[Federal Register Volume 88, Number 150 (Monday, August 7, 2023)]
[Rules and Regulations]
[Pages 53200-53347]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2023-16249]



[[Page 53199]]

Vol. 88

Monday,

No. 150

August 7, 2023

Part III





Department of Health and Human Services





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Centers for Medicare & Medicaid Services





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42 CFR Parts 411, 413, 488, et al.





Medicare Program; Prospective Payment System and Consolidated Billing 
for Skilled Nursing Facilities; Updates to the Quality Reporting 
Program and Value-Based Purchasing Program for Federal Fiscal Year 
2024; Final Rule

  Federal Register / Vol. 88 , No. 150 / Monday, August 7, 2023 / Rules 
and Regulations  

[[Page 53200]]


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DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Medicare & Medicaid Services

42 CFR Parts 411, 413, 488, and 489

[CMS-1779-F]
RIN 0938-AV02


Medicare Program; Prospective Payment System and Consolidated 
Billing for Skilled Nursing Facilities; Updates to the Quality 
Reporting Program and Value-Based Purchasing Program for Federal Fiscal 
Year 2024

AGENCY: Centers for Medicare & Medicaid Services (CMS), Department of 
Health and Human Services (HHS).

ACTION: Final rule.

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SUMMARY: This final rule updates payment rates, including implementing 
the second phase of the Patient Driven Payment Model (PDPM) parity 
adjustment recalibration. This final rule also updates the diagnosis 
code mappings used under PDPM, the SNF Quality Reporting Program (QRP), 
and the SNF Value-Based Purchasing (VBP) Program. We are also 
eliminating the requirement for facilities to actively waive their 
right to a hearing in writing, treating as a constructive waiver when 
the facility does not submit a request for hearing.

DATES: These regulations are effective October 1, 2023, except for the 
amendments to Sec. Sec.  411.15 and 489.20 in instructions 2 and 11, 
which are effective January 1, 2024.

FOR FURTHER INFORMATION CONTACT: [email protected] for issues related to 
the SNF PPS.
    Heidi Magladry, (410) 786-6034, for information related to the 
skilled nursing facility quality reporting program.
    Alexandre Laberge, (410) 786-8625, for information related to the 
skilled nursing facility value-based purchasing program.
    Lorelei Kahn, (443) 803-8643, for information related to the Civil 
Money Penalties Waiver of Hearing.

SUPPLEMENTARY INFORMATION:

Availability of Certain Tables Exclusively Through the Internet on the 
CMS Website

    As discussed in the FY 2014 SNF PPS final rule (78 FR 47936), 
tables setting forth the Wage Index for Urban Areas Based on CBSA Labor 
Market Areas and the Wage Index Based on CBSA Labor Market Areas for 
Rural Areas are no longer published in the Federal Register. Instead, 
these tables are available exclusively through the internet on the CMS 
website. The wage index tables for this final rule can be accessed on 
the SNF PPS Wage Index home page, at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/WageIndex.html.
    Readers who experience any problems accessing any of these online 
SNF PPS wage index tables should contact Kia Burwell at (410) 786-7816.
    To assist readers in referencing sections contained in this 
document, we are providing the following Table of Contents.

Table of Contents

I. Executive Summary
    A. Purpose
    B. Summary of Major Provisions
    C. Summary of Cost and Benefits
    D. Advancing Health Information Exchange
II. Background on SNF PPS
    A. Statutory Basis and Scope
    B. Initial Transition for the SNF PPS
    C. Required Annual Rate Updates
III. Analysis and Responses to Public Comments on the FY 2024 SNF 
PPS Proposed Rule
    A. General Comments on the FY 2024 SNF PPS Proposed Rule
IV. SNF PPS Rate Setting Methodology and FY 2024 Update
    A. Federal Base Rates
    B. SNF Market Basket Update
    C. Case-Mix Adjustment
    D. Wage Index Adjustment
    E. SNF Value-Based Purchasing Program
    F. Adjusted Rate Computation Example
V. Additional Aspects of the SNF PPS
    A. SNF Level of Care--Administrative Presumption
    B. Consolidated Billing
    C. Payment for SNF-Level Swing-Bed Services
    D. Revisions to the Regulation Text
VI. Other SNF PPS Issues
    A. Technical Updates to PDPM ICD-10 Mappings
VII. Skilled Nursing Facility Quality Reporting Program (SNF QRP)
    A. Background and Statutory Authority
    B. General Considerations Used for the Selection of Measures for 
the SNF QRP
    C. SNF QRP Quality Measures
    D. Principles for Selecting and Prioritizing SNF QRP Quality 
Measures and Concepts Under Consideration for Future Years: Request 
for Information (RFI)
    E. Health Equity Update
    F. Form, Manner, and Timing of Data Submission Under the SNF QRP
    G. Policies Regarding Public Display of Measure Data for the SNF 
QRP
VIII. Skilled Nursing Facility Value-Based Purchasing Program (SNF 
VBP)
    A. Statutory Background
    B. SNF VBP Program Measures
    C. SNF VBP Performance Period and Baseline Periods
    D. SNF VBP Performance Standards
    E. SNF VBP Performance Scoring Methodology
    F. Updates to the Extraordinary Circumstances Exception Policy 
Regulation Text
    G. Updates to the Validation Process for the SNF VBP Program
    H. SNF Value-Based Incentive Payments for FY 2024
    I. Public Reporting on the Provider Data Catalog website
IX. Civil Money Penalties: Waiver of Hearing, Automatic Reduction of 
Penalty Amount
X. Waiver of Proposed Rulemaking
XI. Collection of Information Requirements
XII. Economic Analyses
    A. Regulatory Impact Analysis
    B. Regulatory Flexibility Act Analysis
    C. Unfunded Mandates Reform Act Analysis
    D. Federalism Analysis
    E. Regulatory Review Costs

I. Executive Summary

A. Purpose

    This final rule updates the SNF prospective payment rates for 
fiscal year (FY) 2024, as required under section 1888(e)(4)(E) of the 
Social Security Act (the Act). It also responds to section 
1888(e)(4)(H) of the Act, which requires the Secretary to provide for 
publication of certain specified information relating to the payment 
update (see section II.C. of the FY 2024 SNF PPS proposed rule) in the 
Federal Register before the August 1 that precedes the start of each 
FY. In addition, this final rule includes requirements for the Skilled 
Nursing Facility Quality Reporting Program (SNF QRP) for the FY 2025 
and FY 2026 program years. This final rule will add two new measures to 
the SNF QRP, remove three measures from the SNF QRP, and modify one 
measure in the SNF QRP. This final rule will also make policy changes 
to the SNF QRP, and begin public reporting of four measures. In 
addition, this final rule includes a summary of comments received in 
response to our request for information on principles we will use to 
select and prioritize SNF QRP quality measures in future years and on 
the update on our health equity efforts. Finally, this final rule 
includes requirements for the Skilled Nursing Facility Value-Based 
Purchasing (SNF VBP) Program, including adopting new quality measures 
for the SNF VBP Program, finalizing several updates to the Program's 
scoring methodology, including a Health Equity Adjustment, and 
finalizing new processes to validate SNF VBP data. We are also changing 
the current long-term care (LTC) facility requirements that will 
simplify and streamline the current requirements and thereby increase 
provider flexibility and reduce unnecessary administrative burden, 
while also allowing facilities to focus on providing healthcare to

[[Page 53201]]

residents to meet their needs. This proposal was previously proposed 
and published in the July 18, 2019 Federal Register in the proposed 
rule entitled, ``Medicare and Medicaid Programs; Requirements for Long-
Term Care Facilities: Regulatory Provisions to Promote Efficiency, and 
Transparency'' (84 FR 34718). We are finalizing this revision for a 
facility to waive its hearing rights and receive a reduction in civil 
money penalties. This change to the current LTC requirements will 
simplify and streamline the current requirements and thereby increase 
provider flexibility and reduce excessively burdensome regulations, 
while also allowing facilities to focus on providing high-quality 
healthcare to their residents.

B. Summary of Major Provisions

    In accordance with sections 1888(e)(4)(E)(ii)(IV) and (e)(5) of the 
Act, the Federal rates in this final rule update the annual rates that 
we published in the SNF PPS final rule for FY 2023 (87 FR 47502, August 
3, 2022). In addition, this final rule includes a forecast error 
adjustment for FY 2024 and includes the second phase of the PDPM parity 
adjustment recalibration. This final rule also updates the diagnosis 
code mappings used under the PDPM.
    Beginning with the FY 2025 SNF QRP, we are modifying the COVID-19 
Vaccination Coverage among Healthcare Personnel measure, adopting the 
Discharge Function Score measure, and removing the (1) Application of 
Percent of Long-Term Care Hospital Patients with an Admission and 
Discharge Functional Assessment and a Care Plan That Addresses Function 
measure, (2) the Application of IRF Functional Outcome Measure: Change 
in Self-Care Score for Medical Rehabilitation Patients measure, and (3) 
the Application of IRF Functional Outcome Measure: Change in Mobility 
Score for Medical Rehabilitation Patients measure. Beginning with the 
FY 2026 SNF QRP, we are adopting the COVID-19 Vaccine: Percent of 
Patients/Residents Who Are Up to Date measure. We are also changing the 
SNF QRP data completion thresholds for the Minimum Data Set (MDS) data 
items beginning with the FY 2026 SNF QRP and making certain revisions 
to regulation text at Sec.  413.360. This final rule also contains 
updates pertaining to the public reporting of the (1) Transfer of 
Health Information to the Patient-Post-Acute Care (PAC) measure, (2) 
the Transfer of Health Information to the Provider-PAC measure, (3) the 
Discharge Function Score measure, and (4) the COVID-19 Vaccine: Percent 
of Patients/Residents Who Are Up to Date measure. In addition, we 
summarize comments received in response to the Request for Information 
(RFI) on principles for selecting and prioritizing SNF QRP quality 
measures and concepts and the update on our continued efforts to close 
the health equity gap, including under the SNF QRP.
    We are finalizing several updates for the SNF VBP Program. We are 
adopting a Health Equity Adjustment that rewards top tier performing 
SNFs that serve higher proportions of SNF residents with dual 
eligibility status, effective with the FY 2027 program year and 
adopting a variable payback percentage to maintain an estimated payback 
percentage for all SNFs of no less than 60 percent. We are adopting 
four new quality measures to the SNF VBP Program, one taking effect 
beginning with the FY 2026 program year and three taking effect 
beginning with the FY 2027 program year. We are also refining the 
Skilled Nursing Facility 30-Day Potentially Preventable Readmission 
(SNFPPR) measure specifications and updating the name to the Skilled 
Nursing Facility Within-Stay Potentially Preventable Readmission (SNF 
WS PPR) measure effective with the FY 2028 program year. We are 
adopting new processes to validate SNF VBP program data.
    In addition, we are finalizing our proposal to eliminate the 
requirement for facilities facing a civil money penalty to actively 
waive their right to a hearing in writing in order to receive a penalty 
reduction. We are creating, in its place, a constructive waiver process 
that will operate by default when CMS has not received a timely request 
for a hearing. The accompanying 35 percent penalty reduction will 
remain. This will streamline and reduce the administrative burden for 
CMS, and result in lower administrative costs for most LTC facilities 
facing civil money penalties (CMPs). The accompanying 35 percent 
penalty reduction will remain for now, although we plan to revisit this 
in a future rulemaking. The move to a constructive waiver process in 
this rule purely reflects the need to reduce costs and paperwork burden 
for CMS in order to prioritize current limited Survey and Certification 
resources for enforcement actions, and we continue to consider whether 
the existing penalty reduction is appropriate given this final policy. 
The operational change finalized here will streamline and reduce the 
administrative burden for CMS.

C. Summary of Cost and Benefits

                       Table 1--Cost and Benefits
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       Provision description                Total transfers/costs
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FY 2024 SNF PPS payment rate        The overall economic impact of this
 update.                             final rule is an estimated increase
                                     of $1.4 billion in aggregate
                                     payments to SNFs during FY 2024.
FY 2025 SNF QRP changes...........  The overall economic impact of this
                                     final rule to SNFs is an estimated
                                     benefit of $1,037,261 to SNFs
                                     during FY 2025.
FY 2026 SNF QRP changes...........  The overall economic impact of this
                                     final rule to SNFs is an estimated
                                     increase in aggregate cost from FY
                                     2025 of $778,591.
FY 2024 SNF VBP changes...........  The overall economic impact of the
                                     SNF VBP Program is an estimated
                                     reduction of $184.85 million in
                                     aggregate payments to SNFs during
                                     FY 2024.
FY 2026 SNF VBP changes...........  The overall economic impact of the
                                     SNF VBP Program is an estimated
                                     reduction of $196.50 million in
                                     aggregate payments to SNFs during
                                     FY 2026.
FY 2027 SNF VBP changes...........  The overall economic impact of the
                                     SNF VBP Program is an estimated
                                     reduction of $166.86 million in
                                     aggregate payments to SNFs during
                                     FY 2027.
FY 2028 SNF VBP changes...........  The overall economic impact of the
                                     SNF VBP Program is an estimated
                                     reduction of $170.98 million in
                                     aggregate payments to SNFs during
                                     FY 2028.
FY 2024 Enforcement Provisions for  The overall impact of this
 LTC Facilities Requirements         regulatory change is an estimated
 Changes.                            administrative cost savings of
                                     $2,299,716 to LTC facilities and
                                     $772,044 to the Federal Government
                                     during FY 2024.
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D. Advancing Health Information Exchange

    The Department of Health and Human Services (HHS) has a number of 
initiatives designed to encourage and support the adoption of 
interoperable health information technology and to promote nationwide 
health information exchange to improve health care and patient access 
to their digital health information.
    To further interoperability in post-acute care settings, CMS and 
the Office of the National Coordinator for Health Information 
Technology (ONC) participate in the Post-Acute Care Interoperability 
Workgroup (PACIO) to facilitate collaboration with interested parties 
to develop Health Level Seven International[supreg] (HL7) Fast 
Healthcare Interoperability Resource[supreg] (FHIR) standards. These 
standards could support the exchange and reuse of patient assessment 
data derived from the post-acute care (PAC) setting assessment tools, 
such as the minimum data set (MDS), inpatient rehabilitation facility -
patient assessment instrument (IRF-PAI), Long-Term Care Hospital (LTCH) 
continuity assessment record and evaluation (CARE) Data Set (LCDS), 
outcome and assessment information set (OASIS), and other 
sources.1 2 The PACIO Project has focused on HL7 FHIR 
implementation guides for: functional status, cognitive status and new 
use cases on advance directives, re-assessment timepoints, and Speech, 
language, swallowing, cognitive communication and hearing (SPLASCH) 
pathology.\3\ We encourage PAC provider and health IT vendor 
participation as the efforts advance.
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    \1\ HL7 FHIR Release 4. Available at https://www.hl7.org/fhir/.
    \2\ HL7 FHIR. PACIO Functional Status Implementation Guide. 
Available at https://paciowg.github.io/functional-status-ig/.
    \3\ PACIO Project. Available at http://pacioproject.org/about/.
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    The CMS Data Element Library (DEL) continues to be updated and 
serves as a resource for PAC assessment data elements and their 
associated mappings to health IT standards such as Logical Observation 
Identifiers Names and Codes (LOINC) and Systematized Nomenclature of 
Medicine Clinical Terms (SNOMED).\4\ The DEL furthers CMS' goal of data 
standardization and interoperability. Standards in the DEL can be 
referenced on the CMS website and in the ONC Interoperability Standards 
Advisory (ISA). The 2023 ISA is available at https://www.healthit.gov/sites/isa/files/inline-files/2023%20Reference%20Edition_ISA_508.pdf.
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    \4\ Centers for Medicare & Medicaid Services. Newsroom. Fact 
sheet: CMS Data Element Library Fact Sheet. June 21, 2018. Available 
at https://www.cms.gov/newsroom/fact-sheets/cms-data-element-library-fact-sheet.
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    We are also working with ONC to advance the United States Core Data 
for Interoperability (USCDI), a standardized set of health data classes 
and constituent data elements for nationwide, interoperable health 
information exchange.\5\ We are collaborating with ONC and other 
Federal agencies to define and prioritize additional data 
standardization needs and develop consensus on recommendations for 
future versions of the USCDI. We are also directly collaborating with 
ONC to build requirements to support data standardization and alignment 
with requirements for quality measurement. ONC has launched the USCDI+ 
initiative to support the identification and establishment of domain 
specific datasets that build on the core USCDI foundation.\6\ The 
USCDI+ quality measurement domain currently being developed aims to 
support defining additional data specifications for quality measurement 
that harmonize, where possible, with other Federal agency data needs 
and inform supplemental standards necessary to support quality 
measurement, including the needs of programs supporting quality 
measurement for long-term and post-acute care.
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    \5\ USCDI. Available at https://www.healthit.gov/isa/united-states-core-data-interoperability-uscdi.
    \6\ USCDI+. Available at https://www.healthit.gov/topic/interoperability/uscdi-plus.
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    The 21st Century Cures Act (Cures Act) (Pub. L. 114-255, enacted 
December 13, 2016) required HHS and ONC to take steps to promote 
adoption and use of electronic health record (EHR) technology. 
Specifically, section 4003(b) of the Cures Act required ONC to take 
steps to advance interoperability through the development of a Trusted 
Exchange Framework and Common Agreement aimed at establishing full 
network-to network exchange of health information nationally. On 
January 18, 2022, ONC announced a significant milestone by releasing 
the Trusted Exchange Framework \7\ and Common Agreement Version 1.\8\ 
The Trusted Exchange Framework is a set of non-binding principles for 
health information exchange, and the Common Agreement is a contract 
that advances those principles. The Common Agreement and the Qualified 
Health Information Network Technical Framework Version 1 (incorporated 
by reference into the Common Agreement) establish the technical 
infrastructure model and governing approach for different health 
information networks and their users to securely share clinical 
information with each other, all under commonly agreed to terms. The 
technical and policy architecture of how exchange occurs under the 
Common Agreement follows a network-of-networks structure, which allows 
for connections at different levels and is inclusive of many different 
types of entities at those different levels, such as health information 
networks, healthcare practices, hospitals, public health agencies, and 
Individual Access Services (IAS) Providers.\9\ On February 13, 2023, 
HHS marked a new milestone during an event at HHS headquarters,\10\ 
which recognized the first set of applicants accepted for onboarding to 
the Common Agreement as Qualified Health Information Networks (QHINs). 
QHINs will be entities that will connect directly to each other to 
serve as the core for nationwide interoperability.\11\ For more 
information, we refer readers to https://www.healthit.gov/topic/interoperability/trusted-exchange-framework-and-common-agreement.
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    \7\ The Trusted Exchange Framework (TEF): Principles for Trusted 
Exchange (Jan. 2022). Available at https://www.healthit.gov/sites/default/files/page/2022-01/Trusted_Exchange_Framework_0122.pdf.
    \8\ Common Agreement for Nationwide Health Information 
Interoperability Version 1 (Jan. 2022). Available at https://www.healthit.gov/sites/default/files/page/2022-01/Common_Agreement_for_Nationwide_Health_Information_Interoperability_Version_1.pdf.
    \9\ The Common Agreement defines Individual Access Services 
(IAS) as ``with respect to the Exchange Purposes definition, the 
services provided utilizing the Connectivity Services, to the extent 
consistent with Applicable Law, to an Individual with whom the QHIN, 
Participant, or Subparticipant has a Direct Relationship to satisfy 
that Individual's ability to access, inspect, or obtain a copy of 
that Individual's Required Information that is then maintained by or 
for any QHIN, Participant, or Subparticipant.'' The Common Agreement 
defines ``IAS Provider'' as: ``Each QHIN, Participant, and 
Subparticipant that offers Individual Access Services.'' See Common 
Agreement for Nationwide Health Information Interoperability Version 
1, at 7 (Jan. 2022), https://www.healthit.gov/sites/default/files/page/2022-01/Common_Agreement_for_Nationwide_Health_Information_Interoperability_Version_1.pdf.
    \10\ ``Building TEFCA,'' Micky Tripathi and Mariann Yeager, 
Health IT Buzz Blog. February 13, 2023. https://www.healthit.gov/buzz-blog/electronic-health-and-medical-records/interoperability-electronic-health-and-medical-records/building-tefca.
    \11\ The Common Agreement defines a QHIN as ``to the extent 
permitted by applicable SOP(s), a Health Information Network that is 
a U.S. Entity that has been Designated by the RCE and is a party to 
the Common Agreement countersigned by the RCE.'' See Common 
Agreement for Nationwide Health Information Interoperability Version 
1, at 10 (Jan. 2022), https://www.healthit.gov/sites/default/files/page/2022-01/Common_Agreement_for_Nationwide_Health_Information_Interoperability_Version_1.pdf.

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    We invite providers to learn more about these important 
developments and how they are likely to affect SNFs.

II. Background on SNF PPS

A. Statutory Basis and Scope

    As amended by section 4432 of the Balanced Budget Act of 1997 (BBA 
1997) (Pub. L. 105-33, enacted August 5, 1997), section 1888(e) of the 
Act provides for the implementation of a PPS for SNFs. This methodology 
uses prospective, case-mix adjusted per diem payment rates applicable 
to all covered SNF services defined in section 1888(e)(2)(A) of the 
Act. The SNF PPS is effective for cost reporting periods beginning on 
or after July 1, 1998, and covers virtually all costs of furnishing 
covered SNF services (routine, ancillary, and capital-related costs) 
other than costs associated with approved educational activities and 
bad debts. Under section 1888(e)(2)(A)(i) of the Act, covered SNF 
services include post-hospital extended care services for which 
benefits are provided under Part A, as well as those items and services 
(other than a small number of excluded services, such as physicians' 
services) for which payment may otherwise be made under Part B and 
which are furnished to Medicare beneficiaries who are residents in a 
SNF during a covered Part A stay. A comprehensive discussion of these 
provisions appears in the May 12, 1998 interim final rule (63 FR 
26252). In addition, a detailed discussion of the legislative history 
of the SNF PPS is available online at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/Downloads/Legislative_History_2018-10-01.pdf.
    Section 215(a) of the Protecting Access to Medicare Act of 2014 
(PAMA) (Pub. L. 113-93, enacted April 1, 2014) added section 1888(g) to 
the Act requiring the Secretary to specify an all-cause all-condition 
hospital readmission measure and an all-condition risk-adjusted 
potentially preventable hospital readmission measure for the SNF 
setting. Additionally, section 215(b) of PAMA added section 1888(h) to 
the Act requiring the Secretary to implement a VBP program for SNFs. 
Finally, section 2(c)(4) of the Improving Medicare Post-Acute Care 
Transformation (IMPACT) Act of 2014 (Pub. L. 113-185, enacted October 
6, 2014) amended section 1888(e)(6) of the Act, which requires the 
Secretary to implement a QRP for SNFs under which SNFs report data on 
measures and resident assessment data. Finally, section 111 of the 
Consolidated Appropriations Act, 2021 (CAA, 2021) amended section 
1888(h) of the Act, authorizing the Secretary to apply up to nine 
additional measures to the VBP program for SNFs.

B. Initial Transition for the SNF PPS

    Under sections 1888(e)(1)(A) and (e)(11) of the Act, the SNF PPS 
included an initial, three-phase transition that blended a facility-
specific rate (reflecting the individual facility's historical cost 
experience) with the Federal case-mix adjusted rate. The transition 
extended through the facility's first 3 cost reporting periods under 
the PPS, up to and including the one that began in FY 2001. Thus, the 
SNF PPS is no longer operating under the transition, as all facilities 
have been paid at the full Federal rate effective with cost reporting 
periods beginning in FY 2002. As we now base payments for SNFs entirely 
on the adjusted Federal per diem rates, we no longer include adjustment 
factors under the transition related to facility-specific rates for the 
upcoming FY.

C. Required Annual Rate Updates

    Section 1888(e)(4)(E) of the Act requires the SNF PPS payment rates 
to be updated annually. The most recent annual update occurred in a 
final rule that set forth updates to the SNF PPS payment rates for FY 
2023 (87 FR 47502, August 3, 2022).
    Section 1888(e)(4)(H) of the Act specifies that we provide for 
publication annually in the Federal Register the following:
     The unadjusted Federal per diem rates to be applied to 
days of covered SNF services furnished during the upcoming FY.
     The case-mix classification system to be applied for these 
services during the upcoming FY.
     The factors to be applied in making the area wage 
adjustment for these services.
    Along with other revisions discussed later in this preamble, this 
final rule provides the required annual updates to the per diem payment 
rates for SNFs for FY 2024.

III. Analysis and Responses to Public Comments on the FY 2024 SNF PPS 
Proposed Rule

    In response to the publication of the FY 2024 SNF PPS proposed 
rule, we received 81 public comments from individuals, providers, 
corporations, government agencies, private citizens, trade 
associations, and major organizations. The following are brief 
summaries of each proposed provision, a summary of the public comments 
that we received related to that proposal, and our responses to the 
comments.

A. General Comments on the FY 2024 SNF PPS Proposed Rule

    In addition to the comments we received on specific proposals 
contained within the proposed rule (which we address later in this 
final rule), commenters also submitted the following, more general, 
observations on the SNF PPS and SNF care generally. A discussion of 
these comments, along with our responses, appears below.
    Comment: Several commenters raised concerns with therapy treatment 
under PDPM, specifically reductions in the amount of therapy furnished 
to SNF patients since PDPM was implemented. Some of these commenters 
stated that CMS should revise the existing limit on concurrent and 
group therapy to provide a financial penalty in cases where the 
facility exceeds this limit. These commenters also recommended that CMS 
direct its review contractors to examine the practices of facilities 
that changed their therapy service provision after PDPM was 
implemented. Additionally, commenters want CMS to release the results 
of any monitoring efforts around therapy provision. Finally, several 
commenters recommended that CMS reinstate a more frequent assessment 
schedule to discourage gaming.
    Response: We appreciate commenters raising these concerns around 
therapy provision under PDPM, as compared the RUG-IV. We agree with 
commenters that the amount of therapy that is furnished to patients 
under PDPM is less than that delivered under RUG-IV. As we stated in 
the FY 2020 SNF PPS final rule, we believe that close, real-time 
monitoring is essential to identifying any adverse trends under PDPM. 
While we have identified the same reduction in therapy services and 
therapy staff, we believe that these findings must be considered within 
the context of patient outcomes. To the extent that facilities are able 
to maintain or improve patient outcomes, we believe that this 
supersedes changes in service provision, whether this be in the amount 
of therapy furnished or the mode in which it is furnished. We continue 
to monitor all aspects of PDPM and advise our review contractors on any 
adverse trends.
    With regard to implementing a specific penalty for exceeding the 
group and concurrent therapy threshold, based on our current data, we 
have not identified any widespread misuse of this limit. Should we 
identify such misuse, either at a provider-level or at

[[Page 53204]]

a broader level, we will pursue an appropriate course of action.
    Finally, with regard to the recommendation that we reinstate 
something akin to the assessment schedule that was in effect under RUG-
IV, given that PDPM does not reimburse on the basis of therapy minutes, 
we do not believe that such an increase in administrative burden on 
providers would have an impact on therapy provision. That being said, 
we strongly encourage interested parties to continue to provide 
suggestions on how to ensure that SNF patients receive the care they 
need based on their unique characteristics and goals.
    Comment: One commenter stated that CMS should undertake an analysis 
of the impact of waiving the 3-day stay requirement during the PHE as 
compared to the impact on patient cost and outcomes once the 
requirement has been reinstated. This commenter requests that CMS 
release the results of such an analysis.
    Response: We appreciate this suggestion. We have previously 
conducted analyses of the associated cost of removing the 3-day stay 
requirement and found that it would significantly increase Medicare 
outlays. We have not yet been able to perform such an analysis which 
would compare the impact of waiving this requirement during the PHE to 
the impact of it being re-implemented, but we believe it would likely 
lead to the same result.
    Comment: One commenter requested that we consider including 
recreational therapy time provided to SNF residents by recreational 
therapists into the case-mix adjusted therapy component of PDPM, rather 
than having it be considered part of the nursing component. This 
commenter further suggested that CMS begin collecting data, as part of 
a demonstration project, on the utilization of recreational therapy, as 
a distinct and separate service, and its impact on patient care cost 
and quality.
    Response: We appreciate the commenter raising this issue, but we do 
not believe there is sufficient evidence at this time regarding the 
efficacy of recreational therapy interventions or, more notably, data 
which would substantiate a determination of the effect on payment of 
such interventions, as such services were not considered separately, as 
were physical, occupational and speech-language pathology services, 
when the PDPM was being developed. That being said, we would note that 
Medicare Part A originally paid for institutional care in various 
provider settings, including SNF, on a reasonable cost basis, but now 
makes payment using PPS methodologies, such as the SNF PPS. To the 
extent that one of these SNFs furnished recreational therapy to its 
inpatients under the previous, reasonable cost methodology, the cost of 
the services would have been included in the base payments when SNF PPS 
payment rates were derived. Under the PPS methodology, Part A makes a 
comprehensive payment for the bundled package of items and services 
that the facility furnishes during the course of a Medicare-covered 
stay. This package encompasses nearly all services that the beneficiary 
receives during the course of the stay--including any medically 
necessary recreational therapy--and payment for such services is 
included within the facility's comprehensive SNF PPS payment for the 
covered Part A stay itself. With regard to developing a demonstration 
project focused on this particular service, we do not believe that 
creating such a project would substantially improve the accuracy of the 
SNF PPS payment rates. Moreover, in light of comments discussed above 
on the impact of PDPM implementation on therapy provision more 
generally, we believe that carving out recreational therapy as a 
separate discipline will not have a significant impact on access to 
recreational therapy services for SNF patients.
    Comment: One commenter raised concerns regarding the perceived lack 
of adequate financial reporting and cost report auditing. This 
commenter stated that CMS does not do enough to ensure that the funds 
paid to providers under the SNF PPS are used appropriately for patient 
care. Further, this commenter suggested that CMS impose penalties for 
inaccurate, incomplete and fraudulent SNF ownership and cost data. 
Finally, this commenter urged CMS to establish a medical-loss ratio for 
SNFs to ensure that Medicare funds are used for patient care.
    Response: We appreciate the commenter raising these concerns. With 
regard to the need for regulation and penalties associated with 
incomplete and fraudulent ownership and cost data, we would contend 
that there are consequences for providers when they are found to have 
incomplete cost reports or if the data they are reporting to CMS is 
found to be fraudulent. That being said, we focus on patient outcomes 
as the basis for assessing if the care provided to SNF patients is 
appropriate, as well as the Medicare funding used as the basis for that 
care. Ultimately, it is the responsibility of each SNF provider to 
ensure that the care provided to their patients, using the funds 
provided under the SNF PPS, is appropriate and sufficient to meet the 
unique needs, goals and characteristics of each patient. We encourage 
interested parties to provide future recommendations and suggestions 
for how to use SNF cost reports and other data sources to improve CMS 
auditing and enforcement activities.

IV. SNF PPS Rate Setting Methodology and FY 2024 Update

A. Federal Base Rates

    Under section 1888(e)(4) of the Act, the SNF PPS uses per diem 
Federal payment rates based on mean SNF costs in a base year (FY 1995) 
updated for inflation to the first effective period of the PPS. We 
developed the Federal payment rates using allowable costs from 
hospital-based and freestanding SNF cost reports for reporting periods 
beginning in FY 1995. The data used in developing the Federal rates 
also incorporated a Part B add-on, which is an estimate of the amounts 
that, prior to the SNF PPS, would be payable under Part B for covered 
SNF services furnished to individuals during the course of a covered 
Part A stay in a SNF.
    In developing the rates for the initial period, we updated costs to 
the first effective year of the PPS (the 15-month period beginning July 
1, 1998) using a SNF market basket, and then standardized for 
geographic variations in wages and for the costs of facility 
differences in case-mix. In compiling the database used to compute the 
Federal payment rates, we excluded those providers that received new 
provider exemptions from the routine cost limits, as well as costs 
related to payments for exceptions to the routine cost limits. Using 
the formula that the BBA 1997 prescribed, we set the Federal rates at a 
level equal to the weighted mean of freestanding costs plus 50 percent 
of the difference between the freestanding mean and weighted mean of 
all SNF costs (hospital-based and freestanding) combined. We computed 
and applied separately the payment rates for facilities located in 
urban and rural areas and adjusted the portion of the Federal rate 
attributable to wage-related costs by a wage index to reflect 
geographic variations in wages.

B. SNF Market Basket Update

1. SNF Market Basket
    Section 1888(e)(5)(A) of the Act requires us to establish a SNF 
market basket that reflects changes over time in the prices of an 
appropriate mix of goods and services included in covered SNF services. 
Accordingly, we have developed a SNF market basket that encompasses the 
most commonly used

[[Page 53205]]

cost categories for SNF routine services, ancillary services, and 
capital-related expenses. In the SNF PPS final rule for FY 2018 (82 FR 
36548 through 36566), we rebased and revised the SNF market basket, 
which included updating the base year from FY 2010 to 2014. In the SNF 
PPS final rule for FY 2022 (86 FR 42444 through 42463), we rebased and 
revised the SNF market basket, which included updating the base year 
from 2014 to 2018.
    The SNF market basket is used to compute the market basket 
percentage increase that is used to update the SNF Federal rates on an 
annual basis, as required by section 1888(e)(4)(E)(ii)(IV) of the Act. 
This market basket percentage increase is adjusted by a forecast error 
adjustment, if applicable, and then further adjusted by the application 
of a productivity adjustment as required by section 1888(e)(5)(B)(ii) 
of the Act and described in section IV.B.4. of this final rule.
    As outlined in the proposed rule, we proposed a FY 2024 SNF market 
basket percentage increase of 2.7 percent based on IHS Global Inc.'s 
(IGI's) fourth quarter 2022 forecast of the 2018-based SNF market 
basket (before application of the forecast error adjustment and 
productivity adjustment). We also proposed that if more recent data 
subsequently became available (for example, a more recent estimate of 
the market basket and/or the productivity adjustment), we would use 
such data, if appropriate, to determine the FY 2024 SNF market basket 
percentage increase, labor-related share relative importance, forecast 
error adjustment, or productivity adjustment in the SNF PPS final rule.
    Since the proposed rule, we have updated the FY 2024 market basket 
percentage increase based on IGI's second quarter 2023 forecast with 
historical data through the first quarter of 2023. The FY 2024 growth 
rate of the 2018-based SNF market basket is estimated to be 3.0 
percent.
2. Market Basket Update Factor for FY 2024
    Section 1888(e)(5)(B) of the Act defines the SNF market basket 
percentage increase as the percentage change in the SNF market basket 
from the midpoint of the previous FY to the midpoint of the current FY. 
For the Federal rates outlined in this final rule, we use the 
percentage change in the SNF market basket to compute the update factor 
for FY 2024. This factor is based on the FY 2024 percentage increase in 
the 2018-based SNF market basket reflecting routine, ancillary, and 
capital-related expenses. Sections 1888(e)(4)(E)(ii)(IV) and 
(e)(5)(B)(i) of the Act require that the update factor used to 
establish the FY 2024 unadjusted Federal rates be at a level equal to 
the SNF market basket percentage increase. Accordingly, we determined 
the total growth from the average market basket level for the period of 
October 1, 2022 through September 30, 2023 to the average market basket 
level for the period of October 1, 2023 through September 30, 2024. As 
outlined in the proposed rule, we proposed a FY 2024 SNF market basket 
percentage increase of 2.7 percent. For this final rule, based on IGI's 
second quarter 2023 forecast with historical data through the first 
quarter of 2023, the FY 2024 growth rate of the 2018-based SNF market 
basket is estimated to be 3.0 percent.
    As further explained in section IV.B.3. of this final rule, as 
applicable, we adjust the percentage increase by the forecast error 
adjustment from the most recently available FY for which there is final 
data and apply this adjustment whenever the difference between the 
forecasted and actual percentage increase in the market basket exceeds 
a 0.5 percentage point threshold in absolute terms. Additionally, 
section 1888(e)(5)(B)(ii) of the Act requires us to reduce the market 
basket percentage increase by the productivity adjustment (the 10-year 
moving average of changes in annual economy-wide private nonfarm 
business total factor productivity (TFP) for the period ending 
September 30, 2024) which is estimated to be 0.2 percentage point, as 
described in section IV.B.4. of this final rule.
    We also note that section 1888(e)(6)(A)(i) of the Act provides 
that, beginning with FY 2018, SNFs that fail to submit data, as 
applicable, in accordance with sections 1888(e)(6)(B)(i)(II) and (III) 
of the Act for a fiscal year will receive a 2.0 percentage point 
reduction to their market basket update for the fiscal year involved, 
after application of section 1888(e)(5)(B)(ii) of the Act (the 
productivity adjustment) and section 1888(e)(5)(B)(iii) of the Act (the 
market basket increase). In addition, section 1888(e)(6)(A)(ii) of the 
Act states that application of the 2.0 percentage point reduction 
(after application of section 1888(e)(5)(B)(ii) and (iii) of the Act) 
may result in the market basket percentage change being less than zero 
for a fiscal year and may result in payment rates for a fiscal year 
being less than such payment rates for the preceding fiscal year. 
Section 1888(e)(6)(A)(iii) of the Act further specifies that the 2.0 
percentage point reduction is applied in a noncumulative manner, so 
that any reduction made under section 1888(e)(6)(A)(i) of the Act 
applies only to the fiscal year involved, and that the reduction cannot 
be taken into account in computing the payment amount for a subsequent 
fiscal year.
3. Forecast Error Adjustment
    As discussed in the June 10, 2003 supplemental proposed rule (68 FR 
34768) and finalized in the August 4, 2003 final rule (68 FR 46057 
through 46059), Sec.  413.337(d)(2) provides for an adjustment to 
account for market basket forecast error. The initial adjustment for 
market basket forecast error applied to the update of the FY 2003 rate 
for FY 2004 and took into account the cumulative forecast error for the 
period from FY 2000 through FY 2002, resulting in an increase of 3.26 
percent to the FY 2004 update. Subsequent adjustments in succeeding FYs 
take into account the forecast error from the most recently available 
FY for which there is final data and apply the difference between the 
forecasted and actual change in the market basket when the difference 
exceeds a specified threshold. We originally used a 0.25 percentage 
point threshold for this purpose; however, for the reasons specified in 
the FY 2008 SNF PPS final rule (72 FR 43425), we adopted a 0.5 
percentage point threshold effective for FY 2008 and subsequent FYs. As 
we stated in the final rule for FY 2004 that first issued the market 
basket forecast error adjustment (68 FR 46058), the adjustment will 
reflect both upward and downward adjustments, as appropriate.
    For FY 2022 (the most recently available FY for which there is 
final data), the forecasted or estimated increase in the SNF market 
basket was 2.7 percent, and the actual increase for FY 2022 is 6.3 
percent, resulting in the actual increase being 3.6 percentage points 
higher than the estimated increase. Accordingly, as the difference 
between the estimated and actual amount of change in the market basket 
exceeds the 0.5 percentage point threshold, under the policy previously 
described (comparing the forecasted and actual market basket percentage 
increase), the FY 2024 market basket percentage increase of 3.0 percent 
would be adjusted upward to account for the forecast error adjustment 
of 3.6 percentage points, resulting in a SNF market basket percentage 
increase of 6.6 percent, which is then reduced by the productivity 
adjustment of 0.2 percentage point, discussed in section IV.B.4. of 
this final rule. This results in a SNF market basket update for FY 2024 
of 6.4 percent.

[[Page 53206]]

    Table 2 shows the forecasted and actual market basket increases for 
FY 2022.

            Table 2--Difference Between the Actual and Forecasted Market Basket Increases for FY 2022
----------------------------------------------------------------------------------------------------------------
                                               Forecasted FY 2022       Actual FY 2022
                   Index                           increase *            increase **         FY 2022 difference
----------------------------------------------------------------------------------------------------------------
SNF........................................                   2.7                    6.3                    3.6
----------------------------------------------------------------------------------------------------------------
* Published in Federal Register; based on second quarter 2021 IGI forecast (2018-based SNF market basket).
** Based on the second quarter 2023 IGI forecast (2018-based SNF market basket).

4. Productivity Adjustment
    Section 1888(e)(5)(B)(ii) of the Act, as added by section 3401(b) 
of the Patient Protection and Affordable Care Act (Affordable Care Act) 
(Pub. L. 111-148, enacted March 23, 2010) requires that, in FY 2012 and 
in subsequent FYs, the market basket percentage under the SNF payment 
system (as described in section 1888(e)(5)(B)(i) of the Act) is to be 
reduced annually by the productivity adjustment described in section 
1886(b)(3)(B)(xi)(II) of the Act. Section 1886(b)(3)(B)(xi)(II) of the 
Act, in turn, defines the productivity adjustment to be equal to the 
10-year moving average of changes in annual economy-wide, private 
nonfarm business multifactor productivity (MFP) (as projected by the 
Secretary for the 10-year period ending with the applicable FY, year, 
cost-reporting period, or other annual period).
    The U.S. Department of Labor's Bureau of Labor Statistics (BLS) 
publishes the official measure of productivity for the U.S. We note 
that previously the productivity measure referenced at section 
1886(b)(3)(B)(xi)(II) of the Act was published by BLS as private 
nonfarm business multifactor productivity. Beginning with the November 
18, 2021 release of productivity data, BLS replaced the term MFP with 
TFP. BLS noted that this is a change in terminology only and will not 
affect the data or methodology. As a result of the BLS name change, the 
productivity measure referenced in section 1886(b)(3)(B)(xi)(II) of the 
Act is now published by BLS as private nonfarm business total factor 
productivity. We refer readers to the BLS website at www.bls.gov for 
the BLS historical published TFP data. A complete description of the 
TFP projection methodology is available on our website at https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MedicareProgramRatesStats/MarketBasketResearch. In addition, in 
the FY 2022 SNF final rule (86 FR 42429) we noted that, effective with 
FY 2022 and forward, we changed the name of this adjustment to refer to 
it as the ``productivity adjustment,'' rather than the ``MFP 
adjustment.''
    Per section 1888(e)(5)(A) of the Act, the Secretary shall establish 
a SNF market basket that reflects changes over time in the prices of an 
appropriate mix of goods and services included in covered SNF services. 
Section 1888(e)(5)(B)(ii) of the Act, added by section 3401(b) of the 
Affordable Care Act, requires that for FY 2012 and each subsequent FY, 
after determining the market basket percentage described in section 
1888(e)(5)(B)(i) of the Act, the Secretary shall reduce such percentage 
by the productivity adjustment described in section 
1886(b)(3)(B)(xi)(II) of the Act. Section 1888(e)(5)(B)(ii) of the Act 
further states that the reduction of the market basket percentage by 
the productivity adjustment may result in the market basket percentage 
being less than zero for a FY and may result in payment rates under 
section 1888(e) of the Act being less than such payment rates for the 
preceding fiscal year. Thus, if the application of the productivity 
adjustment to the market basket percentage calculated under section 
1888(e)(5)(B)(i) of the Act results in a productivity-adjusted market 
basket percentage that is less than zero, then the annual update to the 
unadjusted Federal per diem rates under section 1888(e)(4)(E)(ii) of 
the Act would be negative, and such rates would decrease relative to 
the prior FY.
    Based on the data available for the FY 2024 SNF PPS proposed rule, 
the proposed productivity adjustment (the 10-year moving average of 
changes in annual economy-wide private nonfarm business TFP for the 
period ending September 30, 2024) was projected to be 0.2 percentage 
point. We note that, as we typically do, we have updated our data 
between the FY 2024 SNF PPS proposed rule and this final rule. Based on 
IGI's second quarter 2023 forecast, the estimated 10-year moving 
average of changes in annual economy-wide private nonfarm business TFP 
for the period ending September 30, 2024 is estimated to be 0.2 
percentage point.
    Consistent with section 1888(e)(5)(B)(i) of the Act and Sec.  
413.337(d)(2), and as discussed previously in section IV.B.1. of this 
final rule, the market basket percentage for FY 2024 for the SNF PPS is 
based on IGI's second quarter 2023 forecast of the SNF market basket 
percentage increase, which is estimated to be 3.0 percent. This market 
basket update is then increased by 3.6 percentage points, due to 
application of the forecast error adjustment discussed earlier in 
section IV.B.3. of this final rule. Finally, as discussed earlier in 
section IV.B.4. of this final rule, we are applying a 0.2 percentage 
point productivity adjustment to the FY 2024 SNF market basket 
percentage increase. Therefore, the resulting productivity-adjusted FY 
2024 SNF market basket update is equal to 6.4 percent, which reflects a 
market basket percentage increase of 3.0 percent, plus the 3.6 
percentage points forecast error adjustment, and less the 0.2 
percentage point productivity adjustment. Thus, we apply a net SNF 
market basket update factor of 6.4 percent in our determination of the 
FY 2024 SNF PPS unadjusted Federal per diem rates.
    A discussion of the public comments received on the FY 2024 SNF 
market basket percentage increase to the SNF PPS rates, along with our 
responses, can be found below.
    Comment: One commenter suggested CMS consider allowing SNFs to use 
different labor percentages for geographic areas with wage indexes less 
than or greater than 1, similar to IPPS hospitals. They believe this 
methodological change would allow for the wage index adjustment to 
match more closely with the provider's costs.
    Response: We continue to believe it is technically appropriate and 
consistent with our interpretation of the statute to use the market 
basket cost weights, reflecting the national average of SNF costs, to 
determine the labor-related share applicable for all SNFs. In addition, 
our analysis of the 2018 SNF Medicare cost report data used to 
determine the 2018-based SNF market

[[Page 53207]]

basket cost weights, shows that the compensation cost weights for urban 
(accounting for about 70 percent of freestanding SNF costs) and rural 
SNFs, in aggregate, are both 60 percent--consistent with the 2018-based 
SNF market basket compensation cost weight.
    Comment: One commenter requested that CMS work with interested 
parties to explore updates to the SNF market basket methodology, 
potentially with new proxies or alternative data. One commenter 
identified a few detailed methodological issues for CMS to consider 
regarding the SNF market basket.
    Response: We welcome commenters' input on the SNF market basket and 
appreciate the suggestions provided. We will consider them for future 
rulemaking when we propose to rebase and revise the SNF market basket.
    Comment: One commenter appreciated the forecast error adjustments 
during the last two rulemaking cycles but stated that the current 
methodology may not capture impacts such as the entirety of the cost 
changes during times of high healthcare resource utilization (for 
example, during COVID-19 pandemic). The commenter further noted that 
applying the forecast error adjustment to future payments does not 
account for inflation that can alter the time-value of money. The 
commenter requested that CMS consider ways to evaluate the impact of 
addressing these potential shortcomings of the forecast error 
adjustment. One commenter recommended that CMS strongly consider 
including additional labor and cost data into the market basket updates 
prospectively, rather than retroactively, to adjust for the market 
basket projections' inability to accurately project rate increases 
during high inflation periods. One commenter (MedPAC) noted that CMS is 
not required by statute to make automatic forecast error corrections 
and in this instance the forecast error correction results in making a 
larger payment increase in addition to the statutory increases for FY 
2024.
    Response: The SNF market basket is a price index that measures the 
change in price, over time, of the same mix of goods and services 
purchased in the base period. As noted by the commenter, due to the 
availability of data and rates being set by CMS on a prospective basis, 
there is a 2-year lag between the forecast error adjustment and its 
application to the payment rate. For example, as stated in section 
IV.B.3. of this final rule, the FY 2024 SNF PPS payment rate update 
includes an adjustment for the FY 2022 market basket forecast error.
    Subsequent to the initial cumulative adjustment implemented in FY 
2004, the forecast error adjustment has been based on the forecast 
error from the most recently available FY for which there is final 
data, and the difference between the forecasted and actual change in 
the market basket is applied when the difference exceeds a specified 
threshold. The forecast error adjustment (when it exceeds the threshold 
of 0.5 percentage point (in absolute terms)) is intended to adjust for 
when historical price changes differ substantially from the forecasted 
price changes in order to appropriately pay providers for services 
provided, rather than typical minor variances that are inherent in 
statistical measurements. The forecast error adjustment is specifically 
defined to only account for errors in price forecasts and would 
appropriately not take into account differences in non-price factors 
affecting costs.
    Therefore, we disagree with the commenter that the CMS forecast 
error adjustment is inadequate or that it should reflect other factors 
(such as changes in utilization due to case mix or other non-price 
factors or the time value of money). We use the most complete and 
available data for purposes of determining the market basket forecast, 
forecast error adjustment, and productivity adjustment as well as the 
most recent claims data when determining the SNF PPS payment rates. We 
do not forecast changes in the case-mix index.
    Comment: Several commenters supported the net payment update of 3.7 
percent reflecting a 2.7 percent market basket update. Numerous 
commenters also recommended that CMS use the most recently available 
data when determining the market basket update for the final rule.
    Several commenters stated that the proposed 3.7 percent net payment 
update is inadequate when considering the financial hardship and 
increased costs many health care providers are facing as a result of 
the PHE and labor shortages. They recommended that CMS use data that 
better reflects the input price inflation that SNFs have experienced 
and are projected to experience in 2024. They believe CMS should 
reassess market basket data and how it weighs wage and benefits data, 
as they do not believe the updates to the market basket data reasonably 
reflect the reality of these associated costs. Similarly, one commenter 
stated that they believe the 2018-based SNF market basket alone no 
longer serves as an appropriate price proxy due to the growing 
expenditures in labor, which has driven a recent disproportionate 
increase in the labor share portion of the market basket. They 
recommended that CMS use more recent and supplemental labor cost data 
to accurately reflect a recent increase of the market basket's labor.
    One commenter cited a report stating that the average hourly 
nursing wage increased over 17 percent from 2019 to 2022 as reported on 
the Medicare cost reports. They stated that the Medicare market basket 
update had only increased per-stay payments by less than 6 percent 
during that same time period. The commenter acknowledged that CMS will 
refresh the market basket update in the final rule with more recent 
data but expressed concern that the revised update will still be 
insufficient relative to input cost inflation as illustrated by the 
discrepancy between input costs and the market basket update in FY 
2022.
    Several commenters requested CMS exercise its existing authority or 
conditional funding opportunities to revise the proposed update to 
annual rates (either though an updated market basket or other allowable 
means) to account for the rapid rise of costs.
    Response: We recognize the various comments on the proposed net 
payment update of 3.7 percent. Section 1888(e)(5)(A) of the Act states 
the Secretary shall establish a skilled nursing facility market basket 
index that reflects changes over time in the prices of an appropriate 
mix of goods and services included in covered skilled nursing facility 
services. The 2018-based SNF market basket is a fixed-weight, 
Laspeyres-type price index that measures the change in price, over 
time, of the same mix of goods and services purchased in the base 
period. Any changes in the quantity or mix of goods and services (that 
is, intensity) purchased over time relative to a base period that would 
determine change in costs are not measured. For the compensation cost 
weight in the 2018-based SNF market basket (which includes salaried and 
contract labor employees), we use the Employment Cost Indexes (ECIs) 
for wages and salaries and benefits for private industry workers in 
nursing care facilities to proxy the price increase of SNF labor. The 
ECI (published by the Bureau of Labor Statistics, or BLS) measures the 
change in the hourly labor cost to employers, independent of the 
influence of employment shifts among occupations and industry 
categories. Therefore, we believe the ECI for private industry workers 
in nursing care facilities, which only reflects the price

[[Page 53208]]

change associated with the labor used to provide SNF care and 
appropriately does not reflect other factors that might affect labor 
costs, is an appropriate measure to use in the SNF market basket.
    We disagree with the commenter's statement that the 2018-based SNF 
market basket is not adequately reflecting growing expenditures in 
labor, which has driven a recent disproportionate increase in the labor 
share portion of the market basket. Our preliminary analysis of the 
2021 Medicare cost report data shows the compensation cost weight for 
freestanding SNFs is 59.9 percent--relatively unchanged from 2018 with 
60.2 percent as increases in the contract labor cost weight were 
accompanied by decreasing wages and salaries and benefit cost weights. 
We will continue to analyze more recent freestanding skilled nursing 
Medicare cost report data to assess whether the SNF market basket 
should be rebased and revised. Any changes to the SNF market basket 
will be proposed in future rulemaking.
    While the forecasted productivity-adjusted market basket update was 
2.4 percent in FY 2020, 2.2 percent in FY 2021, and 2.0 percent in FY 
2022, the increases in FY 2023 and FY 2024 reflect additional increases 
from forecast errors over this period (CMS provided a forecast error 
adjustment for FY 2021 of 1.5 percentage points in the FY 2023 SNF net 
payment update and a forecast error adjustment for FY 2022 of 3.6 
percentage points, which is being applied to the FY 2024 SNF net 
payment update in this final rule).
    While the average hourly wage for nursing from the reported SNF 
Medicare cost report data increased roughly 17 percent from 2019 to 
2021 (the most complete data available), the hourly wages of nearly all 
other medical occupational categories, which make up approximately 15 
percent of wages and salaries, have not increased by nearly as much. We 
found that the combined average wage for all other medical occupational 
categories, weighted by each occupation's percentage of total Adjusted 
Salaries as indicated on Worksheet S-3, Part V, Column 3 of the 
Medicare cost report, increased by less than 1 percent over the same 
time period. The compensation price proxy used in the SNF market basket 
would reflect trends in all occupations combined, which would partly 
explain why the ECI for wages and salaries for private industry workers 
in nursing care facilities has not increased at the pace of nursing 
wages alone.
    As proposed, for this final rule, we are updating the SNF market 
basket percentage increase to reflect more recent data. Based on IGI's 
second quarter 2023 forecast with historical data through the first 
quarter of 2023, we are finalizing a 2018-based SNF market basket 
percentage increase of 3.0 percent which reflects a projected increase 
in compensation prices of 3.4 percent. This is faster projected price 
growth compared to the proposed FY 2024 market basket increase of 2.7 
percent, which reflected a 3.0 percent compensation price growth. Both 
of the final FY 2024 increases are faster than the 10-year historical 
average price growth (2.6 percent for the 2018-based SNF market basket, 
with compensation prices increasing 2.7 percent).
    As noted previously, section 1888(e)(5)(A) of the Act requires us 
to establish a SNF market basket index that reflects changes over time 
in the prices of an appropriate mix of goods and services included in 
covered SNF services. This market basket percentage update is adjusted 
by a forecast error correction, if applicable, and then further 
adjusted by the application of a productivity adjustment as required by 
section 1888(e)(5)(B)(ii) of the Act. Section 1888(e)(5)(A) of the Act 
does not provide the Secretary with the authority to apply a different 
update factor to SNF PPS payment rates for FY 2024. Additionally, 
MedPAC annually conducts an analysis of payment adequacy for SNF 
providers. In its March 2023 Report to Congress (https://www.medpac.gov/document/march-2023-report-to-the-congress-medicare-payment-policy/) MedPAC noted the combination of Federal relief 
policies and the implementation of the new case-mix system resulted in 
overall improved financial performance for SNFs and recommended a 3 
percent reduction to the SNF base payment rates.
    Comment: Given that CMS is required by statute to implement a 
productivity adjustment to the market basket update, several commenters 
urged CMS to closely monitor the impact of such productivity 
adjustments and requested that the agency work with Congress to 
permanently eliminate or offset this reduction to SNF payments. 
Further, they requested that CMS use its exceptions authority under 
section 1888(e)(3)(A) of the Act to remove the productivity adjustment 
for any fiscal year that was covered under PHE determination (that is, 
2020 (0.4 percent), 2021 (0.0 percent), 2022 (0.7 percent), and 2023 
(0.3 percent)) from the calculation of the market basket for FY 2024 
and any year thereafter.
    Response: Section 1888(e)(5)(B)(ii) of the Act requires the 
application of the productivity adjustment described in section 
1886(b)(3)(xi)(II) of the Act to the SNF PPS market basket increase 
factor. As required by statute, the FY 2024 productivity adjustment is 
derived based on the 10-year moving average growth in economy-wide 
productivity for the period ending in FY 2024. We recognize the 
concerns of the commenters regarding the appropriateness of the 
productivity adjustment; however, we are required pursuant to section 
1888(e)(5)(B)(ii) of the Act to apply the specific productivity 
adjustment described here.
    Comment: MedPAC commented that while they understand that CMS is 
required to implement the statutory payment update, the combination of 
Federal relief policies and the implementation of the new case-mix 
system resulted in overall improved financial performance for SNFs. 
Thus, they recommended a 3 percent reduction to the SNF base payment 
rates.
    Response: We thank the commenter for their recommendation. However, 
we are required to update SNF PPS payments by the market basket 
percentage increase, as directed by section 1888(e)(4)(E)(ii)(IV) of 
the Act. This market basket percentage increase is adjusted by a 
forecast error correction, if applicable, and then further adjusted by 
the application of a productivity adjustment as required by section 
1888(e)(5)(B)(ii) of the Act.
    Comment: While many commenters were appreciative of the forecast 
error adjustment, one commenter noted that the application of the 
forecast error correction results in making a larger payment increase 
in addition to the statutory increase for FY 2024, even though the 
aggregate Medicare margin for SNFs is already high.
    Response: As most recently discussed in the FY 2023 SNF PPS final 
rule (87 FR 47502), forecast error adjustments for the SNF market 
basket were introduced in the FY 2004 SNF PPS final rule (68 FR 46035), 
with the intended goal ``to pay the appropriate amount, to the correct 
provider, for the proper service, at the right time''. We note that 
since implementation, forecast errors have generally been relatively 
small and clustered near zero and that for FY 2008 and subsequent 
years, we increased the threshold at which adjustments are triggered 
from 0.25 to 0.5 percentage

[[Page 53209]]

point. Our intent in raising the threshold was to distinguish typical 
statistical variances from more major unanticipated impacts and 
unforeseen disruptions of the economy (such as the recent PHE), or 
unexpected inflationary patterns (either at lower or higher than 
anticipated rates).
    Comment: One commenter suggested that the forecast error adjustment 
be adopted and utilized across every CMS payment program.
    Response: We appreciate the commenter's suggestion and will share 
this recommendation with our colleagues in other settings.
5. Unadjusted Federal Per Diem Rates for FY 2024
    As discussed in the FY 2019 SNF PPS final rule (83 FR 39162), in FY 
2020 we implemented a new case-mix classification system to classify 
SNF patients under the SNF PPS, the PDPM. As discussed in section 
V.B.1. of that final rule (83 FR 39189), under PDPM, the unadjusted 
Federal per diem rates are divided into six components, five of which 
are case-mix adjusted components (Physical Therapy (PT), Occupational 
Therapy (OT), Speech-Language Pathology (SLP), Nursing, and Non-Therapy 
Ancillaries (NTA)), and one of which is a non-case-mix component, as 
existed under the previous RUG-IV model. We proposed to use the SNF 
market basket, adjusted as described previously in sections IV.B.1. 
through IV.B.4. of this final rule, to adjust each per diem component 
of the Federal rates forward to reflect the change in the average 
prices for FY 2024 from the average prices for FY 2023. We also 
proposed to further adjust the rates by a wage index budget neutrality 
factor, described in section IV.D. of this final rule.
    Further, in the past, we used the revised Office of Management and 
Budget (OMB) delineations adopted in the FY 2015 SNF PPS final rule (79 
FR 45632, 45634), with updates as reflected in OMB Bulletin Nos. 15-01 
and 17-01, to identify a facility's urban or rural status for the 
purpose of determining which set of rate tables would apply to the 
facility. As discussed in the FY 2021 SNF PPS proposed and final rules, 
we adopted the revised OMB delineations identified in OMB Bulletin No. 
18-04 (available at https://www.whitehouse.gov/wp-content/uploads/2018/09/Bulletin-18-04.pdf) to identify a facility's urban or rural status 
effective beginning with FY 2021.
    Tables 3 and 4 reflect the updated unadjusted Federal rates for FY 
2024, prior to adjustment for case-mix.

                                                Table 3--FY 2024 Unadjusted Federal Rate Per Diem--URBAN
--------------------------------------------------------------------------------------------------------------------------------------------------------
                  Rate component                           PT               OT              SLP            Nursing            NTA          Non-case-mix
--------------------------------------------------------------------------------------------------------------------------------------------------------
Per Diem Amount...................................          $70.27           $65.41           $26.23          $122.48           $92.41          $109.69
--------------------------------------------------------------------------------------------------------------------------------------------------------


                                                Table 4--FY 2024 Unadjusted Federal Rate Per Diem--RURAL
--------------------------------------------------------------------------------------------------------------------------------------------------------
                  Rate component                           PT               OT              SLP            Nursing            NTA          Non-case-mix
--------------------------------------------------------------------------------------------------------------------------------------------------------
Per Diem Amount...................................          $80.10           $73.56           $33.05          $117.03           $88.29          $111.72
--------------------------------------------------------------------------------------------------------------------------------------------------------

C. Case-Mix Adjustment

    Under section 1888(e)(4)(G)(i) of the Act, the Federal rate also 
incorporates an adjustment to account for facility case-mix, using a 
classification system that accounts for the relative resource 
utilization of different patient types. The statute specifies that the 
adjustment is to reflect both a resident classification system that the 
Secretary establishes to account for the relative resource use of 
different patient types, as well as resident assessment data and other 
data that the Secretary considers appropriate. In the FY 2019 final 
rule (83 FR 39162, August 8, 2018), we finalized a new case-mix 
classification model, the PDPM, which took effect beginning October 1, 
2019. The previous RUG-IV model classified most patients into a therapy 
payment group and primarily used the volume of therapy services 
provided to the patient as the basis for payment classification, thus 
creating an incentive for SNFs to furnish therapy regardless of the 
individual patient's unique characteristics, goals, or needs. PDPM 
eliminates this incentive and improves the overall accuracy and 
appropriateness of SNF payments by classifying patients into payment 
groups based on specific, data-driven patient characteristics, while 
simultaneously reducing the administrative burden on SNFs.
    The PDPM uses clinical data from the MDS to assign case-mix 
classifiers to each patient that are then used to calculate a per diem 
payment under the SNF PPS, consistent with the provisions of section 
1888(e)(4)(G)(i) of the Act. As discussed in section V.A. of this final 
rule, the clinical orientation of the case-mix classification system 
supports the SNF PPS's use of an administrative presumption that 
considers a beneficiary's initial case-mix classification to assist in 
making certain SNF level of care determinations. Further, because the 
MDS is used as a basis for payment, as well as a clinical assessment, 
we have provided extensive training on proper coding and the timeframes 
for MDS completion in our Resident Assessment Instrument (RAI) Manual. 
As we have stated in prior rules, for an MDS to be considered valid for 
use in determining payment, the MDS assessment should be completed in 
compliance with the instructions in the RAI Manual in effect at the 
time the assessment is completed. For payment and quality monitoring 
purposes, the RAI Manual consists of both the Manual instructions and 
the interpretive guidance and policy clarifications posted on the 
appropriate MDS website at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/MDS30RAIManual.html.
    Under section 1888(e)(4)(H) of the Act, each update of the payment 
rates must include the case-mix classification methodology applicable 
for the upcoming FY. The FY 2024 payment rates set forth in this final 
rule reflect the use of the PDPM case-mix classification system from 
October 1, 2023, through September 30, 2024. The case-mix adjusted PDPM 
payment rates for FY 2024 are listed separately for urban and rural 
SNFs, in Tables 5 and 6 with corresponding case-mix values.
    Given the differences between the previous RUG-IV model and PDPM in 
terms of patient classification and billing, it was important that the 
format of Tables 5 and 6 reflect these differences. More specifically, 
under both RUG-IV and PDPM, providers use a Health Insurance 
Prospective Payment System (HIPPS) code on a claim to bill

[[Page 53210]]

for covered SNF services. Under RUG-IV, the HIPPS code included the 
three-character RUG-IV group into which the patient classified, as well 
as a two-character assessment indicator code that represented the 
assessment used to generate this code. Under PDPM, while providers 
still use a HIPPS code, the characters in that code represent different 
things. For example, the first character represents the PT and OT group 
into which the patient classifies. If the patient is classified into 
the PT and OT group ``TA'', then the first character in the patient's 
HIPPS code would be an A. Similarly, if the patient is classified into 
the SLP group ``SB'', then the second character in the patient's HIPPS 
code would be a B. The third character represents the Nursing group 
into which the patient classifies. The fourth character represents the 
NTA group into which the patient classifies. Finally, the fifth 
character represents the assessment used to generate the HIPPS code.
    Tables 5 and 6 reflect the PDPM's structure. Accordingly, Column 1 
of Tables 5 and 6 represents the character in the HIPPS code associated 
with a given PDPM component. Columns 2 and 3 provide the case-mix index 
and associated case-mix adjusted component rate, respectively, for the 
relevant PT group. Columns 4 and 5 provide the case-mix index and 
associated case-mix adjusted component rate, respectively, for the 
relevant OT group. Columns 6 and 7 provide the case-mix index and 
associated case-mix adjusted component rate, respectively, for the 
relevant SLP group. Column 8 provides the nursing case-mix group (CMG) 
that is connected with a given PDPM HIPPS character. For example, if 
the patient qualified for the nursing group CBC1, then the third 
character in the patient's HIPPS code would be a ``P.'' Columns 9 and 
10 provide the case-mix index and associated case-mix adjusted 
component rate, respectively, for the relevant nursing group. Finally, 
columns 11 and 12 provide the case-mix index and associated case-mix 
adjusted component rate, respectively, for the relevant NTA group.
    Tables 5 and 6 do not reflect adjustments which may be made to the 
SNF PPS rates as a result of the SNF VBP Program, discussed in section 
VII. of this final rule, or other adjustments, such as the variable per 
diem adjustment. Further, in the past, we used the revised OMB 
delineations adopted in the FY 2015 SNF PPS final rule (79 FR 45632, 
45634), with updates as reflected in OMB Bulletin Nos, 15-01 and 17-01, 
to identify a facility's urban or rural status for the purpose of 
determining which set of rate tables would apply to the facility. As 
discussed in the FY 2021 SNF PPS final rule (85 FR 47594), we adopted 
the revised OMB delineations identified in OMB Bulletin No. 18-04 
(available at https://www.whitehouse.gov/wp-content/uploads/2018/09/Bulletin-18-04.pdf) to identify a facility's urban or rural status 
effective beginning with FY 2021.
    In the FY 2023 SNF PPS final rule (87 FR 47502), we finalized a 
proposal to recalibrate the PDPM parity adjustment over 2 years 
starting in FY 2023, which means that, for each of the PDPM case-mix 
adjusted components, we lowered the PDPM parity adjustment factor from 
46 percent to 42 percent in FY 2023 and we will further lower the PDPM 
parity adjustment factor from 42 percent to 38 percent in FY 2024. 
Following this methodology, which is further described in the FY 2023 
SNF PPS final rule (87 FR 47525 through 47534), Tables 5 and 6 
incorporate the second phase of the PDPM parity adjustment 
recalibration.

               Table 5--PDPM Case-Mix Adjusted Federal Rates and Associated Indexes--URBAN (Including the Parity Adjustment Recalibration)
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                                                    Nursing    Nursing    Nursing
           PDPM group              PT CMI    PT rate     OT CMI    OT rate    SLP CMI    SLP rate     CMG        CMI        rate     NTA CMI    NTA rate
--------------------------------------------------------------------------------------------------------------------------------------------------------
A..............................       1.45    $101.89       1.41     $92.23       0.64     $16.79        ES3       3.84    $470.32       3.06    $282.77
B..............................       1.61     113.13       1.54     100.73       1.72      45.12        ES2       2.90     355.19       2.39     220.86
C..............................       1.78     125.08       1.60     104.66       2.52      66.10        ES1       2.77     339.27       1.74     160.79
D..............................       1.81     127.19       1.45      94.84       1.38      36.20       HDE2       2.27     278.03       1.26     116.44
E..............................       1.34      94.16       1.33      87.00       2.21      57.97       HDE1       1.88     230.26       0.91      84.09
F..............................       1.52     106.81       1.51      98.77       2.82      73.97       HBC2       2.12     259.66       0.68      62.84
G..............................       1.58     111.03       1.55     101.39       1.93      50.62       HBC1       1.76     215.56  .........  .........
H..............................       1.10      77.30       1.09      71.30        2.7      70.82       LDE2       1.97     241.29  .........  .........
I..............................       1.07      75.19       1.12      73.26       3.34      87.61       LDE1       1.64     200.87  .........  .........
J..............................       1.34      94.16       1.37      89.61       2.83      74.23       LBC2       1.63     199.64  .........  .........
K..............................       1.44     101.19       1.46      95.50        3.5      91.81       LBC1       1.35     165.35  .........  .........
L..............................       1.03      72.38       1.05      68.68       3.98     104.40       CDE2       1.77     216.79  .........  .........
M..............................       1.20      84.32       1.23      80.45  .........  .........       CDE1       1.53     187.39  .........  .........
N..............................       1.40      98.38       1.42      92.88  .........  .........       CBC2       1.47     180.05  .........  .........
O..............................       1.47     103.30       1.47      96.15  .........  .........        CA2       1.03     126.15  .........  .........
P..............................       1.02      71.68       1.03      67.37  .........  .........       CBC1       1.27     155.55  .........  .........
Q..............................  .........  .........  .........  .........  .........  .........        CA1       0.89     109.01  .........  .........
R..............................  .........  .........  .........  .........  .........  .........       BAB2       0.98     120.03  .........  .........
S..............................  .........  .........  .........  .........  .........  .........       BAB1       0.94     115.13  .........  .........
T..............................  .........  .........  .........  .........  .........  .........       PDE2       1.48     181.27  .........  .........
U..............................  .........  .........  .........  .........  .........  .........       PDE1       1.39     170.25  .........  .........
V..............................  .........  .........  .........  .........  .........  .........       PBC2       1.15     140.85  .........  .........
W..............................  .........  .........  .........  .........  .........  .........        PA2       0.67      82.06  .........  .........
X..............................  .........  .........  .........  .........  .........  .........       PBC1       1.07     131.05  .........  .........
Y..............................  .........  .........  .........  .........  .........  .........        PA1       0.62      75.94  .........  .........
--------------------------------------------------------------------------------------------------------------------------------------------------------


[[Page 53211]]


               Table 6--PDPM Case-Mix Adjusted Federal Rates and Associated Indexes--RURAL (Including the Parity Adjustment Recalibration)
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                                                    Nursing    Nursing    Nursing
           PDPM group              PT CMI    PT rate     OT CMI    OT rate    SLP CMI    SLP rate     CMG        CMI        rate     NTA CMI    NTA rate
--------------------------------------------------------------------------------------------------------------------------------------------------------
A..............................       1.45    $116.15       1.41    $103.72       0.64     $21.15        ES3       3.84    $449.40       3.06    $270.17
B..............................       1.61     128.96       1.54     113.28       1.72      56.85        ES2       2.90     339.39       2.39     211.01
C..............................       1.78     142.58       1.60     117.70       2.52      83.29        ES1       2.77     324.17       1.74     153.62
D..............................       1.81     144.98       1.45     106.66       1.38      45.61       HDE2       2.27     265.66       1.26     111.25
E..............................       1.34     107.33       1.33      97.83       2.21      73.04       HDE1       1.88     220.02       0.91      80.34
F..............................       1.52     121.75       1.51     111.08       2.82      93.20       HBC2       2.12     248.10       0.68      60.04
G..............................       1.58     126.56       1.55     114.02       1.93      63.79       HBC1       1.76     205.97  .........  .........
H..............................       1.10      88.11       1.09      80.18        2.7      89.24       LDE2       1.97     230.55  .........  .........
I..............................       1.07      85.71       1.12      82.39       3.34     110.39       LDE1       1.64     191.93  .........  .........
J..............................       1.34     107.33       1.37     100.78       2.83      93.53       LBC2       1.63     190.76  .........  .........
K..............................       1.44     115.34       1.46     107.40        3.5     115.68       LBC1       1.35     157.99  .........  .........
L..............................       1.03      82.50       1.05      77.24       3.98     131.54       CDE2       1.77     207.14  .........  .........
M..............................       1.20      96.12       1.23      90.48  .........  .........       CDE1       1.53     179.06  .........  .........
N..............................       1.40     112.14       1.42     104.46  .........  .........       CBC2       1.47     172.03  .........  .........
O..............................       1.47     117.75       1.47     108.13  .........  .........        CA2       1.03     120.54  .........  .........
P..............................       1.02      81.70       1.03      75.77  .........  .........       CBC1       1.27     148.63  .........  .........
Q..............................  .........  .........  .........  .........  .........  .........        CA1       0.89     104.16  .........  .........
R..............................  .........  .........  .........  .........  .........  .........       BAB2       0.98     114.69  .........  .........
S..............................  .........  .........  .........  .........  .........  .........       BAB1       0.94     110.01  .........  .........
T..............................  .........  .........  .........  .........  .........  .........       PDE2       1.48     173.20  .........  .........
U..............................  .........  .........  .........  .........  .........  .........       PDE1       1.39     162.67  .........  .........
V..............................  .........  .........  .........  .........  .........  .........       PBC2       1.15     134.58  .........  .........
W..............................  .........  .........  .........  .........  .........  .........        PA2       0.67      78.41  .........  .........
X..............................  .........  .........  .........  .........  .........  .........       PBC1       1.07     125.22  .........  .........
Y..............................  .........  .........  .........  .........  .........  .........        PA1       0.62      72.56  .........  .........
--------------------------------------------------------------------------------------------------------------------------------------------------------

    Commenters submitted the following comments related to the proposed 
Federal per diem rates for FY 2024. A discussion of these comments, 
along with our responses, appears below.
    Comment: One commenter stated that the case-mix adjusted rates for 
PT, OT, SLP, and nursing categories are higher in urban areas than in 
rural areas, which exacerbate inequalities between rural and urban 
SNFs.
    Response: We disagree with the commenter's statement that the case-
mix adjusted rates for the PT, OT and SLP components are higher in 
urban than rural areas as shown in Tables 5 and 6. As most recently 
noted in the FY 2023 SNF PPS final rule (87 FR 47502), the Federal per 
diem rates were established separately for urban and rural areas using 
allowable costs from FY 1995 cost reports, and therefore, account for 
and reflect the relative costs differences between urban and rural 
facilities. We note that the SNF PPS payment rates are updated annually 
by an increase factor that reflects changes over time in the prices of 
an appropriate mix of goods and services included in the covered SNF 
services and a portion of these rates are further adjusted by a wage 
index to reflect geographic variations in wages. We will continue to 
monitor our SNF payment policies to ensure they reflect as accurately 
as possible the current costs of care in the SNF setting.
    Comment: One commenter was appreciative of the increase in payment 
for FY 2024 and encouraged CMS to maximize support for rural SNFs.
    Response: We thank the commenter for their support of the payment 
rate update for FY 2024 and note that rural SNFs are expected to 
experience, on average, a 3.3 percent increase in payments compared 
with FY 2023.
    Comment: Commenters encouraged CMS to continue to monitor the 
impact of the PDPM on beneficiaries' access to appropriate SNF 
services, including therapy services to address any emerging problems 
affecting SNF residents.
    Response: We thank the commenter for their suggestion. We will 
continue to monitor the impact of the PDPM implementation on patient 
outcomes and other metrics to identify any adverse trends accompanying 
the revisions to the PPS.
    Comment: Commenters generally expressed appreciation that the 
parity adjustment was phased in over 2 years but expressed concern that 
there would be a reduction to the SNF payment rates for FY 2024 due to 
this adjustment. A few commenters requested that the PDPM parity 
adjustment be delayed, reduced, cancelled or be phased in over an 
additional 2 years. One commenter indicated that they support 
implementing the remainder of the recalibrated parity adjustment in FY 
2024 to prevent continued SNF payments in excess of the intended budget 
neutral implementation of the PDPM.
    Response: We thank the commenters for their support of the phase in 
of the parity adjustment. We believe the 2-year phase-in was sufficient 
to mitigate adverse payment impacts while also ensuring that payment 
rates for all SNFs are set accurately and appropriately. As such, we do 
not believe it would be appropriate to expand the phase-in period 
beyond than what was finalized in the FY 2023 SNF PPS final rule. We 
refer readers to the FY 2023 SNF PPS final rule (87 FR 47502), for a 
full discussion of the rationale related to the implementation of this 
policy.

D. Wage Index Adjustment

    Section 1888(e)(4)(G)(ii) of the Act requires that we adjust the 
Federal rates to account for differences in area wage levels, using a 
wage index that the Secretary determines appropriate. Since the 
inception of the SNF PPS, we have used hospital inpatient wage data in 
developing a wage index to be applied to SNFs. We will continue this 
practice for FY 2024, as we continue to believe that in the absence of 
SNF-specific wage data, using the hospital inpatient wage index data is 
appropriate and reasonable for the SNF PPS. As explained in the update 
notice for FY 2005 (69 FR 45786), the SNF PPS does not use the hospital 
area wage index's occupational mix adjustment, as this adjustment

[[Page 53212]]

serves specifically to define the occupational categories more clearly 
in a hospital setting; moreover, the collection of the occupational 
wage data under the inpatient prospective payment system (IPPS) also 
excludes any wage data related to SNFs. Therefore, we believe that 
using the updated wage data exclusive of the occupational mix 
adjustment continues to be appropriate for SNF payments. As in previous 
years, we would continue to use the pre-reclassified IPPS hospital wage 
data, without applying the occupational mix, rural floor, or 
outmigration adjustment, as the basis for the SNF PPS wage index. For 
FY 2024, the updated wage data are for hospital cost reporting periods 
beginning on or after October 1, 2019 and before October 1, 2020 (FY 
2020 cost report data).
    We note that section 315 of the Medicare, Medicaid, and SCHIP 
Benefits Improvement and Protection Act of 2000 (BIPA) (Pub. L. 106-
554, enacted December 21, 2000) gave the Secretary the discretion to 
establish a geographic reclassification procedure specific to SNFs, but 
only after collecting the data necessary to establish a SNF PPS wage 
index that is based on wage data from nursing homes. To date, this has 
proven to be unfeasible due to the volatility of existing SNF wage data 
and the significant amount of resources that would be required to 
improve the quality of the data. More specifically, auditing all SNF 
cost reports, similar to the process used to audit inpatient hospital 
cost reports for purposes of the IPPS wage index, would place a burden 
on providers in terms of recordkeeping and completion of the cost 
report worksheet. Adopting such an approach would require a significant 
commitment of resources by CMS and the Medicare Administrative 
Contractors (MACs), potentially far in excess of those required under 
the IPPS, given that there are nearly five times as many SNFs as there 
are inpatient hospitals. While we continue to believe that the 
development of such an audit process could improve SNF cost reports, 
which is determined to be adequately accurate for cost development 
purposes, in such a manner as to permit us to establish a SNF-specific 
wage index, we do not believe this undertaking is feasible.
    In addition, we will continue to use the same methodology discussed 
in the SNF PPS final rule for FY 2008 (72 FR 43423) to address those 
geographic areas in which there are no hospitals, and thus, no hospital 
wage index data on which to base the calculation of the FY 2022 SNF PPS 
wage index. For rural geographic areas that do not have hospitals and, 
therefore, lack hospital wage data on which to base an area wage 
adjustment, we will continue using the average wage index from all 
contiguous Core-Based Statistical Areas (CBSAs) as a reasonable proxy. 
For FY 2024, there are no rural geographic areas that do not have 
hospitals, and thus, this methodology will not be applied. For rural 
Puerto Rico, we will not apply this methodology due to the distinct 
economic circumstances there; due to the close proximity of almost all 
of Puerto Rico's various urban and non-urban areas, this methodology 
will produce a wage index for rural Puerto Rico that is higher than 
that in half of its urban areas. Instead, we will continue using the 
most recent wage index previously available for that area. For urban 
areas without specific hospital wage index data, we will continue using 
the average wage indexes of all urban areas within the State to serve 
as a reasonable proxy for the wage index of that urban CBSA. For FY 
2024, the only urban area without wage index data available is CBSA 
25980, Hinesville-Fort Stewart, GA.
    In the SNF PPS final rule for FY 2006 (70 FR 45026, August 4, 
2005), we adopted the changes discussed in OMB Bulletin No. 03-04 (June 
6, 2003), which announced revised definitions for MSAs and the creation 
of micropolitan statistical areas and combined statistical areas. In 
adopting the CBSA geographic designations, we provided for a 1-year 
transition in FY 2006 with a blended wage index for all providers. For 
FY 2006, the wage index for each provider consisted of a blend of 50 
percent of the FY 2006 MSA-based wage index and 50 percent of the FY 
2006 CBSA-based wage index (both using FY 2002 hospital data). We 
referred to the blended wage index as the FY 2006 SNF PPS transition 
wage index. As discussed in the SNF PPS final rule for FY 2006 (70 FR 
45041), after the expiration of this 1-year transition on September 30, 
2006, we used the full CBSA-based wage index values.
    In the FY 2015 SNF PPS final rule (79 FR 45644 through 45646), we 
finalized changes to the SNF PPS wage index based on the newest OMB 
delineations, as described in OMB Bulletin No. 13-01, beginning in FY 
2015, including a 1-year transition with a blended wage index for FY 
2015. OMB Bulletin No. 13-01 established revised delineations for 
Metropolitan Statistical Areas, Micropolitan Statistical Areas, and 
Combined Statistical Areas in the United States and Puerto Rico based 
on the 2010 Census, and provided guidance on the use of the 
delineations of these statistical areas using standards published in 
the June 28, 2010 Federal Register (75 FR 37246 through 37252). 
Subsequently, on July 15, 2015, OMB issued OMB Bulletin No. 15-01, 
which provided minor updates to and superseded OMB Bulletin No. 13-01 
that was issued on February 28, 2013. The attachment to OMB Bulletin 
No. 15-01 provided detailed information on the update to statistical 
areas since February 28, 2013. The updates provided in OMB Bulletin No. 
15-01 were based on the application of the 2010 Standards for 
Delineating Metropolitan and Micropolitan Statistical Areas to Census 
Bureau population estimates for July 1, 2012 and July 1, 2013 and were 
adopted under the SNF PPS in the FY 2017 SNF PPS final rule (81 FR 
51983, August 5, 2016). In addition, on August 15, 2017, OMB issued 
Bulletin No. 17-01 which announced a new urban CBSA, Twin Falls, Idaho 
(CBSA 46300) which was adopted in the SNF PPS final rule for FY 2019 
(83 FR 39173, August 8, 2018).
    As discussed in the FY 2021 SNF PPS final rule (85 FR 47594), we 
adopted the revised OMB delineations identified in OMB Bulletin No. 18-
04 (available at https://www.whitehouse.gov/wp-content/uploads/2018/09/Bulletin-18-04.pdf) beginning October 1, 2020, including a 1-year 
transition for FY 2021 under which we applied a 5 percent cap on any 
decrease in a hospital's wage index compared to its wage index for the 
prior fiscal year (FY 2020). The updated OMB delineations more 
accurately reflect the contemporary urban and rural nature of areas 
across the country, and the use of such delineations allows us to 
determine more accurately the appropriate wage index and rate tables to 
apply under the SNF PPS.
    In the FY 2023 SNF PPS final rule (87 FR 47521 through 47525), we 
finalized a policy to apply a permanent 5 percent cap on any decreases 
to a provider's wage index from its wage index in the prior year, 
regardless of the circumstances causing the decline. Additionally, we 
finalized a policy that a new SNF would be paid the wage index for the 
area in which it is geographically located for its first full or 
partial FY with no cap applied because a new SNF would not have a wage 
index in the prior FY. We amended the SNF PPS regulations at 42 CFR 
413.337(b)(4)(ii) to reflect this permanent cap on wage index 
decreases. A full discussion of the adoption of this policy is found in 
the FY 2023 SNF PPS final rule.
    As we previously stated in the FY 2008 SNF PPS proposed and final 
rules (72 FR 25538 through 25539, and 72 FR

[[Page 53213]]

43423), this and all subsequent SNF PPS rules and notices are 
considered to incorporate any updates and revisions set forth in the 
most recent OMB bulletin that applies to the hospital wage data used to 
determine the current SNF PPS wage index. OMB issued further revised 
CBSA delineations in OMB Bulletin No. 20-01, on March 6, 2020 
(available on the web at https://www.whitehouse.gov/wp-content/uploads/2020/03/Bulletin-20-01.pdf). However, we determined that the changes in 
OMB Bulletin No. 20-01 do not impact the CBSA-based labor market area 
delineations adopted in FY 2021. Therefore, we did not propose to adopt 
the revised OMB delineations identified in OMB Bulletin No. 20 01 for 
FY 2022 or 2023, and for these reasons we are likewise not making such 
a requirement for FY 2024. The wage index applicable to FY 2024 is set 
forth in Tables A and B available on the CMS website at http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/WageIndex.html.
    Once calculated, we will apply the wage index adjustment to the 
labor-related portion of the Federal rate. Each year, we calculate a 
labor-related share, based on the relative importance of labor-related 
cost categories (that is, those cost categories that are labor-
intensive and vary with the local labor market) in the input price 
index. In the SNF PPS final rule for FY 2022 (86 FR 42437), we 
finalized a proposal to revise the labor-related share to reflect the 
relative importance of the 2018-based SNF market basket cost weights 
for the following cost categories: Wages and Salaries; Employee 
Benefits; Professional Fees: Labor-Related; Administrative and 
Facilities Support services; Installation, Maintenance, and Repair 
Services; All Other: Labor-Related Services; and a proportion of 
Capital-Related expenses. The methodology for calculating the labor-
related portion beginning in FY 2022 is discussed in detail in the FY 
2022 SNF PPS final rule (86 FR 42461 through 42463).
    We calculate the labor-related relative importance from the SNF 
market basket, and it approximates the labor-related portion of the 
total costs after taking into account historical and projected price 
changes between the base year and FY 2024. The price proxies that move 
the different cost categories in the market basket do not necessarily 
change at the same rate, and the relative importance captures these 
changes. Accordingly, the relative importance figure more closely 
reflects the cost share weights for FY 2024 than the base year weights 
from the SNF market basket. We calculate the labor-related relative 
importance for FY 2024 in four steps. First, we compute the FY 2024 
price index level for the total market basket and each cost category of 
the market basket. Second, we calculate a ratio for each cost category 
by dividing the FY 2024 price index level for that cost category by the 
total market basket price index level. Third, we determine the FY 2024 
relative importance for each cost category by multiplying this ratio by 
the base year (2018) weight. Finally, we add the FY 2024 relative 
importance for each of the labor-related cost categories (Wages and 
Salaries; Employee Benefits; Professional Fees: Labor-Related; 
Administrative and Facilities Support Services; Installation, 
Maintenance, and Repair Services; All Other: Labor-Related Services; 
and a portion of Capital-Related expenses) to produce the FY 2024 
labor-related relative importance.
    For the proposed rule, the labor-related share for FY 2024 was 
based on IGI's fourth quarter 2022 forecast of the 2018-based SNF 
market basket with historical data through the third quarter of 2022. 
As outlined in the proposed rule, we noted that if more recent data 
became available (for example, a more recent estimate of the labor-
related share relative importance) we would use such data, if 
appropriate, for the SNF final rule. For this final rule, we base the 
labor-related share for FY 2024 on IGI's second quarter 2023 forecast, 
with historical data through the first quarter of 2023 of the 2018-
based SNF market basket.
    Table 7 summarizes the labor-related share for FY 2024, based on 
IGI's second quarter 2023 forecast of the 2018-based SNF market basket, 
compared to the labor-related share that was used for the FY 2023 SNF 
PPS final rule.

                                Table 7--Labor-Related Share, FY 2023 and FY 2024
----------------------------------------------------------------------------------------------------------------
                                                                  Relative importance,     Relative importance,
                                                                labor-related share, FY  labor-related share, FY
                                                                 2023 22:2 forecast \1\   2024 23:2 forecast \2\
----------------------------------------------------------------------------------------------------------------
Wages and salaries............................................                     51.9                     52.5
Employee benefits.............................................                      9.5                      9.3
Professional fees: Labor-related..............................                      3.5                      3.4
Administrative & facilities support services..................                      0.6                      0.6
Installation, maintenance & repair services...................                      0.4                      0.4
All other: Labor-related services.............................                      2.0                      2.0
Capital-related (.391)........................................                      2.9                      2.9
                                                               -------------------------------------------------
    Total.....................................................                     70.8                     71.1
----------------------------------------------------------------------------------------------------------------
\1\ Published in the Federal Register; Based on the second quarter 2022 IHS Global Inc. forecast of the 2018-
  based SNF market basket.
\2\ Based on the second quarter 2023 IHS Global Inc. forecast of the 2018-based SNF market basket.

    To calculate the labor portion of the case-mix adjusted per diem 
rate, we will multiply the total case-mix adjusted per diem rate, which 
is the sum of all five case-mix adjusted components into which a 
patient classifies, and the non-case-mix component rate, by the FY 2024 
labor-related share percentage provided in Table 7. The remaining 
portion of the rate would be the non-labor portion. Under the previous 
RUG-IV model, we included tables which provided the case-mix adjusted 
RUG-IV rates, by RUG-IV group, broken out by total rate, labor portion 
and non-labor portion, such as Table 9 of the FY 2019 SNF PPS final 
rule (83 FR 39175). However, as we discussed in the FY 2020 final rule 
(84 FR 38738), under PDPM, as the total rate is calculated as a 
combination of six different component rates, five of which are case-
mix adjusted, and given the sheer volume of possible combinations of 
these five case-mix adjusted components, it is not feasible to provide 
tables similar to those that existed in the prior rulemaking.
    Therefore, to aid interested parties in understanding the effect of 
the wage

[[Page 53214]]

index on the calculation of the SNF per diem rate, we have included a 
hypothetical rate calculation in Table 9.
    Section 1888(e)(4)(G)(ii) of the Act also requires that we apply 
this wage index in a manner that does not result in aggregate payments 
under the SNF PPS that are greater or less than would otherwise be made 
if the wage adjustment had not been made. For FY 2024 (Federal rates 
effective October 1, 2023), we apply an adjustment to fulfill the 
budget neutrality requirement. We meet this requirement by multiplying 
each of the components of the unadjusted Federal rates by a budget 
neutrality factor, equal to the ratio of the weighted average wage 
adjustment factor for FY 2023 to the weighted average wage adjustment 
factor for FY 2024. For this calculation, we will use the same FY 2022 
claims utilization data for both the numerator and denominator of this 
ratio. We define the wage adjustment factor used in this calculation as 
the labor portion of the rate component multiplied by the wage index 
plus the non-labor portion of the rate component. The finalized budget 
neutrality factor for FY 2024 is 0.9997.
    We note that if more recent data become available (for example, 
revised wage data), we would use such data, as appropriate, to 
determine the wage index budget neutrality factor in the SNF PPS final 
rule.
    We solicited public comment on the proposed SNF wage adjustment for 
FY 2024. The following is a summary of the comments we received and our 
responses.
    Comment: One commenter did not support any increases in the labor-
related share as any facility that has a wage index less than 1.0 will 
suffer financially from a rise in the labor-related share. They stated 
that across the country, there is a growing disparity between the high-
wage and low-wage States.
    Response: We appreciate the commenter's concern. However, each year 
we calculate a labor-related share based on the relative importance of 
labor-related cost categories, to account historical and projected 
price changes between the base year and the payment year (FY 2024 in 
this rule). The price proxies that move the different cost categories 
in the market basket do not necessarily change at the same rate, and 
the relative importance captures these changes. As shown in Table 7, 
the slight increase in the labor-related share is due to an increase in 
the wages and salaries relative importance cost weight, reflecting the 
faster wage prices compared to other nonwage prices in the SNF market 
basket. This increase is consistent with comments we have received 
during this rulemaking about faster wage prices.
    As discussed above, based on IGI's second quarter 2023 forecast 
with historical data through the first quarter of 2023, we are 
finalizing the FY 2024 labor-related share of 71.1 percent based on the 
relative importance of each of the labor-related cost categories in the 
2018-based SNF market basket.
    Comment: Commenters stated support of the permanent 5-percent cap 
on wage index decreases. One commenter encouraged CMS to implement 
these caps in a non-budget neutral manner to stabilize provider 
reimbursement and avoid further unexpected reductions for other 
providers.
    Response: We appreciate the commenters' support of the permanent 
cap on wage index decreases. As for budget neutrality, we do not 
believe that the permanent 5-percent cap policy for the SNF wage index 
should be applied in a non-budget-neutral manner. The statute at 
section 1888(e)(4)(G)(ii) of the Act requires that adjustments for 
geographic variations in labor costs for a FY are made in a budget-
neutral. We refer readers to the FY 2023 SNF PPS final rule (87 FR 
47521 through 47523) for a detailed discussion and for responses to 
these and other comments relating to the wage index cap policy.
    Comment: While commenters support the current wage index 
methodology for FY 2024, including not requiring the commitment of 
resources needed to do audits on cost reports at this time, others 
encourage CMS to continue to reform the wage index policies (for 
example, SNF-specific wage index utilizing SNF audited cost report and 
nursing wage data).
    Response: We appreciate the commenters' support of the proposed 
wage index policies for FY 2024. In the absence of a SNF-specific wage 
index, we believe the use of the pre-reclassified and pre-floor 
hospital wage data (without the occupational mix adjustment) continue 
to be an appropriate and reasonable proxy for the SNF PPS. For a 
detailed discussion of the rationale for our current wage index 
policies and for responses to these recurring comments, we refer 
readers to the FY 2023 SNF PPS final rule (87 FR 47513 through 47516) 
and the FY 2016 SNF PPS final rule (80 FR 46401 through 46402).
    Comment: One commenter recommended that CMS should, as a matter of 
policy, require that SNFs provide wages on parity with hospitals for 
nursing staff. This commenter stated that, given that the SNF wage 
index is based on hospital wages, CMS should require that SNFs pay the 
same wages as the hospitals for nursing staff.
    Response: We appreciate the commenter's suggestion. While we 
continue to believe that the pre-reclassified and pre-floor hospital 
wage index serves as an appropriate proxy for the SNF PPS, we do not 
believe that it would be appropriate for us to require SNFs to pay a 
certain amount to their staff. How a SNF chooses to reimburse their 
staff is a private financial arrangement between the facility and its 
staff, which means that we believe it would be inappropriate to 
establish regulations that govern this matter since there is no 
statutory authority present.
    After consideration of public comments, we are finalizing our 
proposal regarding the wage index adjustment for FY 2024.

E. SNF Value-Based Purchasing Program

    Beginning with payment for services furnished on October 1, 2018, 
section 1888(h) of the Act requires the Secretary to reduce the 
adjusted Federal per diem rate determined under section 1888(e)(4)(G) 
of the Act otherwise applicable to a SNF for services furnished during 
a fiscal year by 2 percent, and to adjust the resulting rate for a SNF 
by the value-based incentive payment amount earned by the SNF based on 
the SNF's performance score for that fiscal year under the SNF VBP 
Program. To implement these requirements, we finalized in the FY 2019 
SNF PPS final rule the addition of Sec.  413.337(f) to our regulations 
(83 FR 39178).
    Please see section VIII. of this final rule for further discussion 
of the updates we are finalizing for the SNF VBP Program.

F. Adjusted Rate Computation Example

    Tables 8 through 10 provide examples generally illustrating payment 
calculations during FY 2024 under PDPM for a hypothetical 30-day SNF 
stay, involving the hypothetical SNF XYZ, located in Frederick, MD 
(Urban CBSA 23224), for a hypothetical patient who is classified into 
such groups that the patient's HIPPS code is NHNC1. Table 8 shows the 
adjustments made to the Federal per diem rates (prior to application of 
any adjustments under the SNF VBP Program as discussed previously and 
taking into account the second phase of the parity adjustment 
recalibration discussed in section IV.C. of this final rule) to compute 
the provider's case-mix adjusted per diem rate for FY 2024, based on 
the patient's PDPM classification, as well as how the variable per diem 
(VPD) adjustment

[[Page 53215]]

factor affects calculation of the per diem rate for a given day of the 
stay. Table 9 shows the adjustments made to the case-mix adjusted per 
diem rate from Table 8 to account for the provider's wage index. The 
wage index used in this example is based on the FY 2024 SNF PPS wage 
index that appears in Table A available on the CMS website at http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/WageIndex.html. Finally, Table 10 provides the case-mix and wage index 
adjusted per-diem rate for this patient for each day of the 30-day 
stay, as well as the total payment for this stay. Table 10 also 
includes the VPD adjustment factors for each day of the patient's stay, 
to clarify why the patient's per diem rate changes for certain days of 
the stay. As illustrated in Table 10, SNF XYZ's total PPS payment for 
this particular patient's stay would equal $21,717.98.

                            Table 8--PDPM Case-Mix Adjusted Rate Computation Example
----------------------------------------------------------------------------------------------------------------
                                            Per diem rate calculation
-----------------------------------------------------------------------------------------------------------------
                                                     Component                    VPD adjustment
                    Component                          group      Component rate      factor       VPD adj. rate
----------------------------------------------------------------------------------------------------------------
PT..............................................               N          $98.38            1.00          $98.38
OT..............................................               N           92.88            1.00           92.88
SLP.............................................               H           70.82            1.00           70.82
Nursing.........................................               N          180.05            1.00          180.05
NTA.............................................               C          160.79            3.00          482.37
Non-Case-Mix....................................  ..............          109.69  ..............          109.69
                                                 ---------------------------------------------------------------
    Total PDPM Case-Mix Adj. Per Diem...........  ..............  ..............  ..............        1,034.19
----------------------------------------------------------------------------------------------------------------



                                                  Table 9--Wage Index Adjusted Rate Computation Example
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                         PDPM wage index adjustment calculation
---------------------------------------------------------------------------------------------------------------------------------------------------------
                                                     PDPM  case-mix                                                                       Total case mix
                    HIPPS code                        adjusted per    Labor portion      Wage index       Wage index       Non-labor      and wage index
                                                          diem                                          adjusted rate       portion         adj. rate
--------------------------------------------------------------------------------------------------------------------------------------------------------
NHNC1.............................................       $1,034.19          $735.31           0.9637          $708.62          $298.88        $1,007.50
--------------------------------------------------------------------------------------------------------------------------------------------------------



                                   Table 10--Adjusted Rate Computation Example
----------------------------------------------------------------------------------------------------------------
                                                                                                   Case mix and
                                                                      NTA VPD        PT/OT VPD      wage Index
                           Day of stay                              adjustment      adjustment     adjusted per
                                                                      factor          factor         diem rate
----------------------------------------------------------------------------------------------------------------
1...............................................................             3.0             1.0       $1,007.50
2...............................................................             3.0             1.0        1,007.50
3...............................................................             3.0             1.0        1,007.50
4...............................................................             1.0             1.0          694.22
5...............................................................             1.0             1.0          694.22
6...............................................................             1.0             1.0          694.22
7...............................................................             1.0             1.0          694.22
8...............................................................             1.0             1.0          694.22
9...............................................................             1.0             1.0          694.22
10..............................................................             1.0             1.0          694.22
11..............................................................             1.0             1.0          694.22
12..............................................................             1.0             1.0          694.22
13..............................................................             1.0             1.0          694.22
14..............................................................             1.0             1.0          694.22
15..............................................................             1.0             1.0          694.22
16..............................................................             1.0             1.0          694.22
17..............................................................             1.0             1.0          694.22
18..............................................................             1.0             1.0          694.22
19..............................................................             1.0             1.0          694.22
20..............................................................             1.0             1.0          694.22
21..............................................................             1.0            0.98          690.49
22..............................................................             1.0            0.98          690.49
23..............................................................             1.0            0.98          690.49
24..............................................................             1.0            0.98          690.49
25..............................................................             1.0            0.98          690.49
26..............................................................             1.0            0.98          690.49
27..............................................................             1.0            0.98          690.49
28..............................................................             1.0            0.96          686.77
29..............................................................             1.0            0.96          686.77

[[Page 53216]]

 
30..............................................................             1.0            0.96          686.77
                                                                 -----------------------------------------------
    Total Payment...............................................  ..............  ..............       21,717.98
----------------------------------------------------------------------------------------------------------------

V. Additional Aspects of the SNF PPS

A. SNF Level of Care--Administrative Presumption

    The establishment of the SNF PPS did not change Medicare's 
fundamental requirements for SNF coverage. However, because the case-
mix classification is based, in part, on the beneficiary's need for 
skilled nursing care and therapy, we have attempted, where possible, to 
coordinate claims review procedures with the existing resident 
assessment process and case-mix classification system discussed in 
section III.C. of the FY 2024 SNF PPS proposed rule. This approach 
includes an administrative presumption that utilizes a beneficiary's 
correct assignment, at the outset of the SNF stay, of one of the case-
mix classifiers designated for this purpose to assist in making certain 
SNF level of care determinations.
    In accordance with Sec.  413.345, we include in each update of the 
Federal payment rates in the Federal Register a discussion of the 
resident classification system that provides the basis for case-mix 
adjustment. We also designate those specific classifiers under the 
case-mix classification system that represent the required SNF level of 
care, as provided in 42 CFR 409.30. This designation reflects an 
administrative presumption that those beneficiaries who are correctly 
assigned one of the designated case-mix classifiers on the initial 
Medicare assessment are automatically classified as meeting the SNF 
level of care definition up to and including the assessment reference 
date (ARD) for that assessment.
    A beneficiary who does not qualify for the presumption is not 
automatically classified as either meeting or not meeting the level of 
care definition, but instead receives an individual determination on 
this point using the existing administrative criteria. This presumption 
recognizes the strong likelihood that those beneficiaries who are 
correctly assigned one of the designated case-mix classifiers during 
the immediate post-hospital period would require a covered level of 
care, which would be less likely for other beneficiaries.
    In the July 30, 1999 final rule (64 FR 41670), we indicated that we 
would announce any changes to the guidelines for Medicare level of care 
determinations related to modifications in the case-mix classification 
structure. The FY 2018 final rule (82 FR 36544) further specified that 
we would henceforth disseminate the standard description of the 
administrative presumption's designated groups via the SNF PPS website 
at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/index.html (where such designations appear in the paragraph 
entitled ``Case Mix Adjustment''), and would publish such designations 
in rulemaking only to the extent that we actually intend to propose 
changes in them. Under that approach, the set of case-mix classifiers 
designated for this purpose under PDPM was finalized in the FY 2019 SNF 
PPS final rule (83 FR 39253) and is posted on the SNF PPS website 
(https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/index.html), in the paragraph entitled ``Case Mix Adjustment.''
    However, we note that this administrative presumption policy does 
not supersede the SNF's responsibility to ensure that its decisions 
relating to level of care are appropriate and timely, including a 
review to confirm that any services prompting the assignment of one of 
the designated case-mix classifiers (which, in turn, serves to trigger 
the administrative presumption) are themselves medically necessary. As 
we explained in the FY 2000 SNF PPS final rule (64 FR 41667), the 
administrative presumption is itself rebuttable in those individual 
cases in which the services actually received by the resident do not 
meet the basic statutory criterion of being reasonable and necessary to 
diagnose or treat a beneficiary's condition (according to section 
1862(a)(1) of the Act). Accordingly, the presumption would not apply, 
for example, in those situations where the sole classifier that 
triggers the presumption is itself assigned through the receipt of 
services that are subsequently determined to be not reasonable and 
necessary. Moreover, we want to stress the importance of careful 
monitoring for changes in each patient's condition to determine the 
continuing need for Part A SNF benefits after the ARD of the initial 
Medicare assessment.

B. Consolidated Billing

    Sections 1842(b)(6)(E) and 1862(a)(18) of the Act (as added by 
section 4432(b) of the BBA 1997) require a SNF to submit consolidated 
Medicare bills to its Medicare Administrative Contractor (MAC) for 
almost all of the services that its residents receive during the course 
of a covered Part A stay. In addition, section 1862(a)(18) of the Act 
places the responsibility with the SNF for billing Medicare for 
physical therapy, occupational therapy, and speech-language pathology 
services that the resident receives during a noncovered stay. Section 
1888(e)(2)(A) of the Act excludes a small list of services from the 
consolidated billing provision (primarily those services furnished by 
physicians and certain other types of practitioners), which remain 
separately billable under Part B when furnished to a SNF's Part A 
resident. These excluded service categories are discussed in greater 
detail in section V.B.2. of the May 12, 1998 interim final rule (63 FR 
26295 through 26297).
    Effective with services furnished on or after January 1, 2024, 
section 4121(a)(4) of the CAA, 2023 added marriage and family 
therapists and mental health counselors to the list of practitioners at 
section 1888(e)(2)(A)(ii) of the Act whose services are excluded from 
the consolidated billing provision. We note that there are no rate 
adjustments required to the per diem to offset these exclusions, as 
payments for services made under section 1888(e)(2)(A)(ii) of the Act 
are not specified under the requirement at section 1888(e)(4)(G)(iii) 
of the Act as services for which the Secretary must ``provide for an 
appropriate proportional reduction . . .equal to the aggregate increase 
in payments attributable to the exclusion''. See section IV.D. of the 
FY 2024 SNF PPS

[[Page 53217]]

proposed rule for a discussion of the proposed regulatory updates 
implementing this change.
    A detailed discussion of the legislative history of the 
consolidated billing provision is available on the SNF PPS website at 
https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/Downloads/Legislative_History_2018-10-01.pdf. In particular, section 
103 of the Medicare, Medicaid, and SCHIP Balanced Budget Refinement Act 
of 1999 (BBRA 1999) (Pub. L. 106-113, enacted November 29, 1999) 
amended section 1888(e)(2)(A)(iii) of the Act by further excluding a 
number of individual high-cost, low probability services, identified by 
HCPCS codes, within several broader categories (chemotherapy items, 
chemotherapy administration services, radioisotope services, and 
customized prosthetic devices) that otherwise remained subject to the 
provision. We discuss this BBRA 1999 amendment in greater detail in the 
SNF PPS proposed and final rules for FY 2001 (65 FR 19231 through 
19232, April 10, 2000, and 65 FR 46790 through 46795, July 31, 2000), 
as well as in Program Memorandum AB-00-18 (Change Request #1070), 
issued March 2000, which is available online at www.cms.gov/transmittals/downloads/ab001860.pdf.
    As explained in the FY 2001 proposed rule (65 FR 19232), the 
amendments enacted in section 103 of the BBRA 1999 not only identified 
for exclusion from this provision a number of particular service codes 
within four specified categories (that is, chemotherapy items, 
chemotherapy administration services, radioisotope services, and 
customized prosthetic devices), but also gave the Secretary the 
authority to designate additional, individual services for exclusion 
within each of these four specified service categories. In the proposed 
rule for FY 2001, we also noted that the BBRA 1999 Conference report 
(H.R. Conf. Rep. No. 106-479 at 854 (1999)) characterizes the 
individual services that this legislation targets for exclusion as 
high-cost, low probability events that could have devastating financial 
impacts because their costs far exceed the payment SNFs receive under 
the PPS. According to the conferees, section 103(a) of the BBRA 1999 is 
an attempt to exclude from the PPS certain services and costly items 
that are provided infrequently in SNFs. By contrast, the amendments 
enacted in section 103 of the BBRA 1999 do not designate for exclusion 
any of the remaining services within those four categories (thus, 
leaving all of those services subject to SNF consolidated billing), 
because they are relatively inexpensive and are furnished routinely in 
SNFs.
    As we further explained in the final rule for FY 2001 (65 FR 
46790), and as is consistent with our longstanding policy, any 
additional service codes that we might designate for exclusion under 
our discretionary authority must meet the same statutory criteria used 
in identifying the original codes excluded from consolidated billing 
under section 103(a) of the BBRA 1999: they must fall within one of the 
four service categories specified in the BBRA 1999; and they also must 
meet the same standards of high cost and low probability in the SNF 
setting, as discussed in the BBRA 1999 Conference report. Accordingly, 
we characterized this statutory authority to identify additional 
service codes for exclusion as essentially affording the flexibility to 
revise the list of excluded codes in response to changes of major 
significance that may occur over time (for example, the development of 
new medical technologies or other advances in the state of medical 
practice) (65 FR 46791).
    Effective with items and services furnished on or after October 1, 
2021, section 134 in Division CC of the CAA, 2021 established an 
additional category of excluded codes in section 1888(e)(2)(A)(iii)(VI) 
of the Act, for certain blood clotting factors for the treatment of 
patients with hemophilia and other bleeding disorders along with items 
and services related to the furnishing of such factors under section 
1842(o)(5)(C) of the Act. Like the provisions enacted in the BBRA 1999, 
section 1888(e)(2)(A)(iii)(VI) of the Act gives the Secretary the 
authority to designate additional items and services for exclusion 
within the category of items and services related to blood clotting 
factors, as described in that section. Finally, as noted previously in 
this final rule, section 4121(a)(4) of Division FF of CAA, 2023 amended 
section 1888(e)(2)(A)(ii) of the Act to exclude marriage and family 
therapist services and mental health counselor services from 
consolidated billing effective January 1, 2024.
    In the proposed rule, we specifically solicited public comments 
identifying HCPCS codes in any of these five service categories 
(chemotherapy items, chemotherapy administration services, radioisotope 
services, customized prosthetic devices, and blood clotting factors) 
representing recent medical advances that might meet our criteria for 
exclusion from SNF consolidated billing. We may consider excluding a 
particular service if it meets our criteria for exclusion as specified 
previously. We requested that commenters identify in their comments the 
specific HCPCS code that is associated with the service in question, as 
well as their rationale for requesting that the identified HCPCS 
code(s) be excluded.
    We note that the original BBRA amendment and the CAA, 2021 
identified a set of excluded items and services by means of specifying 
individual HCPCS codes within the designated categories that were in 
effect as of a particular date (in the case of the BBRA 1999, July 1, 
1999, and in the case of the CAA, 2021, July 1, 2020), as subsequently 
modified by the Secretary. In addition, as noted in this section of the 
preamble, the statute (sections 1888(e)(2)(A)(iii)(II) through (VI) of 
the Act) gives the Secretary authority to identify additional items and 
services for exclusion within the five specified categories of items 
and services described in the statute, which are also designated by 
HCPCS code. Designating the excluded services in this manner makes it 
possible for us to utilize program issuances as the vehicle for 
accomplishing routine updates to the excluded codes to reflect any 
minor revisions that might subsequently occur in the coding system 
itself, such as the assignment of a different code number to a service 
already designated as excluded, or the creation of a new code for a 
type of service that falls within one of the established exclusion 
categories and meets our criteria for exclusion.
    Accordingly, in the event that we identify through the current 
rulemaking cycle any new services that will actually represent a 
substantive change in the scope of the exclusions from SNF consolidated 
billing, we will identify these additional excluded services by means 
of the HCPCS codes that are in effect as of a specific date (in this 
case, October 1, 2023). By making any new exclusions in this manner, we 
can similarly accomplish routine future updates of these additional 
codes through the issuance of program instructions. The latest list of 
excluded codes can be found on the SNF Consolidated Billing website at 
https://www.cms.gov/Medicare/Billing/SNFConsolidatedBilling.
    We received public comments on these proposals. The following is a 
summary of the comments we received and our responses.
    Comment: Several commenters requested that CMS create a new 
exclusion category that excludes expensive items and services based on 
a price threshold. Another commenter requested that CMS review the 
statute and change the statute to provide equal access and payment for 
DME items for residents in a SNF. Some commenters

[[Page 53218]]

suggested that CMS exclude expensive antibiotics. Finally, some 
commenters requested that CMS add clinical social workers to the SNF 
exclusion list.
    Response: As we noted in the proposed rule, sections 
1888(e)(2)(A)(iii)(II) through (VI) of the Act give the Secretary 
authority to identify additional items and services for exclusion only 
within the categories of items and services described in the statute. 
Accordingly, it is beyond the statutory authority of CMS to exclude 
services that do not fit these categories, or to create additional 
categories of excluded services. The changes requested by these 
commenters are beyond the scope of CMS authority and would require 
Congressional action.
    Comment: A commenter requested that CMS add Altuviio, a new class 
of factor VIII therapy for adults and children with hemophilia A, the 
list of blood clotting factor exclusions. Altuviio is currently billed 
using the miscellaneous J code--J 7199, Hemophilia Clotting Factor, not 
otherwise classified, and has not been assigned its own J code.
    Response: As we noted in the proposed rule, we are only able to add 
services to the exclusion list once they have actually been assigned a 
HCPCS code. The approach that Congress adopted to identify the 
individual blood clotting factor drugs being designated for exclusion 
consisted of listing them by HCPCS code in the statute itself (section 
1888(e)(2)(A)(iii)(VI) of the Act). Thus, a blood clotting factor 
drug's assignment to its own specific code serves as the mechanism of 
designating it for exclusion, as well as the means by which the claims 
processing system is able to recognize that exclusion. Accordingly, the 
assignment of a blood clotting factor drug to its own code is a 
necessary prerequisite to consider that service for exclusion from 
consolidated billing under the SNF PPS. We cannot add a miscellaneous 
non-descriptive code such as J7199. When the code is assigned, we will 
review it as part of our standard review of new HCPCS codes for 
exclusion.
    Comment: Several commenters named specific suggestions of drugs for 
exclusion in the chemotherapy category, including: Tecvayli; Denosumab, 
Leuprolide, and Keytruda; Ponatinib, Gilteritinib, Idhifa, Onureg, 
Midostaurin, Sprycel, Venetoclax, Promacta, Fulphila, Neulasta, Zarxio, 
Udenyca; Imatinib, Dasatinib, Nilotinib, Cabozantinib, Sunitinib, and 
Lenalidomide.
    Response: For the reasons discussed previously in this final rule 
as well as prior rulemaking, the particular drugs cited in these 
comments remain subject to consolidated billing.
    In the case of leuprolide acetate and denosumab, we have addressed 
these when suggested in past rulemaking cycles, most recently in the 
SNF PPS final rules for FY 2023 (87 FR 47502, August 3, 2022). In those 
rules, we explained that these drugs are unlikely to meet the criterion 
of ``low probability'' specified in the BBRA.
    With regard to all other specific drugs mentioned, these are not 
actually chemotherapy drugs, but rather either immunotherapy or other 
non-chemotherapy treatments for cancer, or non-chemotherapy services 
related to or used in conjunction with chemotherapy or in treatment of 
chemotherapy symptoms. As such, these services do not fit the 
chemotherapy category or any existing exclusion categories. As we noted 
in the proposed rule, sections 1888(e)(2)(A)(iii)(II) through (VI) of 
the Act give the Secretary authority to identify additional items and 
services for exclusion only within the categories of items and services 
described in the statute. Accordingly, it is beyond the statutory 
authority of CMS to exclude services that do not fit these categories, 
or to create additional categories of excluded services. Such changes 
would require Congressional action. Additionally, some of these drugs 
do not have unique HCPCS codes assigned, which as we explained in the 
preceding comment, is a necessary prerequisite to consider that service 
for exclusion from consolidated billing under the SNF PPS.
    Comment: A commenter noted that CMS website and manual materials 
contain out of date material with regard to the exclusion of blood 
clotting factors enacted in the Consolidated Appropriations Act (CAA) 
of 2021 and implemented by the FY 2022 SNF Final Rule (86 FR 42442).
    Response: We appreciate the commenter bringing this to our 
attention and will update our online materials accordingly.
    Comment: One commenter requested a copy of the consolidated billing 
exclusion list or instructions on how to find it. The statutory 
language specifying exclusion categories is set out in sections 
1888(e)(2)(A)(ii) and (iii) of the Act.
    Response: The consolidated billing exclusion list is available 
online at: https://www.cms.gov/Medicare/Billing/SNFConsolidatedBilling.

C. Payment for SNF-Level Swing-Bed Services

    Section 1883 of the Act permits certain small, rural hospitals to 
enter into a Medicare swing-bed agreement, under which the hospital can 
use its beds to provide either acute- or SNF-level care, as needed. For 
critical access hospitals (CAHs), Part A pays on a reasonable cost 
basis for SNF-level services furnished under a swing-bed agreement. 
However, in accordance with section 1888(e)(7) of the Act, SNF-level 
services furnished by non-CAH rural hospitals are paid under the SNF 
PPS, effective with cost reporting periods beginning on or after July 
1, 2002. As explained in the FY 2002 final rule (66 FR 39562), this 
effective date is consistent with the statutory provision to integrate 
swing-bed rural hospitals into the SNF PPS by the end of the transition 
period, June 30, 2002.
    Accordingly, all non-CAH swing-bed rural hospitals have now come 
under the SNF PPS. Therefore, all rates and wage indexes outlined in 
earlier sections of this final rule for the SNF PPS also apply to all 
non-CAH swing-bed rural hospitals. As finalized in the FY 2010 SNF PPS 
final rule (74 FR 40356 through 40357), effective October 1, 2010, non-
CAH swing-bed rural hospitals are required to complete an MDS 3.0 
swing-bed assessment which is limited to the required demographic, 
payment, and quality items. As discussed in the FY 2019 SNF PPS final 
rule (83 FR 39235), revisions were made to the swing bed assessment to 
support implementation of PDPM, effective October 1, 2019. A discussion 
of the assessment schedule and the MDS effective beginning FY 2020 
appears in the FY 2019 SNF PPS final rule (83 FR 39229 through 39237). 
The latest changes in the MDS for swing-bed rural hospitals appear on 
the SNF PPS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/index.html.

D. Revisions to the Regulation Text

    We proposed to make the following revisions in the regulation text. 
Section 4121(a)(4) of Division FF of the CAA, 2023 requires Medicare to 
exclude marriage and family therapist (MFT) services and mental health 
counselor services (MHC) from SNF consolidated billing for services 
furnished on or after January 1, 2024. Exclusion from consolidated 
billing allows these services to be billed separately by the performing 
clinician rather than being included in the SNF payment. To reflect the 
recently-enacted exclusion of MFT services and MHC services from SNF 
consolidated billing at section 1888(e)(2)(A)(ii) of the Act (as 
discussed in section V.B of the proposed rule), we proposed to 
redesignate current Sec.  411.15(p)(2)(vi) through (xviii) as Sec.  
411.15(p)(2)(viii) through (xx),

[[Page 53219]]

respectively. In addition, we proposed to redesignate Sec.  
489.20(s)(6) through (18) as Sec.  489.20(s)(8) through (20), 
respectively. We also proposed to add new regulation text at Sec. Sec.  
411.15(p)(2)(vi) and (vii) and 489.20(s)(6) and (7). Specifically, 
proposed new Sec. Sec.  411.15(p)(2)(vi) and 489.20(s)(6) would reflect 
the exclusion of services performed by an MFT, as defined in section 
1861(lll)(2) of the Act. Proposed new Sec. Sec.  411.15(p)(2)(vii) and 
489.20(s)(7) would reflect the exclusion of services performed by an 
MHC, as defined in section 1861(lll)(4) of the Act.
    Subsequently, we identified the need for additional conforming 
changes to the regulatory text. In addition to adding the two new 
exclusions themselves to the regulation text as set forth in the 
proposed rule, the existing exclusion for certain telehealth services 
will need to be revised as well, because it cross-refers to 
subparagraphs that are now being renumbered as a result of adding the 
new exclusions. Specifically, a conforming change is needed in the 
consolidated billing exclusion provision on telehealth services at 
existing Sec.  411.15(p)(2)(xii) (which, as a result of the other 
regulation text changes finalized in this rule, will be redesignated 
Sec.  411.15(p)(2)(xiv)) and in the parallel provider agreement 
provision on telehealth services at existing Sec.  489.20(s)(12) 
(which, as a result of the other regulation text changes finalized in 
this rule, will be redesignated Sec.  489.20(s)(14)). As these 
additional conforming edits serve to ensure effective implementation of 
this new exclusion, and because these new conforming edits additionally 
serve to expand access to telehealth services, we are confident in 
making these additional changes in this final rule.
    We received public comments on these proposals. The following is a 
summary of the comments we received and our responses.
    Comment: Commenters agreed and appreciated the new exclusion of MFT 
and MHC services. A few commenters stated that, in light of the 
exclusion of MFT and MHC services, CMS should consider also excluding 
services furnished by clinical social workers (CSW). One commenter 
cited a recent nursing home study which recommended that nursing homes 
should retain more clinical social workers and CMS should allow for 
Medicare reimbursement for services furnished by these practitioners.
    Response: We appreciate the support that we received in relation to 
the proposed regulatory text changes. With regard to the additional 
exclusion of CSW services, we would note that unlike the services of 
certain other types of practitioners (such as physicians and clinical 
psychologists), CSW services do not appear in the list of services that 
the law specifies in section 1888(e)(2)(A)(ii) through (iv) of the Act 
as being excluded from the consolidated billing requirement. Adding CSW 
services to the statutory list of services that are excluded from SNF 
consolidated billing would require legislation by Congress to amend the 
law itself.
    In light of the comments received on this issue, we are finalizing 
the additions as proposed, with the additional conforming edits that we 
identified during the comment period.

VI. Other SNF PPS Issues

A. Technical Updates to the PDPM ICD-10 Mappings

1. Background
    In the FY 2019 SNF PPS final rule (83 FR 39162), we finalized the 
implementation of the Patient Driven Payment Model (PDPM), effective 
October 1, 2019. The PDPM utilizes the International Classification of 
Diseases, 10th Revision, Clinical Modification (ICD-10-CM, hereafter 
referred to as ICD-10) codes in several ways, including using the 
patient's primary diagnosis to assign patients to clinical categories 
under several PDPM components, specifically the PT, OT, SLP, and NTA 
components. While other ICD-10 codes may be reported as secondary 
diagnoses and designated as additional comorbidities, the PDPM does not 
use secondary diagnoses to assign patients to clinical categories. The 
PDPM ICD-10 code to clinical category mapping, ICD-10 code to SLP 
comorbidity mapping, and ICD-10 code to NTA comorbidity mapping 
(hereafter collectively referred to as the PDPM ICD-10 code mappings) 
are available on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/PDPM.
    In the FY 2020 SNF PPS final rule (84 FR 38750), we outlined the 
process by which we maintain and update the PDPM ICD-10 code mappings, 
as well as the SNF Grouper software and other such products related to 
patient classification and billing, to ensure that they reflect the 
most up to date codes. Beginning with the updates for FY 2020, we apply 
nonsubstantive changes to the PDPM ICD-10 code mappings through a 
subregulatory process consisting of posting the updated PDPM ICD-10 
code mappings on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/PDPM. Such nonsubstantive 
changes are limited to those specific changes that are necessary to 
maintain consistency with the most current PDPM ICD-10 code mappings.
    On the other hand, substantive changes that go beyond the intention 
of maintaining consistency with the most current PDPM ICD-10 code 
mappings, such as changes to the assignment of a code to a clinical 
category or comorbidity list, would be through notice and comment 
rulemaking because they are changes that affect policy. We note that, 
in the case of any diagnoses that are either currently mapped to Return 
to Provider or that we are finalizing to classify into this category, 
this is not intended to reflect any judgment on the importance of 
recognizing and treating these conditions. Rather, we believe that 
there are more specific or appropriate diagnoses that would better 
serve as the primary diagnosis for a Part-A covered SNF stay.
2. Clinical Category Changes for New ICD-10 Codes for FY 2023
    Each year, we review the clinical category assigned to new ICD-10 
diagnosis codes and propose changing the assignment to another clinical 
category if warranted. This year, we proposed changing the clinical 
category assignment for the following five new ICD-10 codes that were 
effective on October 1, 2022:
     D75.84 Other platelet-activating anti-platelet factor 4 
(PF4) disorders was mapped to the clinical category of Return to 
Provider. Patients with anti-PF4 disorders have blood clotting 
disorders. Examples of disorders to be classified with D75.84 are 
spontaneous heparin-induced thrombocytopenia (without heparin 
exposure), thrombosis with thrombocytopenia syndrome, and vaccine-
induced thrombotic thrombocytopenia. Due to the similarity of this code 
to other anti-PF4 disorders, we proposed changing the assignment to 
Medical Management.
     F43.81 Prolonged grief disorder and F43.89 Other reactions 
to severe stress were mapped to the clinical category of Medical 
Management. However, while we believe that SNFs serve an important role 
in providing services to those beneficiaries suffering from mental 
illness, the SNF setting is not the setting that would be most 
beneficial to treat a patient for whom these diagnoses are coded as the 
patient's primary diagnosis. For this reason, we proposed changing the 
clinical category of both codes to Return to Provider. We would 
encourage providers to continue reporting these codes as secondary 
diagnoses, to ensure that we are able to

[[Page 53220]]

identify these patients and that they are receiving appropriate care.
     G90.A Postural orthostatic tachycardia syndrome (POTS) was 
mapped to the clinical category of Acute Neurologic. POTS is a type of 
orthostatic intolerance that causes the heart to beat faster than 
normal when transitioning from sitting or lying down to standing up, 
causing changes in blood pressure, increase in heart rate, and 
lightheadedness. The treatment for POTS involves hydration, physical 
therapy, and vasoconstrictor medications, which are also treatments for 
codes such as E86.0 Dehydration and E86.1 Hypovolemia that are mapped 
to the Medical Management category. Since the medical interventions are 
similar, we proposed changing the assignment for POTS to Medical 
Management.
     K76.82 Hepatic encephalopathy was mapped to the clinical 
category of Return to Provider. Hepatic encephalopathy is a condition 
resulting from severe liver disease, where toxins build up in the blood 
that can affect brain function and lead to a change in medical status. 
Prior to the development of this code, multiple codes were used to 
characterize this condition such as K76.6 Portal hypertension, K76.7 
Hepatorenal syndrome, and K76.89 Other unspecified diseases of liver, 
which are mapped to the Medical Management category. Since these codes 
describe similar liver conditions, we proposed changing the assignment 
to Medical Management.
    We solicited comments on the proposed substantive changes to the 
PDPM ICD-10 code mappings discussed in this section, as well as 
comments on additional substantive and nonsubstantive changes that 
commenters believe are necessary.
    We received public comments on these proposals. The following is a 
summary of the comments we received and our responses.
    Comment: Several commenters stated that they appreciate the ongoing 
refinements to the PDPM ICD-10 code mappings and the opportunity to 
provide input to the proposals. Some commenters stated that they would 
like CMS to identify effective dates on the PDPM website along with 
educational materials and resources.
    Response: We appreciate the positive comments that we received 
supporting our efforts to map diagnoses more accurately under the PDPM. 
We also appreciate the suggestion to develop additional educational 
materials and resources, which we will consider as we update the CMS 
website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/PDPM.
    Comment: Some commenters did not support the proposal to change the 
assignment of F43.81 Prolonged grief disorder and F43.89 Other 
reactions to severe stress to Return to Provider instead of Medical 
Management. Their rationale was that a subset of SNFs that specialize 
in behavioral and mental health treatment may require use of these two 
new diagnosis codes as the primary diagnosis codes to meet beneficiary 
needs.
    Response: We believe that even in such cases as the commenters 
described, there are many other behavioral and mental health diagnoses 
available that would serve as a more appropriate primary diagnosis for 
a SNF stay and, therefore, assigning these two codes to Return to 
Provider would not impede access to care for beneficiaries.
    Comment: Several commenters suggested additional changes to the 
PDPM ICD-10 code mappings that were outside the scope of this 
rulemaking. Specifically, they requested that we consider changing 
M62.81 Muscle weakness (generalized) from Return to Provider to the 
Non-surgical orthopedic/musculoskeletal clinical category; adding 
several dysphasia codes to the SLP comorbidity mapping (namely, R13.14 
Dysphagia, pharyngoesophageal phase, R13.11 Dysphagia, oral phase, 
R13.12 Dysphagia, oropharyngeal phase, R13.13 Dysphagia, pharyngeal 
phase, and R13.19 Other dysphagia); and adding a range of ICD-10 codes 
from J00 Acute nasopharyngitis [common cold] to J06.9 Acute upper 
respiratory infection, unspecified to the SLP comorbidity mapping.
    Response: We note that the changes suggested by these commenters 
are outside the scope of this rulemaking, and will not be addressed in 
this rule. We will further consider the suggested changes to the ICD-10 
code mappings and may implement them in the future as appropriate. To 
the extent that such changes are non-substantive, we may issue them in 
a future subregulatory update if appropriate; however, if such changes 
are substantive changes, in accordance with the update process 
established in the FY 2020 SNF PPS final rule, such changes must 
undergo full notice and comment rulemaking, and thus may be included in 
future rulemaking. See the discussion of the update process for the 
ICD-10 code mappings in the FY 2020 SNF PPS final rule (84 FR 38750) 
for more information.
    After consideration of public comments, we are finalizing the 
changes as proposed.
3. Clinical Category Changes for Unspecified Substance Use Disorder 
Codes
    Effective with stays beginning on and after October 1, 2022, ICD-10 
diagnosis codes F10.90 Alcohol use, unspecified, uncomplicated, F10.91 
Alcohol use, unspecified, in remission, F11.91 Opioid use, unspecified, 
in remission, F12.91 Cannabis use, unspecified, in remission, F13.91 
Sedative, hypnotic or anxiolytic use, unspecified, in remission, and 
F14.91 Cocaine use, unspecified, in remission went into effect and were 
mapped to the clinical category of Medical Management. We reviewed 
these 6 new substance use disorder (SUD) codes and changed the 
assignment from Medical Management to Return to Provider because the 
codes are not specific as to if they refer to abuse or dependence, and 
there are other specific codes available for each of these conditions 
that would be more appropriate as a primary diagnosis for a SNF stay. 
For example, diagnosis code F10.90 Alcohol use, unspecified, 
uncomplicated is not specific as to whether the patient has alcohol 
abuse or alcohol dependence. There are more specific codes that could 
be used instead, such as F10.10 Alcohol abuse, uncomplicated or F10.20 
Alcohol dependence, uncomplicated, that may serve as the primary 
diagnosis for a SNF stay and are appropriately mapped to the clinical 
category of Medical Management.
    Moreover, we believe that increased accuracy of coding a patient's 
primary diagnosis aligns with CMS' broader efforts to ensure better 
quality of care. Therefore, we reviewed all 458 ICD-10 SUD codes from 
code categories F10 to F19 and finalized reassigning 162 additional 
unspecified SUD codes to Return to Provider from Medical Management 
because the codes are not specific as to if they refer to abuse or 
dependence. We would note that this policy change would not affect a 
large number of SNF stays. Our data from FY 2021 show that the 162 
unspecified SUD codes were used as primary diagnoses for only 323 SNF 
stays (0.02 percent) and as secondary diagnoses for 9,537 SNF stays 
(0.54 percent). The purpose of enacting this policy is to continue an 
ongoing effort to refine the PDPM ICD-10 code mappings each year to 
ensure more accurate coding of primary diagnoses. We would encourage 
providers to continue reporting these codes as secondary diagnoses, to 
ensure that we are able to identify these patients and that they are 
receiving appropriate care.

[[Page 53221]]

    Table 1, Proposed Clinical Category Changes for Unspecified 
Substance Use Disorder Codes, which lists all 168 codes included in 
this proposal, was posted on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/PDPM. We solicited 
comments on the proposed substantive changes to the PDPM ICD-10 code 
mappings discussed in this section, as well as comments on additional 
substantive and nonsubstantive changes that commenters believe are 
necessary.
    We received public comments on these proposals. The following is a 
summary of the comments we received and our responses.
    Comment: Commenters supported the PDPM clinical category changes 
for unspecified SUD codes as proposed. However, several commenters did 
not agree with the use of F10.10 Alcohol abuse, uncomplicated or F10.20 
Alcohol dependence, uncomplicated, as these examples do not align with 
the ICD-10-CM Official Guidelines for Coding and Reporting and the SNF 
provider would not be able to assign a code such as F10.10 or F10.20 
without physician documentation to support that alcohol abuse or 
dependence was present.
    Response: We appreciate the positive comments that we received 
supporting our efforts to map SUD diagnoses more accurately under the 
PDPM. We would note that the examples provided for alcohol abuse and 
dependence diagnosis were not intended to be diagnostic guidance, and 
the facility should assess the patient to identify the specific primary 
diagnosis that requires daily skilled care.
    Comment: Some commenters opposed the PDPM clinical category changes 
for unspecified SUD codes due to concerns about administrative burden. 
While they acknowledged that there are more appropriate codes that can 
be used to indicate whether the patient has substance abuse or 
dependence, they believe that it is the responsibility of the referring 
physician to code at the highest level of specificity, and query rules 
make it complex for SNFs to recommend more specific codes to the 
physician.
    Response: We appreciate that commenters agree there are more 
appropriate codes that can be used to indicate whether the patient has 
substance abuse or dependence. We continue to believe that appropriate 
treatment requires specificity in the coding of the diagnoses, which 
aligns with CMS' broader efforts to ensure better quality of care. 
Moreover, we believe that the plan of care for a patient should not 
only depend upon the diagnoses of the referring physician, but also on 
the assessment of the SNF care team, which includes the clinicians 
caring for the patient at the facility.
    After consideration of public comments, we are finalizing the 
changes as proposed.
4. Clinical Category Changes for Certain Subcategory Fracture Codes
    Each year, we solicit comments on additional substantive and 
nonsubstantive changes that commenters believe are necessary to the 
PDPM ICD-10 code mappings. In the FY 2023 final rule (87 FR 47524), we 
described how one commenter recommended that CMS consider revising the 
PDPM ICD-10 code mappings to reclassify certain subcategory S42.2--
humeral fracture codes. The commenter highlighted that certain 
encounter codes for humeral fractures, such as those ending in the 7th 
character of A for an initial encounter for fracture, are permitted the 
option to be mapped to a surgical clinical category, denoted on the 
PDPM ICD-10 code mappings as May be Eligible for One of the Two 
Orthopedic Surgery Categories (that is, major joint replacement or 
spinal surgery, or orthopedic surgery) if the patient had a major 
procedure during the prior inpatient stay that impacts the SNF care 
plan. However, the commenter noted that other encounter codes within 
the same code family, such as those ending in the 7th character of D 
for subsequent encounter for fracture with routine healing, are mapped 
to the Non-Surgical Orthopedic/Musculoskeletal without the surgical 
option. The commenter requested that we review all subcategory S42.2--
fracture codes to ensure that the appropriate surgical clinical 
category could be selected for joint aftercare. Since then, the 
commenter has also contacted CMS with a similar suggestion for M84.552D 
Pathological fracture in neoplastic disease, left femur, subsequent 
encounter for fracture with routine healing.
    We have since reviewed the suggested code subcategories to 
determine the most efficient manner for addressing this discrepancy. We 
proposed adding the surgical option that allows 45 subcategory S42.2--
codes for displaced fractures to be eligible for one of two orthopedic 
surgery categories. However, we noted that this does not extend to 
subcategory S42.2--codes for nondisplaced fractures, which typically do 
not require surgery. We also proposed adding the surgical option to 
subcategory 46 M84.5--codes for pathological fractures to certain major 
weight-bearing bones to be eligible for one of two orthopedic surgery 
categories.
    Table 2, Proposed Clinical Category Changes for S42.2 and M84.5 
Fracture Codes, which lists all 91 codes included in this proposal, was 
posted on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/PDPM. We solicited comments on the proposed 
substantive changes to the PDPM ICD-10 code mappings discussed in this 
section, as well as comments on additional substantive and 
nonsubstantive changes that commenters believe are necessary.
    We did not receive public comments on this provision, and 
therefore, we are finalizing the changes as proposed.
5. Clinical Category Changes for Unacceptable Principal Diagnosis Codes
    In the FY 2023 final rule (87 FR 47525), we described how several 
commenters referred to instances when SNF claims were denied for 
including a primary diagnosis code that was listed on the PDPM ICD-10 
code mappings as a valid code, but was not accepted by some Medicare 
Administrative Contractors (MACs) that use the Hospital Inpatient 
Prospective Payment System (IPPS) Medicare Code Editor (MCE) lists when 
evaluating the primary diagnosis codes listed on SNF claims. In the 
IPPS, a patient's diagnosis is entered into the Medicare claims 
processing systems and subjected to a series of automated screens 
called the MCE. The MCE lists are designed to identify cases that 
require further review before classification into an MS-DRG. We noted 
that all codes on the MCE lists are able to be reported; however, a 
code edit may be triggered that the MAC may either choose to bypass or 
return to the provider to resubmit. Updates to the MCE lists are 
proposed on an annual basis and discussed through IPPS rulemaking when 
new codes or policies involving existing codes are introduced.
    Commenters recommended that CMS seek to align the PDPM ICD-10 code 
mappings with the MCE in treating diagnoses that are Return to 
Provider, specifically referring to the Unacceptable Principal 
Diagnosis edit code list in the Definition of Medicare Code Edits, 
which was posted on the CMS website at https://www.cms.gov/medicare/medicare-fee-for-service-payment/acuteinpatientpps/ms-drg-classifications-and-software. The Unacceptable Principal Diagnosis edit 
code list contains selected codes that describe a circumstance that 
influences an individual's health status but not a current illness or 
injury, or codes that are not specific manifestations but may be due to 
an underlying cause, and

[[Page 53222]]

which are considered unacceptable as a principal diagnosis.
    We identified 95 codes from the MCE Unacceptable Principal 
Diagnosis edit code list that were mapped to a valid clinical category 
on the PDPM ICD-10 code mappings, and that were coded as primary 
diagnoses for 14,808 SNF stays (0.84 percent) in FY 2021. Table 3, 
Proposed Clinical Category Changes for Unacceptable Principal Diagnosis 
Codes, which lists all 95 codes included in this proposal, was posted 
on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/PDPM. As stated previously in this section of 
this final rule, we note that reporting these codes as a primary 
diagnosis for a SNF stay may trigger an edit that the MAC may either 
choose to bypass or return to the provider to resubmit, and therefore 
not all of these 14,808 stays were denied by the MACs.
    After clinical review, we concurred that the 95 codes listed in 
Table 3 on the CMS website should be assigned to Return to Provider. 
For the diagnosis codes listed in Table 3 on the CMS website that are 
from the category B95 to B97 range and contain the suffix ``as the 
cause of diseases classified elsewhere'', the ICD-10 coding convention 
for such etiology and manifestation codes, where certain conditions 
have both an underlying etiology and multiple body system 
manifestations due to the underlying etiology, dictates that the 
underlying condition should be sequenced first, followed by the 
manifestation. The ICD-10 coding guidelines also state that codes from 
subcategory G92.0--Immune effector cell-associated neurotoxicity 
syndrome, subcategory R40.2--Coma scale, and subcategory S06.A--
Traumatic brain injury should only be reported as secondary diagnoses, 
as there are more specific codes that should be sequenced first. 
Additionally, the ICD-10 coding guidelines state that diagnosis codes 
in categories Z90 and Z98 are status codes, indicating that a patient 
is either a carrier of a disease or has the sequelae or residual of a 
past disease or condition, and are not reasons for a patient to be 
admitted to a SNF. Lastly, our clinicians determined that diagnosis 
code Z43.9 Encounter for attention to unspecified artificial opening 
should be assigned to the clinical category Return to Provider because 
there are more specific codes that identify the site for the artificial 
opening.
    Therefore, we proposed to reassign the 95 codes listed in Table 3 
on the CMS website from the current default clinical category on the 
PDPM ICD-10 code mappings to Return to Provider. We also proposed to 
make future updates to align the PDPM ICD-10 code mappings with the MCE 
Unacceptable Principal Diagnosis edit code list on a subregulatory 
basis going forward. Moreover, we solicited comment on aligning with 
the MCE Manifestation codes not allowed as principal diagnosis edit 
code list, which contains diagnosis codes that are the manifestation of 
an underlying disease, not the disease itself, and therefore should not 
be used as a principal diagnosis, and the Questionable admission codes 
edit code list, which contains diagnoses codes that are not usually 
sufficient justification for admission to an acute care hospital. While 
these MCE lists were not mentioned by commenters, we believed that some 
MACs may be applying these edit lists to SNF claims and this could 
cause continued differences between the PDPM ICD-10 code mappings and 
the IPPS MCE. Finally, we proposed to make future updates to align the 
PDPM ICD-10 code mappings with the MCE Manifestation codes not allowed 
as principal diagnosis edit code list and the Questionable admission 
codes edit code list on a subregulatory basis going forward.
    We solicited comments on the proposed substantive changes to the 
PDPM ICD-10 code mappings discussed in this section, as well as 
comments on additional substantive and nonsubstantive changes that 
commenters believe are necessary. We did not receive public comments on 
this provision, and therefore, we are finalizing as proposed.

VII. Skilled Nursing Facility Quality Reporting Program (SNF QRP)

A. Background and Statutory Authority

    The Skilled Nursing Facility Quality Reporting Program (SNF QRP) is 
authorized by section 1888(e)(6) of the Act, and it applies to 
freestanding SNFs, SNFs affiliated with acute care facilities, and all 
non-critical access hospital (CAH) swing-bed rural hospitals. Section 
1888(e)(6)(A)(i) of the Act requires the Secretary to reduce by 2 
percentage points the annual market basket percentage increase 
described in section 1888(e)(5)(B)(i) of the Act applicable to a SNF 
for a fiscal year (FY), after application of section 1888(e)(5)(B)(ii) 
of the Act (the productivity adjustment) and section 1888(e)(5)(B)(iii) 
of the Act, in the case of a SNF that does not submit data in 
accordance with sections 1888(e)(6)(B)(i)(II) and (III) of the Act for 
that FY. Section 1890A of the Act requires that the Secretary establish 
and follow a pre-rulemaking process, in coordination with the 
consensus-based entity (CBE) with a contract under section 1890(a) of 
the Act, to solicit input from certain groups regarding the selection 
of quality and efficiency measures for the SNF QRP. We have codified 
our program requirements in our regulations at 42 CFR part 413.
    In the proposed rule, we proposed to adopt three new measures, 
remove three existing measures, and modify one existing measure. 
Second, we sought information on principles we could use to select and 
prioritize SNF QRP quality measures in future years. Third, we provided 
an update on our health equity efforts. Fourth, we proposed several 
administrative changes, including a change to the SNF QRP data 
completion thresholds and a new data submission method for the proposed 
CoreQ: Short Stay Discharge questionnaire. Finally, we proposed to 
begin the public reporting of four measures.

B. General Considerations Used for the Selection of Measures for the 
SNF QRP

    For a detailed discussion of the considerations we use for the 
selection of SNF QRP quality, resource use, or other measures, we refer 
readers to the FY 2016 SNF PPS final rule (80 FR 46429 through 46431).
1. Quality Measures Currently Adopted for the FY 2024 SNF QRP
    The SNF QRP currently has 16 measures for the FY 2024 SNF QRP, 
which are listed in Table C1. For a discussion of the factors used to 
evaluate whether a measure should be removed from the SNF QRP, we refer 
readers to Sec.  413.360(b)(2).

  Table 11--Quality Measures Currently Adopted for the FY 2024 SNF QRP
------------------------------------------------------------------------
               Short name                   Measure name & data source
------------------------------------------------------------------------
   Resident Assessment Instrument Minimum Data Set (Assessment-Based)
------------------------------------------------------------------------
Pressure Ulcer/Injury..................  Changes in Skin Integrity Post-
                                          Acute Care: Pressure Ulcer/
                                          Injury.
Application of Falls...................  Application of Percent of
                                          Residents Experiencing One or
                                          More Falls with Major Injury
                                          (Long Stay).
Application of Functional Assessment/    Application of Percent of Long-
 Care Plan.                               Term Care Hospital (LTCH)
                                          Patients with an Admission and
                                          Discharge Functional
                                          Assessment and a Care Plan
                                          That Addresses Function.

[[Page 53223]]

 
Change in Mobility Score...............  Application of IRF Functional
                                          Outcome Measure: Change in
                                          Mobility Score for Medical
                                          Rehabilitation Patients.
Discharge Mobility Score...............  Application of IRF Functional
                                          Outcome Measure: Discharge
                                          Mobility Score for Medical
                                          Rehabilitation Patients.
Change in Self-Care Score..............  Application of the IRF
                                          Functional Outcome Measure:
                                          Change in Self-Care Score for
                                          Medical Rehabilitation
                                          Patients.
Discharge Self-Care Score..............  Application of IRF Functional
                                          Outcome Measure: Discharge
                                          Self-Care Score for Medical
                                          Rehabilitation Patients.
DRR....................................  Drug Regimen Review Conducted
                                          With Follow-Up for Identified
                                          Issues-Post-Acute Care (PAC)
                                          Skilled Nursing Facility (SNF)
                                          Quality Reporting Program
                                          (QRP).
TOH-Provider *.........................  Transfer of Health (TOH)
                                          Information to the Provider
                                          Post[dash]Acute Care (PAC).
TOH-Patient *..........................  Transfer of Health (TOH)
                                          Information to the Patient
                                          Post-Acute Care (PAC).
------------------------------------------------------------------------
                              Claims-Based
------------------------------------------------------------------------
MSPB SNF...............................  Medicare Spending Per
                                          Beneficiary (MSPB)--Post Acute
                                          Care (PAC) Skilled Nursing
                                          Facility (SNF) Quality
                                          Reporting Program (QRP).
DTC....................................  Discharge to Community (DTC)--
                                          Post Acute Care (PAC) Skilled
                                          Nursing Facility (SNF) Quality
                                          Reporting Program (QRP).
PPR....................................  Potentially Preventable 30-Day
                                          Post-Discharge Readmission
                                          Measure for Skilled Nursing
                                          Facility (SNF) Quality
                                          Reporting Program (QRP).
SNF HAI................................  SNF Healthcare-Associated
                                          Infections (HAI) Requiring
                                          Hospitalization.
------------------------------------------------------------------------
                                  NHSN
------------------------------------------------------------------------
HCP COVID-19 Vaccine...................  COVID-19 Vaccination Coverage
                                          among Healthcare Personnel
                                          (HCP).
HCP Influenza Vaccine..................  Influenza Vaccination Coverage
                                          among Healthcare Personnel
                                          (HCP).
------------------------------------------------------------------------
* In response to the public health emergency (PHE) for the Coronavirus
  Disease 2019 (COVID-19), we released an Interim Final Rule (85 FR
  27595 through 27597) which delayed the compliance date for collection
  and reporting of the Transfer of Health (TOH) Information measures for
  at least 2 full fiscal years after the end of the PHE. The compliance
  date for the collection and reporting of the Transfer of Health
  Information measures was revised to October 1, 2023 in the FY 2023 SNF
  PPS final rule (87 FR 47547 through 47551).

C. SNF QRP Quality Measure Updates

    In the proposed rule, we included SNF QRP proposals for the FY 2025 
and FY 2026 program years. We proposed to add new measures to the SNF 
QRP as well as remove measures from the SNF QRP. Beginning with the FY 
2025 SNF QRP, we proposed to (1) modify the COVID-19 Vaccination 
Coverage among Healthcare Personnel (HCP) measure, (2) adopt the 
Discharge Function Score measure,\12\ which we specified under section 
1888(e)(6)(B)(i) of the Act, and (3) remove three current measures: (i) 
the Application of Percent of Long-Term Care Hospital (LTCH) Patients 
with an Admission and Discharge Functional Assessment and a Care Plan 
That Addresses Function measure, (ii) the Application of IRF Functional 
Outcome Measure: Change in Self-Care Score for Medical Rehabilitation 
Patients measure, and (iii) the Application of IRF Functional Outcome 
Measure: Change in Mobility Score for Medical Rehabilitation Patients 
measure.
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    \12\ This measure was submitted to the Measures Under 
Consideration (MUC) List as the Cross-Setting Discharge Function 
Score. Subsequent to the MAP Workgroup meetings, the measure 
developer modified the name. Discharge Function Score for Skilled 
Nursing Facilities (SNFs) Technical Report. https://www.cms.gov/files/document/snf-discharge-function-score-technical-report-february-2023.pdf.
---------------------------------------------------------------------------

    We also proposed two new measures beginning with the FY 2026 SNF 
QRP: (i) the CoreQ: Short Stay Discharge measure which we are 
specifying under section 1899B(d)(1) of the Act, and (ii) the COVID-19 
Vaccine: Percent of Patients/Residents Who Are Up to Date measure, 
which we are specifying under section 1899B(d)(1) of the Act.
1. SNF QRP Quality Measure Updates Beginning With the FY 2025 SNF QRP
a. Modification of the COVID-19 Vaccination Coverage Among Healthcare 
Personnel (HCP) Measure Beginning With the FY 2025 SNF QRP
(1) Background
    On January 31, 2020, the Secretary declared a public health 
emergency (PHE) for the United States in response to the global 
outbreak of SARS-CoV-2, a novel (new) coronavirus that causes a disease 
named ``coronavirus disease 2019'' (COVID-19).\13\ Subsequently, in the 
FY 2022 SNF PPS final rule (86 FR 42480 through 42489), we adopted the 
COVID-19 Vaccination Coverage among Healthcare Personnel (HCP) (HCP 
COVID-19 Vaccine) measure for the SNF QRP. The HCP COVID-19 Vaccine 
measure requires each SNF to submit data on the percentage of HCP 
eligible to work in the SNF for at least one day during the reporting 
period, excluding persons with contraindications to FDA-authorized or -
approved COVID-19 vaccines, who have received a complete vaccination 
course against SARS-CoV-2. Since that time, COVID-19 has continued to 
spread domestically and around the world with more than 103.9 million 
cases and 1.13 million deaths in the United States as of June 19, 
2023.\14\ In recognition of the ongoing significance and complexity of 
COVID-19, the Secretary has renewed the PHE on April 21, 2020, July 23, 
2020, October 2, 2020, January 7, 2021, April 15, 2021, July 19, 2021, 
October 15, 2021, January 14, 2022, April 12, 2022, July 15, 2022, 
October 13, 2022, January 11, 2023, and February 9, 2023.\15\ The 
Department of Health and Human Services (HHS) let the PHE expire on May 
11, 2023. However, HHS stated that the public health response to COVID-
19 remains a public health priority with a whole of government approach 
to combating the virus, including through vaccination efforts.\16\
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    \13\ U.S. Department of Health and Human Services, 
Administration for Strategic Preparedness and Response. 
Determination that a Public Health Emergency Exists. January 31, 
2020. https://aspr.hhs.gov/legal/PHE/Pages/2019-nCoV.aspx.
    \14\ Centers for Disease Control and Prevention. COVID Data 
Tracker. June 19, 2023. https://covid.cdc.gov/covid-data-tracker/#datatracker-home.
    \15\ U.S. Department of Health and Human Services, 
Administration for Strategic for Preparedness and Response. Renewal 
of Determination that a Public Health Emergency Exists. February 9, 
2023. https://aspr.hhs.gov/legal/PHE/Pages/COVID19-9Feb2023.aspx.
    \16\ U.S. Department of Health and Human Services. Fact Sheet: 
COVID-19 Public Health Emergency Transition Roadmap. February 9, 
2023. https://www.hhs.gov/about/news/2023/02/09/fact-sheet-covid-19-public-health-emergency-transition-roadmap.html.

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[[Page 53224]]

    In the FY 2022 SNF PPS final rule (86 FR 42480 through 42489) and 
in the Revised Guidance for Staff Vaccination Requirements,\17\ we 
stated that vaccination is a critical part of the nation's strategy to 
effectively counter the spread of COVID-19. We continue to believe it 
is important to incentivize and track HCP vaccination in SNFs through 
quality measurement in order to protect HCP, residents, and caregivers, 
and to help sustain the ability of SNFs to continue serving their 
communities after the PHE. At the time we issued the FY 2022 SNF PPS 
final rule (86 FR 42480 through 42489) where we adopted the HCP COVID-
19 Vaccine measure, the Food and Drug Administration (FDA) had issued 
emergency use authorizations (EUAs) for COVID-19 vaccines manufactured 
by Pfizer-BioNTech,\18\ Moderna,\19\ and Janssen.\20\ The Pfizer-
BioNTech vaccine was authorized for ages 12 and older and the Moderna 
and Janssen vaccines for ages 18 and older. Shortly following the 
publication of the FY 2022 SNF PPS final rule, on August 23, 2021, the 
FDA issued an approval for the Pfizer-BioNTech vaccine, marketed as 
Comirnaty.\21\ The FDA issued approval for the Moderna vaccine, 
marketed as Spikevax, on January 31, 2022 \22\ and an EUA for the 
Novavax vaccine, on July 13, 2022.\23\ The FDA also issued EUAs for 
single booster doses of the then authorized COVID-19 vaccines. As of 
November 19, 2021 24 25 26 a single booster dose of each 
COVID-19 vaccine was authorized for all eligible individuals 18 years 
of age and older. EUAs were subsequently issued for a second booster 
dose of the Pfizer-BioNTech and Moderna vaccines in certain populations 
in March 2022.\27\ FDA first authorized the use of a booster dose of 
bivalent or ``updated'' COVID-19 vaccines from Pfizer-BioNTech and 
Moderna in August 2022.\28\
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    \17\ Centers for Medicare & Medicaid Services. Revised Guidance 
for Staff Vaccination Requirements QSO-23-02-ALL. October 26, 2022. 
https://www.cms.gov/files/document/qs0-23-02-all.pdf.
    \18\ Food and Drug Administration. FDA Takes Key Action in Fight 
Against COVID-19 By Issuing Emergency Use Authorization for First 
COVID-19 Vaccine. December 11, 2020. https://www.fda.gov/news-events/press-announcements/fda-takes-key-action-fight-against-covid-19-issuing-emergency-use-authorization-first-covid-19.
    \19\ Food and Drug Administration. FDA Takes Additional Action 
in Fight Against COVID-19 By Issuing Emergency Use Authorization for 
Second COVID-19 Vaccine. December 18, 2020. https://www.fda.gov/news-events/press-announcements/fda-takes-additional-action-fight-against-covid-19-issuing-emergency-use-authorization-second-covid.
    \20\ Food and Drug Administration. FDA Issues Emergency Use 
Authorization for Third COVID-19 Vaccine. February 27, 2021. https://www.fda.gov/news-events/press-announcements/fda-issues-emergency-use-authorization-third-covid-19-vaccine.
    \21\ Food and Drug Administration. FDA Approves First COVID-19 
Vaccine. August 23, 2021. https://www.fda.gov/news-events/press-announcements/fda-approves-first-covid-19-vaccine.
    \22\ Food and Drug Administration. Coronavirus (COVID-19) 
Update: FDA Takes Key Action by Approving Second COVID-19 Vaccine. 
January 31, 2022. https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-takes-key-action-approving-second-covid-19-vaccine.
    \23\ Food and Drug Administration. Coronavirus (COVID-19) 
Update: FDA Authorizes Emergency Use of Novavax COVID-19 Vaccine, 
Adjuvanted. July 13, 2022. https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-authorizes-emergency-use-novavax-covid-19-vaccine-adjuvanted.
    \24\ Food and Drug Administration. FDA Authorizes Booster Dose 
of Pfizer-BioNTech COVID-19 Vaccine for Certain Populations. 
September 22, 2021. https://www.fda.gov/news-events/press-announcements/fda-authorizes-booster-dose-pfizer-biontech-covid-19-vaccine-certain-populations.
    \25\ Food and Drug Administration. Coronavirus (COVID-19) 
Update: FDA Takes Additional Actions on the Use of a Booster Dose 
for COVID-19 Vaccines. October 20, 2021. https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-takes-additional-actions-use-booster-dose-covid-19-vaccines.
    \26\ Food and Drug Administration. Coronavirus (COVID-19) 
Update: FDA Expands Eligibility for COVID-19 Vaccine Boosters. 
November 19, 2021. https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-expands-eligibility-covid-19-vaccine-boosters.
    \27\ Food and Drug Administration. Coronavirus (COVID-19) 
Update: FDA Authorizes Second Booster Dose of Two COVID-19 Vaccines 
for Older and Immunocompromised Individuals. March 29, 2022. https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-authorizes-second-booster-dose-two-covid-19-vaccines-older-and.
    \28\ Food and Drug Administration. Coronavirus (COVID-19) 
Update: FDA Authorizes Moderna, Pfizer-BioNTech Bivalent COVID-19 
Vaccines for Use as a Booster Dose. August 31, 2022. https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-authorizes-moderna-pfizer-biontech-bivalent-covid-19-vaccines-use.
---------------------------------------------------------------------------

(a) Measure Importance
    While the impact of COVID-19 vaccines on asymptomatic infection and 
transmission is not yet fully known, there are now robust data 
available on COVID-19 vaccine effectiveness across multiple populations 
against severe illness, hospitalization, and death. Two-dose COVID-19 
vaccines from Pfizer-BioNTech and Moderna were found to be 88 percent 
and 93 percent effective against hospitalization for COVID-19, 
respectively, over 6 months for adults over age 18 without 
immunocompromising conditions.\29\ During a SARS-CoV-2 surge in the 
spring and summer of 2021, 92 percent of COVID-19 hospitalizations and 
91 percent of COVID-19-associated deaths were reported among persons 
not fully vaccinated.\30\ Real-world studies of population-level 
vaccine effectiveness indicated similarly high rates of efficacy in 
preventing SARS-CoV-2 infection among frontline workers in multiple 
industries, with a 90 percent effectiveness in preventing symptomatic 
and asymptomatic infection from December 2020 through August 2021.\31\ 
Vaccines have also been highly effective in real-world conditions at 
preventing COVID-19 in HCP with up to 96 percent efficacy for fully 
vaccinated HCP, including those at risk for severe infection and those 
in racial and ethnic groups disproportionately affected by COVID-
19.\32\ In the presence of high community prevalence of COVID-19, 
residents of nursing homes with low staff vaccination coverage had 
cases of COVID-19 related deaths 195 percent higher than those among 
residents of nursing homes with high staff vaccination coverage.\33\ 
Overall, data demonstrate that COVID-19 vaccines are effective and 
prevent severe disease, hospitalization, and death.
---------------------------------------------------------------------------

    \29\ Self WH, Tenforde MW, Rhoads JP, et al. Comparative 
Effectiveness of Moderna, Pfizer-BioNTech, and Janssen (Johnson & 
Johnson) Vaccines in Preventing COVID-19 Hospitalizations Among 
Adults Without Immunocompromising Conditions--United States, March-
August 2021. MMWR Morb Mortal Wkly Rep 2021;70:1337-1343. doi: 
10.15585/mmwr.mm7038e1. https://www.cdc.gov/mmwr/volumes/70/wr/mm7038e1.htm.
    \30\ Scobie HM, Johnson AG, Suthar AB, et al. Monitoring 
Incidence of COVID-19 Cases, Hospitalizations, and Deaths, by 
Vaccination Status--13 U.S. Jurisdictions, April 4-July 17, 2021. 
MMWR Morb Mortal Wkly Rep 2021;70:1284-1290. doi: 10.15585/
mmwr.mm7037e1. https://www.cdc.gov/mmwr/volumes/70/wr/mm7037e1.htm.
    \31\ Fowlkes A, Gaglani M, Groover K, et al. Effectiveness of 
COVID-19 Vaccines in Preventing SARS-CoV-2 Infection Among Frontline 
Workers Before and During B.1.617.2 (Delta) Variant Predominance--
Eight U.S. Locations, December 2020-August 2021. MMWR Morb Mortal 
Wkly Rep 2021 Aug 27;70(34):1167-1169. doi: 10.15585/mmwr.mm7034e4. 
https://cdc.gov/mmwr/volume/70/wr/mm7034e4.htm?s_cid=mm7034e4_w.
    \32\ Pilishvili T, Gierke R, Fleming-Dutra KE, et al. 
Effectiveness of mRNA Covid-19 Vaccine among U.S. Health Care 
Personnel. N Engl J Med. 2021 Dec 16;385(25):e90. doi: 10.1056/
NEJMoa2106599. PMID: 34551224; PMCID: PMC8482809.
    \33\ McGarry BE, Barnett ML, Grabowski DC, Gandhi AD. Nursing 
Home Staff Vaccination and Covid-19 Outcomes. N Engl J Med. 2022 Jan 
27;386(4):397-398. doi: 10.1056/NEJMc2115674. PMID: 34879189; PMCID: 
PMC8693685.
---------------------------------------------------------------------------

    As SARS-CoV-2 persists and evolves, our COVID-19 vaccination 
strategy must remain responsive. When we adopted the HCP COVID-19 
Vaccine measure in the FY 2022 SNF PPS final rule, we stated that the 
need for booster doses of COVID-19 vaccine had not been established and 
no additional doses had been recommended (86 FR 42484 through 42485). 
We also stated

[[Page 53225]]

that we believed the numerator was sufficiently broad to include 
potential future boosters as part of a ``complete vaccination course'' 
and that the measure was sufficiently specified to address boosters (86 
FR 42485). Since we adopted the HCP COVID-19 Vaccine measure in the FY 
2022 SNF PPS final rule, new variants of SARS-CoV-2 have emerged around 
the world and within the United States. Specifically, the Omicron 
variant (and its related subvariants) is listed as a variant of concern 
by the Centers for Disease Control and Prevention (CDC) because it 
spreads more easily than earlier variants.\34\ Vaccine manufacturers 
have responded to the Omicron variant by developing bivalent COVID-19 
vaccines, which include a component of the original virus strain, to 
provide broad protection against COVID-19 and a component of the 
Omicron variant, to provide better protection against COVID-19 caused 
by the Omicron variant.\35\ These booster doses of the bivalent COVID-
19 vaccines have been shown to increase immune response to SARS-CoV-2 
variants, including Omicron, particularly in individuals that are more 
than 6 months removed from receipt of their primary series.\36\ The FDA 
issued EUAs for booster doses of two bivalent COVID-19 vaccines, one 
from Pfizer-BioNTech \37\ and one from Moderna,\38\ and strongly 
encourages anyone who is eligible to consider receiving a booster dose 
with a bivalent COVID-19 vaccine to provide better protection against 
currently circulating variants.\39\ COVID-19 booster doses are 
associated with a greater reduction in infections among HCP relative to 
those who only received primary series vaccination, with a rate of 
breakthrough infections among HCP who received only a two-dose regimen 
of 21.4 percent compared to a rate of 0.7 percent among boosted 
HCP.40 41
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    \34\ Centers for Disease Control and Prevention. Variants of the 
Virus. https://www.cdc.gov/coronavirus/2019-ncov/variants/index.html.
    \35\ Food and Drug Administration. COVID-19 Bivalent Vaccine. 
https://www.fda.gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/covid-19-bivalent-vaccines.
    \36\ Chalkias S, Harper C, Vrbicky K, et al. A Bivalent Omicron-
Containing Booster Vaccine Against COVID-19. N Engl J Med. 2022 Oct 
6;387(14):1279-1291. doi: 10.1056/NEJMoa2208343. PMID: 36112399; 
PMCID: PMC9511634.
    \37\ Food and Drug Administration. Pfizer-BioNTech COVID-19 
Vaccines. https://www.fda.gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/pfizer-biontech-covid-19-vaccines.
    \38\ Food and Drug Administration. Moderna COVID-19 Vaccines. 
https://www.fda.gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/moderna-covid-19-vaccines.
    \39\ Food and Drug Administration. Coronavirus (COVID-19) 
Update: FDA Authorizes Moderna, Pfizer-BioNTech Bivalent COVID-19 
Vaccines for Use as a Booster Dose. August 31, 2022. https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-authorizes-moderna-pfizer-biontech-bivalent-covid-19-vaccines-use.
    \40\ Prasad N, Derado G, Nanduri SA, et al. Effectiveness of a 
COVID-19 Additional Primary or Booster Vaccine Dose in Preventing 
SARS-CoV-2 Infection Among Nursing Home Residents During Widespread 
Circulation of the Omicron Variant--United States, February 14-March 
27, 2022. MMWR Morb Mortal Wkly Rep. 2022 May 6;71(18):633-637. doi: 
10.15585/mmwr.mm7118a4. PMID: 35511708; PMCID: PMC9098239.
    \41\ Oster Y, Benenson S, Nir-Paz R, Buda I, Cohen MJ. The 
Effect of a Third BNT162b2 Vaccine on Breakthrough Infections in 
Health Care Workers: a Cohort Analysis. Clin Microbiol Infect. 2022 
May;28(5):735.e1-735.e3. doi: 10.1016/j.cmi.2022.01.019. PMID: 
35143997; PMCID: PMC8820100.
---------------------------------------------------------------------------

    We believe that vaccination remains the most effective means to 
prevent the severe consequences of COVID-19, including severe illness, 
hospitalization, and death. Given the availability of vaccine efficacy 
data, EUAs issued by the FDA for bivalent boosters, the continued 
presence of SARS-CoV-2 in the United States, and variance among rates 
of booster dose vaccination, it is important to update the 
specifications of the HCP COVID-19 Vaccine measure to refer to HCP who 
receive primary series and booster doses in a timely manner. Given the 
persistent spread of COVID-19, we continue to believe that monitoring 
and surveillance of vaccination rates among HCP are important and 
provides residents, beneficiaries, and their caregivers with 
information to support informed decision making. Beginning with the FY 
2025 SNF QRP, we proposed to modify the HCP COVID-19 Vaccine measure to 
replace the term ``complete vaccination course'' with the term ``up to 
date'' in the HCP vaccination definition. We also proposed to update 
the numerator to specify the time frames within which an HCP is 
considered up to date with recommended COVID-19 vaccines, including 
booster doses, beginning with the FY 2025 SNF QRP.
(b) Measure Testing
    The CDC conducted beta testing of the modified HCP COVID-19 Vaccine 
measure by assessing if the collection of information on booster doses 
received by HCP was feasible, as information on receipt of booster 
doses is required for determining if HCP are up to date with the 
current COVID-19 vaccination. Feasibility was assessed by calculating 
the proportion of facilities that reported booster doses of the COVID-
19 vaccine. The assessment was conducted in various facility types, 
including SNFs, using vaccine coverage data for the first quarter of 
calendar year (CY) 2022 (January to March), which was reported through 
the CDC's National Healthcare Safety Network (NHSN). Feasibility of 
reporting booster doses is evident by the fact that 99.2 percent of 
SNFs reported vaccination booster dose coverage data to the NHSN for 
the first quarter of 2022.\42\ Additionally, HCP COVID-19 Vaccine 
measure scores calculated using January 1 to March 31, 2022 data had a 
median of 31.8 percent and an interquartile range of 18.9 to 49.7 
percent, indicating a measure performance gap as there are clinically 
significant differences in booster dose vaccination coverage rates 
among SNFs.\43\
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    \42\ National Quality Forum. Measure Application Partnership 
(MAP) Post-Acute Care/Long-Term Care: 2022-2023 Measures Under 
Consideration (MUC) Cycle Measure Specifications. December 1, 2022. 
https://mmshub.cms.gov/sites/default/files/map-pac-muc-measure-specifications-2022-2023.pdf.
    \43\ National Quality Forum. Measure Application Partnership 
(MAP) Post-Acute Care/Long-Term Care: 2022-2023 Measures Under 
Consideration (MUC) Cycle Measure Specifications. December 1, 2022. 
https://mmshub.cms.gov/sites/default/files/map-pac-muc-measure-specifications-2022-2023.pdf.
---------------------------------------------------------------------------

(2) Competing and Related Measures
    Section 1899B(e)(2)(A) of the Act requires that, absent an 
exception under section 1899B(e)(2)(B) of the Act, measures specified 
under section 1899B of the Act be endorsed by a consensus-based entity 
(CBE) with a contract under section 1890(a) of the Act. In the case of 
a specified area or medical topic determined appropriate by the 
Secretary for which a feasible and practical measure has not been 
endorsed, section 1899B(e)(2)(B) of the Act permits the Secretary to 
specify a measure that is not so endorsed, as long as due consideration 
is given to measures that have been endorsed or adopted by a consensus 
organization identified by the Secretary.
    The current version of the HCP COVID-19 Vaccine measure recently 
received endorsement by the CBE on July 26, 2022 under the name 
``Quarterly Reporting of COVID-19 Vaccination Coverage Among Healthcare 
Personnel.'' \44\ However, this measure received endorsement based on 
its specifications depicted in the FY 2022 SNF PPS final rule (86 FR 
42480 through 42489), and does not capture information about whether 
HCP are up to date with their COVID-19 vaccinations. The proposed

[[Page 53226]]

modification of this measure utilizes the term up to date in the HCP 
vaccination definition and updates the numerator to specify the time 
frames within which an HCP is considered up to date with recommended 
COVID-19 vaccines. We are unable to identify any measures endorsed or 
adopted by a consensus organization for SNFs that captured information 
on whether HCP are up to date with their COVID-19 vaccinations, and we 
found no other feasible and practical measure on this topic.
---------------------------------------------------------------------------

    \44\ Partnership for Quality Measurement. Quarterly Reporting of 
COVID-19 Vaccination Coverage among Healthcare Personnel. Accessed 
June 28, 2023. https://p4qm.org/measures/3636.
---------------------------------------------------------------------------

    Therefore, after consideration of other available measures, we 
found that the exception under section 1899B(e)(2)(B) of the Act 
applies and proposed the modified measure, HCP COVID-19 Vaccine, 
beginning with the FY 2025 SNF QRP. The CDC, the measure developer, is 
pursuing CBE endorsement for the modified version of the measure.
(3) Measure Applications Partnership (MAP) Review
    We refer readers to the FY 2022 SNF PPS final rule (86 FR 42482) 
for more information on the initial review of the HCP COVID-19 Vaccine 
measure by the Measure Applications Partnership (MAP).
    In accordance with section 1890A of the Act, the pre-rulemaking 
process includes making publicly available a list of quality and 
efficiency measures, called the Measures Under Consideration (MUC) 
List, that the Secretary is considering adopting for use in the 
Medicare program(s), including our quality reporting programs. This 
allows interested parties to provide recommendations to the Secretary 
on the measures included on the MUC List. We submitted the updated 
version of the HCP COVID-19 Vaccine measure on the MUC List entitled 
``List of Measures under Consideration for December 1, 2022'' \45\ for 
the 2022 to 2023 pre-rulemaking cycle for consideration by the MAP. 
Interested parties submitted four comments to the MAP during the pre-
rulemaking process on the proposed modifications of the HCP COVID-19 
Vaccine measure. Three commenters noted that it is important that HCP 
be vaccinated against COVID-19 and supported measurement and reporting 
as an important strategy to help healthcare organizations assess their 
performance in achieving high rates of up to date vaccination of their 
HCP. One of these commenters noted that the measure would provide 
valuable information to the government as part of its ongoing response 
to the pandemic. The other two commenters do not believe it should be 
used in a pay-for-performance program, and one raised concerns of 
potential unintended consequences, such as frequency of reporting and 
the potential State regulations with which such a requirement might 
conflict. One commenter did not support the measure, raising several 
concerns with the measure, including that the data have never been 
tested for validity or reliability. Finally, three of the four 
commenters raised concern about the difficulty of defining up to date 
for purposes of the modified measure.
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    \45\ Centers for Medicare & Medicaid Services. Overview of the 
List of Measures Under Consideration for December 1, 2022. https://mmshub.cms.gov/sites/default/files/2022-MUC-List-Overview.pdf.
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    Shortly after publication of the MUC List, several MAP workgroups 
met to provide input on the measure. First, the MAP Health Equity 
Advisory Group convened on December 6 to 7, 2022. The MAP Health Equity 
Advisory Group questioned whether the measure excludes residents with 
contraindications to FDA authorized or approved COVID-19 vaccines, and 
whether the measure will be stratified by demographic factors. The 
measure developer (that is, the CDC) confirmed that HCP with 
contraindications to the vaccines are excluded from the measure 
denominator, but the measure will not be stratified since the data are 
submitted at an aggregate rather than an individual level.
    The MAP Rural Health Advisory Group met on December 8 to 9, 2022, 
during which a few members expressed concerns about data collection 
burden, given that small rural hospitals may not have employee health 
software. The measure developer acknowledged the challenge of getting 
adequate documentation and emphasized their goal is to ensure the 
measures do not present a burden on the provider. The measure developer 
also noted that the model used for the HCP COVID-19 Vaccine measure is 
based on the Influenza Vaccination Coverage among HCP measure (CBE 
#0431), and it intends to utilize a similar approach to the modified 
HCP COVID-19 Vaccine measure if vaccination strategy becomes seasonal. 
The measure developer acknowledged that if COVID-19 becomes seasonal, 
the measure model could evolve to capture seasonal vaccination.
    Next, the MAP Post-Acute Care/Long-Term Care (PAC/LTC) workgroup 
met on December 12, 2022 and provided input on the proposed 
modification for the HCP COVID-19 Vaccine measure. The MAP PAC/LTC 
workgroup noted that the previous version of the measure received 
endorsement from the CBE (CBE #3636),\46\ and that the CDC intends to 
submit the updated measure for endorsement. The PAC/LTC workgroup voted 
to support the staff recommendation of conditional support for 
rulemaking pending testing indicating the measure is reliable and 
valid, and endorsement by the CBE.
---------------------------------------------------------------------------

    \46\ Partnership for Quality Measurement. Quarterly Reporting of 
COVID-19 Vaccination Coverage among Healthcare Personnel. Accessed 
June 28, 2023. https://p4qm.org/measures/3636.
---------------------------------------------------------------------------

    Following the PAC/LTC workgroup meeting, a public comment period 
was held in which interested parties commented on the PAC/LTC 
workgroup's preliminary recommendations, and the MAP received three 
comments. Two supported the update to the measure, one of which 
strongly supported the vaccination of HCP against COVID-19. Although 
these commenters supported the measure, one commenter recommended CBE 
endorsement for the updated measure, and encouraged us to monitor any 
unintended consequences from the measure. Two commenters noted the 
challenges associated with the measure's specifications. Specifically, 
one noted the broad definition of the denominator and another 
recommended a vaccination exclusion or exception due to religious 
beliefs. Finally, one commenter raised issues related to the time lag 
between data collection and public reporting on Care Compare and 
encouraged us to provide information as to whether the measure is 
reflecting vaccination rates accurately and encouraging HCP 
vaccination.
    The MAP Coordinating Committee convened on January 24 to 25, 2023, 
during which the measure was placed on the consent calendar and 
received a final recommendation of conditional support for rulemaking 
pending testing indicating the measure is reliable and valid, and 
endorsement by the CBE. We refer readers to the final MAP 
recommendations, titled 2022-2023 MAP Final Recommendations.\47\
---------------------------------------------------------------------------

    \47\ 2022-2023 MAP Final Recommendations. https://mmshub.cms.gov/sites/default/files/2022-2023-MAP-Final-Recommendations-508.xlsx.
---------------------------------------------------------------------------

(4) Quality Measure Calculation
    The HCP COVID-19 Vaccine measure is a process measure developed by 
the CDC to track COVID-19 vaccination coverage among HCP in facilities 
such as SNFs. The HCP COVID-19 Vaccine measure is a process measure and 
is not risk-adjusted.
    The denominator would be the number of HCP eligible to work in the 
facility for at least one day during the reporting period, excluding 
persons

[[Page 53227]]

with contraindications to COVID-19 vaccination that are described by 
the CDC.\48\ SNFs report the following four categories of HCP to NHSN, 
and the first three categories are included in the measure denominator:
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    \48\ Centers for Disease Control and Prevention. 
Contraindications and precautions. https://www.cdc.gov/vaccines/covid-19/clinical-considerations/interim-considerations-us.html#contraindications.
---------------------------------------------------------------------------

     Employees: This includes all persons who receive a direct 
paycheck from the reporting facility (that is, on the facility's 
payroll), regardless of clinical responsibility or patient contact.
     Licensed independent practitioners (LIPs): This includes 
physicians (MD, DO), advanced practice nurses, and physician assistants 
who are affiliated with the reporting facility, but are not directly 
employed by it (that is, they do not receive a paycheck from the 
facility), regardless of clinical responsibility or patient contact. 
Post-residency fellows are also included in this category if they are 
not on the facility's payroll.
     Adult students/trainees and volunteers: This includes all 
medical, nursing, or other health professional students, interns, 
medical residents, or volunteers aged 18 or over who are affiliated 
with the healthcare facility, but are not directly employed by it (that 
is, they do not receive a direct paycheck from the facility), 
regardless of clinical responsibility or patient contact.
     Other contract personnel: Contract personnel are defined 
as persons providing care, treatment, or services at the facility 
through a contract who do not fall into any of the above- mentioned 
denominator categories. This also includes vendors providing care, 
treatment, or services at the facility who may or may not be paid 
through a contract. Facilities are required to enter data on other 
contract personnel for submission in the NHSN application, but data 
from this category are not included in the HCP COVID-19 Vaccine 
measure.\49\
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    \49\ For more details on the reporting of other contract 
personnel, we refer readers to the NHSN COVID-19 Vaccination 
Protocol, Weekly COVID-19 Vaccination Module for Healthcare 
Personnel, https://www.cdc.gov/nhsn/pdfs/hps/covidvax/protocol-hcp-508.pdf.
---------------------------------------------------------------------------

    The denominator excludes denominator-eligible individuals with 
contraindications as defined by the CDC.\50\ We did not propose any 
changes to the denominator exclusions.
---------------------------------------------------------------------------

    \50\ Centers for Disease Control and Prevention. 
Contraindications and precautions. https://www.cdc.gov/vaccines/covid-19/clinical-considerations/interim-considerations-us.html#contraindications.
---------------------------------------------------------------------------

    We proposed the numerator would be the cumulative number of HCP in 
the denominator population who are considered up to date with CDC 
recommended COVID-19 vaccines. Providers would refer to the definition 
of up to date as of the first day of the applicable reporting quarter, 
which can be found at https://www.cdc.gov/nhsn/pdfs/hps/covidvax/UpToDateGuidance-508.pdf. For example, HCP would have been considered 
up to date during quarter 4 of the CY 2022 reporting period for the SNF 
QRP if they met one of the following criteria:
    1. Individuals who received an updated bivalent \51\ booster dose, 
or
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    \51\ The updated (bivalent) Moderna and Pfizer-BioNTech boosters 
target the most recent Omicron subvariants. The updated (bivalent) 
boosters were recommended by the CDC on September 2, 2022. As of 
this date, the original, monovalent mRNA vaccines are no longer 
authorized as a booster dose for people ages 12 years and older.
---------------------------------------------------------------------------

    2a. Individuals who received their last booster dose less than 2 
months ago, or
    2b. Individuals who completed their primary series \52\ less than 2 
months ago.
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    \52\ Completing a primary series means receiving a two-dose 
series of a COVID-19 vaccine or a single dose of Janssen/J&J COVID-
19 vaccine.
---------------------------------------------------------------------------

    We refer readers to https://www.cdc.gov/nhsn/pdfs/nqf/covid-vax-hcpcoverage-rev-2023-508.pdf for more details on the measure 
specifications.\53\
---------------------------------------------------------------------------

    \53\ We highlight that the hyperlink included in the FY 2024 SNF 
PPS proposed rule has been retired as the CDC has uploaded a new 
measure specification document to the NHSN. Therefore, the hyperlink 
has been updated in this FY 2024 SNF PPS final rule.
---------------------------------------------------------------------------

    While we did not propose any changes to the data submission or 
reporting process for the HCP COVID-19 Vaccine measure, we proposed 
that for purposes of meeting FY 2025 SNF QRP compliance, SNFs would 
report HCP who are up to date beginning in quarter 4 of CY 2023. Under 
the data submission and reporting process, SNFs would collect the 
numerator and denominator for the modified HCP COVID-19 Vaccine measure 
for at least one self-selected week during each month of the reporting 
quarter and submit the data to the NHSN Long-Term Care Facility (LTCF) 
Component before the quarterly deadline. In the FY 2024 SNF PPS 
proposed rule (88 FR 21337), we incorrectly stated that SNFs would 
submit data to the NHSN Healthcare Personnel Safety (HPS) Component. We 
clarify that SNFs submit the data for this measure to the NHSN LTCF 
Component. We highlight that SNFs already submit data to the LTCF 
component of the NHSN for reporting of the HCP COVID-19 Vaccine 
measure. If a SNF submits more than 1 week of data in a month, the most 
recent week's data would be used to calculate the measure. Each 
quarter, the CDC would calculate a single quarterly HCP COVID-19 
vaccination coverage rate for each SNF, which would be calculated by 
taking the average of the data from the 3 weekly rates submitted by the 
SNF for that quarter. Beginning with the FY 2026 SNF QRP, we proposed 
SNFs would be required to submit data for the entire calendar year. We 
also proposed that public reporting of the modified version of the HCP 
COVID-19 Vaccine measure would begin with the October 2024 Care Compare 
refresh or as soon as technically feasible.
    We solicited public comment on our proposal to modify the HCP 
COVID-19 Vaccine measure beginning with the FY 2025 SNF QRP. We 
received several comments from interested parties who support 
vaccination of HCP and communities against COVID-19. They also agreed 
with our rationale underlying the proposal to adopt the modified 
measure in the SNF QRP because updating the measure numerator 
definition reflected the current science. However, many of these same 
commenters did not support the proposal itself for various reasons, 
including the lack of CBE endorsement, the perceived burden associated 
with collecting the data, and the definition of up to date. The 
following is a summary of the comments we received on our proposal to 
modify the HCP COVID-19 Vaccine measure beginning with the FY 2025 SNF 
QRP and our responses.
    Comment: We received several supportive comments for our proposal 
to modify the numerator definition for the HCP COVID-19 Vaccine measure 
and to update the numerator to specify the time frames within which an 
HCP is considered up to date with recommended COVID-19 vaccines. 
Commenters note that nursing home residents have been 
disproportionately vulnerable throughout the COVID-19 pandemic, and 
although the PHE has ended, adherence to infection prevention and 
control measures is essential to the health, safety, and well-being of 
residents. Some commenters noted that access to transparent, complete, 
and easily understandable information is essential for residents to 
make informed decisions, and that public display of the vaccination 
rates on Care Compare provides vital information for residents and 
their caregivers. Other commenters also noted that despite CMS's 
withdrawal of the Omnibus COVID-19 Health Care Staff Vaccination 
Requirements,\54\

[[Page 53228]]

vaccinations are still one of the most effective infection prevention 
tools to protect staff, residents, and visitors against severe illness, 
hospitalization, and death.
---------------------------------------------------------------------------

    \54\ We interpret the commenter to be referring to the Medicare 
and Medicaid Programs; Policy and Regulatory Changes to the Omnibus 
COVID-19 Health Care Staff Vaccination Requirements; Additional 
Policy and Regulatory Changes to the Requirements for Long-Term Care 
(LTC) Facilities and Intermediate Care Facilities for Individuals 
with Intellectual Disabilities (ICFs-IID) To Provide COVID-19 
Vaccine Education and Offer Vaccinations to Residents, Clients, and 
Staff; Policy and Regulatory Changes to the Long-Term Care Facility 
COVID-19 Testing Requirements Final Rule (88 FR 36485).
---------------------------------------------------------------------------

    Response: We thank the commenters for their support. We agree that 
vaccination plays a critical part in the nation's strategy to 
effectively counter the spread of COVID-19. We continue to believe it 
is important to incentivize and track HCP vaccination through quality 
measurement across care settings, including SNFs, in order to protect 
HCP, residents, and caregivers, and to help sustain the ability of HCP 
in SNFs to continue serving their communities.
    Comment: Three commenters opposed the proposed modification and 
expressed concern that the modified version of the measure was not 
submitted for endorsement by a CBE before it was proposed for the SNF 
QRP. As a result, one of these commenters is concerned that the measure 
has not received a full evaluation of a range of issues affecting 
measure reliability, accuracy, and feasibility. This commenter also 
stated that the current version of the measure never went through a CBE 
endorsement process, and therefore, it has not yet had a holistic 
evaluation regarding whether the measure is working as intended.
    Response: We refer the commenter to section VII.C.1.a.2. of this 
final rule, where we point out that the current version of the HCP 
COVID-19 Vaccine measure received endorsement by the CBE on July 26, 
2022 under the name ``Quarterly Reporting of COVID-19 Vaccination 
Coverage among Healthcare Personnel.'' \55\ We note, however, that the 
measure received endorsement based on its specifications in the FY 2022 
SNF PPS final rule (86 FR 42480 through 42489). Even though the 
current, endorsed version does not capture information about whether 
HCP are up to date with their COVID-19 vaccinations, we believe its 
previous endorsement speaks to the quality of the measure design for 
the proposed modified version, since many components of the previous 
measure remain intact in this modified version. Since we were unable to 
identify any CBE endorsed measures for SNFs that captured information 
on whether HCP are up to date with their COVID-19 vaccinations, and we 
found no other feasible and practical measure on this topic, we find 
the modification to the HCP COVID-19 Vaccine measure reasonable for SNF 
QRP adoption and implementation. The CDC, the measure developer, is 
pursuing CBE endorsement for the modified version of the HCP COVID-19 
Vaccine measure.
---------------------------------------------------------------------------

    \55\ Partnership for Quality Measurement. Quarterly Reporting of 
COVID-19 Vaccination Coverage among Healthcare Personnel. Accessed 
on June 14, 2023. https://p4qm.org/measures/3636.
---------------------------------------------------------------------------

    In terms of measure testing, as mentioned in section VII.C.1.a.1.b. 
of this final rule, we reiterate that the CDC conducted beta testing of 
the modified HCP COVID-19 Vaccine measure and concluded that the 
collection of information on booster doses received by HCP was feasible 
with a high reporting rate and the measure score displayed a 
performance gap indicating clinically significant differences in 
booster dose vaccination coverage rates among SNFs. We will continue to 
monitor the measure to identify any concerning trends as part of our 
routine monitoring activities to regularly assess measure performance, 
reliability, and reportability for all data submitted for the SNF QRP.
    Comment: A number of commenters expressed concerns with the 
evolving nature of the measure's definition of up to date. Commenters 
suggested that the definition will quickly and frequently become 
outdated, and that a measure with a ``moving set of goalposts'' is 
challenging for HCP to understand. As a result, these changes to the 
definition could result in an inaccurate reporting of HCPs' up to date 
vaccination rates. Another commenter was concerned that any 
inconsistencies in the up to date definitions and potential 
inaccuracies associated with the rapid translation of complex 
vaccination recommendations may cause confusion among SNFs and 
negatively impact vaccine uptake. Finally, one commenter suggested that 
without a regular cadence of boosters or a defined COVID-19 ``season,'' 
like influenza, modifying the numerator definition to up to date is 
premature.
    Response: We recognize that the up to date COVID-19 vaccination 
definition may evolve due to the changing nature of the virus, but we 
are also confident in HCPs' ability to understand these changes as they 
have been at the front lines of managing COVID-19 since the beginning 
of the pandemic. Since the adoption of the current version of the 
measure, the public health response to COVID-19 has necessarily adapted 
to respond to the changing nature of the virus's transmission and 
community spread. As mentioned in the FY 2022 SNF PPS final rule (86 FR 
42481 through 42482), we received several public comments during the 
measure's pre-rulemaking process encouraging us to continue to update 
the measure as new evidence on COVID-19 continues to arise and we 
stated our intention to continue to work with our partners, including 
the FDA and CDC, to consider any updates to the measure in future 
rulemaking as appropriate. We believe that the proposed modification to 
this measure aligns with our responsive approach to COVID-19 and will 
continue to support vaccination as the most effective means to prevent 
the worst consequences of COVID-19, including severe illness, 
hospitalization, and death.
    Comment: One commenter who supported the proposal to modify the HCP 
COVID-19 Vaccine numerator definition also recommended that the measure 
should explicitly specify for HCP to receive primary series and booster 
vaccine doses to align with the recommendations on bivalent booster 
doses, including being up to date.
    Response: We agree with the commenter, and highlight that the 
proposed modification to the HCP COVID-19 Vaccine measure numerator is 
in alignment with CDC recommendations as found on the following CDC 
NHSN web page: https://www.cdc.gov/nhsn/pdfs/hps/covidvax/UpToDateGuidance-508.pdf. At the beginning of each reporting period and 
before collecting or submitting data on this modified measure, SNFs 
must refer to the aforementioned document to determine the then-
applicable definition of up to date to apply when collecting data on 
the vaccination status of HCP for that quarterly reporting period.
    Comment: One commenter noted that CDC's vaccination guidance 
suggests that some individuals with certain risk factors should 
consider receiving a booster dose within 4 months of receiving their 
first bivalent dose. The commenter noted that SNFs usually do not have 
routine access to data to know which of their HCPs may need a booster 
dose. The commenter was concerned that, to collect accurate data, SNFs 
would have to obtain permission to inquire and obtain information on 
each individual HCP's underlying health risk factors and a mechanism to 
keep the data fully secure. As a result, they expressed concern that 
the resource intensiveness of collecting data under the CDC's proposed 
modified definition for the HCP COVID-19 Vaccine measure may outweigh 
its value.
    Response: SNFs have been engaging with their staff for almost 2 
years to obtain information on their COVID-19 vaccination status. The 
proposed modification to the HCP COVID-19

[[Page 53229]]

Vaccine measure should not require any changes to how SNFs currently 
engage with their staff and administer a comprehensive vaccine 
administration strategy. We are also confident in SNF's ability to 
utilize the available CDC resources to keep themselves informed as they 
have been at the front lines of managing COVID-19 since the beginning 
of the pandemic. Specifically, we note that considerations for 
immunocompromised persons are not impacted by the modification proposed 
to this measure as these considerations are present with the primary 
vaccination series for the current HCP COVID-19 Vaccine measure. As 
emphasized in the CDC NHSN ``COVID-19 Vaccination Modules: 
Understanding Key Terms and Up to Date Vaccination'' web page https://www.cdc.gov/nhsn/pdfs/hps/covidvax/UpToDateGuidance-508.pdf referred to 
in section VII.C.1.a.4. of this final rule, the NHSN surveillance 
definition for up to date is currently the same for all HCP regardless 
of immunocompromised status.
    Comment: Two commenters expressed concern that modifications to the 
HCP COVID-19 Vaccine measure may exacerbate workforce shortages. One 
commenter noted that while the measure does not mandate up to date 
COVID-19 vaccinations for HCP, it may affect how SNFs approach 
vaccination requirements. One of these commenters mentioned that HCP 
may choose to work in other health care settings where such a mandate 
or quality measure does not exist, and the other commenter suggested 
they will choose to work in other areas of commerce.
    Response: We disagree that the proposed modification to the 
numerator definition of the HCP COVID-19 Vaccine measure may exacerbate 
workforce shortages. We believe that the risks associated with COVID-19 
warrant direct attention, especially because HCP are working directly 
with, and in close proximity to, residents. We clarify that the HCP 
COVID-19 Vaccine measure does not require SNFs to adopt mandatory 
vaccination policies, and it is a SNF's responsibility to determine 
their own personnel policies. To support a comprehensive vaccine 
administration strategy, we encourage SNFs to voluntarily engage in the 
provision of appropriate and accessible education and vaccine-offering 
activities. Many SNFs across the country are educating staff, 
residents, and residents' representatives, participating in vaccine 
distribution programs, and reporting up to date vaccine administration. 
The CDC has a number of resources available to SNFs to assist in 
building vaccine confidence. CMS also has a web page to help providers, 
including SNFs, find resources related to the COVID-19 vaccines. There 
are several toolkits and videos SNFs can use to stay informed and to 
educate their HCP, residents and communities about the COVID-19 
vaccines.
    Comment: Several commenters expressed concern with the measure's 
administrative burden, especially with having to track whether HCP meet 
the new requirements when the up to date definition changes. Another 
commenter suggested that because SNFs do not currently report booster 
doses to the NHSN, the proposal will require facility staff to spend 
more time tracking this information which will redirect resources away 
from direct resident care, particularly for smaller facilities without 
sophisticated software. Finally, one commenter expressed conditional 
support for the modification to the HCP COVID-19 measure but requested 
CMS reduce the reporting burden associated with the measure. This 
commenter requested that CMS and the CDC work with SNFs to identify 
opportunities to simplify and streamline any reporting burdens 
associated with the measure.
    Response: We appreciate commenters' concerns regarding the 
reporting of the measure. SNFs have been reporting the current version 
of the measure since the measure's initial data submission period 
(October 1, 2021 through December 31, 2021), and we believe that there 
has been sufficient time to allocate the necessary resources required 
to report this measure. We note that the CDC used the up to date 
numerator definition during the Quarter 4 2022 surveillance period 
(September 26, 2022 through December 25, 2022) for purposes of NHSN 
surveillance, and SNFs have been successfully reporting the measure in 
alignment with the proposed modifications since that time. To assess 
the burden of reporting booster doses, the CDC conducted feasibility 
analysis of the modified HCP COVID-19 Vaccine measure by calculating 
the proportion of facilities that reported booster doses of the COVID-
19 vaccine. As mentioned in section VII.C.1.a.1.b. of this final rule, 
feasibility of reporting booster doses of vaccine is evident by the 
fact that 99.2 percent of SNFs reported vaccination booster dose 
coverage data to the NHSN for the first quarter of 2022. Based on the 
high reportability, we do not believe the proposed change would impose 
overwhelming burden.
    The CDC provides frequent communications and education to support 
SNFs' understanding of the latest guidelines. CDC posts an updated 
document approximately 2 weeks before the start of a new reporting 
quarter. If there are any changes to the definition, forms, etc., CDC 
will host a webinar in the 1-2 weeks before the beginning of a new 
reporting quarter. If SNFs have any concerns they would like to address 
regarding the data submission of this measure, they can voice their 
concerns during CMS' SNF/LTC Open Door Forums (ODFs). For more 
information on ODFs and to sign up for email notifications, we refer 
readers to the following CMS web page: https://www.cms.gov/outreach-and-education/outreach/opendoorforums/odf_snfltc.
    Comment: One commenter emphasized that the reporting burden stems 
from the high frequency reporting cadence as well as the number of 
individuals included in the measure denominator. The same commenter 
stated that up to date COVID-19 vaccination data would not be easy to 
track, requires multiple processes, and frequent multiple software 
applications.
    Response: We emphasize that we proposed no changes to the measure's 
reporting frequency, reporting method, or denominator population. SNFs 
have been successfully reporting at this cadence on the same HCP 
population since October 1, 2021.
    Comment: Two commenters recommended the HCP COVID-19 Vaccine 
measure should be voluntary until there is a stable definition for up 
to date.
    Response: The HCP COVID-19 Vaccine measure was adopted into the SNF 
QRP in the FY 2022 SNF PPS Final Rule (86 FR 42480 through 42489). We 
proposed to modify the definition of the measure numerator and the time 
frames for reporting and did not make any proposed changes to the 
measure denominator or the minimum reporting threshold for compliance. 
Therefore, successful reporting of the measure is still part of the SNF 
QRP reporting requirements.
    Comment: One commenter raised concerns with the potential 
inaccuracy of the measure because the term up to date may continue to 
evolve with new vaccines and vaccine formulations.
    Response: In response to the commenter's concerns that the up to 
date numerator definition may evolve, we refer commenters to section 
VII.C.1.a.4. of this final rule where we discuss how SNFs would refer 
to the definition of up to date as of the first day of the quarter, 
which can be found at the following CDC NHSN web page at https://www.cdc.gov/nhsn/pdfs/hps/covidvax/UpToDateGuidance-508.pdf. The CDC 
notes that this document will be updated quarterly to reflect any 
changes as COVID-19 guidance evolves,

[[Page 53230]]

and notes that SNFs would use the definitions for the reporting period 
associated with the reporting weeks included in data submission. As 
such, the up to date vaccination definition that would be applicable 
during a particular reporting period would not change, which addresses 
any concern that there would not be a single consistent resource for 
reporting instructions when the definition of up to date is revised. If 
the requirements do change from one quarter to the next, SNFs would 
have the up to date definition at the beginning of the quarter (using 
the aforementioned CDC NHSN web page), and have a minimum of three 
weeks to assess whether their HCP meet the definition of up to date 
before submitting HCP COVID-19 Vaccine measure data during the self-
selected week of a corresponding month.
    Comment: A number of commenters stated that while they support 
COVID-19 vaccination as one of the strongest measures for preventing 
serious illness and/or death from COVID-19, they do not believe the HCP 
COVID-19 Vaccine measure is an indicator of whether a SNF provides high 
quality of care to residents. Commenters noted that the measure, as 
currently written, reflects personal choice and represents outcomes 
over which SNFs have no control. Another commenter stated that staff 
acceptance of the COVID-19 vaccine reflects the community in which they 
reside, their own culture and beliefs, as well as their own health 
status. This commenter urged CMS to withdraw the HCP COVID-19 Vaccine 
measure from the SNF QRP and instead create a process measure to 
collect data on the outreach and education efforts that SNFs have 
undertaken to encourage up to date vaccination among staff. One 
commenter noted that differences in vaccine uptake are often deeply 
rooted in culture, religion, ethnicity, socioeconomic status, and more. 
Therefore, they believe that while SNFs will continue to educate their 
staff and encourage employee vaccinations, they should not be used to 
measure a SNF's ability to provide a safe environment. Finally, one 
commenter requested that CMS remind the public that vaccination is not 
mandatory for HCP, and as a result, the reported vaccination rate 
performance may vary based on local vaccine hesitancy barriers rather 
than provider effort at encouraging all HCP to be vaccinated.
    Response: We disagree with the commenters and believe that the HCP 
COVID-19 Vaccine measure is an indicator of the quality of care in a 
SNF. We direct readers to section VII.C.1.a.1.a. of this final rule 
where we provide information illustrating that in the presence of a 
high community prevalence of COVID-19, residents of facilities with low 
staff vaccination coverage had cases of COVID-19-related deaths 195 
percent higher than those among residents of facilities with high 
vaccination coverage.\56\ Therefore, we find that a SNF's HCP COVID-19 
vaccination rate, including booster doses, is an important quality 
indicator. We acknowledge that vaccination rates may be influenced by 
staff's culture, beliefs, community, and geographic areas, but we also 
know that HCP may come into contact with SNF residents, increasing the 
risk for HCP-to-resident transmission of infection. Therefore, we 
believe the measure as proposed has the potential to generate 
actionable data on up to date HCP COVID-19 vaccination rates that can 
be used to target quality improvement among SNFs, including increasing 
up to date HCP COVID-19 vaccination coverage in SNFs, while also 
promoting resident safety and increasing the transparency of quality of 
care in the SNF setting. Furthermore, we appreciate the suggestion for 
a quality measure to collect data on the outreach and education efforts 
that SNFs have undertaken to encourage up to date vaccination among 
staff and will use this input to inform our future measure development 
efforts. Finally, in relation to the commenter requesting us to remind 
the public that HCP vaccination is not mandatory, we assume that the 
commenter is recommending adding this reminder to the Care Compare web 
page. We appreciate the commenter's suggestion and will consider it 
when the modified HCP COVID-19 Vaccine measure is publicly reported on 
Care Compare.
---------------------------------------------------------------------------

    \56\ Pilishvili T, Gierke R, Fleming-Dutra KE, et al. 
Effectiveness of mRNA Covid-19 Vaccine among U.S. Health Care 
Personnel. N Engl J Med. 2021 Dec 16;385(25):e90. doi: 10.1056/
NEJMoa2106599. PMID: 34551224; PMCID: PMC8482809.
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    Comment: One commenter opposed the measure's modified numerator 
definition because the FDA has not fully authorized the bivalent 
booster, rather it remains available under an Emergency Use 
Authorization (EUA).
    Response: We note that, on August 31, 2022, the FDA amended the 
EUAs for the Moderna COVID-19 vaccine and the Pfizer-BioNTech COVID-19 
vaccine to authorize bivalent formulations of the vaccines for use as a 
single booster dose at least two months following primary or booster 
vaccination.\57\ See more details in section VII.C.1.a.1. of this final 
rule. We would like to refer readers to the FDA website for additional 
information related to FDA process for evaluating an EUA request at 
https://www.fda.gov/vaccines-blood-biologics/vaccines/emergency-use-authorization-vaccines-explained. In addition, we emphasize that the 
FDA is closely monitoring the safety of the COVID-19 vaccines 
authorized for emergency use. We believe that due to the ongoing risk 
of infection transmissions in the SNF population, the benefits of 
finalizing the modified up to date definition of the measure in this 
year's final rule is essential for patient safety.
---------------------------------------------------------------------------

    \57\ Food and Drug Administration. Coronavirus (COVID-19) 
Update: FDA Authorizes Moderna, Pfizer-BioNTech Bivalent COVID-19 
Vaccines for Use as a Booster Dose. August 31, 2022. https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-authorizes-moderna-pfizer-biontech-bivalent-covid-19-vaccines-use.
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    Comment: Several commenters opposed the proposed modifications to 
the HCP COVID-19 Vaccine measure, and the most frequently cited reason 
was that the COVID-19 PHE ended on May 11, 2023 and CMS subsequently 
lifted staff vaccination requirements established under Sec.  
483.80(i).\58\ One commenter was concerned that the data reporting 
requirements associated with the measure will divert already stretched 
resources from resident care to administrative processes. Another 
commenter thought it was counter-intuitive for CMS to end vaccination 
mandates for HCP while seeking to amend the numerator for this measure. 
One commenter called for an elimination of the HCP COVID-19 Vaccine 
measure in the SNF QRP, while another commenter stated that they were 
comfortable with continuing to report on the measure during 2024 as the 
Administration and the broader healthcare ecosystem continue to assess 
what COVID-19 looks like moving forward. This commenter encouraged CMS 
to continue to evaluate and revisit the measure's requirements.
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    \58\ On June 5, 2023, CMS issued the Medicare and Medicaid 
Programs; Policy and Regulatory Changes to the Omnibus COVID-19 
Health Care Staff Vaccination Requirements; Additional Policy and 
Regulatory Changes to the Requirements for Long-Term Care (LTC) 
Facilities and Intermediate Care Facilities for Individuals With 
Intellectual Disabilities (ICFs-IID) to Provide COVID-19 Vaccine 
Education and Offer Vaccinations to Residents, Clients, and Staff; 
Policy and Regulatory Changes to the Long Term Care Facility COVID-
19 Testing Requirements final rule. This final rule withdrew the 
regulations in the interim final rule with comment (IFC) ``Omnibus 
COVID-19 Health Care Staff Vaccination'' published in the November 
5, 2021 Federal Register.
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    Response: We do not agree with commenters suggesting that because 
the PHE ended, and we lifted the staff vaccination requirements, that 
there is no value in retaining the HCP COVID-19 Vaccine measure in the 
SNF QRP.

[[Page 53231]]

We believe this measure continues to align with our goals to promote 
wellness and disease prevention. Under CMS' Meaningful Measures 
Framework 2.0, the HCP COVID-19 Vaccine measure addresses the quality 
priorities of ``Immunizations'' and ``Public Health'' through the 
Meaningful Measures Area of ` ``Wellness and Prevention.'' \59\ Under 
the National Quality Strategy, the measure addresses the goal of Safety 
under the priority area Safety and Resiliency.\60\ While the end of the 
PHE may result in removing vaccination requirements from the LTC 
Conditions of Participation, we note that the reporting requirements of 
the SNF QRP for the proposed modified version of the HCP COVID-19 
Vaccine measure are distinct from those cited by the commenter. 
Specifically, the SNF QRP is a pay-for-reporting program, and therefore 
the inclusion of this measure in the SNF QRP does not require that HCP 
actually receive these booster vaccine doses in order for the SNF to 
successfully participate in the SNF QRP. Our continued response to 
COVID-19 is not fully dependent on the emergency declaration for the 
COVID-19 PHE, and even beyond the end of the COVID-19 PHE, we will 
continue to work to protect individuals and communities from the virus 
and its worst impacts by supporting access to COVID-19 vaccines, 
treatments, and tests.\61\
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    \59\ Centers for Medicare & Medicaid Services. Meaningful 
Measures 2.0: Moving from Measure Reduction to Modernization. June 
17, 2022Accessed May 26, 2023. https://www.cms.gov/medicare/meaningful-measures-framework/meaningful-measures-20-moving-measure-reduction-modernization.
    \60\ Centers for Medicare & Medicaid Services. CMS National 
Quality Strategy. Accessed May 26, 2023. https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/value-based-programs/cms-quality-strategy.
    \61\ U.S. Department of Health and Human Services. Fact Sheet: 
End of the COVID-19 Public Health Emergency. May 9, 2023. Accessed 
May 22, 2023. https://www.hhs.gov/about/news/2023/05/09/fact-sheet-end-of-the-covid-19-public-health-emergency.html.
---------------------------------------------------------------------------

    Comment: One additional commenter requested clarification on 
whether the White House's announcement to end COVID-19 vaccination 
requirements and/or ``mandates'' will impact the adoption or use of the 
proposed HCP COVID-19 Vaccine measure in the SNF QRP.
    Response: We clarify that the vaccination requirements under Sec.  
483.80(i) (which have now been lifted) are separate from SNF QRP 
requirements to report HCP COVID-19 vaccination data. Even though the 
PHE has ended, and vaccination requirements have been lifted, CMS 
intends to encourage ongoing COVID-19 vaccination through use of its 
quality reporting programs (88 FR 36487). One way to encourage resident 
safety and COVID-19 vaccination is through adoption of the modified up 
to date numerator definition of the HCP COVID-19 Vaccine measure. 
Despite the White House's announcement,\62\ the SNF QRP still requires 
data submission of the HCP COVID-19 Vaccine measure to the NHSN for 
SNFs to remain in compliance with the SNF QRP reporting requirements. 
However, since the SNF QRP is a pay-for-reporting program, HCP 
receiving COVID-19 vaccination is not mandated by this measure.
---------------------------------------------------------------------------

    \62\ White House. The Biden-Harris Administration Will End 
COVID-19 Vaccination Requirements for Federal Employees, 
Contractors, International Travelers, Head Start Educators, and CMS-
Certified Facilities. May 1, 2023. https://www.whitehouse.gov/briefing-room/statements-releases/2023/05/01/the-biden-administration-will-end-covid-19-vaccination-requirements-for-federal-employees-contractors-international-travelers-head-start-educators-and-cms-certified-facilities/.
---------------------------------------------------------------------------

    Comment: One commenter noted the proposed rule stated that data 
will be submitted through the Healthcare Personnel Safety (HPS) 
component of NHSN (88 FR 21337), and they point out that the data are 
actually submitted through the Long-Term Care Facility (LTCF) component 
as part of the SNF regulatorily required reporting.
    Response: We thank the commenter and acknowledge that in the FY 
2024 SNF PPS proposed rule (88 FR 21337), we incorrectly stated that 
SNFs would submit data to the NHSN HPS component. We clarify that, in 
alignment with the current version of the measure established in the FY 
2022 SNF PPS final rule, SNFs will continue to submit HCP COVID-19 
Vaccine data under this modified measure to the LTCF component of the 
CDC's NHSN before the quarterly deadline. We refer readers to section 
VII.C.1.a.4. of this final rule, where we have remediated this error.
    Comment: One commenter questioned why CMS would delay the 
modification to the HCP COVID-19 Vaccine measure to 2025, rather than 
implementing it now. They stated a delay may prove unnecessary given 
the uncertain future of COVID-19 and the efficacy and availability of 
COVID-19 vaccines over time.
    Response: We refer the commenter to section VII.C.1.a.4 of this 
final rule where we proposed SNFs would report individuals who are up 
to date beginning in quarter four of CY 2023. To clarify, data reported 
in CY 2023 comply with the requirements for the FY 2025 SNF QRP.
    Comment: One commenter questioned why CMS has prioritized use of 
the NHSN over State-run Immunization Information Systems (IIS) for data 
reporting. This commenter noted that IIS are more robust and allow for 
greater clarity on vaccination status as healthcare professionals and 
individuals transition throughout the health care system.
    Response: We did not propose to modify the method of data 
submission for the HCP COVID-19 Vaccine measure. As we stated in the FY 
2022 SNF PPS Final Rule (86 FR 42494), we understand IIS to be 
confidential, population-based, computerized databases that record 
immunization doses administered by participating providers to persons 
residing within a given geopolitical area, but these systems are not 
standardized across all SNFs. HHS has an Immunization Information 
Systems Support Branch (IISSB) that facilitates the development, 
implementation, and acceptance of these systems, but they are overseen 
by the States and/or organizations who develop them. In the FY 2022 SNF 
PPS final rule (86 FR 42493), we adopted the use of the NHSN COVID-19 
Modules for tracking HCP COVID-19 vaccination rates across all sites of 
service, including SNFs, because most of the state IIS do not include 
the information needed to calculate the HCP COVID-19 Vaccine measure. 
Since SNFs have successfully reported HCP COVID-19 vaccination rates 
since the measure's initial data submission period (October 1, 2021 
through December 31, 2021), we will continue using the CDC's NHSN as 
the measure's data submission platform.
    Comment: One commenter expressed concerns with the validity of any 
COVID-19 vaccination measure that uses self-reported data from SNFs and 
their HCP and encouraged CMS to develop data sources beyond those that 
are self-reported. This commenter recommends that CMS develop and 
implement auditing and penalty systems to detect and respond to 
inaccurate or falsified data.
    Response: We emphasize that we currently implement multiple 
processes to ensure self-reported data are accurate. As part of our 
measure monitoring and compliance determination processes, we 
scrutinize provider data submission for all SNF QRP measures, including 
those for NHSN measures. We look for any performance gaps or discordant 
performance in measures that may indicate issues with data submission.
    Comment: One commenter suggested that if the measure continues to 
be included in the SNF QRP, CMS should reduce the burden of gathering 
data from all personnel captured within the measure's denominator 
population.

[[Page 53232]]

    Response: We did not propose changes to the measure denominator and 
disagree that the denominator criteria should be loosened. We emphasize 
that any HCP working in the facility for at least one working day 
during the reporting period, meeting denominator eligibility criteria, 
may come into contact with SNF residents, increasing the risk for HCP-
to-resident transmission of infection. Therefore, we believe the 
measure as proposed has the potential to increase up to date COVID-19 
vaccination coverage in SNFs, promote resident safety, and increase the 
transparency of quality of care in the SNF setting.
    Comment: One commenter urged CMS to expand the criteria of HCP that 
are exempted beyond those with contraindications as defined by the CDC 
because there are numerous reasons HCP may decide whether to be up to 
date on vaccinations. One commenter specifically took issue with the 
measure's lack of religious exemptions. Another commenter was concerned 
that a SNF could be unfairly penalized for following CDC guidelines 
while delivering care that focuses on supporting individuals' ability 
to choose the recommended vaccine option that best suits their needs 
and preferences. This commenter suggested alignment of the HCP COVID-19 
Vaccine measure's up to date definition with that of the Advisory 
Committee on Immunization Practices (ACIP) and recommended that the 
measure allow HCP to choose the vaccine option that best suits their 
needs and preferences.
    Response: We acknowledge that numerous factors may impact an 
individual's decision to receive up to date vaccinations, such as 
sincerely held religious beliefs, observances, or practices. However, 
we emphasize that any HCP may come into contact with SNF residents, 
increasing the risk for HCP-to-resident transmission of infection. 
Therefore, we believe the measure as proposed has the potential to 
increase up to date HCP COVID-19 vaccination coverage in SNFs, promote 
resident safety, and increase the transparency of quality of care in 
the SNF setting. Additionally, we want to reiterate that neither the 
current version nor the proposed modified version of the measure 
mandate that HCP be up to date on their COVID-19 vaccine. The HCP 
COVID-19 Vaccine measure only requires reporting of vaccination rates 
for a SNF to successfully participate in the SNF QRP. Therefore, this 
measure is not preventing anyone from choosing a vaccine option that 
best suits their beliefs or preferences. In regard to the comment about 
aligning the measure's up to date definition with that of ACIP, the 
CDC's and ACIP's definitions are currently aligned. Additionally, we 
note that recommendations made by the ACIP are reviewed by the CDC and 
published as the official CDC recommendation if adopted.
    Comment: One commenter stated that the CDC maintains guidance that 
receiving a dose of the COVID-19 vaccine may or should be delayed if a 
person has recently had the COVID-19 infection. This may impact the 
timing of an employee's up to date vaccine dosage.
    Response: The CDC recommends that individuals who recently had a 
COVID-19 infection should still stay up to date with vaccines; however, 
individuals may consider delaying their next vaccine dose by three 
months from when (i) symptoms began, or (ii) initial receipt of a 
positive COVID-19 test. The CDC reiterates that certain factors could 
be reasons for individuals to receive up to date vaccinations sooner 
rather than later, including (i) personal risk of severe disease, (ii) 
risk of disease among close contacts, (iii) local COVID-19 hospital 
admission level, and (iv) the most common COVID-19 variant currently 
causing illness.\63\ Since the CDC recommends that individuals stay up 
to date on vaccines regardless of recent COVID-19 infection, and since 
HCP often come into close contact with individuals at risk of disease, 
we do not agree that a recent COVID-19 infection would prevent HCP from 
receiving up to date COVID-19 vaccinations.
---------------------------------------------------------------------------

    \63\ Centers for Disease Control and Prevention. Stay Up to Date 
with COVID-19 Vaccines. July 17, 2023. https://www.cdc.gov/coronavirus/2019-ncov/vaccines/stay-up-to-date.html#UTD.
---------------------------------------------------------------------------

    Comment: One commenter recommended that the measure should be 
revised to cover all CDC-recommended vaccines, and that the measure can 
be revised periodically as CDC guidance changes.
    Response: We thank the commenter for this suggestion and will use 
this input to inform our future measure development efforts.
    Comment: One commenter requested CMS mandate that all SNF HCP 
receive an up to date COVID-19 vaccination.
    Response: Staff COVID-19 vaccination is no longer required under 
Sec.  483.80(i). We continue to encourage ongoing COVID-19 vaccination 
through our quality reporting and value-based incentive programs. We 
emphasize that the proposed modifications to the HCP COVID-19 Vaccine 
measure for the SNF QRP do not mandate HCP COVID-19 vaccination.
    Comment: Although generally supportive of the HCP COVID-19 Vaccine 
modifications to the up to date numerator definition, a few commenters 
recommended that CMS revise the measure to only require annual 
reporting, which would align with reporting requirements for the HCP 
Influenza Vaccine measure.
    Response: As we stated in the FY 2024 SNF PPS proposed rule (88 FR 
21336), the measure developer, the CDC, noted that the model used for 
this measure is based on the Influenza Vaccination Coverage among HCP 
measure (CBE #0431), and it intends to utilize a similar approach for 
the HCP COVID-19 Vaccine measure if the vaccination strategy becomes 
seasonal. We continue to monitor COVID-19 as part of our public health 
response and will consider these data to inform any potential action 
that may address seasonality in future rulemaking.
    We also received comments related to the public reporting of the 
modified HCP COVID-19 Vaccine measure.
    Comment: One commenter emphasized the importance of publicly 
reporting the HCP COVID-19 Vaccine measure on Care Compare, and 
recommended CMS coordinate public display of the HCP COVID-19 vaccine 
with existing measures of staff and resident COVID-19 vaccination and 
rates to avoid confusion or duplication. This commenter also suggested 
CMS include demographic information in the public display of the data 
in order to highlight potential disparities similar to those already 
uncovered about COVID-19 variation within facilities and among 
residents. Finally, this commenter stated CMS should give strong 
consideration to providing results to facilities that are stratified 
for race, ethnicity, and other social risk factors based on information 
submitted by facilities.
    Response: We thank the commenter for their suggestions. However, as 
described in section VII.C.1.a.3. of this final rule, the measure 
developer (CDC) stated that the measure could not be stratified by 
demographic factors since the data are submitted at an aggregate rather 
than an individual level. We will continue to assess methods of 
incorporating health equity into the SNF QRP. In response to the 
commenter's recommendation to align the way in which measures of staff 
vaccination are presented on Care Compare, we appreciate this 
suggestion and will take it into consideration.
    Comment: Several commenters were concerned with the delay between 
data submission via the NHSN and public reporting on Care Compare. One 
commenter emphasized that staff in SNFs may change over time so 
publicly

[[Page 53233]]

reported measure data will become outdated quickly. Another commenter 
stated the delay between when the information is collected and when it 
is actually publicly reported could cause confusion and damage the 
public's trust and confidence in the quality of care delivered in their 
community if the rate of up to date HCP vaccination is low due to the 
data lag.
    Response: We agree that it is important to make the most up to date 
data available to beneficiaries and ensure timely display of publicly 
reported data. Therefore, as mentioned in the FY 2022 SNF PPS final 
rule (86 FR 42496 through 42497), we revised our public reporting 
policy for this measure to use quarterly reporting, which allows the 
most recent quarter of data to be displayed, as opposed to an average 
of four rolling quarters. Additionally, the public display schedule of 
the HCP COVID-19 Vaccine measure aligns with SNF QRP public display 
policies finalized in the FY 2017 SNF PPS final rule (81 FR 52041), 
which allows SNFs to submit their SNF QRP data up to 4.5 months after 
the end of the reporting quarter. A number of administrative tasks must 
then occur in sequential order between the time SNF QRP data are 
submitted and reported in Care Compare to ensure the validity of data 
and to allow SNFs sufficient time to request a review of their data 
during the preview period if they believe the quality measure scores 
that are displayed within their Preview Reports are inaccurate. We 
believe this reporting schedule, outlined in section VII.C.1.a.4. of 
this final rule is reasonable, and expediting this schedule may 
establish undue burden on SNFs and jeopardize the integrity of the 
data.
    Additionally, in response to the comment that staff in SNFs may 
change over time, we emphasize that it is precisely because staff in 
SNF's change that monitoring COVID-19 up to date vaccination rates over 
time is important.
    Comment: One commenter pointed out that it may mean that HCPs who 
count as up to date in one quarter may no longer be up to date in the 
next quarter and CMS needs to clearly communicate what publicly 
reported data reflect.
    Response: We agree with the commenter that pointed out that HCP who 
count as up to date in one quarter may no longer be up to date in the 
next quarter. We note that each provider will be measured against the 
same criteria within the same quarter, and the guideline for each 
quarter will be shared through the CDC's website ahead of each quarter. 
Regarding the data collection period used for public reporting, this 
information can be retrieved through the Care Compare site through 
``View Quality Measures,'' and then clicking on ``Get current data 
collection period.''
    Comment: One commenter noted that changing CDC definitions are 
challenging for healthcare professionals, and they do not believe that 
this information can be articulated in a manner for residents to fully 
digest in order to make meaningful healthcare decisions.
    Response: We believe residents will be able to understand what 
changes to the up to date definition mean on Care Compare. We note that 
the public has been using the information displayed on Care Compare for 
the current HCP COVID-19 Vaccine measure since it was first publicly 
reported in 2022. We work closely with our Office of Communications and 
consumer groups when onboarding measures to the Care Compare websites, 
and we will do the same with the modified HCP COVID-19 Vaccine measure 
to ensure that the measure description on Care Compare is clear and 
understandable for the general public.
    After careful consideration of the public comments we received, we 
are finalizing our proposal to modify the HCP COVID-19 Vaccine measure 
beginning with the FY 2025 SNF QRP as proposed.
b. Discharge Function Score Measure Beginning With the FY 2025 SNF QRP
(1) Background
    SNFs provide short-term skilled nursing care and rehabilitation 
services, including physical and occupational therapy and speech-
language pathology services. The most common resident conditions are 
septicemia, joint replacement, heart failure and shock, hip and femur 
procedures (not including major joint replacement), and pneumonia.\64\ 
Septicemia progressing to sepsis is often associated with long-term 
functional deficits and increased mortality in survivors.\65\ 
Rehabilitation of function, however, has been shown to be effective and 
is associated with reducing mortality and improving quality of 
life.66 67
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    \64\ Medicare Payment Advisory Commission. Report to the 
Congress: Medicare and the Health Care Delivery System. June 2021. 
https://www.medpac.gov/wp-content/uploads/import_data/scrape_files/docs/default-source/reports/jun21_medpac_report_to_congress_sec.pdf.
    \65\ Winkler D, Rose N, Freytag A, Sauter W, Spoden M, Schettler 
A, Wedekind L, Storch J, Ditscheid B, Schlattmann P, Reinhart K, 
G[uuml]nster C, Hartog CS, Fleischmann-Struzek C. The Effect of 
Post-acute Rehabilitation on Mortality, Chronic Care Dependency, 
Health Care Use and Costs in Sepsis Survivors. Ann Am Thorac Soc. 
2022 Oct 17. doi: 10.1513/AnnalsATS.202203-195OC. Epub ahead of 
print. PMID: 36251451.
    \66\ Chao PW, Shih CJ, Lee YJ, Tseng CM, Kuo SC, Shih YN, Chou 
KT, Tarng DC, Li SY, Ou SM, Chen YT. Association of Post discharge 
Rehabilitation with Mortality in Intensive Care Unit Survivors of 
Sepsis. Am J Respir Crit Care Med. 2014 Nov 1;190(9):1003-11. doi: 
10.1164/rccm.201406-1170OC. PMID: 25210792.
    \67\ Taito S, Taito M, Banno M, Tsujimoto H, Kataoka Y, 
Tsujimoto Y. Rehabilitation for Patients with Sepsis: A Systematic 
Review and Meta-Analysis. PLoS One. 2018 Jul 26;13(7):e0201292. doi: 
10.1371/journal.pone.0201292. Erratum in: PLoS One. 2019 Aug 
21;14(8):e0221224. PMID: 30048540; PMCID: PMC6062068.
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    Section 1888(e)(6)(B)(i) of the Act, cross-referencing subsections 
(b), (c), and (d) of section 1899B of the Act, requires us to develop 
and implement standardized quality measures from five quality measure 
domains, including the domain of functional status, cognitive function, 
and changes in function and cognitive function across the post-acute 
care (PAC) settings, including SNFs. To satisfy this requirement, we 
adopted the Application of Percent of Long-Term Care Hospital Patients 
with an Admission and Discharge Functional Assessment and a Care Plan 
That Addresses Function (Application of Functional Assessment/Care 
Plan) measure, for the SNF QRP in the FY 2016 SNF PPS final rule (80 FR 
46444 through 46453). While this process measure allowed for the 
standardization of functional assessments across assessment instruments 
and facilitated cross-setting data collection, quality measurement, and 
interoperable data exchange, we believe it is now topped out and 
proposed to remove it in the FY 2024 SNF PPS proposed rule (88 FR 
21342). While there are other outcome measures addressing functional 
status \68\ that can reliably distinguish performance among providers 
in the SNF QRP, these outcome measures are not cross-setting in nature 
because they rely on functional status items not collected in all PAC 
settings. In contrast, a cross-setting functional outcome measure would 
align measure specifications across settings, including the use of a 
common set of standardized functional assessment data elements.
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    \68\ The measures include: IRF Functional Outcome Measure: 
Change in Self-Care Score for Medical Rehabilitation Patients, IRF 
Functional Outcome Measure: Change in Mobility Score for Medical 
Rehabilitation Patients, IRF Functional Outcome Measure: Discharge 
Self-Care Score for Medical Rehabilitation Patients, IRF Functional 
Outcome Measure: Discharge Mobility Score for Medical Rehabilitation 
Patients.
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(a) Measure Importance
    Maintenance or improvement of physical function among older adults 
is increasingly an important focus of health care. Adults age 65 years 
and

[[Page 53234]]

older constitute the most rapidly growing population in the United 
States, and functional capacity in physical (non-psychological) domains 
has been shown to decline with age.\69\ Moreover, impaired functional 
capacity is associated with poorer quality of life and an increased 
risk of all-cause mortality, postoperative complications, and cognitive 
impairment, the latter of which can complicate the return of a resident 
to the community from post-acute care.70 71 72 Nonetheless, 
evidence suggests that physical functional abilities, including 
mobility and self-care, are modifiable predictors of resident outcomes 
across PAC settings, including functional recovery or decline after 
post-acute care,73 74 75 76 77 rehospitalization 
rates,78 79 80 discharge to community,81 82 and 
falls.\83\
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    \69\ High KP, Zieman S, Gurwitz J, Hill C, Lai J, Robinson T, 
Schonberg M, Whitson H. Use of Functional Assessment to Define 
Therapeutic Goals and Treatment. J Am Geriatr Soc. 2019 
Sep;67(9):1782-1790. doi: 10.1111/jgs.15975. Epub 2019 May 13. PMID: 
31081938; PMCID: PMC6955596.
    \70\ Clouston SA, Brewster P, Kuh D, Richards M, Cooper R, Hardy 
R, Rubin MS, Hofer SM. The dynamic relationship between physical 
function and cognition in longitudinal aging cohorts. Epidemiol Rev. 
2013;35(1):33-50. doi: 10.1093/epirev/mxs004. Epub 2013 Jan 24. 
PMID: 23349427; PMCID: PMC3578448.
    \71\ Michael YL, Colditz GA, Coakley E, Kawachi I. Health 
behaviors, social networks, and healthy aging: cross-sectional 
evidence from the Nurses' Health Study. Qual Life Res. 1999 
Dec;8(8):711-22. doi: 10.1023/a:1008949428041. PMID: 10855345.
    \72\ High KP, Zieman S, Gurwitz J, Hill C, Lai J, Robinson T, 
Schonberg M, Whitson H. Use of Functional Assessment to Define 
Therapeutic Goals and Treatment. J Am Geriatr Soc. 2019 
Sep;67(9):1782-1790. doi: 10.1111/jgs.15975. Epub 2019 May 13. PMID: 
31081938; PMCID: PMC6955596.
    \73\ Deutsch A, Palmer L, Vaughan M, Schwartz C, McMullen T. 
Inpatient Rehabilitation Facility Patients' Functional Abilities and 
Validity Evaluation of the Standardized Self-Care and Mobility Data 
Elements. Arch Phys Med Rehabil. 2022 Feb 11:S0003-9993(22)00205-2. 
doi: 10.1016/j.apmr.2022.01.147. Epub ahead of print. PMID: 
35157893.
    \74\ Hong I, Goodwin JS, Reistetter TA, Kuo YF, Mallinson T, 
Karmarkar A, Lin YL, Ottenbacher KJ. Comparison of Functional Status 
Improvements Among Patients With Stroke Receiving Postacute Care in 
Inpatient Rehabilitation vs Skilled Nursing Facilities. JAMA Netw 
Open. 2019 Dec 2;2(12):e1916646. doi: 10.1001/
jamanetworkopen.2019.16646. PMID: 31800069; PMCID: PMC6902754.
    \75\ Alcusky M, Ulbricht CM, Lapane KL. Postacute Care Setting, 
Facility Characteristics, and Poststroke Outcomes: A Systematic 
Review. Arch Phys Med Rehabil. 2018;99(6):1124-1140.e9. doi:10.1016/
j.apmr.2017.09.005. PMID: 28965738; PMCID: PMC5874162.
    \76\ Chu CH, Quan AML, McGilton KS. Depression and Functional 
Mobility Decline in Long Term Care Home Residents with Dementia: a 
Prospective Cohort Study. Can Geriatr J. 2021;24(4):325-331. 
doi:10.5770/cgj.24.511. PMID: 34912487; PMCID: PMC8629506.
    \77\ Lane NE, Stukel TA, Boyd CM, Wodchis WP. Long-Term Care 
Residents' Geriatric Syndromes at Admission and Disablement Over 
Time: An Observational Cohort Study. J Gerontol A Biol Sci Med Sci. 
2019;74(6):917-923. doi:10.1093/gerona/gly151. PMID: 29955879; 
PMCID: PMC6521919.
    \78\ Li CY, Haas A, Pritchard KT, Karmarkar A, Kuo YF, Hreha K, 
Ottenbacher KJ. Functional Status Across Post-Acute Settings is 
Associated With 30-Day and 90-Day Hospital Readmissions. J Am Med 
Dir Assoc. 2021 Dec;22(12):2447-2453.e5. doi: 10.1016/
j.jamda.2021.07.039. Epub 2021 Aug 30. PMID: 34473961; PMCID: 
PMC8627458.
    \79\ Middleton A, Graham JE, Lin YL, Goodwin JS, Bettger JP, 
Deutsch A, Ottenbacher KJ. Motor and Cognitive Functional Status Are 
Associated with 30-day Unplanned Rehospitalization Following Post-
Acute Care in Medicare Fee-for-Service Beneficiaries. J Gen Intern 
Med. 2016 Dec;31(12):1427-1434. doi: 10.1007/s11606-016-3704-4. Epub 
2016 Jul 20. PMID: 27439979; PMCID: PMC5130938.
    \80\ Gustavson AM, Malone DJ, Boxer RS, Forster JE, Stevens-
Lapsley JE. Application of High-Intensity Functional Resistance 
Training in a Skilled Nursing Facility: An Implementation Study. 
Phys Ther. 2020;100(10):1746-1758. doi: 10.1093/ptj/pzaa126. PMID: 
32750132; PMCID: PMC7530575.
    \81\ Minor M, Jaywant A, Toglia J, Campo M, O'Dell MW. Discharge 
Rehabilitation Measures Predict Activity Limitations in Patients 
with Stroke Six Months after Inpatient Rehabilitation. Am J Phys Med 
Rehabil. 2021 Oct 20. doi: 10.1097/PHM.0000000000001908. Epub ahead 
of print. PMID: 34686630.
    \82\ Dubin R, Veith JM, Grippi MA, McPeake J, Harhay MO, 
Mikkelsen ME. Functional Outcomes, Goals, and Goal Attainment among 
Chronically Critically Ill Long-Term Acute Care Hospital Patients. 
Ann Am Thorac Soc. 2021;18(12):2041-2048. doi:10.1513/
AnnalsATS.202011-1412OC. PMID: 33984248; PMCID: PMC8641806.
    \83\ Hoffman GJ, Liu H, Alexander NB, Tinetti M, Braun TM, Min 
LC. Posthospital Fall Injuries and 30-Day Readmissions in Adults 65 
Years and Older. JAMA Netw Open. 2019 May 3;2(5):e194276. doi: 
10.1001/jamanetworkopen.2019.4276. PMID: 31125100; PMCID: 
PMC6632136.
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    The implementation of interventions that improve residents' 
functional outcomes and reduce the risks of associated undesirable 
outcomes as a part of a resident-centered care plan is essential to 
maximizing functional improvement. For many people, the overall goals 
of SNF care may include optimizing functional improvement, returning to 
a previous level of independence, maintaining functional abilities, or 
avoiding institutionalization. Studies have suggested that 
rehabilitation services provided in SNFs can improve residents' 
mobility and functional independence for residents with various 
diagnoses, including cardiovascular and pulmonary conditions, 
orthopedic conditions, and stroke.84 85 Moreover, studies 
found an association between the level of therapy intensity and better 
functional improvement, suggesting that assessment of functional status 
as a health outcome in SNFs can provide valuable information in 
determining treatment decisions throughout the care continuum, such as 
the need for rehabilitation services, and discharge 
planning,86 87 88 as well as provide information to 
consumers about the effectiveness of skilled nursing services and 
rehabilitation services delivered. Because evidence shows that older 
adults experience aging heterogeneously and require individualized and 
comprehensive health care, functional status can serve as a vital 
component in informing the provision of health care and thus indicate a 
SNF's quality of care.89 90
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    \84\ Jette DU, Warren RL, Wirtalla C. The Relation Between 
Therapy Intensity and Outcomes of Rehabilitation in Skilled Nursing 
Facilities. Archives of Physical Medicine and Rehabilitation. 
2005;86(3):373-379. doi: 10.1016/j.apmr.2004.10.018. PMID: 15759214.
    \85\ Gustavson AM, Malone DJ, Boxer RS, Forster JE, Stevens-
Lapsley JE. Application of High-Intensity Functional Resistance 
Training in a Skilled Nursing Facility: An Implementation Study. 
Phys Ther. 2020;100(10):1746-1758. doi: 10.1093/ptj/pzaa126. PMID: 
32750132; PMCID: PMC7530575.
    \86\ Harry M, Woehrle T, Renier C, Furcht M, Enockson M. 
Predictive Utility of the Activity Measure for Post-Acute Care `6-
Clicks' Short Forms on Discharge Disposition and Effect on 
Readmissions: A Retrospective Observational Cohort Study. BMJ Open 
2021;11:e044278. doi: 10.1136/bmjopen-2020-044278. PMID: 33478966; 
PMCID: PMC7825271.
    \87\ Warren M, Knecht J, Verheijde J, Tompkins J. Association of 
AM-PAC ``6-Clicks'' Basic Mobility and Daily Activity Scores With 
Discharge Destination. Phys Ther. 2021 Apr;101(4):pzab043. doi: 
10.1093/ptj/pzab043. PMID: 33517463.
    \88\ Covert S, Johnson JK, Stilphen M, Passek S, Thompson NR, 
Katzan I. Use of the Activity Measure for Post-Acute Care ``6 
Clicks'' Basic Mobility Inpatient Short Form and National Institutes 
of Health Stroke Scale to Predict Hospital Discharge Disposition 
After Stroke. Phys Ther. 2020 Aug 31;100(9):1423-1433. doi: 10.1093/
ptj/pzaa102. PMID: 32494809.
    \89\ Criss MG, Wingood M, Staples W, Southard V, Miller K, 
Norris TL, Avers D, Ciolek CH, Lewis CB, Strunk ER. APTA Geriatrics' 
Guiding Principles for Best Practices in Geriatric Physical Therapy: 
An Executive Summary. J Geriatr Phys Ther. 2022 April/June;45(2):70-
75. doi: 10.1519/JPT.0000000000000342. PMID: 35384940.
    \90\ Cogan AM, Weaver JA, McHarg M, Leland NE, Davidson L, 
Mallinson T. Association of Length of Stay, Recovery Rate, and 
Therapy Time per Day With Functional Outcomes After Hip Fracture 
Surgery. JAMA Netw Open. 2020 Jan 3;3(1):e1919672. doi: 10.1001/
jamanetworkopen.2019.19672. PMID: 31977059; PMCID: PMC6991278.
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    We proposed to adopt the Discharge Function Score (DC Function) 
measure \91\ in the SNF QRP beginning with the FY 2025 SNF QRP. This 
assessment-based outcome measure evaluates functional status by 
calculating the percentage of Medicare Part A SNF residents who meet or

[[Page 53235]]

exceed an expected discharge function score. We also proposed to 
replace the topped-out Application of Functional Assessment/Care Plan 
process measure with the DC Function measure. Like the cross-setting 
process measure we proposed to remove in the FY 2024 SNF PPS proposed 
rule (88 FR 21342), the DC Function measure is calculated using 
standardized resident assessment data from the current SNF assessment 
tool, the Minimum Data Set (MDS).
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    \91\ This measure was submitted to the Measures Under 
Consideration (MUC) List as the Cross-Setting Discharge Function 
Score. Subsequent to the MAP workgroup meetings, CMS modified the 
name. For more information, refer to the Discharge Function Score 
for Skilled Nursing Facilities (SNFs) Technical Report. https://www.cms.gov/files/document/snf-discharge-function-score-technical-report-february-2023.pdf.
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    The DC Function measure supports our current priorities. 
Specifically, the measure aligns with the Streamline Quality 
Measurement domain in CMS's Meaningful Measurement 2.0 Framework in two 
ways. First, the proposed outcome measure would further our objective 
to prioritize outcome measures by replacing the current cross-setting 
process measure (see FY 2024 SNF PPS proposed rule 88 FR 21342). This 
proposed DC Function measure uses a set of cross-setting assessment 
items which would facilitate data collection, quality measurement, 
outcome comparison, and interoperable data exchange among PAC settings; 
existing functional outcome measures do not use a set of cross-setting 
assessment items. Second, this measure would add no additional provider 
burden since it would be calculated using data from the MDS that SNFs 
are already required to collect.
    The proposed DC Function measure also follows a calculation 
approach similar to the existing functional outcome measures, which are 
CBE endorsed, with some modifications.\92\ Specifically, the measure 
(1) considers two dimensions of function (self-care and mobility 
activities) and (2) accounts for missing data by using statistical 
imputation to improve the validity of measure performance. The 
statistical imputation approach recodes missing functional status data 
to the most likely value had the status been assessed, whereas the 
current imputation approach implemented in existing functional outcome 
measures recodes missing data to the lowest functional status. A 
benefit of statistical imputation is that it uses resident 
characteristics to produce an unbiased estimate of the score on each 
item with a missing value. In contrast, the current approach treats 
residents with missing values and residents who were coded to the 
lowest functional status similarly, despite evidence suggesting varying 
measure performance between the two groups, which can to lead less 
accurate measure performances.
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    \92\ The existing measures are the IRF Functional Outcome 
Measure: Discharge Self-Care Score for Medical Rehabilitation 
Patients measure (Discharge Self-Care Score), and the Inpatient 
Rehabilitation Facility (IRF) Functional Outcome Measure: Discharge 
Mobility Score for Medical Rehabilitation Patients measure 
(Discharge Mobility Score).
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(b) Measure Testing
    Our measure developer conducted testing using FY 2019 data on the 
DC Function measure to assess validity, reliability, and reportability, 
all of which informed interested parties' feedback and Technical Expert 
Panel (TEP) input (see FY 2024 SNF PPS proposed rule 88 FR 21340 
through 21341). Validity was assessed for the measure performance, the 
risk adjustment model, face validity, and statistical imputation 
models. Validity testing of measure performance entailed determining 
Spearman's rank correlations between the proposed measure's performance 
for providers with 20 or more stays and the performance of other 
publicly reported SNF quality measures. Results indicated that the 
measure captures the intended outcome based on the directionalities and 
strengths of correlation coefficients and are further detailed below in 
Table 12.

  TABLE 12--Spearman's Rank Correlation Results of DC Function Measure
               With Publicly Reported SNF Quality Measures
------------------------------------------------------------------------
       Measure--long name          Measure--short name         [rho]
------------------------------------------------------------------------
Discharge to Community--PAC SNF  Discharge to Community.            0.16
 QRP.
Application of IRF Functional    Change in Self-Care                0.75
 Outcome Measure: Change in       Score.
 Self-Care Score for Medical
 Rehabilitation Patients.
Application of IRF Functional    Change in Mobility                 0.78
 Outcome Measure: Change in       Score.
 Mobility Score for Medical
 Rehabilitation Patients.
Application of IRF Functional    Discharge Self-Care                0.78
 Outcome Measure: Discharge       Score.
 Self-Care Score for Medical
 Rehabilitation Patients.
Application of IRF Functional    Discharge Mobility                 0.80
 Outcome Measure: Discharge       Score.
 Mobility Score for Medical
 Rehabilitation Patients.
Potentially Preventable 30-Day   Potentially Preventable           -0.10
 Post-Discharge Readmission       Readmissions within 30
 Measure--SNF QRP.                Days Post-Discharge.
Medicare Spending Per            Medicare Spending Per             -0.07
 Beneficiary--PAC SNF QRP.        Beneficiary.
------------------------------------------------------------------------

    Validity testing of the risk adjustment model showed good model 
discrimination as the measure model has the predictive ability to 
distinguish residents with low expected functional capabilities from 
those with high expected functional capabilities.\93\ The ratios of 
observed-to-predicted discharge function score across eligible stays, 
by deciles of expected functional capabilities, ranged from 0.99 to 
1.01. Both the Cross-Setting Discharge Function TEPs and resident-
family feedback showed strong support for the face validity and 
importance of the proposed measure as an indicator of quality of care 
(see FY 2024 SNF PPS proposed rule 88 FR 21340 through 21341). Lastly, 
validity testing of the measure's statistical imputation models 
indicated that the models demonstrate good discrimination and produce 
more precise and accurate estimates of function scores for items with 
missing scores when compared to the current imputation approach 
implemented in SNF QRP functional outcome measures, specifically the 
Application of IRF Functional Outcome Measure: Change in Self-Care 
Score for Medical Rehabilitation Patients measure (Change in Self-Care 
Score), the Application of IRF Functional Outcome Measure: Change in 
Mobility Score for Medical Rehabilitation Patients measure (Change in 
Mobility Score), the Application of IRF Functional Outcome Measure: 
Discharge Self-Care Score for Medical Rehabilitation Patients measure 
(Discharge Self-Care Score), and the Application of IRF Functional 
Outcome Measure: Discharge Mobility Score for

[[Page 53236]]

Medical Rehabilitation Patients measure (Discharge Mobility Score) 
measures.
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    \93\ ``Expected functional capabilities'' is defined as the 
predicted discharge function score.
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    Reliability and reportability testing also yielded results that 
support the measure's scientific acceptability. Split-half testing 
revealed the proposed measure's good reliability, indicated by an 
intraclass correlation coefficient value of 0.81. Reportability testing 
indicated high reportability (85 percent) of SNFs meeting the public 
reporting threshold of 20 eligible stays. For additional measure 
testing details, we refer readers to the document titled Discharge 
Function Score for Skilled Nursing Facilities (SNFs) Technical 
Report.\94\
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    \94\ Discharge Function Score for Skilled Nursing Facilities 
(SNFs) Technical Report. https://www.cms.gov/files/document/snf-discharge-function-score-technical-report-february-2023.pdf.
---------------------------------------------------------------------------

(2) Competing and Related Measures
    Section 1899B(e)(2)(A) of the Act requires that, absent an 
exception under section 1899B(e)(2)(B) of the Act, measures specified 
under section 1899B of the Act be endorsed by the CBE with a contract 
under section 1890(a) of the Act. In the case of a specified area or 
medical topic determined appropriate by the Secretary for which a 
feasible and practical measure has not been endorsed, section 
1899B(e)(2)(B) of the Act permits the Secretary to specify a measure 
that is not so endorsed, as long as due consideration is given to 
measures that have been endorsed or adopted by a consensus organization 
identified by the Secretary.
    The proposed DC Function measure is not CBE endorsed, so we 
considered whether there are other available measures that: (1) assess 
both functional domains of self-care and mobility in SNFs and (2) 
satisfy the requirement of the Act to specify quality measures with 
respect to functional status, cognitive function, and changes in 
function and cognitive function across the PAC settings. While the 
Application of Functional Assessment/Care Plan measure assesses both 
functional domains and satisfies the Act's requirement, this cross-
setting process measure is not CBE endorsed and the measure's 
performance among SNFs is so high and unvarying across most SNFs that 
the measure no longer offers meaningful distinctions in performance. 
Additionally, after review of other measures endorsed or adopted by a 
consensus organization, we were unable to identify any measures 
endorsed or adopted by a consensus organization for SNFs that meet the 
aforementioned requirements. While the SNF QRP includes CBE endorsed 
outcome measures addressing functional status,\95\ they each assess a 
single domain of function, and are not cross-setting in nature because 
they rely on functional status items not collected in all PAC settings.
---------------------------------------------------------------------------

    \95\ The measures include: Change in Self-Care Score for Medical 
Rehabilitation Patients (CBE #2633), Change in Mobility for Medical 
Rehabilitation Patients (CBE #2634), Discharge Self-Care Score for 
Medical Rehabilitation Patients (CBE #2635), Discharge Mobility 
Score for Medical Rehabilitation Patients (CBE #2636).
---------------------------------------------------------------------------

    Therefore, after consideration of other available measures, we find 
that the exception under section 1899B(e)(2)(B) of the Act applies and 
proposed to adopt the DC Function measure, beginning with the FY 2025 
SNF QRP. We intend to submit the proposed measure to the CBE for 
consideration of endorsement when feasible.
(3) Interested Parties and Technical Expert Panel (TEP) Input
    In our development and specification of this measure, we employed a 
transparent process in which we sought input from interested parties 
and national experts and engaged in a process that allowed for pre-
rulemaking input, in accordance with section 1890A of the Act. To meet 
this requirement, we provided the following opportunities for input 
from interested parties: a focus group of patient and family/caregiver 
advocates (PFAs), two TEPs, and public comments through a request for 
information (RFI).
    First, the measure development contractor convened a PFA focus 
group, during which residents and caregivers provided support for the 
proposed measure concept. Participants emphasized the importance of 
measuring functional outcomes and found self-care and mobility to be 
critical aspects of care. Additionally, they expressed an interest in 
measures assessing the number of residents discharged from particular 
facilities with improvements in self-care and mobility, and their views 
of self-care and mobility aligned with the functional domains captured 
by the proposed measure. All feedback was used to inform measure 
development efforts.
    The measure development contractor for the DC Function measure 
subsequently convened TEPs on July 14 to 15, 2021 and January 26 to 27, 
2022 to obtain expert input on the development of a cross-setting 
function measure for use in the SNF QRP. The TEPs consisted of 
interested parties with a diverse range of expertise, including SNF and 
PAC subject matter knowledge, clinical expertise, resident and family 
perspectives, and measure development experience. The TEPs supported 
the proposed measure concept and provided substantive feedback 
regarding the measure's specifications and measure testing data.
    First, the TEP was asked whether they prefer a cross-setting 
measure that is modeled after the currently adopted Discharge Mobility 
Score and Discharge Self-Care Score measures, or one that is modeled 
after the currently adopted Change in Mobility Score and Change in 
Self-Care Score measures. With the Discharge Mobility Score and Change 
in Mobility Score measures and the Discharge Self-Care Score and Change 
in Self-Care Score measures being both highly correlated and not 
appearing to measure unique concepts, the TEP favored the Discharge 
Mobility Score and Discharge Self-Care Score measures over the Change 
in Mobility Score and Change in Self-Care Score measures and 
recommended moving forward with utilizing the Discharge Mobility Score 
and Discharge Self-Care Score measures' concepts for the development of 
a cross-setting measure.
    Second, in deciding the standardized functional assessment data 
elements to include in the cross-setting measure, the TEP recommended 
removing redundant data elements. Strong correlations between scores of 
functional items within the same functional domain suggested that 
certain items may be redundant in eliciting information about resident 
function and inclusion of these items could lead to overrepresentation 
of a particular functional area. Subsequently, our measure development 
contractor focused on the Discharge Mobility Score measure as a 
starting point for cross-setting development due to the greater number 
of cross-setting standardized functional assessment data elements for 
mobility while also identifying redundant functional items that could 
be removed from a cross-setting functional measure.
    Third, the TEP supported including the cross-setting self-care 
items such that the cross-setting function measure would capture both 
self-care and mobility. Panelists agreed that self-care items added 
value to the measure and are clinically important to function. Lastly, 
the TEP provided refinements to imputation strategies to more 
accurately represent functional performance across all PAC settings, 
including the support of using statistical imputation over the current 
imputation approach implemented in existing functional outcome measures 
in the PAC QRPs. We considered all recommendations from the TEPs and we 
applied their recommendations where technically feasible and 
appropriate. Summaries of

[[Page 53237]]

the TEP proceedings titled Technical Expert Panel (TEP) for the 
Refinement of Long-Term Care Hospital (LTCH), Inpatient Rehabilitation 
Facility (IRF), Skilled Nursing Facility (SNF)/Nursing Facility (NF), 
and Home Health (HH) Function Measures Summary Report (July 2021 TEP) 
\96\ and Technical Expert Panel (TEP) for Cross-Setting Function 
Measure Development Summary Report (January 2022 TEP) \97\ are 
available on the CMS Measures Management System (MMS) Hub.
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    \96\ Technical Expert Panel (TEP) for the Refinement of Long-
Term Care Hospital (LTCH), Inpatient Rehabilitation Facility (IRF), 
Skilled Nursing Facility (SNF)/Nursing Facility (NF), and Home 
Health (HH) Function Measures Summary Report (July 2021 TEP). 
https://mmshub.cms.gov/sites/default/files/TEP-Summary-Report-PAC-Function.pdf.
    \97\ Technical Expert Panel (TEP) for Cross-Setting Function 
Measure Development Summary Report (January 2022 TEP). https://mmshub.cms.gov/sites/default/files/PAC-Function-TEP-Summary-Report-Jan2022-508.pdf.
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    Finally, we solicited feedback from interested parties on the 
importance, relevance, and applicability of a cross-setting functional 
outcome measure for SNFs through an RFI in the FY 2023 SNF PPS proposed 
rule (87 FR 22754). Commenters were supportive of a cross-setting 
functional outcome measure that is inclusive of both self-care and 
mobility items, but also provided information related to potential 
risk-adjustment methodologies, as well as other measures that could be 
used to capture functional outcomes across PAC settings (87 FR 47553).
(4) Measure Applications Partnership (MAP) Review
    In accordance with section 1890A of the Act, our pre-rulemaking 
process includes making publicly available a list of quality and 
efficiency measures, called the Measures Under Consideration (MUC) 
List, that the Secretary is considering adopting for use in Medicare 
programs. This allows interested parties to provide recommendations to 
the Secretary on the measures included on the list.
    We included the DC Function measure under the SNF QRP in the 
publicly available MUC List for December 1, 2022.\98\ After the MUC 
List was published, the CBE convened MAP received three comments from 
interested parties in the industry on the 2022 MUC List. Two commenters 
were supportive of the measure and one was not. Among the commenters in 
support of the measure, one commenter stated that function scores are 
the most meaningful outcome measure in the SNF setting, as they not 
only assess resident outcomes but also can be used for clinical 
improvement processes. Additionally, this commenter noted the measure's 
good reliability and validity and that the measure is feasible to 
implement. The second commenter noted that the DC Function measure is 
modeled on an CBE endorsed measure and has undergone an extensive 
formal development process. In addition, the second commenter noted 
that the DC Function measure improves on the existing functional 
outcome measures and recommended replacing the existing function 
measures with the DC Function measure.
---------------------------------------------------------------------------

    \98\ Centers for Medicare & Medicaid Services. Overview of the 
List of Measures Under Consideration for December 1, 2022. CMS.gov. 
https://mmshub.cms.gov/sites/default/files/2022-MUC-List-Overview.pdf.
---------------------------------------------------------------------------

    One commenter did not support the DC Function measure and raised 
the following concerns: the ``gameability'' of the expected discharge 
score, the measure's complexity, and the difficulty of implementing a 
composite functional score.
    Shortly after, several CBE convened MAP workgroups met to provide 
input on the DC Function measure. First, the MAP Health Equity Advisory 
Group convened on December 6 to 7, 2022. The MAP Health Equity Advisory 
Group did not share any health equity concerns related to the 
implementation of the DC Function measure, and only requested 
clarification regarding measure specifications from the measure 
steward. The MAP Rural Health Advisory Group met on December 8 to 9, 
2022, during which some of the group's members provided support for the 
DC Function measure and other group members did not express rural 
health concerns regarding the DC Function measure.
    The MAP PAC/LTC workgroup met on December 12, 2022 and provided 
input on the proposed DC Function measure. During this meeting, we were 
able to address several concerns raised by interested parties after the 
publication of the MUC List. Specifically, we clarified that the 
expected discharge scores are not calculated using self-reported 
functional goals, and are simply calculated by risk-adjusting the 
observed discharge scores (see FY 2024 SNF PPS proposed rule 88 FR 
21342). Therefore, we believe that these scores cannot be ``gamed'' by 
reporting less-ambitious functional goals. We also pointed out that the 
measure is highly usable as it is similar in design and complexity to 
existing function measures and that the data elements used in this 
measure are already in use on the MDS submitted by SNFs. Lastly, we 
clarified that the DC Function measure is intended to supplement, 
rather than replace, existing SNF QRP measures for self-care and 
mobility and implements improvements on the existing Discharge Self-
Care Score and Discharge Mobility Score measures that make the measure 
more valid and harder to game.
    The MAP PAC/LTC workgroup went on to discuss other concerns with 
the DC Function measure, including (1) whether the measure is cross-
setting due to denominator populations that differ among settings, (2) 
whether the measure would adequately represent the full picture of 
function, especially for residents who may have a limited potential for 
functional gain, and (3) that the range of expected scores was too 
large to offer a valid facility-level score. We clarified that the 
denominator population in each measure setting represents the assessed 
population within the setting and that the measure satisfies the 
requirement of section 1888(e)(6) of the Act for a cross-setting 
measure in the functional status domain specified under section 
1899B(c)(1) of the Act. Additionally, we noted that the TEP had 
reviewed the item set and determined that all the self-care and 
mobility items were suitable for all settings. Further, we clarified 
that, because the DC Function measure would assess whether a resident 
met or exceeded their expected discharge score, it accounts for 
residents who are not expected to improve. Lastly, we noted that the DC 
Function measure has a high degree of correlation with the existing 
function measures and that the range of expected scores is consistent 
with the range of observed scores. The PAC/LTC workgroup voted to 
support the CBE staff recommendation of conditional support for 
rulemaking, with the condition that we seek CBE endorsement.
    In response to the PAC/LTC workgroup's preliminary recommendation, 
the CBE received two more comments supporting the recommendation and 
one comment that did not. Among the commenters in support of the DC 
Function measure, one supported the measure under the condition that it 
be reviewed and refined such that its implementation supports resident 
autonomy and results in care that aligns with residents' personal 
functional goals. The second commenter supported the DC Function 
measure under the condition that it produces statistically meaningful 
information that can inform improvements in care processes. This 
commenter also expressed concern that the DC Function measure is not 
truly cross-setting because it utilizes different resident populations 
and risk-

[[Page 53238]]

adjustment models with setting-specific covariates across settings. 
Additionally, this commenter noted that using a single set of cross-
setting section GG items is not appropriate since the items in our 
standardized patient/resident assessment data instruments may not be 
relevant across varying resident-setting populations. The commenter who 
did not support the DC Function measure raised concern with the 
usability of a composite functional score for improving functional 
performance, and expressed support for using individual measures, such 
as the current Change in Mobility Score and Change in Self-Care Score 
measures, to attain this goal.
    Finally, the MAP Coordinating Committee convened on January 24 to 
25, 2023, during which the CBE received one comment not in support of 
the PAC/LTC workgroup's preliminary recommendation for conditional 
support of the DC Function measure. The commenter expressed concern 
that the DC Function measure competes with existing self-care and 
mobility measures in the SNF QRP. We noted that we monitor measures to 
determine if they meet any of the measure removal factors, set forth in 
Sec.  413.360(b)(2), and when identified, we may remove such measure(s) 
through the rulemaking process. We noted again that the TEP had 
reviewed the item set and determined that all self-care and mobility 
items were suitable for all settings. The MAP Coordinating Committee 
members expressed support for reviewing existing measures for removal 
as well as support for the DC Function measure, favoring the 
implementation of a single, standardized function measure across PAC 
settings. The MAP Coordinating Committee unanimously upheld the PAC/LTC 
workgroup recommendation of conditional support for rulemaking. We 
refer readers to the final MAP recommendations, titled 2022-2023 MAP 
Final Recommendations.\99\
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    \99\ 2022-2023 MAP Final Recommendations. https://mmshub.cms.gov/sites/default/files/2022-2023-MAP-Final-Recommendations-508.xlsx.
---------------------------------------------------------------------------

(5) Quality Measure Calculation
    The proposed DC Function measure is an outcome measure that 
estimates the percentage of Medicare Part A SNF residents who meet or 
exceed an expected discharge score during the reporting period. The 
proposed DC Function measure's numerator is the number of SNF stays 
with an observed discharge function score that is equal to or greater 
than the calculated expected discharge function score. The observed 
discharge function score is the sum of individual function items values 
at discharge. The expected discharge function score is computed by 
risk-adjusting the observed discharge function score for each SNF stay. 
Risk adjustment controls for resident characteristics such as admission 
function score, age, and clinical conditions. The denominator is the 
total number of SNF stays with an MDS record in the measure target 
period (four rolling quarters) that do not meet the measure exclusion 
criteria. For additional details regarding the numerator, denominator, 
risk adjustment, and exclusion criteria, refer to the Discharge 
Function Score for Skilled Nursing Facilities (SNFs) Technical 
Report.\100\
---------------------------------------------------------------------------

    \100\ Discharge Function Score for Skilled Nursing Facilities 
(SNFs) Technical Report. https://www.cms.gov/files/document/snf-discharge-function-score-technical-report-february-2023.pdf.
---------------------------------------------------------------------------

    The proposed measure implements a statistical imputation approach 
for handling ``missing'' standardized functional assessment data 
elements. The coding guidance for standardized functional assessment 
data elements allows for using ``Activity Not Attempted'' (ANA) codes, 
resulting in ``missing'' information about a resident's functional 
ability on at least some items, at admission and/or discharge, for a 
substantive portion of SNF residents. Currently, functional outcome 
measures in the SNF QRP use a simple imputation method whereby all ANA 
codes or otherwise missing scores, on both admission and discharge 
records, are recoded to ``1'' or ``most dependent.'' Statistical 
imputation, on the other hand, replaces these missing values with a 
variable based on the values of other, non-missing variables in the 
assessment and on the values of other assessments which are otherwise 
similar to the assessment with a missing value. Specifically, the 
proposed DC Function measure's statistical imputation allows missing 
values (for example, the ANA codes) to be replaced with any value from 
1 to 6, based on a resident's clinical characteristics and codes 
assigned on other standardized functional assessment data elements. The 
measure implements separate imputation models for each standardized 
functional assessment data element used in the construction of the 
discharge score and the admission score. Relative to the current simple 
imputation method, this statistical imputation approach increases 
precision and accuracy and reduces the bias in estimates of missing 
item values. We refer readers to the Discharge Function Score for 
Skilled Nursing Facilities (SNFs) Technical Report \101\ for measure 
specifications and additional details.
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    \101\ Discharge Function Score for Skilled Nursing Facilities 
(SNFs) Technical Report. https://www.cms.gov/files/document/snf-discharge-function-score-technical-report-february-2023.pdf.
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    We solicited public comment on our proposal to adopt the Discharge 
Function Score measure beginning with the FY 2025 SNF QRP. We received 
a number of comments from interested parties who support the adoption 
of the proposed measure, and others who supported the concept but 
encouraged CMS to continue to evaluate the methodology for validity. 
However, many commenters did not support the proposed measure for 
various reasons, including the lack of CBE endorsement, the concern 
that the methodology was replacing clinical judgement, and concerns 
around how the expected scores were calculated. The following is a 
summary of the comments we received on our proposal to adopt the DC 
Function measure, beginning with the FY 2025 SNF QRP, and our 
responses.
    Comment: Several commenters supported the adoption of the proposed 
measure. Some of these commenters specifically noted that the 
statistical imputation approach is an improvement over the current 
imputation approach used in the functional outcome measures already in 
the SNF QRP.
    Response: We thank commenters for their support of the adoption of 
the DC Function measure and agree that the statistical imputation 
approach improves upon the approach used in the measures currently in 
the SNF QRP.
    Comment: One commenter who supported the addition of the DC 
Function measure encouraged continual evaluation of the imputation 
methodology for validity and any unintended negative consequences.
    Response: We reevaluate measures implemented in the SNF QRP on an 
ongoing basis to ensure they have strong scientific acceptability and 
appropriately capture the care provided by SNFs. This monitoring 
includes the appropriateness and performance of both the risk models 
and imputation models used to calculate the measure.
    Comment: One commenter agreed with the proposed statistical 
imputation approach utilized in the DC Function measure but suggested 
it might lead to confusion. Specifically, this commenter noted that the 
statistical imputation approach is only proposed for the DC Function 
measure and is not used for the Discharge Self-Care Score and Discharge 
Mobility Score measures,

[[Page 53239]]

despite the measures being similar. The commenter stated the different 
approaches may lead to different outcome percentages when looking at 
the Discharge Self-Care Score and Discharge Mobility Score measures and 
the DC Function measure.
    Response: We thank the commenter for their support of the proposed 
statistical imputation approach utilized in the DC Function measure. We 
acknowledge the value of implementing this imputation approach in other 
measures using section GG items in the MDS, as measure testing has 
shown that this approach improves the validity of the DC Function 
measure over the current imputation approach used in existing measures 
in the SNF QRP. Measures undergo testing and refinement during measure 
development and maintenance activities, and we will consider testing 
the statistical imputation methodology in existing and future measures.
    The DC Function measure captures information that is distinct from 
the Discharge Self-Care Score and Discharge Mobility Score measures. 
Specifically, the DC Function measure considers both dimensions of 
function (utilizing a subset of self-care and mobility GG items), while 
the Discharge Self-Care Score and Discharge Mobility Score measures 
each consider one dimension of function (utilizing all self-care and 
mobility GG items, respectively). For these same reasons, we expect to 
see differences in outcome percentages among these three measures for 
reasons unrelated to the imputation approach.
    Comment: Four commenters did not support the adoption of this 
measure specifically because it lacks CBE endorsement or has not 
undergone the CBE endorsement process. Two of these commenters noted 
that the CBE endorsement process provides information on whether the 
measure provides valuable information that can be used to inform 
improvements in care.
    Response: We direct readers to section VII.C.1.b.2. of this final 
rule, where we discuss this topic in detail. Despite the current 
absence of CBE endorsement for this measure, we still believe it is 
important to adopt the DC Function measure into the SNF QRP because, 
unlike the Discharge Self-Care Score and Discharge Mobility Score 
measures, the DC Function measure relies on functional status items 
collected in all PAC settings, satisfies the requirement of a cross-
setting quality measure set forth in sections 1888(e)(6)(B)(i)(II) and 
1899B(c)(1)(A) of the Act, and assesses both domains of function. We 
also direct readers to section VII.C.1.b.1. of this final rule, where 
we discuss measurement gaps that the DC function measure fulfills in 
relation to competing and related measures. We also acknowledge the 
importance of the CBE endorsement process and plan to submit the 
proposed measure for CBE endorsement in the future. We direct readers 
to section VII.C.1.b.3. of this final rule and the technical report for 
detailed measures testing results demonstrating that the measure 
provides meaningful information which can be used to improve quality of 
care, and to the TEP report summaries 102 103 which detail 
TEP support for the proposed measure concept.
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    \102\ Technical Expert Panel (TEP) for the Refinement of Long-
Term Care Hospital (LTCH), Inpatient Rehabilitation Facility (IRF), 
Skilled Nursing Facility (SNF)/Nursing Facility (NF), and Home 
Health (HH) Function Measures Summary Report (July 2021 TEP). 
https://mms-test.battelle.org/sites/default/files/TEP-Summary-Report-PAC-Function.pdf.
    \103\ Technical Expert Panel (TEP) for Cross-Setting Function 
Measure Development Summary Report (January 2022 TEP). https://mmshub.cms.gov/sites/default/files/PAC-Function-TEP-Summary-Report-Jan2022-508.pdf.
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    Comment: One commenter opposed the adoption of the DC Function 
measure due to concern with the proposed imputation approach. This 
commenter noted that the ``Activity Not Attempted'' codes allow 
clinicians to use their professional judgement when certain activities 
should not or could not be safely attempted by the resident, which may 
be due to medical reasons. Moreover, this commenter stated that among 
some residents not able to attempt certain self-care and mobility tasks 
at the time of admission, the use of ANA codes decreases significantly 
at the time of discharge, which they believe reflects the functional 
outcomes achieved during their SNF stay. With these considerations in 
mind, this commenter does not believe it is appropriate or accurate for 
CMS to override the clinical judgement of the clinicians who are 
treating the resident by using statistical imputation to impute a value 
to a data element where an ANA code was entered. Lastly, the commenter 
recommended that CMS engage with post-acute care clinicians to address 
their concerns that ANA codes are not truly reflective of residents' 
functional abilities and/or deficits.
    Response: We acknowledge that the ``Activity Not Attempted'' (ANA) 
codes allow clinicians to use their professional judgement when certain 
activities should not or could not be safely attempted by the resident 
and that there may be medical reasons that a resident cannot safely 
attempt a task. However, we want to clarify that utilizing statistical 
imputation does not override the clinical judgement of clinicians who 
are expected to continue determining whether certain activities can be 
safely attempted by the residents when completing the MDS and utilizing 
the assessment data to determine appropriate goals for SNF residents. 
Rather, statistical imputation is a component in measure calculation of 
reported data and improves upon the imputation approach currently 
adopted in the Discharge Self-Care Score, Discharge Mobility Score, 
Change in Self-Care Score, and Change in Mobility Score measures by 
improving measure component validity.
    In the Discharge Self-Care Score, Discharge Mobility Score, Change 
in Self-Care Score, and Change in Mobility Score measures, ANA codes 
are imputed to 1 (dependent) when calculating the measure scores, 
regardless of a resident's own clinical and functional information. The 
imputation approach implemented in the proposed DC Function measure 
uses each resident's available functional and clinical information to 
estimate each ANA value had the item been completed. Testing 
demonstrates that, relative to the current simple imputation method, 
the statistical imputation approach used in the DC Function measure 
increases precision and accuracy and reduces bias in estimates of 
missing item values.
    Finally, in regard to the commenter's recommendation that we engage 
with PAC clinicians about the ANA codes, we have engaged with PAC 
clinicians on more than one occasion. As described in section 
VII.C.1.b.3. of this final rule, our measure development contractor 
convened two TEPs to obtain expert clinician input on the development 
of the measure. The TEPs consisted of interested parties with a diverse 
range of expertise, including SNF and other subject matter knowledge 
and clinical expertise, and measure development experience in PAC 
settings. As described in the PAC QRP Functions TEP Summary Report--
March 2022,\104\ panelists agreed that the recode approach used in the 
already adopted functional outcome measures could be improved upon and 
reiterated that not all ANAs reflect dependence on a function activity. 
Based on the extensive testing results presented to the TEP, a majority 
of panelists favored the statistical imputation over alternative

[[Page 53240]]

methodologies and an imputation method that is more accurate.
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    \104\ Technical Expert Panel (TEP) for Cross-Setting Function 
Measure Development Summary Report. Page 20. https://mmshub.cms.gov/sites/default/files/PAC-Function-TEP-Summary-Report-Jan2022-508.pdf.
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    Comment: One commenter expressed concern with the proposed 
statistical imputation approach utilized in the DC Function measure and 
suggested it might lead to this measure score varying significantly 
from the Discharge Self-Care Score and Discharge Mobility Score 
measures' scores.
    Response: The DC Function measure captures information that is 
distinct from the Discharge Self-Care Score and Discharge Mobility 
Score measures. Specifically, the DC Function measure considers both 
dimensions of function (utilizing a subset of self-care and mobility GG 
items in the MDS), while the Discharge Self-Care Score and Discharge 
Mobility Score measures each consider one dimension of function 
(utilizing all self-care and mobility GG items, respectively). For 
these same reasons, we expect to see differences in outcome percentages 
among these three measures for reasons unrelated to the imputation 
approach used.
    Comment: Three commenters believe the measure's imputation and 
risk-adjustment approach are complex and difficult to understand. One 
of these commenters urged CMS to continuously evaluate the imputation 
method and its impact across the PAC settings and urged CMS to provide 
additional coding guidance for ANA use for the GG items in order to 
better standardize and reduce the use of ANA codes. The other two 
commenters suggested that CMS provide greater transparency on the 
``expected'' discharge function score and/or the imputation method.
    Response: The proposed measure uses imputation methods that are 
similar in complexity to the CBE endorsed functional outcome measures 
that have been in the SNF QRP for several years, and will be similarly 
specified. As such, interpreting measure performance should be no more 
difficult than understanding current functional outcome measures. We 
appreciate that statistical imputation adds additional steps to the 
measure's calculation; however, understanding the technical details of 
imputation and, separately, the construction of the expected scores, is 
not needed to correctly interpret the measure scores. For those who are 
interested in the technical details, the methodology and specifications 
are available in the Discharge Function Score for Skilled Nursing 
Facilities (SNFs) Technical Report.\105\ As with all other measures, we 
will routinely monitor this measure's performance, including the 
statistical imputation approach, to ensure the measure remains valid 
and reliable. Finally, we would like to clarify that the adoption of 
this measure does not change how SNFs should complete the GG items. As 
stated in the MDS Resident Assessment Instrument (RAI) Manual, the ANA 
codes should only be used if the activity did not occur; that is, the 
resident did not perform the activity and a helper did not perform that 
activity for the resident. However, we acknowledge that there will be 
instances where an ANA code is the most appropriate code to select. We 
regularly review and update the manual as indicated. Additionally, if 
SNFs have questions related to the completion of these items, they can 
submit questions to the SNF QRP Help Desk at 
[email protected].
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    \105\ Discharge Function Score for Skilled Nursing Facilities 
(SNFs) Technical Report. https://www.cms.gov/files/document/snf-discharge-function-score-technical-report-february-2023.pdf.
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    Comment: Four commenters oppose the adoption of the proposed 
measure due to their doubt regarding the cross-setting applicability of 
the measure given the different resident populations served by the 
various PAC settings and pointed out that the capabilities and goals of 
residents differ widely by setting. One of these commenters stated that 
the measure is only ``cross-setting'' in name and that while the 
measure attempts to take into account the myriad of differences in the 
resident populations across settings, the DC Function measure is 
nevertheless four different measures across four different settings 
because the differences in resident populations alter the underlying 
calculation of the cross-setting measure. Three other commenters 
referenced the Therapy Outcomes in Post-Acute Care Settings study, 
which found significant differences in function across settings, which 
dictate differences in treatment.
    Response: We acknowledge that different resident populations are 
served across the PAC settings and the capabilities and goals of these 
populations differ. However, we would like to clarify that cross-
setting measures do not necessarily suggest that facilities can and 
should be compared across settings. Instead, these measures are 
intended to compare providers within a specific setting while 
standardizing measure specifications across settings. The proposed 
measure does just this, by aligning measure specifications across 
settings and using a common set of standardized functional assessment 
data elements.
    Comment: Three commenters opposed the proposed DC Function measure 
because it combines self-care and mobility items from the MDS. Two 
commenters expressed a preference towards the Discharge Self-Care Score 
and Discharge Mobility Score measures currently adopted in the SNF QRP 
because they reflect the two dimensions of function separately, and 
believe these measures more accurately capture each functional domain 
over the proposed DC Function measure. One commenter noted that 
separate measures would allow for better understanding of the optimal 
interventions and outcomes for residents in each unique PAC setting. 
One of these commenters additionally asked CMS to introduce two 
separate DC Function measures for both mobility and self-care.
    Response: The DC Function measure is intended to summarize several 
cross-setting functional assessment items while meeting the 
requirements of section 1899B(c)(1) of the Act. We agree with the 
commenters that the individual Discharge Self-Care Score and Discharge 
Mobility Score measures will continue to be useful to assess care 
quality in these dimensions. For this reason, the Discharge Self-Care 
Score and Discharge Mobility Score measures, which include additional 
self-care and mobility items, are not proposed for removal. SNFs will 
be able to use information from both the DC Function measure and these 
``individual function measures'' (Discharge Self-Care Score and 
Discharge Mobility Score measures) when determining which functional 
areas may be opportunities for improvement, and for this reason, these 
two measures are not proposed for removal. We routinely reevaluate 
measures and will consider re-specifying the Discharge Self-Care Score 
and Discharge Mobility Score measures such that they more closely align 
with this proposed DC Function measure (for example, using statistical 
imputation).
    Comment: Two commenters disagreed with characterizing items coded 
with an ANA code (codes 07, 09, 10, and 88) as ``missing'' data because 
these ANA codes represent clinical information. Thus, imputing scores 
for ANA codes would be clinically inappropriate. One of these 
commenters stated that imputation of these ANA codes based on other 
function activities would not improve the precision of the score.
    Response: We would like to clarify that the use of the term 
``missing'' data refers to codes that are not coded 01, 02, 03, 04, 05, 
or 06, which represent the amount of (or lack of) helper assistance a 
resident needed to complete a functional activity. ANA codes are 
considered ``missing'' in the context of the measure calculations since 
the observed discharge score is the sum of

[[Page 53241]]

01-06 values from functional assessment items included in the observed 
discharge score. Regarding the comment stating that imputation of these 
ANA codes based on other functional activities would not improve the 
precision of the score, we interpret the commenters to be saying that 
statistical imputation would not improve the precision of the score of 
missing item values. However, we disagree that using statistical 
imputation would not improve the precision of this value. Measure 
testing showed that the statistical imputation models demonstrate good 
discrimination and produce more precise and accurate estimates of 
function scores for items with missing scores when compared to the 
current imputation approach implemented in SNF QRP functional outcome 
measures, which recodes all ANAs as most dependent.
    Comment: One commenter expressed concern that the proposed measure 
numerator is not wholly attributed to a SNF's quality of care and that 
the calculation of the ``expected'' discharge score is opaque, 
resulting in difficulty for SNFs to determine the score for which they 
are striving. This commenter further noted that functional goals are 
not based on statistical regression and are identified via individual-
specific goals related to function, independence, and overall health.
    Response: We agree with the commenter that functional goals are 
identified for each resident as a result of an individual assessment 
and clinical decisions, rather than statistics. We want to remind 
commenters that the DC Function measure is not calculated using the 
goals identified through the clinical process. The ``expected'' 
discharge score is calculated by risk-adjusting the observed discharge 
score (that is, the sum of individual function item values at 
discharge) for admission functional status, age, and clinical 
characteristics using an ordinary least squares linear regression 
model. The model intercept and risk-adjustor coefficients are 
determined by running the risk-adjustment model on all eligible SNF 
stays. For more detailed measure specifications, we direct readers to 
the document titled Discharge Function Score for Skilled Nursing 
Facilities (SNFs) Technical Report.\106\ The risk-adjustment model for 
this measure controls for clinical, demographic, and function 
characteristics to ensure that the score fully reflects a facility's 
quality of care.
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    \106\ Discharge Function Score for Skilled Nursing Facilities 
(SNFs) Technical Report. https://www.cms.gov/files/document/snf-discharge-function-score-technical-report-february-2023.pdf.
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    Comment: Three commenters encouraged CMS to provide SNFs a resource 
to calculate the expected discharge function score in real time, such 
that SNFs can implement these scores in care planning and monitoring 
efforts of residents prior to receiving confidential feedback reports. 
One of these commenters noted that such resources are necessary as 
calculations of the expected scores are complex and beyond easy 
comprehension for SNFs. Another commenter encouraged CMS to work with 
interested parties to develop the tools and educational resources 
necessary for SNFs to be able to obtain the individual resident's risk-
adjusted predicted discharge function score when the assessments are 
completed. One commenter specifically requested that this information 
be included in the SNF's Review and Correct reports found in the 
internet Quality Improvement and Evaluation System (iQIES). 
Additionally, guidance should be developed and disseminated on how to 
use that information as a resource to inform and monitor the plan of 
care, so that necessary reassessments and modifications can be made in 
a timely manner in the event progress toward the predicted discharge 
function outcomes appear not to be satisfactory.
    Response: We do not expect SNFs to replicate the methodology used 
to calculate this measure; however, the resources necessary to carry 
out such calculations will be available in the technical specifications 
posted on the SNF QRP Measures and Technical Specification website. 
Additionally, while the measure relies on statistical imputation to 
impute missing values, the steps used to calculate expected scores 
based on a given set of assessment items and their values are exactly 
the same as the Discharge Self-Care Score, Change in Self-Care Score, 
Discharge Mobility Score, and Change in Mobility Score already adopted 
in the SNF QRP. Given this, the concept of the expected score is no 
more complex than the functional outcome measures that have been in use 
for several years.
    With respect to the comment regarding access to expected scores, we 
want to clarify that expected scores are not intended to be used for 
care planning; rather, care planning should be based on clinical 
judgement, assessment of residents' clinical status (including 
functional abilities and/or deficits), and residents' functional goals. 
Additionally, we have concerns that providing expected scores in such a 
real-time manner prior to the end of the data submission period may 
incentivize some SNFs to modify their scores and/or otherwise influence 
their coding practices. Given that SNFs have been able to use the 
current functional outcome measures to improve their care processes 
without the expected function scores, we maintain that SNFs will be 
able to similarly do so for the DC Function measure. However, we do 
appreciate that understanding how individuals' observed scores compared 
to expected scores can potentially allow SNFs to identify areas for 
improvement and will consider adding resident-level expected scores to 
the confidential feedback reports as technically feasible.
    Comment: Three commenters expressed concern regarding the validity 
of reported functional assessment data. Two commenters oppose the 
adoption of the DC Function measure, stating that provider-reported 
functional assessment information is not accurate and incomplete, so 
when measures are calculated, scores are incorrect. With this in mind, 
two of these commenters recommended CMS improve SNFs' reporting of 
functional assessment data before adopting this measure. One of these 
commenters noted that some SNFs code resident function in response to 
payment incentives and noted that differential coding practices and 
profitability by case type across SNFs may contribute to differential 
profitability. Additionally, this commenter stated that the current 
imputation approach (which recodes all ANAs to 1) would lead to a lower 
motor score and raise Medicare payment for the stay and supported the 
proposal to improve the quality of the MDS data by using statistical 
imputation.
    Response: We are aware of the concerns and challenges related to 
provider-reported data and acknowledge that the coding of GG items may 
be affected by payment and quality reporting considerations. We 
actively monitor SNF (and other PAC) coding practices to identify 
potential threats to the validity, and these analyses ultimately 
resulted in our development of the proposed DC Function measure. By 
using all available relevant information to impute ANAs, rather than 
simply imputing the most dependent value of 1, the statistical 
imputation approach mitigates payment-related incentives to code ANAs, 
while improving validity, as demonstrated through the measure's testing 
results. We acknowledge the importance of utilizing valid assessment 
data, and we remind commenters that we will be implementing a 
validation process for MDS-based measures starting in the same FY as 
the performance period of the measure. We

[[Page 53242]]

believe that adopting this validation process in parallel with the 
adoption of the measure will increase the accuracy of the data 
reported.
    With respect to the comment about coding resident function in 
response to payment incentives, we have processes in place to ensure 
reported patient data are accurate. The MDS process has multiple 
regulatory requirements. Our regulations at Sec. Sec.  
483.20(b)(1)(xviii),(g), and (h) require that (1) the assessment must 
be a comprehensive, accurate assessment of the resident's status, (2) 
the assessment must accurately reflect the resident's status, (3) a 
registered nurse and each individual who completes a portion of the 
assessment must sign and certify the assessment is completed, and (4) 
the assessment process must include direct observation, as well as 
communication with the resident.\107\
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    \107\ 42 CFR 483.20.
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    Comment: Four commenters oppose the adoption of the DC Function 
measure due to the belief that this measure encourages SNFs to favor 
residents with the potential for improvement at discharge over those in 
need of maintenance care. For this reason, three of these commenters 
believe there needs to be an additional measure reflecting maintenance 
care and services; otherwise, incorporation of the DC Function measure 
in the QRP would incentivize SNFs to forgo provision of maintenance 
services to Medicare beneficiaries.
    Response: The DC Function measure does not solely reflect 
improvement of residents at discharge. The measure estimates the 
percentage of residents who meet, as well as exceed, an expected 
discharge function score. In other words, if a resident, based on their 
own demographic and clinical characteristics, is expected to maintain, 
as opposed to improve in, function, then they will still meet the 
numerator criteria for this measure. For many residents, the overall 
goals of SNF care may include optimizing functional improvement, 
returning to a previous level of independence, maintaining functional 
abilities, or avoiding institutionalization. For additional details 
regarding risk adjustment, please refer to the Discharge Function Score 
for Skilled Nursing Facilities (SNFs) Technical Report.\108\
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    \108\ Discharge Function Score for Skilled Nursing Facilities 
(SNFs) Technical Report. https://www.cms.gov/files/document/snf-discharge-function-score-technical-report-february-2023.pdf.
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    Comment: One commenter requested CMS provide more clarity on its 
imputation approach to recoding, specifically contrasting it with a 
Rasch analysis used in the unified PAC PPS prototype, to ensure 
transparency and clinical meaningfulness.
    Response: The Rasch analysis in the unified PAC PPS prototype 
produces a single value to which every single ANA is recoded for a 
given item across all residents and settings. By contrast, under the 
imputation approach for the DC Function measure, we estimate a 
different imputed value for each resident, based on their clinical 
comorbidities, their score on all other GG items, and setting. We 
believe our approach accounts for several likely effects: setting-
specific coding guidance and practice differences; function scores 
being correlated with clinical comorbidities; and functional scores for 
a given GG item being correlated with functional codes on other GG 
items, particularly on ``adjacent'' (similar) items. Therefore, we 
believe recoding ANAs based on each resident's specific clinical risk 
and using all available GG item scores/codes is a more valid approach. 
For more detailed measure specifications, we direct readers to the 
document titled Discharge Function Score for Skilled Nursing Facilities 
(SNFs) Technical Report.\109\
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    \109\ Discharge Function Score for Skilled Nursing Facilities 
(SNFs) Technical Report. https://www.cms.gov/files/document/snf-discharge-function-score-technical-report-february-2023.pdf.
---------------------------------------------------------------------------

    Comment: Two commenters oppose the adoption of the DC Function 
measure due to potential negative effects arising from Medicare 
Advantage (MA) plans focusing on money-saving practices. One commenter 
stated that if discharge measures only examine a discharge functional 
score in SNFs rather than a change in functional score in SNF and other 
PAC settings, MA plans can circumvent measurements of quality by 
sending difficult rehabilitation candidates to home rehabilitation, 
even if SNF or IRF rehabilitation would be better for the resident.
    Response: We do not understand the connections raised by the 
commenter between the adoption of the DC Function measure and 
unintended consequences MA beneficiaries could face. However, if the 
concern stems from a belief that the DC Function measure would only be 
adopted in the SNF setting, we would like to clarify that aligned 
versions of the DC Function measure are also proposed for the IRF, 
LTCH, and HH QRPs.
    Additionally, the Change in Mobility Score and Change in Self-Care 
Score measures rely on functional status items not yet collected in all 
settings and utilize a set of items that are not equally applicable 
across all settings. On the other hand, the DC Function score measure 
is a cross-setting measure that utilizes a standardized set of self-
care and mobility assessment items that are common to all the PAC 
settings and are aligned in terms of the exclusions and risk models 
applied (as appropriate and feasible).
    Comment: One commenter expressed concern that the measure 
performance may not adequately demonstrate functional ability 
improvements across the mobility and selfcare domains during the SNF 
stay. This commenter noted that the measure only includes a subset of 
function items from the assessment instrument and is concerned that 
these items are not necessarily the best indicators of resident 
functional success when discharged; for example, functional abilities 
and goals that better reflect self-care included upper body dressing 
and lower body dressing. This commenter also stated that the functional 
items captured in this measure seem to be based solely on ensuring 
cross-setting applicability and less on the accuracy of an expected 
function score.
    Response: We acknowledge that the cross-setting applicability was a 
motivating factor in determining function items captured in the 
proposed DC Function measure, and upper body dressing and lower body 
dressing function items were not available across settings. 
Nonetheless, the proposed DC Function measure does reflect the progress 
of a resident across both the mobility and selfcare domains. As stated 
in section VII.C.1.b.3. of this final rule, the TEP supported the 
inclusion of both functional domains as self-care items impact mobility 
items and are clinically relevant to function. Additionally, the 
proposed measure is meant to supplement, rather than replace, the 
Discharge Self-Care Score and Discharge Mobility Score measures which 
implement the remaining self-care and mobility function items not 
captured in the DC Function measure. High correlations between the 
proposed measure and the Discharge Self-Care Score and Discharge 
Mobility Score measures (0.85 and 0.88, respectively) demonstrate that 
these three measures capture related, but distinct, aspects of provider 
care in relation to residents' function. The TEP understood these 
considerations and supported the inclusion of the function items 
included in the proposed measure.
    Comment: One commenter believed that the adoption of the proposed 
measure would result in additional burden, stating that its adoption 
will

[[Page 53243]]

require software updates to implement and monitor the measure's complex 
calculations prior to CMS publishing results, as well as additional 
training and education for clinical and administrative personnel. 
Another commenter noted that to achieve high measure scores, SNFs would 
require continuing education, time to perform and report assessments, 
and increased collaboration among clinicians.
    Response: We disagree that the adoption of the proposed measure 
would result in additional burden or require additional training. We 
are not proposing changes to the number of items required or the 
reporting frequency of the items reported in the MDS in order to report 
for this measure. In fact, this measure requires the same set of items 
that are already reported by SNFs in the MDS. Additionally, we 
calculate this measure, and provide SNFs with various resources to 
review and monitor their own performance on this measure, including 
provider preview reports. Therefore, SNFs are not required to update 
software to successfully report or monitor performance. Regarding the 
commenter's concerns about education, we do plan to provide educational 
resources to SNFs about the DC Function measure.
    Comment: Two commenters raised concerns that the measure does not 
account for cognition and communication. One commenter urged CMS to 
consider alternative assessments that better incorporate cognition and 
communication into the measure calculation. The other commenter 
similarly raised concerns that section GG items insufficiently capture 
all elements of function and do not adequately capture the outcomes 
required for safety and independence.
    Response: We agree that cognition and communication are critically 
important and related to the safety and independence of residents. 
Although not directly assessed for the purpose of measure calculation, 
this measure does indirectly capture a facility's ability to impact a 
resident's cognition and communication to the extent that these factors 
are correlated to improvements in self-care and mobility. That said, we 
agree that communication and cognition are important to assess 
directly, and facilities currently do so through completion of the 
Brief Interview for Mental Status (BIMS), Confusion Assessment Method 
(CAM(copyright)), and Speech/Communication items in section 
B of the MDS. Additionally, we regularly assess the measures in the SNF 
QRP for measurement gaps, and as described in section VII.D. of this 
final rule, specifically identified cognitive improvement as a possible 
measurement gap and sought feedback about how to best assess this 
clinical dimension. We will use feedback from this RFI, as well as 
discussion with technical experts and empirical analyses to determine 
how to measure communication and cognition.
    Comment: One commenter urged CMS to monitor the impact of COVID-19 
and social determinants of health on functional outcomes and address 
these impacts in measure refinements.
    Response: We recognize that COVID-19 and social determinants of 
health may have an impact on functional outcomes. Testing indicates 
that adding social determinants of health, such as dual eligibility and 
race/ethnicity, does not substantively affect provider scores for this 
measure. However, we will continue to monitor the impact of the above 
factors, as is feasible, on the measures and incorporate them in 
measure calculations, as needed, to ensure the measure remains valid 
and reliable.
    After careful consideration of the public comments we received, we 
are finalizing our proposal to adopt the DC Function measure as an 
assessment-based outcome measure beginning with the FY 2025 SNF QRP as 
proposed.
c. Removal of the Application of Percent of Long-Term Care Hospital 
Patients With an Admission and Discharge Functional Assessment and a 
Care Plan That Addresses Function Beginning With the FY 2025 SNF QRP
    We proposed to remove the Application of Percent of Long-Term Care 
Hospital Patients with an Admission and Discharge Functional Assessment 
and a Care Plan That Addresses Function (Application of Functional 
Assessment/Care Plan) measure from the SNF QRP beginning with the FY 
2025 SNF QRP. Section 413.360(b)(2) of our regulations describes eight 
factors we consider for measure removal from the SNF QRP, and we 
believe this measure should be removed because it satisfies two of 
these factors.
    First, the Application of Functional Assessment/Care Plan measure 
meets the conditions for measure removal factor one: measure 
performance among SNFs is so high and unvarying that meaningful 
distinctions in improvements in performance can no longer be made.\110\ 
Second, this measure meets the conditions for measure removal factor 
six: there is an available measure that is more strongly associated 
with desired resident functional outcomes. We believe the proposed DC 
Function measure discussed in the FY 2024 SNF PPS proposed rule (88 FR 
21337 through 21342) better measures functional outcomes than the 
current Application of Functional Assessment/Care Plan measure. We 
discuss each of these reasons in more detail.
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    \110\ For more information on the factors CMS uses to base 
decisions for measure removal, we refer readers to the Code of 
Federal Regulations, Sec.  413.360(b)(2). https://www.ecfr.gov/current/title-42/chapter-IV/subchapter-B/part-413/subpart-J/section-413.360.
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    In regard to measure removal factor one, the Application of 
Functional Assessment/Care Plan measure has become topped out,\111\ 
with average performance rates reaching nearly 100 percent over the 
past 3 years (ranging from 99.1 percent to 98.9 percent during CYs 2019 
through 2021).112 113 114 For the 12-month period of Q3 2020 
through Q2 2021 (July 1, 2020 through June 30, 2021), SNFs had an 
average score for this measure of 98.8 percent, with nearly 70 percent 
of SNFs scoring 100 percent \115\ and for CY 2021, SNFs had an average 
score of 98.9 percent, with nearly 63 percent of SNFs scoring 100 
percent.\116\ The proximity of these mean rates to the maximum score of 
100 percent suggests a ceiling effect and a lack of variation that 
restricts distinction among SNFs.
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    \111\ Centers for Medicare & Medicaid Services. 2022 Annual Call 
for Quality Measures Fact Sheet, p. 10. https://www.cms.gov/files/document/mips-call-quality-measures-overview-fact-sheet-2022.pdf.
    \112\ Centers for Medicare & Medicaid Services. Nursing Homes 
including Rehab Services Data Archive, 2020. Annual Files National 
Data 10-20. PQDC, https://data.cms.gov/provider-data/archived-data/nursing-homes.
    \113\ Centers for Medicare & Medicaid Services. Nursing Homes 
including Rehab Services Data Archive, 2022. Annual Files National 
Data 06-22. PQDC, https://data.cms.gov/provider-data/archived-data/nursing-homes.
    \114\ Centers for Medicare & Medicaid Services. Nursing Homes 
including Rehab Services Data Archive, 2022. Annual Files National 
Data 10-22. PQDC, https://data.cms.gov/provider-data/archived-data/nursing-homes.
    \115\ Centers for Medicare & Medicaid Services. Nursing Homes 
including Rehab Services Data Archive, 2022. Annual Files Provider 
Data 05-22. PQDC, https://data.cms.gov/provider-data/archived-data/nursing-homes.
    \116\ Centers for Medicare & Medicaid Services. Nursing Homes 
including Rehab Services Data Archive, 2022. Annual Files Provider 
Data 10-22. PQDC, https://data.cms.gov/provider-data/archived-data/nursing-homes.
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    In regard to measure removal factor six, the proposed DC Function 
measure is more strongly associated with desired resident functional 
outcomes than this current process measure, the Application of 
Functional Assessment/Care Plan measure. As described in the FY 2024 
SNF PPS proposed rule (88 FR 21339 through 213340), the DC Function 
measure has the predictive ability to distinguish residents with low

[[Page 53244]]

expected functional capabilities from those with high expected 
functional capabilities.\117\ We have been collecting standardized 
functional assessment elements across PAC settings since 2016, which 
has allowed for the development of the proposed DC Function measure and 
meets the requirements of the Act to submit standardized patient 
assessment data and other necessary data with respect to the domain of 
functional status, cognitive function, and changes in function and 
cognitive function. In light of this development, this process measure, 
the Application of Functional Assessment/Care Plan measure, which 
measures only whether a functional assessment is completed and a 
functional goal is included in the care plan, is no longer necessary, 
and can be replaced with a measure that evaluates the SNF's outcome of 
care on a resident's function.
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    \117\ ``Expected functional capabilities'' is defined as the 
predicted discharge function score.
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    Because the Application of Functional Assessment/Care Plan measure 
meets measure removal factors one and six, we proposed to remove it 
from the SNF QRP beginning with the FY 2025 SNF QRP. We also proposed 
in the FY 2024 SNF PPS proposed rule (88 FR 21361) that public 
reporting of the Application of Functional Assessment/Care Plan measure 
would end by the October 2024 Care Compare refresh or as soon as 
technically feasible when public reporting of the proposed DC Function 
measure would begin.
    Under our proposal, SNFs would no longer be required to report a 
Self-Care Discharge Goal (that is, GG0130, Column 2) or a Mobility 
Discharge Goal (that is, GG0170, Column 2) beginning with residents 
admitted on or after October 1, 2023. We would remove the items for 
Self-Care Discharge Goal (that is, GG0130, Column 2) and Mobility 
Discharge Goal (that is, GG0170, Column 2) with the next release of the 
MDS. Additionally, these items would not be required to meet SNF QRP 
requirements beginning with the FY 2025 SNF QRP.
    We solicited public comment on our proposal to remove the 
Application of Functional Assessment/Care Plan measure from the SNF QRP 
beginning with the FY 2025 SNF QRP. The following is a summary of the 
comments we received on our proposal to remove the Application of 
Functional Assessment/Care Plan measure from the SNF QRP beginning with 
the FY 2025 SNF QRP and our responses.
    Comment: Several commenters expressed support for the removal of 
the Application of Functional Assessment/Care Plan measure. Some of the 
commenters agreed with the removal of the measure because of the 
measure's topped out performance and due to the costs associated with 
tracking duplicate measures. A few of these commenters believe the DC 
Function measure better reflects the care delivered during a SNF stay.
    Response: We thank the commenters for their support and agree that 
the Application of Functional Assessment/Care Plan measure should be 
removed due to topped-out performance. Additionally, we agree with the 
commenters that the DC Function measure better reflects care delivered 
in SNFs.
    After consideration of the public comments we received, we are 
finalizing our proposal to remove the Application of Functional 
Assessment/Care Plan measure from the SNF QRP beginning with the FY 
2025 SNF QRP as proposed.
d. Removal of the Application of IRF Functional Outcome Measure: Change 
in Self-Care Score for Medical Rehabilitation Patients and Removal of 
the Application of IRF Functional Outcome Measure: Change in Mobility 
Score for Medical Rehabilitation Patients Beginning With the FY 2025 
SNF QRP
    We proposed to remove the Application of the IRF Functional Outcome 
Measure: Change in Self-Care Score for Medical Rehabilitation Patients 
(Change in Self-Care Score) and the Application of IRF Functional 
Outcome Measure: Change in Mobility Score for Medical Rehabilitation 
Patients (Change in Mobility Score) measures from the SNF QRP beginning 
with the FY 2025 SNF QRP. Section 413.360(b)(2) of our regulations 
describe eight factors we consider for measure removal from the SNF 
QRP, and we proposed removal of this measure because it satisfies 
measure removal factor eight: the costs associated with a measure 
outweigh the benefits of its use in the program.
    Measure costs are multifaceted and include costs associated with 
implementing and maintaining the measure. On this basis, we proposed to 
remove these measures for two reasons. First, the costs to SNFs 
associated with tracking similar or duplicative measures in the SNF QRP 
outweigh any benefit that might be associated with the measures. 
Second, our costs associated with program oversight of the measures, 
including measure maintenance and public display, outweigh the benefit 
of information obtained from the measures. We discuss each of these in 
more detail below.
    We adopted the Change in Self-Care Score and Change in Mobility 
Score measures in the FY 2018 SNF PPS final rule (82 FR 36578 through 
36593), under section 1888(e)(6)(B)(i)(II) of the Act because the 
measures meet the functional status, cognitive function, and changes in 
function and cognitive function domain under section 1899B(c)(1) of the 
Act. Two additional measures addressing the functional status, 
cognitive function, and changes in function and cognitive function 
domain were adopted in the same program year: the Application of IRF 
Functional Outcome Measure: Discharge Self-Care Score for Medical 
Rehabilitation Patients (Discharge Self-Care Score) and the Application 
of IRF Functional Outcome Measure: Discharge Mobility Score for Medical 
Rehabilitation Patients (Discharge Mobility Score) measures. At the 
time these four outcome measures were adopted, the amount of 
rehabilitation services received among SNF residents varied. We 
believed that measuring residents' functional changes across all SNFs 
on an ongoing basis would permit identification of SNF characteristics 
associated with better or worse resident risk adjustment outcomes as 
well as help SNFs target their own quality improvement efforts.\118\
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    \118\ Federal Register. Medicare Program; Prospective Payment 
System and Consolidated Billing for Skilled Nursing Facilities for 
FY 2018. https://www.federalregister.gov/documents/2017/05/04/2017-08521/medicare-program-prospective-payment-system-and-consolidated-billing-for-skilled-nursing-facilities#p-397.
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    We proposed to remove the Change in Self-Care Score and Change in 
Mobility Score measures because we believe the SNF costs associated 
with tracking duplicative measures outweigh any benefit that might be 
associated with the measures. Since the adoption of these measures in 
2018, we have been monitoring the data and found that the scores for 
the two self-care functional outcome measures, Change in Self-Care 
Score and Discharge Self-Care Score, are very highly correlated in SNF 
settings (0.93).\119\ Similarly, in the monitoring data, we have found 
that the scores for the two mobility score measures, Change in Mobility 
Score and Discharge Mobility Score, are very highly

[[Page 53245]]

correlated in SNF settings (0.95).\120\ The high correlation between 
these measures suggests that the Change in Self-Care Score and 
Discharge Self-Care Score and the Change in Mobility Score and the 
Discharge Mobility Score measures provide almost identical information 
about this dimension of quality to SNFs and are therefore duplicative.
---------------------------------------------------------------------------

    \119\ Acumen, LLC and Abt Associates. Technical Expert Panel 
(TEP) for the Refinement of Long-Term Care Hospital (LTCH), 
Inpatient Rehabilitation Facility (IRF), Skilled Nursing Facility 
(SNF)/Nursing Facility (NF), and Home Health (HH) Function Measures, 
July 14-15, 2021: Summary Report. February 2022. https://mmshub.cms.gov/sites/default/files/TEP-Summary-Report-PAC-Function.pdf.
    \120\ Acumen, LLC and Abt Associates. Technical Expert Panel 
(TEP) for the Refinement of Long-Term Care Hospital (LTCH), 
Inpatient Rehabilitation Facility (IRF), Skilled Nursing Facility 
(SNF)/Nursing Facility (NF), and Home Health (HH) Function Measures, 
July 14-15, 2021: Summary Report. February 2022. https://mmshub.cms.gov/sites/default/files/TEP-Summary-Report-PAC-Function.pdf.
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    Our proposal to remove the Change in Self-Care Score and the Change 
in Mobility Score measures is supported by feedback received from the 
TEP convened for the Refinement of LTCH, IRF, SNF/NF, and HH Function 
Measures. As described in the FY 2024 SNF PPS proposed rule (88 FR 
21340 through 21341), the TEP panelists were presented with analyses 
that demonstrated the ``Change in Score'' and ``Discharge Score'' 
measure sets are highly correlated and do not appear to measure unique 
concepts, and they subsequently articulated that it would be sensible 
to retire either the ``Change in Score'' or ``Discharge Score'' measure 
sets for both self-care and mobility. Based on responses to the post-
TEP survey, the majority of panelists (nine out of 12 respondents) 
suggested that only one measure set each for self-care and mobility, 
respectively, is necessary. Of those nine respondents, six preferred 
retaining the ``Discharge Score'' measure set over the ``Change in 
Score'' measure set.\121\
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    \121\ Acumen, LLC and Abt Associates. Technical Expert Panel 
(TEP) for the Refinement of Long-Term Care Hospital (LTCH), 
Inpatient Rehabilitation Facility (IRF), Skilled Nursing Facility 
(SNF)/Nursing Facility (NF), and Home Health (HH) Function Measures, 
July 14-15, 2021: Summary Report. February 2022. https://mmshub.cms.gov/sites/default/files/TEP-Summary-Report-PAC-Function.pdf.
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    Additionally, we proposed to remove the Change in Self-Care Score 
and Change in Mobility Score measures because the program oversight 
costs outweigh the benefit of information that CMS, SNFs, and the 
public obtain from the measures. We must engage in various activities 
when administering the QRPs, such as monitoring measure results, 
producing provider preview reports, and ensuring the accuracy of the 
publicly reported data. Because these measures essentially provide the 
same information to SNFs as well as to consumers as the Discharge Self-
Care Score and Discharge Mobility Score measures, our costs associated 
with measure maintenance and public display outweigh the benefit of 
information obtained from the measures.
    Because these measures meet the criteria for measure removal factor 
eight, we proposed to remove the Change in Self-Care Score and Change 
in Mobility Score measures from the SNF QRP beginning with the FY 2025 
SNF QRP. We also proposed that public reporting of the Change in Self-
Care Score and the Change in Mobility Score measures would end by the 
October 2024 Care Compare refresh or as soon as technically feasible.
    We solicited public comment on our proposal to remove the Change in 
Self-Care Score and the Change in Mobility Score measures from the SNF 
QRP beginning with the FY 2025 SNF QRP. The following is a summary of 
the comments we received on our proposal to remove the Change in Self-
Care Score and the Change in Mobility Score measures from the SNF QRP 
beginning with the FY 2025 SNF QRP and our responses.
    Comment: Several commenters expressed their support for the removal 
of the Change in Self-Care Score and the Change in Mobility Score 
measures, noting that these measures are duplicative of other measures 
and that their removal will reduce costs to SNFs and to CMS.
    Response: We thank the commenters for their support on the removal 
of the Change in Self-Care Score and the Change in Mobility Score 
measures. We agree that the measures are duplicative and that their 
removal will reduce costs to SNFs and CMS.
    Comment: Several commenters did not agree with the removal of the 
Change in Self-Care Score and Change in Mobility Score measures because 
they believe these measures provide more information than the Discharge 
Self-Care Score and the Discharge Mobility Score measures. 
Specifically, two of these commenters contended that capturing the 
amount of change in a resident's experience is more valuable than 
capturing whether residents meet or exceed an expected discharge score 
during their stay. One commenter advised CMS to keep the Change in 
Self-Care Score and Change in Mobility Score measures in the SNF QRP 
because the new DC Function measure lacks the positive characteristics 
the Change in Self-Care Score and Change in Mobility Score measures 
capture. Meanwhile, another commenter encouraged CMS to consider how it 
can incorporate the positive aspects of these measures into the new DC 
Function measure.
    Response: We appreciate the perspective of the commenters and 
understand that there are advantages and disadvantages to retiring the 
Change in Self-Care Score and Change in Mobility Score measures rather 
than the Discharge Self-Care Score and Discharge Mobility Score 
measures. We weighed the tradeoffs of these measures in consultation 
with a TEP, comprised of 15 panelists with diverse perspectives and 
areas of expertise, including SNF representation.\122\ The majority of 
the TEP favored the retirement of the Change in Self-Care Score and 
Change in Mobility Score measures because they believed the Discharge 
Self-Care Score and Discharge Mobility Score measures better capture a 
resident's relevant functional ability. We agree that it is important 
for facilities to track the amount of change that occurs over the 
course of a stay for its residents and would like to point out that the 
removal of the Change in Self-Care Score and Change in Mobility Score 
measures does not preclude SNFs' abilities in this regard. However, we 
also believe that the Change in Self-Care Score and Change in Mobility 
Score measures are not intuitive to interpret for the primary audience 
of Care Compare, as the units of change and what constitutes a 
meaningful change are unfamiliar to the vast majority of users, 
particularly prospective or current residents and their caregivers. 
This is in contrast to the Discharge Self-Care Score and Discharge 
Mobility Score measures, which are presented as simple proportions. 
Additionally, the correlations between the Change in Self-Care Score 
and Discharge Self-Care Score measures and Change in Mobility Score and 
Discharge Mobility Score measures are very high (Spearman correlation: 
0.93 and 0.95), indicating the measures capture almost identical 
concepts and lead to very similar rankings.\123\ As such, the testing 
does not support the claim that the Change in Self-Care Score and 
Change in Mobility Score measures provide significantly

[[Page 53246]]

more information on which to compare facilities, as the relative 
rankings of facilities are very similar between the Change in Self-Care 
Score and Discharge Self-Care Score measures and the Change in Mobility 
Score and Discharge Mobility Score measures. Consequently, given the 
TEP's recommendation, the more intuitive interpretation, and the very 
high correlations, we believe there is more value in retiring the 
Change in Self-Care Score and Change in Mobility Score measures and 
retaining the Discharge Self-Care Score and Discharge Mobility Score 
measures.
---------------------------------------------------------------------------

    \122\ Acumen, LLC and Abt Associates. Technical Expert Panel 
(TEP) for the Refinement of Long-Term Care Hospital (LTCH), 
Inpatient Rehabilitation Facility (IRF), Skilled Nursing Facility 
(SNF)/Nursing Facility (NF), and Home Health (HH) Function Measures, 
July 14-15, 2021: Summary Report. February 2022. https://mmshub.cms.gov/sites/default/files/TEP-Summary-Report-PAC-Function.pdf.
    \123\ Acumen, LLC and Abt Associates. Technical Expert Panel 
(TEP) for the Refinement of Long-Term Care Hospital (LTCH), 
Inpatient Rehabilitation Facility (IRF), Skilled Nursing Facility 
(SNF)/Nursing Facility (NF), and Home Health (HH) Function Measures, 
July 14-15, 2021: Summary Report. February 2022. https://mmshub.cms.gov/sites/default/files/TEP-Summary-Report-PAC-Function.pdf.
---------------------------------------------------------------------------

    Comment: One commenter raised concerns that the methodology used to 
calculate the Discharge Self-Care Score and Discharge Mobility Score 
measures does not account for functional abilities at admission in the 
way that the Change in Self-Care Score and Change in Mobility Score 
measures being proposed for removal do. The commenter requested that 
CMS clarify the extent to which the remaining Discharge Self-Care Score 
and Discharge Mobility Score measures would account for change in a 
residents' function over time, as well as resident heterogeneity. These 
commenters also raised concerns about unintended consequences that 
could be introduced through the removal of the Change in Self-Care 
Score and Change in Mobility Score measures, such as the cherry-picking 
of residents or creating limited access to services for those with 
lower functional status. One of these commenters urged CMS to carefully 
evaluate whether the removal of the Change in Self-Care Score and 
Change in Mobility Score measures could lead to such unintended 
consequences.
    Response: We appreciate that measures of functional outcomes must 
account for resident case-mix to ensure fair and meaningful comparisons 
across facilities. Accordingly, the Discharge Self-Care Score and 
Discharge Mobility Score measures that would remain in the SNF QRP do 
in fact account for functional abilities at admission, as well as other 
relevant demographic and clinical characteristics (see, for example, 
Skilled Nursing Facility Quality Reporting Program Measure Calculations 
and Reporting User's Manual Version 4.0.).\124\ Specifically, the 
expected discharge scores, which residents must meet or exceed to meet 
the Discharge Self-Care Score and Discharge Mobility Score measures' 
numerators, are predicted using the residents' observed admission 
function scores plus the same clinical comorbidities and demographic 
characteristics as the corresponding Change in Self-Care Score and 
Change in Mobility Score measures. Given that the Discharge Self-Care 
Score and Discharge Mobility Score measures do account for functional 
abilities at admission, among other relevant clinical characteristics 
that can impact functional improvement, we do not anticipate that the 
removal of the Change in Self-Care Score and Change in Mobility Score 
measures will increase any incentive to cherry -pick residents or block 
access to care. We take the appropriate access to care in SNFs very 
seriously, and routinely monitor the performance of measures in the SNF 
QRP, including performance gaps across SNFs. We will continue to 
monitor closely whether any proposed changes to the SNF QRP have 
unintended consequences on access to care for high-risk residents. 
Should we find any unintended consequences, we will take appropriate 
steps to address these issues in future rulemaking.
---------------------------------------------------------------------------

    \124\ Skilled Nursing Facility Quality Reporting Program Measure 
Calculations and Reporting User's Manual Version 4.0. October 2022. 
https://www.cms.gov/files/document/snf-quality-measure-calculations-and-reporting-users-manual-v40.pdf.
---------------------------------------------------------------------------

    Comment: A few commenters recommended the removal of the Discharge 
Self-Care Score and Discharge Mobility Score measures instead, which 
they believe are duplicative of the proposed DC Function Measure.
    Response: We disagree that the currently adopted Discharge Self-
Care Score and Discharge Mobility Score measures are duplicative of the 
proposed DC Function measure. As discussed in section VII.C.1.b.1.a. of 
the final rule, the Discharge Self-Care Score and Discharge Mobility 
Score measures are not cross-setting because they rely on functional 
status items not collected in all PAC settings and thus do not satisfy 
requirement of a cross-setting quality measure as set forth in sections 
1888(e)(6)(B)(i)(II) and 1899B(c)(1)(A) of the Act. In contrast, the DC 
Function measure does include functional status items collected in each 
of the four PAC settings. Moreover, the DC Function measure captures 
information that is distinct from the Discharge Self-Care and Discharge 
Mobility Score measures. Specifically, the DC Function measure 
considers both dimensions of function within a single measure 
(utilizing a subset of self-care and mobility GG items in the MDS), 
while the Discharge Self-Care Score and Discharge Mobility score 
measures each consider one dimension of function (utilizing all self-
care and mobility GG items, respectively).
    After consideration of the public comments we received, we are 
finalizing our proposal to remove the Change in Self-Care Score and 
Change in Mobility Score measures from the SNF QRP beginning with the 
FY 2025 SNF QRP as proposed.
2. SNF QRP Quality Measures Beginning With the FY 2026 SNF QRP
a. CoreQ: Short Stay Discharge Measure (CBE #2614) Beginning With the 
FY 2026 SNF QRP
(1) Background
    We define person-centered care as integrated healthcare services 
delivered in a setting and manner that is responsive to the individual 
and their goals, values and preferences, in a system that empowers 
residents and providers to make effective care plans together.\125\ 
Person-centered care is achieved when healthcare providers work 
collaboratively with individuals to do what is best for the health and 
well-being of individuals receiving healthcare services, and allows 
individuals to make informed decisions about their treatment that align 
with their preferences and values, such as including more choice in 
medication times, dining options, and sleeping times. Self-reported 
measures, including questionnaires assessing the individual's 
experience and satisfaction in receiving healthcare services, are 
widely used across various types of providers to assess the 
effectiveness of their person-centered care practices.
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    \125\ Centers for Medicare & Medicaid Services. Innovation 
Center. Person-Centered Care. https://innovation.cms.gov/key-concepts/person-centered-care.
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    There is currently no national standardized satisfaction 
questionnaire that measures a resident's satisfaction with the quality 
of care received by SNFs. We identified resident satisfaction with the 
quality of care received by SNFs as a measurement gap in the SNF QRP 
(see section VII.D. of this final rule), as did the MAP in its report 
MAP 2018 Considerations for Implementing Measure in Federal Programs: 
Post-Acute Care and Long-Term Care.\126\ Currently the SNF QRP includes 
measures of processes and outcomes that illustrate whether 
interventions are working to improve delivery of healthcare services. 
However, we believe that measuring resident satisfaction would provide 
clinical teams compelling information to use when examining the results 
of their clinical care, and can help SNFs identify deficiencies that 
other quality

[[Page 53247]]

metrics may struggle to identify, such as communication between a 
resident and the provider.
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    \126\ National Quality Forum. MAP 2018 Considerations for 
Implementing Measures in Federal Programs--PAC-LTC. https://www.qualityforum.org/Publications/2018/02/MAP_2018_Considerations_for_Implementing_Measures_in_Federal_Programs_-_PAC-LTC.aspx.
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    Measuring individuals' satisfaction with healthcare services using 
questionnaires has been shown to be a valid indicator for measuring 
person-centered care practices. The value of measuring consumer 
satisfaction is supported in the peer-reviewed literature using 
respondents from SNFs. One study demonstrated higher (that is, better) 
resident satisfaction is associated with the SNF receiving fewer 
deficiency citations from regulatory inspections of the SNF, and is 
also associated with higher perceived service quality.\127\ Other 
studies of the relationship between resident satisfaction and clinical 
outcomes suggest that higher overall satisfaction may contribute to 
lower 30-day readmission rates 128 129 130 and better 
adherence to treatment recommendations.131 132
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    \127\ Li Y, Li Q, Tang Y. Associations between Family Ratings on 
Satisfaction with Care and Clinical Quality-of-Care Measures for 
Nursing Home Residents. Med Care Res Rev. 2016 Feb;73(1):62-84. doi: 
10.1177/1077558715596470. Epub 2015 Jul 21. PMID: 26199288; PMCID: 
PMC4712136.
    \128\ Boulding W, Glickman SW, Manary MP, Schulman KA, Staelin 
R. Relationship between Patient Satisfaction with Inpatient Care and 
Hospital Readmission within 30 days. Am J Manag Care. 2011 
Jan;17(1):41-8. PMID: 21348567.
    \129\ Carter J, Ward C, Wexler D, Donelan K. The Association 
between Patient Experience Factors and Likelihood of 30-day 
Readmission: a Prospective Cohort Study. BMJ Qual Saf. 2018;27:683-
690. doi: 10.1136/bmjqs-2017-007184. PMID: 29146680.
    \130\ Anderson PM, Krallman R, Montgomery D, Kline-Rogers E, 
Bumpus SM. The Relationship Between Patient Satisfaction With 
Hospitalization and Outcomes Up to 6 Months Post-Discharge in 
Cardiac Patients. J Patient Exp. 2020;7(6):1685-1692. doi: 
10.117712374373520948389. PMID: 33457631 PMCID: PMC7786784.
    \131\ Barbosa CD, Balp MM, Kulich K, Germain N, Rofail D. A 
Literature Review to Explore the Link Between Treatment Satisfaction 
and Adherence, Compliance, and Persistence. Patient Prefer 
Adherence. 2012;6:39-48. doi: 10.2147/PPA.S24752. Epub 2012 Jan 13. 
PMID: 22272068; PMCID: PMC3262489.
    \132\ Krot K, Rudawska I. Is Patient Satisfaction the Key to 
Promote Compliance in Health Care Sector? Econ Sociol. 
2019;12(3):291-300. doi: 10.14254/2071-789X.2019/12-3/19.
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    We currently collect resident satisfaction data in other settings, 
such as home health, hospice, and hospital, using Consumer Assessment 
of Healthcare Providers and Systems (CAHPS[supreg]) patient experience 
surveys.\133\ These CAHPS[supreg] surveys ask individuals (or in some 
cases their families) about their experiences with, and ratings of, 
their healthcare providers, and then we publicly report the results of 
some of these resident experience surveys on Care Compare.\134\ The 
CAHPS[supreg] Nursing Home survey: Discharged Resident Instrument 
(NHCAHPS-D) was developed specifically for short-stay SNF residents 
\135\ by the Agency for Healthcare Research and Quality (AHRQ) and the 
CAHPS[supreg] consortium \136\ in collaboration with CMS. However, due 
to its length and the potential burden on SNFs and residents to 
complete it, we have not adopted it for the SNF QRP.
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    \133\ Centers for Medicare & Medicaid Services. Consumer 
Assessment of Healthcare Providers & Systems (CAHPS). https://cms.gov/Research-Statistics-Data-and-Systems/Research/CAHPS.
    \134\ Care Compare. https://www.medicare.gov/care-compare/.
    \135\ Sangl J, Bernard S, Buchanan J, Keller S, Mitchell N, 
Castle NG, Cosenza C, Brown J, Sekscenski E, Larwood D. The 
development of a CAHPS instrument for nursing home residents. J 
Aging Soc Policy. 2007;19(2):63-82. doi: 10.1300/J031v19n02_04. 
PMID: 17409047.
    \136\ The CAHPS consortium included Harvard Medical School, The 
RAND Corporation, and Research Triangle Institute International.
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    The CoreQ is another suite of questionnaires developed by a team of 
nursing home providers and researchers \137\ to assess satisfaction 
among residents and their families. The CoreQ suite of five measures is 
used to capture resident and family data for SNFs and assisted living 
(AL) facilities. The CoreQ was developed in 2012 by SNFs and ALs that 
partnered with researchers to develop a valid resident satisfaction 
survey for SNFs and ALs since, at the time, there was no standard 
questionnaire or set of identical questions that could be used to 
compare meaningful differences in quality between SNFs. As part of the 
development of the CoreQ measures, extensive psychometric testing was 
conducted to further refine the CoreQ measures into a parsimonious set 
of questions that capture the domain of resident and family 
satisfaction. Since 2017, the CoreQ has been used in the American 
Health Care Association (AHCA) professional recognition program, and 
several States (including New Jersey, Tennessee, and Georgia) have 
incorporated the CoreQ into their Medicaid quality incentive programs. 
In addition, 42 SNF and AL customer satisfaction vendors currently 
administer the CoreQ measures' surveys or have added the CoreQ 
questions to their questionnaires.
---------------------------------------------------------------------------

    \137\ The CoreQ was developed by Nicholas Castle, Ph.D., the 
American Health Care Association/National Center for Assisted Living 
(AHCA/NCAL), and providers with input from customer satisfaction 
vendors and residents.
---------------------------------------------------------------------------

    The CoreQ measures were designed to be different from other 
resident satisfaction surveys. The primary difference between the CoreQ 
questionnaires for residents discharged from a SNF after receiving 
short-stay services and the NHCAHPS-D survey is its length: the CoreQ 
questionnaire consists of four questions while the NHCAHPS-D has 50 
questions. Another difference is that the CoreQ measures provide one 
score that reflects a resident's overall satisfaction, while other 
satisfaction surveys do not. The CoreQ questionnaires use a 5-point 
Likert scale, and the number of respondents with an average score 
greater than or equal to 3.0 across the four questions is divided by 
the total number of valid responses to yield the SNF's satisfaction 
score.\138\
---------------------------------------------------------------------------

    \138\ What is CoreQ? www.coreq.org.
---------------------------------------------------------------------------

    The CoreQ measures are also instruments that are familiar to the 
SNF community, and the CoreQ: Short Stay Discharge (CoreQ: SS DC) 
survey has already been voluntarily adopted by a large number of SNFs 
with ease. The number of SNFs voluntarily using the CoreQ: SS DC survey 
increased from 372 in the first quarter of 2016 to over 1,500 in the 
third quarter of 2019.\139\ Additionally, the measure steward, AHCA, 
reported that there have been no reported difficulties with the current 
implementation of the measure, and in fact, providers, vendors, and 
residents have reported they like the fact that the questionnaire is 
short and residents report appreciation that their satisfaction (or 
lack thereof) is being measured.
---------------------------------------------------------------------------

    \139\ 
CoreQ_Short_Stay_Appendix_Final_updated_Jan2020_Corrected_April2020_F
inalforSubmission-637229961612228954.docx. Available in the 
measure's specifications from the Patient Experience and Function 
Spring Cycle 2020 project. https://nqfappservicesstorage.blob.core.windows.net/proddocs/36/Spring/2020/measures/2614/shared/2614.zip.
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(a) Measure Importance
    Measuring residents' satisfaction is an effective method to assess 
whether the goals of person-centered care are achieved. Measuring 
residents' satisfaction can help SNFs identify deficiencies that the 
other quality metrics adopted in the SNF QRP cannot identify, such as 
communication between a resident and the SNF's healthcare providers. We 
believe collecting and assessing satisfaction data from SNF residents 
is important for understanding residents' experiences and preferences, 
while the collection process ensures each resident can easily and 
discreetly share their information in a manner that may help other 
potential consumers choose a SNF. Collection of resident satisfaction 
data also aligns with the person-centered care domain of CMS's 
Meaningful Measures 2.0

[[Page 53248]]

Framework,\140\ and would provide SNFs with resident-reported outcome 
information to incorporate into their quality assessment and 
performance improvement (QAPI) strategies to improve their quality of 
care.
---------------------------------------------------------------------------

    \140\ Centers for Medicare & Medicaid Services. Meaningful 
Measures 2.0: Moving from Measure Reduction to Modernization. 
https://www.cms.gov/meaningful-measures-20-moving-measure-reduction-modernization.
---------------------------------------------------------------------------

    The CoreQ: SS DC measure is a resident-reported outcome measure 
using the CoreQ: SS DC measure questionnaire which calculates the 
percentage of residents discharged in a 6-month period from a SNF, 
within 100 days of admission, who are satisfied with their SNF stay. 
The CoreQ: SS DC measure received initial CBE endorsement in 2016 and 
re-endorsement in 2020, and is a widely accepted instrument for 
measuring resident satisfaction. The measure includes a parsimonious 
set of four questions, and represents an important aspect of quality 
improvement and person-centered care. We believe it could be used to 
fill the identified gap in the SNF QRP's measure set, that is, 
measuring residents' experience of care. Therefore, we proposed to 
adopt the CoreQ: SS DC measure for the SNF QRP beginning with the FY 
2026 SNF QRP. More information about the CoreQ questionnaire is 
available at http://www.coreq.org.
(b) Measure Testing
    The measure steward, AHCA, conducted extensive testing on the 
CoreQ: SS DC measure to assess reliability and validity prior to its 
initial CBE endorsement in 2016 and conducted additional analyses for 
the CoreQ: SS DC measure's CBE re-endorsement in 2020. These analyses 
found the CoreQ: SS DC measure to be highly reliable, valid, and 
reportable.\141\ We describe the results of these analyses in this 
section.
---------------------------------------------------------------------------

    \141\ 
CoreQ_Short_Stay_Testing_Final_v7.1_Corrected_4_20_20_FinalforSubmiss
ion-637229958835088042.docx. Available in the measure's 
specifications from the Patient Experience and Function Spring Cycle 
2020 project. https://nqfappservicesstorage.blob.core.windows.net/proddocs/36/Spring/2020/measures/2614/shared/2614.zip.
---------------------------------------------------------------------------

    Reliability testing included administering a pilot survey to 853 
residents, re-administering the survey to 100 of these residents, and 
then examining results at the data element level, the respondent/
questionnaire level, and the measure (that is, facility) level. The 
data elements of the CoreQ: SS DC measure were found to be highly 
repeatable, with pilot and re-administered responses agreeing between 
94 percent and 97 percent of the time, depending on the question. In 
other words, the same results were produced a high proportion of the 
time when assessed in the same population in the same time period. The 
questionnaire-level scores were also highly repeatable, with pilot and 
re-administered responses agreeing 98 percent of the time. Finally, 
reliability at the measure (that is, facility) level was also strong. 
Bootstrapping analyses in which repeated draws of residents were 
randomly selected from the measure population and scores were 
recalculated showed that 17.82 percent of scores were within 1 
percentage point of the original score, 38.14 percent were within 3 
percentage points of the original score, and 61.05 percent were within 
5 percentage points of the original score. These results demonstrate 
that the CoreQ: SS DC measure scores from the same facility are very 
stable across bootstrapped samples.
    The measure steward also conducted extensive validity testing of 
the CoreQ: SS DC measure's questionnaire, which included examination of 
the items in the questionnaire, the questionnaire format, and the 
validity of the CoreQ: SS DC measure itself.\142\
---------------------------------------------------------------------------

    \142\ 
CoreQ_Short_Stay_Testing_Final_v7.1_Corrected_4_20_20_FinalforSubmiss
ion-637229958835088042.docx. Available in the measure's 
specifications from the Patient Experience and Function Spring Cycle 
2020 project. https://nqfappservicesstorage.blob.core.windows.net/proddocs/36/Spring/2020/measures/2614/shared/2614.zip.
---------------------------------------------------------------------------

    First, the measure steward tested the items in the CoreQ: SS DC 
questionnaire to determine if a subset of items could reliably be used 
to produce an overall indicator of customer satisfaction. The measure 
steward started with 22 pilot questions, which assessed an individual's 
satisfaction with a number of concepts, such as food, environment, 
activities, communication, and responsiveness. Through repeated 
analyses, the number of questions was narrowed down to four. The four 
questions in the CoreQ: SS DC measure's final questionnaire were found 
to have a high degree of criterion validity, supporting that the 
instrument measures a single concept of ``customer satisfaction,'' 
rather than multiple areas of satisfaction.
    Next, the validity of the four-question CoreQ: SS DC measure 
summary score was compared to the more expansive set of 22 pilot 
questions, and was found to have a correlation value of 0.94, 
indicating that the CoreQ: SS DC measure's questionnaire consisting of 
four questions adequately represents the overall satisfaction of the 
facility.
    Finally, the measure steward found moderate levels of construct 
validity and convergent validity when the CoreQ: SS DC measure's 
relationship with Certification and Survey Provider Enhanced Reports 
(CASPER) Quality Indicators, Nursing Home Compare Quality Indicators, 
Five Star Ratings and staffing levels was examined. Therefore, the 
CoreQ: SS DC measure's questionnaire format has a high degree of both 
face validity and content validity.\143\
---------------------------------------------------------------------------

    \143\ 
CoreQ_Short_Stay_Testing_Final_v7.1_Corrected_4_20_20_FinalforSubmiss
ion-637229958835088042.docx. Available in the measure's 
specifications from the Patient Experience and Function Spring Cycle 
2020 project. https://nqfappservicesstorage.blob.core.windows.net/proddocs/36/Spring/2020/measures/2614/shared/2614.zip.
---------------------------------------------------------------------------

    Since the CoreQ: SS DC measure's original CBE endorsement in 2018, 
and its subsequent use by SNFs in quality improvement (see section 
VI.C.2.a.(1) of the proposed rule), the measure steward conducted 
additional testing, including examining the reportability of the 
measure. Testing found that when the CoreQ: SS DC measure's 
questionnaires were administered within one week of facility discharge, 
the response rate was 8 percent higher than if it was administered 2 
weeks after facility discharge. The measure steward analyzed responses 
when it allowed up to 2 months for a resident to respond, and found the 
average time to respond to the CoreQ: SS DC questionnaire was 2 weeks, 
while the response rate dropped much lower in the second month after 
facility discharge.\144\ The measure steward also conducted additional 
analyses to determine if there was any bias introduced into the 
responses to the CoreQ: SS DC's questionnaires that were returned 
during the second month, and found that average scores for the 
questionnaires returned in the second month were almost identical to 
those returned in the first month. Finally, the measure steward 
examined the time period required to collect the CoreQ: SS DC measure's 
data, and found that a majority of SNFs (that is, 90 percent) could 
achieve the minimum sample size of 20 completed CoreQ: SS DC 
questionnaires necessary for the satisfaction score to be reported as 
reliable for the SNF, when given up to 6 months. Additionally, once 125 
consecutive completed CoreQ: SS DC questionnaires were received for a

[[Page 53249]]

particular SNF, the measure steward found that including additional 
CoreQ: SS DC questionnaires had no additional effect on the SNF's 
satisfaction score. As a result of these additional analyses, the 
recommendations to allow up to 2 months for CoreQ: SS DC questionnaire 
returns, a 6-month reporting period, and a ceiling of 125 completed 
questionnaires in a 6-month period were incorporated into the CoreQ: SS 
DC measure's specification.
---------------------------------------------------------------------------

    \144\ CoreQ Measure Worksheet-2614-Spring 2020 Cycle. Patient 
Experience and Function Project. https://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=93879.
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(2) Competing and Related Measures
    Section 1899B(e)(2)(A) of the Act requires that, absent an 
exception under section 1899B(e)(2)(B) of the Act, measures specified 
under section 1899B of the Act be endorsed by a CBE with a contract 
under section 1890(a) of the Act. In the case of a specified area or 
medical topic determined appropriate by the Secretary for which a 
feasible and practical measure has not been endorsed, section 
1899B(e)(2)(B) of the Act permits the Secretary to specify a measure 
that is not so endorsed, as long as due consideration is given to 
measures that have been endorsed or adopted by a consensus organization 
identified by the Secretary.
    Although the CoreQ measure is CBE endorsed for SNFs, we did 
consider whether there were other CBE endorsed measures capturing SNF 
resident satisfaction after discharge from a SNF in less than 100 days. 
We found several CBE endorsed measures used in other programs that 
assess resident experiences for specific resident populations, such as 
residents at end of life, residents with low back pain, and residents 
receiving psychiatric care. However, we did not find other CBE endorsed 
measures that assess satisfaction of residents discharged within 100 
days of their admission to the SNF.
(3) Interested Parties and Technical Expert Panel (TEP) Input
    We employ a transparent process to seek input from interested 
parties and national experts and engage in a process that allows for 
pre-rulemaking input on each measure, under section 1890A of the Act. 
To meet this requirement, we solicited feedback from interested parties 
through an RFI in the FY 2022 SNF PPS proposed rule (86 FR 19998) on 
the importance, relevance, and applicability of patient-reported 
outcome (PRO) measures for SNFs. In the FY 2022 SNF PPS final rule (86 
FR 42490 through 42491), we noted that several commenters supported the 
concept of PROs while others were uncertain what we intended with the 
term ``patient-reported outcomes.'' One commenter stressed the 
importance of PROs since they determine outcomes based on information 
obtained directly from residents, and therefore provide greater insight 
into residents' experience of the outcomes of care. Another commenter 
agreed and stated that residents and caregivers are the best sources of 
information reflecting the totality of the resident experience.
    We solicited public comments from interested parties specifically 
on the inclusion of the CoreQ: SS DC measure in a future SNF QRP year 
through an RFI in the FY 2023 SNF PPS proposed rule (87 FR 22761 
through 22762). In the FY 2023 SNF PPS final rule (87 FR 47555), we 
noted that support for the CoreQ: SS DC measure specifically was mixed 
among commenters. One commenter stated that since the CoreQ: SS DC 
measure has a limited number of questions, it may not fully reflect 
resident experience at a given facility. Another commenter would not 
support the CoreQ: SS DC measure since it excludes residents who leave 
a facility against medical advice and residents with guardians, and 
this commenter stated it would be important to hear from both of these 
resident populations. Two commenters cautioned us to consider the 
burden associated with contracting with third-party vendors to 
administer the CoreQ: SS DC measure.
(4) Measure Application Partnership (MAP) Review
    The CoreQ: SS DC measure was initially endorsed by the CBE in 2016. 
It was originally reviewed by the CBE's Person- and Family-Centered 
Care (PFCC) Committee on June 6, 2016. The PFCC Committee members noted 
the importance of measuring residents' experiences and their 
preferences given health care's changing landscape. Overall, the PFCC 
Committee members liked that there was a conceptual framework 
associated with the measure submission that linked the CoreQ: SS DC 
measure with other improvement programs and organizational change 
initiatives that can help SNFs improve the quality of care they 
provide. Some PFCC Committee members expressed concern around the 
consistency of implementation across SNFs and whether scores could be 
compromised by a low response rate. All PFCC Committee members agreed 
to not risk-adjust the CoreQ: SS DC measure as it would be 
inappropriate to control for differences based on sociodemographic 
factors. We refer readers to the PFCC Final Report--Phase 3.\145\
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    \145\ Person and Family Centered Care Final Report--Phase 3. 
https://www.qualityforum.org/Publications/2017/01/Person_and_Family_Centered_Care_Final_Report_-_Phase_3.aspx.
---------------------------------------------------------------------------

    The following year, the CoreQ: SS DC measure was included on the 
publicly available ``List of Measures under Consideration for December 
1, 2017'' \146\ for the SNF QRP Program, but the MAP did not receive 
any comments from interested parties. The CBE convened MAP PAC/LTC 
workgroup met on December 13, 2017 and provided input on the CoreQ: SS 
DC measure. The MAP PAC/LTC workgroup offered support of the CoreQ: SS 
DC measure for rulemaking, noting that it adds value by adding 
addressing a gap area for the SNF QRP. The MAP PAC/LTC workgroup 
emphasized the value of resident-reported outcomes and noted that the 
CoreQ: SS DC measure would reflect quality of care from the resident's 
perspective. However, the MAP PAC/LTC workgroup also noted the 
potential burden of collecting the data and cautioned that the 
implementation of a new data collection requirement should be done with 
the least possible burden to the SNF.\147\
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    \146\ Centers for Medicare & Medicaid Services. List of Measures 
under Consideration for December 1, 2017. https://mmshub.cms.gov/sites/default/files/map-2017-2018-preliminary-recommendations.xlsx.
    \147\ MAP Post-Acute Care/Long-Term Care Workgroup Project. 
2017-2018 Preliminary Recommendations. https://mmshub.cms.gov/measure-lifecycle/measure-implementation/pre-rulemaking/lists-and-reports.
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(5) Quality Measure Calculation
    The CoreQ: SS DC measure is a resident-reported outcome measure 
based on the CoreQ: SS DC questionnaire that calculates the percentage 
of residents discharged in a 6-month period from a SNF, within 100 days 
of admission, who are satisfied with their SNF stay. Unless otherwise 
exempt from collecting and reporting on the CoreQ: SS DC measure (as 
discussed in section VI.F.3.b. of the FY 2024 SNF PPS proposed rule), 
we proposed that each SNF must contract with an independent CMS-
approved CoreQ survey vendor to administer the CoreQ: SS DC measure 
questionnaire, and report the results to us, on behalf of the SNF (as 
specified in sections VI.F.3.a. and VI.F.3.c. of the FY 2024 SNF PPS 
proposed rule).
    The CoreQ: SS DC measure questionnaire utilizes four questions 
(hereafter referred to as the four primary questions) and uses a 5-
point Likert scale as illustrated in Table C3.

[[Page 53250]]



         Table 13--CoreQ: Short Stay Discharge Primary Questions
------------------------------------------------------------------------
                                               Response options for the
 Primary questions used in the CoreQ: short       four CoreQ primary
        stay discharge questionnaire                  questions
------------------------------------------------------------------------
1. In recommending this facility to your     Poor (1).
 friends and family, how would you rate it
 overall?
2. Overall, how would you rate the staff?    Average (2).
3. How would you rate the care you           Good (3).
 received?
4. How would you rate how well your          Very Good (4).
 discharge needs were met?                   Excellent (5).
------------------------------------------------------------------------

    We also proposed to add two ``help provided'' questions to the end 
(as questions five and six) of the CoreQ: SS DC questionnaire to 
determine whether to count the CoreQ: SS DC questionnaire as a 
completed questionnaire for the CoreQ: SS DC measure denominator or 
whether the questionnaire should be excluded as described in the Draft 
CoreQ: SS DC Survey Protocols and Guidelines Manual \148\ available on 
the SNF QRP Measures and Technical Information web page. These two 
``help provided'' questions are:
---------------------------------------------------------------------------

    \148\ Draft CoreQ: SS DC Survey Protocols and Guidelines Manual. 
Chapter VIII. Data Processing and Coding. Available on the SNF QRP 
Measures and Technical Information web page at https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/nursinghomequalityinits/skilled-nursing-facility-quality-reporting-program/snf-quality-reporting-program-measures-and-technical-information.
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    5. Did someone help you [the resident] complete the survey?
    6. How did that person help you [the resident]?
(a) Denominator
    The denominator is the sum of all of the questionnaire-eligible 
residents, regardless of payer, who (1) are admitted to the SNF and 
discharged within 100 days, (2) receive the CoreQ: SS DC questionnaire, 
and (3) respond to the CoreQ: SS DC questionnaire within 2 months of 
discharge from the SNF. However, certain residents are excluded from 
the denominator and therefore are not sent a CoreQ: SS DC questionnaire 
by the CMS-approved CoreQ survey vendor or contacted by the CMS-
approved CoreQ survey vendor for a phone interview. The residents who 
are not eligible to respond to the questionnaire, and therefore are 
excluded from the denominator for the CoreQ: SS DC measure are: (1) 
residents discharged to another hospital, another SNF, a psychiatric 
facility, an IRF, or an LTCH; (2) residents who die during their SNF 
stay; (3) residents with court-appointed legal guardians with authority 
to make decisions on behalf of the resident; (4) residents discharged 
to hospice; (5) residents who have dementia impairing their ability to 
answer the questionnaire; \149\ (6) residents who left the SNF against 
medical advice; and (7) residents with a foreign address. Additionally, 
residents are excluded from the denominator if after the CoreQ: SS DC 
questionnaire is returned: (1) the CMS-approved CoreQ survey vendor 
received the CoreQ: SS DC completed questionnaire more than 2 months 
after the resident was discharged from the SNF or the resident did not 
respond to attempts to conduct the interview by phone within 2 months 
of their SNF discharge date; (2) the CoreQ: SS DC questionnaire ``help 
provided'' question six indicates the questionnaire answers were 
answered for the resident by an individual(s) other than the resident; 
or (3) the received CoreQ: SS DC questionnaire is missing more than one 
response to the four primary questions (that is, missing two or more 
responses).
---------------------------------------------------------------------------

    \149\ Patients who have dementia impairment in their ability to 
answer the questionnaire are defined as having a BIMS score on the 
MDS 3.0 as 7 or lower. https://cmit.cms.gov/CMIT_public/ViewMeasure?MeasureId=3436.
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(b) Numerator
    The numerator is the sum of the resident respondents in the 
denominator that submitted an average satisfaction score of greater 
than or equal to 3 for the four primary questions on the CoreQ: SS DC 
questionnaire. If a CoreQ: SS DC questionnaire is received and is 
missing only one response (out of the four primary questions in the 
questionnaire), imputation is used which represents the average value 
from the other three available responses. If a CoreQ: SS DC 
questionnaire is received and is missing more than one response to the 
four primary questions (that is, missing two or more responses), the 
CoreQ: SS DC questionnaire is excluded from the analysis (that is, no 
imputation will be used for these residents). The CoreQ: SS DC measure 
is not risk-adjusted by sociodemographic status (SDS), as the measure 
steward found no statistically significant differences (at the 5 
percent level) in scores between the SDS categories.\150\ Additional 
information about how the CoreQ: SS DC measure is calculated is 
available in the Draft CoreQ: SS DC Survey Protocols and Guidelines 
Manual \151\ on the SNF QRP Measures and Technical Information web 
page.
---------------------------------------------------------------------------

    \150\ The measure developer examined the following SDS 
categories: age, race, gender, and highest level of education. 
CoreQ: Short Stay Discharge Measure.
    \151\ Draft CoreQ: SS DC Survey Protocols and Guidelines Manual. 
Chapter VIII. Data Processing and Coding. Available on the SNF QRP 
Measures and Technical Information web page at https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/nursinghomequalityinits/skilled-nursing-facility-quality-reporting-program/snf-quality-reporting-program-measures-and-technical-information.
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    We solicited public comment on our proposal to adopt the CoreQ: SS 
DC Measure beginning with the FY 2026 SNF QRP. The following is a 
summary of the comments we received and our responses.
    Comment: A number of commenters supported the adoption of the 
CoreQ: SS DC measure in the SNF QRP as a reliable and valid tool for 
assessing resident satisfaction. Several commenters noted the measure 
is CBE endorsed and expressed appreciation to CMS for proposing a 
measure that was supported by the MAP PAC/LTC workgroup for rulemaking. 
Two commenters pointed out that the CoreQ: SS DC survey is more 
efficient than other tools that have over 50 questions and provides a 
concise satisfaction rate that is intuitive for providers to act on and 
for consumers to understand. Another commenter supported the adoption 
of the CoreQ: SS DC measure not only because they believe it is an 
accurate measure of resident-centered care, but also because of its 
long tenure, validity testing, utilization in other settings, and 
cooperative development with SNFs and assisted living communities. One 
commenter noted the importance of residents/families providing direct 
feedback regarding the care and services received.
    Response: We thank the commenters for their support of the CoreQ: 
SS DC measure. We agree that this CBE endorsed measure's survey is an 
efficient tool for both SNFs to implement and residents to complete, 
which would increase the likelihood

[[Page 53251]]

that SNFs would receive robust responses they could use to advance 
their person-centered care practices. We agree that capturing 
residents' direct feedback is valuable and the proposed measure would 
fill a measurement gap in the SNF QRP.
    We also received several comments that did not support our proposal 
to adopt the CoreQ: SS DC measure. Commenters gave various reasons 
including: a preference for using the NHCAHPS-D survey because it 
includes a greater number of questions; concern about the number of 
residents that would be excluded from receiving a CoreQ: SS DC survey; 
the imputation method used to calculate a CoreQ: SS DC measure score; 
and the burden of submitting resident information files to the CoreQ 
survey vendor on a weekly basis. The following is a summary of the 
comments we received and our responses.
    Comment: While several commenters agreed that resident satisfaction 
surveys would provide clinical teams information to use when examining 
the results of their clinical care, and help SNFs identify areas for 
improvement, they did question why CMS did not choose to use the 
standardized measures contained in the Consumer Assessment of 
Healthcare Providers and Systems (CAHPS) that were developed by CMS 
with the Agency for Healthcare Research and Quality (AHRQ), and 
specifically the CAHPS Nursing Home survey: Discharged Resident 
Instrument (NHCAHPS-D)--or a portion of this instrument. Two of these 
commenters cited the National Academies of Sciences, Engineering, and 
Medicine (NASEM) report, ``The National Imperative to Improve Nursing 
Home Quality,'' which recommended the use of the CAHPS survey, which 
was developed by the AHRQ, in conjunction with CMS.\152\ Another 
commenter suggested that the use of surveys other than CAHPS conflicts 
with the CMS Foundational Measurement Strategy, which aims to align all 
adult and pediatric person-centered care domain measures with CAHPS 
surveys.
---------------------------------------------------------------------------

    \152\ National Academies of Sciences, Engineering, and Medicine. 
2022. The National Imperative to Improve Nursing Home Quality: 
Honoring Our Commitment to Residents, Families, and Staff. 
Washington, DC: The National Academies Press. https://doi.org/10.17226/26526.
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    A number of these commenters also questioned why CMS would use a 
tool that was developed by the American Health Care Association (AHCA), 
which is the major nursing home trade association. These commenters 
pointed to the NASEM report's findings that many nursing homes promote 
and advertise high scores from self-designed and administered surveys 
of their residents. One of these commenters expressed concern that CMS 
is proposing to adopt an instrument developed by the very industry 
whose members it will be used to measure.
    Response: We acknowledge that the NHCAHPS-D was developed for 
short-stay SNF residents \153\ by the AHRQ and the CAHPS[supreg] 
consortium \154\ in collaboration with us. We also recognize that there 
are other measures of resident satisfaction that are available, but we 
proposed the CoreQ for two primary reasons: (1) it is the only CBE 
endorsed survey of SNF resident satisfaction, and (2) its extensive 
testing prior to initial CBE endorsement in 2016 and subsequent CBE re-
endorsement in 2020 and its strong item and response reliability and 
validity. We also considered the length of the NHCAHPS-D tool and the 
potential burden on respondents to complete it.
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    \153\ Sangl J, Bernard S, Buchanan J, Keller S, Mitchell N, 
Castle NG, Cosenza C, Brown J, Sekscenski E, Larwood D. The 
development of a CAHPS instrument for nursing home residents. J 
Aging Soc Policy. 2007;19(2):63-82. doi: 10.1300/J031v19n02_04. 
PMID: 17409047.
    \154\ The CAHPS consortium included Harvard Medical School, The 
RAND Corporation, and Research Triangle Institute International.
---------------------------------------------------------------------------

    We refer the commenters to section VII.2.a.1. of this final rule 
where we describe how the CoreQ was developed by a team led by 
researchers from the University of Pittsburgh with input from an AHCA 
workgroup, providers, and residents \155\ specifically for assessing 
satisfaction among residents and their families. Furthermore, since the 
measure has been endorsed by a CBE on two occasions, it means that a 
panel of experts and interested parties representing providers, 
residents, and payers support this measure for inclusion in the SNF 
QRP.
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    \155\ Castle NG, Gifford D, Schwartz LB. The CoreQ: Development 
and Testing of a Nursing Facility Resident Satisfaction Survey. J 
Appl Gerontol. 2021 Jun;40(6):629-637. doi: 10.1177/
0733464820940871. Epub 2020 Jul 29. PMID: 32723121.
---------------------------------------------------------------------------

    We also refer commenters to section VII.D. of this final rule, 
where we discuss the measurement gaps we identified for the SNF QRP, 
including the measurement concepts of resident experience and resident 
satisfaction. We sought feedback in the FY 2024 SNF PPS proposed rule 
(88 FR 21355) on the value of adding a resident experience measure, 
such as the NHCAHPS-D, to the SNF QRP.
    Comment: Several commenters opposed the adoption of the CoreQ: SS 
DC measure because they believe it provides limited actionable feedback 
for performance improvement. One of these commenters believed that 
organizations tend to improve resident experiences when they have data 
and feedback that are actionable, which comes through measuring 
behaviors. They do not believe the CoreQ: SS DC measure asks about 
behavior and therefore fails to capture meaningful feedback. They 
disagree with using the CoreQ: SS DC survey because it does not ask 
questions about whether a specific action occurred, how often it 
occurred, or the quality of the action or interaction. Two commenters 
noted that a single score would be meaningless.
    Response: We understand the commenter's concerns to be related to 
the fact that the CoreQ: SS DC measure represents the overall 
satisfaction with the nursing facility. However, we believe this to be 
advantageous for several reasons, including its simplicity and its 
utility for ranking/rating purposes.
    First, the simple format may be important in helping older adults 
and their families choose a SNF. That is, the CoreQ: SS DC measure 
score is understandable. At the same time, testing demonstrated the 
range of CoreQ measure scores was large, indicating that the scores can 
be used to differentiate facilities with varying levels of customer 
satisfaction.\156\ Second, a single score may also be useful for 
facilities to easily track their performance over time and a tool they 
might use to gauge the effectiveness of their own quality improvement 
processes. It is also a score a SNF could use to compare its overall 
level of satisfaction with other SNFs. This is something that might be 
much more difficult to achieve with a resident satisfaction survey that 
includes multiple questions about specific actions and interactions and 
the quality of those actions and interactions. Moreover, other resident 
satisfaction surveys we found were not developed or tested to produce 
an overall satisfaction score.
---------------------------------------------------------------------------

    \156\ Castle NG, Gifford D, Schwartz LB. The CoreQ: Development 
and Testing of a Nursing Facility Resident Satisfaction Survey. J 
Appl Gerontol. 2021 Jun;40(6):629-637. doi: 10.1177/
0733464820940871. Epub 2020 Jul 29. PMID: 32723121.
---------------------------------------------------------------------------

    We acknowledge that the CoreQ: SS DC measure score would not 
provide a detailed set of information about specific actions and 
interactions, but a facility could have its survey vendor add as many 
specific questions to the survey as it wants, so it could obtain more 
details about why a resident responded the way they did. For more 
information, we refer commenters to the

[[Page 53252]]

Draft CoreQ: SS DC Survey Protocols and Guidelines Manual found at 
https://www.cms.gov/files/document/draft-coreq-ss-dc-manual508compliant.pdf.
    Comment: One commenter opposed the adoption of the CoreQ: SS DC 
measure because it is not currently endorsed by a CBE.
    Response: We refer the commenter to section VII.C.2.a.4. of this 
final rule for details about the CoreQ: SS DC measure's CBE 
endorsement. The CoreQ: SS DC measure was initially endorsed by the CBE 
in 2016 and re-endorsed in 2020.\157\
---------------------------------------------------------------------------

    \157\ https://www.qualityforum.org/QPS/2614.
---------------------------------------------------------------------------

    Comment: One commenter noted that in the proposed rule, CMS 
described comments of interested parties and the Technical Expert Panel 
(TEP), some of whom were critical of CoreQ and whose concerns the 
proposed rule did not address. This commenter acknowledged that they 
were a member of a TEP that reviewed the CoreQ and this commenter 
remains extremely critical of the tool.
    Response: Contrary to the commenter's assertion, we did not 
describe comments from a CoreQ: SS DC measure TEP in the proposed rule. 
As described in section VII.C.2.a.1. of the final rule, the CoreQ: SS 
DC survey was developed by SNFs and ALs that partnered with researchers 
to develop the CoreQ: SS DC survey for SNFs and ALs. TEPs are groups of 
experts assembled by our contractors involved in quality activities. 
Since neither we nor our quality measure development contractors 
developed the survey tool, we cannot speak to discussions that may have 
occurred in a provider-assembled panel associated with the measure.
    However, as discussed in section VII.C.2.a.4. of this final rule, 
the CoreQ: SS DC measure was reviewed by the CBE's Person- and Family-
Centered Care (PFCC) Committee on June 6, 2016, and subsequently the 
measure appeared on the List of Measures under Consideration for 
December 1, 2017 \158\ for the SNF QRP Program. The CBE-convened MAP 
PAC/LTC workgroup met on December 13, 2017, and offered support of the 
CoreQ: SS DC measure for rulemaking, noting that it adds value by 
addressing a gap area for the SNF QRP.
---------------------------------------------------------------------------

    \158\ Centers for Medicare & Medicaid Services. List of Measures 
under Consideration for December 1, 2017. https://www.cms.gov/files/document/2017amuc-listclearancerpt.pdf.
---------------------------------------------------------------------------

    Comment: One commenter acknowledged that it is vital to collect 
information on resident experience in SNFs but suggested the CoreQ: SS 
DC measure is not ready to be proposed for inclusion in the SNF QRP 
because the CoreQ questionnaire is a proprietary tool and thus requires 
administration by third-party vendors, as opposed to a CAHPS survey, 
which is maintained by the AHRQ.
    Response: We agree with the commenter that it is vital to collect 
information on resident experience in SNFs. We do want to clarify, 
however, that the CoreQ: SS DC measure's survey is not a proprietary 
tool and is free to SNFs and vendors. All of the CoreQ surveys (along 
with instructions for use) are provided on a free publicly accessible 
website. The website does not ask for any fees for using the CoreQ 
surveys.
    Comment: Several commenters stated that the CoreQ: SS DC measure 
has not been adequately tested for reliability, nor has it been tested 
to determine if it produces valid data or that the data are meaningful. 
One of these commenters stated that the fact that many facilities have 
``voluntarily adopted'' CoreQ, and use it ``with ease,'' suggests that 
the tool is useful to facilities. However, the commenter asserted that 
facilities have historically used satisfaction surveys for marketing 
purposes, and the CoreQ's usability does not suggest that the tool is 
equally useful or meaningful to government regulators. Another one of 
these commenters noted that calculating measure scores by only 
including responses with an average score greater than or equal to 3.0 
will impact the statistical reliability of the measure and expressed 
concern that this issue, combined with the low item count of only four 
questions, could potentially produce a measure with extremely low 
statistical reliability and compromising validity.
    One commenter recommended that CMS use the CAHPS measures of 
resident and family experience which they noted are based on actual 
experiences and have been thoroughly tested for validity. This 
commenter went on to say that they disagree with CMS' conclusion that 
reproduction of CoreQ: SS DC survey results indicates the measure's 
reliability. Instead, they stated that the CoreQ's measure properties 
(that is, the limited number of questions in the measure, the vagueness 
of the questions, and the inherent bias in the scale, the computation 
process, and the selection process) increase the likelihood of repeated 
results.
    Response: As described in section VII.C.2.a.(1)(b) of this final 
rule, the development of the CoreQ: SS DC measure involved multiple 
interested parties, involved rigorous testing and review on two 
separate occasions, and has been thoroughly vetted. Three steps were 
used in developing the CoreQ: SS DC questionnaire. The first step was 
the development of the general approach used in the questionnaire (that 
is, domains, format, and potential items). The data collection for this 
first step mostly involved using consumers in SNFs. The second step 
included validity testing to further refine items that should be 
included in the questionnaire. The data collection for this second step 
involved using residents in a national sample of nursing facilities. 
The third step included testing to examine the reliability of the 
CoreQ: SS DC measure (that is, facility and summary score validity). 
The data collection for this third step involved using residents from a 
national sample of nursing facilities. These three steps in the 
questionnaire development follow an approach used by the CAHPS nursing 
home surveys.\159\ Since this initial testing, the CoreQ: SS DC survey 
has been used with tens of thousands of additional residents. The 
response rate and score distributions have remained in-line with the 
initial testing.
---------------------------------------------------------------------------

    \159\ Castle NG, Gifford D, Schwartz LB. The CoreQ: Development 
and Testing of a Nursing Facility Resident Satisfaction Survey. J 
Appl Gerontol. 2021 Jun;40(6):629-637. doi: 10.1177/
0733464820940871. Epub 2020 Jul 29. PMID: 32723121.
---------------------------------------------------------------------------

    We acknowledge the commenter's point that SNFs have historically 
used satisfaction surveys for marketing purposes. However, this fact 
does not diminish the importance of adding a resident satisfaction 
measure to the SNF QRP. We recognize there are other instruments to 
measure SNF resident satisfaction, but no one universal instrument has 
been adopted by SNFs. Additionally, as described in section 
VII.C.2.a.(2) of this final rule, we did look at and consider other 
measure tools to meet this gap in the SNF QRP measure set. We decided 
to propose the CoreQ: SS DC measure specifically because it has been 
exhaustively tested for validity and reliability (as described in 
section VII.C.2.a.(1)(b) of this final rule) and it is endorsed by a 
CBE.
    Comment: We received a number of comments about residents who would 
be excluded from receiving a CoreQ: SS DC survey. Most commenters were 
concerned that residents who left against medical advice (AMA) were 
excluded from the CoreQ: SS DC measure's denominator. As a result, they 
fear that residents who are may have been very dissatisfied with their 
care will not receive a survey. One of these commenters pointed out 
that residents leaving AMA are at a higher risk of adverse events and 
readmissions, and that SNFs could use these residents'

[[Page 53253]]

experiences and reasons for leaving in the SNF's risk management and 
readmission prevention strategies. This commenter also pointed out that 
by surveying these residents, resident feedback could highlight areas 
where resident-SNF communication can be improved and SNFs could 
identify recurring problems and implement necessary changes.
    Other commenters stated that residents who transfer to another SNF, 
psychiatric facility, IRF, LTCH, or hospice should not be excluded 
either.
    Two commenters also noted that residents living with Alzheimer's 
disease or other forms of dementia should not automatically be excluded 
because some residents with dementia could give meaningful opinions 
about their SNF stay. They maintain that CMS and the public have a 
significant interest in assessing the care quality provided to 
residents with dementia. These commenters also disagree with the 
exclusions for surveys completed by (i) a family member (however a 
resident defines ``family''), (ii) a representative of a former 
resident with dementia or of a resident who dies during their SNF stay, 
and (iii) a legal guardian of a resident under any circumstance. 
Another commenter referenced these exclusions as ``discriminatory,'' 
and stated that they are likely to skew the results to former residents 
who were temporarily in the facility for rehabilitation, went home, and 
were satisfied.
    Response: We acknowledge the commenters' concerns about the CoreQ: 
SS DC measure exclusions. In developing the CoreQ: SS DC measure, the 
measure developer convened an expert panel to advise them on which 
exclusions to apply to the measure. The expert panel advised the 
measure developer to exclude residents who died, residents who were 
discharged to a hospital, residents with durable power of attorney for 
all decisions, residents on hospice, residents with low BIMS scores, 
and residents who left against medical advice.
    Regarding the exclusion for residents who left AMA, residents who 
leave AMA generally do so within the first few days of admission to the 
SNF. As a result, the SNF has not yet had time to develop and implement 
a full care plan to address the resident's needs. The measure developer 
was not confident they could validate their answers as accurate or 
unbiased.
    Regarding the exclusion for residents who transfer to another SNF, 
IRF, LTCH, or hospice, the exclusions were applied because such 
residents were incapable or unlikely to complete a questionnaire.
    Regarding the exclusion for residents living with Alzheimer's 
disease or other forms of dementia, the exclusion applied in the 
denominator is for residents with a BIMS score of 7 or lower. A BIMS 
score of 7 represents residents with severe cognitive impairment, and 
the measure developer determined that they were unable to validate the 
responses as reliable, and the response rate dropped considerably in 
this population.
    With respect to the exclusion for surveys completed by a family 
member, representative, legal guardian, or other proxy, the exclusion 
was applied because the measure developer could not be confident the 
responses were accurate or unbiased. However, we are intentional in our 
efforts to increase the resident's voice in the assessment process and 
SNF QRP. All residents capable of any communication should be asked to 
provide information for the CoreQ: SS DC measure. Self-reporting is the 
single most reliable indicator of resident satisfaction. For that 
reason, we proposed to add two additional ``help provided'' questions 
to the original four primary questions in the CoreQ: SS DC measure. 
These questions would be used by the vendor to identify and code all 
completed surveys where a helper assisted the respondent. A decision 
algorithm was proposed to determine whether a CoreQ survey would be 
included or excluded from the CoreQ: SS DC measure numerator based on 
whether a helper completed the survey for the resident or whether the 
helper only assisted the resident due to visual, hearing, or motor 
coordination impairments.\160\ Residents requiring assistance only due 
to visual, hearing, or motor coordination impairments would be not be 
excluded.
---------------------------------------------------------------------------

    \160\ For more details about the decision algorithm, see Chapter 
8 of the CoreQ: SS DC Protocols and Guidelines Manual at https://www.cms.gov/files/document/draft-coreq-ss-dc-manual508compliant.pdf.
---------------------------------------------------------------------------

    Comment: Several commenters disagreed with using the CoreQ: SS DC 
survey because they found the number of questions to be too small, and 
they found the questions too vague to provide enough meaningful 
information for actionable improvement. One of these commenters 
suggested that CMS proposed a measure that is so simple that it tells 
consumers almost nothing about the resident's experience. This 
commenter, and two others, provided extensive examples of why they 
found each of the CoreQ: SS DC survey questions problematic. One of 
these commenters acknowledged that 50 questions may be very long for 
some residents but noted that the questions on such a survey provide 
much more meaningful information than the very vague four questions 
that constitute the CoreQ. One commenter stated the wording of the 
CoreQ: SS DC survey is potentially coercive in nature, implying an 
expected recommendation. In comparison, they noted the CAHPS Nursing 
Home Survey tactfully phrases similar questions to avoid such 
implications.
    Finally, several commenters noted the CoreQ: SS DC survey does not 
adequately capture resident satisfaction with all types of HCP and does 
not represent the totality of SNF care. These commenters noted that SNF 
care is multifaceted, encompassing multiple disciplines and components, 
including activities, diet, nursing, social work, and therapies. These 
commenters stated that residents may have positive experiences in some 
aspects of their stay and negative experiences in others. One of these 
commenters expressed concern that the measure could potentially be 
gamed through a SNF's emphasis on activities that may be appealing to 
residents and caregivers, but do not meaningfully improve function or 
other outcomes. Another one of these commenters suggested that CMS 
should use surveyor interviews with residents, resident councils, and 
families to create a satisfaction score.
    Response: We found the process that was used to develop the CoreQ: 
SS DC measure to be iterative, comprehensive, and widely published. We 
provide more details here and refer readers the CoreQ website at http://coreq.org/ to learn more.
    The first step of the development of the CoreQ: SS DC measure was 
to determine the domains, format, and potential items to include in the 
survey. This first step involved using consumers in nursing facilities. 
Following prior research in this area,\161\ a literature review was 
conducted to examine (a) important areas of satisfaction for long-term 
care residents (commonly called domains), (b) response scales used, and 
(c) individual items used in existing surveys. The research team 
examined 15 commonly used satisfaction surveys and reports addressing 
consumer satisfaction in long-term care settings.
---------------------------------------------------------------------------

    \161\ Robinson, J., Lucas, J., Castle, N.G., Lowe, T.J., & 
Crystal, S. (2004). Consumer satisfaction in nursing homes: Current 
practices and resident priorities. Research on Aging, 26(4), 454-
480.
---------------------------------------------------------------------------

    Next, a total of 35 domains of interest were identified. The face 
validity of these 35 domains was examined using nursing facility 
residents. That is, residents were asked to rank the importance of the 
domains. Residents

[[Page 53254]]

were asked to rank only 12 of the 35 domains to help simplify the 
process. After analyzing the responses, there was a substantial 
reduction in ranking of the tenth and subsequent domains, so the nine 
most highly ranked domains were chosen. For the nine domains of 
interest, individual items (questions) were selected. That is, as many 
items as could be found in these domains were taken from the 15 
commonly used satisfaction surveys identified previously in this 
section.
    A list of 140 items resulted, and these were reduced in three 
steps. First, a team of five satisfaction survey experts, in an 
iterative process consisting of six rounds of consultation, identified 
items that most represented the domains. In each round of consultation, 
100 percent agreement was used for deleting items in each domain. This 
process is generally known as ``Member Checking.'' \162\ In the second 
step, the survey experts were asked to isolate individual items that 
measured the satisfaction of each domain globally. In each round of 
consultation, 100 percent agreement was used for deleting items in each 
domain. The items thus could potentially be used to measure overall 
issues in this domain, rather than more focused issues in the domain. 
Third, the items were further reduced, again using member checking. The 
five satisfaction survey experts identified items they believed to be 
the most easily understood by potential respondents.
---------------------------------------------------------------------------

    \162\ Creswell, J.W., & Miller, D. L. (2000). Determining 
validity in qualitative inquiry. Theory into Practice, 39(3), 124-
130.
---------------------------------------------------------------------------

    The resulting items were included as part of the Pilot CoreQ: Short 
Stay Discharge questionnaire, which consisted of 24 items. The intent 
of the pilot instrument was to have items that represented the most 
important areas of satisfaction and to be parsimonious. Additional 
analyses were used to eliminate items in the Pilot instrument. The 
Pilot CoreQ: Short Stay Discharge questionnaire items were subsequently 
examined to first determine the validity of the items included and 
second to determine if the items could be reduced with the objective of 
finding the lowest number of items providing the most consumer 
satisfaction information.
    The Pilot CoreQ: Short Stay Discharge questionnaire was then sent 
to 865 residents who had been discharged from a SNF in less than 100 
days and who met the inclusion criteria.\163\ The Pilot CoreQ: Short 
Stay Discharge questionnaire items were examined to determine the 
fewest number of items providing the most consumer satisfaction 
information. That is, the 24 items were examined to determine if some 
were globally representing the residents' overall rating of their 
satisfaction with the facility. Conceptually, the intent of the item 
reduction was to identify items (a) highly correlated with overall 
satisfaction, (b) having low correlations with each other, and (c) in 
different domains. The steps previously mentioned resulted in a short 
four-item instrument, the CoreQ: Short Stay Discharge questionnaire. 
From this instrument, a single metric was developed, the CoreQ: Short 
Stay Discharge measure. To determine if the 4 items in the CoreQ: Short 
Stay Discharge questionnaire were a reliable indicator of satisfaction, 
the correlation between these four items in the CoreQ: Short Stay 
Discharge Measure and all of the items on the Pilot CoreQ instrument 
was conducted. The correlation was identified as having a value of 
0.94. That is, the correlation score between the final CoreQ: Short 
Stay Discharge Measure and all of the 22 items used in the Pilot 
instrument indicates that the satisfaction information is approximately 
the same if the survey included the four items or the 22 item Pilot 
instrument.
---------------------------------------------------------------------------

    \163\ The inclusion criteria for the Pilot testing is identical 
to the inclusion criteria for the proposed CoreQ: SS DC measure.
---------------------------------------------------------------------------

    In summary, the CoreQ: SS DC measure questions were not found to be 
vague by the SNF residents who participated in the testing of the CoreQ 
survey. The CoreQ: Short Stay Discharge questionnaire was purposefully 
written using simple language. No a priori goal for reading level was 
set; however, a Flesch-Kinkaid scale score of six, or lower, is 
achieved for all questions.\164\ The CoreQ: SS DC survey was developed 
with extensive input from residents, nursing home personnel, other 
survey vendors, and clinical researchers. As outlined previously in 
this section, the CoreQ: SS DC measure represents a resident's overall 
satisfaction with the SNF, including all types of HCP and SNF care. 
Additionally, three State Medicaid programs have incorporated the 
CoreQ: SS DC measure into their Medicaid quality incentive programs. As 
we noted before, SNFs could work with their vendors to add additional 
questions to their survey instrument in order to ask about other 
aspects of their care that they believe would help them in their 
quality improvement efforts.
---------------------------------------------------------------------------

    \164\ The Flesch-Kincaid grade level readability formula 
analyzes and rates text based on a U.S. grade school educational 
level. The formula uses the average number of words per sentence and 
the average number of syllables per word to generate a result. A 
grade level score of 8.0 means that an eighth grader can understand 
the text. We aim for a grade level of sixth- to eighth-grade level 
for our notices. SSA Program Operations Manual System. NL 10605.105. 
https://secure.ssa.gov/poms.nsf/lnx/0910605105.
---------------------------------------------------------------------------

    Finally, we were unable to determine what the commenter means when 
they suggested the wording of the CoreQ: SS DC survey is potentially 
coercive in nature. The language used in the CoreQ: SS DC measure is 
similar to language found in other survey instruments, including the 
NHCAHPS-D.
    Comment: One commenter was concerned that if the CoreQ: SS DC 
measure was implemented in the SNF QRP, it would overlap considerably 
with a SNF's own satisfaction survey activity. This commenter also 
considers the CoreQ: SS DC measure to be an imperfect gauge of care 
quality. Specifically, they take issue with the question that asks 
whether a resident's discharge needs were met. They are concerned that 
residents may respond based on dissatisfaction with how their discharge 
needs were met based on limitations of their insurance network which 
are beyond the control of the SNF. Therefore, they recommended CMS 
reconsider the elements of the CoreQ questionnaire.
    Response: The CoreQ: SS DC measure could be an adjunct to a SNF's 
own satisfaction survey activity. As described in Chapter 6 of the 
Draft CoreQ: SS DC Short Stay Discharge Survey Protocols and Guidelines 
Manual,\165\ the CoreQ: SS DC measure's set of four primary questions 
and two help-provided questions could be added to existing surveys used 
by SNFs or could be used alone to collect satisfaction information.
---------------------------------------------------------------------------

    \165\ Draft CoreQ SS DC Manual. Located in the Downloads section 
of the SNF QRP Measures and Technical Information web page. https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/nursinghomequalityinits/skilled-nursing-facility-quality-reporting-program/snf-quality-reporting-program-measures-and-technical-information.
---------------------------------------------------------------------------

    Regarding the comment that the CoreQ: SS DC measure is an imperfect 
gauge of care quality, reliability testing results at both the data 
element and the measure level were strong. The CoreQ: SS DC measure has 
a high degree of both face validity and content validity. In response 
to the concern that residents may respond based on dissatisfaction with 
how their discharge needs were met for reasons beyond the control of 
the SNF, we note that during the discharge planning process, it is 
incumbent on SNFs to make reasonable assurances that the resident's 
needs will be met in the next care setting.
    Comment: Several commenters did not support adoption of the CoreQ: 
SS

[[Page 53255]]

DC survey because they found the response scale to be skewed and 
lacking objectivity.
    As described in section VII.C.2.a.(1) of this final rule, the CoreQ 
questionnaires use a 5-point Likert scale, and the number of 
respondents with an average score greater than or equal to 3.0 across 
the four questions is divided by the total number of valid responses to 
yield the SNF's satisfaction score. The five responses options are: 
Excellent (5), Very Good (4), Good (3), Average (2), and Poor (1). 
These commenters objected to the fact that the scale had no middle 
``neutral'' choice and believe this grading system could create bias in 
the survey instrument by leading the resident to a more positive 
response and skews the results to the positive side. One commenter 
questioned what the term ``average'' may mean to a resident who had 
only experienced care in one SNF, and as a result they would not know 
whether the care they received was ``average.'' This commenter was also 
concerned that since the term ``average'' is used as a choice, then all 
the other terms refer to it, so that Good (3), Very Good (4), and 
Excellent (5) must all be better than average under this scoring 
system. Another commenter provided the example that because the middle 
score, Good (3), is a positive response, and not a neutral answer, 
there is only a single negative response (Poor [1]). As a result, they 
believe this methodology overstates positive responses. Another 
commenter pointed out that CAHPS surveys use a top box score 
methodology and other survey-based measures may use a simple mean, but 
the CoreQ: SS DC measure calculates a score by using an unbalanced 
response scale, and only includes data from residents that provide an 
average rating of greater than or equal to three.
    Several of these commenters also quoted the NASEM report which 
noted that consumer advocates and survey methodologists have raised 
concerns that item wording and the choice of response formats may 
increase the tendency of respondents to provide socially appropriate 
response choices and thus provide only minimal variation in the 
scale.\166\
---------------------------------------------------------------------------

    \166\ National Academies of Sciences, Engineering, and Medicine. 
2022. The National Imperative to Improve Nursing Home Quality: 
Honoring Our Commitment to Residents, Families, and Staff. 
Washington, DC: The National Academies Press. https://doi.org/10.17226/26526.
---------------------------------------------------------------------------

    Response: During the development of the CoreQ: SS DC measure, a 
total of 14 different scales were tested, including scales ranging from 
1 to 10. Respondents were asked whether they fully understood how the 
response scale worked, could complete the scale, and in cognitive 
testing understood the scale. The scale used in the CoreQ: SS DC 
measure performed as well or better than the other scales tested.\167\ 
Based on testing conducted by the measure developer at that time, as 
well as since the use of the CoreQ: SS DC measure by interested 
parties, the distribution of CoreQ scores is large, and the measure 
developer has not observed a ceiling effect, which would be expected if 
the scale only allowed for minimal variation in responses.
---------------------------------------------------------------------------

    \167\ Castle NG, Gifford D, Schwartz LB. The CoreQ: Development 
and Testing of a Nursing Facility Resident Satisfaction Survey. J 
Appl Gerontol. 2021 Jun;40(6):629-637. doi: 10.1177/
0733464820940871. Epub 2020 Jul 29. PMID: 32723121.
---------------------------------------------------------------------------

    In response to the comment about how item wording and choice of 
response formats may increase the tendency of responses to provide 
``socially appropriate'' response choices, the NASEM report did not 
reference the CoreQ specifically when making this statement, and it is 
unclear to us how to interpret the statement in the context of our 
proposal.
    Comment: One commenter supported the addition of two questions to 
the four primary questions of the CoreQ: SS DC survey that would allow 
CMS to determine the level of possible intermediary assistance, and 
therefore, exclude only surveys that met the exclusion criteria 
outlined in the draft CoreQ: SS Protocols and Guidelines manual. Two 
commenters were concerned that a significant number of eligible 
residents would be excluded from the measure simply because an adult 
child or neighbor assists with completion of the survey. These 
commenters pointed out that a number of residents served in a SNF face 
limitations and if they need assistance from a family member or trusted 
friend to complete the CoreQ: SS DC survey, they should not be excluded 
from the data files.
    Response: We thank the commenter for their support of the two 
additional helper provided questions to determine the level of possible 
intermediary assistance a resident receives when completing the CoreQ: 
SS DC measure survey. Additionally, just because a resident is assisted 
by an adult child or neighbor does not mean they would automatically be 
excluded. As described in Chapter 8 of the Draft CoreQ: SS DC Protocols 
and Guidelines Manual, a decision algorithm would be used to determine 
whether a CoreQ survey is included or excluded from the CoreQ: SS DC 
measure denominator based on whether a helper completed the survey for 
the resident or whether the helper only assisted the resident due to 
visual, hearing, or motor coordination impairments.\168\ Residents 
would not be automatically excluded just because they required 
assistance with reading the survey, having the survey translated into 
their own primary language, or completing the mailed survey due to 
physical impairments.
---------------------------------------------------------------------------

    \168\ For more details about the decision algorithm, see Chapter 
8 of the Draft CoreQ: SS DC Protocols and Guidelines Manual at 
https://www.cms.gov/files/document/draft-coreq-ss-dc-manual508compliant.pdf.
---------------------------------------------------------------------------

    Comment: Two commenters suggested that most SNF residents require 
in-person interviews for data collection because many residents have 
vision, hearing, and cognitive problems. They stated CMS' plan does not 
allow for adequate data sampling and data collection and could result 
in biased results.
    Response: As discussed in the Draft CoreQ: SS DC Survey Protocols 
and Guidelines Manual,\169\ CMS-approved CoreQ survey vendors would be 
required to offer a toll-free assistance line and an electronic mail 
address which respondents could use to seek help with completing the 
survey. Additionally, residents could ask a family member or friend to 
assist them by reading the survey to them or translating the survey 
into their primary language. Such methods of assisted data collection 
have been used successfully for surveys in other PAC settings, 
including home health agencies.
---------------------------------------------------------------------------

    \169\ Available on the SNF QRP Measures and Technical 
Information web page at https://www.cms.gov/medicare/qualityinitiatives-patient-assessmentinstruments/nursinghomequalityinits/skilled-nursing-facility-qualityreporting-program/snf-quality-reportingprogram-measures-and-technicalinformation.
---------------------------------------------------------------------------

    Comment: Several commenters opposed the use of imputing a response 
to obtain a score when only one of the questions is missing a response. 
One of these commenters noted that imputation for missing data is 
appropriate only if it is assumed that all measures are equivalent or 
redundant to each other and the sum of the remaining responses can 
``stand in'' for missing data. The commenter suggested that if 
individual measures are intended to address unique facets of 
experience, or if different populations or groups of respondents might 
have reason to skip particular items, imputation would be inappropriate 
and misleading. Another one of these commenters suggested that survey 
questionnaires with missing data should be discarded.
    Response: We appreciate the concerns that some commenters may have 
with

[[Page 53256]]

imputation of a missing score. However, the measure developer tested 
the imputation method as part of their overall measure development 
process. Two methods of imputing missing data were tested: (1) using 
the average value from the three available questions as the imputed 
value, and (2) using the lowest value from the three available 
questions as the imputed value. They found that imputing the average 
score or imputing the lowest score had no influence on the overall 
CoreQ measure scores for SNFs.\170\ The measure developer also 
correlated cases with one missing value imputed and cases with no 
missing values with quality indicators (that is, restraint use, 
pressure ulcers, catheter use, antipsychotic use, antidepressant use, 
antianxiety use, use of hypnotics, and deficiency citations). They 
found the correlation with these quality indicators unchanged and 
therefore bias from imputation was minimal.\171\
---------------------------------------------------------------------------

    \170\ Castle NG, Gifford D, Schwartz LB. The CoreQ: Development 
and Testing of a Nursing Facility Resident Satisfaction Survey. J 
Appl Gerontol. 2021 Jun;40(6):629-637. doi: 10.1177/
0733464820940871. Epub 2020 Jul 29. PMID: 32723121. 
CoreQ_Short_Stay_Testing_Final_v7.1_Corrected_4_20_20_FinalforSubmiss
ion-637229958835088042.docx. Available in the measure's 
specifications from the Patient Experience and Function Spring Cycle 
2020 project. https://nqfappservicesstorage.blob.core.windows.net/proddocs/36/Spring/2020/measures/2614/shared/2614.zip.
    \171\ 
CoreQ_Short_Stay_Testing_Final_v7.1_Corrected_4_20_20_FinalforSubmiss
ion-637229958835088042.docx. Available in the measure's 
specifications from the Patient Experience and Function Spring Cycle 
2020 project. https://nqfappservicesstorage.blob.core.windows.net/proddocs/36/Spring/2020/measures/2614/shared/2614.zip.
---------------------------------------------------------------------------

    Comment: While one commenter believed a short stay discharge 
measure is long overdue within the SNF QRP, they stated that CMS should 
first provide additional guidance on how it will benchmark and/or risk-
adjust the measure among SNFs and over time. They stated any final 
methodology must factor in improvements over time, and not just the 
absolute score relative to all SNFs or even a smaller cohort of peers. 
This commenter recommended that CMS also carefully consider whether/
which kinds of SNFs will perform well or poorly depending on multiple 
variables. They stated that facilities in underserved areas with high 
prevalence of social determinants of health (SDOH) and predominated by 
SNFs with lower star ratings will not perform well on measures of 
resident satisfaction, resulting in exacerbation of access in 
underserved communities. Another commenter is concerned that the 
measure is not risk-adjusted.
    Response: As described in section VII.C.2.a.(5)(b) of this final 
rule, the CoreQ: SS DC measure is not risk-adjusted by resident level 
sociodemographic status (SDS) variables, as the measure steward found 
no statistically significant differences (at the 5 percent level) in 
scores between the SDS variables.\172\ We do reevaluate measures 
implemented in the SNF QRP on an ongoing basis to ensure they have 
strong scientific acceptability as well as appropriately capture the 
care provided by SNFs. Lastly, we take the appropriate access to care 
in SNFs very seriously and monitor closely to determine whether new SNF 
QRP measures have unintended consequences on access to care for high-
risk residents.
---------------------------------------------------------------------------

    \172\ The measure developer examined the following SDS 
categories: age, race, gender, and highest level of education. 
CoreQ: Short Stay Discharge Measure.
---------------------------------------------------------------------------

    Comment: One commenter disagreed with how the CoreQ: SS DC measure 
is calculated. They believe that since it only includes respondents 
that have an average score greater than or equal to 3.0 and then 
dividing that number by the total number of valid responses to the 
survey that SNFs will only be incentivized to drive improvement from 
Poor or Average to Good. They stated the methodology used to calculate 
a score for the CoreQ: SS DC measure is inconsistent with the 
calculations of other measures used by CMS and generally viewed as 
statistically unreliable. Another commenter was concerned that the 
CoreQ: SS DC survey focuses less on rating the quality of resident 
experience and more on summative satisfaction ratings.
    Response: We do not agree with the commenter that the CoreQ: SS DC 
measure score will only incentivize SNFs to drive improvement from Poor 
or Average to Good. The CoreQ: SS DC measure is expressed as the 
percentage of the SNF short stay population whose average score is 
three or higher. Other SNF QRP measures are also expressed as the 
percentage of the SNF population who meet or exceed a threshold.\173\
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    \173\ Examples include: (1) The Discharge Self-Care Score 
measure and Discharge Mobility Score measure are expressed as the 
percentage of SNF patients who meet or exceed an expected discharge 
score, and (2) The Drug Regimen Review measure is expressed as the 
number of patients who received a drug regimen review at admission 
and throughout their Part A stay and when a potentially clinically 
significant issue was found, it was addressed bv midnight of the 
next calendar day.
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    We believe that the CBE endorsed CoreQ: SS DC measure has been 
extensively tested and is highly reliable, valid, and reportable, and 
would fill a critical measurement gap within the SNF QRP. However, we 
acknowledge the concerns raised by commenters that the CoreQ: SS DC 
measure may not have enough questions to adequately measure residents' 
satisfaction with the quality of care received by SNFs. We also 
recognize the concerns raised by commenters that finalizing the CoreQ: 
SS DC measure would require SNFs to contract with a survey vendor and 
implement a workflow to create and send a resident information file 
(RIF) to the vendor on a weekly basis. Therefore, after consideration 
of the public comments we received on this proposal, we have decided 
that at this time, we will not finalize the proposal to add the CoreQ: 
SS DC measure beginning with the FY 2026 SNF QRP. However, we remain 
committed to the timely adoption of a meaningful measure that addresses 
resident satisfaction or resident experience for the SNF QRP. As we 
stated in the FY 2024 SNF PPS proposed rule (88 FR 21344), there is 
currently no national standardized satisfaction questionnaire that 
measures a resident's satisfaction with the quality of care received in 
SNFs. While it may require time to conduct further research to identify 
and/or develop a meaningful measure that meets the needs of both SNFs 
and consumers, we intend to propose a resident satisfaction or resident 
experience measure for the SNF QRP in future rulemaking.
b. COVID-19 Vaccine: Percent of Patients/Residents Who Are Up to Date 
Measure Beginning With the FY 2026 SNF QRP
(1) Background
    COVID-19 has been and continues to be a major challenge for PAC 
facilities, including SNFs. The Secretary first declared COVID-19 a PHE 
on January 31, 2020. As of June 19, 2023, the U.S. has reported 103.9 
million cases of COVID-19 and 1.13 million deaths due to COVID-19.\174\ 
Although all age groups are at risk of contracting COVID-19, older 
persons are at a significantly higher risk of mortality and severe 
disease following infection; those over age 80 dying at five times the 
average rate.\175\ Older adults, in general, are prone to both acute 
and chronic infections owing to reduced immunity, and are a high-risk 
population.\176\

[[Page 53257]]

Adults age 65 and older comprise over 75 percent of total COVID-19 
deaths despite representing 13.4 percent of reported cases.\177\ COVID-
19 has impacted older adults' access to care, leading to poorer 
clinical outcomes, as well as taking a serious toll on their mental 
health and well-being due to social distancing.\178\
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    \174\ Centers for Disease Control and Prevention. COVID Data 
Tracker. https://covid.cdc.gov/covid-data-tracker/#cases_totalcases. 
June 19, 2023.
    \175\ United Nations. Policy Brief: The Impact of COVID-19 on 
Older Persons. May 2020. https://unsdg.un.org/sites/default/files/2020-05/Policy-Brief-The-Impact-of-COVID-19-on-Older-Persons.pdf.
    \176\ Lekamwasam R, Lekamwasam S. Effects of COVID-19 Pandemic 
on Health and Wellbeing of Older People: a Comprehensive Review. Ann 
Geriatr Med Res. 2020;24(3):166-172. doi: 10.4235/agmr.20.0027. 
PMID: 32752587; PMCID: PMC7533189.
    \177\ Centers for Disease Control and Prevention. Demographic 
Trends of COVID-19 Cases and Deaths in the U.S. Reported to CDC. 
COVID Data Tracker. https://covid.cdc.gov/covid-data-tracker/#demographics.
    \178\ United Nations. Policy Brief: The Impact of COVID-19 on 
Older Persons. May 2020. https://unsdg.un.org/sites/default/files/2020-05/Policy-Brief-The-Impact-of-COVID-19-on-Older-Persons.pdf.
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    Since the development of the vaccines to combat COVID-19, studies 
have shown they continue to provide strong protection against severe 
disease, hospitalization, and death in adults, including during the 
predominance of Omicron BA.4 and BA.5 variants.\179\ Initial studies 
showed the efficacy of FDA-approved or authorized COVID-19 vaccines in 
preventing COVID-19. Prior to the emergence of the Delta variant of the 
virus, vaccine effectiveness against COVID-19-associated 
hospitalizations among adults age 65 and older was 91 percent for those 
who were fully vaccinated with a full mRNA vaccination (Pfizer-BioNTech 
or Moderna), and 84 percent for those receiving a viral vector vaccine 
(Janssen). Adults age 65 and older who were fully vaccinated with an 
mRNA COVID-19 vaccine had a 94 percent reduction in risk of COVID-19 
hospitalizations, while those who were partially vaccinated had a 64 
percent reduction in risk.\180\ Further, after the emergence of the 
Delta variant, vaccine effectiveness against COVID-19-associated 
hospitalizations for adults who were fully vaccinated was 76 percent 
among adults age 75 and older.\181\
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    \179\ Chalkias S, Harper C, Vrbicky K, et al. A Bivalent 
Omicron-Containing Booster Vaccine Against COVID-19. N Engl J Med. 
2022 Oct 6;387(14):1279-1291. doi: 10.1056/NEJMoa2208343. PMID: 
36112399; PMCID: PMC9511634.
    \180\ Centers for Disease Control and Prevention. Fully 
Vaccinated Adults 65 and Older Are 94% Less Likely to Be 
Hospitalized with COVID-19. April 28, 2021. https://www.cdc.gov/media/releases/2021/p0428-vaccinated-adults-less-hospitalized.html.
    \181\ Interim Estimates of COVID-19 Vaccine Effectiveness 
Against COVID-19-Associated Emergency Department or Urgent Care 
Clinic Encounters and Hospitalizations Among Adults During SARS-CoV-
2 B.1.617.2 (Delta) Variant Predominance--Nine States, June-August 
2021. (Grannis SJ, et al. MMWR Morb Mortal Wkly Rep. 
2021;70(37):1291-1293. doi: 10.15585/mmwr.mm7037e2). https://www.cdc.gov/mmwr/volumes/70/wr/mm7037e2.htm.
---------------------------------------------------------------------------

    More recently, since the emergence of the Omicron variants and the 
availability of booster doses, multiple studies have shown that while 
vaccine effectiveness has waned, protection is higher among those 
receiving booster doses than among those receiving only the primary 
series.182 183 184 CDC data show that, among people age 50 
and older, those who have received both a primary vaccination series 
and booster doses have a lower risk of hospitalization and dying from 
COVID-19 than their non-vaccinated counterparts.\185\ Additionally, a 
second vaccine booster dose has been shown to reduce risk of severe 
outcomes related to COVID-19, such as hospitalization or death, among 
nursing home residents. Nursing home residents who received their 
second booster dose were more likely to have additional protection 
against severe illness compared to those who received only one booster 
dose after their initial COVID-19 vaccination.\186\ Early evidence also 
demonstrates that the bivalent boosters, specifically aimed to provide 
better protection against disease caused by Omicron subvariants, have 
been quite effective, and underscores the role of up to date 
vaccination protocols in effectively countering the spread of COVID-
19.187 188
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    \182\ Surie D, Bonnell L, Adams K, et al. Effectiveness of 
monovalent mRNA vaccines against COVID-19-associated hospitalization 
among immunocompetent adults during BA.1/BA.2 and BA.4/BA.5 
predominant periods of SARS-CoV-2 Omicron variant in the United 
States--IVY Network, 18 States, December 26, 2021-August 31, 2022. 
MMWR Morb Mortal Wkly Rep. 2022;71(42):1327-1334. doi: 10.15585/
mmwr.mm7142a3.
    \183\ Andrews N, Stowe J, Kirsebom F, et al. Covid-19 Vaccine 
Effectiveness against the Omicron (B.1.1.529) Variant. N Engl J Med. 
2022;386(16):1532-1546. doi: 10.1056/NEJMoa2119451. PMID: 35249272; 
PMCID: PMC8908811.
    \184\ Buchan SA, Chung H, Brown KA, et al. Estimated 
Effectiveness of COVID-19 Vaccines Against Omicron or Delta 
Symptomatic Infection and Severe Outcomes. JAMA Netw Open. 
2022;5(9):e2232760. doi:10.1001/jamanetworkopen.2022.32760. PMID: 
36136332; PMCID: PMC9500552.
    \185\ Centers for Disease Control and Prevention. Rates of 
laboratory-confirmed COVID-19 hospitalizations by vaccination 
status. COVID Data Tracker. 2023, February 9. Last accessed March 
22, 2023. https://covid.cdc.gov/covid-data-tracker/#covidnet-hospitalizations-vaccination.
    \186\ Centers for Disease Control and Prevention. COVID-19 
Vaccine Effectiveness Monthly Update. COVID Data Tracker. November 
10, 2022. https://covid.cdc.gov/covid-data-tracker/#vaccine-effectiveness.
    \187\ Chalkias S, Harper C, Vrbicky K, et al. A Bivalent 
Omicron-Containing Booster Vaccine Against COVID-19. N Engl J Med. 
2022 Oct 6;387(14):1279-1291. doi: 10.1056/NEJMoa2208343. PMID: 
36112399; PMCID: PMC9511634.
    \188\ Tan, S.T., Kwan, A.T., Rodr[iacute]guez-Barraquer, I. et 
al. Infectiousness of SARS-CoV-2 breakthrough infections and 
reinfections during the Omicron wave. Nat Med 29, 358-365 (2023). 
https://doi.org/10.1038/s41591-022-02138-x.
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(a) Measure Importance
    Despite the availability and demonstrated effectiveness of COVID-19 
vaccinations, significant gaps continue to exist in vaccination 
rates.\189\ As of March 22, 2023, vaccination rates among people age 65 
and older are generally high for the primary vaccination series (94.3 
percent) but lower for the first booster (73.6 percent among those who 
received a primary series) and even lower for the second booster (59.9 
percent among those who received a first booster).\190\ Additionally, 
though the uptake in boosters among people age 65 and older has been 
much higher than among people of other ages, booster uptake still 
remains relatively low compared to primary vaccination among older 
adults.\191\ Variations are also present when examining vaccination 
rates by race, gender, and geographic location.\192\ For example, 66.2 
percent of the Asian, non-Hispanic population have completed the 
primary series and 21.2 percent have received a bivalent booster dose, 
whereas 44.9 percent of the Black, non-Hispanic population have 
completed the primary series and only 8.9 percent have received the 
bivalent booster dose. Among Hispanic populations, 57.1 percent of the 
population have completed the primary series and 8.5 percent have 
received the bivalent booster dose, while in White, non-Hispanic 
populations, 51.9 percent have completed the primary series and 16.2 
percent have received a bivalent

[[Page 53258]]

booster dose.\193\ Disparities have been found in vaccination rates 
between rural and urban areas, with lower vaccination rates found in 
rural areas.194 195 Data show that 55.2 percent of the 
eligible population in rural areas have completed the primary 
vaccination series, as compared to 66.5 percent of the eligible 
population in urban areas.\196\ Receipt of bivalent booster doses among 
those eligible has been lower: 18 percent of the urban population have 
received a booster dose, and 11.5 percent of the rural population have 
received a booster dose.\197\
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    \189\ Centers for Disease Control and Prevention. COVID-19 
Vaccinations in the United States. COVID Data Tracker. https://covid.cdc.gov/covid-data-tracker/#vaccinations_vacc-people-booster-percent-pop5.
    \190\ Centers for Disease Control and Prevention. COVID-19 
Vaccination Age and Sex Trends in the United States, National and 
Jurisdictional. https://data.cdc.gov/Vaccinations/COVID-19-Vaccination-Age-and-Sex-Trends-in-the-Uni/5i5k-6cmh.
    \191\ Freed M, Neuman T, Kates J, Cubanski J. Deaths Among Older 
Adults Due to COVID-19 Jumped During the Summer of 2022 Before 
Falling Somewhat in September. Kaiser Family Foundation. October 6, 
2022. https://www.kff.org/coronavirus-covid-19/issue-brief/deaths-among-older-adults-due-to-covid-19-jumped-during-the-summer-of-2022-before-falling-somewhat-in-september/.
    \192\ Saelee R, Zell E, Murthy BP, et al. Disparities in COVID-
19 Vaccination Coverage Between Urban and Rural Counties--United 
States, December 14, 2020-January 31, 2022. MMWR Morb Mortal Wkly 
Rep. 2022;71:335-340. doi: 10.15585/mmwr.mm7109a2.
    \193\ Centers for Disease Control and Prevention. Trends in 
Demographic Characteristics of People Receiving COVID-19 
Vaccinations in the United States. COVID Data Tracker. 2023, January 
20. Last accessed January 17, 2023. https://covid.cdc.gov/covid-data-tracker/#vaccination-demographics-trends.
    \194\ Saelee R, Zell E, Murthy BP, et al. Disparities in COVID-
19 Vaccination Coverage Between Urban and Rural Counties--United 
States, December 14, 2020-January 31, 2022. MMWR Morb Mortal Wkly 
Rep. 2022;71:335-340. doi: 10.15585/mmwr.mm7109a2.
    \195\ Sun Y, Monnat SM. Rural-Urban and Within-Rural Differences 
in COVID-19 Vaccination Rates. J Rural Health. 2022;38(4):916-922. 
doi: 10.1111/jrh.12625. PMID: 34555222; PMCID: PMC8661570.
    \196\ Centers for Disease Control and Prevention. Vaccination 
Equity. COVID Data Tracker; 2023, January 20. Last accessed January 
17, 2023. https://covid.cdc.gov/covid-data-tracker/#vaccination-equity.
    \197\ Centers for Disease Control and Prevention. Vaccination 
Equity. COVID Data Tracker; 2023, January 20. Last accessed January 
17, 2023. https://covid.cdc.gov/covid-data-tracker/#vaccination-equity.
---------------------------------------------------------------------------

    We proposed to adopt the COVID-19 Vaccine: Percent of Patients/
Residents Who Are Up to Date (Patient/Resident COVID-19 Vaccine) 
measure for the SNF QRP beginning with the FY 2026 SNF QRP. The 
proposed measure has the potential to increase COVID-19 vaccination 
coverage of residents in SNFs, as well as prevent the spread of COVID-
19 within the SNF resident population. This measure would also support 
the goal of the CMS Meaningful Measure Initiative 2.0 to ``Empower 
consumers to make good health care choices through patient-directed 
quality measures and public transparency objectives.'' The proposed 
Patient/Resident COVID-19 Vaccine measure would be reported on Care 
Compare and would provide residents and caregivers, including those who 
are at high risk for developing serious complications from COVID-19, 
with valuable information they can consider when choosing a SNF. The 
proposed Patient/Resident COVID-19 Vaccine measure would also 
facilitate resident care and care coordination during the hospital 
discharge planning process. A discharging hospital, in collaboration 
with the resident and family, could use this proposed measure's 
information on Care Compare to coordinate care and ensure resident 
preferences are considered in the discharge plan. Additionally, the 
proposed Patient/Resident COVID-19 Vaccine measure would be an indirect 
measure of SNF action. Since the resident's COVID-19 vaccination status 
would be reported at discharge from the SNF, if a resident is not up to 
date with their COVID-19 vaccine per applicable CDC guidance at the 
time they are admitted, the SNF has the opportunity to educate the 
resident and provide information on why they should become up to date 
with their COVID-19 vaccine. SNFs may also choose to administer the 
vaccine to the resident prior to their discharge from the SNF or 
coordinate a follow-up visit for the resident to obtain the vaccine at 
their physician's office or local pharmacy.
(b) Item Testing
    Our measure development contractor conducted testing of the 
proposed standardized patient/resident COVID-19 vaccination coverage 
assessment item for the Patient/Resident COVID-19 Vaccine measure using 
resident scenarios, draft guidance manual coding instructions, and 
cognitive interviews to assess SNFs' comprehension of the item and the 
associated guidance. A team of clinical experts assembled by our 
measure development contractor developed these resident scenarios to 
represent the most common scenarios that SNFs would encounter. The 
results of the item testing demonstrated that SNFs that used the draft 
guidance manual coding instructions had strong agreement (that is, 84 
percent) with the correct responses, supporting its reliability. The 
testing also provided information to improve both the item itself and 
the accompanying guidance.
(2) Competing and Related Measures
    Section 1899B(e)(2)(A) of the Act requires that, absent an 
exception under section 1899B(e)(2)(B) of the Act, each measure 
specified under section 1899B of the Act be endorsed by a CBE with a 
contract under section 1890(a) of the Act. In the case of a specified 
area or medical topic determined appropriate by the Secretary for which 
a feasible and practical measure has not been endorsed, section 
1899B(e)(2)(B) of the Act permits the Secretary to specify a measure 
that is not so endorsed, as long as due consideration is given to the 
measures that have been endorsed or adopted by a CBE identified by the 
Secretary. The proposed Patient/Resident COVID-19 Vaccine measure is 
not CBE endorsed and, after review of other measures endorsed or 
adopted by consensus organizations, we were unable to identify any 
measures endorsed or adopted by consensus organizations for SNFs 
focused on capturing COVID-19 vaccination coverage of SNF residents. We 
found only one related measure addressing COVID-19 vaccination, the 
COVID-19 Vaccination Coverage among Healthcare Personnel (HCP) measure, 
adopted for the FY 2023 SNF QRP (86 FR 42480 through 42489), which 
captures the percentage of HCP who receive a complete COVID-19 primary 
vaccination series, but not booster doses.
    Although SNFs' COVID-19 vaccination rates are posted on Care 
Compare, these data are aggregated at the facility level, and SNFs are 
not required to report beneficiary-level data to the CDC's NHSN. The 
COVID-19 vaccination rates currently posted on Care Compare are 
obtained from CDC's NHSN and reflect ``residents who completed primary 
vaccination series'' and ``residents who are up-to-date on their 
vaccines'' across the entire nursing home (NH) resident population. 
Residents receiving SNF care under the Medicare fee-for-service program 
differ from residents receiving long-term care in nursing homes in 
several ways. SNF residents typically enter the facility after an 
inpatient hospital stay for temporary specialized post-acute care, 
while NH residents typically have chronic or progressive medical 
conditions, requiring maintenance and supportive levels of care, and 
may reside in the NH for years. Additionally, the SNF QRP includes data 
submitted by non-CAH swing bed units whose data are only represented 
through the SNF QRP and are not included in the COVID-19 vaccination 
data reported to the NHSN by nursing homes. The proposed Patient/
Resident COVID-19 Vaccine measure would be calculated using data 
collected on the MDS (as described in section VI.F.4. of the FY 2024 
SNF proposed rule) at the beneficiary level, which would enhance SNFs' 
ability to monitor their own infection prevention efforts with 
information on which they can act.
    Additionally, the COVID-19 reporting requirements set forth in 42 
CFR 483.80(g), finalized in the interim final rule with comment period 
(IFC) published on May 13, 2021 entitled ``Medicare and Medicaid 
Programs; COVID-19 Vaccine Requirements for Long-Term Care (LTC) 
Facilities and Intermediate Care Facilities for Individuals with 
Intellectual Disabilities (ICFs-IID) Residents, Clients, and Staff''

[[Page 53259]]

(86 FR 26315 through 26316) (hereafter referred to as the May 2021 IFC) 
are directed at the LTC facilities' requirements and are separate from 
the SNF QRP. The purpose of the May 2021 IFC was to collect information 
which would allow the CDC to identify and alert us to facilities that 
may need additional support in regard to vaccine administration and 
education. While the COVID-19 staff vaccination requirements are being 
withdrawn from the Conditions of Participation, SNFs must continue to 
educate and offer the COVID-19 vaccine to their residents, clients, and 
staff, as well as perform the appropriate documentation for these 
activities.\198\
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    \198\ Medicare and Medicaid Programs; Policy and Regulatory 
Changes to the Omnibus COVID-19 Health Care Staff Vaccination 
Requirements; Additional Policy and Regulatory Changes to the 
Requirements for Long-Term Care (LTC) Facilities and Intermediate 
Care Facilities for Individuals With Intellectual Disabilities 
(ICFs-IID) To Provide COVID-19 Vaccine Education and Offer 
Vaccinations to Residents, Clients, and Staff; Policy and Regulatory 
Changes to the Long Term Care Facility COVID-19 Testing Requirements 
(88 FR 36502).
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    The purpose of the proposed Patient/Resident COVID-19 Vaccine 
measure is to allow for the collection of resident vaccination data 
under the SNF QRP and subsequent public reporting of SNFs' facility-
level resident vaccination rates on Care Compare so that Medicare 
beneficiaries who require short stays can make side-by-side SNF 
comparisons. Adoption of the proposed measure would also promote 
measure harmonization across quality reporting programs and provide 
Medicare beneficiaries the information to make side-by-side comparisons 
across other facility types to facilitate informed decision making in 
an accessible and user-friendly manner. Finally, the proposed Patient/
Resident COVID-19 Vaccine measure would generate actionable data on 
vaccination rates that can be used to target quality improvement among 
SNFs.
    Therefore, after consideration of other available measures that 
assess COVID-19 vaccination rates among SNF residents, we believe the 
exception under section 1899B(e)(2)(B) of the Act applies. We intend to 
submit the proposed measure to the CBE for consideration of endorsement 
when feasible.
(3) Interested Parties and Technical Expert Panel (TEP) Input
    First, the measure development contractor convened a focus group of 
patient and family/caregiver advocates (PFAs) to solicit input. The 
PFAs believed a measure capturing raw vaccination rate, irrespective of 
SNF action, would be most helpful in resident and caregiver decision-
making. Next, TEP meetings were held on November 19, 2021, and December 
15, 2021 to solicit feedback on the development of patient/resident 
COVID-19 vaccination measures and assessment items for the PAC 
settings. The TEP panelists voiced their support for PAC patient/
resident COVID-19 vaccination measures and agreed that developing a 
measure to report the rate of vaccination in a SNF/NH setting without 
denominator exclusions was an important goal. We considered the TEP's 
recommendations, and we applied the recommendations, where technically 
feasible and appropriate. A summary of the TEP proceedings titled 
Technical Expert Panel (TEP) for the Development of Long-Term Care 
Hospital (LTCH), Inpatient Rehabilitation Facility (IRF), Skilled 
Nursing Facility (SNF)/Nursing Facility (NF), and Home Health (HH) 
COVID-19 Vaccination-Related Items and Measures Summary Report \199\ is 
available on the CMS MMS Hub.
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    \199\ Technical Expert Panel (TEP) for the Development of Long-
Term Care Hospital (LTCH), Inpatient Rehabilitation Facility (IRF), 
Skilled Nursing Facility (SNF)/Nursing Facility (NF), and Home 
Health (HH) COVID-19 Vaccination-Related Items and Measures Summary 
Report is available on the CMS MMS Hub at https://mmshub.cms.gov/sites/default/files/COVID19-Patient-Level-Vaccination-TEP-Summary-Report-NovDec2021.pdf.
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    To seek input on the importance, relevance, and applicability of a 
patient/resident COVID-19 vaccination coverage measure, we solicited 
public comments in an RFI for publication in the FY 2023 SNF PPS 
proposed rule (87 FR 42424). Commenters were mixed on whether they 
supported the concept of a measure addressing COVID-19 vaccination 
coverage among SNF residents. Two commenters noted the measure should 
account for other variables, such as whether the vaccine was offered, 
as well as excluding residents with medical contraindications to the 
vaccine (87 FR 47553).
(4) Measure Applications Partnership (MAP) Review
    In accordance with section 1890A of the Act, the pre-rulemaking 
process includes making publicly available a list of quality and 
efficiency measures, called the Measures Under Consideration (MUC) 
List, that the Secretary is considering adopting for use in Medicare 
programs. This allows interested parties to provide recommendations to 
the Secretary on the measures included on the list. The Patient/
Resident COVID-19 Vaccine measure was included on the publicly 
available 2022 MUC List for the SNF QRP.\200\
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    \200\ CMS Measures Management System (MMS). Measure 
Implementation: Pre-rulemaking MUC Lists and Recommendation Reports. 
https://mmshub.cms.gov/measure-lifecycle/measure-implementation/pre-rulemaking/lists-and-reports.
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    After the MUC List was published, MAP received seven comments by 
interested parties during the measure's MAP pre-rulemaking process. 
Commenters were mostly supportive of the measure and recognized the 
importance of resident COVID-19 vaccination, and that measurement and 
reporting is one important method to help healthcare organizations 
assess their performance in achieving high rates of up to date 
vaccination. One commenter also noted that resident engagement is 
critical at this stage of the pandemic because best available 
information indicates COVID-19 variants will continue to require 
additional boosters to avert case surges. Another commenter noted the 
benefit of less-specific criteria for inclusion in the numerator and 
denominator of the proposed Patient/Resident COVID-19 Vaccine measure, 
which would provide flexibility for the measure to remain relevant to 
current circumstances. Several commenters noted their conditional 
support, however, and raised several issues about the measure. 
Specifically, one questioned whether our intent was to replace the 
required NHSN reporting if this measure were finalized and noted it did 
not collect data on Medicare Advantage residents. Another commenter 
suggested that nursing homes might refuse to admit unvaccinated 
residents, and was concerned about the costs SNFs would incur 
purchasing the vaccines. Another commenter raised concerns about the 
measure since it did not directly measure provider actions to increase 
vaccine uptake in the numerator and that it would only collect 
vaccination information on Medicare fee-for-service residents, rather 
than all residents, regardless of payer. Finally, one commenter was 
concerned because there were no exclusions for residents who refused to 
become up to date with their COVID-19 vaccination.
    Subsequently, several MAP workgroups met to provide input on the 
measure. First, the MAP Health Equity Advisory Group convened on 
December 6, 2022. One MAP Health Equity Advisory Group member noted 
that the percentage of true contraindications for the COVID-19 vaccine 
is low, and the lack of exclusions on the measure is reasonable in 
order to minimize

[[Page 53260]]

variation in what constitutes a contraindication.\201\ The MAP Rural 
Health Advisory Group met on December 8, 2022, and requested 
clarification of the term ``up to date'' and noted concerns with the 
perceived level of burden for collection of data.\202\
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    \201\ CMS Measures Management System (MMS). Measure 
Implementation: Pre-rulemaking MUC Lists and Recommendation Reports. 
https://mmshub.cms.gov/measure-lifecycle/measure-implementation/pre-rulemaking/lists-and-reports.
    \202\ CMS Measures Management System (MMS). Measure 
Implementation: Pre-rulemaking MUC Lists and MAP reports. https://mmshub.cms.gov/measure-lifecycle/measure-implementation/pre-rulemaking/lists-and-reports.
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    Next, the MAP PAC/LTC workgroup met on December 12, 2022. The 
voting workgroup members noted the importance of reporting residents' 
vaccination status, but discussed their concerns about: (1) the 
duplication of data collection with the NHSN if an assessment-based 
measure were adopted into the SNF QRP; (2) how publicly reported rates 
would differ from the rates reported by the NHSN; (3) that the Patient/
Resident COVID-19 Vaccine measure does not account for resident 
refusals or those who are unable to respond; and (4) the difficulty of 
implementing the definition of ``up to date.'' We clarified during the 
PAC/LTC workgroup meeting that this measure was intended to only 
include Medicare Part A-covered SNF stays. We further noted that the 
proposed Patient/Resident COVID-19 Vaccine measure does not have 
exclusions for resident refusals because the proposed measure was 
intended to report raw rates of vaccination. We explained that raw 
rates of vaccination collected by the proposed Patient/Resident COVID-
19 Vaccine measure are important for consumer choice and PAC providers, 
including SNFs, are in a unique position to leverage their care 
processes to increase vaccination coverage in their settings to protect 
residents and prevent negative outcomes. We also clarified that the 
measure defines ``up to date'' in a manner that provides flexibility to 
reflect future changes in the CDC's guidance with respect to COVID-19 
vaccination. Finally, we clarified that, like the existing HCP COVID-19 
Vaccine measure, this measure would continue to be reported quarterly 
because the CDC has not yet determined whether COVID-19 is seasonal. 
Ultimately, the PAC/LTC workgroup did not achieve a 60 percent 
consensus vote to accept the CBE's preliminary analysis assessment of 
conditional support for the Patient/Resident COVID-19 Vaccine measure 
for SNF QRP rulemaking pending testing demonstrating the measure is 
reliable and valid, and CBE endorsement.\203\ Since the PAC/LTC 
workgroup did not reach consensus to accept, or subsequently to 
overturn the CBE staff's preliminary analysis assessment, the 
preliminary analysis assessment became the final recommendation of the 
PAC/LTC workgroup.
---------------------------------------------------------------------------

    \203\ National Quality Forum MAP Post-Acute Care/Long Term Care 
Workgroup Materials. Meeting Summary--MUC Review Meeting. Accessed 
January 20, 2023. https://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=97960.
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    The CBE received 10 comments by interested parties in response to 
the PAC/LTC workgroup recommendations. Interested parties generally 
understood the importance of COVID-19 vaccinations' role in preventing 
the spread of COVID-19 infections, although a majority of commenters 
did not recommend the inclusion of the proposed Patient/Resident COVID-
19 Vaccine measure in the SNF QRP and raised several concerns. 
Specifically, several commenters were concerned about vaccine 
hesitancy, SNFs' inability to influence measure results based on 
factors outside of their control, duplication with NHSN reporting 
requirements, data lag in public reporting of QRP data relative to 
NHSN's current reporting of the measure, and that the proposed Patient/
Resident COVID-19 Vaccine measure is not representative of the full SNF 
population, noting that the proposed Patient/Resident COVID-19 Vaccine 
measure has not been fully tested, and encouraged us to monitor the 
measure for unintended consequences and ensure that the measure has 
meaningful results. One commenter was in support of the proposed 
Patient/Resident COVID-19 Vaccine measure and provided recommendations 
for us to consider, including an exclusion for medical 
contraindications and submitting the measure for CBE endorsement. 
Another commenter questioned why the PAC/LTC workgroup recommendation 
for SNF was not consistent with their recommendation for the proposed 
Patient/Resident COVID-19 Vaccine measure in other PAC QRPs.
    Finally, the MAP Coordinating Committee convened on January 24, 
2023, and noted concerns which were previously discussed in the PAC/LTC 
workgroup, such as the duplication of NHSN reporting requirements and 
potential for selection bias based on the resident's vaccination 
status. We were able to clarify that this measure was intended to 
include only Medicare Part A-covered SNF stays for facilities required 
to report to the SNF QRP, since the Medicare Advantage resident 
population is not part of the SNF QRP reporting requirements. We also 
noted that this measure does not have exclusions for resident refusals 
since this is a process measure intended to report raw rates of 
vaccination and is not intended to be a measure of SNFs' actions. We 
acknowledged that a measure accounting for variables, such as SNFs' 
actions to vaccinate residents, could be important, but noted that we 
are focused on a measure which would provide and publicly report 
vaccination rates for consumers given the importance of this 
information to residents and their caregivers.
    The MAP Coordinating Committee recommended three mitigation 
strategies for the Patient/Resident COVID-19 Vaccine measure: (i) 
reconsider exclusions for medical contraindications, (ii) complete 
reliability and validity measure testing, and (iii) seek CBE 
endorsement. The Coordinating Committee ultimately reached 90 percent 
consensus on its recommendation of ``Do not Support with potential for 
mitigation.'' \204\ Despite the MAP Coordinating Committee's vote, we 
believe it is still important to propose the Patient/Resident COVID-19 
Vaccine measure for the SNF QRP. As we stated in section VI.C.2.b.(3) 
of the FY 2024 SNF PPS proposed rule, we did not include exclusions for 
medical contraindications because the PFAs we met with told us that a 
measure capturing raw vaccination rate, irrespective of any medical 
contraindications, would be most helpful in resident and family/
caregiver decision-making. We do plan to conduct reliability and 
validity measure testing once we have collected enough data, and we 
intend to submit the proposed measure to the CBE for consideration of 
endorsement when feasible. We refer readers to the final MAP 
recommendations, titled 2022-2023 MAP Final Recommendations.\205\
---------------------------------------------------------------------------

    \204\ National Quality Forum Measure Applications Partnership. 
2022-2023 MAP Final Recommendations. https://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=98102.
    \205\ 2022-2023 MAP Final Recommendations. https://mmshub.cms.gov/measure-lifecycle/measure-implementation/pre-rulemaking/lists-and-reports.
---------------------------------------------------------------------------

(5) Quality Measure Calculation
    The proposed Patient/Resident COVID-19 Vaccine measure is a process 
measure that reports the percent of stays in which residents in a SNF 
are up to date on their COVID-19 vaccinations

[[Page 53261]]

per the CDC's latest guidance.\206\ This measure has no exclusions, and 
is not risk adjusted.
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    \206\ The definition of ``up to date'' may change based on CDC's 
latest guidelines and can be found on the CDC web page, ``Stay Up to 
Date with COVID-19 Vaccines Including Boosters,'' at https://www.cdc.gov/coronavirus/2019-ncov/vaccines/stay-up-to-date.html 
(updated January 9, 2023).
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    The numerator for this measure would be the total number of 
Medicare Part A-covered SNF stays in which residents are up to date 
with their COVID-19 vaccine per CDC's latest guidance during the 
reporting year. The denominator for this measure would be the total 
number of Medicare Part A-covered SNF stays discharged during the 
reporting period. For the SNF QRP, this would apply to all freestanding 
SNFs, SNFs affiliated with acute care facilities, and all non-CAH 
swing-bed rural hospitals.
    The data source for the proposed Patient/Resident COVID-19 Vaccine 
measure is the MDS assessment instrument for SNF residents. For more 
information about the proposed data submission requirements for this 
measure, we refer readers to section VII.F.4. of this final rule. For 
additional technical information about this proposed measure, we refer 
readers to the draft measure specifications document titled Patient-
Resident-COVID-Vaccine-Draft-Specs.pdf \207\ available on the SNF QRP 
Measures and Technical Information web page.
---------------------------------------------------------------------------

    \207\ Patient-Resident-COVID-Vaccine-Draft-Specs.pdf. https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/nursinghomequalityinits/skilled-nursing-facility-quality-reporting-program/snf-quality-reporting-program-measures-and-technical-information.
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    We solicited public comments on our proposal to adopt the Patient/
Resident COVID-19 Vaccine measure beginning with the FY 2026 SNF QRP. 
The following is a summary of the comments we received on our proposal 
to adopt the Patient/Resident COVID-19 Vaccine measure beginning with 
the FY 2026 SNF QRP and our responses.
    Comment: A number of commenters supported the adoption of this 
measure into the SNF QRP because of the importance to the safety of 
residents. Commenters agreed that this measure would provide another 
source of valuable information to current and prospective SNF residents 
and their family/caregivers in their decision-making process. One 
commenter suggested that rather than remaining specific to COVID-19, 
the measure could be revised to include all CDC-recommended vaccines. 
Two commenters also appreciated that collection of this data would only 
require minimal burden since it consists of only one MDS item on the 
discharge assessment and the item is similar to the existing resident 
influenza vaccination item.
    Response: We thank the commenters for their support and agree that 
the Patient/Resident COVID-19 Vaccine measure would provide residents 
and caregivers, including those who are at high risk for developing 
serious complications from COVID-19, with valuable information they can 
consider when choosing a SNF. We also agree with the commenter that the 
measure would not add significant burden since the data item would 
consist of a single MDS item and SNFs would be able to use multiple 
sources of information available to obtain the vaccination data, such 
as resident interviews, medical records, proxy response, and 
vaccination cards provided by the resident or their caregivers. We 
would also publish coding guidance for the new item and SNFs will also 
have access to guidance from the CDC to further aid their collection of 
these data.\208\ Finally, we appreciate the commenter's suggestion that 
the measure could be revised to include all CDC-recommended vaccines 
and will use this input to inform our future measure development 
efforts.
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    \208\ COVID-19 Vaccine: Percent of Patients/Residents Who Are Up 
to Date Draft Measure Specifications. https://www.cms.gov/files/document/patient-resident-covid-vaccine-draft-specs.pdf.
---------------------------------------------------------------------------

    Comment: Several commenters stated that the proposed measure was 
not a measure of quality of care because it did not reflect provider 
action. They noted that there may be medical, religious, and/or 
cultural reasons for a resident's decision not to receive a vaccine 
that are out of a SNF's control. One commenter noted that it is 
possible for a SNF to have a robust effort to encourage vaccination 
among its patients/residents, but still have a relatively low rate of 
vaccination. Another commenter noted that resident vaccination may also 
be influenced by political beliefs and the political environment in a 
resident's region. One commenter noted that continuing disparities in 
vaccine uptake do not reflect the local SNFs' efforts to bring their 
residents up to date, but often reflect differences deeply rooted in 
culture, religion, ethnicity, socioeconomic status, and more. Some 
commenters pointed out that residents have the right to refuse 
vaccination, in the same way they have the right to refuse other 
medical and nursing interventions.
    Response: While we agree with the commenters that residents have 
the right to refuse vaccination, we disagree with the commenters who 
suggested the proposed Patient/Resident COVID-19 Vaccine measure is an 
invalid measure of quality of care. On the contrary, we believe it 
would be a beneficial addition to the other vaccination measures in the 
SNF QRP. We believe it is an indirect measure of provider action since 
SNFs have the opportunity to encourage, as well as coordinate, 
vaccinations among residents. This is particularly important for 
residents at SNFs, who tend to be older and thus more vulnerable to 
serious complications from COVID-19. CDC data show that, among people 
age 50 and older, those who have received both a primary vaccination 
series and booster doses have a lower risk of hospitalization and dying 
from COVID-19 than their non-vaccinated counterparts.\209\ 
Additionally, a second vaccine booster dose has been shown to reduce 
risk of severe outcomes related to COVID-19, such as hospitalization or 
death, among nursing home residents. Nursing home residents who 
received their second booster dose were more likely to have additional 
protection against severe illness compared to those who received only 
one booster dose after their initial COVID-19 vaccination.\210\
---------------------------------------------------------------------------

    \209\ Centers for Disease Control and Prevention. Rates of 
laboratory-confirmed COVID-19 hospitalizations by vaccination 
status. COVID Data Tracker. 2023, February 9. Last accessed March 
22, 2023. https://covid.cdc.gov/covid-data-tracker/#covidnet-hospitalizations-vaccination.
    \210\ Centers for Disease Control and Prevention. COVID-19 
Vaccine Effectiveness Monthly Update. COVID Data Tracker. November 
10, 2022. https://covid.cdc.gov/covid-data-tracker/#vaccine-effectiveness.
---------------------------------------------------------------------------

    We acknowledge that individual residents have a choice regarding 
whether to receive a COVID-19 vaccine or booster dose(s), but residents 
and their caregivers also have choices about selecting PAC providers, 
and it is our role to empower them with the information they need to 
make an informed decision by publicly reporting the data we receive 
from SNFs on this measure. We understand that despite a SNF's best 
efforts, there may be instances where a resident may choose not to 
receive a booster dose of the COVID-19 vaccine. However, we want to 
remind SNFs that this measure does not mandate residents be up to date 
with their COVID-19 vaccine. The number of residents who have been 
vaccinated in a SNF does not impact a SNF's ability to successfully 
report the measure to comply with the requirements of the SNF QRP. 
Finally, we do appreciate SNFs' commitment and efforts at ensuring 
residents are educated and encouraged to become and

[[Page 53262]]

remain up to date with their COVID-19 vaccinations.
    Comment: One commenter noted that, while some SNFs have been 
extremely successful, especially with their long-stay residents, in 
having a high degree of acceptance of the COVID-19 vaccines throughout 
the last 3 years, this success is not a proxy for providing the actual 
care and services a resident has come to the SNF to receive. Another 
commenter noted that CMS's statement ``SNFs could choose to administer 
the vaccine to the resident prior to discharge'' seemed to indicate 
that vaccination is a SNF's choice, and not a resident's choice.
    Response: The primary intent of the Patient/Resident COVID-19 
Vaccine measure is to promote transparency of raw data regarding COVID-
19 vaccination rates for residents and their caregivers to make 
informed decisions for selecting facilities. This measure will provide 
potential residents and their caregivers with an important piece of 
information regarding vaccination rates as part of their process of 
identifying SNFs they would want to seek care from, alongside other 
measures available on Care Compare, to make an informed, comprehensive 
decision. In response to the comment about our statement in the 
proposed rule that seemed to indicate vaccination is a SNF's choice, 
and not a resident's choice, we appreciate the opportunity to clarify 
the statement. We acknowledge and support a resident's choice about 
whether to receive an up to date vaccine. Our statement was meant to 
convey that the SNF could work with the resident to determine the most 
appropriate approach for them.
    Comment: One commenter noted that sometimes patients/residents may 
not have the opportunity to ``shop'' for a facility outside of their 
region simply based on the COVID-19 vaccinations rates. They noted that 
insurance and proximity to loved ones are often the drivers for 
selecting a post-acute care facility.
    Response: We acknowledge that sometimes residents may not have 
access to as many SNF choices as others. However, we believe that the 
information provided by this measure will still be valuable to 
potential SNF residents/caregivers who may have geographic limitations.
    Comment: One commenter noted that vaccination administration rates 
can ebb and flow significantly based on factors outside the control of 
SNFs, including holidays, weather, vaccine/pharmaceutical supply chain 
management, staff availability and more.
    Response: We are unaware of any access issues to COVID-19 vaccines 
or vaccine production delays. While we believe SNFs will be able to 
administer the COVID-19 vaccine if a resident consents, this measure 
does not require SNFs to administer the vaccine themselves. They could 
arrange for the resident to obtain the vaccine outside of their 
facility, or work with community pharmacies to obtain vaccines.
    Comment: One commenter agreed with CMS's proposed justification 
that the measure has the potential to drive COVID-19 vaccination uptake 
among SNF residents and prevent the spread of COVID-19 in the SNF 
population and agreed that the measure could help empower consumers in 
making decisions about their care. Despite this, they still urged CMS 
to ensure that measures are appropriately specified and adequately 
tested and validated prior to implementation. This commenter also noted 
that unlike the proposed HCP COVID-19 Vaccine measure, the 
specifications for this Patient/Resident COVID-19 Vaccine measure 
solely reference the definition of up to date as described on CDC's 
``Stay Up to Date'' website. Even though this definition more 
accurately reflects the most current Advisory Committee on Immunization 
Practices (ACIP) recommendation, the commenter urged CMS to ensure that 
this approach to specifying measures is valid and will not serve to 
cause confusion or reporting challenges in the future.
    However, several commenters did not support the proposal due to the 
measure not being fully tested for reliability and validity, and one 
commenter raised concerns about the feasibility to report this measure 
as well as the measure's ability to produce statistically meaningful 
information.
    Response: We are pleased that the commenter agrees with our 
proposed rationale that the measure has the potential to drive COVID-19 
vaccination uptake among SNF residents, prevent the spread of COVID-19 
in the SNF population, and empower consumers in making decisions about 
their care.
    While we acknowledge that we have not yet tested the measure for 
reliability and validity, we have tested the item proposed for the MDS 
to capture data for this measure and its feasibility and 
appropriateness. Since a COVID-19 vaccination item does not yet exist 
within the MDS, we developed clinical vignettes to test item-level 
reliability of a draft Patient/Resident COVID-19 Vaccine measure. The 
clinical vignettes were a proxy for resident records with the most 
common and challenging cases SNFs would encounter, similar to the 
approach that we use to train SNFs on all new assessment items, and the 
results demonstrated strong agreement (that is, 84 percent).
    Validity testing has not yet been completed, since the COVID-19 
vaccination item does not yet exist on the MDS. However, the Patient/
Resident COVID-19 Vaccine measure was constructed based on prior use of 
similar items, such as the Percent of Residents or Patients Who Were 
Assessed and Appropriately Given the Seasonal Influenza Vaccine (Short 
Stay) for the IRF and LTCH QRPs.\211\ Four Nursing Home Quality 
Initiative (NHQI) pneumococcal vaccination measures also use similar 
item construction. We have used these types of patient/resident 
vaccination assessment items in the calculation of vaccination quality 
measures in our PAC QRPs and intend to conduct reliability and validity 
testing for this specific Patient/Resident COVID-19 Vaccine measure 
once the COVID-19 vaccination item has been added to the MDS and we 
have collected sufficient data. Additionally, we solicited feedback 
from our Technical Expert Panel (TEP) on the proposed assessment item 
and its feasibility. No concerns were raised by the TEP regarding 
obtaining the information that would be required to complete the new 
COVID-19 vaccination item.\212\
---------------------------------------------------------------------------

    \211\ 78 FR 47859 and 77 FR 53257.
    \212\ Technical Expert Panel (TEP) for the Development of Long-
Term Care Hospital (LTCH), Inpatient Rehabilitation Facility (IRF), 
Skilled Nursing Facility (SNF)/Nursing Facility (NF), and Home 
Health (HH) COVID-19 Vaccination-Related Items and Measures Summary 
Report. https://mmshub.cms.gov/sites/default/files/COVID19-Patient-Level-Vaccination-TEP-Summary-Report-NovDec2021.pdf.
---------------------------------------------------------------------------

    Comment: Several commenters did not support the measure and pointed 
to the fact that the MAP Coordinating Committee reached 90 percent 
consensus on its recommendation of ``do not support with potential for 
mitigation'' when evaluating this proposed measure. Two of these 
commenters also urged CMS to delay adoption of the measure until 
concerns raised by the MAP Coordinating Committee have been addressed. 
Specifically, they encouraged CMS to address the MAP's recommendations 
for adding exclusions to the measure, conducting measure testing, and 
submitting the measure for CBE endorsement. One commenter noted they 
were deeply concerned about the proposal to adopt the Patient/Resident 
COVID-19 Vaccine measure because it

[[Page 53263]]

appeared as though CMS disregarded the recommendations of the MAP.
    Response: As part of the pre-rulemaking process, HHS takes into 
consideration the recommendations of the MAP in selecting candidate 
quality and efficiency measures. HHS selects candidate measures and 
publishes proposed rules in the Federal Register, which allows for 
public comment and further consideration before a final rule is issued. 
If the CBE has not endorsed a candidate measure, then HHS must publish 
a rationale for the use of the measure described in section 
1890(b)(7)(B) of the Act in the notice. The MAP Coordinating Committee 
recommended three mitigation strategies for the Patient/Resident COVID-
19 Vaccine measure: (i) reconsider exclusions for medical 
contraindications, (ii) complete reliability and validity measure 
testing, and (iii) seek CBE endorsement. We would like to reiterate 
that this measure is intended to promote transparency of raw data 
regarding COVID-19 vaccination rates for residents/caregivers to make 
informed decisions for selecting facilities, providing potential 
residents with an important piece of information regarding vaccination 
rates as part of their process of identifying SNFs they would want to 
seek care from. As we stated in section VI.C.2.a.(3) of the FY 2024 SNF 
PPS proposed rule, we did not include exclusions for medical 
contraindications because the PFAs we met with told us that a measure 
capturing raw vaccination rate, irrespective of any medical 
contraindications, would be most helpful in resident and family/
caregiver decision-making. We intend to conduct measure testing once 
sufficient data on the COVID-19 vaccination item are collected through 
the MDS and plan to submit the measure for CBE endorsement when it is 
technically feasible to do so.
    Comment: Several commenters were concerned about the burden this 
measure places on SNFs as a result of having a new assessment item in 
the MDS, especially in light of changing guidelines around vaccine 
requirements, and workforce shortages. One commenter noted that the 
proposed changes to the measure will require SNFs to track CDC guidance 
on a quarterly basis and will also require SNFs to change their 
processes to track whether residents have received multiple doses. Two 
commenters noted that if CDC were to update its guidance and require 
booster doses, SNFs would then need to validate and track whether all 
residents met the new requirements, creating an added burden for SNFs 
to adapt to the new recommendations that will take both time and staff 
resources.
    Response: To ensure appropriate coding of the assessment item, SNFs 
would be able to use multiple sources of information to obtain a 
resident's vaccination status, such as resident interviews, medical 
records, proxy response, and vaccination cards provided by the resident 
or their caregivers.\213\ As with any assessment item in the MDS, we 
will also publish coding guidance and instructions to further aid SNFs 
in collection of these data. Additionally, we believe SNFs should be 
assessing whether residents are up to date with COVID-19 vaccination as 
a part of their routine care and infection control processes, and 
during our item testing, we heard from SNFs that they are routinely 
inquiring about COVID-19 vaccination status when admitting residents 
already.
---------------------------------------------------------------------------

    \213\ COVID-19 Vaccine: Percent of Patients/Residents Who Are Up 
to Date Draft Measure Specifications. https://www.cms.gov/files/document/patient-resident-covid-vaccine-draft-specs.pdf.
---------------------------------------------------------------------------

    Comment: One commenter was concerned that the proposed Patient/
Resident COVID-19 Vaccine measure could have unintended consequences if 
adopted. Another commenter stated the adoption of the measure would 
create a difficult dynamic for SNFs. They suggested SNFs would have two 
choices when making a decision whether to admit a resident who is not 
up to date with their COVID-19 vaccine: (1) not offer admission to 
residents who are not up to date with CDC recommendations, because they 
stated it would result in the SNF receiving a low-quality score on this 
measure, or (2) admit the resident, administer a COVID-19 vaccination 
to bring them in line with CDC recommendations even though the vaccine 
may increase the resident's risk of adverse health outcomes. One 
commenter pointed to the concerns raised by MAP and other interested 
parties and states CMS should consider the potential impacts of its 
approach on vaccination efforts. They caution that as SNFs are 
endeavoring to follow the vaccine guidelines and gain resident trust, 
this measure--as constructed--has the potential to adversely impact 
resident-provider relationships, trust, and provider performance.
    Response: We do not anticipate issues with resident access to SNF 
care if this measure is adopted. Use or adoption of other vaccination 
measures in PAC settings have not previously impacted access to care. 
Additionally, SNFs have been required to ``educate and offer'' COVID-19 
vaccine to residents, clients, and staff, and report COVID-19 
vaccination status to the CDC's NHSN, on a weekly basis, since May 13, 
2021.\214\ More recently, we finalized certain infection control 
requirements at Sec.  483.80(d) that SNFs and LTC facilities must meet 
to participate in the Medicare and Medicaid programs.\215\ As finalized 
in the ``Medicare and Medicaid Programs; Policy and Regulatory Changes 
to the Omnibus COVID-19 Health Care Staff Vaccination Requirements; 
Additional Policy and Regulatory Changes to the Requirements for Long-
Term Care (LTC) Facilities and Intermediate Care Facilities for 
Individuals with Intellectual Disabilities (ICFs-IID) to Provide COVID-
19 Vaccine Education and Offer Vaccinations to Residents, Clients, and 
Staff; Policy and Regulatory Changes to the Long Term Care Facility 
COVID-19 Testing Requirements'' (88 FR 36491 to 36492), SNFs must 
continue to educate residents, resident representatives, and staff 
about COVID-19 vaccines and offer a COVID-19 vaccine to residents, 
resident representatives, and staff, as well as complete the 
appropriate documentation for these activities. Since the information 
captured by the Patient/Resident COVID-19 Vaccine measure is consistent 
with these activities a SNF is already required to perform to meet 42 
CFR 483.80(d)(3)(iii) through (vi), we believe SNFs are having those 
discussions with their residents every day, and the adoption of this 
measure should not have adverse impacts on resident-provider 
relationships.
---------------------------------------------------------------------------

    \214\ Medicare and Medicaid Programs; COVID-19 Vaccine 
Requirements for Long-Term Care (LTC) Facilities and Intermediate 
Care Facilities for Individuals with Intellectual Disabilities 
(ICFs-IID) Residents, Clients, and Staff (86 FR 26315-26316).
    \215\ Medicare and Medicaid Programs; Policy and Regulatory 
Changes to the Omnibus COVID-19 Health Care Staff Vaccination 
Requirements; Additional Policy and Regulatory Changes to the 
Requirements for Long-Term Care (LTC) Facilities and Intermediate 
Care Facilities for Individuals With Intellectual Disabilities 
(ICFs-IID) To Provide COVID-19 Vaccine Education and Offer 
Vaccinations to Residents, Clients, and Staff; Policy and Regulatory 
Changes to the Long Term Care Facility COVID-19 Testing Requirements 
(88 FR 36502).
---------------------------------------------------------------------------

    We believe SNFs consider resident care of paramount importance and 
will not refuse care to residents based on their vaccination status. We 
also believe SNFs should use clinical judgement to determine if a 
resident is eligible to receive the vaccination. Lastly, we take the 
appropriate access to care in SNFs very seriously, and routinely 
monitor the performance of measures in the SNF QRP, including 
performance gaps across SNFs. We intend to monitor closely whether any 
proposed change to the

[[Page 53264]]

SNF QRP has unintended consequences on access to care. Should we find 
any unintended consequences, we will take appropriate steps to address 
these issues in future rulemaking.
    Comment: Several commenters were concerned regarding the lack of a 
well-defined definition of up to date, and the burden it poses on SNFs 
to collect these data from residents due to the constantly changing 
guidelines. One commenter characterized it as a ``moving-target'' 
definition, and another commenter noted that the CDC maintains 
different definitions of ``up to date'' and ``fully vaccinated.'' This 
commenter states that the public has a limited appreciation for the 
differences in these definitions and could easily misreport their 
vaccination status to facility staff when asked, giving the public a 
misleading picture of the vaccination levels of a SNF's resident 
population. Another commenter noted that it was unclear whether most 
residents would have an understanding of the CDC's specific definition 
of ``up to date'' when answering a yes/no question for the resident 
assessment, leading to potentially inaccurate data.
    Response: The concept of up to date is not new and is currently in 
use by SNFs for the short stay and long stay Percent of Residents 
Assessed and Appropriately Given the Pneumococcal Vaccine and Percent 
of Residents Who Received the Pneumococcal Vaccine measures. Beyond the 
historical use of this concept, ensuring that standards of care are up 
to date according to the relevant authorities remains a widespread goal 
for all SNFs. We believe that SNFs should be staying current on the 
latest care guidelines of COVID-19 vaccination as part of best 
practice. Additionally, SNFs would be able to use multiple sources of 
information available to obtain the vaccination data, such as resident 
interviews, medical records, proxy response, and vaccination cards 
provided by the resident or their caregivers. Gathering this 
information gives the SNF the opportunity to educate residents about 
what it means to be up to date per CDC guidelines, so that the item can 
be completed accurately. Further, the MDS Resident Assessment 
Instrument (RAI) Guidance Manual will indicate how to code the item and 
SNFs could access the CDC website at any time to find the definition of 
up to date. The CDC has published FAQs that clearly state the 
difference in the terms ``fully vaccinated'' and ``up to date.'' \216\ 
Finally, as described in section VII.C.2.b.(1)(b) of this final rule, 
our item testing demonstrated strong agreement with the correct 
responses when facilities used the available guidance, and rates 
increased when facilities accessed the CDC website.
---------------------------------------------------------------------------

    \216\ Frequently Asked Questions about COVID-19 Vaccination. May 
15, 2023. https://www.cdc.gov/coronavirus/2019-ncov/vaccines/faq.html.
---------------------------------------------------------------------------

    Comment: One commenter noted that given the various lengths of stay 
for residents, residents may be up to date one month and then with 
additional boosters and evidence on the horizon, they would move to 
being not up to date.
    Response: Given this assessment item is completed at discharge, 
SNFs would only code the item using guidance in place at the time of 
resident discharge.
    Comment: One commenter raised concerns about the evolving 
recommendation landscape from FDA and CDC as well as lack of full 
authorization from FDA for bivalent vaccines. They stated expert 
advisory groups will meet in June 2023 to provide additional 
recommendations to the agencies and to the public and encouraged CMS to 
delay measure amendment or adoption until future years when greater 
clarity from experts and other agencies is available. Another commenter 
was concerned about the uncertainty about the seasonality of COVID-19, 
future vaccination schedules, and how often new versions of a COVID-19 
vaccine will be available.
    Response: We disagree with the commenter and do not believe the 
evolving landscape and recommendations will affect this measure 
negatively. We recognize that the up to date COVID-19 vaccination 
definition may evolve due to the changing nature of the virus. As the 
COVID-19 virus mutates, this vaccination measure takes a forward-
thinking approach to ensure that SNF residents are protected in the 
event of COVID-19 infection. Given that CDC guidelines may change over 
time in response to the virus, we believe the use of ``up to date'' 
will actually be simpler for facilities since it ensures that the 
measure specifications, item responses, and accompanying item guidance 
would not have to continually change. The public health response to 
COVID-19 has necessarily adapted to respond to the changing nature of 
the virus's transmission and community spread. Just as we stated when 
we finalized the adoption of the HCP COVID-19 Vaccine measure in the FY 
2022 SNF PPS final rule (86 FR 42481), we intend to continue to work 
with partners including FDA and CDC to consider any updates to the 
Patient/Resident COVID-19 Vaccine measure in future rulemaking as 
appropriate. We believe that the proposed measure aligns with our 
responsive approach to COVID-19 and will continue to support 
vaccination as the most effective means to prevent the worst 
consequences of COVID-19, including severe illness, hospitalization, 
and death. Additionally, FDA recently authorized the bivalent vaccine 
to be used for all doses administered to individuals 6 months of age 
and older, including for an additional dose or doses for certain 
populations.\217\ Lastly, we regularly review our measures as part of 
the measure maintenance process and welcome feedback and expert input 
on our measures, and will re-specify the measure in the future, if 
needed, based on any changes to guidelines.
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    \217\ Coronavirus (COVID-19) Update: FDA Authorizes Changes to 
Simplify Use of Bivalent mRNA COVID-19 Vaccines. April 18, 2023. 
https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-authorizes-changes-simplify-use-bivalent-mrna-covid-19-vaccines.
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    Comment: Several commenters did not support the measure due to the 
lack of exclusions in the measure for reasons such as medical 
contraindications, religious beliefs, cultural norms, and resident 
refusals. Some commenters encouraged CMS to consider the MAP's 
recommendations to add exclusions to the measure calculation. One 
commenter suggested CMS include a follow-up question to learn why the 
vaccine is not up to date, like MDS item O0300B for the pneumococcal 
vaccine, with three response options: ``Not eligible--medical 
contraindication,'' ``Offered and declined,'' and ``Not offered.''
    Response: We thank the commenters for their recommendations about 
adding exclusions to the measure. Our measure development contractor 
convened a focus group of PFAs as well as a TEP that included 
interested parties from every PAC setting, to solicit input on patient/
resident COVID-19 vaccination measures and assessment items. The PFAs 
told us that a measure capturing raw vaccination rates would be most 
helpful in resident and family/caregiver decision-making. Our TEP 
agreed that developing a measure to report the rate of vaccination 
without denominator exclusions was an important goal.\218\

[[Page 53265]]

Based on this feedback, we believe excluding patients/residents with 
contraindications from the measure would distort the intent of the 
measure of providing raw COVID-19 resident vaccination rates, while 
making the information more difficult for residents/caregivers to 
interpret, and hence did not include any exclusions.
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    \218\ Technical Expert Panel (TEP) for the Development of Long-
Term Care Hospital (LTCH), Inpatient Rehabilitation Facility (IRF), 
Skilled Nursing Facility (SNF)/Nursing Facility (NF), and Home 
Health (HH) COVID-19 Vaccination-Related Items and Measures Summary 
Report. https://mmshub.cms.gov/sites/default/files/COVID19-Patient-Level-Vaccination-TEP-Summary-Report-NovDec2021.pdf.
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    Comment: Some commenters did not support adoption of this measure 
in light of the Administration's announcement of the end of the COVID-
19 PHE on May 11, 2023. One of these commenters noted that it will be 
even more challenging for residents to stay informed on the most recent 
guidance from the CDC. Another one of these commenters noted that with 
the end of the PHE and the end of the Federal vaccination mandates, CMS 
should eliminate any tracking of vaccines. Finally, one of these 
commenters commended CMS for recognizing the burden of such a 
requirement included in the SNF Conditions of Participation and working 
to remove it, but now questions the ``juxtaposition'' of proposing a 
vaccine uptake measure as a metric for quality of care.
    Response: Despite the announcement of the end of the COVID-19 PHE, 
many people continue to be affected by COVID-19, particularly seniors, 
people who are immunocompromised, and people with disabilities. As 
mentioned in the End of COVID-9 Public Health Emergency Fact 
Sheet,\219\ our response to the spread of SARS-CoV-2, the virus that 
causes COVID-19, remains a public health priority. Even beyond the end 
of the COVID-19 PHE, we will continue to work to protect Americans from 
the virus and its worst impacts by supporting access to COVID-19 
vaccines, treatments, and tests, including for people without health 
insurance. Given the continued impacts of COVID-19, we believe it is 
important to promote resident vaccination and education, which this 
measure aims to achieve. Accordingly, we are aligning our approach with 
those for other infectious diseases, such as influenza by encouraging 
ongoing COVID-19 vaccination.\220\ Further, published coding guidance 
will indicate how to code the item taking into account CDC guidelines, 
and SNFs could access the CDC website at any time to find the 
definition of up to date. Lastly, this measure as proposed for the SNF 
QRP is not associated with the PHE declaration, or the Conditions of 
Participation. This measure is being proposed to address our priority 
to empower consumers to make informed health care choices through 
resident-directed quality measures and public transparency, as with 
previous vaccination measures.
---------------------------------------------------------------------------

    \219\ Fact Sheet: End of the COVID-19 Public Health Emergency. 
U.S. Department of Health and Human Services. May 9, 2023. https://www.hhs.gov/about/news/2023/05/09/fact-sheet-end-of-the-covid-19-public-health-emergency.html.
    \220\ Medicare and Medicaid Programs; Policy and Regulatory 
Changes to the Omnibus COVID-19 Health Care Staff Vaccination 
Requirements; Additional Policy and Regulatory Changes to the 
Requirements for Long-Term Care (LTC) Facilities and Intermediate 
Care Facilities for Individuals With Intellectual Disabilities 
(ICFs-IID) To Provide COVID-19 Vaccine Education and Offer 
Vaccinations to Residents, Clients, and Staff; Policy and Regulatory 
Changes to the Long Term Care Facility COVID-19 Testing 
Requirements. (88 FR 36487).
---------------------------------------------------------------------------

    Comment: One commenter did not support the measure for the SNF QRP 
because residents entering a Medicare Part A SNF stay have had an acute 
care stay and they believe the hospital has already determined the 
person's interest in receiving the COVID-19 vaccine.
    Response: We believe that COVID-19 vaccination for high-risk 
populations, such as those in SNF settings, is of paramount importance. 
This is particularly important for residents at SNFs, who tend to be 
older and thus more vulnerable to serious complications from COVID-19. 
Therefore, if a resident is not vaccinated at the time they are 
admitted, the SNF has the opportunity to continue to educate the 
resident and provide information on why they should receive the 
vaccine, irrespective of whether the resident has received prior 
education.
    Comment: Some commenters provided alternate recommendations for a 
measure of a SNF's action, such as a count of the number of documented 
encounters facility staff had with a resident and/or their family 
concerning the COVID-19 vaccine, or a process measure that collects 
data on vaccines that are offered to residents in SNFs that are 
eligible for boosters. One commenter recommended a ``balancing 
measure'' which would track whether a SNF recommended the resident 
become up to date with their COVID-19 vaccine as opposed to tracking 
whether the resident accepted and received a COVID-19 vaccine.
    Response: We appreciate the input from the commenters. We did not 
propose a measure of SNF action related to the measure but will use 
this input to inform our future measure development efforts.
    After consideration of the public comments we received, we are 
finalizing our proposal to adopt the Patient/Resident COVID-19 Vaccine 
measure as an assessment-based measure beginning with the FY 2026 SNF 
QRP as proposed.

D. Principles for Selecting and Prioritizing SNF QRP Quality Measures 
and Concepts Under Consideration for Future Years--Request for 
Information (RFI)

1. Solicitation of Comments
    We solicited general comments on the principles for identifying SNF 
QRP measures, as well as additional thoughts about measurement gaps, 
and suitable measures for filling these gaps. Specifically, we 
solicited comment on the following questions:
     Principles for Selecting and Prioritizing QRP Measures
    ++ To what extent do you agree with the principles for selecting 
and prioritizing measures?
    ++ Are there principles that you believe CMS should eliminate from 
the measure selection criteria?
    ++ Are there principles that you believe CMS should add to the 
measure selection criteria?
     SNF QRP Measurement Gaps
    ++ We requested input on the identified measurement gaps, including 
in the areas of cognitive function, behavioral and mental health, 
resident experience and resident satisfaction, chronic conditions and 
pain management.
    ++ Are there gaps in the SNF QRP measures that have not been 
identified in this RFI?
     Measures and Measure Concepts Recommended for Use in the 
SNF QRP.
    ++ Are there measures that you believe are either currently 
available for use, or that could be adapted or developed for use in the 
SNF QRP program to assess performance in the areas of (1) cognitive 
functioning, (2) behavioral and mental health, (3) resident experience 
and resident satisfaction, (4) chronic conditions, (5) pain management, 
or (6) other areas not mentioned in this RFI?
    We also sought input on data available to develop measures, 
approaches for data collection, perceived challenges or barriers, and 
approaches for addressing challenges. We received several comments in 
response to this RFI, which are summarized below.
    Comments on Principles for Selecting and Prioritizing QRP Measures: 
Many commenters expressed support for the measure selection and 
prioritization criteria identified by CMS in the FY 2024 SNF PPS 
proposed rule (88 FR

[[Page 53266]]

21353), as well as those espoused through the National Quality Strategy 
and the ``Universal Foundation'' of quality measures. In addition to 
support for these principles, commenters emphasized the importance of 
prioritizing measures that are meaningful to residents and their 
caregivers; support shared decision-making; promote continuity or 
consistency across a range of accountability programs; are constructed 
from data that are clearly defined, validated, and standardized; for 
which the SNF is able to influence outcomes; and are consensus-based.
    A couple of commenters expressed appreciation for CMS' interest in 
adopting quality measures that do not impose undue administrative or 
financial burden on SNFs. These commenters urged that, when considering 
whether to adopt a measure, CMS assess SNF (including rural SNF) costs 
in terms of time, money, and staff resources.
    Many commenters suggested principles that relate to the types of 
data that are used in measure construction. For instance, one commenter 
recommended that measures that are incorporated into the SNF QRP 
emphasize resident-reported outcomes. Other commenters recommended that 
measures not be based on facility self-reported data, such as the MDS, 
due to concerns about data accuracy and completeness. Some commenters 
recommended that CMS focus on data sources considered to be more 
objective, such as claims-based measures, the Payroll Based Journal 
(PBJ), and State surveys. One commenter emphasized the importance of 
ensuring that regardless of the assessment tool used, requirements for 
staff training, certification, and interim certification are met.
    Comments on Principles for Selecting and Prioritizing QRP Measures 
and Measures and Measure Concepts Recommended for Use in the SNF QRP: 
Several commenters agreed with CMS that SNF QRP measurement gaps exist 
in domains that include cognitive function, behavioral and mental 
health, resident experiences of care and satisfaction, and chronic 
condition and pain management.
Cognitive Function
    Although several commenters noted the importance of developing 
quality measures that focus on cognitive function, one commenter 
suggested caution in selecting measures of cognitive functioning. 
According to this commenter, SNFs have limited ability to meaningfully 
influence cognitive functioning during a typical SNF stay.
    One commenter indicated that despite the usefulness of a cognitive 
function measure, the MDS is one of the only available data sources to 
develop this measure which, according to the commenter, is neither 
reliable nor accurate.
    A few commenters voiced concerns about the use of the BIMS and 
CAM(copyright) in measure development. Some commenters 
indicated that the BIMS, for example, was designed to screen for the 
presence of cognitive impairment and determine residents' need for 
further cognitive assessment. Commenters noted that the BIMS was not 
intended to diagnose or track changes in cognition; and it only 
effectively assesses basic elements of cognition (for example, 
attention, short-term memory), rather than executive functioning, 
judgment, and other higher-level cognitive functions. One commenter 
also stated that the constructs that are measured by the BIMS are not 
those that are the typical focus of therapy.
    Other concerns about the BIMS or CAM(copyright) for use 
in development of measures of cognitive functioning included the lack 
of physician buy-in, variation in the reliability of scoring, and 
limited utility of the BIMS for measuring and risk adjusting resident 
cognition and communication.
    A commenter indicated that instruments identified in the FY 2024 
SNF PPS proposed rule (88 FR 21353 to 21354) RFI (for example, PROMIS 
Cognitive Function Short Form) are not utilized by many SNFs. Because 
therapy practitioners are more familiar with the BIMS and 
CAM(copyright) than with other cognitive function 
instruments mentioned in the RFI--the PROMIS short forms and the PROMIS 
Neuro-QoL--the commenter thought that use of PROMIS measures would 
present a greater burden to SNFs. This commenter further indicated that 
the PROMIS tools were developed for use in broad populations or to 
measure specific cognitive functions and, as such, would not readily 
translate to a SNF QRP measure. The commenter recommended that CMS 
perform feasibility, reliability, and validity testing to ensure that 
QRP measures could be effectively developed from these instruments.
    Commenters encouraged CMS to collaborate with SNFs and experts in 
cognition to assess and consider other measures that not only offer 
information on a broad set of elements related to cognitive function 
but could also be used to assess change in cognitive abilities 
throughout the course of the SNF episode. One commenter indicated that 
the proprietary nature of many instruments that assess cognitive 
functioning could be a challenge for measure development.
Behavioral and Mental Health
    A few commenters agreed with CMS that measurement gaps exist in the 
areas of behavioral and mental health. One commenter indicated that 
although a measure of behavioral and mental health would be useful, the 
MDS is one of the only available data sources that could be used to 
develop this measure. The commenter questioned the accuracy and 
reliability of the MDS.
    One commenter noted that because occupational therapists have a key 
role in addressing residents' behavioral and mental health needs, that 
they need to be included in quality measures in this area. Another 
commenter suggested caution in selecting measures of behavioral and 
mental health functioning, indicating that SNFs are not specialized in 
treating behavioral and mental health issues.
Resident Experience and Resident Satisfaction
    One commenter expressed support for the use of the CAHPS measure to 
measure resident experience and satisfaction but cautioned that an 
independent contractor should be used to identify the resident sample--
rather than having SNFs identify this sample--and CMS should ensure 
that the survey sample mirrors the SNF population using a random sample 
process.
Chronic Condition and Pain Management
    One commenter acknowledged the importance of measures of chronic 
condition and pain management. However, they did not support 
development of measures in this area as they believed the MDS to be 
inaccurate and subject to gaming by nursing facilities.
Other Measurement Gaps
    Some commenters believed measurement gaps do exist in domains not 
identified in the RFI. Noting the importance of good nutrition in 
reducing readmissions and increasing SNF resident quality of life, two 
commenters recommended the inclusion of a malnutrition screening and 
intervention measures in the SNF QRP to promote both quality and health 
equity. These commenters suggested that malnutrition-related quality 
measures that CMS has adopted in other quality programs be considered 
as the foundation for a SNF QRP malnutrition measure. These include the 
Global

[[Page 53267]]

Composite Malnutrition Score which will be used in the Hospital 
Inpatient Quality Reporting program beginning in 2024, and the Food 
Insecurity/Nutrition Risk Identification and Treatment Improvement 
Activity that is part of the Merit-based Incentive Payment System.
    Another commenter recommended the adoption of structural measures 
that indicate hours of service provided by physicians, social workers, 
and therapists to ensure that residents receive needed services. The 
commenter supported the use of data from the CMS PBJ to develop these 
measures.
    Commenters expressed support for the development of measures 
focused on degenerative cognitive conditions, for which maintenance of 
function is the primary focus. One commenter suggested consideration of 
a measure related to residents' ability to safely and effectively 
return to the community.
    Other measures and measurement concepts identified by commenters 
include health equity, psychosocial issues, caregiver status (for 
example, availability of caregiver), receipt of or referral for smoking 
cessation counseling among residents with COPD, referrals to pulmonary 
rehabilitation for residents with COPD, and resident vaccination 
status, including adult Td/Tdap (tetanus, diphtheria, and pertussis) 
and herpes zoster (shingles) vaccinations.
    Response: We appreciate the input provided by commenters. While we 
will not be responding to specific comments submitted in response to 
this RFI in this final rule, we intend to use this input to inform our 
future measure development efforts.

E. Health Equity Update

1. Background
    In the FY 2023 SNF PPS proposed rule (87 FR 22754 through 22760), 
we included an RFI entitled ``Overarching Principles for Measuring 
Equity and Healthcare Quality Disparities Across CMS Quality 
Programs.'' We define health equity as ``the attainment of the highest 
level of health for all people, where everyone has a fair and just 
opportunity to attain their optimal health regardless of race, 
ethnicity, disability, sexual orientation, gender identity, 
socioeconomic status, geography, preferred language, or other factors 
that affect access to care and health outcomes.'' \221\ We are working 
to advance health equity by designing, implementing, and 
operationalizing policies and programs that support health for all the 
people served by our programs and models, eliminating avoidable 
differences in health outcomes experienced by people who are 
disadvantaged or underserved, and providing the care and support that 
our beneficiaries need to thrive. Our goals outlined in the CMS 
Framework for Health Equity 2022-2023 \222\ are in line with Executive 
Order 13985, ``Advancing Racial Equity and Support for Underserved 
Communities Through the Federal Government.'' \223\ The goals included 
in the CMS Framework for Health Equity serve to further advance health 
equity, expand coverage, and improve health outcomes for the more than 
170 million individuals supported by our programs, and set a foundation 
and priorities for our work, including: strengthening our 
infrastructure for assessment; creating synergies across the healthcare 
system to drive structural change; and identifying and working to 
eliminate barriers to CMS-supported benefits, services, and coverage. 
The CMS Framework for Health Equity outlines the approach CMS will use 
to promote health equity for enrollees, mitigate health disparities, 
and prioritize CMS's commitment to expanding the collection, reporting, 
and analysis of standardized data.\224\
---------------------------------------------------------------------------

    \221\ Centers for Medicare & Medicaid Services. Health Equity. 
https://www.cms.gov/pillar/health-equity. Accessed February 1, 2023.
    \222\ Centers for Medicare & Medicaid Services. CMS Framework 
for Health Equity 2022-2032. https://www.cms.gov/files/document/cms-framework-health-equity-2022.pdf.
    \223\ Executive Order 13985, ``Advancing Racial Equity and 
Support for Underserved Communities Through the Federal 
Government,'' can be found at https://www.whitehouse.gov/briefing-room/presidential-actions/2021/01/20/executive-order-advancing-racial-equity-and-support-for-underserved-communities-through-the-federal-government/.
    \224\ Centers for Medicare and Medicaid Services. The Path 
Forward: Improving Data to Advance Health Equity Solutions. https://www.cms.gov/files/document/path-forwardhe-data-paper.pdf.
---------------------------------------------------------------------------

    In addition to the CMS Framework for Health Equity, we seek to 
advance health equity and whole-person care as one of eight goals 
comprising the CMS National Quality Strategy (NQS).\225\ The NQS 
identifies a wide range of potential quality levers that can support 
our advancement of equity, including: (1) establishing a standardized 
approach for resident-reported data and stratification; (2) employing 
quality and value-based programs to address closing equity gaps; and 
(3) developing equity-focused data collections, analysis, regulations, 
oversight strategies, and quality improvement initiatives.
---------------------------------------------------------------------------

    \225\ Centers for Medicare & Medicaid Services. What Is the CMS 
Quality Strategy? https://www.cms.gov/Medicare/Quality-Initiatives-
Patient-Assessment-Instruments/Value-Based-Programs/CMS-Quality-
Strategy.
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    A goal of the NQS is to address persistent disparities that 
underlie our healthcare system. Racial disparities in health, in 
particular, are estimated to cost the U.S. $93 billion in excess 
medical costs and $42 billion in lost productivity per year, in 
addition to economic losses due to premature deaths.\226\ At the same 
time, racial and ethnic diversity has increased in recent years with an 
increase in the percentage of people who identify as two or more races 
accounting for most of the change, rising from 2.9 percent to 10.2 
percent between 2010 and 2020.\227\ Therefore, we need to consider ways 
to reduce disparities, achieve equity, and support our diverse 
beneficiary population through the way we measure quality and display 
the data.
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    \226\ Turner A. The Business Case for Racial Equity: A Strategy 
for Growth. April 24, 2018. W.K. Kellogg Foundation and Altarum. 
https://altarum.org/RacialEquity2018.
    \227\ World Health Organization. Social Determinants of Health. 
https://www.who.int/health-topics/social-determinants-of-health#tab=tab_1.
---------------------------------------------------------------------------

    We solicited public comments via the aforementioned RFI on changes 
that we should consider in order to advance health equity. We refer 
readers to the FY 2023 SNF PPS final rule (87 FR 47553 through 47555) 
for a summary of the public comments and suggestions we received in 
response to the health equity RFI. In the proposed rule, we stated that 
we would take these comments into account as we continue to work to 
develop policies, quality measures, and measurement strategies on this 
important topic.
2. Anticipated Future State
    We are committed to developing approaches to meaningfully 
incorporate the advancement of health equity into the SNF QRP. One 
option we are considering is including social determinants of health 
(SDOH) as part of new quality measures.
    Social determinants of health are the conditions in the 
environments where people are born, live, learn, work, play, worship, 
and age that affect a wide range of health, functioning, and quality-
of-life outcomes and risks. They may have a stronger influence on the 
population's health and well-being than services delivered by 
practitioners and healthcare delivery organizations.\228\ Measure 
stratification by CMS is important for better understanding differences 
in health outcomes from across different patient population groups 
according to specific demographic and SDOH variables. For example, when 
``pediatric measures

[[Page 53268]]

over the past two decades are stratified by race, ethnicity, and 
income, they show that outcomes for children in the lowest income 
households and for Black and Hispanic children have improved faster 
than outcomes for children in the highest income households or for 
White children, thus narrowing an important health disparity.'' \229\ 
This analysis and comparison of the SDOH items in the assessment 
instruments support our desire to understand the benefits of measure 
stratification. Hospital providers receive such information in their 
confidential feedback reports, and we believe this learning opportunity 
would benefit PAC providers. The goal of the confidential feedback 
reports is to provide SNFs with their results so they can compare 
certain quality measures stratified by dual eligible status and race 
and ethnicity. The process is meant to increase provider's awareness of 
their data. We will solicit feedback from SNFs for future enhancements 
to the confidential feedback reports.
---------------------------------------------------------------------------

    \228\ Agency for Healthcare Research and Quality. 2022 National 
Healthcare Quality and Disparities Report. November 2022. https://www.ahrq.gov/research/findings/nhqrdr/nhqdr22/index.html.
    \229\ Agency for Healthcare Research and Quality. 2022 National 
Healthcare Quality and Disparities Report. Content last reviewed 
November 2022. https://www.ahrq.gov/research/findings/nhqrdr/nhqdr22/index.html.
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    In the proposed rule, we stated that we are considering whether 
health equity measures we have adopted for other settings,\230\ such as 
hospitals, could be adopted in PAC settings. We stated that we are 
exploring ways to incorporate SDOH elements into the measure 
specifications. For example, we could consider a future health equity 
measure like screening for social needs and interventions using our 
current SDOH Data items of preferred language, interpreter services, 
health literacy, transportation, and social isolation. With 30 percent 
to 55 percent of health outcomes attributed to SDOH,\231\ a measure 
capturing and addressing SDOH could encourage SNFs to identify 
residents' specific needs and connect them with the community resources 
necessary to overcome social barriers to their wellness. We could 
specify a health equity measure using the same SDOH data items that we 
currently collect as standardized patient assessment data elements 
under the SNF. These SDOH data items assess health literacy, social 
isolation, transportation problems, and preferred language (including 
need or want of an interpreter). We also see value in aligning SDOH 
data items according to existing health IT vocabulary and codes sets 
where applicable and appropriate such as those included in the Office 
of the National Coordinator for Health Information (ONC) United States 
Core Data for Interoperability (USCDI) \232\ across all care settings 
as we develop future health equity quality measures under our SNF QRP 
statutory authority. This would further the goals of the NQS to align 
quality measures across our programs as part of the Universal 
Foundation.\233\
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    \230\ Medicare Program; Hospital Inpatient Prospective Payment 
Systems for Acute Care Hospitals and the Long-Term Care Hospital 
Prospective Payment System and Policy Changes and Fiscal Year 2023 
Rates; Quality Programs and Medicare Promoting Interoperability 
Program Requirements for Eligible Hospitals and Critical Access 
Hospitals; Costs Incurred for Qualified and Non-Qualified Deferred 
Compensation Plans; and Changes to Hospital and Critical Access 
Hospital Conditions of Participation. (87 FR 49202-49215).
    \231\ World Health Organization. Social Determinants of Health. 
https://www.who.int/health-topics/social-determinants-of-health#tab=tab_1.
    \232\ United States Core Data for Interoperability (USCDI), 
https://www.healthit.gov/isa/united-states-core-data-interoperability-uscdi.
    \233\ Jacobs DB, Schreiber M, Seshamani M, Tsai D, Fowler E, 
Fleisher LA. Aligning Quality Measures across CMS--The Universal 
Foundation. N Engl J Med. 2023 Mar 2;338:776-779. doi: 10.1056/
NEJMp2215539. PMID: 36724323.
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    Although we did not directly solicit feedback to our update, we did 
receive some public comments, which we summarize later in this section.
    Comment: Commenters were generally supportive of CMS' efforts to 
develop ways to measure and mitigate health inequities. Four commenters 
applauded CMS' continuing efforts to advance health equity and 
encouraged CMS to continue to develop and adopt measures of SDOH into 
the SNF QRP. One of these commenters referenced their belief that 
collection of SDOH will enhance holistic care, call attention to 
impairments that might be mitigated or resolved, and facilitate clear 
communication between residents and SNFs. Another commenter shared 
strategies they are using with their member organizations to assess 
organizational leadership's commitment to identify and address health 
equity, as well as evaluating the impact of health equity on care 
delivery.
    We also received comments supporting measure stratification and 
adoption of screening measures in the SNF QRP. One commenter noted the 
importance of stratification to understanding the differences in 
outcomes across different groups. Some commenters suggested CMS 
incorporate screening measures similar to those adopted in the FY 2023 
Inpatient Prospective Payment System (IPPS) final rule for the Hospital 
Inpatient Quality Reporting Program.
    We also received feedback on other ways to incorporate health 
equity into the SNF QRP. One commenter recommended CMS incorporate 
workforce equity measures into the SNF QRP, suggesting that workforce 
factors are related to a worker's ability to provide quality care. We 
received some comments on other data points that may be useful in 
identifying and addressing health disparities. One commenter noted that 
while it is important to still try to understand differences by race 
and ethnicity to identify and address disparities that might root from 
racism and social/economic inequities, they recommended against making 
generalizations about differences in health and health care simply 
based on race and ethnicity and to instead conduct more in-depth 
evaluations of underlying social and economic drivers of health. This 
commenter suggested CMS incentivize the collection and analysis of data 
on factors such as, but not limited to, disability status, veteran 
status, primary or preferred language, health literacy, food security, 
transportation access, housing stability, social support after 
discharge from a SNF, and a person's access to care. This same 
commenter, however, pointed out that any program must account for the 
fact that there are many contributors to health inequities, including 
personal factors, many of which are outside the control of SNFs. They 
encouraged CMS to have ongoing engagement from interested parties to 
best understand structural and socioeconomic barriers to health and to 
monitor for any unintended consequences. Finally, this commenter urged 
CMS to focus on improving care coordination as residents move between 
settings.
    One commenter recommended CMS consider including SDOH in new 
quality measures and in SNF payment and suggested it could be 
accomplished through the use of ICD-10 Z-codes as indicators of the 
additional resources required to care for residents. There were also 
several commenters who urged CMS to balance any reporting requirements 
so as not to create an undue administrative burden on clinicians. One 
of these commenters noted that quantifying health care disparities and 
barriers faced by residents is extremely nuanced due to the sensitive 
nature of this issue, and an overly burdensome reporting approach may 
impact the critical relationship between the SNF and resident.
    One commenter was critical of our efforts to meaningfully 
incorporate the advancement of health equity into the SNF QRP, noting 
that it disregards a person's behavior and accountability for their own 
health. This commenter raised a concern that these efforts presuppose 
systemic bias on the part of

[[Page 53269]]

the healthcare system or bigotry on the part of medical providers, or 
that medical providers' bias is responsible for differences in the 
health outcomes among demographic minority groups. This commenter also 
cautioned CMS against expecting providers to view treatments through 
the lens of race, as it could result in allocating resources to one 
group at the expense of another.
    Finally, one commenter suggested that the abbreviated term for 
``social determinants of health'' was incorrect, believing it should be 
SDoH.
    Response: We thank all the commenters for responding to our update 
on this important CMS priority. When abbreviating ``social determinants 
of health,'' we consistently use SDOH across our agencies and 
programs.234 235 236 237 238 We also want to be transparent 
about our efforts to provide SNFs with information that they find 
beneficial as they seek to improve clinical outcomes for all SNF 
residents and are not intended to be critical of any health system or 
provider. As we stated in the FY 2024 SNF PPS proposed rule (88 FR 
21355-21356), our goals outlined in the CMS Framework for Health Equity 
2022-2023 \239\ are in line with Executive Order 13985, ``Advancing 
Racial Equity and Support for Underserved Communities Through the 
Federal Government.'' \240\ We will continue to prioritize our efforts 
to advance health equity by designing, implementing, and 
operationalizing policies and programs that support health for all 
people served by our program. As we move this important work forward, 
we will take these comments into account as we work to develop 
policies, quality measures, and measurement strategies.
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    \234\ Centers for Disease Control and Prevention. Social 
Determinants of Health at CDC. https://www.cdc.gov/about/sdoh/index.html.
    \235\ Office of the Assistant Secretary for Health. Social 
Determinants of Health. https://health.gov/healthypeople/priority-areas/social-determinants-health.
    \236\ National Institutes of Health. PhenX Social Determinants 
of Health Assessments Collection. https://www.nimhd.nih.gov/resources/phenx/.
    \237\ Office of Minority Health. Using Z Codes: The Social 
Determinants of Health (SDOH) Data Journey to Better Outcomes. 
https://www.cms.gov/files/document/zcodes-infographic.pdf.
    \238\ Assistant Secretary for Planning and Evaluation. 
Addressing Social Determinants of Health in Federal Programs. 
https://aspe.hhs.gov/topics/health-health-care/social-drivers-health/addressing-social-determinants-health-federal-programs.
    \239\ Centers for Medicare & Medicaid Services. CMS Framework 
for Health Equity 2022-2032. April 2022. https://www.cms.gov/files/document/cms-framework-health-equity-2022.pdf.
    \240\ Executive Order 13985, ``Advancing Racial Equity and 
Support for Underserved Communities Through the Federal 
Government.'' https://www.whitehouse.gov/briefing-room/presidential-actions/2021/01/20/executive-order-advancing-racial-equity-and-support-for-underserved-communities-through-the-federal-government/.
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F. Form, Manner, and Timing of Data Submission Under the SNF QRP

1. Background
    We refer readers to the current regulatory text at Sec.  413.360(b) 
for information regarding the policies for reporting SNF QRP data.
2. Reporting Schedule for the Minimum Data Set (MDS) Assessment Data 
for the Discharge Function Score Measure Beginning With the FY 2025 SNF 
QRP
    As discussed in section VI.C.1.b. of the FY 2024 SNF PPS proposed 
rule, we proposed to adopt the DC Function measure beginning with the 
FY 2025 SNF QRP. We proposed that SNFs would be required to report 
these MDS assessment data beginning with residents admitted and 
discharged on October 1, 2023 for purposes of the FY 2025 SNF QRP. 
Starting in CY 2024, SNFs would be required to submit data for the 
entire calendar year beginning with the FY 2026 SNF QRP. Because the DC 
Function measure is calculated based on data that are currently 
submitted to the Medicare program, there would be no new burden 
associated with data collection for this measure.
    We solicited public comment on this proposal. We did not receive 
public comments on this proposed schedule for data submission of the DC 
Function measure beginning with the FY 2025 SNF QRP, and therefore, we 
are finalizing as proposed.
3. Method of Data Submission and Reporting Schedule for the CoreQ: 
Short Stay Discharge Measure Beginning With the FY 2026 SNF QRP
a. Method of Data Submission To Meet SNF QRP Requirements Beginning 
With the FY 2026 Program Year
    As discussed in section VII.C.2.a. of this final rule, we proposed 
to adopt the CoreQ: SS DC measure beginning with the FY 2026 SNF QRP. 
In the FY 2024 SNF PPS proposed rule (88 FR 21357), we proposed that 
Medicare-certified SNFs and all non-CAH swing bed rural hospitals would 
be required to contract with a third-party vendor that is CMS-trained 
and approved to administer the CoreQ: SS DC survey on their behalf 
(referred to as a ``CMS-approved CoreQ survey vendor''). Under this 
proposal, SNFs would have been required to contract with a CMS-approved 
CoreQ survey vendor to ensure that the data are collected by an 
independent organization that is trained to collect this type of data 
and given the independence of the CMS-approved CoreQ survey vendor from 
the SNF, ensure that the data collected are unbiased. The CMS-approved 
CoreQ survey vendor would have been the business associate of the SNF 
and required to follow the minimum business requirements described in 
the Draft CoreQ: SS DC Survey Protocols and Guidelines Manual.\241\ 
This method of data collection has been used successfully in other 
settings, including for Medicare-certified home health agencies and 
hospices.
---------------------------------------------------------------------------

    \241\ Draft CoreQ: SS DC Survey Protocols and Guidelines Manual. 
Chapter III. CoreQ Survey Participation Requirements. Available on 
the SNF QRP Measures and Technical Information web page at https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/nursinghomequalityinits/skilled-nursing-facility-quality-reporting-program/snf-quality-reporting-program-measures-and-technical-information.
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    As described in the FY 2024 SNF PPS proposed rule (88 FR 21357), it 
was proposed that CMS-approved CoreQ survey vendors administering the 
CoreQ: SS DC survey would be required to offer a toll-free assistance 
line and an electronic mail address which respondents could use to seek 
help.
    We also proposed in the FY 2024 SNF PPS proposed rule (88 FR 21357) 
to require SNFs to use the protocols and guidelines for the proposed 
CoreQ: SS DC measure as defined by the Draft CoreQ: SS Survey Protocols 
and Guidelines Manual in effect at the time the questionnaires are sent 
to eligible residents. The Draft CoreQ: SS DC Survey Protocols and 
Guidelines Manual is available on the SNF QRP Measures and Technical 
Information web page at https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/nursinghomequalityinits/skilled-nursing-facility-quality-reporting-program/snf-quality-reporting-program-measures-and-technical-information. We also proposed 
that CMS-approved CoreQ survey vendors and SNFs be required to 
participate in CoreQ: SS DC measure oversight activities to ensure 
compliance with the protocols, guidelines, and questionnaire 
requirements. Additionally, we proposed that all CMS-approved CoreQ 
survey vendors develop a Quality Assurance Plan (QAP) for CoreQ: SS DC 
survey administration in accordance with the Draft CoreQ: SS DC Survey 
Protocols and Guidelines Manual.
    At Sec.  413.360, we also proposed redesignating paragraph (b)(2) 
as paragraph (b)(3) and add new paragraph (b)(2) for the CoreQ: SS DC 
measure's data submission requirements. Finally,

[[Page 53270]]

we proposed to codify the requirements for being a CMS-approved CoreQ: 
SS DC survey vendor at paragraphs (b)(2)(ii) through (b)(2)(iii) in 
regulation. The proposed revisions are outlined in the FY 2024 SNF PPS 
proposed rule (88 FR 21422).
    In the FY 2024 SNF PPS proposed rule (88 FR 21358), we proposed 
that SNFs would send a resident information file (RIF) to the CMS-
approved CoreQ survey vendor on a weekly basis so the vendor can start 
administering the CoreQ: SS DC questionnaire within seven days after 
the reporting week closes. However, we received a significant number of 
comments expressing concern about the burden associated with weekly 
data submission.
    We solicited public comment on this proposal to require Medicare-
certified SNFs to contract with a third-party vendor to administer the 
CoreQ: SS DC measure questionnaire on their behalf beginning with the 
FY 2026 SNF QRP. We received comments that supported and opposed our 
proposal to require Medicare-certified SNFs to contract with a third-
party vendor to administer the CoreQ: SS DC measure questionnaire on 
their behalf, but we will not be responding to these. As described in 
section VII.C.2.a.5.b of this final rule, we have decided that, at this 
time, we will not finalize the proposal to add the CoreQ: SS DC measure 
beginning with the FY 2026 SNF QRP. Therefore, we are not finalizing 
our proposal to require Medicare-certified SNFs to contract with a 
third-party vendor to administer the CoreQ: SS DC measure questionnaire 
on their behalf beginning with the FY 2026 SNF QRP.
b. Exemptions for the CoreQ: SS DC Measure Reporting Requirements 
Beginning With the FY 2026 Program Year
(1) Low Volume Exemptions
    We are aware that there is a wide variation in the size of 
Medicare-certified SNFs. Therefore, we proposed that SNFs with less 
than 60 residents, regardless of payer, discharged within 100 days of 
SNF admission in the prior calendar year would be exempt from the 
CoreQ: SS DC measure data collection and reporting requirements. A 
SNF's total number of short-stay discharged residents for the period of 
January 1 through December 31 for a given year would have been used to 
determine if the SNF would have to participate in the CoreQ: SS DC 
measure in the next calendar year. To qualify for the exemptions, SNFs 
would have been required to submit their request using the 
Participation Exemption Request form no later than December 31 of the 
CY prior to the reporting CY.
(2) New Provider Exemptions
    We also proposed in the FY 2024 SNF PPS proposed rule (88 FR 21357 
through 21358), that newly Medicare-certified SNFs (that is, those 
certified on or after January 1, 2024) be excluded from the CoreQ: SS 
DC measure reporting requirement for CY 2024, because there would be no 
information from the previous CY to determine whether the SNF would be 
required to report or exempt from reporting the CoreQ: SS DC measure.
    In future years, we proposed requiring that SNFs certified for 
Medicare participation on or after January 1 of the reporting year 
would be excluded from reporting on the CoreQ: SS DC measure for the 
applicable SNF QRP program year.
    We solicited public comment on this proposal to exempt SNFs with 
less than 60 residents, regardless of payer, discharged within 100 days 
of SNF admission in the prior calendar year, and to exempt newly 
Medicare-certified SNFs in their first-year of certification, from the 
CoreQ: SS DC measure reporting requirements for the applicable SNF QRP 
program year.
    We received comments that supported and opposed our proposal to 
exempt SNFs with less than 60 residents, regardless of payer, 
discharged within 100 days of SNF admission in the prior calendar year, 
and to exempt newly Medicare-certified SNFs in their first year of 
certification from the CoreQ: SS DC measure reporting requirements for 
the applicable SNF QRP program year, but we will not be responding to 
these. As described in section VII.C.2.a.5.b of this final rule, we 
have decided that, at this time, we will not finalize the proposal to 
add the CoreQ: SS DC measure beginning with the FY 2026 SNF QRP. 
Therefore, we are not finalizing our proposal to exempt SNFs with less 
than 60 residents, regardless of payer, discharged within 100 days of 
SNF admission in the prior calendar year, and to exempt newly Medicare-
certified SNFs in their first year of certification from the CoreQ: SS 
DC measure reporting requirements for the applicable SNF QRP program 
year.
c. Reporting Schedule for the Data Submission of the CoreQ: Short Stay 
Discharge Measure Beginning With the FY 2026 SNF QRP
    In the FY 2024 SNF PPS proposed rule (88 FR 21358 through 21360), 
we proposed that the CoreQ: SS DC measure questionnaire be a component 
of the SNF QRP for the FY 2026 SNF QRP and subsequent years. To comply 
with the SNF QRP reporting requirements for the FY 2026 SNF QRP, we 
proposed that SNFs would be required to collect data for the CoreQ: SS 
DC measure by utilizing CMS-approved CoreQ survey vendors in compliance 
with the proposed revisions outlined at Sec.  413.360(b)(2)(i) through 
(b)(2)(iii) in the regulation text of the FY 2024 SNF PPS proposed 
rule.
    For the CoreQ: SS DC measure, we proposed that SNFs would send a 
resident information file to the CMS-approved CoreQ survey vendor on a 
weekly basis so the CMS-approved CoreQ survey vendor could start 
administering the CoreQ: SS DC questionnaire within 7 days after the 
reporting week closes. The resident information file, whose data is 
listed in Table 14, represented the minimum required information the 
CMS-approved CoreQ survey vendor would need to determine the residents' 
eligibility for the CoreQ: SS DC measure's questionnaire to administer 
the survey to eligible residents.

Table 14--Data Elements in the CoreQ: SS DC Measure Resident Information
                                  File
------------------------------------------------------------------------
 
-------------------------------------------------------------------------
SNF name.
SNF CMS Certification Number (CCN).
National Provider Identifier (NPI).
Reporting week.
Reporting year.
Number of eligible residents.
Resident First Name.
Resident Middle Initial.
Resident Last Name.
Resident Date of Birth.
Resident Mailing Address 1.
Resident Mailing Address 2.
Resident address, City.
Resident address, State.
Resident address, Zip Code.
Telephone number, including area code.
Resident email address.
Gender.
Payer.
HMO indicator.
Dual eligibility indicator.
End stage renal disease.
Resident date of admission.
Resident date of discharge.
Brief Interview for Mental Status (BIMS) score.
Discharge status.
Left against medical advice.
Court appointed guardian.
Are you of Hispanic, Latino/a, or Spanish origin?
What is your race?

[[Page 53271]]

 
What is your preferred language?
------------------------------------------------------------------------
For additional information about the data elements that would be
  included in the resident information file, see the Draft CoreQ
  Protocols and Guidelines Manual located at https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/nursinghomequalityinits/skilled-nursing-facility-quality-reporting-program/snf-quality-reporting-program-measures-and-technical-information information.

    For the CoreQ: SS DC measure, we proposed that SNFs would be 
required to meet or exceed two separate data completeness thresholds: 
(1) one threshold, set at 75 percent, for submission of weekly resident 
information files to the CMS-approved CoreQ survey vendor for the full 
reporting year; and (2) a second threshold, set at 90 percent, for 
completeness of the resident information files. In other words, as 
proposed, SNFs would have submitted resident information files on a 
weekly basis that included at least 90 percent of the required data 
fields to their CMS-approved CoreQ survey vendors for at least 75 
percent of the weeks in a reporting year. SNFs could have chosen to 
submit resident information files more frequently but would have been 
required meet the minimum threshold to avoid receiving a 2-percentage-
point reduction to their Annual Payment Update (APU). We also proposed 
to codify this data completeness threshold requirement at our 
regulation at Sec.  413.360(f)(1)(iv) as described in the regulation 
text of the FY 2024 SNF PPS proposed rule.
    We also proposed an initial data submission period from January 1, 
2024, through June 30, 2024. As described in Table 15 in the FY 2024 
SNF PPS proposed rule (88 FR 21359), we proposed that to meet the pay-
for-reporting requirement of the SNF QRP for the first half of the FY 
2026 program year, SNFs would only be required to contract with a CMS-
approved CoreQ survey vendor and submit one resident information file 
to their CMS-approved CoreQ survey vendor for at least 1 week during 
January 1, 2024 through June 30, 2024. During this period, the CMS-
approved CoreQ survey vendor would follow the procedures as described 
in the Draft CoreQ: SS DC Survey Protocols and Guidelines Manual.\242\ 
Beginning July 1, 2024, SNFs would have been required to submit weekly 
resident information files for at least 75 percent of the weeks 
remaining in CY 2024.
---------------------------------------------------------------------------

    \242\ Draft CoreQ: SS DC Survey Protocols and Guidelines Manual. 
Available on the SNF QRP Measures and Technical Information web page 
at https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/nursinghomequalityinits/skilled-nursing-facility-quality-reporting-program/snf-quality-reporting-program-measures-and-technical-information.
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    Starting in CY 2025, SNFs would be required to submit resident 
information files no less than weekly for the entire calendar year 
beginning with the FY 2027 SNF QRP, as described in Table 16 in the FY 
2024 SNF PPS proposed rule (88 FR 21359).
    We proposed that the CMS-approved CoreQ survey vendor administer 
the CoreQ: SS DC measure's questionnaire to discharged residents within 
2 weeks of their discharge date through the U.S. Postal Service or by 
telephone. If administered by mail, the questionnaires must be returned 
to the CMS-approved CoreQ survey vendor within 2 months of the 
resident's discharge date from the SNF.
    Although the CMS-approved CoreQ survey vendor would administer the 
CoreQ: SS DC measure's survey on a SNF's behalf, each SNF would have 
been responsible for ensuring required data are collected and submitted 
to CMS in accordance with the SNF QRP's requirements. We also 
recommended that SNFs submitting CoreQ: SS DC resident information 
files to their CMS-approved CoreQ survey vendor promptly review the 
Data Submission Summary Reports that are described in the Draft CoreQ: 
SS DC Survey Protocols and Guidelines Manual.\243\
---------------------------------------------------------------------------

    \243\ Draft CoreQ: SS DC Survey Protocols and Guidelines Manual. 
Chapter X. SNF CoreQ Survey website Reports. Available on the SNF 
QRP Measures and Technical Information web page at https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/nursinghomequalityinits/skilled-nursing-facility-quality-reporting-program/snf-quality-reporting-program-measures-and-technical-information.
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    We solicited public comment on the proposed schedule for data 
submission and the participation requirements for the CoreQ: SS DC 
measure beginning with the FY 2026 SNF QRP. We received several 
comments on our proposed schedule for data submission and the 
participation requirements for the CoreQ: SS DC measure beginning with 
the FY 2026 SNF QRP, but we will not be responding to these. As 
described in section VII.C.2.a.5.b of this final rule, we have decided 
that, at this time, we will not finalize the proposal to add the CoreQ: 
SS DC measure beginning with the FY 2026 SNF QRP. Therefore, we are not 
finalizing our proposed schedule for data submission and the 
participation requirements for the CoreQ: SS DC Measure beginning with 
the FY 2026 SNF QRP.
4. Reporting Schedule for the Data Submission of Minimum Data Set (MDS) 
Assessment Data for the COVID-19 Vaccine: Percent of Patients/Residents 
Who Are Up to Date Measure Beginning With the FY 2026 SNF QRP
    As discussed in section VI.C.2.b. of the FY 2024 SNF PPS proposed 
rule, we proposed to adopt the Patient/Resident COVID-19 Vaccine 
measure beginning with the FY 2026 SNF QRP. We proposed that SNFs would 
be required to report this new MDS assessment data item beginning with 
Medicare Part A residents discharged on October 1, 2024, for purposes 
of the FY 2026 SNF QRP. Starting in CY 2025, SNFs would be required to 
submit data for the entire calendar year beginning with the FY 2027 SNF 
QRP.
    We also proposed to add a new item to the MDS for SNFs to report 
the proposed Patient/Resident COVID-19 Vaccine measure. Specifically, a 
new item would be added to the MDS discharge item sets to collect 
information on whether a resident is up to date with their COVID-19 
vaccine at the time of discharge from the SNF. A draft of the new item 
is available in the COVID-19 Vaccine: Percent of Patients/Residents Who 
Are Up to Date Draft Measure Specifications.\244\
---------------------------------------------------------------------------

    \244\ COVID-19 Vaccine: Percent of Patients/Residents Who Are Up 
to Date Draft Measure Specifications. https://www.cms.gov/files/document/patient-resident-covid-vaccine-draft-specs.pdf.
---------------------------------------------------------------------------

    We solicited public comment on this proposal. The following is a 
summary of the comments we received on our proposal to require SNFs to 
report a new MDS assessment data item for the Patient/Resident COVID-19 
Vaccine measure on Medicare Part A residents beginning with residents 
discharged on October 1, 2024 and our responses.
    Comment: Several commenters raised concerns about the data 
collected using the assessment item on the MDS being duplicative of 
what is currently being reported to NHSN. They noted that this 
reporting adds additional burden on SNFs and could confuse residents 
looking for information. One commenter recommended that in order to 
remove burdensome duplication of reporting for the same process, CMS 
should issue a regulatory revision to the requirements promulgated 
through a prior COVID-19 IFC \245\ to end reporting of resident COVID-
19 vaccination up to date status

[[Page 53272]]

requirements through the NHSN no later than September 30, 2024.
---------------------------------------------------------------------------

    \245\ Medicare and Medicaid Programs; COVID-19 Vaccine 
Requirements for Long-Term Care (LTC) Facilities and Intermediate 
Care Facilities for Individuals with Intellectual Disabilities 
(ICFs-IID) Residents, Clients, and Staff (86 FR 26315-26316).
---------------------------------------------------------------------------

    Response: We acknowledge the commenters' concerns and thank them 
for their recommendations regarding the duplication of reporting 
resident COVID-19 vaccination status on the MDS and to NHSN. We will 
take the recommendations into consideration.
    Comment: Some commenters noted their preference for the NHSN 
reported data, since it includes the entire nursing home population 
regardless of payer source and provides more valuable information, as 
opposed to this proposed SNF QRP measure which only reflects short-stay 
residents.
    Response: While the data that SNFs report to the NHSN are 
aggregated resident vaccination data, SNF's are not required to report 
beneficiary-level data to the CDC's NHSN. However, since the proposed 
Patient/Resident COVID-19 Vaccine measure would be collected using an 
MDS assessment item at the resident-level, the data submitted would be 
included in the SNF's Review and Correct reports as well as the Quality 
Measure (QM) resident- and facility-level confidential feedback reports 
and would allow SNFs to track resident-level information for quality 
improvement purposes. These data would also allow for granular analyses 
of vaccinations, including identification of potential disparities 
within the SNF QRP.
    Comment: A few commenters raised concerns about this measure being 
based on facility self-reported MDS data and its reliability. 
Commenters urged CMS to consider alternative data sources or implement 
auditing and penalty systems for inaccurate or falsified data, if an 
MDS assessment item was finalized as the source to collect this 
information. One commenter suggested that having a single yes or no 
item on the MDS without any requirements for documentation or 
validation of vaccination status would amount to a mere checkmark in a 
box with no evidence that it leads to improved quality of care.
    Response: We acknowledge the commenters' concerns regarding the MDS 
data. However we note that the RAI process has multiple regulatory 
requirements. Our regulations at Sec. Sec.  483.20(b)(1)(xviii), (g), 
and (h) \246\ require that (1) the assessment must be a comprehensive, 
accurate assessment of the resident's status, (2) the assessment must 
accurately reflect the resident's status, (3) a registered nurse and 
each individual who completes a portion of the assessment must sign and 
certify the assessment is completed, and (4) the assessment process 
includes direct observation, as well as communication with the 
resident.
---------------------------------------------------------------------------

    \246\ https://www.ecfr.gov/current/title-42/chapter-IV/subchapter-G/part-483/subpart-B/section-483.20.
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    We intend to monitor this measure closely to identify any 
concerning trends, and we will continue to do so as part of our routine 
monitoring activities to regularly assess measure performance, 
reliability, and reportability for all data submitted for the SNF QRP.
    After consideration of the public comments we received, we are 
finalizing our proposal to require SNFs to report the new MDS 
assessment data item for the Patient/Resident COVID-19 Vaccine measure 
on Medicare Part A residents beginning with residents discharged on 
October 1, 2024 for the FY 2026 SNF QRP.
5. SNF QRP Data Completion Thresholds for MDS Data Items Beginning With 
the FY 2026 SNF QRP
    In the FY 2016 SNF PPS final rule (80 FR 46458), we finalized that 
SNFs would need to complete 100 percent of the data on 80 percent of 
MDSs submitted in order to be in compliance with the SNF QRP reporting 
requirements for the applicable program year, as codified in regulation 
at Sec.  413.360(f). We established this data completion threshold 
because SNFs were accustomed to submitting MDS assessments for other 
purposes and they should easily be able to meet this requirement for 
the SNF QRP. We also noted at that time our intent to raise the 
proposed 80 percent threshold in subsequent program years.\247\
---------------------------------------------------------------------------

    \247\ 80 FR 22077; 80 FR 46458.
---------------------------------------------------------------------------

    We proposed that, beginning with the FY 2026 SNF QRP, SNFs would be 
required to report 100 percent of the required quality measure data and 
standardized patient assessment data collected using the MDS on at 
least 90 percent of the assessments they submit through the CMS-
designated submission system.
    Complete data are needed to help ensure the validity and 
reliability of SNF QRP data items, including risk-adjustment models. 
The proposed threshold of 90 percent is based on the need for 
substantially complete records, which allows appropriate analysis of 
SNF QRP measure data for the purposes of updating quality measure 
specifications as they undergo yearly and triennial measure maintenance 
reviews with the CBE. Additionally, we want to ensure complete SNF QRP 
measure data from SNFs, which will ultimately be reported to the 
public, allowing our beneficiaries to gain a more complete 
understanding of SNF performance related to these metrics, helping them 
to make informed healthcare choices. Finally, the proposal would 
contribute to further alignment of data completion thresholds across 
the PAC settings.
    We believe SNFs should be able to meet the proposed requirement for 
the SNF QRP. Our data suggest that the majority of SNFs are already in 
compliance with, or exceeding, the proposed threshold. The complete 
list of items required under the SNF QRP is updated annually and posted 
on the SNF QRP Measures and Technical Information page.\248\
---------------------------------------------------------------------------

    \248\ The SNF QRP Measures and Technical Information page. 
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-
Instruments/NursingHomeQualityInits/Skilled-Nursing-Facility-
Quality-Reporting-Program/SNF-Quality-Reporting-Program-Measures-
and-Technical-Information.
---------------------------------------------------------------------------

    We proposed that SNFs would be required to comply with the proposed 
new data completion threshold beginning with the FY 2026 SNF QRP. 
Starting in CY 2024, SNFs would be required to report 100 percent of 
the required quality measures data and standardized patient assessment 
data collected using the MDS on at least 90 percent of all assessments 
submitted January 1 through December 31 for that calendar year's 
payment determination. Any SNF that does not meet the proposed 
requirement will be subject to a reduction of 2 percentage points to 
the applicable FY APU beginning with the FY 2026 SNF QRP. We proposed 
to update Sec.  413.360(f) of our regulations to reflect this new 
policy, as well as to clarify and make non-substantive edits to improve 
clarity of the regulation.
    We solicited public comment on the proposed schedule for the 
increase of SNF QRP data completion thresholds for the MDS data items 
beginning with the FY 2026 program year. The following is a summary of 
the comments we received and our responses.
    Comment: A number of commenters opposed our proposal to increase 
the SNF QRP data completion thresholds for MDS data items beginning 
with the FY 2026 SNF QRP because they believe SNFs need more time to 
adjust to the collection of the new standardized patient assessment 
data elements that begins October 1, 2023. These commenters do not 
believe that 3 months is adequate time for SNFs to adjust to the new 
data elements. One of these commenters noted that the proposed increase 
in the data completion threshold comes at a time when CMS is 
significantly expanding

[[Page 53273]]

the MDS 3.0, and there is additional health IT programming that will 
need to be done to accommodate these data as well. One of these 
commenters suggested that CMS apply the higher 90 percent threshold 
only to the current required data elements and implement a 75 percent 
threshold for the new standardized patient assessment data element.
    Response: We acknowledge the commenters' concerns, but as we stated 
in the SNF PPS proposed rule, our data suggest that the majority of 
SNFs are already in compliance with, or exceeding, this proposed 
threshold. As the commenters noted, SNFs will begin collecting new 
standardized patient assessment data elements beginning October 1, 
2023.\249\ However, many of these items are not ``new'' to SNFs. SNFs 
have been collecting the Brief Interview for Mental Status (BIMS), 
Confusion Assessment Method (CAM(copyright)), the Patient Health 
Questionnaire (PHQ), some of the Nutritional Approaches, and even some 
of the Special Treatments, Procedures, and Programs for several years, 
but they have not counted toward the SNF's data completion threshold 
for the SNF QRP. We also want to note that three of the new items have 
a response option (``None of the above'') that SNFs can select for 
residents who are not receiving special nutritional approaches, high-
risk drug classes, and special treatments, procedures, and programs. 
When ``None of the above'' is selected, 46 of the items are eliminated 
and SNFs do not have to complete them. To support SNFs, we have already 
begun to provide extensive education and training opportunities on the 
standardized patient assessment data elements for SNFs, and will 
continue to do so, in addition to answering all questions through our 
SNF QRP Helpdesk.
---------------------------------------------------------------------------

    \249\ A list of the new and revised standardized patient 
assessment data elements to be collected beginning October 1, 2023 
can be found in the FY 2025 SNF QRP APU Table for Reporting 
Assessment Based Measures and Standardized Patient Assessment Data 
Elements document available here: https://www.cms.gov/files/document/fy-2025-snf-qrp-apu-table-reporting-assessment-based-measures-and-standardized-patient-assessment.pdf.
---------------------------------------------------------------------------

    We also do not believe it would be appropriate to implement a lower 
threshold for the new standardized patient assessment data elements. As 
noted earlier, many of these items are not ``new'' to SNFs, even though 
they did not count towards the SNF's data completion threshold for the 
SNF QRP. We must maintain our commitment to the quality of care for all 
residents, and we continue to believe that the collection of the 
standardized patient assessment data elements and TOH Information 
measures will contribute to this effort. We note that in response to 
the ``Request for Information to Close the Health Equity Gap'' in the 
FY 2022 SNF PPS proposed rule (86 FR 20000), we heard from interested 
parties that it is important to gather additional information about 
race, ethnicity, gender, language, and other SDOH, and some SNFs noted 
they had already begun to collect some of this information for use in 
their operations. We believe capturing complete information on these 
new items is equally important and therefore do not plan to implement a 
lower threshold for these items.
    Comment: One commenter noted it would place additional burden on 
the important role of the Nurse Assessment Coordinators at a time when 
they are already in short supply. Another suggested that because SNF 
residents are often extremely sick, there are often situations outside 
of the facility's control that may prevent them from being able to 
complete an MDS in its entirety. Another commenter echoed that point 
and added that for facilities that serve larger proportions of complex 
and/or acutely ill residents, these cases are more frequent, and that 
20 percent buffer is necessary. This commenter also added that CMS 
rationale for increasing the data completion threshold--that is, that 
the majority of SNFs already meet or exceed the 90 percent threshold--
is moot since these SNFs clearly do not need the motivation of a higher 
threshold to report a larger proportion of complete assessments.
    Response: While we acknowledge the impacts of the COVID-19 PHE on 
the healthcare system, including staffing shortages, it also makes it 
especially important now to monitor quality of care.\250\ Still, we are 
mindful of burden that may occur from the collection and reporting of 
our measures. We emphasize, however, that several of the standardized 
patient assessment data elements reflect activities that align with the 
existing Requirements of Participation for SNFs.\251\ As a result, the 
information gathered will reflect a process that SNFs should already be 
conducting and will demonstrate the quality of care provided by SNFs. 
Additionally, for each of the items, the MDS RAI manual provides 
instructions for how to code the items if the item does not apply to 
the resident or the resident is unable to respond. Selecting these 
responses when applicable counts toward the data completion threshold. 
Additionally, the assessments of the special services, treatments, and 
interventions with multiple responses are formatted as a ``check all 
that apply'' format. Therefore, when treatments do not apply, the 
assessor need only check one row for ``None of the Above,'' and the 
data completion requirement is met, and when a resident has to leave 
emergently, the resident interview questions are not required.
---------------------------------------------------------------------------

    \250\ https://psnet.ahrq.gov/primer/nursing-and-patient-safety.
    \251\ Code of Federal Regulations. Title 42--Public Health. Part 
483--Requirements for States and Long Term Care Facilities. https://www.govinfo.gov/content/pkg/CFR-2018-title42-vol5/xml/CFR-2018-title42-vol5-part483.xml.
---------------------------------------------------------------------------

    Finally, we do not believe that shortages in staffing will affect 
implementation of the new MDS because many of the data elements adopted 
as standardized patient assessment data elements in the FY 2020 SNF PPS 
final rule are already collected on the MDS 1.17.2 using current SNF 
staffing levels. Therefore, MDS 1.18.11 results in fewer ``new'' 
standardized patient assessment data elements for SNFs, as compared to 
other PAC settings.
    Comment: One commenter noted that starting with FY 2026, if 
finalized, SNFs will have additional reporting requirements for weekly 
submissions to the approved vendor for the CoreQ: SS Discharge measure. 
This commenter suggested that delaying the threshold increase would 
allow time to analyze whether the increase in data elements 
significantly impacts the SNF's ability to maintain compliance with the 
QRP requirements.
    Response: As described in section VII.C.2.a.(5)(b) of this final 
rule, we have decided at this time, not to finalize the proposal to add 
the CoreQ: SS DC measure beginning with the FY 2026 SNF QRP.
    After consideration of the public comments we received, we are 
finalizing our proposal to require SNFs to report 100 percent of the 
required quality measures data and standardized patient assessment data 
collected using the MDS on at least 90 percent of all assessments 
submitted beginning with the FY 2026 SNF QRP as proposed.

G. Policies Regarding Public Display of Measure Data for the SNF QRP

1. Background
    Section 1899B(g) of the Act requires the Secretary to establish 
procedures for making the SNF QRP data available to the public, 
including the performance of individual SNFs, after ensuring that SNFs 
have the opportunity to review their data prior to public display. For 
a more detailed discussion about our

[[Page 53274]]

policies regarding public display of SNF QRP measure data and 
procedures for the SNF's opportunity to review and correct data and 
information, we refer readers to the FY 2017 SNF PPS final rule (81 FR 
52045 through 52048).
2. Public Reporting of the Transfer of Health Information to the 
Provider--Post-Acute Care Measure and Transfer of Health Information to 
the Patient--Post-Acute Care Measure Beginning With the FY 2025 SNF QRP
    We proposed to begin publicly displaying data for the measures: (1) 
Transfer of Health (TOH) Information to the Provider--Post-Acute Care 
(PAC) Measure (TOH-Provider); and (2) TOH Information to the Patient--
PAC Measure (TOH-Patient) beginning with the October 2025 Care Compare 
refresh or as soon as technically feasible.
    We adopted these measures in the FY 2020 SNF PPS final rule (84 FR 
38761 through 38764). In response to the COVID-19 PHE, we released an 
Interim Final Rule (85 FR 27595 through 27597) which delayed the 
compliance date for collection and reporting of the TOH-Provider and 
TOH-Patient measures to October 1 of the year that is at least 2 full 
fiscal years after the end of the COVID-19 PHE. Subsequently, in the FY 
2023 SNF PPS final rule (87 FR 47502), the compliance date for the 
collection and reporting of the TOH-Provider and TOH-Patient measures 
was revised to October 1, 2023. Data collection for these two 
assessment-based measures will begin with residents discharged on or 
after October 1, 2023.
    We proposed to publicly display data for these two assessment-based 
measures based on four rolling quarters of data, initially using 
discharges from January 1, 2024, through December 31, 2024 (Quarter 1 
2024 through Quarter 4 2024), and to begin publicly reporting these 
measures with the October 2025 refresh of Care Compare, or as soon as 
technically feasible. To ensure the statistical reliability of the 
data, we proposed that we would not publicly report a SNF's performance 
on a measure if the SNF had fewer than 20 eligible cases in any four 
consecutive rolling quarters for that measure. SNFs that have fewer 
than 20 eligible cases would be distinguished with a footnote that 
states: ``The number of cases/resident stays is too small to report.''
    We solicited public comment on our proposal for the public display 
of the (1) Transfer of Health (TOH) Information to the Provider--Post-
Acute Care (PAC) Measure (TOH-Provider), and (2) Transfer of Health 
(TOH) Information to the Patient--Post-Acute Care (PAC) Measure (TOH-
Patient) assessment-based measures. The following is a summary of the 
comments we received and our responses.
    Comment: Several commenters supported the proposal to publicly 
report the Transfer of Health Information to the Provider-PAC Measure 
and the Transfer of Health Information to the Patient-PAC Measure 
beginning with the October 2024 Care Compare refresh or as soon as 
possible. One commenter expressed their appreciation at CMS' decision 
to delay the implementation of these process measures during the COVID-
19 PHE and stated their members are in a better position to be 
successful with these measures with the timelines presented in the 
proposed rule.
    Another commenter supported these two measures as a starting point 
to reflect that health information is shared with the next applicable 
setting as well as the resident.
    Response: We appreciate these commenters' support for the proposed 
public reporting of these measures.
    Comment: Two commenters were not supportive of the proposal. One of 
these commenters believed the publication of the information will be 
confusing for consumers and burdensome to SNFs.
    Response: We want to clarify that the proposal would add no 
additional reporting requirements to the SNF QRP. Additionally, we 
believe that publicly reporting these measures will provide consumers 
with meaningful information about a SNF's communication of health 
information, which is critical to ensuring safe and effective 
transitions from one healthcare setting to another. We work closely 
with our Office of Communications and consumer groups when onboarding 
new measures to the Care Compare websites, and we will do the same with 
the TOH-Patient and TOH-Provider measures.
    Comment: Another commenter stated CMS should reconsider publicly 
reporting the information, and requested CMS delay public display until 
2025, using information based on discharges beginning January 1, 2024. 
They stated the calculation of the measure is confusing, and 
instructions provided by CMS and its contractors were not made clear 
until very recently.
    Response: SNFs will begin collecting the TOH Information data 
elements for all residents discharged beginning October 1, 2023. 
Consistent with the implementation of these measures in other PAC 
settings, we began providing provider education earlier this year. 
Additionally, our helpdesks have been responding to provider questions 
about these measures since the compliance date for the collection of 
the TOH Information data elements was finalized in the FY 2023 SNF PPS 
final rule (87 FR 47544 through 47551). We proposed using data 
collected from January 1, 2024 through December 31, 2024, and believe 
this will provide SNFs ample time to adjust to their collection. This 
schedule is consistent with the inaugural display of other new SNF QRP 
measures.
    Comment: We received several additional comments that were outside 
the scope of our proposal for public reporting of these measures. One 
commenter urged CMS to expand the measure to include additional 
information at the time of transfer to facilitate appropriate infection 
prevention and control, such as other transmission-based precautions a 
resident may have, presence of indwelling catheters and a resident's 
vaccination status. One commenter suggested that CMS should consider 
that sharing the medication list with the resident may not be enough if 
the resident is unable to understand or follow that list and that it 
might be more appropriate to assess whether, in those instances, the 
list was provided to the resident and the family or caregiver. One 
commenter noted that providing an electronic list to the next provider 
can be problematic when the PAC provider and the resident's primary 
care practitioner utilize different Electronic Medical Record (EMR) 
systems.
    Response: We thank the commenters for bringing these issues to our 
attention and will take these comments into consideration for potential 
policy refinements.
    After consideration of the public comments we received, we are 
finalizing our proposal to begin publicly displaying data for the 
measures: (1) Transfer of Health (TOH) Information to the Provider--
Post-Acute Care (PAC) Measure (TOH-Provider); and (2) TOH Information 
to the Patient--PAC Measure (TOH-Patient) beginning with the October 
2025 Care Compare refresh or as soon as technically feasible.
3. Public Reporting of the Discharge Function Score Measure Beginning 
With the FY 2025 SNF QRP
    We proposed to begin publicly displaying data for the DC Function 
measure beginning with the October 2024 refresh of Care Compare, or as 
soon as technically feasible, using data collected from January 1, 2023 
through December 31, 2023 (Quarter 1 2023 through Quarter 4 2023). We 
proposed, that a SNF's DC Function score would be displayed based on 
four quarters of data. Provider preview reports would be

[[Page 53275]]

distributed in July 2024, or as soon as technically feasible. 
Thereafter, a SNF's DC Function score would be publicly displayed based 
on four quarters of data and updated quarterly. To ensure the 
statistical reliability of the data, we proposed that we would not 
publicly report a SNF's performance on the measure if the SNF had fewer 
than 20 eligible cases in any quarter. SNFs that have fewer than 20 
eligible cases would be distinguished with a footnote that states: 
``The number of cases/resident stays is too small to report.''
    We solicited public comment on the proposal for the public display 
of the Discharge Function Score assessment-based measure beginning with 
the October 2024 refresh of Care Compare, or as soon as technically 
feasible. The following is a summary of the comments we received and 
our responses.
    Comment: Two commenters provided support to publicly report the DC 
Function measure.
    Response: We thank the commenters for their support to publicly 
report the proposed measure.
    Comment: One commenter opposed public reporting for this measure as 
it may inappropriately skew the decision-making process when residents 
and facilities are reviewing SNF performance prior to admission to a 
SNF. Although the commenter does not explicitly state the rationale for 
how this measure would skew decision-making processes, they urge CMS to 
wait to adopt this measure until it has undergone CBE endorsement.
    Response: We do not believe the publication of this measure 
inappropriately skews residents' decision-making process, and on the 
contrary will allow Care Compare users to base healthcare decisions on 
a measure that, as testing demonstrated, more accurately measures 
functional ability. We direct readers to section VII.C.1.b.1.b. of this 
final rule, and the technical report for detailed measures testing 
results demonstrating that the measure provides meaningful information 
which can be used to improve quality of care, and to the TEP report 
summaries 252 253 which detail TEP support for the proposed 
measure concept. We also acknowledge the importance of the CBE 
endorsement process and plan to submit the proposed measure for CBE 
endorsement in the future.
---------------------------------------------------------------------------

    \252\ Technical Expert Panel (TEP) for the Refinement of Long-
Term Care Hospital (LTCH), Inpatient Rehabilitation Facility (IRF), 
Skilled Nursing Facility (SNF)/Nursing Facility (NF), and Home 
Health (HH) Function Measures Summary Report (July 2021 TEP).) is 
available at https://mms-test.battelle.org/sites/default/files/TEP-Summary-Report-PAC-Function.pdf.
    \253\ Technical Expert Panel (TEP) for Cross-Setting Function 
Measure Development Summary Report (January 2022 TEP) is available 
at https://mmshub.cms.gov/sites/default/files/PAC-Function-TEP-Summary-Report-Jan2022-508.pdf.
---------------------------------------------------------------------------

    Comment: One commenter expressed concern about consumer confusion 
with the public reporting of multiple SNF functional outcome measures, 
as the DC Function measure correlates highly with the Discharge Self-
Care Score and Discharge Mobility Score measures. This commenter asks 
CMS to consider whether reporting only the DC Function measure is 
sufficient to help the public make informed care decisions.
    Response: We work closely with our Office of Communications and 
consumer groups when onboarding new measures to the Care Compare 
websites, and we will do the same with the DC Function measure. We will 
also provide additional training and outreach materials for SNFs before 
the measure is publicly reported.
    After consideration of the public comments we received, we are 
finalizing our proposal to begin publicly displaying data for the DC 
Function measure beginning with the October 2024 Care Compare refresh 
or as soon as technically feasible.
4. Public Reporting of the COVID-19 Vaccine: Percent of Patients/
Residents Who Are Up to Date Measure Beginning With the FY 2026 SNF QRP
    We proposed to begin publicly displaying data for the COVID-19 
Vaccine: Percent of Patients/Residents Who Are Up to Date measure 
beginning with the October 2025 refresh of Care Compare or as soon as 
technically feasible using data collected for Q4 2024 (October 1, 2024 
through December 31, 2024). We proposed that a SNF's Patient/Resident 
COVID-19 Vaccine percent of residents who are up to date would be 
displayed based on one quarter of data. Provider preview reports would 
be distributed in July 2025 for data collected in Quarter 4 of CY 2024, 
or as soon as technically feasible. Thereafter, the percent of SNF 
residents who are up to date with their COVID-19 vaccinations would be 
publicly displayed based on 1 quarter of data updated quarterly. To 
ensure the statistical reliability of the data, we proposed that we 
would not publicly report a SNF's performance on the measure if the SNF 
had fewer than 20 eligible cases in any quarter. SNFs that have fewer 
than 20 eligible cases would be distinguished with a footnote that 
states: ``The number of cases/resident stays is too small to report.''
    We solicited public comment on the proposal for the public display 
of the COVID-19 Vaccine: Percent of Patients/Residents Who Are Up to 
Date measure beginning with the October 2025 refresh of Care Compare, 
or as soon as technically feasible. The following is a summary of the 
comments we received and our responses.
    Comment: A few commenters supported public reporting of this 
measure on Care Compare, to aid beneficiaries and families in selecting 
a facility, while protecting resident privacy. One commenter suggested 
that CMS provide contextual guidance that the vaccine is not mandatory 
and that community vaccine hesitancy factors may influence the 
vaccination rate in any particular SNF. One commenter suggested that 
CMS should explicitly detail alongside any public reporting the scoring 
methodology and exclusions for the measure. Another commenter noted 
that these data on Care Compare should be coordinated with existing 
measures of staff and resident COVID-19 vaccination rates to avoid 
confusion and duplication. They also suggested that reported data on 
Care Compare include demographic information and be stratified by race, 
ethnicity and other social risk factors to highlight potential 
disparities and help address health equity gaps. One commenter noted 
that if adopted this measure should not be reported through the NHSN.
    Response: We thank the commenter for their support and appreciate 
the additional suggestions provide by other commenters. We work closely 
with our Office of Communications and consumer groups when onboarding 
new measures to the Care Compare websites, and we will do the same with 
the Patient/Resident COVID-19 Vaccine measure. We will also provide 
additional training and outreach materials for SNFs before the measure 
is publicly reported. Additionally, we set public reporting thresholds 
for each measure to ensure we are protecting resident privacy. We also 
did not propose stratified reporting of these data for this measure; 
however, we continue to take all concerns, comments, and suggestions 
into account for future development and expansion of policies to 
advance health equity across the SNF QRP, including by supporting SNFs 
in their efforts to ensure equity for all of their residents, and to 
identify opportunities for improvements in health outcomes. Any updates 
to specific program requirements related to quality measurement and 
reporting provisions would be addressed through separate and future 
notice-and-comment rulemaking, as necessary. Lastly, this SNF QRP 
measure will be reported on Care Compare using data collected

[[Page 53276]]

through an assessment item on the MDS. This measure was not proposed to 
be reported through the NHSN.
    Comment: One commenter disagrees with CMS's statement that public 
reporting of the resident/patients who are up to date measure ``would 
provide residents and caregivers, including those who are at high risk 
for developing serious complications from COVID-19, with valuable 
information they can consider when choosing a SNF.'' They believe the 
measure reflects only short-stay residents who are a small portion of 
the total resident population that is generally not segregated from the 
broader population, and no longer resides in the nursing home. They 
noted that the measure tells nothing about risks to potential residents 
due to the vaccination status of the individuals with whom they will be 
living and interacting, and that this information is not beneficial to 
individuals considering SNF care. Another commenter was concerned that 
scores from both sets of data would be publicly reported and could lead 
to confusion when a SNF's scores appearing on Care Compare would 
display two different data sets for the same measure.
    Response: We acknowledge that the proposed measure captures only 
short-stay residents. As mentioned in section VII.C.2.b.2. of this 
final rule, residents receiving SNF care under the Medicare fee-for-
service program may differ from residents receiving long-term care in 
nursing homes. We also note that SNFs are not required to report 
beneficiary-level data to the CDC's NHSN, and data from non-CAH swing 
bed units are not included in the COVID-19 vaccination data reported to 
the NHSN by nursing homes. Therefore, reporting of this data through 
the MDS would capture additional resident characteristics and resident 
populations that may not be covered under the NHSN reporting. 
Additionally, we believe that adding this measure to the SNF QRP as an 
assessment-based measure will give SNFs more visibility into their 
patient-level vaccination rates in order to identify opportunities to 
improve COVID-19 vaccination rates.
    We also acknowledge the commenter's concern regarding the public 
display of resident vaccination rates using NHSN and MDS data. We work 
closely with the Office of Communications and consumer groups when 
onboarding new measures to the Care Compare websites and will take this 
concern under consideration.
    Comment: One commenter raised concerns regarding the reliability of 
this data collected due to a moving-target definition in addition to 
there being a lag time from when the vaccine is administered, the data 
gathered and submitted, and its eventual display online.
    Response: We intend to publicly report one quarter of data, so that 
each Care Compare refresh would include the most up to date information 
available. We believe this mitigates concerns that the data would not 
reflect ``recent'' information to consumers.
    After consideration of the public comments we received, we are 
finalizing our proposal to begin publicly displaying data for the 
Patient/Resident COVID-19 Vaccine measure beginning with the October 
2025 Care Compare refresh or as soon as technically feasible.

VIII. Skilled Nursing Facility Value-Based Purchasing (SNF VBP) Program

A. Statutory Background

    Through the Skilled Nursing Facility Value-Based Purchasing (SNF 
VBP) Program, we award incentive payments to SNFs to encourage 
improvements in the quality of care provided to Medicare beneficiaries. 
The SNF VBP Program is authorized by section 1888(h) to the Act, and it 
applies to freestanding SNFs, SNFs affiliated with acute care 
facilities, and all non-CAH swing bed rural hospitals. We believe the 
SNF VBP Program has helped to transform how Medicare payment is made 
for SNF care, moving increasingly towards rewarding better value and 
outcomes instead of merely rewarding volume. Our codified policies for 
the SNF VBP Program can be found in our regulations at 42 CFR 
413.337(f) and 413.338.

B. SNF VBP Program Measures

1. Background
    For background on the measures we have adopted for the SNF VBP 
Program, we refer readers to the following prior final rules:
     In the FY 2016 SNF PPS final rule (80 FR 46411 through 
46419), we finalized the Skilled Nursing Facility 30 Day All-Cause 
Readmission Measure (SNFRM) as required under section 1888(g)(1) of the 
Act.
     In the FY 2017 SNF PPS final rule (81 FR 51987 through 
51995), we finalized the Skilled Nursing Facility 30-Day Potentially 
Preventable Readmission (SNFPPR) Measure as required under section 
1888(g)(2) of the Act.
     In the FY 2020 SNF PPS final rule (84 FR 38821 through 
38822), we updated the name of the SNFPPR measure to the ``Skilled 
Nursing Facility Potentially Preventable Readmissions after Hospital 
Discharge measure'' (Sec.  413.338(a)(14)).
     In the FY 2021 SNF PPS final rule (85 FR 47624), we 
amended the definition of ``SNF Readmission Measure'' in our 
regulations to reflect the updated name for the SNFPPR measure.
     In the FY 2022 SNF PPS final rule (86 FR 42503 through 
42507), we finalized a measure suppression policy for the duration of 
the PHE for COVID-19, and finalized suppression of the SNFRM for 
scoring and payment purposes for the FY 2022 SNF VBP Program. We also 
updated the lookback period for risk-adjustment in the FY 2023 
performance period (FY 2021).
     In the FY 2023 SNF PPS final rule (87 FR 47559 through 
47580), we finalized suppression of the SNFRM for scoring and payment 
purposes for the FY 2023 SNF VBP Program. We also modified the SNFRM 
beginning with the FY 2023 program year by adding a risk-adjustment 
variable for both patients with COVID-19 during the prior proximal 
hospitalization (PPH) and patients with a history of COVID-19. We also 
finalized three new quality measures for the SNF VBP Program as 
permitted under section 1888(h)(2)(A)(ii) of the Act. We finalized two 
new measures beginning with the FY 2026 program year: (1) Skilled 
Nursing Facility Healthcare Associated Infections Requiring 
Hospitalization (SNF HAI) measure; and (2) Total Nursing Hours per 
Resident Day Staffing (Total Nurse Staffing) measure. We finalized an 
additional measure beginning with the FY 2027 program year: Discharge 
to Community--Post-Acute Care Measure for Skilled Nursing Facilities 
(DTC PAC SNF) measure.
2. Refinements to the SNFPPR Measure Specifications and Updates to the 
Measure Name
a. Background
    Section 1888(g)(2) of the Act requires the Secretary to specify a 
resource use measure that reflects an all-condition, risk-adjusted 
potentially preventable hospital readmission rate for skilled nursing 
facilities. To meet this statutory requirement, we finalized the 
Skilled Nursing Facility Potentially Preventable Readmission (SNFPPR) 
measure in the FY 2017 SNF PPS final rule (81 FR 51987 through 51995). 
In the FY 2020 SNF PPS final rule (84 FR 38821 through 38822), we 
updated the SNFPPR measure name to the Skilled Nursing Facility 
Potentially Preventable Readmissions after Hospital Discharge measure, 
while maintaining SNFPPR as the measure short name.

[[Page 53277]]

    Although our testing results indicated that the SNFPPR measure was 
sufficiently developed, valid, and reliable for use in the SNF VBP 
Program at the time we adopted it, we have since engaged in additional 
measure development work to further align the measure's specifications 
with the specifications of other potentially preventable readmission 
(PPR) measures, including the SNF PPR post-discharge (PD) measure 
specified for the SNF QRP, and the within-stay PPR measure used in the 
IRF QRP. Based on those efforts, we proposed to refine the SNFPPR 
measure specifications as follows: (1) changing the outcome observation 
window from a fixed 30-day window following acute care hospital 
discharge to within the SNF stay; and (2) changing the length of time 
allowed between a qualifying prior proximal inpatient discharge (that 
is, the inpatient discharge that occurs prior to admission to the index 
SNF stay) and SNF admission from one day to 30 days. To align with 
those measure refinements, we also proposed to update the measure name 
to the ``Skilled Nursing Facility Within-Stay Potentially Preventable 
Readmission (SNF WS PPR) Measure.''
b. Overview of the Updated Measure
    The SNF WS PPR measure estimates the risk-standardized rate of 
unplanned, potentially preventable readmissions (PPR) that occur during 
SNF stays among Medicare fee-for-service (FFS) beneficiaries. 
Specifically, this outcome measure reflects readmission rates for SNF 
residents who are readmitted to a short-stay acute-care hospital or 
long-term care hospital (LTCH) with a principal diagnosis considered to 
be unplanned and potentially preventable while within SNF care. The 
measure is risk-adjusted and calculated using 2 consecutive years of 
Medicare FFS claims data.
    We have tested the updated SNF WS PPR measure for reliability and 
validity. The random split-half correlation tests indicated good 
reliability with the intraclass correlation coefficient being notably 
better than that of the SNFRM. In addition, we tested the validity of 
the SNF WS PPR measure by comparing SNF WS PPR measure scores with 
those of nine other measures. The testing results indicated that the 
SNF WS PPR measure is not duplicative of those nine measures and 
provides unique information about quality of care not captured by the 
other nine measures. Validity tests also showed that the measure can 
accurately predict PPRs while controlling for differences in resident 
case-mix. We refer readers to the SNF WS PPR measure technical 
specifications available at https://www.cms.gov/files/document/snfvbp-snfwsppr-draft-technical-measure-specification.pdf.
(1) Measure Applications Partnership (MAP) Review
    We included the SNF WS PPR measure as a SNF VBP measure under 
consideration in the publicly available ``2022 Measures Under 
Consideration List.'' \254\ The MAP offered conditional support of the 
SNF WS PPR measure for rulemaking, contingent upon endorsement by the 
consensus-based entity, noting that the measure would add value to the 
Program because PPRs are disruptive and burdensome to patients. We 
refer readers to the final 2022-2023 MAP recommendations for further 
details available at https://mmshub.cms.gov/measure-lifecycle/measure-implementation/pre-rulemaking/lists-and-reports.
---------------------------------------------------------------------------

    \254\ 2022 Measures Under Consideration Spreadsheet available at 
https://mmshub.cms.gov/sites/default/files/2022-MUC-List.xlsx.
---------------------------------------------------------------------------

c. Data Sources
    The SNF WS PPR measure is calculated using 2 consecutive years of 
Medicare FFS claims data to estimate the risk-standardized rate of 
unplanned PPRs that occur during SNF stays. Specifically, the stay 
construction, exclusions, and risk-adjustment model utilize data from 
the Medicare eligibility files and inpatient hospital claims. 
Calculating the SNF WS PPR measure using 2 years of data improved the 
measure's statistical reliability relative to 1 year of data, which is 
used in the current version of the SNFPPR measure. Because the SNF WS 
PPR measure is calculated entirely using administrative data, we stated 
that our proposed adoption of the measure would not impose any 
additional data collection or submission burden for SNFs.
d. Measure Specifications
(1) Denominator
    The population included in the measure denominator is Medicare FFS 
beneficiaries who are admitted to a SNF during a 2-year measurement 
period who are not then excluded based on the measure exclusion 
criteria, which we describe in the next section. For SNF residents with 
multiple SNF stays during the 2-year readmission window, each of those 
SNF stays is eligible for inclusion in the measure. In addition, the 
index SNF admission must have occurred within 30 days of discharge from 
a prior proximal hospital (PPH) stay, which is defined in the measure 
specifications as an inpatient stay in an IPPS hospital, a CAH, or an 
inpatient psychiatric facility. Residents who expire during the 
readmission window are included in the measure.
    The measure denominator is the risk-adjusted ``expected'' number of 
residents with a PPR that occurred during the SNF stay. This estimate 
includes risk adjustment for certain resident characteristics without 
the facility effect, which we further discuss in section VIII.B.2.e. of 
this final rule. The ``expected'' number of residents with a PPR is 
derived from the predicted number of residents with a PPR if the same 
residents were treated at the average SNF, which is defined for 
purposes of this measure as a SNF whose facility effect is zero.
(2) Denominator Exclusions
    A SNF stay is excluded from the measure denominator if it meets at 
least one of the following conditions:
     The SNF resident is less than 18 years old.
     The SNF resident did not have at least 12 months of 
continuous FFS Medicare enrollment prior to SNF admission, which is 
defined as the month of SNF admission and the 11 months prior to that 
admission.
     The SNF resident did not have continuous FFS Medicare 
enrollment for the entire risk period (defined as enrollment during the 
month of SNF admission through the month of SNF discharge).
     SNF stays where there was a gap of greater than 30 days 
between discharge from the PPH and the SNF admission.
     The SNF resident was discharged from the SNF against 
medical advice.
     SNF stays in which the principal diagnosis for the PPH was 
for the medical treatment of cancer. Residents with cancer whose 
principal diagnosis from the PPH was for other medical diagnoses or for 
surgical treatment of their cancer remain included in the measure.
     SNF stays in which the principle diagnosis for the PPH was 
for pregnancy (this is an atypical reason for resident to be admitted 
to SNFs).
     The SNF resident who the SNF subsequently transfers to a 
Federal hospital. A transfer to a Federal hospital is identified when 
discharge code 43 is entered for the patient discharge status field on 
the Medicare claim.
     The SNF resident received care from a provider outside of 
the United States, Puerto Rico, or a U.S. territory, as identified by 
the provider's CCN on the Medicare claim.
     SNF stays with data that are problematic (for example, 
anomalous

[[Page 53278]]

records for hospital stays that overlap wholly or in part or are 
otherwise erroneous or contradictory).
     SNF stays that occurred in a CAH swing bed.
    For additional details on the denominator exclusions, we refer 
readers to the SNF WS PPR measure technical specifications available at 
https://www.cms.gov/files/document/snfvbp-snfwsppr-draft-technical-measure-specification.pdf.
(3) Numerator
    The numerator is defined as the number of SNF residents included in 
the measure denominator who also have an unplanned PPR during an index 
SNF stay. For the purposes of this measure, an unplanned PPR is defined 
as a readmission from a SNF to an acute care hospital or a long-term 
care hospital, with a diagnosis considered to be unplanned and 
potentially preventable. The numerator only includes unplanned PPRs 
that occur during the within-SNF stay period (that is, from the date of 
the SNF admission through and including the date of discharge), which 
can be a hospital readmission that occurs within the SNF stay or a 
direct transfer to a hospital on the date of the SNF discharge. Because 
this measure focuses on potentially preventable and unplanned 
readmissions, we do not count planned readmissions in the numerator. 
Further, because we consider readmissions to inpatient psychiatric 
facilities to be planned, they are also not counted in the numerator.
    The measure numerator is the risk-adjusted ``predicted'' estimate 
of the number of residents with an unplanned PPR that occurred during a 
SNF stay. This estimate starts with the unadjusted, observed count of 
the measure outcome (the number of residents with an unplanned PPR 
during a SNF stay), which is then risk-adjusted for resident 
characteristics and a statistical estimate of the SNF's facility 
effect, to become the risk-adjusted numerator.
e. Risk Adjustment
    The SNF WS PPR measure is risk-adjusted to control for risk factor 
differences across SNF residents and SNF facilities. Specifically, the 
statistical model utilizes a hierarchical logistic regression to 
estimate the effect of resident characteristics on the probability of 
readmission across all SNFs and the effect of each SNF on readmissions 
that differs from that of the average SNF (``facility effect''). The 
denominator is risk-adjusted for resident characteristics only, while 
the numerator is risk-adjusted for both resident characteristics and 
the facility effect. The specific risk adjustment variables included in 
the statistical model for this measure are the following:
     Age and sex category.
     Original reason for Medicare entitlement (disability or 
other).
     Indicator of End-Stage Renal Disease (ESRD).
     Surgery category if present (for example, cardiothoracic, 
orthopedic), as defined in the Hospital Wide Readmission (HWR) measure 
model software. The surgical procedures are grouped using the Clinical 
Classification Software (CCS) classes for ICD-10 procedures developed 
by the Agency for Healthcare Research and Quality (AHRQ).
     Principal diagnosis on PPH inpatient claim. The ICD-10 
codes are grouped clinically using the CCS mappings developed by AHRQ.
     Comorbidities from secondary diagnoses on the PPH 
inpatient claim and diagnoses from earlier hospital inpatient claims up 
to 1 year before the date of the index SNF admission (these are 
clustered using the Hierarchical Condition Categories (HCC) groups used 
by CMS).
     Length of stay in the PPH (categorical to account for 
nonlinearity).
     Prior acute intensive care unit (ICU) or critical care 
unit (CCU) utilization.
     Number of prior acute care hospital discharges in the 
prior year.
    For additional details on the risk adjustment model, we refer 
readers to the SNF WS PPR measure technical specifications available at 
https://www.cms.gov/files/document/snfvbp-snfwsppr-draft-technical-measure-specification.pdf.
f. Measure Calculation
    The SNF WS PPR measure estimates the risk-standardized rate of 
unplanned PPRs that occur during SNF stays among Medicare FFS 
beneficiaries. A lower score on this measure indicates better 
performance. The provider-level risk-standardized readmission rate 
(RSRR) of unplanned PPRs is calculated by multiplying the standardized 
risk ratio (SRR) by the mean readmission rate in the population (that 
is, all Medicare FFS residents included in the measure). The SRR is 
calculated as the predicted number of readmissions at the SNF divided 
by the expected number of readmissions for the same residents if 
treated at the average SNF. For additional details on the calculation 
method, we refer readers to the SNF WS PPR measure technical 
specifications available at https://www.cms.gov/files/document/snfvbp-snfwsppr-draft-technical-measure-specification.pdf.
g. Scoring of SNF Performance on the SNF WS PPR Measure
(1) Background
    In the FY 2017 SNF PPS final rule (81 FR 52000 through 52001), we 
finalized a policy to invert SNFRM measure rates such that a higher 
measure rate reflects better performance on the SNFRM. In that final 
rule, we also stated our belief that this inversion is important for 
incentivizing improvement in a clear and understandable manner, and 
because a ``lower is better'' rate could cause confusion among SNFs and 
the public. In the FY 2023 SNF PPS final rule (87 FR 47568), we applied 
this policy to the SNF HAI measure such that a higher measure rate 
reflects better performance on the SNF HAI measure. We also stated our 
intent to apply this inversion scoring policy to all measures in the 
Program for which the calculation produces a ``lower is better'' 
measure rate. We continue to believe that inverting measure rates such 
that a higher measure rate reflects better performance on a measure is 
important for incentivizing improvement in a clear and understandable 
manner.
    The measure rate inversion scoring policy does not change the 
measure specifications or the calculation method. We use this measure 
rate inversion only as part of the scoring methodology under the SNF 
VBP Program. The measure rate inversion is part of the methodology we 
use to generate measure scores, and resulting SNF Performance Scores, 
that are clear and understandable for SNFs and the public.
(2) Inversion of the SNF WS PPR Measure Rate for SNF VBP Scoring 
Purposes
    In the previous section, we stated that a lower risk-standardized 
rate for the SNF WS PPR measure indicates better performance. 
Therefore, we proposed to apply our measure rate inversion scoring 
policy to the SNF WS PPR measure because a ``lower is better'' rate 
could cause confusion among SNFs and the public. Specifically, we 
proposed to calculate the scores for this measure for the SNF VBP 
Program by inverting the SNF WS PPR measure rates using the following 
calculation:

SNF WS PPR Inverted Rate = 1-Facility's SNF WS PPR Risk Standardized 
Rate

    This calculation will invert SNF WS PPR measure rates such that a 
higher measure rate would reflect better performance.

[[Page 53279]]

h. Confidential Feedback Reports and Public Reporting for the SNF WS 
PPR Measure
    Our confidential feedback reports and public reporting policies are 
codified at Sec.  413.338(f) of our regulations. In the FY 2023 SNF PPS 
final rule (87 FR 47591 through 47592), we revised our regulations such 
that the confidential feedback reports and public reporting policies 
apply to each measure specified for a fiscal year, which includes the 
SNF WS PPR measure beginning with the FY 2028 program year.
    We solicited public comment on our proposal to refine the measure 
specifications for the SNFPPR measure, and our proposal to update the 
measure's name to the ``Skilled Nursing Facility Within-Stay 
Potentially Preventable Readmissions (SNF WS PPR) measure.'' We also 
solicited public comment on our proposal to invert the SNF WS PPR 
measure rate for SNF VBP Program scoring purposes.
    We received public comments on these proposals. The following is a 
summary of the comments we received and our responses.
    Comment: Several commenters supported the proposal to refine the 
SNFPPR measure specifications and update the measure name to the SNF WS 
PPR measure because those proposals more appropriately align the 
measure with changes and improvements within the SNF's control. 
Specifically, commenters supported the change to a within-SNF stay 
readmission specification because it allows for a fairer comparison of 
SNF performance given the socioeconomic and other community factors 
outside a SNF's control that may impact hospital readmissions during 
the periods before SNF admission and after SNF discharge.
    Response: We thank the commenters for their support. We agree that 
this measure refinement allows us to accurately measure the rates of 
PPRs across SNFs and to assess performance based on factors within a 
SNF's control.
    Comment: One commenter, while supporting the proposal to refine the 
SNFPPR measure specifications and update the measure name generally, 
recommended that CMS delay adoption of the SNF WS PPR measures until it 
has been endorsed by the consensus-based entity (CBE).
    Response: SNF VBP measures are not required to be endorsed by the 
CBE to be included in the Program. We will consider submitting this 
measure for endorsement by the CBE in the future.
    Comment: One commenter expressed concern about the proposal to 
implement the SNF WS PPR measure because we would score it using 
predicted and expected outcomes for residents, which may not be 
accurate.
    Response: We do not agree with commenter's concern regarding the 
accuracy and use of predicted and expected outcomes for residents as 
part of the calculation for the SNF WS PPR measure. The ``expected'' 
and ``predicted'' values are estimates of the measure outcome 
(denominator and numerator, respectively) and are calculated by risk 
adjusting the data obtained from the Medicare FFS claims. As we discuss 
in section VIII.G. of this final rule, claims data are validated for 
accuracy by Medicare Administrative Contractors (MACs) and therefore, 
we believe these data are sufficiently validated and accurate for use 
in calculating SNF VBP claims-based measures. Further, the risk 
adjustment model helps ensure we are assessing SNF performance based on 
the quality of care delivered by SNFs. We also note that the current 
measure (SNFRM) is calculated in a similar manner.
    Comment: A few commenters expressed concern about the proposal to 
implement the SNF WS PPR measure, due to the potential to attribute 
preventable hospital readmissions to the SNF when the hospital 
readmission is due to other factors, such as being prematurely 
discharged from a hospital or if a patient's condition worsened before 
admission to a SNF. Specifically, one commenter expressed concern that 
refining the SNFPPR measure specifications to increase the number of 
days between the hospital inpatient discharge and SNF admission could 
increase the potential for factors outside the hospital or SNF's 
control to influence a resident's condition prior to the SNF admission. 
A few commenters recommended that CMS consider expanding the exclusion 
criteria to exclude residents with more complex care and applying 
appropriate risk adjustment. One commenter expressed concern that the 
SNF WS PPR measure could produce counterproductive SNF behavior, such 
as incentivizing SNFs to not admit patients discharged from the 
hospital who have multiple co-morbidities and are at higher risk of 
being readmitted to the hospital, and to only admit those perceived to 
have a lower risk of hospital readmission. One commenter recommended 
that CMS continue to measure how transitioning to the SNF WS PPR 
measure impacts the conditions residents present with at admission.
    Response: We recognize that the measure cannot completely eliminate 
the potential risk of attributing a PPR to a SNF when that readmission 
occurred due to factors outside the SNFs control. However, we believe 
that the SNF WS PPR measure specifications minimize that risk to the 
extent feasible. For example, the SNF WS PPR measure has a robust risk-
adjustment model that controls for numerous variables including 
comorbidities, principal diagnoses for the prior proximal hospital 
inpatient claim, and measures of prior acute care utilization. We also 
note that the WS PPR definition was developed based on findings from an 
environmental scan, empirical analyses, and clinical team evaluations 
to ensure that hospital readmissions included in this measure are 
potentially preventable and unplanned, and that readmissions include 
only PPR conditions associated with post-acute care. For additional 
details on the PPR definition used for the measure, we refer commenters 
to the SNF WS PPR measure technical specifications available at https://www.cms.gov/files/document/snfvbp-snfwsppr-draft-technical-measure-specification.pdf. In addition, we note that section 1888(g)(2) of the 
Act requires that the SNF WS PPR measure be ``all-condition,'' which we 
believe necessitates attributing readmissions to SNFs even in the cases 
the commenter specified.
    The original SNFPPR measure excluded SNF stays with a gap of 
greater than one day between discharge from the prior proximal 
hospitalization and SNF admission in order to harmonize with the SNFRM 
measure specifications. We received public comments and feedback from a 
Technical Expert Panel (TEP) expressing concern with the 1-day prior 
proximal hospitalization lookback window noting that this 1-day 
lookback window does not consider medically complex patients and that 
this criterion did not align with the measure specifications for other 
PPR measures. In response to that feedback, we refined the SNF WS PPR 
measure specifications such that the SNF admission must occur within 30 
days of discharge from the prior proximal hospitalization. This 
refinement aligns the SNF WS PPR measure specifications with those of 
PPR measures used in other CMS Programs, including the SNF PPR post-
discharge measure specified for the SNF QRP. We note that the SNF WS 
PPR measure refinements are associated with improved measure 
reliability and validity. We intend to monitor performance on this 
measure as part of ongoing evaluation efforts.
    We believe the exclusion criteria for the SNF WS PPR measure, as 
detailed in section VIII.B.2.d.(2) of this final rule, in addition to 
the variables included in

[[Page 53280]]

the risk-adjustment model, are sufficient for controlling for medically 
complex residents. For example, the risk-adjustment model includes 
variables relating to comorbidities, principal diagnoses for the prior 
proximal hospital inpatient claim, and measures of prior acute care 
utilization. Therefore, we do not believe it is necessary to expand the 
exclusion criteria to include medically complex residents at this time. 
However, we will take this into consideration as we monitor performance 
on this measure.
    After consideration of public comments, we are finalizing the 
updates to the SNFPPR measure specifications and finalizing our 
proposal to update the measure's name to the ``Skilled Nursing Facility 
Within-Stay Potentially Preventable Readmissions (SNF WS PPR) 
measure.''
3. Replacement of the SNFRM With the SNF WS PPR Measure Beginning With 
the FY 2028 SNF VBP Program Year
    Section 1888(h)(2)(B) of the Act requires the Secretary to apply 
the measure specified under section 1888(g)(2) of the Act, instead of 
the measure specified under section 1888(g)(1) of the Act as soon as 
practicable. To meet that statutory requirement, we proposed to replace 
the SNFRM with the SNF WS PPR measure beginning with the FY 2028 
program year. This is the first program year that we can feasibly 
implement the SNF WS PPR measure after taking into consideration its 
proposed performance period and a number of other statutory 
requirements.
    We proposed a 2-year performance period for the proposed SNF WS PPR 
measure, and we believe the earliest the first performance period can 
occur is FY 2025 and FY 2026 (October 1, 2024 through September 30, 
2026). This will provide us with sufficient time to calculate and 
announce the performance standards for the SNF WS PPR measure at least 
60 days before the beginning of that performance period, as required 
under section 1888(h)(3)(C) of the Act. Additionally, we are required 
under section 1888(h)(7) of the Act to announce the net payment 
adjustments for SNFs no later than 60 days prior to the start of the 
applicable fiscal year. We calculate these payment adjustments using 
performance period data. To provide us with sufficient time to 
calculate and announce the net payment adjustments after the end of the 
performance period (FY 2025 and FY 2026), we believe the earliest 
program year in which we can feasibly adopt the proposed SNF WS PPR 
measure is FY 2028.
    We solicited public comment on our proposal to replace the SNFRM 
with the SNF WS PPR measure beginning with the FY 2028 SNF VBP program 
year.
    We received public comments on this proposal. The following is a 
summary of the comments we received and our responses.
    Comment: Several commenters supported the proposal to replace the 
SNFRM with the SNF WS PPR measure beginning with the FY 2028 program 
year because they agreed that this is the earliest CMS can implement 
this change and that the SNF WS PPR measure is more reflective of 
actions SNF's can take to reduce hospital readmissions.
    Response: We thank the commenters for their support. We agree that 
replacing the SNFRM with the SNF WS PPR measure more appropriately 
assesses the quality of care within the SNF's control.
    Comment: One commenter opposed the proposal to replace the SNFRM 
with the SNF WS PPR measure because the SNFRM is already publicly 
reported and available to consumers.
    Response: The commenter is correct in that we do publicly report 
information on the performance of SNFs with respect to the SNFRM. 
However, we are required at section 1888(h)(2)(B) of the Act to replace 
the measure specified under section 1888(g)(1) of the Act, currently 
the SNFRM, with the measure specified under section 1888(g)(2) of the 
Act, which we proposed as the SNF WS PPR measure. We will also begin 
publicly reporting information on the performance of SNFs with respect 
to the SNF WS PPR measure when the measure is implemented beginning 
with the FY 2028 SNF VBP program year.
    After consideration of public comments, we are finalizing our 
proposal to replace the SNFRM with the SNF WS PPR measure beginning 
with the FY 2028 SNF VBP program year.
4. Adoption of Quality Measures for the SNF VBP Expansion Beginning 
With the FY 2026 Program Year
a. Background
    Section 1888(h)(2)(A)(ii) of the Act (as amended by section 
111(a)(2)(C) of the CAA 2021) allows the Secretary to expand the SNF 
VBP Program to include up to 10 quality measures with respect to 
payments for services furnished on or after October 1, 2023. These 
measures may include measures of functional status, patient safety, 
care coordination, or patient experience. Section 1888(h)(2)(A)(ii) of 
the Act also requires that the Secretary consider and apply, as 
appropriate, quality measures specified under section 1899B(c)(1) of 
the Act.
    In the FY 2023 SNF PPS final rule (87 FR 47564 through 47580), we 
adopted the first three measures for the Program expansion: (1) SNF HAI 
measure; (2) Total Nurse Staffing measure; and (3) DTC PAC SNF measure. 
We adopted the SNF HAI and Total Nurse Staffing measures beginning with 
the FY 2026 program year (FY 2024 is the first performance period). We 
also adopted the DTC PAC SNF measure beginning with the FY 2027 program 
year (FY 2024 and FY 2025 is the first performance period).
    In the proposed rule, we proposed to adopt four additional measures 
for the Program. We proposed one new measure beginning with the FY 2026 
program year (FY 2024 would be the first performance period): Total 
Nursing Staff Turnover (``Nursing Staff Turnover'') measure. We also 
proposed to adopt three new measures beginning with the FY 2027 program 
year (FY 2025 would be the first performance period): (1) Percent of 
Residents Experiencing One or More Falls with Major Injury (Long-Stay) 
(``Falls with Major Injury (Long-Stay)'') measure; (2) Discharge 
Function Score for SNFs (``DC Function measure''); and (3) Number of 
Hospitalizations per 1,000 Long Stay Resident Days (``Long Stay 
Hospitalization'') measure.
    Therefore, for the FY 2024 performance period, we proposed that SNF 
data would be collected for five measures: SNFRM, SNF HAI, Total Nurse 
Staffing, Nursing Staff Turnover, and DTC PAC SNF measures. Performance 
on the first four measures would affect SNF payment in the FY 2026 
program year. Since the DTC PAC SNF measure is a 2-year measure, 
performance on that measure would affect SNF payment in the FY 2027 
program year.
    Beginning with the FY 2025 performance period, SNF data would be 
collected for nine measures: SNFRM, SNF HAI, Total Nurse Staffing, 
Nursing Staff Turnover, DC Function, Falls with Major Injury (Long-
Stay), Long Stay Hospitalization, DTC PAC SNF, and SNF WS PPR measures. 
Performance on the first eight measures will affect SNF payment in the 
FY 2027 program year. Since the SNF WS PPR measure is a 2-year measure, 
performance on this measure will affect SNF payment in the FY 2028 
program year. Further, we refer readers to section VIII.B.3. of this 
final rule for additional details on our replacement of the SNFRM with 
the SNF WS PPR measure beginning with the FY 2028 program year, which 
will mean that the FY 2027 and FY 2028

[[Page 53281]]

program years will each only have eight measures that affect SNF 
payment for those program years. Finally, there is no additional burden 
on SNFs to submit data on these previously adopted and proposed 
measures for the SNF VBP Program.
    Table 15 provides the list of the currently adopted measures and 
proposed measures for the SNF VBP Program.

                         Table 15--Currently Adopted and Newly Proposed SNF VBP Measures
----------------------------------------------------------------------------------------------------------------
                                                                              First program   First performance
           Measure name             Measure short name     Measure status         year             period *
----------------------------------------------------------------------------------------------------------------
SNF 30-Day All-Cause Readmission   SNFRM..............  Adopted,                 ** FY 2017  FY 2015.
 Measure.                                                implemented.
SNF Healthcare-Associated          SNF HAI Measure....  Adopted, not                FY 2026  FY 2024.
 Infections Requiring                                    implemented.
 Hospitalization Measure.
Total Nurse Staffing Hours per     Total Nurse          Adopted, not                FY 2026  FY 2024.
 Resident Day Measure.              Staffing Measure.    implemented.
Total Nursing Staff Turnover       Nursing Staff        Proposed...........     \+\ FY 2026  FY 2024.
 Measure.                           Turnover Measure.
Discharge to Community--Post-      DTC PAC SNF Measure  Adopted, not                FY 2027  FY 2024 and FY
 Acute Care Measure for SNFs.                            implemented.                         2025.
Percent of Residents Experiencing  Falls with Major     Proposed...........     \+\ FY 2027  FY 2025.
 One or More Falls with Major       Injury (Long-Stay)
 Injury (Long-Stay) Measure.        Measure.
Discharge Function Score for SNFs  DC Function Measure  Proposed...........     \+\ FY 2027  FY 2025.
 Measure.
Number of Hospitalizations per     Long Stay            Proposed...........     \+\ FY 2027  FY 2025.
 1,000 Long Stay Resident Days      Hospitalization
 Measure.                           Measure.
SNF Within-Stay Potentially        SNF WS PPR Measure.  Proposed...........     \+\ FY 2028  FY 2025 and FY
 Preventable Readmissions Measure.                                                            2026.
----------------------------------------------------------------------------------------------------------------
* For each measure, we have adopted a policy to automatically advance the beginning of the performance period by
  1-year from the previous program year. We refer readers to section VIII.C.3 of this final rule for additional
  information.
** Will be replaced with the SNF WS PPR measure beginning with the FY 2028 program year.
\+\ First program year in which the measure would be included in the Program.

b. Adoption of the Total Nursing Staff Turnover Measure Beginning With 
the FY 2026 SNF VBP Program Year
(1) Background
    Nursing home staffing, including nursing staff turnover, has long 
been considered an important indicator of nursing home 
quality.255 256 257 Longer-tenured nursing staff are more 
familiar with the residents and are better able to detect changes in a 
resident's condition. They are also more acclimated to their facility's 
procedures and thus, operate more efficiently. In contrast, higher 
nursing staff turnover can mean that nursing staff are less familiar 
with resident needs and facility procedures, which can contribute to 
lower quality of care.
---------------------------------------------------------------------------

    \255\ Centers for Medicare and Medicaid Services. 2001 Report to 
Congress: Appropriateness of Minimum Nurse Staffing Ratios in 
Nursing Homes, Phase II. Baltimore, MD: Centers for Medicare and 
Medicaid Services. http://phinational.org/wp-content/uploads/legacy/clearinghouse/PhaseIIVolumeIofIII.pdf.
    \256\ Institute of Medicine. Nursing Staff in Hospitals and 
Nursing Homes: Is It Adequate? Washington, DC: National Academy 
Press; 1996.
    \257\ ``To Advance Information on Quality of Care, CMS Makes 
Nursing Home Staffing Data Available [verbar] CMS.'' Accessed 
December 22, 2022. https://www.cms.gov/newsroom/press-releases/advance-information-quality-care-cms-makes-nursing-home-staffing-data-available.
---------------------------------------------------------------------------

    There is considerable evidence demonstrating the impact of nursing 
staff turnover on resident outcomes, with higher turnover associated 
with poorer quality of care.258 259 260 261 262 263 264 A 
recent 2019 study comparing nursing home's annualized turnover rates 
with the overall five-star ratings for the facilities found that the 
average total nursing staff annual turnover rates were 53.4 percent 
among one-star nursing homes and 40.7 percent for five-star 
facilities.\265\ The same study found a statistically significant 
relationship between higher turnover rates and lower performance on 
clinical quality measures, including hospitalization rates, readmission 
rates, and emergency department visits.\266\ Studies have also shown 
that nursing staff turnover is a meaningful factor in nursing home 
quality of care and that staff turnover influences quality 
outcomes.267 268 For example, higher staff turnover is 
associated with an increased likelihood of receiving an infection 
control citation.\269\
---------------------------------------------------------------------------

    \258\ Zheng Q, Williams CS, Shulman ET, White AJ. Association 
between staff turnover and nursing home quality--evidence from 
payroll-based journal data. Journal of the American Geriatrics 
Society. May 2022. doi:10.1111/jgs.17843.
    \259\ Bostick JE, Rantz MJ, Flesner MK, Riggs CJ. Systematic 
review of studies of staffing and quality in nursing homes. J Am Med 
Dir Assoc. 2006;7:366-376. https://pubmed.ncbi.nlm.nih.gov/16843237/.
    \260\ Backhaus R, Verbeek H, van Rossum E, Capezuti E, Hamer 
JPH. Nursing staffing impact on quality of care in nursing homes: a 
systemic review of longitudinal studies. J Am Med Dir Assoc. 
2014;15(6):383-393. https://pubmed.ncbi.nlm.nih.gov/24529872/.
    \261\ Spilsbury K, Hewitt C, Stirk L, Bowman C. The relationship 
between nurse staffing and quality of care in nursing homes: a 
systematic review. Int J Nurs Stud. 2011; 48(6):732-750. https://pubmed.ncbi.nlm.nih.gov/21397229/.
    \262\ Castle N. Nursing home caregiver staffing levels and 
quality of care: a literature review. J Appl Gerontol. 2008;27:375-
405. https://doi.org/10.1177%2F0733464808321596.
    \263\ Spilsbury et al.
    \264\ Castle NG, Engberg J. Staff turnover and quality of care 
in nursing homes. Med Care. 2005 Jun;43(6):616-26. doi: 10.1097/
01.mlr.0000163661.67170.b9. PMID: 15908857.
    \265\ Zheng, Q, Williams, CS, Shulman, ET, White, AJ. 
Association between staff turnover and nursing home quality--
evidence from payroll-based journal data. J Am Geriatr Soc. 2022; 
70(9): 2508-2516. doi:10.1111/jgs.17843.
    \266\ Ibid.
    \267\ Centers for Medicare & Medicaid Services. 2001 Report to 
Congress: Appropriateness of Minimum Nurse Staffing Ratios in 
Nursing Homes, Phase II. Baltimore, MD: Centers for Medicare and 
Medicaid Services. http://phinational.org/wp-content/uploads/legacy/clearinghouse/PhaseIIVolumeIofIII.pdf.
    \268\ Loomer, L., Grabowski, DC, Yu, H., & Gandhi, A. (2021). 
Association between nursing home staff turnover and infection 
control citations. Health Services Research. https://doi.org/10.1111/1475-6773.13877.
    \269\ Loomer, L., Grabowski, DC, Yu, H., & Gandhi, A. (2021). 
Association between nursing home staff turnover and infection 
control citations. Health Services Research. https://doi.org/10.1111/1475-6773.13877.
---------------------------------------------------------------------------

    Recently, the National Academies of Sciences, Engineering, and 
Medicine formed the Committee on the Quality of Care in Nursing Homes 
to examine the delivery of care and the complex array of factors that 
influence the quality of

[[Page 53282]]

care in nursing homes. The committee published a report in 2022 titled 
``The National Imperative to Improve Nursing Home Quality.'' The report 
details the complex array of factors that influence care quality in 
nursing homes, including staffing variables such as staffing levels and 
turnover, and identifies several broad goals and recommendations to 
improve the quality of care in nursing homes.\270\ In the 2022 report, 
the National Academies of Sciences, Engineering, and Medicine 
highlighted the association between the high turnover of many nursing 
home staff, including RNs, and lower quality of care delivery in 
nursing homes.\271\ The report also recognized the need for quality 
measures that report on turnover rates, citing that increased 
transparency will improve patient care. Because of its central role in 
the quality of care for Medicare beneficiaries, HHS and the Biden-
Harris Administration are also committed to improving the quality of 
care in nursing homes with respect to staffing, as stated in the fact 
sheets entitled ``Protecting Seniors by Improving Safety and Quality of 
Care in the Nation's Nursing Homes'' and ``Biden-Harris Administration 
Announces New Steps to Improve Quality of Nursing Homes.'' 
272 273 While much of this research has been conducted in 
long-term care facilities or nursing homes, we believe this research is 
relevant to the SNF setting, because approximately 94 percent of long-
term care facilities are dually certified as both SNFs and nursing 
facilities (86 FR 42508).
---------------------------------------------------------------------------

    \270\ National Academies of Sciences, Engineering, and Medicine. 
2022. The National Imperative to Improve Nursing Home Quality: 
Honoring Our Commitment to Residents, Families, and Staff. 
Washington, DC: The National Academies Press. https://doi.org/10.17226/26526.
    \271\ National Academies of Sciences, Engineering, and Medicine, 
2022.
    \272\ The White House. (2022, February 28). FACT SHEET: 
Protecting Seniors by Improving Safety and Quality of Care in the 
Nation's Nursing Homes. https://www.whitehouse.gov/briefing-room/statements-releases/2022/02/28/fact-sheet-protecting-seniors-and-people-with-disabilities-by-improving-safety-and-quality-of-care-in-the-nations-nursing-homes/.
    \273\ The White House. (2021, October 21). FACT SHEET: Biden-
Harris Administration Announces New Steps to Improve Quality of 
Nursing Homes. https://www.whitehouse.gov/briefing-room/statements-releases/2022/10/21/fact-sheet-biden-harris-administration-announces-new-steps-to-improve-quality-of-nursing-homes/.
---------------------------------------------------------------------------

    In light of the strong association between high nursing staff 
turnover rates and negative resident outcomes, including the nursing 
staff turnover measure in the SNF VBP Program will provide a 
comprehensive assessment of the quality of care provided to residents. 
This measure may also drive improvements in nursing staff turnover that 
are likely to translate into positive resident outcomes.
    Although the Nursing Staff Turnover measure is not specified under 
section 1899B(c)(1) of the Act, we believe this measure supports the 
Program's goals to improve the quality of care provided to Medicare 
beneficiaries throughout their entire SNF stay. We have long identified 
staffing as one of the vital components of a SNF's ability to provide 
quality care and use staffing data to gauge a facility's impact on 
quality of care in SNFs with more accuracy and efficacy. The proposed 
measure aligns with the topics listed under section 1888(h)(2)(A)(ii) 
of the Act and with HHS and Biden-Harris Administration priorities. We 
also believe that the Nursing Staff Turnover measure would complement 
the Total Nursing Hours per Resident Day (Total Nurse Staffing) 
measure, adopted in the FY 2023 SNF PPS final rule (87 FR 47570 through 
47576). Together, these measures emphasize and align with our current 
priorities and focus areas for the Program.
(2) Overview of Measure
    The Nursing Staff Turnover measure is a structural measure that 
uses auditable electronic data reported to CMS' Payroll-Based Journal 
(PBJ) system to calculate annual turnover rates for nursing staff, 
including registered nurses (RNs), licensed practical nurses (LPNs), 
and nurse aides. Given the well-documented impact of nurse staffing on 
resident outcomes and quality of care, this measure will align the 
Program with the Care Coordination domain of CMS' Meaningful Measures 
2.0 Framework. The Nursing Staff Turnover measure is currently being 
measured and publicly reported for nursing facilities on the Care 
Compare website https://www.medicare.gov/care-compare/ and is used in 
the Five-Star Quality Rating System. For more information on measure 
specifications and how this measure is used in the Five-Star Quality 
Rating System, we referred readers to the January 2023 Technical Users' 
Guide available at https://www.cms.gov/medicare/provider-enrollment-and-certification/certificationandcomplianc/downloads/usersguide.pdf.
    This measure is constructed using daily staffing information 
submitted through the PBJ system by nursing facilities. Specifically, 
turnover is identified based on gaps in days worked, which helps ensure 
that Nursing Staff Turnover is defined the same way across all nursing 
facilities with SNF beds and that it does not depend on termination 
dates that may be reported inconsistently by these facilities. 
Individuals are identified based on the employee system ID and SNF 
identifiers in the PBJ data. We refer readers to the Nursing Staff 
Turnover measure specifications available at https://www.cms.gov/medicare/provider-enrollment-and-certification/certificationandcomplianc/downloads/usersguide.pdf.
    Payroll data are considered the gold standard for nurse staffing 
measures and are a significant improvement over the manual data 
previously used, wherein staffing information was calculated based on a 
form (CMS-671) filled out manually by the facility.\274\ The PBJ 
staffing data are electronically submitted and auditable back to 
payroll and other verifiable sources. Analyses of PBJ-based staffing 
measures show a relationship between higher nurse staffing levels and 
higher ratings for other dimensions of quality such as health 
inspection survey results and quality measures.\275\
---------------------------------------------------------------------------

    \274\ https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/SurveyCertificationGenInfo/Downloads/QSO18-17-NH.pdf.
    \275\ Zheng, Q, Williams, CS, Shulman, ET, White, AJ. 
Association between staff turnover and nursing home quality--
evidence from payroll-based journal data. J Am Geriatr Soc. 2022; 
70(9): 2508-2516.
---------------------------------------------------------------------------

(a) Interested Parties and TEP Input
    In 2019 through 2022, CMS tested this measure based on input from 
the CMS Five-Star Quality Rating Systems' TEP, as well as input from 
interested parties. We began publicly reporting this measure on the 
Care Compare website via the Nursing Home Five-Star Rating System in 
January 2022.
    We solicited public feedback on this measure in a ``Request for 
Comment on Additional SNF VBP Program Measure Considerations for Future 
Years'' in the FY 2023 SNF PPS proposed rule (87 FR 22786 through 
22787). We considered the input we received as we developed our 
proposal for this measure. We refer readers to the FY 2023 SNF PPS 
final rule (87 FR 47592 through 475963) for a detailed summary of the 
feedback we received on this measure.
(b) Measure Applications Partnership (MAP) Review
    We included the Nursing Staff Turnover measure as a SNF VBP measure 
under consideration in the publicly available ``2022 Measures Under 
Consideration List.'' \276\ The MAP offered conditional support of the 
Nursing Staff Turnover measure for rulemaking, contingent upon 
endorsement by the consensus-based

[[Page 53283]]

entity, noting that the measure would add value to the Program because 
staffing turnover is a longstanding indicator of nursing home quality, 
and it addresses the Care Coordination domain of the Meaningful 
Measures 2.0 Framework. We refer readers to the final 2022-2023 MAP 
recommendations available at https://mmshub.cms.gov/measure-lifecycle/measure-implementation/pre-rulemaking/lists-and-reports.
---------------------------------------------------------------------------

    \276\ 2022 Measures Under Consideration Spreadsheet available at 
https://mmshub.cms.gov/sites/default/files/2022-MUC-List.xlsx.
---------------------------------------------------------------------------

(3) Data Sources
    The Nursing Staff Turnover measure is calculated using auditable, 
electronic staffing data submitted by each SNF for each quarter through 
the PBJ system. Specifically, this measure utilizes five data elements 
from the PBJ data, including employee ID, facility ID, hours worked, 
work date, and job title code.
(4) Inclusion and Exclusion Criteria
    We proposed that SNFs will be excluded from the measure under the 
following conditions:
     Any SNF with 100 percent total nursing staff turnover for 
any day in the six-quarter period during which there were at least five 
eligible nurse staff. A 100 percent daily turnover is typically the 
result of changes in the employee IDs used by SNFs and does not reflect 
actual staff turnover.
     SNFs that do not submit staffing data or submit data that 
are considered invalid (using the current exclusion rules for the 
staffing domain) for one or more of the quarters used to calculate the 
Nursing Staff turnover measure.
     SNFs that do not have resident census information (derived 
from MDS assessments).
     SNFs with fewer than five eligible nurses (RNs, LPNs and 
nurse aides) in the denominator.
(a) Denominator
    The denominator for the Nursing Staff Turnover measure includes all 
eligible employees, defined as RNs, LPNs, and nurse aides, who are 
regular employees and agency staff who work at a Medicare certified SNF 
and use the same job category codes as other nurse staffing measures 
that are reported on the Care Compare website. For the purposes of this 
measure, the RN category is defined as RNs (job code 7), RN director of 
nursing (job code 5), and RNs with administrative duties (job code 6). 
The LPN category is defined as LPNs (job code 9) and LPNs with 
administrative duties (job code 8). The nurse aide category is defined 
as certified nurse aides (job code 10), aides in training (job code 
11), and medication aides/technicians (job code 12). This measure only 
includes eligible employees who work at least 120 hours in a 90-day 
period. The timeframe for the 90-day period begins on the first workday 
observed during the quarter prior to the start of the performance 
period (termed the baseline quarter) and ends on the last workday, of 
the last month, of the second quarter of the performance period. 
Eligible employees who work infrequently (that is, those who work fewer 
than 120 hours during a 90-day period, including those who only 
occasionally cover shifts at a nursing home) would be excluded from the 
denominator calculation.
(b) Numerator
    The numerator includes eligible employees who were included in the 
denominator and who are not identified in the PBJ data as having worked 
at the SNF for at least 60 consecutive days during the performance 
period. The 60-day gap must start during the period covered by the 
turnover measure. The turnover date is defined as the last workday 
prior to the start of the 60-day gap.
(5) Measure Calculation
    The Nursing Staff Turnover measure is calculated using six 
consecutive quarters of PBJ data. Data from a baseline quarter,\277\ 
Q0, along with the first two quarters of the performance period, are 
used for identifying employees who are eligible to be included in the 
measure (denominator). The four quarters of data (Q1 through Q4) of the 
performance period are used for identifying the number of employment 
spells, defined as a continuous period of work, that ended in turnover 
(numerator). Data from the sixth quarter (Q5), which occurs after the 
four-quarter numerator (performance) period, are used to identify gaps 
in days worked that started in the last 60 days of the fifth quarter 
(Q4) used for the measure. To calculate the measure score, we first 
determine the measure denominator by identifying the total number of 
employment spells, defined as a continuous period of work. For example, 
for the FY 2026 program year, the denominator will be calculated as the 
number of eligible employees who worked 120 or more hours in a 90-day 
period with the first workday of the 90-day period occurring in FY 2023 
Q4, the quarter prior to the start of the performance period (Q0), 
through FY 2024 Q2, the first 2 quarters of the performance period 
(July 1, 2023 through March 31, 2024). The numerator is calculated as 
the total number of eligible employees who had a 60-day gap from 
October 1, 2023 through September 30, 2024 during which they did not 
work. Data from FY 2025 Q1, defined as Q5 above, is also used to 
identify gaps that start within 60 days of the end of the performance 
period (August 2, 2024 through September 30, 2024).
---------------------------------------------------------------------------

    \277\ The baseline quarter is specific to this measure 
calculation and not related to the SNF VBP Program's measure 
baseline period, which is part of the performance standards used to 
score the measure. The baseline quarter is the quarter prior to the 
first quarter of either the baseline period or the performance 
period for a program year.
---------------------------------------------------------------------------

    We proposed to calculate the Nursing Staff Turnover measure rate 
for the SNF VBP Program using the following formula:

[GRAPHIC] [TIFF OMITTED] TR07AU23.707

    We also note that based on analysis and previous research on 
turnover measures, and a review by a technical expert panel, the 
Nursing Staff Turnover measure is not risk-adjusted.
    We solicited public comment on our proposal to adopt the Total 
Nursing Staff Turnover measure beginning with the FY 2026 SNF VBP 
program year.
    We received public comments on this proposal. The following is a 
summary of the comments we received and our responses.
    Comment: Many commenters supported CMS's proposal to adopt the 
Total Nursing Staff Turnover Measure because it provides a meaningful 
assessment of the quality of care provided to SNF residents.
    Response: We thank the commenters for their support. We agree that 
this measure will provide valuable insight

[[Page 53284]]

into the quality of care that SNF residents are receiving.
    Comment: A few commenters that supported the proposed measure also 
recommended that a retention measure either be added or used in place 
of the turnover measure to help incentivize positive behavior by SNFs. 
One commenter recommended that CMS develop a resident ``dumping'' 
measure as a metric to reduce facility-initiated transfers and 
discharges which negatively impact residents and their quality of care.
    Response: We thank the commenters for their recommendations and 
will take this feedback into consideration as we develop additional 
measures for future rulemaking.
    Comment: A few commenters supported the measure generally but 
recommended that CMS consider a number of factors with respect to both 
the proposed measure and potential future measures. One commenter 
suggested that CMS revise the proposed measure to exclude team members 
that move, or float, within a health system. A few commenters 
recommended that CMS consider the impact of staffing changes when 
employees do not work for a period of time that exceeds 60 days (for 
example, because of family or medical leave) but indicate their 
intention to return. Several commenters did not support the proposed 
measure because it does not exclude staff that have taken parental 
leave or are students or seasonal workers. A few commenters recommended 
expanding the length of the gap beyond 60 days or providing an 
adjustment for workers returning from an approved leave. One commenter 
stated that the proposed measure should take into consideration a 
differential impact of staff turnover on residents depending on the 
role of the exiting nursing staff member within the SNF. One commenter 
suggested that the measure be revised to include all direct care 
workers and rehabilitation professionals in SNFs because they all 
impact performance and quality of care. One commenter recommended that 
CMS monitor the impact of the measure by assessing the relationship 
between resident outcomes and staff turnover to see if SNFs change 
their behavior in ways that may lower quality of care.
    Response: We carefully considered different turnover 
specifications, including the 60-day gap threshold for turnover, the 
inclusion of agency and other types of nursing staff, and the minimum 
number of hours required to be included in the measure. The final 
measure specifications were developed based on extensive data analyses, 
as well as recommendations to us from the project's Technical Expert 
Panel (TEP) convened by a CMS contractor. We believe this measure, as 
proposed, is both a reliable and valid measure of nursing staff 
turnover. We tested the validity of the measure by examining the 
association between the Nursing Staff Turnover measure and a 
comprehensive set of measures that capture nursing home quality, 
including nursing home ratings from Care Compare's Five-Star Quality 
Rating System and claims-based measures of hospitalizations and 
outpatient Emergency Department visits for both short- and long-stay 
residents. We found a consistent and statistically significant 
relationship between the Nursing Staff Turnover measure and this 
comprehensive set of measures that capture nursing home quality.\278\ 
For reliability testing, we used split-sample reliability testing. We 
calculated the Shrout-Fleiss intraclass correlation coefficient (ICC) 
between the split-half scores to measure reliability. The split-sample 
ICC was 0.834. The results of this extensive testing indicate the 
strong relationship between nursing staff turnover, as proposed, and 
quality of care. It shows that the quality of care is impacted when a 
caregiver does not report any hours worked for 60 days or more whether 
they are still officially employed by the SNF or not. Additionally, we 
conducted analyses that showed a very high correlation in nursing home 
turnover rates for a measure based on different gaps in days worked 
(for example, 30, 60, 90 days) suggesting extending the number of days 
in the gap would have little impact on the measure rate. Lastly, the 
PBJ data that we use to calculate the turnover measures do not allow us 
to identify individuals who have taken a period of leave but intend to 
return to work.
---------------------------------------------------------------------------

    \278\ Zheng, Q, Williams, CS, Shulman, ET, White, AJ. 
Association between staff turnover and nursing home quality--
evidence from payroll-based journal data. J Am Geriatr Soc. 2022; 
70(9): 2508-2516.
---------------------------------------------------------------------------

    Although we recognize that all staff may have an impact on resident 
quality, there is substantial literature documenting the relationship 
between nursing staff turnover and quality.279 280 281 282 
Additional research supports that all nursing staff, including 
certified nursing assistants and LPNs, play a critical role in 
providing care to Medicare beneficiaries in SNFs.\283\ Because of this 
extensive evidence, we chose to focus on nursing staff turnover at this 
time.
---------------------------------------------------------------------------

    \279\ Zheng Q, Williams CS, Shulman ET, White AJ Association 
between staff turnover and nursing home quality--evidence from 
payroll-based journal data. Journal of the American Geriatrics 
Society. May 2022. doi:10.1111/jgs.17843.
    \280\ Bostick JE, Rantz MJ, Flesner MK, Riggs CJ Systematic 
review of studies of staffing and quality in nursing homes. J Am Med 
Dir Assoc. 2006;7:366-376. https://pubmed.ncbi.nlm.nih.gov/16843237/.
    \281\ Backhaus R, Verbeek H, van Rossum E, Capezuti E, Hamer JPH 
Nursing staffing impact on quality of care in nursing homes: a 
systemic review of longitudinal studies. J Am Med Dir Assoc. 
2014;15(6):383-393. https://pubmed.ncbi.nlm.nih.gov/24529872/.
    \282\ Spilsbury K., Hewitt C., Stirk L., Bowman C. The 
relationship between nurse staffing and quality of care in nursing 
homes: a systematic review. Int J Nurs Stud. 2011; 48(6):732-750. 
https://pubmed.ncbi.nlm.nih.gov/21397229/.
    \283\ Bostick JE, Rantz MJ, Flesner MK, Riggs CJ. Systematic 
review of studies of staffing and quality in nursing homes. J Am Med 
Dir Assoc. 2006;7:366-376. https://pubmed.ncbi.nlm.nih.gov/16843237/.
---------------------------------------------------------------------------

    Comment: A few commenters supported the proposed measure in concept 
but expressed concern that the measure may not accurately reflect true 
nursing staff turnover. A few commenters stated that the measure should 
distinguish between voluntary and involuntary turnover because they 
believe SNFs should not be negatively impacted by the latter. A few 
commenters stated that the inclusion of contracted nursing staff would 
lead to inaccurate nursing staff turnover counts. One commenter stated 
that the inclusion of nursing staff who work solely in an 
administrative capacity and do not perform direct resident care would 
lead to inaccurate nursing staff turnover counts. One commenter 
suggested that CMS delay the implementation of this measure to develop 
a way to index SNFs to a regional nursing staff turnover measure that 
would better reflect local labor market variance and factors within a 
SNF's control.
    Response: There is significant research connecting nursing staff 
turnover with resident outcomes (88 FR 21366). The TEP convened by our 
contractor concluded that continuity of care is impacted when a 
caregiver does not work for 60 or more days, regardless of whether they 
are still employed by the facility or the reason they are no longer 
employed (on a voluntary or involuntary basis). This was further 
supported by the analysis we conducted that showed a strong 
relationship between the Nursing Staff Turnover measure, as proposed, 
and quality of care.\284\ In addition to evidence linking nursing staff 
turnover to quality, there is also evidence of a significant 
relationship between directors of nursing and nursing administrator 
turnover and resident quality of care.

[[Page 53285]]

Specifically, retention of directors of nursing and nursing 
administrators is associated with better resident outcomes and fewer 
facility health and safety deficiencies.\285\ Thus, we believe it is 
appropriate to include nurses with administrative responsibilities in 
this measure. We also note that we do not believe delaying this measure 
to incorporate regional differences is necessary or appropriate at this 
time. As described previously in this section, this measure went 
through extensive reliability and validity testing and thus we are 
confident that this measure, as proposed, is reliable, valid, and an 
excellent indicator of quality. However, we will continue to assess the 
measure and if needed, propose measure updates in future rulemaking.
---------------------------------------------------------------------------

    \284\ Zheng Q, Williams CS, Shulman ET, White AJ. Association 
between staff turnover and nursing home quality--evidence from 
payroll-based journal data. Journal of the American Geriatrics 
Society. May 2022. doi:10.1111/jgs.17843.
    \285\ Bostick JE, Rantz MJ, Flesner MK, Riggs CJ. Systematic 
review of studies of staffing and quality in nursing homes. J Am Med 
Dir Assoc. 2006;7:366-376. https://pubmed.ncbi.nlm.nih.gov/16843237/.
---------------------------------------------------------------------------

    Comment: Many commenters did not support the proposed Nursing Staff 
Turnover measure because they believe it is unrelated to the intent of 
the program and reflects circumstances outside of SNFs' control such as 
market conditions. One commenter stated that the proposed measure is 
not a good indicator of high-quality care because of current healthcare 
workforce challenges that are outside the control of SNFs. One 
commenter believed this measure is solving a problem that does not 
exist and that current staffing standards are adequate to ensure 
patient safety. One commenter requested that CMS delay implementing the 
proposed measure until the nurse staffing minimum standards that the 
agency is developing are finalized and implemented in long-term care 
facilities. One commenter noted that the proposed measure will not be 
risk-adjusted and urged CMS to consider adding risk adjustment to the 
measure.
    Response: We recognize the relationship between nursing staff 
turnover and quality of care is multi-faceted, but we disagree that 
this measure is unrelated to the intent of the Program to reward SNFs 
that provide high quality care. We refer commenters to the proposed 
rule (88 FR 21366 through 21367) where we discussed several studies 
that emphasize the evidence of a relationship between nursing staff 
turnover, quality of care, and patient outcomes. We have selected this 
measure as a complement to the Total Nursing Staffing measure we 
finalized in the FY 2023 SNF PPS final rule (87 FR 47576) and as an 
additional step towards addressing this complex relationship between 
nurse staffing and quality of care. There are ongoing efforts at CMS to 
address staffing, including discussions around nurse staffing minimum 
standards. However, nursing staff minimums and turnover are distinct, 
and we do not believe those efforts need to be in place prior to 
finalizing this Nursing Staff Turnover measure for the SNF VBP Program. 
We reiterate that the proposed Nursing Staff Turnover measure is 
reliable and valid, and we do not anticipate staffing minimums having 
significant impact on this proposed measure. Regarding risk-adjustment, 
as we stated in the proposed rule (88 FR 21368), based on analysis and 
previous research on turnover measures, and a review by a TEP convened 
by our contractor, we do not believe the Nursing Staff Turnover measure 
needs to be risk-adjusted at this time. We do not believe that 
differences in nursing home turnover rates are related to nursing home 
acuity. Rather, we believe that turnover is related to management 
practices such as high-quality leadership, valuing and respecting 
nursing staff, positive human resource practices, work organization and 
care practices that help to retain staff and build relationships, and 
compensation and benefits, among others. It would not be appropriate to 
have any type of adjustment for these factors; however, we will 
continue to monitor the data and adjust as needed in future rulemaking.
    Comment: Several commenters did not support the proposed measure 
because SNFs are being impacted by widespread healthcare personnel 
shortages for which they believe SNFs should not be penalized. A few 
commenters expressed concern that SNFs do not have the financial 
support for retention and recruitment and that finalizing this measure 
could make turnover worse as facilities will be penalized and will then 
have less money to hire and train additional staff. One commenter 
suggested CMS instead focus on limiting the number of staffing agencies 
that are contributing to the staffing crisis. One commenter was 
concerned that SNFs will have to choose between having enough staff and 
accepting agency staff at the cost of poor performance on the measure.
    Response: We recognize that the past few years, which included the 
COVID-19 PHE, have significantly affected SNF operations and staffing. 
We also remain committed to the importance of value-based care and 
incentivizing quality care tied to payment. SNF staffing, including 
turnover, is a high priority for us because of its central role in the 
quality of care for SNF residents. As described previously in this 
section, the measure specifications were developed based on extensive 
data analyses, as well as recommendations to us from the project's TEP 
convened by a CMS contractor. This measure is both a reliable and valid 
measure of nursing staff turnover as proposed, and therefore, we 
continue to believe that this measure will provide a more comprehensive 
assessment of, and accountability for, the quality of care provided to 
residents despite staffing challenges. Further, this measure, which 
includes agency staff, has been shown to have a strong relationship 
with quality of care, and thus we do not believe it is appropriate to 
revise the measure.\286\ We will continue to evaluate the impact on 
SNFs' behaviors, staffing levels, and quality outcomes as the measure 
is implemented in the Program.
---------------------------------------------------------------------------

    \286\ Zheng Q, Williams CS, Shulman ET, White AJ. Association 
between staff turnover and nursing home quality--evidence from 
payroll-based journal data. Journal of the American Geriatrics 
Society. May 2022. doi:10.1111/jgs.17843.
---------------------------------------------------------------------------

    Comment: One commenter did not support the measure without 
endorsement by the CBE.
    Response: We note the SNF VBP Program is not required to seek 
endorsement by the CBE to include measures in the Program. We will 
consider submitting this measure for endorsement by the CBE in the 
future.
    Comment: A few commenters believed the measure is overly 
complicated. One commenter expressed that the measure will only add to 
the reporting burden for SNFs.
    Response: The Nursing Staff Turnover measure should already be 
familiar to SNFs that are dually certified as nursing facilities (NFs) 
because nursing facilities are currently required to report to us the 
data needed to calculate the measure. We publicly report data on the 
measure on the Care Compare website (https://www.medicare.gov/care-compare/) for the Five-Star Quality Rating System. We chose to align 
the specifications for the proposed measure with the specifications for 
the turnover measure being reported by NFs to reduce the reporting 
burden for SNFs under the SNF VBP.
    Comment: One commenter suggested that CMS should collaborate with 
congressional leaders to provide additional funding to both State and 
Federal VBP programs instead of offering quality measures that are 
poorly conceived, like the Nursing Staff Turnover measure.
    Response: As noted previously, we believe the Nursing Staff 
Turnover measure has strong reliability and validity, and the measure 
was strongly supported in recommendations made by

[[Page 53286]]

the TEP convened by CMS contractors. For the SNF VBP Program, the 
Medicare Payment Advisory Commission (MedPAC) found, according to the 
2023 Report to Congress on Medicare Payment Policy, that Medicare 
payments for SNFs were adequate in the latest year of available 
data.\287\ Additionally, this same report found that a combination of 
federal policies and the implementation of the new case-mix system 
resulted in improved financial performance for SNFs, indicating 
providing additional funding for SNFs unrelated to quality is not 
appropriate at this time. The goal of this Program is to incentivize 
high quality care. We believe the addition of the Nursing Staff 
Turnover measure helps us meet this goal because the measure displays a 
strong relationship to quality.\288\
---------------------------------------------------------------------------

    \287\ MedPAC, 2023 https://www.medpac.gov/wp-content/uploads/2023/03/Mar23_MedPAC_Report_To_Congress_SEC.pdf.
    \288\ Zheng Q, Williams CS, Shulman ET, White AJ. Association 
between staff turnover and nursing home quality--evidence from 
payroll-based journal data. Journal of the American Geriatrics 
Society. May 2022. doi:10.1111/jgs.17843.
---------------------------------------------------------------------------

    Comment: One commenter requested CMS amend the PBJ data submission 
policies to allow facilities to submit payroll data used to calculate 
the Nursing Staff Turnover measure after the submission deadline to 
allow SNFs to provide the most complete and accurate staffing data for 
consumers.
    Response: We thank the commenter for their suggestion. This request 
would be a considerable update to our current policies around data 
submission that impacts programs beyond the SNF VBP Program. However, 
we will take it into consideration for future rulemaking.
    After consideration of public comments, we are finalizing adoption 
of the Total Nursing Staff Turnover measure beginning with the FY 2026 
SNF VBP program year.
c. Adoption of the Percent of Residents Experiencing One or More Falls 
With Major Injury (Long-Stay) Measure Beginning With the FY 2027 SNF 
VBP Program Year
    We proposed to adopt the Percent of Residents Experiencing One or 
More Falls with Major Injury (Long-Stay) Measure (``Falls with Major 
Injury (Long-Stay) measure'') beginning with the FY 2027 SNF VBP 
program year. The Falls with Major Injury (Long-Stay) measure is an 
outcome measure that estimates the percentage of long-stay residents 
who have experienced one or more falls with major injury. We refer 
readers to the specifications for this measure, which are located in 
the Minimum Data Set (MDS) 3.0 Quality Measures User's Manual Version 
15 available at https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/nursinghomequalityinits/nhqiqualitymeasures. The Falls with Major Injury (Long-Stay) measure 
was endorsed by the consensus-based entity (CBE) in 2011. The measure 
is currently reported by nursing facilities under the CMS Nursing Home 
Quality Initiative (NHQI) and the Five-Star Quality Rating System and 
those results are publicly reported on the Care Compare website, 
available at https://www.medicare.gov/care-compare/.
(1) Background
    Falls are the leading cause of injury-related death among persons 
aged 65 years and older. According to the Centers for Disease Control 
and Prevention (CDC), approximately one in four adults aged 65 years 
and older fall each year, and fall-related emergency department visits 
are estimated at approximately 3 million per year.\289\ In 2016, nearly 
30,000 U.S. residents aged 65 years and older died as the result of a 
fall, resulting in an age-adjusted mortality rate of 61.6 deaths per 
100,000 people. This represents a greater than 30 percent increase in 
fall-related deaths from 2007, where the age-adjusted mortality rate 
was 47.0 deaths per 100,000 people.\290\ Additionally, the death rate 
from falls was higher among adults aged 85 years and older as indicated 
by a mortality rate of 257.9 deaths per 100,000 people.\291\
---------------------------------------------------------------------------

    \289\ Burns E, Kakara R. Deaths from Falls Among Persons Aged 
>=65 Years--United States, 2007-2016. MMWR Morb Mortal Wkly Rep 
2018;67:509-514. DOI: http://dx.doi.org/10.15585/mmwr.mm6718a1.
    \290\ Ibid.
    \291\ Ibid.
---------------------------------------------------------------------------

    Of the 1.6 million residents in U.S. nursing facilities, 
approximately half fall annually, with one in three having two or more 
falls in a year. One in every ten residents who falls has a serious 
related injury, and about 65,000 residents suffer a hip fracture each 
year.\292\ An analysis of MDS data from FY 2019 Q2 found that, among 
the 14,586 nursing facilities included in the sample, the percent of 
long-stay residents who experienced one or more falls with major injury 
ranged from zero percent to nearly 21 percent. This wide variation in 
facility-level fall rates indicates a performance gap and suggests that 
there are opportunities to improve performance on this measure.
---------------------------------------------------------------------------

    \292\ The Falls Management Program: A Quality Improvement 
Initiative for Nursing Facilities: Chapter 1. introduction and 
program overview. Agency for Healthcare Research and Quality. 
https://www.ahrq.gov/patient-safety/settings/long-term-care/resource/injuries/fallspx/man1.html. Published December 2017. 
Accessed December 13, 2022.
---------------------------------------------------------------------------

    It is important to monitor injurious falls among the long-stay 
population because of the potentially negative impacts on resident 
health outcomes and quality of life. Research has found that injurious 
falls are one of the leading causes of disability and death for all 
nursing home residents. Specifically, falls have serious health 
consequences, such as reduced quality of life, decreased functional 
abilities, anxiety and depression, serious injuries, and increased risk 
of morbidity and mortality.293 294
---------------------------------------------------------------------------

    \293\ The Falls Management Program: A Quality Improvement 
Initiative for Nursing Facilities: Chapter 1. Introduction and 
Program Overview. Agency for Healthcare Research and Quality. 
https://www.ahrq.gov/patient-safety/settings/long-term-care/resource/injuries/fallspx/man1.html. Published December 2017. 
Accessed December 13, 2022.
    \294\ Bastami M, Azadi A. Effects of a Multicomponent Program on 
Fall Incidence, Fear of Falling, and Quality of Life among Older 
Adult Nursing Home Residents. Ann Geriatr Med Res. 2020;24(4):252-
258. doi:10.4235/agmr.20.0044.
---------------------------------------------------------------------------

    Injurious falls are also a significant cost burden to the entire 
healthcare system. The U.S. spends approximately $50 billion on medical 
costs related to non-fatal fall-related injuries and $754 million on 
medical costs related to fatal falls annually.\295\ Of the amount paid 
on non-fatal fall injuries, Medicare pays approximately $29 billion, 
while private or out-of-pocket payers pay $12 billion. Research 
suggests that acute care costs incurred for falls among nursing home 
residents range from $979 for a typical case with a simple fracture to 
$14,716 for a typical case with multiple injuries.\296\ Other research 
examining hospitalizations of nursing home residents with serious fall-
related injuries (intracranial bleed, hip fracture, or other fracture) 
found an average cost of $23,723.\297\
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    \295\ Cost of older adult falls. Centers for Disease Control and 
Prevention. https://www.cdc.gov/falls/data/fall-cost.html. Published 
July 9, 2020. Accessed December 13, 2022.
    \296\ Sorensen SV, de Lissovoy G, Kunaprayoon D, Resnick B, 
Rupnow MF, Studenski S. A taxonomy and economic consequence of 
nursing home falls. Drugs Aging. 2006;23(3):251-62.
    \297\ Quigley PA, Campbell RR, Bulat T, Olney RL, Buerhaus P, 
Needleman J. Incidence and cost of serious fall-related injuries in 
nursing homes. Clin Nurs Res. Feb 2012;21(1):10-23.
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    Research has found that 78 percent of falls are anticipated 
physiologic falls, which are defined as falls among individuals who 
scored high on a risk assessment scale, meaning their risk could have 
been identified in advance of the fall.\298\ To date, studies have

[[Page 53287]]

identified a number of risk factors for falls within the long-stay 
population, including impaired cognitive function, history of falls, 
difficulties with walking and balancing, vitamin D deficiency, and use 
of psychotropic medications.299 300 301 In addition, 
residents who experience dementia or depression, are underweight, or 
are over the age of 85 are at a higher risk of 
falling.302 303 304 While much of this research has been 
conducted in long-term care facilities or nursing homes, we believe 
this research is relevant to the SNF setting, because approximately 94 
percent of long-term care facilities are dually certified as both SNFs 
and nursing facilities (86 FR 42508). Therefore, these risk factors 
described above suggest that SNFs may be able to identify, reduce, and 
prevent the incidence of falls among their 
residents.305 306 307 308
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    \298\ Morse, J.M. Enhancing the safety of hospitalization by 
reducing patient falls. Am J Infect Control 2002; 30(6): 376-80.
    \299\ Cost of older adult falls. Centers for Disease Control and 
Prevention. https://www.cdc.gov/falls/data/fall-cost.html. Published 
July 9, 2020. Accessed December 13, 2022.
    \300\ Galik, E., Resnick, B., Hammersla, M., & Brightwater, J. 
(2014). Optimizing function and physical activity among nursing home 
residents with dementia: testing the impact of function-focused 
care. Gerontologist 54(6), 930-943. https://doi.org/10.1093/geront/gnt108.
    \301\ Broe KE, Chen TC, Weinberg J, Bischoff-Ferrari HA, Holick 
MF, Kiel DP. A higher dose of vitamin d reduces the risk of falls in 
nursing home residents: a randomized, multiple-dose study. J Am 
Geriatr Soc. 2007;55(2):234-239. doi:10.1111/j.1532-
5415.2007.01048.x.
    \302\ Zhang N, Lu SF, Zhou Y, Zhang B, Copeland L, Gurwitz JH. 
Body Mass Index, Falls, and Hip Fractures Among Nursing Home 
Residents. J Gerontol A Biol Sci Med Sci. 2018;73(10):1403-1409. 
doi:10.1093/gerona/gly039.
    \303\ Fernando E, Fraser M, Hendriksen J, Kim CH, Muir-Hunter 
SW. Risk Factors Associated with Falls in Older Adults with 
Dementia: A Systematic Review. Physiother Can. 2017;69(2):161-170. 
doi:10.3138/ptc.2016-14.
    \304\ Grundstrom AC, Guse CE, Layde PM. Risk factors for falls 
and fall-related injuries in adults 85 years of age and older. Arch 
Gerontol Geriatr. 2012;54(3):421-428. doi:10.1016/
j.archger.2011.06.008.
    \305\ Morris JN, Moore T, Jones R, et al. Validation of long-
term and post-acute care quality indicators. CMS Contract No: 500-
95-0062.
    \306\ Chen XL, Liu YH, Chan DK, Shen Q, Van Nguyen H. Chin Med J 
(Engl). Characteristics associated with falls among the elderly 
within aged care wards in a tertiary hospital: A Retrospective. 2010 
Jul; 123(13):1668-72.
    \307\ Fonad E, Wahlin TB, Winblad B, Emami A, Sandmark H. Falls 
and fall risk among nursing home residents. J Clin Nurs. 2008 Jan; 
17(1):126-34.
    \308\ Lee JE, Stokic DS. Risk factors for falls during inpatient 
rehabilitation. Am J Phys Med Rehabil. 2008 May; 87(5):341-50; quiz 
351, 422.
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    Given the effects of falls with major injury, preventing and 
reducing their occurrence in SNFs is critical to delivering safe and 
high-quality care. We believe the Falls with Major Injury (Long-Stay) 
measure aligns with this goal by monitoring the occurrence of falls 
with major injury and assessing SNFs on their performance on fall 
prevention efforts. In doing so, we believe this measure will promote 
patient safety and increase the transparency of care quality in the SNF 
setting, and it will align the Program with the Patient Safety domain 
of CMS' Meaningful Measures 2.0 Framework.\309\
---------------------------------------------------------------------------

    \309\ Centers for Medicare & Medicaid Services. Meaningful 
Measures Framework. Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/CMS-Quality-Strategy.
---------------------------------------------------------------------------

    We believe there are effective interventions that SNFs can 
implement to reduce and prevent falls, including those that cause major 
injury. Specifically, several studies observed that multifactorial 
interventions such as exercise, medication review, risk assessment, 
vision assessment, and environmental assessment significantly reduce 
fall rates.310 311 312 Another study found that a single 
intervention of exercise reduced the number of resident falls in the 
nursing home setting by 36 percent and the number of recurrent fallers 
by 41 percent.\313\ Additionally, various systematic reviews link 
facility structural characteristics to falls with major injury. For 
example, the incorporation of adequate equipment throughout the 
facility, such as hip protectors or equipment used for staff education 
tasks, may reduce fall rates or fall-related 
injuries.314 315 In addition, poor communication between 
staff, inadequate staffing levels, and limited facility equipment have 
been identified as barriers to implementing fall prevention programs in 
facilities.\316\ Other studies have shown that proper staff education 
can significantly reduce fall rates.317 318 The 
effectiveness of these interventions suggest improvement of fall rates 
among SNF residents is possible through modification of provider-led 
processes and interventions, which supports the overall goal of the SNF 
VBP Program.
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    \310\ Gulka, H.J., Patel, V., Arora, T., McArthur, C., & Iaboni, 
A. (2020). Efficacy and generalizability of falls prevention 
interventions in nursing homes: A systematic review and meta-
analysis. Journal of the American Medical Directors Association, 
21(8), P1024-1035.E4. https://doi.org/10.1016/j.jamda.2019.11.012.
    \311\ Tricco, A.C., Thomas, S. M., Veroniki, A.A., Hamid, J.S., 
Cogo, E., Strifler, L., Khan, P.A., Robson, R., Sibley, K.M., 
MacDonald, H., Riva, J.J., Thavorn, K., Wilson, C., Holroyd-Leduc, 
J., Kerr, G.D., Feldman, F., Majumdar, S.R., Jaglal, S.B., Hui, W., 
& Straus, S.E. (2017). Comparisons of interventions for preventing 
falls in older adults: A systematic review and meta-analysis. 
Journal of the American Medical Association, 318(17), 1687-1699. 
https://doi.org/10.1001/jama.2017.15006.
    \312\ Vlaeyen, E., Coussement, J., Leysens, G., Van der Elst, 
E., Delbaere, K., Cambier, D., Denhaerynck, K., Goemaere, S., 
Wertelaers, A., Dobbels, F., Dejaeger, E., & Milisen, K. (2015). 
Characteristics and effectiveness of fall prevention programs in 
nursing homes: A systematic review and meta-analysis of randomized 
control trials. Journal of the American Geriatrics Society, 6(3), 
211-21. https://doi.org/10.1111/jgs.13254.
    \313\ Gulka, H.J., Patel, V., Arora, T., McArthur, C., & Iaboni, 
A. (2020). Efficacy and generalizability of falls prevention 
interventions in nursing homes: A systematic review and meta-
analysis. Journal of the American Medical Directors Association, 
21(8), P1024-1035.E4. https://doi.org/10.1016/j.jamda.2019.11.012.
    \314\ Crandall, M., Duncan, T., Mallat, A., Greene, W., Violano, 
P., & Christmas, B. (2016). Prevention of fall-related injuries in 
the elderly: An eastern association for the surgery of trauma 
practice management guideline. Journal of Trauma and Acute Care 
Surgery, 81(1), 196-206. https://doi.org/10.1097/TA.0000000000001025.
    \315\ Vlaeyen, E., Stas, J., Leysens, G., Van der Elst, E., 
Janssens, E., Dejaeger, E., Dobbels, F., & Milisen, K. (2017). 
Implementation of fall prevention in residential care facilities: A 
systematic review of barriers and facilitators. International 
Journal of Nursing Studies, 70, 110-121. https://doi.org/10.1016/j.ijnurstu.2017.02.002.
    \316\ Ibid.
    \317\ Gulka, H.J., Patel, V., Arora, T., McArthur, C., & Iaboni, 
A. (2020). Efficacy and generalizability of falls prevention 
interventions in nursing homes: A systematic review and meta-
analysis. Journal of the American Medical Directors Association, 
21(8), P1024-1035.E4. https://doi.org/10.1016/j.jamda.2019.11.012.
    \318\ Tricco, A.C., Thomas, S.M., Veroniki, A.A., Hamid, J.S., 
Cogo, E., Strifler, L., Khan, P.A., Robson, R., Sibley, K.M., 
MacDonald, H., Riva, J.J., Thavorn, K., Wilson, C., Holroyd-Leduc, 
J., Kerr, G.D., Feldman, F., Majumdar, S.R., Jaglal, S.B., Hui, W., 
& Straus, S.E. (2017). Comparisons of interventions for preventing 
falls in older adults: A systematic review and meta-analysis. 
Journal of the American Medical Association, 318(17), 1687-1699. 
https://doi.org/10.1001/jama.2017.15006.
---------------------------------------------------------------------------

(2) Overview of Measure
    The Falls with Major Injury (Long-Stay) measure is an outcome 
measure that reports the percentage of long-stay residents in a nursing 
home who have experienced one or more falls with major injury using 1 
year of data from the Minimum Data Set (MDS) 3.0. This measure defines 
major injuries as bone fractures, joint dislocations, closed head 
injuries with altered consciousness, or subdural hematomas. Long-stay 
residents are defined as residents who have received 101 or more 
cumulative days of nursing home care by the end of the measure 
reporting period (performance period). This measure is a patient safety 
measure reported at the facility-level.
    Although the Falls with Major Injury (Long-Stay) measure is a long-
stay measure, we believe that including a long-stay measure in the SNF 
VBP Program is appropriate because it will better capture the quality 
of care provided to the entirety of the population that resides in 
facilities that are dually certified as SNFs and nursing facilities, 
including long-stay residents who continue to receive Medicare coverage 
for certain services provided

[[Page 53288]]

by nursing facilities. We discussed the potential to include long -stay 
measures in the SNF VBP Program in the FY 2022 SNF PPS final rule 
Summary of Comments Received on Potential Future Measures for the SNF 
VBP Program (86 FR 42507 through 42510). Specifically, we stated that 
the majority of long-stay residents are Medicare beneficiaries, 
regardless of whether they are in a Medicare Part A SNF stay, because 
they are enrolled in Medicare Part B and receive Medicare coverage of 
certain services provided by long-term care facilities even if they are 
a long-stay resident. We did not receive any negative comments on 
inclusion of this specific Falls with Major Injury (Long-Stay) measure 
or long-stay measures generally in the Program in response to this 
request for comment.
    We have adopted a similar measure in the SNF QRP, the Application 
of Percent of Residents Experiencing One or More Falls with Major 
Injury (Long Stay) (80 FR 46440 through 46444), but that measure 
excludes long-stay residents. We believe it is important to hold SNFs 
accountable for the quality of care provided to long-stay residents 
given that the majority of long-term care facilities are dually 
certified as SNFs and nursing facilities. Additionally, we believe the 
Falls with Major Injury (Long-Stay) measure satisfies the requirement 
to consider and apply, as appropriate, quality measures specified under 
section 1899B(c)(1) of the Act, in which this measure aligns with the 
domain, incidence of major falls, described at section 1899B(c)(1)(D) 
of the Act. Therefore, we believe it is appropriate for the SNF VBP 
program to include a falls with major injury for long-stay resident 
measure.
    Testing for this measure has demonstrated that the Falls with Major 
Injury (Long-Stay) measure has sufficient reliability and validity. For 
example, signal-to-noise and split-half reliability analyses found that 
the measure exhibited moderate reliability. Validity testing showed 
that there are meaningful differences in nursing facility-level scores 
for this measure, indicating good validity. For additional details on 
measure testing, we refer readers to the MAP PAC/LTC: 2022-2023 MUC 
Cycle Measure Specifications Manual available at https://mmshub.cms.gov/sites/default/files/map-pac-muc-measure-specifications-2022-2023.pdf.
(a) Interested Parties and TEP Input
    In considering the selection of this measure for the SNF VBP 
Program, CMS convened a TEP in March 2022 which focused on the 
identification of measurement gaps and measure development priorities 
for the Program. Panelists were largely supportive of including a falls 
with major injury measure compared to a general falls measure or a 
falls with injury measure for several reasons including: (1) the broad 
definition of falls; and (2) the consensus-based entity endorsement of 
the Falls with Major Injury (Long-Stay) measure in the Nursing Home 
Quality Initiative Program. A summary of the TEP meeting is available 
at https://mmshub.cms.gov/sites/default/files/SNF-VBP-TEP-Summary-Report-Mar2022.pdf.
(b) Measure Applications Partnership (MAP) Review
    We included the Falls with Major Injury (Long-Stay) measure as a 
SNF VBP measure under consideration in the publicly available ``2022 
Measures Under Consideration List''.\319\ The MAP supported the Falls 
with Major Injury (Long-Stay) measure for rulemaking, noting that the 
measure would add value to the Program because of the lack of an 
existing falls measure and that it would help improve patient safety. 
We refer readers to the final 2022-2023 MAP recommendations available 
at https://mmshub.cms.gov/measure-lifecycle/measure-implementation/pre-rulemaking/lists-and-reports.
---------------------------------------------------------------------------

    \319\ 2022 Measures Under Consideration Spreadsheet available at 
https://mmshub.cms.gov/sites/default/files/2022-MUC-List.xlsx.
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(3) Data Sources
    The Falls with Major Injury (Long-Stay) measure is calculated using 
1 year of resident data collected through the MDS. The collection 
instrument is the Resident Assessment Instrument (RAI), which contains 
the MDS 3.0. The RAI is a tool used by nursing home staff to collect 
information on residents' strengths and needs. We describe the measure 
specifications in more detail below and also refer readers to the MDS 
3.0 Quality Measures User's Manual Version 15.0 for further details on 
how these data components are utilized in calculating the Falls with 
Major Injury (Long-Stay) measure available at https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/nursinghomequalityinits/nhqiqualitymeasures. Technical information for 
the MDS 3.0 is also available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/NHQIMDS30TechnicalInformation. The Falls with Major Injury (Long-Stay) 
measure is calculated using data from the MDS, which all Medicare-
certified SNFs and Medicaid-certified nursing facilities are currently 
required to report. Therefore, this measure will not impose any 
additional data collection or submission burden for SNFs.
(4) Measure Specifications
(a) Denominator
    All long-stay residents with one or more look-back scan assessments 
no more than 275 days prior to the target assessment, except those that 
meet the exclusion criteria, are included in the measure denominator. 
Long-stay residents are defined as those who have 101 or more 
cumulative days of nursing home care by the end of the measure 
reporting period (performance period). Residents who return to the 
nursing home following a hospital discharge would not have their 
cumulative days in the facility reset to zero, meaning that days of 
care from a previous admission will be added to any subsequent 
admissions.
    The MDS includes a series of assessments and tracking documents, 
such as Omnibus Budget Reconciliation Act (OBRA) Comprehensive 
Assessments, OBRA Quarterly Assessments, OBRA Discharge Assessments or 
PPS assessments. For the purposes of this measure, a target assessment, 
which presents the resident's status at the end of the episode of care 
or their latest status if their episode of care is ongoing, is selected 
for each long-stay resident. Target assessments may be an Omnibus 
Budget Reconciliation Act (OBRA) admission, quarterly, annual, or 
significant change/correction assessment; or PPS 5-day assessments; or 
discharge assessment with or without anticipated return. For more 
information on how we define target assessments, we refer readers to 
the MDS 3.0 Quality Measures User's Manual Version 15.0 available at 
https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/nursinghomequalityinits/nhqiqualitymeasures.
(b) Denominator Exclusions
    Residents are excluded from the denominator if the number of falls 
with major injury was not coded for all of the look-back scan 
assessments. A SNF will not be scored on this measure if it does not 
have long-stay residents, or residents with 101 or more cumulative days 
of care. The measure also excludes all SNF swing beds because they do 
not provide care to long-stay residents.

[[Page 53289]]

(c) Numerator
    The measure numerator includes long-stay residents with one or more 
look-back scan assessments that indicate one or more falls that 
resulted in major injury. Major injuries include bone fractures, joint 
dislocations, closed-head injuries with altered consciousness, or 
subdural hematomas. The selection period for the look-back scan 
consists of the target assessment and all qualifying earlier 
assessments in the scan.
    An assessment should be included in the scan if it meets all of the 
following conditions: (1) it is contained within the resident's 
episode, (2) it has a qualifying Reason for Assessment (RFA), (3) its 
target date is on or before the target date for the target assessment, 
and (4) its target date is no more than 275 days prior to the target 
date of the target assessment. For the purposes of this measure, we 
defined the target date as the event date of an MDS record (that is, 
entry date for an entry record or discharge date for a discharge record 
or death-in-facility record) or the assessment reference date (for all 
records that are not entry, discharge, or death-in-facility). For 
additional target date details, we refer readers to Chapter 1 of the 
MDS 3.0 Quality Measures User's Manual Version 15.0 available at 
https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/nursinghomequalityinits/nhqiqualitymeasures.
    A 275-day time period is used to include up to three quarterly OBRA 
assessments. The earliest of these assessments would have a look-back 
period of up to 93 days, which would cover a total of about 1 year. To 
calculate the measure, we scan these target assessments and any 
qualifying earlier assessments described in the previous paragraph for 
indicators of falls with major injury.
(5) Risk Adjustment
    The Falls with Major Injury (Long-Stay) measure is not risk-
adjusted. We considered risk adjustment during measure development, and 
we tested various risk-adjustment models, but none had sufficient 
predictive ability.
(6) Measure Calculation
    The Falls with Major Injury (Long-Stay) measure is calculated and 
reported at the facility level. Specifically, to calculate the measure 
score, we proposed to first determine the measure denominator by 
identifying the total number of long-stay residents with a qualifying 
target assessment (OBRA, PPS, or discharge), one or more look-back scan 
assessments, and who do not meet the exclusion criteria. Using that set 
of residents, we calculate the numerator by identifying the total 
number of those residents with one or more look-back scan assessments 
that indicate one or more falls that resulted in major injury. We then 
divide the numerator by the denominator and multiply the resulting 
ratio by 100 to obtain the percentage of long-stay residents who 
experience one or more falls with major injury. A lower measure rate 
indicates better performance on the measure. For additional details on 
the calculation method, we refer readers to the specifications for the 
Falls with Major Injury (Long-Stay) measure included in the MDS 3.0 
Quality Measures User's Manual available at https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/nursinghomequalityinits/nhqiqualitymeasures.
    We solicited public comment on our proposal to adopt the Percent of 
Residents Experiencing One or More Falls with Major Injury (Long-Stay) 
measure beginning with the FY 2027 SNF VBP program year.
    We received public comments on this proposal. The following is a 
summary of the comments we received and our responses.
    Comment: Several commenters expressed support for the proposed 
Falls with Major Injury (Long-Stay) measure.
    Response: We thank the commenters for their support.
    Comment: Several commenters expressed concerns about the proposed 
measure. One commenter did not believe that MDS data were sufficiently 
valid for the SNF VBP program without an auditing program. One 
commenter expressed concern that the measure is not risk-adjusted. 
Another commenter was uncertain about the measure's use in the SNF VBP 
Program because it has not been adopted in the SNF QRP. One commenter 
did not believe that measures of long-stay residents' care were 
appropriate for the Program. Another commenter worried that facilities 
may restrict residents' movements to avoid falls and injuries, which 
would reduce residents' quality of life and affect their physical 
strength, balance, and flexibility.
    Response: We thank the commenters for this feedback. We proposed to 
adopt a validation process for SNF VBP measures that are calculated 
using MDS data and refer readers to section VIII.G.4. of this final 
rule for additional details regarding that proposal, which we are 
finalizing, as well as our responses to comments on it.
    We appreciate the commenter's concern about risk adjustment. As we 
explained in the proposed rule (88 FR 21371), we tested risk-adjustment 
models for this measure but found that none had sufficient predictive 
ability. Injurious falls are one of the leading causes of disability 
and death for all nursing home residents, and falls have serious health 
consequences, such as reduced quality of life, decreased functional 
abilities, anxiety and depression, serious injuries, and increased risk 
of morbidity and mortality.320 321 Based on these risks, we 
continue to believe that the measure is appropriate for adoption in the 
SNF VBP Program as part of our ongoing efforts to ensure nursing home 
residents' safety in that care setting. We will continue assessing the 
feasibility of risk-adjustment for this measure in the future.
---------------------------------------------------------------------------

    \320\ The Falls Management Program: A Quality Improvement 
Initiative for Nursing Facilities: Chapter 1. Introduction and 
Program Overview. Agency for Healthcare Research and Quality. 
https://www.ahrq.gov/patient-safety/settings/longterm-care/resource/injuries/fallspx/man1.html. Published December 2017. Accessed 
December 13, 2022.
    \321\ Bastami M, Azadi A. Effects of a Multicomponent Program on 
Fall Incidence, Fear of Falling, and Quality of Life among Older 
Adult Nursing Home Residents. Ann Geriatr Med Res. 2020;24(4):252-
258. doi:10.4235/agmr.20.0044.
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    We proposed to adopt this measure in the SNF VBP Program because 
falls represent a significant risk to nursing home residents. We 
believe that the SNF VBP Program's structure will provide strong 
incentives for SNFs to protect residents from those falls. We further 
note that, as we discussed in the proposed rule (88 FR 21370), we have 
adopted a similar measure for the SNF QRP. We also explained our 
reasoning for applying measures of long-stay residents' care in the 
proposed rule (88 FR 21370), where we stated that we believe long-stay 
measures better capture the quality of care provided to the entirety of 
the population residing in facilities that are dually certified as SNFs 
and nursing facilities. Even though Medicare Part A does not cover 
nursing facility stays, long-stay residents who are enrolled in 
Medicare Part B can still obtain Medicare Part B coverage of certain 
services, such as physical therapy, that are provided by nursing 
facilities.
    Finally, while we agree with the commenter that no facility should 
restrict residents' movement to maximize its performance on this 
measure, we do not believe that

[[Page 53290]]

facilities will violate their duties to their residents' care and 
safety in such a manner. We believe that facilities will take 
appropriate steps to protect their residents from injurious falls while 
providing them with the support they need to maintain mobility, 
physical strength, balance, and flexibility. We further add that we are 
also adopting the DC Function measure, in which facilities must improve 
their resident function from admission to perform well on the measure 
which may reduce the incentive to restrict patient movements. We will 
monitor performance on the measure as well as potential unintended 
consequences carefully.
    Comment: One commenter suggested that CMS monitor all injurious 
falls based on the risk of injury associated with them. The commenter 
also suggested that CMS adopt requirements for SNFs to develop 
protective interventions to protect residents from injury. Another 
commenter urged CMS to require Medicare Advantage (MA) plans to report 
falls data. One commenter suggested that CMS consider providing 
positive incentives for SNFs to encourage them to create falls 
management programs and protocols. One commenter expressed concern 
about the risk of facilities cherry-picking residents to avoid poor 
performance on this measure.
    Response: We have not developed a measure of all falls for the SNF 
VBP Program at this time, nor are we aware of other measure developers 
having developed that type of measure. We will consider whether such a 
measure is appropriate for the Program in the future. We intend to work 
with Quality Improvement Organizations (QIOs) to promote safety 
initiatives in the nursing facility setting. Further, while we do not 
currently incorporate a measure of falls in our Star Ratings system for 
MA plans, we will consider whether such a measure would be appropriate 
in the future.
    We note that patient safety is both one of the measure categories 
described at section 1888(h)(2)(A)(ii) and that prevention of falls 
specifically is a patient safety issue and one of the agency's 
priorities. We believe the positive incentives provided by the Program, 
including the policy changes we have proposed this year related to the 
Health Equity Adjustment and increase in payback percentage, provide 
strong incentives for SNFs to design and implement safety protocols, 
including falls management.
    We share the commenter's concern about facilities' potentially 
cherry-picking residents to avoid poor performance on this measure and 
will monitor performance and any unintended consequences carefully.
    Comment: Several commenters opposed the proposal to adopt the Falls 
with Major Injury (Long-Stay) measure. Some commenters were concerned 
that MDS data are not sufficiently accurate for quality measurement and 
suggested that CMS adopt a claims-based measure of falls instead. One 
commenter believed that the measure does not align with the SNF VBP 
Program's intent to link FFS reimbursement with care and outcomes of 
FFS beneficiaries. Another commenter opposed the measure's adoption 
based on population differences and suggested that CMS adopt the SNF 
QRP's Falls with Major Injury instead, which they stated is better 
aligned with Part A reimbursements affected by the SNF VBP Program. One 
commenter opposed the measure because it is already publicly reported 
and available to consumers.
    Response: We appreciate the commenters' concerns. As explained 
below, we are finalizing a proposal to validate the MDS data used to 
calculate SNF VBP measures, and we believe that this policy will help 
to ensure that those data are accurate for quality purposes.
    We disagree with the commenter's assertion that this measure does 
not align with the SNF VBP Program's intent. As we described in the 
proposed rule (88 FR 21370), we believe that this measure better 
captures the quality of care provided to the entirety of the population 
that resides in facilities that are dually certified as SNFs and 
nursing facilities, including long-stay residents who continue to 
receive Medicare coverage for certain services provided by nursing 
facilities. While we considered the SNF QRP's measure on a similar 
topic, we noted in the proposed rule that the SNF QRP's measure 
excludes long-stay residents and that we believe it is important to 
hold SNFs accountable for the quality of care they provide to long-stay 
residents since the majority of long-term care facilities are dually 
certified as SNFs and nursing facilities.
    Finally, we agree with the commenter's reasoning that public 
reporting of quality data is an important feature of quality programs. 
We continue to believe, however, that providing financial incentives 
for quality performance through our pay-for-performance programs takes 
the next step beyond public reporting and provides direct incentives 
for quality improvement in clinical care.
    After consideration of public comments, we are finalizing adoption 
of the Percent of Residents Experiencing One or More Falls with Major 
Injury (Long-Stay) measure beginning with the FY 2027 SNF VBP program 
year.
d. Adoption of the Discharge Function Score Measure Beginning With the 
FY 2027 SNF VBP Program Year
    We proposed to adopt the Discharge Function Score (``DC Function'') 
measure beginning with the FY 2027 SNF VBP Program.\322\ We also 
proposed to adopt this measure in the SNF QRP (see section VII. of this 
final rule).
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    \322\ This measure was submitted to the Measure Under 
Consideration (MUC) List as the Cross-Setting Discharge Function 
Score. Subsequent to the MAP workgroup meetings, the measure 
developer modified the name.
---------------------------------------------------------------------------

(1) Background
    Maintenance or improvement of physical function among older adults 
is increasingly an important focus of healthcare. Adults aged 65 years 
and older constitute the most rapidly growing population in the United 
States, and functional capacity in physical (non-psychological) domains 
has been shown to decline with age.\323\ Moreover, impaired functional 
capacity is associated with poorer quality of life and an increased 
risk of all-cause mortality, postoperative complications, and cognitive 
impairment, the latter of which can complicate the return of a resident 
to the community from post-acute care.324 325 326 
Nonetheless, evidence suggests that physical
---------------------------------------------------------------------------

    \323\ High KP, Zieman S, Gurwitz J, Hill C, Lai J, Robinson T, 
Schonberg M, Whitson H. Use of Functional Assessment to Define 
Therapeutic Goals and Treatment. J Am Geriatr Soc. 2019 
Sep;67(9):1782-1790. doi: 10.1111/jgs.15975. Epub 2019 May 13. PMID: 
31081938; PMCID: PMC6955596.
    \324\ Clouston SA, Brewster P, Kuh D, Richards M, Cooper R, 
Hardy R, Rubin MS, Hofer SM. The dynamic relationship between 
physical function and cognition in longitudinal aging cohorts. 
Epidemiol Rev. 2013;35(1):33-50. doi: 10.1093/epirev/mxs004. Epub 
2013 Jan 24. PMID: 23349427; PMCID: PMC3578448.
    \325\ Michael YL, Colditz GA, Coakley E, Kawachi I. Health 
behaviors, social networks, and healthy aging: cross-sectional 
evidence from the Nurses' Health Study. Qual Life Res. 1999 
Dec;8(8):711-22. doi: 10.1023/a:1008949428041. PMID: 10855345.
    \326\ High KP, Zieman S, Gurwitz J, Hill C, Lai J, Robinson T, 
Schonberg M, Whitson H. Use of Functional Assessment to Define 
Therapeutic Goals and Treatment. J Am Geriatr Soc. 2019 
Sep;67(9):1782-1790. doi: 10.1111/jgs.15975. Epub 2019 May 13. PMID: 
31081938; PMCID: PMC6955596.

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[[Page 53291]]

functional abilities, including mobility and self-care, are modifiable 
predictors of resident outcomes across PAC settings, including 
functional recovery or decline after post-acute 
care,327 328 329 330 331 rehospitalization 
rates,332 333 334 discharge to community,335 336 
and falls.\337\ Because evidence shows that older adults experience 
aging heterogeneously and require individualized and comprehensive 
healthcare, functional status can serve as a vital component in 
informing the provision of healthcare and thus indicate a SNF's quality 
of care.338 339
---------------------------------------------------------------------------

    \327\ Deutsch A, Palmer L, Vaughan M, Schwartz C, McMullen T. 
Inpatient Rehabilitation Facility Patients' Functional Abilities and 
Validity Evaluation of the Standardized Self-Care and Mobility Data 
Elements. Arch Phys Med Rehabil. 2022 Feb 11:S0003-9993(22)00205-2. 
doi: 10.1016/j.apmr.2022.01.147. Epub ahead of print. PMID: 
35157893.
    \328\ Hong I, Goodwin JS, Reistetter TA, Kuo YF, Mallinson T, 
Karmarkar A, Lin YL, Ottenbacher KJ. Comparison of Functional Status 
Improvements Among Patients With Stroke Receiving Postacute Care in 
Inpatient Rehabilitation vs Skilled Nursing Facilities. JAMA Netw 
Open. 2019 Dec 2;2(12):e1916646. doi: 10.1001/
jamanetworkopen.2019.16646. PMID: 31800069; PMCID: PMC6902754.
    \329\ Alcusky M, Ulbricht CM, Lapane KL. Postacute Care Setting, 
Facility Characteristics, and Poststroke Outcomes: A Systematic 
Review. Arch Phys Med Rehabil. 2018;99(6):1124-1140.e9. doi:10.1016/
j.apmr.2017.09.005. PMID: 28965738; PMCID: PMC5874162.
    \330\ Chu CH, Quan AML, McGilton KS. Depression and Functional 
Mobility Decline in Long Term Care Home Residents with Dementia: a 
Prospective Cohort Study. Can Geriatr J. 2021;24(4):325-331. 
doi:10.5770/cgj.24.511. PMID: 34912487; PMCID: PMC8629506.
    \331\ Lane NE, Stukel TA, Boyd CM, Wodchis WP. Long-Term Care 
Residents' Geriatric Syndromes at Admission and Disablement Over 
Time: An Observational Cohort Study. J Gerontol A Biol Sci Med Sci. 
2019;74(6):917-923. doi:10.1093/gerona/gly151. PMID: 29955879; 
PMCID: PMC6521919.
    \332\ Li CY, Haas A, Pritchard KT, Karmarkar A, Kuo YF, Hreha K, 
Ottenbacher KJ. Functional Status Across Post-Acute Settings is 
Associated With 30-Day and 90-Day Hospital Readmissions. J Am Med 
Dir Assoc. 2021 Dec;22(12):2447-2453.e5. doi: 10.1016/
j.jamda.2021.07.039. Epub 2021 Aug 30. PMID: 34473961; PMCID: 
PMC8627458.
    \333\ Middleton A, Graham JE, Lin YL, Goodwin JS, Bettger JP, 
Deutsch A, Ottenbacher KJ. Motor and Cognitive Functional Status Are 
Associated with 30-day Unplanned Rehospitalization Following Post-
Acute Care in Medicare Fee-for-Service Beneficiaries. J Gen Intern 
Med. 2016 Dec;31(12):1427-1434. doi: 10.1007/s11606-016-3704-4. Epub 
2016 Jul 20. PMID: 27439979; PMCID: PMC5130938.
    \334\ Gustavson AM, Malone DJ, Boxer RS, Forster JE, Stevens-
Lapsley JE. Application of High-Intensity Functional Resistance 
Training in a Skilled Nursing Facility: An Implementation Study. 
Phys Ther. 2020;100(10):1746-1758. doi: 10.1093/ptj/pzaa126. PMID: 
32750132; PMCID: PMC7530575.
    \335\ Minor M, Jaywant A, Toglia J, Campo M, O'Dell MW. 
Discharge Rehabilitation Measures Predict Activity Limitations in 
Patients with Stroke Six Months after Inpatient Rehabilitation. Am J 
Phys Med Rehabil. 2021 Oct 20. doi: 10.1097/PHM.0000000000001908. 
Epub ahead of print. PMID: 34686630.
    \336\ Dubin R, Veith JM, Grippi MA, McPeake J, Harhay MO, 
Mikkelsen ME. Functional Outcomes, Goals, and Goal Attainment among 
Chronically Critically Ill Long-Term Acute Care Hospital Patients. 
Ann Am Thorac Soc. 2021;18(12):2041-2048. doi:10.1513/
AnnalsATS.202011-1412OC. PMID: 33984248; PMCID: PMC8641806.
    \337\ Hoffman GJ, Liu H, Alexander NB, Tinetti M, Braun TM, Min 
LC. Posthospital Fall Injuries and 30-Day Readmissions in Adults 65 
Years and Older. JAMA Netw Open. 2019 May 3;2(5):e194276. doi: 
10.1001/jamanetworkopen.2019.4276. PMID: 31125100; PMCID: 
PMC6632136.
    \338\ Criss MG, Wingood M, Staples W, Southard V, Miller K, 
Norris TL, Avers D, Ciolek CH, Lewis CB, Strunk ER. APTA Geriatrics' 
Guiding Principles for Best Practices in Geriatric Physical Therapy: 
An Executive Summary. J Geriatr Phys Ther. 2022 April/June;45(2):70-
75. doi: 10.1519/JPT.0000000000000342. PMID: 35384940.
    \339\ Cogan AM, Weaver JA, McHarg M, Leland NE, Davidson L, 
Mallinson T. Association of Length of Stay, Recovery Rate, and 
Therapy Time per Day With Functional Outcomes After Hip Fracture 
Surgery. JAMA Netw Open. 2020 Jan 3;3(1):e1919672. doi: 10.1001/
jamanetworkopen.2019.19672. PMID: 31977059; PMCID: PMC6991278.
---------------------------------------------------------------------------

    As stated in section VII. of this final rule, we proposed this 
measure for the SNF QRP, and we also proposed it for adoption in the 
SNF VBP Program under section 1888(h)(2)(A)(ii) of the Act. We believe 
it is important to measure quality across the full range of outcomes 
for Medicare beneficiaries during a SNF stay. Further, adoption of this 
measure will ensure that the SNF VBP Program's measure set aligns with 
the Person-Centered Care domain of CMS' Meaningful Measures 2.0 
Framework.
    We included the DC Function measure on the 2022-2023 MUC list for 
the Inpatient Rehabilitation Facility QRP, Home Health QRP, Long Term 
Care Hospital QRP, SNF QRP, and SNF VBP Program. While the DC Function 
measure is not yet implemented in the SNF QRP or other PAC programs, 
SNFs already report many of the elements that will be used to calculate 
this measure.\340\ As such, we believe SNFs have had sufficient time to 
ensure successful reporting of the data elements needed for this 
measure.
---------------------------------------------------------------------------

    \340\ National Quality Forum. (2022, December 29). MAP PAC/LTC 
Workgroup: 2022-2023 Measures Under Consideration (MUC) Review 
Meeting. Retrieved from https://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=97960.
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(2) Overview of Measure
    The DC Function measure is an outcome measure that estimates the 
percentage of SNF residents who meet or exceed an expected discharge 
score during the reporting period. The DC Function measure's numerator 
is the number of SNF stays with an observed discharge function score 
that is equal to or higher than the calculated expected discharge 
function score. The observed discharge function score is the sum of 
individual function items at discharge. The expected discharge function 
score is computed by risk adjusting the observed discharge function 
score for each SNF stay. Risk adjustment controls for resident 
characteristics, such as admission function score, age, and clinical 
conditions. The denominator is the total number of SNF stays with a MDS 
record in the measure target period (four rolling quarters) which do 
not meet the measure exclusion criteria. For additional details 
regarding the numerator, denominator, risk adjustment, and exclusion 
criteria, we refer readers to the Discharge Function Score for Skilled 
Nursing Facilities (SNFs) Technical Report.\341\
---------------------------------------------------------------------------

    \341\ Discharge Function Score for Skilled Nursing Facilities 
(SNFs) Technical Report, which is available on the SNF Quality 
Reporting Program Measures and Technical Information web page at 
https://www.cms.gov/files/document/snf-discharge-function-score-technical-report-february-2023.pdf.
---------------------------------------------------------------------------

    The DC Function measure implements a statistical imputation 
approach for handling ``missing'' standardized functional assessment 
data elements. The coding guidance for standardized functional 
assessment data elements allows for using ``Activity Not Attempted'' 
(ANA) codes, resulting in ``missing'' information about a patient's 
functional ability on at least some items, at admission and/or 
discharge, for a substantive portion of SNF patients. Currently, 
functional outcome measures in the SNF QRP use a simple imputation 
method whereby all ANA codes or otherwise missing scores, on both 
admission and discharge records, are recoded to ``1'' or ``most 
dependent.'' Statistical imputation, on the other hand, replaces these 
missing values for a variable based on the values of other, non-missing 
variables in the data and which are otherwise similar to the assessment 
with a missing value. Specifically, the DC Function measure's 
statistical imputation allows missing values (for example, the ANA 
codes) to be replaced with any value from 1 to 6, based on a patient's 
clinical characteristics and codes assigned on other standardized 
functional assessment data elements. The measure implements separate 
imputation models for each standardized functional assessment data 
elements used in measure construction at admission and discharge. 
Relative to the current simple imputation method, this statistical 
imputation approach increases the precision and accuracy and reduces 
the bias in estimates for missing item scores. We refer readers to the 
Discharge Function Score for Skilled Nursing

[[Page 53292]]

Facilities (SNFs) Technical Report \342\ for measure specifications and 
additional details. We also refer readers to the SNF QRP section 
VII.C.1.b.(1) of this final rule for additional information on Measure 
Importance and Measure Testing.
---------------------------------------------------------------------------

    \342\ Discharge Function Score for Skilled Nursing Facilities 
(SNFs) Technical Report, which is available on the SNF Quality 
Reporting Program Measures and Technical Information web page at 
https://www.cms.gov/files/document/snf-discharge-function-score-technical-report-february-2023.pdf.
---------------------------------------------------------------------------

(a) Interested Parties and TEP Input
    We convened two TEP meetings (July 2021 and January 2022), as well 
as a Patient and Family Engagement Listening Session, to collect 
feedback from interested parties on the measure's potential use in 
quality programs in the future. The TEP members expressed support for 
the measure's validity and agreed with the conceptual and operational 
definition of the measure.
    The feedback we received during the Patient and Family Engagement 
Listening Session demonstrated that this measure resonates with 
patients and caregivers. For example, participants' views of self-care 
and mobility were aligned with the functional domains captured by the 
measure, and participants found that those domains included critical 
aspects of care in post-acute care settings. Participants also 
emphasized the importance of measuring functional outcomes when 
assessing quality for SNF residents. We refer readers to the SNF QRP 
section VII.C.1.b.(3) of this final rule for additional discussion on 
the TEP.
(b) MAP Review
    The DC Function measure was included as a SNF VBP measure under 
consideration in the publicly available ``2022 Measures Under 
Consideration List.'' \343\ The MAP offered conditional support of the 
DC Function measure for rulemaking, contingent upon endorsement by the 
consensus-based entity, noting that the measure will add value to the 
Program because there are currently no measures related to functional 
status in the Program, and this measure serves as an indicator for 
whether the care provided is effective and high quality. We refer 
readers to section VII.C.1.b.(4) of this final rule for further details 
on the MAP's recommendations and the final 2022-2023 MAP 
recommendations available at https://mmshub.cms.gov/measure-lifecycle/measure-implementation/pre-rulemaking/lists-and-reports.
---------------------------------------------------------------------------

    \343\ 2022 Measures Under Consideration Spreadsheet available at 
https://mmshub.cms.gov/sites/default/files/2022-MUC-List.xlsx.
---------------------------------------------------------------------------

    We solicited public comment on our proposal to adopt the Discharge 
Function Score measure beginning with the FY 2027 SNF VBP program year.
    We received public comments on this proposal. The following is a 
summary of the comments we received and our responses.
    Comment: Many commenters supported adoption of the DC Function 
measure in the SNF VBP Program because it assesses performance on both 
self-care and mobility items. One commenter stated that implementing 
the measure in the FY 2027 program year allows SNFs enough time to 
evaluate their current performance on the measure.
    Response: We thank the commenters for their feedback. We also note 
that many of the same commenters expressed support for the inclusion of 
this measure in both the SNF QRP and SNF VBP. We responded to those 
more general comments in section VII.C.1.b. of this final rule.
    Comment: One commenter supported the proposal to adopt this measure 
for the SNF VBP Program, but they recommended that the measure be 
scored on the resident's change in the DC Function score so that the 
Program rewards facilities based on the degree of a resident's 
improvement in function rather than if they met or exceeded an expected 
discharge score.
    Response: We appreciate the commenter's recommendation however, we 
believe the measure as proposed is the best measure for the Program at 
this time because it has strong reliability and validity, has received 
positive feedback from a TEP and other interested parties, and has high 
reportability and usability. We also do not believe at this time that 
rewarding facilities for any improvement in resident function, 
especially those residents who may not achieve a discharge function 
benchmark, are sufficient incentives for improving the quality of care 
for SNF residents. While we agree that it is important for facilities 
to track the amount of change that occurs over the course of a stay for 
its residents, we would like to point out that ``Change in Score'' 
measures are not as intuitive to interpret because the units of change 
and what constitutes a meaningful change has not been determined for 
residents with differing diagnoses and clinical complexities that seek 
care at SNFs. This is in contrast to the proposed Discharge Function 
Score measure which is presented as a simple proportion.
    As stated in section VII.C.1.b.(3) of the proposed rule, a TEP was 
convened and asked whether they prefer a measure that is modeled after 
the currently adopted Discharge Mobility Score and Discharge Self Care 
Score measures, or one that is modeled after the currently adopted 
Change in Mobility Score and Change in Self Care Score measures. We 
note that the Discharge Mobility Score and Change in Mobility Score 
measures were highly correlated and did not appear to measure unique 
concepts. The Discharge Self Care Score and Change in Self Care Score 
measures were also highly correlated and did not appear to measure 
unique concepts. Because both the discharge and change measure types 
did not appear to measure unique concepts, the TEP favored the 
Discharge Mobility Score and Discharge Self Care Score measures over 
the Change in Mobility Score and Change in Self Care Score measures. 
Based on the TEP's recommendation to our contractor, we made a policy 
decision to pursue the DC Function measure for the measure of 
functional status in the SNF VBP Program.
    Comment: A few commenters who supported the DC Function measure 
recommended that CMS include the expected discharge function score, a 
score that is already calculated during the measure evaluation, along 
with the observed function score on the provider reports, so that 
providers have transparency into their performance.
    Response: We will take this feedback into consideration as we 
develop our quarterly confidential feedback reports that are provided 
after the end of the data submission period. We also note that many of 
the same commenters expressed this recommendation for both the SNF QRP 
and SNF VBP. We responded to those comments in section VII.C.1.b. of 
this final rule.
    Comment: A few commenters did not support the adoption of the DC 
Function measure in the SNF VBP Program because the MDS-data are not 
validated for accuracy, and providers have not had enough time using 
the measure prior to use in a performance-based program.
    Response: We thank the commenters for their feedback. As explained 
below, we are finalizing a proposal to validate the MDS data used to 
calculate SNF VBP measures, and we believe that this policy will help 
to ensure that those data are accurate for quality purposes. As stated 
in section VII.F.2 of this final rule, the SNF QRP is adopting this 
measure in FY 2025 SNF QRP year with data collection beginning with 
October 1, 2023 discharges. We are finalizing the adoption of this 
measure for the SNF VBP Program beginning with the FY 2027 program 
year, with data collection beginning with October 1, 2024

[[Page 53293]]

discharges. This timeline will enable SNFs to report the data for a 
full year in the SNF QRP before they are required to report them for 
the SNF VBP Program. We believe that reporting this measure in the SNF 
QRP for one year is sufficient time for providers to gain familiarity 
with the measure. As we stated in the proposed rule (88 FR 21372), the 
DC Function measure contains similar data elements to the Discharge 
Self-Care Score and Discharge Mobility Score measures, which have been 
included in the SNF QRP measure set for several years. We believe that 
SNFs are well acquainted with the Self-Care Score and Discharge 
Mobility Score measures so adopting the DC Function measure at a 
similar time for both the SNF QRP and SNF VBP Program is reasonable. We 
also note that many of the same commenters did not support the 
inclusion of this measure in both the SNF QRP and SNF VBP Program. We 
responded to those more general comments in section VII.C.1.b. of this 
final rule.
    Comment: One commenter believed that SNFs will need to update their 
software in order to create and implement the measure's complex 
calculations, as well as to monitor the expected and observed discharge 
function score progression. This commenter also stated SNFs will need 
to provide additional training and education for clinical and 
administrative personnel with the adoption of new measures.
    Response: We interpret the commenter to be saying that SNFs will 
need to update their software to perform the measure calculations prior 
to receiving the CMS generated reports, as well as provide training and 
education to their clinical staff on the DC Function measure and their 
administrative personnel on reporting the data or monitoring the data.
    We acknowledge the commenter's concern regarding updating software; 
however, SNFs are not required to update their own software to 
successfully report the MDS items or monitor their performance on the 
DC Function measure. Additionally, we disagree that the adoption of the 
proposed measure would result in additional burden or require 
additional training. We did not propose to change the items SNFs report 
for the measure calculation nor the frequency at which SNFs would 
report these items. In fact, this measure uses the same set of MDS 
items that SNFs have been reporting at admission and discharge since 
October 1, 2018. We also will calculate this measure and provide SNFs 
with various educational resources on the DC Function measure they can 
use in preparation for reviewing and monitoring their own performance 
on this measure, thus eliminating the need for SNFs to create training 
and education for their clinical and administrative personnel.
    After consideration of public comments, we are finalizing adoption 
of the Discharge Function Score measure for the SNF VBP Program 
beginning with the FY 2027 program year.
e. Adoption of the Number of Hospitalizations per 1,000 Long-Stay 
Resident Days Measure Beginning With the FY 2027 SNF VBP Program Year
(1) Background
    Unplanned hospitalizations of long-stay residents can be disruptive 
and burdensome to residents. ``They can cause discomfort for residents, 
anxiety for loved ones, morbidity due to iatrogenic events, and excess 
healthcare costs.'' \344\ Studies have found that many unplanned 
hospitalizations could have been safely avoided by early intervention 
by the facility. For example, one structured review by expert 
clinicians of hospitalizations of SNF residents found that two-thirds 
were potentially avoidable, citing a lack of primary care clinicians 
on-site and delays in assessments and lab orders as primary reasons 
behind unplanned hospitalizations.\345\ Another study found that 
standardizing advanced care planning and physician availability has a 
considerable impact on reducing hospitalizations.\346\ The Missouri 
Quality Initiative reduced hospitalizations by 30 percent by having a 
clinical resource embedded to influence resident care outcomes. Another 
study found that reducing hospitalizations did not increase the 
mortality risk for long-stay nursing home residents.\347\
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    \344\ Ouslander, J.G., Lamb, G., Perloe, M., Givens, J.H., 
Kluge, L., Rutland, T., Atherly, A., & Saliba, D. (2010). 
Potentially avoidable hospitalizations of nursing home residents: 
frequency, causes, and costs. Journal of the American Geriatrics 
Society, 58(4), 627-635. https://doi.org/10.1111/j.1532-5415.2010.02768.x.
    \345\ Ouslander, J.G., Lamb, G., Perloe, M., Givens, J.H., 
Kluge, L., Rutland, T., Atherly, A., & Saliba, D. (2010). 
Potentially avoidable hospitalizations of nursing home residents: 
frequency, causes, and costs. Journal of the American Geriatrics 
Society, 58(4), 627-635. https://doi.org/10.1111/j.1532-5415.2010.02768.x.
    \346\ Giger, M., Voneschen, N., Brunkert, T., & Z[uacute]niga, 
F. (2020). Care workers' view on factors leading to unplanned 
hospitalizations of nursing home residents: a cross-sectional 
multicenter study. Geriatric Nursing, 41(2), 110-117.
    \347\ Feng, Z., Ingber, M.J., Segelman, M., Zheng, N.T., Wang, 
J.M., Vadnais, A., . . . & Khatutsky, G. (2018). Nursing facilities 
can reduce avoidable hospitalizations without increasing mortality 
risk for residents. Health Affairs, 37(10), 1640-1646.
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    A review of data that were publicly reported on Care Compare shows 
that there is considerable variation in performance across nursing 
homes when it comes to unplanned hospitalizations, suggesting that 
improvement is possible through modification of facility-led processes 
and interventions. Specifically, performance on this measure ranges 
from 0.841 hospital admissions per 1,000 long-stay resident days at the 
10th percentile to 2.656 hospital admissions per 1,000 long-stay 
resident days at the 90th percentile.\348\ In other words, the top 
decile of performers (10th percentile) has less than half the number of 
hospitalizations compared to the bottom decile (90th percentile). We 
also reported in 2020 that the rate of unplanned hospitalizations was 
1.4 per 1,000 nursing home resident days, suggesting these disruptive 
events are fairly common.\349\ Adopting this measure will align 
measures between Care Compare and the SNF VBP program without 
increasing the reporting burden.
---------------------------------------------------------------------------

    \348\ Data is pulled from the public facing scorecard in 2020, 
available at https://www.medicaid.gov/state-overviews/scorecard/hospitalizations-per-1000-long-stay-nursing-home-days/index.html.
    \349\ Data is pulled from the public facing scorecard in 2020, 
available at https://www.medicaid.gov/state-overviews/scorecard/hospitalizations-per-1000-long-stay-nursing-home-days/index.html.
---------------------------------------------------------------------------

    Although the Long Stay Hospitalization measure is not specified 
under section 1899B(c)(1) of the Act, it aligns with the topics listed 
under section 1888(h)(2)(A)(ii) of the Act. We believe this outcome 
measure supports the Program's goals to improve the quality of care 
provided to Medicare beneficiaries throughout their entire SNF stay. 
Furthermore, the measure will align the Program with the Care 
Coordination domain of CMS' Meaningful Measures 2.0 Framework.
    We examined the relationship between long-stay hospitalization 
rates and other measures of quality from CMS' Five-Star Quality Rating 
System using data from the December 2019 Nursing Home Compare update. 
Analyses showed that facilities with lower hospitalization rates tend 
to perform better on other dimensions of quality such as health 
inspection survey results, staffing level, other quality measures, and 
overall ratings.
    Although the Long Stay Hospitalization measure is a long-stay 
measure, we believe that including a long-stay measure in the SNF VBP 
Program is appropriate because it will better capture the quality of 
care provided to the entirety of the population that resides in 
facilities that

[[Page 53294]]

are dually certified as SNFs and nursing facilities, including long-
stay residents who continue to receive Medicare coverage for certain 
services provided by nursing facilities. We discussed the potential of 
including long-stay measures in the SNF VBP Program in the FY 2022 SNF 
PPS final rule Summary of Comments Received on Potential Future 
Measures for the SNF VBP Program (86 FR 42507 through 42510). 
Specifically, we stated that the majority of long-stay residents are 
Medicare beneficiaries, regardless of whether they are in a Medicare 
Part A SNF stay, because they are enrolled in Medicare Part B and 
receive Medicare coverage of certain services provided by long-term 
care facilities even if they are a long-stay resident. We did not 
receive any negative comments on inclusion of this specific Long Stay 
Hospitalization measure or long-stay measures generally in the Program 
in response to the request for comment.
(2) Overview of Measure
    The Long Stay Hospitalization measure calculates the number of 
unplanned inpatient admissions to an acute care hospital or critical 
access hospital, or outpatient observation stays that occurred among 
long-stay residents per 1,000 long-stay resident days using 1 year of 
Medicare fee-for-service (FFS) claims data. A long-stay day is defined 
as any day after a resident's one-hundredth cumulative day in the 
nursing home or the beginning of the 12-month target period (whichever 
is later) and until the day of discharge, the day of death, or the end 
of the 12-month target period (whichever is earlier). We proposed to 
risk adjust this measure, as explained in more detail below.
(a) Measure Applications Partnership (MAP) Review
    We included the Long Stay Hospitalization measure in the publicly 
available ``2022 Measures Under Consideration List.'' \350\ The MAP 
offered conditional support of the Long Stay Hospitalization measure 
for rulemaking, contingent upon endorsement by the consensus-based 
entity, noting that the measure will add value to the Program because 
unplanned hospitalizations are disruptive and burdensome to long-stay 
residents. We refer readers to the final 2022-2023 MAP recommendations 
available at https://mmshub.cms.gov/measure-lifecycle/measure-implementation/pre-rulemaking/lists-and-reports.
---------------------------------------------------------------------------

    \350\ 2022 Measures Under Consideration Spreadsheet available at 
https://mmshub.cms.gov/sites/default/files/2022-MUC-List.xlsx.
---------------------------------------------------------------------------

(3) Data Sources
    The Long Stay Hospitalization measure is calculated using Medicare 
FFS claims data. We use the inpatient hospital claims data to determine 
the hospital admission, outpatient hospital claims data to determine 
the outpatient observation stay, and items from the Minimum Data Set 
for building resident stays and for risk-adjustment.
(4) Inclusion and Exclusion Criteria
    All Medicare beneficiaries enrolled in both Part A and Part B are 
included. The measure excludes any resident enrolled in Medicare 
managed care during any portion of the resident's stay. The measure 
also excludes all days and any hospital admissions during which the 
resident was enrolled in hospice.
    The measure does not count days prior to a resident's 101st 
cumulative day, which is when the resident meets long-stay criteria. 
Furthermore, we do not include any long-stay days prior to the 
beginning of the applicable performance period. For example, if a 
resident becomes a long-stay resident on September 25, 2024, and is 
discharged on October 5, 2024, we would only count 5 days in the 
denominator during the performance period for the FY 2027 program year.
    Any days a resident was not in the facility for any reason will not 
be counted in the denominator, defined as the total observed number of 
long stay days at the facility. This means we do not count in the 
denominator any days the resident is admitted to another type of 
inpatient facility, or days temporarily residing in the community, so 
long as the NF with beds that are also certified as SNF beds submits an 
MDS discharge assessment for the temporary discharge. For example, if a 
resident became a long-stay resident on December 20, but stayed with 
family on December 24 and December 25 but returned to the facility on 
December 26, we would not count those two days (24 and 25) in the 
denominator because the NF with beds that are also certified as SNF 
beds completed an MDS discharge assessment. We would also not count the 
days when a resident was admitted to a hospital, and therefore, is not 
residing at the facility in the denominator.
    We will not count an observed hospitalization of a resident, the 
numerator count, if the hospitalization occurred while the resident was 
not in the facility and had a completed MDS discharge assessment for 
the temporary discharge. In the example in the prior paragraph, if the 
resident was admitted to the hospital on December 25, during which they 
were residing with family with a completed MDS temporary discharge 
assessment, the admission would not be counted as a hospitalization for 
the NF with beds that are also certified as SNF beds (in the 
numerator). If, however, the resident returned to the NF with beds that 
are also certified as SNF beds on December 26 and was admitted to the 
hospital on December 27, then it would count as a hospitalization (in 
the numerator).
    If a resident spends 31 or more days in a row residing outside the 
NF with beds that are also certified as SNF beds, which could be in 
another facility or in the community, we will consider the resident 
discharged and they will no longer meet long-stay status. If a resident 
is discharged and then admitted to the same facility within 30 days, we 
will consider the resident still in a long-stay status, and we will 
count the days in this admission in the measure denominator.
    The measure numerator includes all admissions to an acute care 
hospital or critical access hospital, for an inpatient or outpatient 
observation stay, that occur while the resident meets the long-stay 
status criteria. Observation stays are included in the numerator 
regardless of diagnosis. Planned inpatient admissions are not counted 
in the numerator since they are unrelated to the quality of care at the 
facility. Hospitalizations are classified as planned or unplanned using 
the same version of CMS' Planned Readmissions Algorithm that is used to 
calculate the percentage of short-stay residents who were re-
hospitalized after a nursing home admission in the Nursing Home Compare 
Five-Star Rating System. The algorithm identifies planned admission 
using the principal discharge diagnosis category and all procedure 
codes listed on inpatient claims, coded using the AHRQ Clinical 
Classification System (CCS) software.
(5) Risk Adjustment
    The risk adjustment model used for this measure is a negative 
binomial regression. Specifically, we proposed to risk adjust the 
observed number of hospitalizations after the resident met the long-
stay status to determine the expected number of hospitalizations for 
each long-stay resident given the resident's clinical and demographic 
profile. The goal of risk adjustment is to account for differences 
across facilities in medical acuity, functional impairment, and frailty 
of the long-stay residents but not factors related to the quality of 
care provided by the facility. The data for the risk adjustment model

[[Page 53295]]

are derived from Medicare inpatient claims data prior to the day the 
resident became a long-stay resident and from the most recent quarterly 
or comprehensive MDS assessment within 120 days prior to the day the 
resident became a long-stay resident.
    The risk adjustment variables derived from the claims-based data 
include age, sex, number of hospitalizations in the 365 days before the 
day the resident became a long-stay resident or beginning of the 1-year 
measurement period (whichever is later), and an outcome-specific 
comorbidity index. The MDS-based covariates span multiple domains 
including functional status, clinical conditions, clinical treatments, 
and clinical diagnoses.
    We refer readers to the measure specifications for additional 
details on the risk-adjustment model for this measure available at 
https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/CertificationandComplianc/Downloads/Nursing-Home-Compare-Claims-based-Measures-Technical-Specifications-April-2019.pdf.
(6) Measure Calculation
    To get the risk adjusted rate (risk standardized rate), we take the 
observed Long Stay Hospitalization rate divided by the expected Long 
Stay Hospitalization rate, multiplied by the national Long Stay 
Hospitalization rate, as shown by the following formula:
[GRAPHIC] [TIFF OMITTED] TR07AU23.700

    The observed Long Stay Hospitalization rate is the actual number of 
hospital admissions or observation stays that met the previously 
discussed inclusion criteria divided by the actual total number of 
long-stay days that met the previously discussed inclusion criteria 
divided by 1,000 days. The observed rate is shown by the following 
formula:
[GRAPHIC] [TIFF OMITTED] TR07AU23.701

    The expected Long Stay Hospitalization rate is the expected number 
of hospital admission or observation stays that were calculated using 
the risk adjustment methodology discussed in section VIII.B.4.e.(5) of 
this final rule, divided by the actual total number of long-stay days 
that met the previously discussed inclusion criteria divided by 1,000 
days. The expected Long Stay Hospitalization rate is shown by the 
following formula:
[GRAPHIC] [TIFF OMITTED] TR07AU23.702

    The national Long Stay Hospitalization rate is the total number of 
inpatient hospital admission or observation stays meeting the numerator 
criteria, divided by the total number of all long stay days that met 
the denominator criteria divided by 1,000. The national Long Stay 
Hospitalization rate is shown by the following formula:
[GRAPHIC] [TIFF OMITTED] TR07AU23.703

    We refer readers to the measure specifications for additional 
details for this measure calculation available at https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/CertificationandComplianc/Downloads/Nursing-Home-Compare-Claims-based-Measures-Technical-Specifications-April-2019.pdf.
    We solicited public comment on our proposal to adopt the Number of 
Hospitalizations per 1,000 Long-Stay Resident Days measure beginning 
with the FY 2027 SNF VBP program year.
    We received public comments on this proposal. The following is a 
summary of the comments we received and our responses.
    Comment: Several commenters expressed support for the proposal to 
adopt the measure. One commenter suggested that CMS monitor rates of 
hospitalization for long-stay residents to assess whether this measure 
will remain appropriate in the long-term.
    Response: We thank the commenters for their support. We agree with 
the suggestion and intend to monitor all SNF VBP Program measures to 
ensure that they remain relevant to the care quality provided to 
Medicare beneficiaries in this setting.
    Comment: Some commenters supported the measure's adoption but 
expressed concerns about its use in the Program. One commenter wondered 
what this measure adds to the Program that isn't captured by the 
proposed SNF WS PPR measure. Another commenter stated its belief that 
CMS should focus the SNF VBP Program on Medicare Part A patients, which 
does not include long-stay residents, because the Program itself 
affects payments for Part A services. Two commenters were concerned 
that the measure excludes Medicare Advantage residents, thus not 
covering a significant portion of Medicare beneficiaries.
    Response: We thank the commenters for their feedback. As we stated 
in the proposed rule (88 FR 21373 through 21374), our analysis of the 
relationship between long-stay hospitalization rates and other measures 
of quality from

[[Page 53296]]

CMS's Five-Star Quality Rating System showed that facilities with lower 
hospitalization rates tend to perform better on other dimensions of 
quality such as health inspection survey results, staffing level, other 
quality measures, and overall ratings. We further explained our 
reasoning for including a long-stay measure in the SNF VBP Program in 
the proposed rule (88 FR 21370), where we stated that we believe long-
stay measures better capture the quality of care provided to the 
entirety of the population that resides in facilities that are dually 
certified as SNFs and nursing facilities. Long-stay residents who are 
enrolled in Medicare Part B receive Medicare Part B coverage for 
certain services provided by nursing facilities. We believe that 
presenting more quality information for beneficiaries helps improve the 
care they receive and the health system generally. We would also like 
to clarify that the SNF WS PPR assesses readmission rates for SNF 
residents who are admitted to a short-stay acute care hospital or long-
term care hospital with a principal diagnosis considered to be 
unplanned and potentially preventable while within SNF care, while the 
Long-Stay Hospitalization measure focuses on the risks experienced by 
long-stay residents. We therefore view these measures as complementary 
assessments of readmissions in dually certified facilities. The 
majority of long-stay residents are enrolled in Medicare Part B. For 
those residents, Medicare Part B provides coverage of certain services, 
such as physical therapy, that are provided by the nursing facility. We 
therefore believe that the measure is appropriate for the Program.
    We also appreciate commenters' concerns about Medicare Advantage 
residents. However, we would like to clarify that our Star Ratings 
system provides quality information to Medicare beneficiaries about the 
care they receive from the specific facility regardless of whether the 
beneficiary is enrolled in the Medicare FFS program or in a Medicare 
Advantage plan. We are also interested in including Medicare Advantage 
beneficiaries in the measure's calculations, but Medicare Advantage 
claims are not generally available for our use on the same timing or in 
the same way that FFS claims are used to calculate this measure.
    Comment: Some commenters opposed the proposal to adopt this 
measure. One commenter did not believe the measure aligned with the 
Program's intent to link Medicare FFS reimbursement with care and 
outcomes experienced by Medicare FFS beneficiaries. A few commenters 
were concerned about assessing facilities using long-stay measures for 
a short-stay Medicare benefit. One commenter worried that the measure 
would impose additional burdens on SNFs.
    Response: We thank the commenters for this feedback. However, as we 
explained in the proposed rule (88 FR 21373 through 21374), performance 
on the Long Stay Hospitalization measure is correlated with numerous 
other measures of quality in the SNF sector, meaning that, in our view, 
the measure supports quality improvement in the SNF sector. We continue 
to believe that measures like this one provide significant benefits to 
Medicare beneficiaries.
    We would also like to clarify that the Long Stay Hospitalization 
measure is calculated using Medicare claims data, so it imposes no 
additional reporting or validation burden on SNFs.
    After consideration of public comments, we are finalizing adoption 
of the Number of Hospitalizations per 1,000 Long-Stay Resident Days 
measure beginning with the FY 2027 SNF VBP program year.
f. Scoring of SNF Performance on the Nursing Staff Turnover, Falls With 
Major Injury (Long-Stay), and Long Stay Hospitalization Measures
(1) Background
    In the FY 2017 SNF PPS final rule (81 FR 52000 through 52001), we 
finalized a policy to invert SNFRM measure rates such that a higher 
measure rate reflects better performance on the SNFRM. In that final 
rule, we also stated our belief that this inversion is important for 
incentivizing improvement in a clear and understandable manner because 
a ``lower is better'' rate could cause confusion among SNFs and the 
public. In the FY 2023 SNF PPS final rule (87 FR 47568), we applied 
this policy to the SNF HAI measure such that a higher measure rate 
reflects better performance on the SNF HAI measure. We also stated our 
intent to apply this inversion scoring policy to all measures in the 
Program for which the calculation produces a ``lower is better'' 
measure rate. We continue to believe that inverting measure rates such 
that a higher measure rate reflects better performance on a measure is 
important for incentivizing improvement in a clear and understandable 
manner.
    This measure rate inversion scoring policy does not change the 
measure specifications or the calculation method. We use this measure 
rate inversion as part of the scoring methodology under the SNF VBP 
Program. The measure rate inversion is part of the methodology we use 
to generate measure scores, and resulting SNF Performance Scores, that 
are clear and understandable for SNFs and the public.
(2) Inversion of the Nursing Staff Turnover, Falls With Major Injury 
(Long-Stay), and Long Stay Hospitalization Measures Rates for SNF VBP 
Program Scoring Purposes
    In sections VII.B.4.b., VII.B.4.c., and VII.B.4.e. of the proposed 
rule, we stated that a lower measure rate for the Nursing Staff 
Turnover, Falls with Major Injury (Long-Stay), and Long Stay 
Hospitalization measures indicate better performance on those measures. 
Therefore, we proposed to apply our measure rate inversion scoring 
policy to these measures. We proposed to calculate the score for these 
measures for the SNF VBP Program by inverting the measure rates using 
the calculations shown in Table 16. We did not propose to apply this 
policy to the DC Function measure because that measure, as currently 
specified and calculated, produces a ``higher is better'' measure rate.

[[Page 53297]]

[GRAPHIC] [TIFF OMITTED] TR07AU23.704

    We believe that inverting the measure rates for the Nursing Staff 
Turnover, Falls with Major Injury (Long-Stay), and Long Stay 
Hospitalization measure is important for incentivizing improvement in a 
clear and understandable manner, and for ensuring a consistent message 
that a higher measure rate reflects better performance on the measures.
    We solicited public comment on our proposal to invert the measure 
rates for the Nursing Staff Turnover, Falls with Major Injury (Long-
Stay), and Long Stay Hospitalization measures for the purposes of 
scoring under the SNF VBP Program.
    We received public comments on this proposal. The following is a 
summary of the comments we received and our responses.
    Comment: One commenter supported the proposal to invert the Nursing 
Staff Turnover, Falls with Major Injury (Long-Stay), and Long Stay 
Hospitalization measure rates for SNF VBP program scoring purposes 
because the proposal is important for incentivizing improvement in a 
clear and understandable manner, and for ensuring a consistent message 
that a higher measure rate reflects better performance.
    Response: We thank this commenter for their support. We agree that 
this proposed score inversion will provide a clearer depiction of 
quality in our performance scoring.
    Comment: One commenter recommended that in addition to the proposed 
inversion of the Nursing Staff Turnover, Falls with Major Injury (Long-
Stay), and Long Stay Hospitalization measure rates for SNF VBP Program 
scoring purposes, non-inverted rates be included in feedback reports to 
providers to help them track their performance relative to benchmark 
rates in their quality improvement effort.
    Response: We thank this commenter for their recommendation. We note 
that we currently include the non-inverted rates for the SNFRM in the 
quarterly confidential feedback reports, and we intend to continue that 
practice for all new measures for which we invert the measure rates for 
scoring purposes. As mentioned in the proposed rule (88 FR 21376), the 
measure rate inversion is solely part of the methodology we use to 
generate measure scores and resulting SNF Performance Scores.
    Comment: One commenter opposed the proposal to invert the nursing 
staff turnover, falls with major injury (long-stay), and long stay 
hospitalization measure rates for SNF VBP program scoring purposes. 
This commenter believes the proposed score inversion overly complicates 
an already complex quality initiative. The commenter further expressed 
that the application of inverted scores is inconsistent with public 
reporting for other measures.
    Response: We believe that our policy to invert measure rates such 
that a higher measure rate reflects better performance is important for 
incentivizing improvement through clear and understandable SNF 
Performance Scores. This measure rate inversion scoring policy is only 
used for the purposes of generating SNF Performance Scores under the 
SNF VBP Program's scoring methodology. The measure rate inversions do 
not change the measure specifications and are not publicly reported.
    After consideration of public comments, we are finalizing our 
proposal to invert the measure rates for the Nursing Staff Turnover, 
Falls with Major Injury (Long-Stay), and Long Stay Hospitalization 
measures for the purposes of scoring under the SNF VBP Program.
g. Confidential Feedback Reports and Public Reporting for Quality 
Measures
    Our confidential feedback reports and public reporting policies are 
codified at Sec.  413.338(f) of our regulations. In the FY 2023 SNF PPS 
final rule (87 FR 47591 through 47592), we revised our regulations such 
that the confidential feedback reports and public reporting policies 
apply to each measure specified for a fiscal year, which includes the 
Nursing Staff Turnover measure beginning with the FY 2026 program year, 
and the Falls with Major Injury (Long-Stay), DC Function, and Long Stay 
Hospitalization measures beginning with the FY 2027 program year.
    We did not propose any changes to these policies in the proposed 
rule.

C. SNF VBP Performance Periods and Baseline Periods

1. Background
    We refer readers to the FY 2016 SNF PPS final rule (80 FR 46422) 
for a discussion of our considerations for determining performance 
periods and baseline periods under the SNF VBP Program. In the FY 2019 
SNF PPS final rule (83 FR 39277 through 39278), we adopted a policy 
whereby we will automatically adopt the performance period and baseline 
period for a SNF VBP program year by advancing the performance period 
and baseline period by 1 year from the previous program year. In the FY 
2023 SNF PPS final rule (87 FR 47580 through 47583), we adopted 
performance periods and baseline periods for three new quality measures 
beginning with the FY 2026 program year: (1) SNF HAI measure, (2) Total 
Nurse Staffing measure, and (3) DTC PAC SNF measure, and finalized the 
application of our policy to automatically adopt performance periods 
and baseline periods for subsequent program years to those new 
measures.
2. SNFRM Performance and Baseline Periods for the FY 2024 SNF VBP 
Program Year
    Under the policy finalized in the FY 2019 SNF PPS final rule (83 FR 
39277 through 39278), the baseline period for the SNFRM for the FY 2024 
program year would be FY 2020 and the performance period for the SNFRM 
for the FY 2024 program year would be FY

[[Page 53298]]

2022. However, in the FY 2022 SNF PPS final rule (85 FR 42512 through 
42513), we updated the FY 2024 baseline period for the SNFRM to FY 2019 
since the ECE we granted on March 22, 2020, due to the PHE for COVID-
19, excepted qualifying claims for a 6-month period in FY 2020 (January 
1, 2020 through June 30, 2020) from the calculation of the 
SNFRM.351 352 We refer readers to that final rule for 
additional discussion of our considerations for updating the FY 2024 
baseline period for the SNFRM. Therefore, for the FY 2024 program year, 
the baseline period for the SNFRM is FY 2019 and the performance period 
for the SNFRM is FY 2022.
---------------------------------------------------------------------------

    \351\ CMS. (2020). Press Release: CMS Announces Relief for 
Clinicians, Providers, Hospitals, and Facilities Participating in 
Quality Reporting Programs in Response to COVID-19. https://www.cms.gov/newsroom/press-releases/cms-announces-relief-clinicians-providers-hospitals-and-facilities-participating-quality-reporting.
    \352\ CMS memorandum (2020) available at https://www.cms.gov/files/document/guidance-memo-exceptions-and-extensions-quality-reporting-and-value-based-purchasing-programs.pdf.
---------------------------------------------------------------------------

3. Performance Periods and Baseline Periods for the Nursing Staff 
Turnover, Falls With Major Injury (Long-Stay), DC Function, and Long 
Stay Hospitalization Measures
a. Performance Periods for the Nursing Staff Turnover, Falls With Major 
Injury (Long-Stay), DC Function, and Long Stay Hospitalization Measures
    In considering the appropriate performance periods for the Nursing 
Staff Turnover, Falls with Major Injury (Long-Stay), DC Function, and 
Long Stay Hospitalization measures, we recognize that we must balance 
the length of the performance periods with our need to calculate valid 
and reliable performance scores and announce the resulting payment 
adjustments no later than 60 days prior to the program year involved, 
in accordance with section 1888(h)(7) of the Act. In addition, we refer 
readers to the FY 2017 SNF PPS final rule (81 FR 51998 through 51999) 
for a discussion of the factors we should consider when specifying 
performance periods for the SNF VBP Program, as well as our stated 
preference for 1-year performance periods. Based on these 
considerations, we believe that 1-year performance periods for these 
measures would be operationally feasible for the SNF VBP Program and 
would provide sufficiently accurate and reliable measure rates and 
resulting performance scores for the measures.
    We also recognize that we must balance our desire to specify 
performance periods for a fiscal year as close to the fiscal year's 
start date as possible to ensure clear connections between quality 
measurement and value-based payment with our need to announce the net 
results of the Program's adjustments to Medicare payments not later 
than 60 days prior to the fiscal year involved, in accordance with 
section 1888(h)(7) of the Act. In considering these constraints, and in 
alignment with other SNF VBP measures, we believe that performance 
periods that occur 2 fiscal years prior to the applicable fiscal 
program year is most appropriate for these measures.
    For these reasons, we proposed to adopt the following performance 
periods:
     FY 2024 (October 1, 2023 through September 30, 2024) as 
the performance period for the Nursing Staff Turnover measure for the 
FY 2026 SNF VBP program year.
     FY 2025 (October 1, 2024, through September 30, 2025) as 
the performance period for the Falls with Major Injury (Long-Stay) 
measure for the FY 2027 SNF VBP program year.
     FY 2025 (October 1, 2024 through September 30, 2025) as 
the performance period for the DC Function measure for the FY 2027 SNF 
VBP program year.
     FY 2025 (October 1, 2024 through September 30, 2025) as 
the performance period for the Long Stay Hospitalization measure for 
the FY 2027 SNF VBP program year.
    In alignment with the previously adopted SNF VBP measures, we also 
proposed that, for these measures, we will automatically adopt the 
performance period for a SNF VBP program year by advancing the 
beginning of the performance period by 1 year from the previous program 
year.
    We solicited public comment on our proposals to adopt performance 
periods for the Nursing Staff Turnover, Falls with Major Injury (Long-
Stay), DC Function, and Long Stay Hospitalization measures. We provide 
a summary of the comments we received and our responses in the next 
section. As stated in that section, we are finalizing the performance 
periods for the Nursing Staff Turnover, Falls with Major Injury (Long-
Stay), DC Function, and Long Stay Hospitalization measures.
b. Baseline Periods for the Nursing Staff Turnover, Falls With Major 
Injury (Long-Stay), DC Function, and Long Stay Hospitalization Measures
    In the FY 2016 SNF PPS final rule (80 FR 46422) we discussed that, 
as with other Medicare quality programs, we generally adopt baseline 
periods for a fiscal year that occurs prior to the performance periods 
for that fiscal year to establish measure performance standards. We 
also discussed our intent to adopt baseline periods that are as close 
as possible in duration as performance periods for a fiscal year, as 
well as our intent to seasonally align baseline periods with 
performance periods to avoid any effects on quality measurement that 
may result from tracking SNF performance during different times in a 
year. Therefore, to align with the performance period length for the 
Nursing Staff Turnover, Falls with Major Injury (Long-Stay), DC 
Function, and Long Stay Hospitalization measures, we proposed to adopt 
1-year baseline periods for those measures.
    We also recognize that we are required, under section 1888(h)(3)(C) 
of the Act, to calculate and announce performance standards no later 
than 60 days prior to the start of performance periods. Therefore, we 
believe that baseline periods that occur 4 fiscal years prior to the 
applicable fiscal program year, and 2 fiscal years prior to the 
performance periods, is most appropriate for these measures and will 
provide sufficient time to calculate and announce performance standards 
prior to the start of the performance periods.
    For these reasons, we proposed to adopt the following baseline 
periods:
     FY 2022 (October 1, 2021 through September 30, 2022) as 
the baseline period for the Nursing Staff Turnover measure for the FY 
2026 SNF VBP program year.
     FY 2023 (October 1, 2022 through September 30, 2023) as 
the baseline period for the Falls with Major Injury (Long-Stay) measure 
for the FY 2027 SNF VBP program year.
     FY 2023 (October 1, 2022 through September 30, 2023) as 
the baseline period for the DC Function measure for the FY 2027 SNF VBP 
program year.
     FY 2023 (October 1, 2022 through September 30, 2023) as 
the baseline period for the Long Stay Hospitalization measure for the 
FY 2027 SNF VBP program year.
    In alignment with the previously adopted SNF VBP measures, we also 
proposed that, for these measures, we will automatically adopt the 
baseline period for a SNF VBP program year by advancing the beginning 
of the baseline period by 1 year from the previous program year.
    We solicited public comment on our proposals to adopt baseline 
periods for the Nursing Staff Turnover, Falls with Major Injury (Long-
Stay), DC Function, and Long Stay Hospitalization measures.
    We received public comments on these proposals. The following is a 
summary of the comments we received and our responses.

[[Page 53299]]

    Comment: One commenter supported the performance periods and 
baseline periods for the Nursing Staff Turnover, Falls with Major 
Injury (Long-Stay), DC Function, and Long Stay Hospitalization measures 
as proposed.
    Response: We thank the commenter for their support of the 
performance periods and baseline periods for the Nursing Staff 
Turnover, Falls with Major Injury (Long-Stay), DC Function, and Long 
Stay Hospitalization measures.
    After consideration of public comments, we are finalizing the 
performance periods and baseline periods for the Nursing Staff 
Turnover, Falls with Major Injury (Long-Stay), DC Function, and Long 
Stay Hospitalization measures.
4. Performance Periods and Baseline Periods for the SNF WS PPR Measure 
Beginning With the FY 2028 SNF VBP Program Year
a. Performance Periods for the SNF WS PPR Measure Beginning With the FY 
2028 SNF VBP Program Year
    The SNF WS PPR measure is calculated using 2 consecutive years of 
Medicare FFS claims data, and therefore, we proposed to adopt a 2-year 
performance period for this measure. During the re-specification 
process for the SNF WS PPR measure, we determined that using 2 years of 
data improved the measure reliability. Specifically, the intraclass 
correlation coefficient (with the Spearman-Brown correction applied) 
for the SNF WS PPR measure was 0.71 compared to 0.56 for the SNFRM. We 
refer readers to section VIII.B.2. of this final rule and the SNF WS 
PPR measure technical specifications, available at https://www.cms.gov/files/document/snfvbp-snfwsppr-draft-technical-measure-specification.pdf, for additional details.
    Accordingly, we proposed to adopt October 1, 2024 through September 
30, 2026 (FY 2025 and FY 2026) as the performance period for the SNF WS 
PPR measure for the FY 2028 SNF VBP program year. We believe that using 
October 1, 2024 through September 30, 2026 (FY 2025 and FY 2026) as the 
performance period for the FY 2028 program year best balances our need 
for sufficient data to calculate valid and reliable performance scores 
with our requirement under section 1888(h)(7) of the Act to announce 
the resulting payment adjustments no later than 60 days prior to the 
program year involved.
    In alignment with the previously adopted SNF VBP measures, we also 
proposed that for the SNF WS PPR measure, we will automatically adopt 
the performance period for a SNF VBP program year by advancing the 
beginning of the performance period by 1 year from the previous program 
year.
    We solicited public comment on our proposals related to the 
performance periods for the SNF WS PPR measure beginning with the FY 
2028 program year. We provide a summary of the comments we received and 
our responses in the next section. As stated in that section, we are 
finalizing the performance periods for the SNF WS PPR measure beginning 
with the FY 2028 program year.
b. Baseline Periods for the SNF WS PPR Measure Beginning With the FY 
2028 SNF VBP Program Year
    Our policy is to generally adopt a baseline period for a fiscal 
year that occurs prior to the performance period for that fiscal year 
in order to establish a measure's performance standards. We also 
generally adopt baseline periods that are as close as possible in 
duration as the performance period for a fiscal year, as well as 
seasonally aligning the baseline periods with performance periods to 
avoid any effects on quality measurement that may result from tracking 
SNF performance during different times in a year. Therefore, to align 
with the performance period length for the SNF WS PPR measure, we 
proposed a 2-year baseline period for this measure.
    We also recognize that we are required, under section 1888(h)(3)(C) 
of the Act, to calculate and announce performance standards no later 
than 60 days prior to the start of the performance period. Therefore, 
we believe that a baseline period that begins 6 fiscal years prior to 
the applicable fiscal program year, and 3 fiscal years prior to the 
applicable performance period, is most appropriate for the SNF WS PPR 
measure and will provide sufficient time to calculate and announce 
performance standards prior to the start of the performance period. For 
these reasons, we proposed to adopt October 1, 2021 through September 
30, 2023 (FY 2022 and FY 2023) as the baseline period for the SNF WS 
PPR measure for the FY 2028 SNF VBP program year.
    In alignment with the previously adopted SNF VBP measures, we also 
proposed that for the SNF WS PPR measure, we will automatically adopt 
the baseline period for a SNF VBP program year by advancing the 
beginning of the baseline period by 1 year from the previous program 
year.
    We solicited public comment on our proposals related to the 
baseline periods for the SNF WS PPR measure beginning with FY 2028 
program year.
    We received public comments on these proposals. The following is a 
summary of the comments we received and our responses.
    Comment: One commenter supported the proposed performance periods 
and baseline periods for the SNF WS PPR measure.
    Response: We thank the commenter for their support of the 
performance periods and baseline periods for the SNF WS PPR measure 
beginning with the FY 2028 program year.
    After consideration of public comments, we are finalizing the 
performance periods and baseline periods for the SNF WS PPR measure 
beginning with the FY 2028 program year.
c. SNFRM and SNF WS PPR Performance Period and Baseline Period 
Considerations
    As discussed in the previous section, we are finalizing our 
proposal that the first performance period for the SNF WS PPR measure 
will be October 1, 2024 through September 30, 2026 (FY 2025 and FY 
2026), and the first baseline period will be October 1, 2021 through 
September 30, 2023 (FY 2022 and FY 2023). In section VIII.B.3. of this 
final rule, we are finalizing our proposal to replace the SNFRM with 
the SNF WS PPR beginning with the FY 2028 program year. Therefore, the 
last program year that will include the SNFRM will be FY 2027. The last 
performance period for the SNFRM will be FY 2025 and the last baseline 
period will be FY 2023. We note that because the SNF WS PPR measure is 
a 2-year measure and the SNFRM is a 1-year measure, the data used to 
calculate the baseline and performance period for the SNF WS PPR 
measure for the FY 2028 program year will include data that is also 
used to calculate the baseline and performance period for the SNFRM for 
the FY 2027 program year. We believe the overlap is necessary to ensure 
that we can transition from the SNFRM to the SNF WS PPR seamlessly, 
without any gaps in the use of either measure.

D. SNF VBP Performance Standards

1. Background
    We refer readers to the FY 2017 SNF PPS final rule (81 FR 51995 
through 51998) for a summary of the statutory provisions governing 
performance standards under the SNF VBP Program and our finalized 
performance standards policy. In the FY 2019 SNF PPS final rule (83 FR 
39276 through 39277), we also adopted a policy allowing us to

[[Page 53300]]

correct the numerical values of the performance standards. Further, in 
the FY 2023 SNF PPS final rule (87 FR 47583 through 47584), we amended 
the definition of ``Performance Standards,'' redesignated that 
definition as Sec.  413.338(a)(12) and added additional detail for our 
performance standards correction policy at Sec.  413.338(d)(6).
    We adopted the final numerical values for the FY 2024 performance 
standards in the FY 2022 SNF PPS final rule (86 FR 42513) and adopted 
the final numerical values for the FY 2025 performance standards in the 
FY 2023 SNF PPS final rule (87 FR 47584).
    We did not propose any changes to these performance standards 
policies.
2. Performance Standards for the FY 2026 Program Year
    In the FY 2023 SNF PPS final rule (87 FR 47564 through 47576), we 
adopted two new quality measures for the FY 2026 program year: SNF HAI 
and Total Nurse Staffing measures. In section VIII.B.4.b. of this final 
rule, we are also finalizing adoption of the Nursing Staff Turnover 
measure beginning with the FY 2026 program year. We are finalizing that 
the performance period for the Nursing Staff Turnover measure for the 
FY 2026 program year will be FY 2024 (October 1, 2023 through September 
30, 2024). Therefore, the FY 2026 program year will consist of four 
measures (SNFRM, SNF HAI, Total Nurse Staffing, and Nursing Staff 
Turnover measures).
    To meet the requirements at section 1888(h)(3)(C) of the Act, we 
are providing the final numerical performance standards for the FY 2026 
program year for the three previously adopted measures (SNFRM, SNF HAI, 
and Total Nurse Staffing measures), as well as the Nursing Staff 
Turnover measure. In accordance with our previously finalized 
methodology for calculating performance standards (81 FR 51996 through 
51998), the final numerical values for the FY 2026 program year 
performance standards are shown in Table 17.

                          Table 17--Final FY 2026 SNF VBP Program Performance Standards
----------------------------------------------------------------------------------------------------------------
                      Measure short name                         Achievement threshold          Benchmark
----------------------------------------------------------------------------------------------------------------
SNFRM.........................................................                  0.78800                  0.82971
SNF HAI Measure...............................................                  0.92315                  0.95004
Total Nurse Staffing Measure..................................                  3.18523                  5.70680
Nursing Staff Turnover Measure................................                  0.35912                  0.72343
----------------------------------------------------------------------------------------------------------------

3. Performance Standards for the DTC PAC SNF Measure for the FY 2027 
Program Year
    In the FY 2023 SNF PPS final rule (87 FR 47576 through 47580), we 
adopted the DTC PAC SNF measure beginning with the FY 2027 program 
year. In that final rule (87 FR 47582 through 47583), we also finalized 
that the baseline and performance periods for the DTC PAC SNF measures 
would be 2 consecutive years, and that FY 2024 and FY 2025 would be the 
performance period for the DTC PAC SNF measure for the FY 2027 program 
year.
    To meet the requirements at section 1888(h)(3)(c) of Act, we are 
providing the final numerical performance standards for the DTC PAC SNF 
measure for the FY 2027 program year. In accordance with our previously 
finalized methodology for calculating performance standards (81 FR 
51996 through 51998), the final numerical values for the DTC PAC SNF 
measure for the FY 2027 program year performance standards are shown in 
Table 18.
    We note that we will provide the estimated numerical performance 
standard values for the remaining measures applicable in the FY 2027 
program year in the FY 2025 SNF PPS proposed rule.

            Table 18--Final FY 2027 SNF VBP Program Performance Standards for the DTC PAC SNF Measure
----------------------------------------------------------------------------------------------------------------
                      Measure short name                         Achievement threshold          Benchmark
----------------------------------------------------------------------------------------------------------------
DTC PAC SNF Measure...........................................                  0.42946                  0.66370
----------------------------------------------------------------------------------------------------------------

E. SNF VBP Performance Scoring Methodology

1. Background
    Our performance scoring policies are codified at Sec.  413.338(d) 
and (e) of our regulations. We also refer readers to the following 
prior final rules for detailed background on the scoring methodology 
for the SNF VBP Program:
     In the FY 2017 SNF PPS final rule (81 FR 52000 through 
52005), we finalized several scoring methodology policies, including a 
policy to use the higher of a SNF's achievement and improvement scores 
as that SNF's performance score for a given program year.
     In the FY 2018 SNF PPS final rule (82 FR 36614 through 
36616), we finalized: (1) a rounding policy, (2) a logistic exchange 
function, (3) a 60 percent payback percentage, and (4) a SNF 
performance ranking policy.
     In the FY 2019 SNF PPS final rule (83 FR 39278 through 
39281), we finalized several scoring methodology policies, including a 
scoring policy for SNFs without sufficient baseline period data and an 
extraordinary circumstances exception policy.
     In the FY 2022 SNF PPS final rule (86 FR 42513 through 
42515), we finalized a special scoring and payment policy for the FY 
2022 SNF VBP Program due to the impact of the PHE for COVID-19.
     In the FY 2023 SNF PPS final rule (87 FR 47584 through 
47590), we finalized a special scoring and payment policy for the FY 
2023 SNF VBP Program due to the continued impact of the PHE for COVID-
19. In that final rule, we also finalized several scoring methodology 
policies to accommodate the addition of new measures to the Program, 
including: (1) case minimum and measure minimum policies, including 
case minimums for the SNFRM, SNF HAI, Total Nurse Staffing, and DTC PAC 
SNF measures, (2) updates to the scoring policy for SNFs without 
sufficient baseline period data, (3) removal of the low-volume 
adjustment policy, and (4) a measure-level and normalization scoring 
policy to replace the previously adopted scoring methodology policies 
beginning with the FY 2026 program year.

[[Page 53301]]

2. Case Minimum and Measure Minimum Policies
a. Background
    We refer readers to the FY 2023 SNF PPS final rule (87 FR 47585 
through 47587) for a detailed description of our considerations for 
adopting case minimums and measure minimums. Our case minimum and 
measure minimum policies are also codified at Sec.  413.338(b) of our 
regulations.
    We proposed to adopt the Nursing Staff Turnover measure beginning 
with the FY 2026 program year; the Falls with Major Injury (Long-Stay), 
DC Function, and Long Stay Hospitalization measures beginning with the 
FY 2027 program year; and the SNF WS PPR measure beginning with the FY 
2028 program year. Therefore, we also proposed to adopt case minimums 
for the new measures and proposed to update the previously finalized 
measure minimum for the FY 2027 program year. Although the addition of 
the Nursing Staff Turnover measure beginning with FY 2026 will increase 
the total number of measures for that program year, we believe that the 
previously finalized measure minimum of two measures remains sufficient 
for that program year.
b. Case Minimums During a Performance Period for the Nursing Staff 
Turnover, Falls With Major Injury (Long-Stay), DC Function, Long Stay 
Hospitalization, and SNF WS PPR Measures
    We proposed to adopt the Nursing Staff Turnover measure beginning 
with the FY 2026 program year; the Falls with Major Injury (Long-Stay), 
Long Stay Hospitalization, and DC Function measures beginning with the 
FY 2027 program year; and the SNF WS PPR measure beginning with the FY 
2028 program year. Therefore, to meet the requirements at section 
1888(h)(1)(C)(i) of the Act, we also proposed to adopt case minimums 
for those proposed measures.
    For the Nursing Staff Turnover measure, we proposed that SNFs must 
have a minimum of 1 eligible stay during the 1-year performance period 
and at least 5 eligible nursing staff (RNs, LPNs, and nurse aides) 
during the 3 quarters of PBJ data included in the measure denominator. 
SNFs must meet both of these requirements in order to be eligible to 
receive a score on the measure for the applicable program year. We 
believe this case minimum requirement is appropriate and consistent 
with the findings of measure testing analyses and the measure 
specifications. For example, using FY 2021 data, we estimated that 80 
percent of SNFs met the 5-eligible nursing staff minimum. In addition, 
we note that the 1-eligible stay and 5-eligible nursing staff minimums 
were determined to be appropriate for publicly reporting this measure 
on the Care Compare website.
    For the Falls with Major Injury (Long-Stay) measure, we proposed 
that SNFs must have a minimum of 20 residents in the measure 
denominator during the 1-year performance period to be eligible to 
receive a score on the measure for the applicable fiscal program year. 
We believe this case minimum requirement is appropriate and consistent 
with the findings of measure testing analyses. For example, using FY 
2021 data, we estimated that nearly 96 percent of SNFs met the 20-
resident minimum. In addition, testing results indicated that a 20-
resident minimum produced moderately reliable measure rates for the 
purposes of public reporting.\353\
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    For the Long Stay Hospitalization measure, we proposed that SNFs 
must have a minimum of 20 eligible stays during the 1-year performance 
period to be eligible to receive a score on the measure for the 
applicable fiscal program year. We believe this case minimum 
requirement is appropriate and consistent with the findings of measure 
testing analyses. For example, using CY 2021 data, we estimated that 
approximately 80 percent of SNFs met the 20-eligible stay minimum. In 
addition, we note that the 20-eligible stay minimum was determined to 
be appropriate for publicly reporting this measure under the Five-Star 
Quality Rating System.
    For the DC Function measure, we proposed that SNFs must have a 
minimum of 20 eligible stays during the 1-year performance period in 
order to be eligible to receive a score on the measure for the 
applicable fiscal program year. We believe this case minimum 
requirement is appropriate and consistent with the findings of measure 
testing analyses. For example, testing results, which used FY 2019 
data, found that nearly 84 percent of SNFs met the 20-eligible stay 
minimum.\354\ In addition, those testing results indicated that a 20-
eligible stay minimum produced sufficiently reliable measure rates.
---------------------------------------------------------------------------

    \354\ Discharge Function Score for Skilled Nursing Facilities 
(SNFs) Technical Report, which is available on the SNF Quality 
Reporting Program Measures and Technical Information web page at 
https://www.cms.gov/files/document/snf-discharge-function-score-technical-report-february-2023.pdf.
---------------------------------------------------------------------------

    For the SNF WS PPR measure, we proposed that SNFs must have a 
minimum of 25 eligible stays during the 2-year performance period in 
order to be eligible to receive a score on the measure for the 
applicable fiscal program year. We believe this case minimum 
requirement is appropriate and consistent with the findings of measure 
testing analyses. For example, using FY 2020 through FY 2021 data, we 
estimated that nearly 91 percent of non-swing bed SNFs met the 25-
eligible stay minimum. In addition, testing results indicated that a 
25-eligible stay minimum produced sufficiently reliable measure 
rates.\355\
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    We believe these case minimum standards for public reporting 
purposes are also appropriate standards for establishing a case minimum 
for these measures under the SNF VBP Program. We also believe these 
case minimum requirements support our objective, which is to establish 
case minimums that appropriately balance quality measure reliability 
with our continuing desire to score as many SNFs as possible on these 
measures.
    We solicited public comment on our proposal to adopt case minimums 
for the Nursing Staff Turnover, Falls with Major Injury (Long-Stay), 
Long Stay Hospitalization, DC Function, and SNF WS PPR measures.
    We received public comments on this proposal. The following is a 
summary of the comments we received and our responses.
    Comment: One commenter supported the proposed case minimums during 
a performance period for the Nursing Staff Turnover, Falls with Major 
Injury (Long-Stay), DC Function, Long Stay Hospitalization, and SNF WS 
PPR measures based on the rationale that the proposed case minimums are 
appropriate and consistent with measure testing analyses and 
appropriately balance quality measure reliability with the desire to 
score as many SNFs as possible on these measures, which is further 
detailed in section VII.E.2. of the proposed rule (88 FR 21379 through 
21380).
    Response: We thank the commenters for their support. We agree that 
these case minimums are consistent with the findings of the measure 
testing analyses we referenced in section VII.E.2. of the proposed rule 
(88 FR 21379 through 21380), and support our objective, which is to 
establish case minimums that appropriately balance quality measure 
reliability with our continuing desire to score as many SNFs as 
possible on these measures.

[[Page 53302]]

    Comment: One commenter recommended that CMS adopt case minimum 
requirements that meet a reliability standard of 0.7. This commenter 
further recommended that CMS could expand the number of SNFs meeting 
this higher reliability standard by including multiple years in a 
performance period, adding that more recent years could be weighted 
more heavily than preceding years.
    Response: We believe that the proposed case minimums ensure that 
SNF VBP measures are sufficiently reliable for purposes of scoring and 
payment adjustments under the Program. Our testing has also indicated 
that increasing the case minimum requirements to achieve the 
reliability standard of 0.7 would result in minimal improvements to a 
measure's reliability while simultaneously increasing the number of 
SNFs that would not meet the higher case minimum requirement, which 
does not align with our goal to ensure as many SNFs as possible have 
the opportunity to receive a score on a given measure. Therefore, we do 
not believe it is currently necessary or feasible to adopt case minimum 
requirements that meet a reliability standard of 0.7.
    We acknowledge the commenter's recommendation to increase measure 
reliability using longer performance periods and baseline periods and 
agree that this could increase measure reliability. However, we stated 
our preference in the FY 2016 SNF PPS final rule (80 FR 46422) and the 
FY 2017 SNF PPS final rule (81 FR 51998 through 51999), to adopt 1-year 
performance and baseline periods because that length of time typically 
provides sufficient levels of data accuracy and reliability for scoring 
performance, while also allowing us to link SNF performance on a 
measure as closely as possible to the payment year to ensure clear 
connections between quality measurement and value-based payment. Where 
appropriate, we have extended the performance periods and baseline 
periods for purposes of improving individual measure reliability. For 
example, in section VIII.C.4. of this final rule, we are finalizing 2-
year performance periods and baseline periods for the SNF WS PPR 
measure because our analytical testing found that using 2-years of data 
improve the measure's statistical reliability relative to one year of 
data. In finalizing the 2-year performance periods and baseline periods 
for the SNF WS PPR measure, we believe that we are appropriately 
balancing measure reliability with recency of data. We intend to 
continue considering the balance of these factors when proposing 
performance periods and baseline periods for any future SNF VBP 
measure.
    After consideration of public comments, we are finalizing the case 
minimums for the Nursing Staff Turnover, Falls with Major Injury (Long-
Stay), Long Stay Hospitalization, DC Function, and SNF WS PPR measures.
c. FY 2026 Measure Minimum
    In the FY 2023 SNF PPS final rule (87 FR 47587), we finalized the 
measure minimum for the FY 2026 program year. Specifically, we 
finalized that for the FY 2026 program year, SNFs must report the 
minimum number of cases for two of the three measures during the 
applicable performance period to receive a SNF Performance Score and 
value-based incentive payment.
    We proposed to adopt an additional measure for the FY 2026 program 
year: Nursing Staff Turnover measure, which means the FY 2026 SNF VBP 
measure set will consist of a total of four measures. Although we 
proposed the Nursing Staff Turnover measure beginning with the FY 2026 
program year, which will increase the total number of measures 
applicable in FY 2026, we believe that our previously finalized minimum 
of two measures for FY 2026 remains sufficient because if we required a 
minimum of three or four measures, all swing-bed facilities would be 
excluded from the Program. Two of the four measures that will be 
included in the FY 2026 program year are PBJ-based measures. Since 
swing-bed facilities do not submit PBJ data, those facilities will not 
meet the measure minimum of reporting three or four measures to the 
Program. Therefore, to ensure swing-bed facilities continue to have the 
opportunity to be included in the Program, we did not propose to update 
the measure minimum for the FY 2026 program year. SNFs must report the 
minimum number of cases for two of the four measures during the 
performance period to be included in the FY 2026 program year.
    While we did not propose any changes to the measure minimum for FY 
2026, we did receive one comment. The following is a summary of the 
comment and our response.
    Comment: One commenter supported the measure minimum for FY 2026.
    Response: We thank the commenter for their support of the measure 
minimum for FY 2026.
d. Updates to the FY 2027 Measure Minimum
    In the FY 2023 SNF PPS final rule (87 FR 47587), we finalized the 
measure minimum for the FY 2027 program year. Specifically, we 
finalized that for the FY 2027 program year, SNFs must report the 
minimum number of cases for three of the four measures during the 
performance period to receive a SNF Performance Score and value-based 
incentive payment.
    In addition to the Nursing Staff Turnover measure beginning with 
the FY 2026 program year, we also proposed to adopt three additional 
measures beginning with the FY 2027 program year: Falls with Major 
Injury (Long-Stay), DC Function, and Long Stay Hospitalization 
measures. Therefore, the FY 2027 SNF VBP measure set will consist of a 
total of eight measures. Given the changes to the number of measures 
applicable in FY 2027, we also proposed to update the measure minimum 
for the FY 2027 program year.
    Specifically, we proposed that for the FY 2027 program year, SNFs 
must report the minimum number of cases for four of the eight measures 
during the performance period to receive a SNF Performance Score and 
value-based incentive payment. SNFs that do not meet these minimum 
requirements will be excluded from the FY 2027 program and will receive 
their adjusted Federal per diem rate for that fiscal year. Under these 
measure minimum requirements, we estimate that approximately 8 percent 
of SNFs would be excluded from the FY 2027 Program. We found that 
increasing the measure minimum requirement from three to four measures 
out of a total of eight measures would cause the number of SNFs 
excluded from the Program to increase from approximately 3 percent to 8 
percent of SNFs for FY 2027. However, the measure minimum requirement 
that we finalized for FY 2027 in the FY 2023 SNF PPS final rule (87 FR 
47587), which was based on a measure set of four measures, excluded 
approximately 16 percent of SNFs. We also found that increasing the 
measure minimum requirement would have little effect on the percentage 
of SNFs that would receive a net-positive incentive payment multiplier 
(IPM) of the overall distribution of IPMs. Based on these testing 
results, we believe the updates to the measure minimum for FY 2027 
aligns with our desire to ensure that as many SNFs as possible can 
receive a reliable SNF Performance Score and value-based incentive 
payment.
    We solicited public comment on our proposal to update the measure 
minimum for the FY 2027 SNF VBP program year.
    We received public comments on this proposal. The following is a 
summary of

[[Page 53303]]

the comments we received and our responses.
    Comment: One commenter supported the proposed FY 2027 measure 
minimum.
    Response: We thank the commenter for their support of the updated 
measure minimum for FY 2027.
    After consideration of public comments, we are finalizing the 
update to the measure minimum for the FY 2027 SNF VBP program year.
3. Application of the SNF VBP Scoring Methodology to Proposed Measures
a. Background
    In the FY 2023 SNF PPS final rule (87 FR 47588 through 47590), we 
finalized several updates to the scoring methodology for the SNF VBP 
Program beginning with the FY 2026 program year. We finalized a 
measure-level scoring policy such that SNFs have the opportunity to 
earn a maximum of 10 points on each measure for achievement, and a 
maximum of nine points on each measure for improvement. The higher of 
these two scores will then be the SNF's score for each measure and used 
to calculate the SNF Performance Score, except if the SNF does not meet 
the case minimum for a given measure during the applicable baseline 
period, in which case that SNF will only be scored on achievement for 
that measure. We also finalized a normalization policy such that we 
will calculate a raw point total for each SNF by adding up that SNF's 
score on each of the measures applicable for the given program year. We 
will then normalize the raw point totals such that the SNF Performance 
Score is reflected on a 100-point scale.
    We proposed to adopt the Nursing Staff Turnover measure beginning 
with the FY 2026 program year; and the Falls with Major Injury (Long-
Stay), Long Stay Hospitalization, and DC Function measures beginning 
with the FY 2027 program year. To accommodate those measures in our 
scoring methodology, we proposed to adjust our scoring methodology for 
the FY 2026 and FY 2027 program years, which we discuss in the next 
section.
    We also note that we proposed to replace the SNFRM with the SNF WS 
PPR measure beginning with the FY 2028 program year, which will not 
affect the total number of measures applicable in the Program for FY 
2028. We intend to address the FY 2028 performance scoring methodology 
in future rulemaking.
b. FY 2026 Performance Scoring
    We proposed the Nursing Staff Turnover measure beginning with the 
FY 2026 program year, and therefore, the FY 2026 program year measure 
set will include four measures (SNFRM, SNF HAI, Total Nurse Staffing, 
and Nursing Staff Turnover measures).
    We proposed to apply our previously finalized scoring methodology, 
which is codified at Sec.  413.338(e) of our regulations, to the 
Nursing Staff Turnover measure. Specifically, we will award up to 10 
points based on achievement, and up to nine points based on 
improvement, so long as the SNF meets the case minimum for the measure. 
The higher of these two scores will be the SNF's score for the measure 
for FY 2026, except in the instance that the SNF does not meet the case 
minimum for the measure during the applicable baseline period, in which 
case that SNF will only be scored on achievement for the measure.
    As previously finalized, we will then add the score for each of the 
four measures for which the SNF met the case minimum to get the raw 
point total. The maximum raw point total for the FY 2026 program year 
will be 40 points. We will then normalize each SNF's raw point total, 
based on the number of measures for which that SNF met the case 
minimum, to get a SNF Performance Score that is on a 100-point scale 
using our previously finalized normalization policy. We will only award 
a SNF Performance Score to SNFs that meet the measure minimum for FY 
2026.
    We solicited public comment on our proposal to apply our previously 
finalized scoring methodology to the Nursing Staff Turnover measure 
beginning with the FY 2026 SNF VBP program year.
    We received public comments on this proposal. The following is a 
summary of the comments we received and our responses.
    Comment: One commenter, while supporting the FY 2026 performance 
scoring methodology proposal, disagrees with the using the mean of the 
top decile of SNFs during the baseline period as the benchmark 
performance standard.
    Response: In the FY 2017 SNF PPS final rule (81 FR 51996 through 
51997) we stated that our finalized definition of the benchmark 
represents a demonstrably high but achievable standard of excellence 
for all SNFs. We refer readers to that final rule for additional 
details on that policy. We continue to believe that our definition of 
the benchmark is appropriate for incentivizing high-quality care across 
SNFs.
    Comment: One commenter opposed the FY 2026 performance scoring 
proposal and recommended that CMS score SNFs on achievement only.
    Response: We disagree with the recommendation to score SNFs on 
achievement only as we are required under section 1888(h)(3)(B) of the 
Act to include levels of achievement and improvement in the performance 
standards we use to assess SNF performance under the SNF VBP.
    After consideration of public comments, we are finalizing the 
application of our previously finalized scoring methodology to the 
Nursing Staff Turnover measure beginning with the FY 2026 SNF VBP 
program year.
c. FY 2027 Performance Scoring
    We proposed the Falls with Major Injury (Long-Stay), DC Function, 
and Long Stay Hospitalization measures beginning with the FY 2027 
program year, and therefore, the FY 2027 program year measure set will 
include eight measures.
    Our current scoring methodology is codified at Sec.  413.338(e) of 
our regulations. Under that scoring methodology, we award up to 10 
points for each measure based on achievement, and up to nine points for 
each measure based on improvement, so long as the SNF meets the case 
minimum for a given measure. The higher of these two scores is the 
SNF's score on that measure for FY 2027, except in the instance that 
the SNF does not meet the case minimum for a given measure during the 
applicable baseline period, in which case that SNF is only scored on 
achievement for that measure. As previously finalized, we then sum the 
scores for each of the eight measures for which the SNF met the case 
minimum to get the raw measure point total. The maximum raw measure 
point total for the FY 2027 program year will be 80 points.
    We proposed to apply these elements of the scoring methodology to 
Falls with Major Injury (Long-Stay), DC Function, and Long Stay 
Hospitalization measures. In addition, and as discussed further in 
section VIII.E.4. of this final rule, we proposed to adopt a Health 
Equity Adjustment in which eligible SNFs could earn a maximum of two 
points for each measure (including all previously finalized and newly 
proposed measures) if they are a top tier performing SNF, which we 
proposed to define as a SNF whose score on the measure for the program 
year falls in the top third of performance (greater than or equal to 
the 66.67th percentile) on a given measure, and the SNF's resident 
population during the performance period that applies to the program 
year includes at least 20 percent of residents

[[Page 53304]]

with dual eligibility status (DES). This combination of a SNF's 
performance and proportion of residents with DES would be used to 
determine a SNF's Health Equity Adjustment (HEA) bonus points. We would 
then add the total number of HEA bonus points to the normalized measure 
point total on a scale from 0 to 100, and that total would be the SNF 
Performance Score earned by the SNF for the program year. We will only 
award a SNF Performance Score to SNFs that meet the proposed measure 
minimum for FY 2027.
    We solicited public comment on our proposal to apply our previously 
finalized scoring methodology to the Falls with Major Injury (Long-
Stay), DC Function, and Long Stay Hospitalization measures beginning 
with the FY 2027 SNF VBP program year.
    We received public comments on this proposal. The following is a 
summary of the comments we received on our proposal to apply our 
previously finalized scoring methodology to the Falls with Major Injury 
(Long-Stay), DC Function, and Long Stay Hospitalization measures and 
our responses. We provide a summary of comments related to the Health 
Equity Adjustment, and our responses, in section VIII.E.4. of this 
final rule.
    Comment: A few commenters supported the proposal to apply the 
previously finalized scoring methodology to the Falls with Major Injury 
(Long-Stay), DC Function, and Long Stay Hospitalization measures 
beginning with the FY 2027 program year noting that these changes are 
needed to accommodate the new quality measures in the SNF VBP Program 
scoring methodology.
    Response: We thank the commenters for their support. We agree that 
applying our scoring methodology to these measures will incentivize 
high-quality care across all SNFs.
    Comment: One commenter, while supporting the FY 2027 performance 
scoring methodology proposal, disagrees with the using the mean of the 
top decile of SNFs during the baseline period as the benchmark 
performance standard.
    Response: In the FY 2017 SNF PPS final rule (81 FR 51996 through 
51997) we stated that our finalized definition of the benchmark 
represents a demonstrably high but achievable standard of excellence 
for all SNFs. We refer readers to that final rule for additional 
details on that policy. We continue to believe that our definition of 
the benchmark is appropriate for incentivizing high-quality care across 
SNFs.
    After consideration of public comments, we are finalizing our 
proposal to apply our previously finalized scoring methodology to the 
Falls with Major Injury (Long-Stay), DC Function, and Long Stay 
Hospitalization measures beginning with the FY 2027 SNF VBP program 
year.
4. Incorporating Health Equity Into the SNF VBP Program Scoring 
Methodology Beginning With the FY 2027 Program Year
a. Background
    Significant and persistent inequities in health outcomes exist in 
the U.S. Belonging to a racial or ethnic minority group; living with a 
disability; being a member of the lesbian, gay, bisexual, transgender, 
queer, and intersex (LGBTQI+) communities; living in a rural area; 
being a member of a religious minority; being near or below the poverty 
level; or being dually enrolled in Medicare and Medicaid, is often 
associated with worse health 
outcomes.356 357 358 359 360 361 362 363 364 Executive Order 
13985 on Advancing Racial Equity and Support for Underserved 
Communities Through the Federal Government, (January 20, 2021) defines 
``equity'' as ``the consistent and systematic fair, just, and impartial 
treatment of all individuals, including individuals who belong to 
underserved communities that have been denied such treatment, such as 
Black, Latino, and Indigenous and Native American persons, Asian 
Americans and Pacific Islanders and other persons of color; members of 
religious minorities; lesbian, gay, bisexual, transgender, queer, [and 
intersex] (LGBTQ[I] +); \365\ persons with disabilities; persons who 
live in rural areas; and persons otherwise adversely affected by 
persistent poverty or inequality'' (86 FR 7009). CMS defines ``health 
equity'' as the ``attainment of the highest level of health for all 
people, where everyone has a fair and just opportunity to attain their 
optimal health regardless of race, ethnicity, disability, sexual 
orientation, gender identity, socioeconomic status, geography, 
preferred language, or other factors that affect access to care and 
health outcomes.'' \366\
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    \356\ Lindenauer PK, Lagu T, Rothberg MB, et al. (2013). Income 
inequality and 30 day outcomes after acute myocardial infarction, 
heart failure, and pneumonia: Retrospective cohort study. British 
Medical Journal, 346.
    \357\ Trivedi AN, Nsa W, Hausmann LRM, et al. (2014). Quality 
and equity of care in U.S. hospitals. New England Journal of 
Medicine, 371(24):2298-2308.
    \358\ Polyakova, M., et al. (2021). Racial disparities in excess 
all-cause mortality during the early COVID-19 pandemic varied 
substantially across states. Health Affairs, 40(2): 307-316.
    \359\ Rural Health Research Gateway. (2018). Rural communities: 
age, income, and health status. Rural Health Research Recap. https://www.ruralhealthresearch.org/assets/2200-8536/rural-communities-age-income-health-status-recap.pdf.
    \360\ https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf.
    \361\ Vu, M. et al. Predictors of Delayed Healthcare Seeking 
Among American Muslim Women, Journal of Women's Health 26(6) (2016) 
at 58; S.B.
    \362\ Nadimpalli, et al., The Association between Discrimination 
and the Health of Sikh Asian Indians Health Psychol. 2016 Apr; 
35(4): 351-355.
    \363\ Poteat TC, Reisner SL, Miller M, Wirtz AL. (2020). COVID-
19 vulnerability of transgender women with and without HIV infection 
in the Eastern and Southern U.S. preprint. medRxiv. 2020;2020.07.21. 
20159327. doi:10.1101/2020.07.21.20159327.
    \364\ Sorbero, M.E., A.M. Kranz, K.E. Bouskill, R. Ross, A.I. 
Palimaru, and A. Meyer. 2018. Addressing social determinants of 
health needs of dually enrolled beneficiaries in Medicare Advantage 
plans: Findings from interviews and case studies. RAND Corporation. 
Available at https://www.rand.org/pubs/research_reports/RR2634.html 
(accessed December 8, 2022).
    \365\ We note that the original, cited definition only 
stipulates, ``LGBTQ+'', however, HHS and the White House now 
recognize individuals who are intersex/have intersex traits. 
Therefore, we have updated the term to reflect these changes.
    \366\ CMS Strategic Plan Pillar: Health Equity. (2022). https://www.cms.gov/files/document/health-equity-fact-sheet.pdf.
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    Advancing health equity is a key pillar of our strategic 
vision,\367\ and we are working to advance health equity by designing, 
implementing, and operationalizing policies and programs aimed at 
identifying and reducing health disparities. This includes the CMS 
Mapping Medicare Disparities Tool,\368\ the CMS Innovation Center's 
Accountable Health Communities Model,\369\ the CMS Disparity Methods 
stratified reporting program,\370\ the collection of standardized 
patient assessment data elements in the post-
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    \367\ CMS Strategic Vision. (2022). https://www.cms.gov/cms-strategic-plan.
    \368\ https://www.cms.gov/About-CMS/Agency-Information/OMH/OMH-Mapping-Medicare-Disparities.
    \369\ https://innovation.cms.gov/innovation-models/ahcm.
    \370\ https://qualitynet.cms.gov/inpatient/measures/disparity-methods.

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[[Page 53305]]

acute care setting,\371\ and health equity program adjustments like the 
Medicare Shared Savings Program's recently adopted health equity 
adjustment for Accountable Care Organizations that report all-payer 
eCQMs/MIPS CQMs (87 FR 69838 through 69857). Further, the 2022-2032 CMS 
Framework for Health Equity outlines CMS' priorities to advance health 
equity, expand coverage, and improve health outcomes for the more than 
170 million individuals supported by CMS programs.\372\ We also 
recently updated the CMS National Quality Strategy (NQS), which 
includes advancing health equity as one of eight strategic goals.\373\ 
As we continue to leverage our programs to improve quality of care, we 
note it is important to implement strategies that ``create aligned 
incentives that drive providers to improve health outcomes for all 
beneficiaries.'' \374\
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    \371\ https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/-IMPACT-Act-Standardized-Patient-Assessment-Data-Elements.
    \372\ CMS Framework for Health Equity (2022). https://www.cms.gov/about-cms/agency-information/omh/health-equity-programs/cms-framework-for-health-equity.
    \373\ CMS National Quality Strategy (2022). Centers for Medicare 
and Medicaid Services. https://www.cms.gov/files/document/cms-national-quality-strategy-fact-sheet.pdf.
    \374\ Office of the Assistant Secretary for Planning and 
Evaluation, U.S. Department of Health & Human Services. Second 
Report to Congress on Social Risk Factors and Performance in 
Medicare's Value-Based Purchasing Program. 2020. https://aspe.hhs.gov/reports/second-report-congress-social-risk-medicares-value-based-purchasing-programs.
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    Prioritizing the achievement of health equity is essential in the 
SNF VBP Program because disparities in SNFs appear to be widespread, 
from admissions to quality of care to nurse staffing and 
turnover.375 376 In the 2016 Report to Congress, the Office 
of the Assistant Secretary for Planning and Evaluation (ASPE) reported 
that individuals with social risk factors, such as dual eligibility 
status, had worse outcomes and were more likely to be cared for by 
lower-quality SNFs.\377\ Individuals with dual eligibility status (DES) 
are those who are eligible for both Medicare and Medicaid coverage. 
Individuals with DES are more likely to have disabilities or functional 
impairments, more likely to be medically complex, more likely to have 
greater social needs, and have a greater risk of negative health 
outcomes compared to individuals without DES.\378\ They are also more 
likely to be admitted to SNFs that have lower staffing levels, have a 
higher share of residents who are enrolled in Medicaid in their total 
resident population, and experience resource constraints.\379\ In 
addition, studies have found that DES is an important predictor of 
admission to a low-quality SNF.\380\ All of these factors indicate that 
individuals with DES represent an underserved population that is more 
clinically complex, has greater social needs and is more often admitted 
to lower-resourced SNFs than those without DES. This presents 
significant challenges to provide quality care to patients with greater 
resource-intensive needs by providers that may have fewer resources, as 
effectively implementing quality improvement initiatives requires time, 
money, staff, and technology.381 382 383 384 As a result, 
competitive programs, like the current SNF VBP Program, may place some 
SNFs that serve this underserved population at a disadvantage.
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    \375\ Rivera-Hernandez, M., Rahman, M., Mor, V., & Trivedi, A.N. 
(2019). Racial Disparities in Readmission Rates among Patients 
Discharged to Skilled Nursing Facilities. Journal of the American 
Geriatrics Society, 67(8), 1672-1679. https://doi.org/10.1111/jgs.15960.
    \376\ Konetzka, R., Yan, K., & Werner, R.M. (2021). Two Decades 
of Nursing Home Compare: What Have We Learned? Medical Care Research 
and Review, 78(4), 295-310. https://doi.org/10.1177/1077558720931652.
    \377\ Office of the Assistant Secretary for Planning and 
Evaluation, U.S. Department of Health & Human Services. First Report 
to Congress on Social Risk Factors and Performance in Medicare's 
Value-Based Purchasing Program. 2016. https://aspe.hhs.gov/sites/default/files/migrated_legacy_files/171041/ASPESESRTCfull.pdf.
    \378\ Johnston, K.J., & Joynt Maddox, K.E. (2019). The Role of 
Social, Cognitive, And Functional Risk Factors In Medicare Spending 
For Dual And Nondual Enrollees. Health Affairs (Project Hope), 
38(4), 569-576. https://doi.org/10.1377/hlthaff.2018.05032.
    \379\ Rahman, M., Grabowski, D.C., Gozalo, P.L., Thomas, K.S., & 
Mor, V. (2014). Are Dual Eligibles Admitted to Poorer Quality 
Skilled Nursing Facilities? Health Services Research, 49(3), 798-
817. https://doi.org/10.1111/1475-6773.12142.
    \380\ Zuckerman, R.B., Wu, S., Chen, L.M., Joynt Maddox, K.E., 
Sheingold, S.H., & Epstein, A.M. (2019). The Five-Star Skilled 
Nursing Facility Rating System and Care of Disadvantaged 
Populations. Journal of the American Geriatrics Society, 67(1), 108-
114. https://doi.org/10.1111/jgs.15629.
    \381\ Reidt, S.L., Holtan, H.S., Larson, T.A., Thompson, B., 
Kerzner, L.J., Salvatore, T.M., & Adam, T.J. (2016). 
Interprofessional Collaboration to Improve Discharge from Skilled 
Nursing Facility to Home: Preliminary Data on Postdischarge 
Hospitalizations and Emergency Department Visits. Journal of the 
American Geriatrics Society, 64(9), 1895-1899. https://doi.org/10.1111/jgs.14258.
    \382\ Au, Y., Holbrook, M., Skeens, A., Painter, J., McBurney, 
J., Cassata, A., & Wang, S.C. (2019). Improving the quality of 
pressure ulcer management in a skilled nursing facility. 
International Wound Journal, 16(2), 550-555. https://doi.org/10.1111/iwj.13112.
    \383\ Berkowitz, R.E., Fang, Z., Helfand, B.K.I., Jones, R.N., 
Schreiber, R., & Paasche-Orlow, M.K. (2013). Project ReEngineered 
Discharge (RED) Lowers Hospital Readmissions of Patients Discharged 
From a Skilled Nursing Facility. Journal of the American Medical 
Directors Association, 14(10), 736-740. https://doi.org/10.1016/j.jamda.2013.03.004.
    \384\ Chisholm, L., Zhang, N.J., Hyer, K., Pradhan, R., Unruh, 
L., & Lin, F.-C. (2018). Culture Change in Nursing Homes: What Is 
the Role of Nursing Home Resources? INQUIRY: The Journal of Health 
Care Organization, Provision, and Financing, 55, 0046958018787043. 
https://doi.org/10.1177/0046958018787043.
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    In the FY 2023 SNF PPS proposed rule (87 FR 22789), we requested 
public comment on policy changes that we should consider on the topic 
of health equity. In the FY 2023 SNF PPS final rule (87 FR 47596 
through 47597), we provided a detailed summary of the feedback we 
received on this topic. Commenters overwhelmingly supported our 
commitment to advancing health equity for SNF residents, with some 
suggesting that we examine factors that may lead to care inequities. 
One commenter suggested we adopt risk adjustment or incentive payments 
for SNFs that admit individuals that other SNFs will not admit. Another 
commenter recommended pairing clinical data measures with social risk 
metrics to help providers deliver more comprehensive care. Overall, 
commenters were interested in understanding where disparities may exist 
and wanted us to work with SNFs and other interested parties to 
understand the greatest needs in achieving health equity to ensure any 
revisions to the Program could be implemented with minimal data burden. 
We considered all the comments we received as we developed our Health 
Equity Adjustment for the SNF VBP Program described below.
    We believe that SNFs and providers across all settings can 
consistently perform well even when caring for a high proportion of 
individuals who are underserved,\385\ and, with the right program 
components, VBP programs can create meaningful incentives for SNFs that 
serve a high proportion of individuals who are underserved to
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    \385\ Office of the Assistant Secretary for Planning and 
Evaluation, U.S. Department of Health & Human Services. First Report 
to Congress on Social Risk Factors and Performance in Medicare's 
Value-Based Purchasing Program. 2016. https://aspe.hhs.gov/sites/default/files/migrated_legacy_files/171041/ASPESESRTCfull.pdf.

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[[Page 53306]]

deliver high quality care.386 387 388 389 390 391 We believe 
updating the scoring methodology, as detailed in the following 
sections, would appropriately measure performance and create these 
meaningful incentives for SNFs that care for a high proportions of 
residents with DES.
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    \386\ Crook, H.L., Zheng, J., Bleser, W.K., Whitaker, R.G., 
Masand, J., & Saunders, R.S. (2021). How Are Payment Reforms 
Addressing Social Determinants of Health? Policy Implications and 
Next Steps. Milbank Memorial Fund, Duke Margolis Center for Health 
Policy. https://www.milbank.org/wp-content/uploads/2021/02/Duke-SDOH-and-VBP-Issue-Brief_v3.pdf.
    \387\ Johnston, K.J., & Joynt Maddox, K.E. (2019). The Role of 
Social, Cognitive, And Functional Risk Factors In Medicare Spending 
For Dual And Nondual Enrollees. Health Affairs (Project Hope), 
38(4), 569-576. https://doi.org/10.1377/hlthaff.2018.05032.
    \388\ Konetzka, R., Yan, K., & Werner, R.M. (2021). Two Decades 
of Nursing Home Compare: What Have We Learned? Medical Care Research 
and Review, 78(4), 295-310. https://doi.org/10.1177/1077558720931652.
    \389\ Weech-Maldonado, R., Pradhan, R., Dayama, N., Lord, J., & 
Gupta, S. (2019). Nursing Home Quality and Financial Performance: Is 
There a Business Case for Quality? Inquiry: A Journal of Medical 
Care Organization, Provision and Financing, 56, 46958018825191. 
https://doi.org/10.1177/0046958018825191.
    \390\ Rivera-Hernandez, M., Rahman, M., Mukamel, D., Mor, V., & 
Trivedi, A. (2019). Quality of Post-Acute Care in Skilled Nursing 
Facilities That Disproportionately Serve Black and Hispanic 
Patients. The Journals of Gerontology. Series A, Biological Sciences 
and Medical Sciences, 74(5). https://doi.org/10.1093/gerona/gly089.
    \391\ Burke, R.E., Xu, Y., & Rose, L. (2022). Skilled Nursing 
Facility Performance and Readmission Rates Under Value-Based 
Purchasing. JAMA Network Open, 5(2), e220721. https://doi.org/10.1001/jamanetworkopen.2022.0721.
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b. Health Equity Adjustment Summary
    Section 1888(h)(4)(A) of the Act requires the Secretary to develop 
a methodology for assessing the total performance of each SNF based on 
performance standards established under section 1888(h)(3) of the Act 
with respect to the measures applied under section 1888(h)(2) of the 
Act. To further align with our goals to achieve health equity, address 
health disparities, and assess SNF performance more accurately and 
completely under the SNF VBP Program, we proposed to apply an 
adjustment that will be added to the normalized sum of a SNF's measure 
points on SNF VBP Program measures. As described previously, residents 
with DES are an underserved population that is clinically complex, has 
significant social needs and is more frequently admitted to SNFs that 
have larger populations of Medicaid residents and fewer resources than 
SNFs that do not care for individuals with DES.392 393 394 
These lower-resourced SNFs are less likely to receive positive payment 
adjustments, which is a considerable limitation of the current SNF VBP 
program's ability to incentivize equitable care.\395\ Careful 
consideration must be taken to modify the Program in a way that 
addresses this issue and ensures that we provide appropriate rewards 
and incentives to all SNFs, including those that serve residents with 
DES. The goal of this Health Equity Adjustment is to not only 
appropriately measure performance by rewarding SNFs that overcome the 
challenges of caring for higher proportions of SNF residents with DES 
but also to incentivize those who have not achieved such high-quality 
care to work towards improvement. We believe this Health Equity 
Adjustment incentivizes high-quality care across all SNFs. We also 
believe this scoring change, through the adoption of an adjustment 
designed to award points based on the quality of care provided and the 
proportion of residents with DES, is consistent with our strategy to 
advance health equity.\396\
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    \392\ Johnston, K.J., & Joynt Maddox, K.E. (2019). The Role of 
Social, Cognitive, And Functional Risk Factors In Medicare Spending 
For Dual And Nondual Enrollees. Health Affairs (Project Hope), 
38(4), 569-576. https://doi.org/10.1377/hlthaff.2018.05032.
    \393\ Rahman, M., Grabowski, D.C., Gozalo, P.L., Thomas, K.S., & 
Mor, V. (2014). Are Dual Eligibles Admitted to Poorer Quality 
Skilled Nursing Facilities? Health Services Research, 49(3), 798-
817. https://doi.org/10.1111/1475-6773.12142.
    \394\ Zuckerman, R.B., Wu, S., Chen, L.M., Joynt Maddox, K.E., 
Sheingold, S.H., & Epstein, A.M. (2019). The Five-Star Skilled 
Nursing Facility Rating System and Care of Disadvantaged 
Populations. Journal of the American Geriatrics Society, 67(1), 108-
114. https://doi.org/10.1111/jgs.15629.
    \395\ Hefele JG, Wang XJ, Lim E. Fewer Bonuses, More Penalties 
at Skilled Nursing Facilities Serving Vulnerable Populations. Health 
Aff (Millwood). 2019;38(7):1127-1131. doi:10.1377/
hlthaff.2018.05393.
    \396\ Centers for Medicare & Medicaid Services. (2022) CMS 
Outlines Strategy to Advance Health Equity, Challenges Industry 
Leaders to Address Systemic Inequities. Available at https://
www.cms.gov/newsroom/press-releases/cms-outlines-strategy-advance-
health-equity-challenges-industry-leaders-address-systemic-
inequities#:~:text=In%20effort%20to%20address%20systemic%20inequities
%20across%20the,Medicare%2C%20Medicaid%20or%20Marketplace%20coverage%
2C%20need%20to%20thrive.
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    The Health Equity Adjustment (HEA) will be calculated using a 
methodology that considers both the SNF's performance on the SNF VBP 
Program measures, and the proportion of residents with DES out of the 
total resident population in a given program year at each SNF. To be 
eligible to receive HEA bonus points, a SNF's performance will need to 
meet or exceed a certain threshold and its resident population during 
the applicable performance period for the program year will have to 
include at least 20 percent of residents with DES. Thus, SNFs that 
perform well on quality measures and serve a higher proportion of SNF 
residents with DES will receive a larger adjustment. We provide the HEA 
calculation methodology in section VIII.E.4.d. of this final rule. By 
providing this HEA to SNFs that serve higher proportions of SNF 
residents with DES and that perform well on quality measures, we 
believe we can appropriately recognize the resource intensity expended 
to achieve high performance on quality measures by SNFs that serve a 
high proportion of SNF residents with DES, while also mitigating the 
worse health outcomes experienced by underserved populations through 
incentivizing better care across all SNFs.
    An analysis of payment from October 2018 for the SNF VBP Program 
found that SNFs that served higher proportions of Medicaid residents 
were less likely to receive positive payment adjustments. As noted 
previously, residents with DES are more likely to be admitted to SNFs 
with higher proportions of Medicaid residents \397\ suggesting that 
SNFs serving higher proportions of SNF residents with DES face 
challenges in utilizing their limited resources to improve the quality 
of care for their complex residents.\398\ Thus, we aimed to adjust the 
current program scoring methodology to ensure that all SNF residents, 
including those with DES, receive high-quality care. We conducted an 
analysis utilizing FY 2018 through FY 2021 measure data for our 
previously finalized and newly proposed measures, including a 
simulation of performance on all 8 measures for the FY 2027 Program, 
and found that the HEA significantly increased the proportion of SNFs 
with high proportions of SNF residents with DES that received a 
positive value-based incentive payment adjustment indicating that this 
approach would modify the SNF VBP Program in the way it is intended.
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    \397\ Rahman, M., Grabowski, D.C., Gozalo, P.L., Thomas, K.S., & 
Mor, V. (2014). Are Dual Eligibles Admitted to Poorer Quality 
Skilled Nursing Facilities? Health Services Research, 49(3), 798-
817. ws://doi.org/10.1111/1475-6773.12142.
    \398\ Hefele JG, Wang XJ, Lim E. Fewer Bonuses, More Penalties 
at Skilled Nursing Facilities Serving Vulnerable Populations. Health 
Aff (Millwood). 2019;38(7):1127-1131. doi:10.1377/
hlthaff.2018.05393.
---------------------------------------------------------------------------

    We proposed to call this adjustment the Health Equity Adjustment 
(HEA)

[[Page 53307]]

and to adopt it beginning with the FY 2027 program year.
c. Health Equity Adjustment Beginning With the FY 2027 SNF VBP Program 
Year
    We proposed to define the term ``underserved population'' as 
residents with DES for purposes of this HEA. DES has been established 
in the literature, including research specifically looking at 
SNFs,399 400 and has been found to be an important factor 
that impacts pay for performance and other quality 
programs.401 402 In addition, DES is currently utilized in 
the Hospital Readmissions Reduction Program.
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    \399\ Rahman, M., Grabowski, D.C., Gozalo, P.L., Thomas, K.S., & 
Mor, V. (2014). Are Dual Eligibles Admitted to Poorer Quality 
Skilled Nursing Facilities? Health Services Research, 49(3), 798-
817. https://doi.org/10.1111/1475-6773.12142.
    \400\ Zuckerman, R.B., Wu, S., Chen, L.M., Joynt Maddox, K.E., 
Sheingold, S.H., & Epstein, A.M. (2019). The Five-Star Skilled 
Nursing Facility Rating System and Care of Disadvantaged 
Populations. Journal of the American Geriatrics Society, 67(1), 108-
114. https://doi.org/10.1111/jgs.15629.
    \401\ Office of the Assistant Secretary for Planning and 
Evaluation, U.S. Department of Health & Human Services. First Report 
to Congress on Social Risk Factors and Performance in Medicare's 
Value-Based Purchasing Program. 2016. https://aspe.hhs.gov/sites/default/files/migrated_legacy_files/171041/ASPESESRTCfull.pdf.
    \402\ Zuckerman, R.B., Wu, S., Chen, L.M., Joynt Maddox, K.E., 
Sheingold, S.H., & Epstein, A.M. (2019). The Five-Star Skilled 
Nursing Facility Rating System and Care of Disadvantaged 
Populations. Journal of the American Geriatrics Society, 67(1), 108-
114. https://doi.org/10.1111/jgs.15629.
---------------------------------------------------------------------------

    The Medicare Shared Savings Program recently adopted a health 
equity adjustment for Accountable Care Organizations that report all-
payer eCQMs/MIPS CQMs, are high-performing on quality, and serve a 
large proportion of underserved beneficiaries, as defined by dual-
eligibility/enrollment in the Medicare Part D low income subsidy (LIS) 
(meaning the individual is enrolled in a Part D plan and receives LIS) 
and an Area Deprivation Index (ADI) score of 85 or above, as detailed 
in the CY 2023 PFS final rule (87 FR 69838 through 69857). At this 
time, for the SNF VBP Program's HEA, we believe that it is preferable 
to use DES to identify SNF residents who are underserved. We also 
explored alternative indicators to identify populations that are 
underserved for purposes of this HEA, such as a resident's eligibility 
for the Medicare Part D Low-Income Subsidy (LIS) program or whether the 
resident lives in an area with high deprivation, as measured by the 
Area Deprivation Index (ADI), however, we determined that for the HEA, 
utilizing residents with DES to identify underserved populations will 
best serve the goals of the adjustment. Individuals who are eligible 
for the LIS program have incomes up to 150 percent of the Federal 
poverty level.\403\ Utilizing residents who are eligible for the LIS 
program would include most residents with DES, as well as additional 
residents who may be underserved; however, the data on the LIS program 
are only available for those enrolled in Medicare Part D, which may 
limit its effectiveness, and it is not uniform across both States and 
territories. Further, those eligible for the LIS program have not been 
studied extensively in the SNF setting and the effect of using those 
eligible for the LIS program to determine a SNF's underserved 
population has also not been studied extensively. Geographic-based or 
neighborhood-level economic indices, such as the ADI, have been 
utilized to look at characteristics of healthcare facilities in low-
resourced areas and could be used as a proxy for negative health 
outcomes due to medical and social risk factors.404 405 ADI 
appears to be an important predictor of poor health outcomes, even when 
adjusting for individual characteristics, suggesting neighborhood or 
geography may play an even more important role in health than 
individual characteristics.406 407 However, there is not 
much literature or analysis that has been conducted linking these 
indices to negative health outcomes specifically in the SNF setting. 
Therefore, we proposed to only use DES data at this time to identify 
SNF residents who are underserved for this HEA, given that the DES data 
are readily available, are evidenced based in the SNF setting, and are 
already used in the Hospital Readmissions Reduction Program. We intend 
to consider how to best incorporate the LIS, ADI, and other indicators 
to identify those who are underserved in future health equity 
adjustment proposals for the SNF VBP Program as more research is made 
available. We solicited public comment, and provide a summary of the 
comments we received, on the potential future use of these additional 
indicators in section VIII.E.5 of this final rule. We provide 
additional detail on how we will calculate SNF residents with DES for 
the purpose of this adjustment later in this section.
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    \403\ Office of the Assistant Secretary for Planning and 
Evaluation, U.S. Department of Health & Human Services. First Report 
to Congress on Social Risk Factors and Performance in Medicare's 
Value-Based Purchasing Program. 2016. https://aspe.hhs.gov/sites/default/files/migrated_legacy_files/171041/ASPESESRTCfull.pdf.
    \404\ The University of Wisconsin Neighborhood Atlas website 
(https://www.neighborhoodatlas.medicine.wisc.edu/).
    \405\ Falvey, J.R., Hade, E.M., Friedman, S., Deng, R., Jabbour, 
J., Stone, R.I., & Travers, J.L. (2022). Severe neighborhood 
deprivation and nursing home staffing in the United States. Journal 
of the American Geriatrics Society. https://doi.org/10.1111/jgs.17990.
    \406\ Chamberlain, A.M., Finney Rutten, L.J., Wilson, P.M., Fan, 
C., Boyd, C.M., Jacobson, D.J., Rocca, W.A., & St. Sauver, J.L. 
(2020). Neighborhood socioeconomic disadvantage is associated with 
multimorbidity in a geographically-defined community. BMC Public 
Health, 20(1), 13. https://doi.org/10.1186/s12889-019-8123-0.
    \407\ Hu, J., Kind, A.J.H., & Nerenz, D. (2018). Area 
Deprivation Index (ADI) Predicts Readmission Risk at an Urban 
Teaching Hospital. American Journal of Medical Quality: The Official 
Journal of the American College of Medical Quality, 33(5), 493-501. 
https://doi.org/10.1177/1062860617753063.
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    In order to calculate the HEA, we first proposed to assign each SNF 
2 points for each measure for which it is a top tier performing SNF. We 
proposed to define a top tier performing SNF as a SNF whose performance 
during the program year is in the top third (greater than or equal to 
the 66.67th percentile) of the performance of all SNFs on the measure 
during the same program year. Each measure will be assessed 
independently such that a SNF that is a top tier performing SNF for one 
measure will be assigned 2 points for that measure even if they are not 
a top tier performing SNF for any other measure. Similarly, if a SNF is 
a top tier performing SNF for all measures, that SNF will be assigned 2 
points for all measures.
    We also proposed to assign a measure performance scaler for each 
SNF that will be equal to the total number of assigned HEA points that 
the SNF earns on all measures as a result of its performance. Under 
this approach, for the FY 2027 program year, a SNF will receive a 
maximum measure performance scaler of 16 if the SNF is a top tier 
performing SNF on all 8 measures for that program year. As described in 
more detail in the following paragraph and in section VIII.E.4.e of 
this final rule, we decided on assigning a maximum point value of 2 
points for each measure because we believe that it provides an 
appropriate incentive to top tier performing SNFs that serve a high 
proportion of SNF residents with DES to continue their quality efforts, 
as well as an incentive for all SNFs that serve SNF residents with DES 
to improve their quality.
    Based on our calculation of measure data from FY 2018 through FY 
2021, the average SNF Performance Score for SNFs in the top third of 
performance that care for high proportions of residents with DES (SNFs 
with proportions of residents with DES in the top third) is 8.4 points 
lower than the SNF Performance Score for SNFs in the top third of 
performance that do not

[[Page 53308]]

care for high proportions of residents with DES (40.8 for high 
performing SNFs with high proportions of residents with DES and 49.2 
for all other high performing SNFs). Allowing for a maximum measure 
performance scaler of 16 for the FY 2027 program year will provide an 
opportunity for top tier performing SNFs that treat a high proportion 
of SNF residents with DES to close this gap. We also considered 
assigning 3 points for each measure to calculate the measure 
performance scaler. However, we determined that the maximum measure 
performance scaler a SNF could earn based on the assignment of 3 points 
per measure, 24 points, would exceed the number of points that many 
SNFs receive for their SNF Performance Score based on all Program 
measures, which diminishes the intent of the HEA as a bonus. We further 
discuss this option in section VIII.E.4.e of this final rule. We also 
considered assigning a point value of 2 to SNFs in the middle third of 
performance (SNFs whose performance falls between the 33.33rd 
percentile and 66.67th percentile in performance) and assigning a point 
value of 4 to top tier performing SNFs for each measure to align with 
the Medicare Shared Savings Program's health equity adjustment (87 FR 
69843 through 69845). This approach would provide a greater number of 
SNFs the opportunity to benefit from the adjustment. However, in the 
SNF VBP Program, this approach could reduce the size of the payment 
adjustment available to SNFs whose performance is in the top tier, 
reducing the incentives to improve and deviating considerably from the 
primary goal of the Program to appropriately assess performance and 
reward high quality performance among SNFs that care for high 
proportions of residents with DES.
    We proposed to define the term ``underserved multiplier'' for a SNF 
as the number representing the SNF's proportion of residents with DES 
out of its total resident population in the applicable program year, 
translated using a logistic exchange function. Due to the structure of 
the logistic exchange function, those SNFs with lower proportions of 
residents with DES have smaller underserved multipliers than their 
actual proportion of residents with DES and those SNFs with higher 
proportions of SNF residents with DES have underserved multipliers 
higher than their proportion of SNF residents with DES. The specific 
logistic function used to translate the SNF's proportion of residents 
with DES is described in section VIII.E.4.d. of this final rule. We 
proposed to define the total resident population at each SNF as 
Medicare beneficiaries identified from the SNF's Part A claims during 
the performance period of the 1-year measures. We proposed to define 
residents with DES, for purposes of the HEA, as the percentage of 
Medicare SNF residents who are also eligible for Medicaid. We proposed 
to assign DES for any Medicare beneficiary who was deemed by Medicaid 
agencies to be eligible to receive Medicaid benefits for any month 
during the performance period of the 1-year measures. For example, 
during the FY 2027 program year, we will calculate the proportion of 
residents with DES during any month of FY 2025 (October 1, 2024 through 
September 30, 2025), which is the performance period for the FY 2027 
program year's 1-year measures. Similarly, a SNF's total resident 
population of Medicare beneficiaries identified from the SNF's Part A 
claims will be calculated from the SNF's Part A claims during FY 2025. 
Data on DES is sourced from the State Medicare Modernization Act (MMA) 
file of dually eligible beneficiaries, which each of the 50 States and 
the District of Columbia submit to CMS at least monthly. This file is 
utilized to deem individuals with DES automatically eligible for the 
Medicare Part D Low Income Subsidy, as well as other CMS program needs 
and thus can be considered the gold standard for determining DES. We 
note that this is the same file used for determining DES in the 
Hospital Readmissions Reduction Program. Additional details on this 
file can be found on the CMS website at https://www.cms.gov/Medicare-Medicaid-Coordination/Medicare-and-Medicaid-Coordination/Medicare-Medicaid-Coordination-Office/DataStatisticalResources/StateMMAFile and 
at the Research Data Assistance Center website at https://resdac.org/cms-data/variables/monthly-medicare-medicaid-dual-eligibility-code-january.
    We proposed to calculate an underserved multiplier for a SNF if 
that SNF's proportion of residents with DES out of its total resident 
population during the applicable performance period of the 1-year 
measures is at least 20 percent. Imposing a floor of 20 percent for the 
underserved multiplier for a SNF to be eligible to receive HEA bonus 
points, reinforces that the adjustment is intended to appropriately 
measure performance by rewarding SNFs that are serving higher 
proportions of SNF residents with DES while also achieving high levels 
of quality performance. We describe this 20 percent floor in further 
detail in section VIII.E.4.d. of this final rule. Lastly, we proposed 
to define HEA bonus points for a SNF as the product of the SNF's 
measure performance scaler and the SNF's underserved multiplier. The 
HEA bonus points will then be added to the normalized sum of all points 
a SNF is awarded for each measure.
    Through the HEA bonus points, we seek to improve outcomes by 
providing incentives to SNFs to strive for high performance across 
measures, as well as to care for high proportions of residents with 
DES. The HEA bonus points calculation is purposefully designed to not 
reward poor quality. Instead, the HEA incentivizes SNFs that care for 
higher proportions of SNF residents with DES to improve their overall 
quality of care across the entire SNF population. As described more 
fully in section VIII.E.4.d. of this final rule, the combination of the 
measure performance scaler and the underserved multiplier will result 
in a range of possible HEA bonus points that is designed to give the 
highest rewards to SNFs caring for a larger proportion of SNF residents 
with DES and delivering high quality care.
    We proposed to amend our regulations at Sec.  413.338(a) to define 
these new scoring methodology terms, including underserved population, 
the measure performance scaler, top tier performing SNF, the 
underserved multiplier, and the HEA bonus points. We also proposed to 
codify the HEA in our regulations by adding a new paragraph (k) at 
Sec.  413.338 of our regulations. We solicited public comments on these 
proposals. We provide a summary of the comments we received, and our 
responses, later in this section.
d. Alternatives Considered
    In developing the HEA, we considered approaches other than 
providing HEA bonus points to top tier performing SNFs with a high 
proportion of SNF residents with DES that could be implemented in the 
SNF VBP Program. More specifically, we considered the addition of risk 
adjustment to the payment methodology, peer grouping, or providing an 
opportunity to earn additional improvement points. First, we considered 
risk adjusting the measures used in the SNF VBP program. Currently, 
most measures in the SNF VBP Program are risk adjusted for the clinical 
characteristics of the resident that are included in the calculation of 
the measure. We do not risk adjust for social risk factors. Although it 
would require us to respecify the measures and then revisit the pre-
rulemaking process for each measure, it is an operationally feasible 
approach. However, there is a

[[Page 53309]]

significant concern around adding additional risk adjustment to the 
measures in the Program to account for social risk factors. Although 
additional risk adjustment can help account for factors outside of a 
SNF's control, such as social risk factors like socioeconomic 
status,\408\ it can also have potential unintended consequences. For 
instance, in a 2021 Report to Congress on Medicare and the Health Care 
Delivery System, the Medicare Payment Advisory Commission (MedPAC) 
recommended against adjusting SNF VBP measures results for social risk 
factors, stating that those types of adjustments can mask 
disparities.\409\ This would mean that disparities that currently exist 
would be more challenging to identify in the data, and thus harder for 
providers or the Program to eliminate. Additionally, in an analysis 
conducted by ASPE, it did not appear that additional risk adjustment 
would significantly impact SNF performance in the Program.\410\ Thus, 
we decided against incorporating additional risk adjustment into the 
SNF VBP Program at this time.
---------------------------------------------------------------------------

    \408\ https://mmshub.cms.gov/sites/default/files/Risk-Adjustment-in-Quality-Measurement.pdf.
    \409\ MedPAC, 2021 https://www.medpac.gov/wp-content/uploads/import_data/scrape_files/docs/default-source/reports/jun21_medpac_report_to_congress_sec.pdf.
    \410\ Office of the Assistant Secretary for Planning and 
Evaluation, U.S. Department of Health & Human Services. Second 
Report to Congress on Social Risk Factors and Performance in 
Medicare's Value-Based Purchasing Program. 2020. https://aspe.hhs.gov/reports/second-report-congress-social-risk-medicares-value-based-purchasing-programs.
---------------------------------------------------------------------------

    Second, we considered adding a peer grouping component to our 
scoring methodology, under which we would divide SNFs into groups based 
on the proportion of residents with DES that a SNF serves. With this 
peer grouping, different performance standards would then be set for 
each group, and thus payment adjustments would be made based on the 
group or strata in which a SNF falls.\411\ However, ASPE noted in their 
second report to congress on Social Risk Factors and Performance in 
Medicare's Value-Based Purchasing Program that although they support 
stratifying quality measures by DES to identify disparities, they had 
concerns that peer grouping could risk setting different standards of 
care for SNFs caring for underserved populations.\412\
---------------------------------------------------------------------------

    \411\ Chen, A., Ghosh, A., Gwynn, K.B., Newby, C., Henry, T.L., 
Pearce, J., Fleurant, M., Schmidt, S., Bracey, J., & Jacobs, E.A. 
(2022). Society of General Internal Medicine Position Statement on 
Social Risk and Equity in Medicare's Mandatory Value-Based Payment 
Programs. Journal of General Internal Medicine, 37(12), 3178-3187. 
https://doi.org/10.1007/s11606-022-07698-9.
    \412\ Office of the Assistant Secretary for Planning and 
Evaluation, U.S. Department of Health & Human Services. Second 
Report to Congress on Social Risk Factors and Performance in 
Medicare's Value-Based Purchasing Program. 2020. https://aspe.hhs.gov/reports/second-report-congress-social-risk-medicares-value-based-purchasing-programs.
---------------------------------------------------------------------------

    Finally, we considered an approach of adding additional improvement 
points to the Program. This could be achieved by either providing bonus 
points to SNFs for measures in which they had significant improvement 
or by increasing the points available for improvement from 9 points to 
some higher quantity, such as 15 points. It is important that even 
poorer performing SNFs be provided incentives to improve as all 
residents should have the opportunity to receive high quality care, and 
currently lower performers have the greatest opportunity for 
improvement. Since SNFs that care for higher proportions of SNF 
residents with DES tend to have lower SNF Performance Scores compared 
to SNFs that do not care for higher proportions of SNF residents with 
DES, this Program adjustment could address health equity by providing 
lower performing SNFs that care for higher proportions of SNF residents 
with DES additional incentives to improve the care they provide. 
However, we had concerns with this approach. First, this approach is 
not focused specifically on populations that are underserved, and it is 
unclear whether the additional improvement points available would 
provide sufficient incentives for SNFs that care for higher proportions 
of SNF residents with DES to invest the limited resources they have to 
make the changes necessary to benefit from it. We were also concerned 
that this change could primarily incentivize poorer performing SNFs 
that do not care for a higher proportion of SNF residents with DES. 
Although we aim to incentivize improvement in care for all SNFs, this 
alternative approach has a significant risk of not meeting the goals of 
a health equity-focused adjustment in the Program. Therefore, in 
considering how to modify the existing SNF VBP Program to advance 
health equity, we believe that rather than utilizing risk adjustment, 
peer grouping or adjusting the improvement point allocation process, it 
would be more appropriate to adopt an approach that rewards overall 
high-quality performance and incentivizes health equity.
    In conclusion, we believe the HEA proposal allows us to 
appropriately measure performance by rewarding SNFs that overcome the 
challenges of caring for higher proportions of SNF residents with DES 
and to incentivize those who have not achieved such high-quality care 
to work towards improvement. As the Program expands beyond one measure, 
we believe this HEA will support high-quality care for all populations 
and recognize top tier performing SNFs serving residents with DES.
e. HEA Calculation Steps and Examples
    In this section, we outline the calculation steps and provide 
examples of the determination of HEA bonus points and the application 
of these HEA bonus points to the normalized sum of a SNF's measure 
points. These example calculations illustrate possible HEA bonus points 
resulting from this approach, which accounts for both a SNF's quality 
performance and its proportion of residents with DES. For each SNF, the 
HEA bonus points would be calculated according to the following 
formula:

HEA bonus points = measure performance scaler x underserved multiplier

    The calculation of the HEA bonus points will be as follows:
Step One--Calculate the Measure Performance Scaler for Each SNF
    We will first calculate a measure performance scaler based on a 
SNF's score on each of the SNF VBP program measures. We will assign a 
point value of 2 for each measure where a SNF is a top tier performing 
SNF on that measure, such that for the FY 2027 program year, a SNF 
could receive a maximum 16-point measure performance scaler for being a 
top tier performing SNF for each of the 8 measures. Top tier 
performance on each measure is calculated by determining the percentile 
that the SNF falls in based on their score on the measure as compared 
to the score earned by other SNFs who are eligible to receive a score 
on the measure. A SNF whose score is greater than or equal to the 
66.67th (two-thirds) percentile on a given measure compared to all 
other SNFs will be considered a top tier performing SNF and will be 
assigned a point value of 2 for that measure. This is depicted in Table 
19 for the FY 2027 program year. We note that if a SNF performs in the 
bottom two-thirds (less than 66.67th percentile) of performance on all 
measures, that SNF would be assigned a point value of 0 for each 
measure, resulting in a measure performance scaler of 0.
    As described previously, we proposed to assign to each SNF a point 
value of 2 for each measure for which it is a top tier performing SNF, 
and we proposed that the measure performance scaler would be the sum of 
the point values

[[Page 53310]]

assigned to each measure in the SNF VBP Program. We modeled this 
measure performance scaler after the performance scaler finalized in 
the Medicare Shared Savings Program's health equity adjustment (87 FR 
69843 through 69845) for consistency across CMS programs, although that 
adjustment allows for a middle performance group as well. However, as 
described previously, because we aim to specifically target the highest 
performing SNFs for this adjustment, we are limiting our adjustment to 
the top third of performers only.

                          Table 19--Example of the Measure Performance Scaler Assigned to SNFs Based on Performance by Measure
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                          Example SNF 1                  Example SNF 2                 Example SNF 3                Example SNF 4
             Measure              ----------------------------------------------------------------------------------------------------------------------
                                    Performance group    Value   Performance group     Value     Performance group    Value   Performance group    Value
--------------------------------------------------------------------------------------------------------------------------------------------------------
SNFRM *..........................  Top third..........       2  Top Third..........          2  Top Third..........       2  Bottom Two-Thirds..       0
SNF HAI Measure..................  Top third..........       2  Top Third..........          2  Top Third..........       2  Bottom Two-Thirds..       0
Total Nurse Staffing Measure.....  Top third..........       2  Bottom Two-Thirds..          0  Bottom Two-Thirds..       0  Top Third..........       2
DTC-PAC SNF Measure..............  Top third..........       2  Top Third..........          2  Bottom Two-Thirds..       0  Bottom Two-Thirds..       0
Falls with Major Injury (Long-     Top Third..........       2  Top Third..........          2  Bottom Two-Thirds..       0  Bottom Two-Thirds..       0
 Stay) Measure **.
DC Function Measure **...........  Top Third..........       2  Top Third..........          2  Top Third..........       2  Bottom Two-Thirds..       0
Long Stay Hospitalization Measure  Top Third..........       2  Top Third..........          2  Top Third..........       2  Bottom Two-Thirds..       0
 **.
Nursing Staff Turnover Measure **  Top Third..........       2  Top Third..........          2  Top Third..........       2  Bottom Two-Thirds..       0
                                   Measure Performance      16  Measure Performance         14  Measure Performance      10  Measure Performance       2
                                    Scaler.                      Scaler.                         Scaler.                      Scaler.
--------------------------------------------------------------------------------------------------------------------------------------------------------
Notes:
* We proposed to replace the SNFRM would be replaced with the SNF WS PPR beginning with the FY 2028 program year.
** We proposed to adopt the Nursing Staff Turnover Measure beginning with the FY 2026 program year and the Falls with Major Injury (Long-Stay) Measure,
  DC Function Measure, and Long Stay Hospitalization Measure beginning with the FY 2027 program year.

Step Two--Calculate the Underserved Multiplier
    We proposed to calculate an underserved multiplier, which, as 
stated previously, we proposed to define as, for a SNF, the number 
representing the SNF's proportion of residents with DES out of its 
total resident population in the applicable program year, translated 
using a logistic exchange function. As stated previously, the primary 
goal of the adjustment is to appropriately measure performance by 
rewarding SNFs that are able to overcome the challenges of caring for 
high proportions of residents with DES while still providing high 
quality care. We can also accomplish the goal of this adjustment by 
utilizing a logistic exchange function to calculate the underserved 
multiplier, which will provide SNFs who care for the highest 
proportions of SNF residents with DES with the most HEA bonus points. 
Thus, we proposed to utilize a logistic exchange function to calculate 
the underserved multiplier for scoring SNFs such that there would be a 
lower rate of increase at the beginning and the end of the curve. The 
formula for the underserved multiplier using a logistic exchange 
function would be as follows:
[GRAPHIC] [TIFF OMITTED] TR07AU23.705

    Due to the structure of the logistic exchange function, those SNFs 
with lower proportions of residents with DES have smaller underserved 
multipliers than their actual proportion of residents with DES and 
those SNFs with higher proportions of SNF residents with DES have 
underserved multipliers higher than their proportion of SNF residents 
with DES. A logistic exchange function assumes a large difference 
between SNFs treating the most and fewest residents with DES. 
Therefore, the logistic exchange function provides higher HEA bonus 
points to SNFs serving greater proportions of SNF residents with DES. 
For example, as shown in Figure A, if a SNF serves 70 percent of SNF 
residents with DES, the SNF would receive an underserved multiplier of 
0.78.

[[Page 53311]]

[GRAPHIC] [TIFF OMITTED] TR07AU23.706

    We proposed that SNFs will receive an underserved multiplier of 0 
if the SNF's proportions of SNF residents with DES is less than 20 
percent, thereby establishing a ``floor'' on the magnitude of the SNF's 
underserved population proportion in order for the SNF to be eligible 
for any HEA bonus points. Because SNFs with proportions of SNF 
residents with DES below 20 percent receive a value of 0 for their 
underserved multiplier, any multiplication with the measure performance 
scaler will be 0 and will lead to those SNFs receiving no HEA bonus 
points. Imposing a floor of 20 percent for the underserved multiplier 
for a SNF to be eligible to receive HEA bonus points, reinforces that 
the adjustment is intended to appropriately measure performance by 
rewarding SNFs that are serving higher proportions of SNF residents 
with DES while also achieving high levels of quality performance. We 
believe this approach is necessary to remain consistent with the goal 
to reward high quality care specifically among SNFs that care for 
higher proportions of SNF residents with DES. We anticipate the vast 
majority of SNFs will be able to earn HEA bonus points despite this 
floor, and we expect the percent of SNFs meeting the 20 percent floor 
for the underserved multiplier may increase over time, as existing SNFs 
seek to expand their resident population to earn HEA bonus points. We 
also believe that the challenges associated with caring for residents 
with DES, a complex resident population, will be negligible if 80 
percent of a SNF's resident population is not underserved. This 20 
percent floor is consistent with the new health equity adjustment for 
ACOs that report all payer eCQMs/MIPS CQMs, as finalized in the CY 2023 
PFS final rule (87 FR 69849 through 69852).
    Alternatively, we considered establishing a floor of 60 percent 
such that all SNFs with proportions of SNF residents with DES below 60 
percent would receive an underserved multiplier of 0, and therefore, 
would not receive any HEA bonus points. Although this would provide a 
greater value-based incentive payment amount to top tier performing 
SNFs that serve the highest proportions of SNF residents with DES and 
thus would support the primary goal of the adjustment, it would also 
mean SNFs that care for high proportions of SNF residents with DES who 
likely face similar challenges, albeit to a lesser extent, would 
receive no adjustment at all.
Step Three--Calculate the HEA Bonus Points
    We proposed to calculate the HEA bonus points that apply to a SNF 
for a program year by multiplying the measure performance scaler by the 
underserved multiplier. We believe that combining the measure 
performance scaler and the underserved multiplier to calculate the HEA 
bonus points allows for us to reward those SNFs with high quality that 
are also serving high proportions of SNF residents with DES, while 
incentivizing other SNFs to improve their performance (by a higher 
measure performance scaler) and serve more SNF residents with DES (by a 
higher underserved multiplier) in order to earn more HEA bonus points. 
Table 20 shows examples of how the measure performance scaler and 
underserved multiplier would be used to calculate the HEA bonus points. 
It also demonstrates how the logistic exchange function that we 
proposed to use to calculate the underserved multiplier interacts with 
the measure performance scaler and results in SNFs serving higher 
proportion of SNF residents with DES receiving more HEA bonus points. 
For instance, example SNF 1 with 16 points and a proportion of 
residents with DES of 50 percent received a measure performance scaler 
of 16 and an underserved multiplier of 0.22. In other words, they would 
receive 22 percent of the points from their measure performance scaler 
because of how the logistic exchange function translates their 
proportion of residents with DES. Their measure performance scaler of 
16 and underserved multiplier of 0.22 would then be multiplied together 
to get their HEA bonus points of 3.52. Alternatively, example SNF 2 
with 14 points and a proportion of residents with DES of 70 percent, 
received an underserved multiplier of 0.78. Their measure performance 
scaler of 14 and underserved multiplier of 0.78 would then be 
multiplied together to get their HEA bonus points of 10.92. Note that 
although SNF 1 had a higher measure performance scaler, they received 
fewer HEA bonus points because they had a lower proportion of residents 
with DES. Finally, example SNF 3 had a proportion of SNF residents with 
DES of less than 20 percent and so they received an underserved 
multiplier of 0, resulting in no HEA bonus points

HEA bonus points = Measure Performance Scaler x Underserved Multiplier


[[Page 53312]]



                              TABLE 20--Example of the HEA Bonus Points Calculation
----------------------------------------------------------------------------------------------------------------
                                                      Measure      Proportion of
                   Example SNF                      performance   Residents with    Underserved      HEA bonus
                                                      scaler          DES (%)       multiplier        points
----------------------------------------------------------------------------------------------------------------
                                                             [A]             [B]             [C]   [D] ([A]*[C])
SNF 1...........................................              16              50            0.22            3.52
SNF 2...........................................              14              70            0.78           10.92
SNF 3...........................................              10              10               0               0
SNF 4...........................................               2              80            0.92            1.84
----------------------------------------------------------------------------------------------------------------

Step Four--Add HEA Bonus Points to the Normalized Sum of all Points 
Awarded for each Measure
    Finally, we proposed that we will add a SNF's HEA bonus points as 
calculated in Step Three of this section to the normalized sum of all 
points awarded to a SNF across all measures. This resulting sum will be 
the SNF Performance Score earned by the SNF for the program year, 
except that we will cap the SNF's Performance Score at 100 points to 
ensure the HEA creates a balanced incentive that has the potential to 
increase the SNF Performance Score without dominating the score and 
creating unintended incentives. Table 21 displays the final HEA bonus 
points added to the normalized sum of all points awarded to a SNF for 
each measure for 4 example SNFs.

                              TABLE 21--Example of the HEA Bonus Points Calculation
----------------------------------------------------------------------------------------------------------------
                                                             Normalized sum
                                                              of all points   HEA bonus points   SNF performance
                        Example SNF                         awarded for each   (Step 3, column        score
                                                                 measure            [D])
----------------------------------------------------------------------------------------------------------------
                                                                         [A]               [B]       ([A] + [B])
SNF 1.....................................................                80              3.52             83.52
SNF 2.....................................................                65             10.92             75.92
SNF 3.....................................................                42                 0             42.00
SNF 4.....................................................                10              1.84             11.84
----------------------------------------------------------------------------------------------------------------

    By adding these HEA bonus points to the normalized sum of all 
points awarded to a SNF for each measure, SNFs can be rewarded for 
delivering excellent care to all residents they serve and can be 
appropriately recognized for the resource intensity expended to achieve 
high performance when caring for higher proportion of SNF residents 
with DES. We believe this scoring adjustment, designed to advance 
health equity through the SNF VBP Program, is consistent with CMS's 
goal to incentivize greater inclusion of underserved populations, as 
well as the delivery of high-quality care to all.
    We proposed the scoring change and calculations including the use 
of the measure performance scaler, underserved multiplier, and HEA 
bonus points. We also proposed to codify this proposal by adding a new 
paragraph (k) at Sec.  413.338 of our regulations and by updating Sec.  
413.338(e) of our regulations to incorporate the health equity scoring 
adjustment into our performance scoring methodology. We solicited 
public comment on the HEA.
    We received public comments on the HEA proposal. The following is a 
summary of the comments we received and our responses.
    Comment: Many commenters supported our HEA noting that it 
appropriately recognizes the additional challenges and increased 
resource utilization in meeting the healthcare needs of the underserved 
population while also rewarding high quality performance for all 
residents.
    Response: We agree that this adjustment recognizes the resource 
intensity required to care for residents with DES while also supporting 
high quality care for all residents.
    Comment: A few commenters supported the HEA and also suggested next 
steps for CMS. One commenter encouraged CMS to adequately fund State 
Medicaid programs. One commenter urged CMS to increase scrutiny on how 
SNFs that are eligible for the HEA spend their Medicare and Medicaid 
funds. Another commenter recommended that CMS monitor the HEA for 
unintended consequences. One commenter suggested that CMS consider 
whether adjustments to the scoring methodology are necessary to account 
for an organization's performance specifically within the DES 
population if it differs from the performance in the rest of the 
patient population. One commenter requested that CMS consider how the 
HEA compares to a peer grouping approach.
    Response: We intend to closely monitor the data for potential 
unintended consequences that could arise as a result of the HEA. We 
agree that it is also important to consider an organization's 
performance specifically within the DES population, although that is 
not what this HEA is intended to do. As we explained in the proposed 
rule (88 FR 21392), we have concerns with utilizing a peer grouping 
approach because it may set different standards of care. We will take 
these suggestions into consideration as we develop additional ways to 
incorporate health equity into the Program.
    Comment: A few commenters supported adjusting the SNF VBP Program 
for health equity but expressed concerns about the details of the 
proposed HEA. One commenter believed the scoring methodology was too 
complex and stated that complexity in measures makes changes at the 
facility level more challenging. One commenter was concerned that high 
performing facilities with high proportions of residents with DES will 
get payment adjustments and lower performing facilities with high 
proportions of residents with DES will not get payment adjustments. The 
same commenter requested that CMS explore how these lower performing 
facilities might access scoring adjustments. One commenter was 
concerned that the HEA may reward facilities for their resident 
population instead of their quality scores. One commenter suggested CMS

[[Page 53313]]

use the term ``patient'' instead of ``resident'' to describe the 
population of SNF short -stay patients with original Medicare-covered 
stays.
    Response: We disagree that the HEA is too complex. We believe that 
the scoring methodology addresses the challenges of adding a HEA to 
high performing SNFs that also care for high proportions of residents 
with DES in a straightforward way. As stated in the proposed rule (88 
FR 21382 through 21392), if a SNF, relative to other SNFs, is in the 
top third of performance for any measure, they are eligible for HEA 
bonus points. The number of HEA bonus points that a SNF is eligible to 
receive depends on its proportion of residents with DES. The HEA bonus 
points are then incorporated into the calculation of the SNF 
Performance Score, which is used to determine a SNF's payment 
adjustment. A SNF that provides care for high proportions of residents 
with DES and performs well on any measure is likely to receive a higher 
adjustment due to this addition to the program. Resources will be 
developed to support SNFs in understanding this new adjustment.
    We also reiterate that the HEA is intended to reward high quality 
performance and not solely adjust for resident population, which may 
leave lower performing facilities with high proportions of residents 
with DES without a payment adjustment. We do not intend to reward lower 
quality performance and we believe the proposed HEA incentivizes lower 
performing facilities to improve their quality scores. We also agree 
that it is important to measure health equity in other ways, which is 
why we included in the proposed rule a request for information on 
additional ways to incorporate health equity into the Program.
    We disagree that the adjustment may reward facilities for their 
resident population instead of their quality scores as we specifically 
designed the adjustment to first determine whether the provider is high 
performing and then apply the underserved multiplier. Lastly, we have 
used the term ``resident'' to refer to both short- and long-stay 
residents when referencing the HEA because we use this language 
throughout the entire proposed and final rules for all measures, 
including both short and long-stay measures.
    Comment: A few commenters did not support our proposed HEA. One 
commenter believed it was premature to add a health equity component 
into a payment program and also believed that the long stay measures 
are unrelated to health equity because the DES population is calculated 
using Medicare Part A claims. The same commenter also believed the HEA 
does not provide meaningful data to address health equity, and that the 
HEA doesn't appropriately incentivize SNFs with a low proportion of 
residents who are in a Medicare Part A stay or SNFs with a large 
population of residents enrolled in Medicare Advantage. One commenter 
believed the proposal is discriminatory and does not consider health 
equity and instead stated that CMS should include social determinants 
of health as part of the new quality measures.
    Response: We believe the HEA is inclusive as all SNFs that meet the 
proposed floor of 20 percent of residents with DES are eligible to earn 
HEA bonus points. As we explained in the proposed rule, there is 
considerable literature linking negative health outcomes to residents 
with DES specifically in the SNF setting (88 FR 21383). We designed the 
HEA to reward high quality care for all residents and to recognize the 
resource intensity required to care for residents with DES, who are 
more likely to have disabilities or functional impairments, more likely 
to be medically complex, more likely to have greater social needs, and 
have a greater risk of negative health outcomes compared to individuals 
without DES.\413\ We disagree that it is premature to add a health 
equity component into a payment program. We note that the HEA will not 
be included until the FY 2027 program year, and we believe it is 
imperative to incentivize high quality care for all residents in the 
Program without additional delay. Further, as described above, 
advancing health equity is a key pillar of our strategic vision \414\ 
and we have already been working to advance health equity by designing, 
implementing, and operationalizing policies and programs aimed at 
identifying and reducing health disparities.
---------------------------------------------------------------------------

    \413\ Johnston, K.J., & Joynt Maddox, K.E. (2019). The Role of 
Social, Cognitive, And Functional Risk Factors In Medicare Spending 
For Dual And Nondual Enrollees. Health Affairs (Project Hope), 
38(4), 569-576. https://doi.org/10.1377/hlthaff.2018.05032.
    \414\ CMS Strategic Vision. (2022). https://www.cms.gov/cms-strategic-plan.
---------------------------------------------------------------------------

    We also disagree that long stay measures are unrelated to health 
equity because the DES population is calculated using Medicare Part A 
claims. The HEA aims to incentivize high quality care under the SNF VBP 
Program, while recognizing the resource intensity required to care for 
residents with DES, by providing health equity bonus points to SNFs 
that perform well on Program measures and have at least 20 percent of 
residents with DES. SNFs with a higher proportion of residents with DES 
also have a higher share of residents who are enrolled in Medicaid in 
their total resident population, which adds to their resource 
constraints.\415\ Many long-stay residents are enrolled in Medicare 
Part B, which covers certain services provided by nursing facilities. 
Thus, to accomplish the goals of the HEA, we feel it is appropriate to 
include all measures in the SNF VBP Program, including long-stay 
measures when calculating the HEA.
---------------------------------------------------------------------------

    \415\ Rahman, M., Grabowski, D.C., Gozalo, P.L., Thomas, K.S., & 
Mor, V. (2014). Are Dual Eligibles Admitted to Poorer Quality 
Skilled Nursing Facilities? Health Services Research, 49(3), 798-
817. https://doi.org/10.1111/1475-6773.12142.
---------------------------------------------------------------------------

    Regarding the data provided by the HEA, we reiterate the intent of 
the HEA is not to specifically incentivize improvement among residents 
with DES but rather incentivize high quality care among all residents 
in the facility and to recognize the additional resources required to 
care for residents with DES. Current data relating to the Program, 
available on the Provider Data Catalog website, provide SNFs with 
information on their quality performance. We believe the HEA is an 
important first step in adding a health equity component to the 
Program; however, we also intend to explore additional ways to 
incorporate health equity into the Program, which we intend to allow 
commenters to provide feedback on in future rulemaking.
    We disagree with concerns that this HEA might not appropriately 
incentivize SNFs that have large populations of residents enrolled in 
Medicare Advantage. We believe this HEA has the ability to improve care 
for all residents in a SNF as SNFs will need to perform in the top 
third of performance for at least one measure to be eligible to receive 
the HEA. Further, SNFs that have a low proportion of Medicare Part A 
beneficiaries will still be able to earn the HEA based on the 
proportion of those Medicare Part A beneficiaries who have DES and 
their performance under the Program. However, we will continue to 
monitor the HEA after implementation.
    We will take the commenter's suggestion to include social 
determinants of health as part of the new quality measures into 
consideration as we develop additional ways to incorporate health 
equity into the Program.
    We received public comments on our proposal to utilize DES to 
define the term ``underserved population''. The following is a summary 
of the comments we received and our responses.

[[Page 53314]]

    Comment: Many commenters supported using dual eligibility status 
(DES) to define the underserved population because it is consistently 
recorded in administrative data, has a strong link to other social 
drivers of health, and reflects those who face the most significant 
social needs.
    Response: We thank commenters for their support and agree DES is an 
important indicator of social need because individuals with DES are 
more likely to have disabilities or functional impairments, more likely 
to be medically complex, more likely to have greater social needs, and 
have a greater risk of negative health outcomes compared to individuals 
without DES.
    Comment: Many commenters encouraged CMS generally to explore other 
options for defining the underserved population in the future as there 
are many other social risk factors that impact resident outcomes. A few 
commenters suggested considering the proportion of Medicaid residents 
in a facility as part of the definition of ``underserved.'' A few 
commenters suggested CMS encourage collection of race and ethnicity 
data and adjust based on the racial composition of facilities.
    Response: We thank the commenters for these suggestions.
    Comment: A few commenters requested CMS consider adding additional 
indicators to the definition of ``underserved'' before implementing the 
HEA in order to create multiple ways to recognize the challenges 
residents and SNFs may face in achieving better outcomes. One commenter 
requested the Low-Income Subsidy (LIS) be included in the definition, 
and one commenter suggested both the LIS and Area Deprivation Index 
(ADI) be included in the definition of ``underserved.''
    Response: As we explained in the proposed rule (88 FR 21384 through 
21385), we are concerned that including the ADI or residents eligible 
for the LIS program as part of our definition of ``underserved'' in the 
HEA is premature until more research is conducted linking these 
indicators to negative health outcomes specifically in the SNF setting. 
We intend to consider these and other indicators as we explore 
additional ways to incorporate health equity into the Program.
    Comment: A few commenters expressed concern over using DES alone to 
define the underserved population because Medicaid eligibility varies 
by State. One commenter requested that CMS consider how fluctuations in 
the number of residents with DES within a SNF over time would impact 
the scoring methodology and whether this indicator would be stable over 
the time the measures are collected.
    Response: As explained in the proposed rule (88 FR 21386), we 
proposed to define residents with DES, for purposes of this proposal, 
as the percentage of Medicare SNF residents who are also eligible for 
Medicaid. We proposed to assign DES for any Medicare beneficiary who 
was deemed by Medicaid agencies to be eligible to receive Medicaid 
benefits for any month during the performance period of the 1-year 
measures. Because of the concern that Medicaid eligibility varies by 
state, we are clarifying in this final rule that this definition 
includes beneficiaries with partial DES. Residents with full DES 
qualify for full Medicare and Medicaid benefits, whereas residents with 
partial DES qualify fully for Medicare, but only for some Medicaid 
benefits, as they have higher amounts of assets and income.\416\ We 
believe this expanded definition of dual eligibility is appropriate for 
SNF VBP as it allows for the inclusion of a larger number of residents 
who are underserved. In our modeling that includes residents with 
partial and full DES, we also considered using eligibility for the 
Medicare Low Income Subsidy to meet the 20 percent threshold, which 
does not differ by State and may capture different low-income 
beneficiaries and found only a small increase in SNFs that became 
eligible to receive the HEA, compared to only using those with partial 
and full DES. Given this, we believe that using the definition of DES, 
which includes residents with both partial and full DES, captures a 
sufficient proportion of low-income Medicare beneficiaries and is 
sufficiently consistent across States.
---------------------------------------------------------------------------

    \416\ Office of the Assistant Secretary for Planning and 
Evaluation, U.S. Department of Health & Human Services. First Report 
to Congress on Social Risk Factors and Performance in Medicare's 
Value-Based Purchasing Program. 2016. https://aspe.hhs.gov/sites/default/files/migrated_legacy_files/171041/ASPESESRTCfull.pdf.
---------------------------------------------------------------------------

    As requested by the commenter, we would like to explain further how 
fluctuations in the number of residents with DES, including both 
partial and full DES, within a SNF over time would impact the scoring 
methodology. We proposed to define the underserved multiplier as the 
number representing the SNF's proportion of residents with DES out of 
its total resident population in the applicable program year, 
translated using a logistic exchange function (88 FR 21385 through 
21386). We further defined the total resident population as Medicare 
beneficiaries identified from the SNF's Part A claims during the 
performance period of the 1-year measures (88 FR 21385 through 21386). 
In SNF VBP, the program year refers to the year in which a SNF's 
payment is impacted and has a corresponding baseline and performance 
period for each measure. Thus, because the calculation of the program 
year payment adjustment is dependent on both the performance period and 
baseline period, we would like to clarify that the underserved 
multiplier is for a SNF, the mathematical result of applying a logistic 
function to the number of SNF residents who are members of the 
underserved population out of the SNF's total Medicare population, as 
identified from the SNF's Part A claims, during the performance period 
that applies to the 1-year measures for the applicable program year. A 
single underserved multiplier will be calculated using the performance 
period of the 1-year measures and will be applied to all measures in 
the Program. The periods for calculating measure performance and 
calculating the proportion of residents with DES therefore overlap. 
This means that a SNF's proportion of residents with DES may change for 
each SNF VBP program year, and thus the SNF's underserved multiplier 
may change for each program year, in the same way that the set of 
residents used to calculate measure scores for each measure changes. 
For example, as a SNF's proportion of residents with DES increases, if 
their performance remains in the top third for the same measure or 
measures, they will likely receive additional HEA bonus points. As a 
SNF's proportion of residents with DES decreases, even if their 
performance remains in the top third for the same measure or measures 
from previous program years, they will likely receive fewer HEA bonus 
points. The combination of a SNF's proportion of residents with DES and 
performance on each measure will determine how many HEA bonus points a 
SNF receives, and both proportion of residents and performance on each 
measure can change from year to year.
    Comment: One commenter did not support using DES until additional 
research is conducted as they believe utilizing DES to define the 
underserved population could lead to unintended consequences. 
Specifically, they believe CMS may unintentionally increase the 
financial disparity that exists between for-profit and not-for-profit 
nursing homes by rewarding for-profit nursing homes with higher DES 
percentages and not rewarding not-for-profit nursing homes that care 
for higher proportions of Medicaid-only residents.
    Response: We disagree that the HEA will necessarily increase the 
disparity

[[Page 53315]]

between SNFs that care for higher proportions of residents with DES 
compared to those with higher proportions of Medicaid-only residents as 
our definition of DES includes the total resident population, which we 
further defined as Medicare beneficiaries identified from the SNF's 
Part A claims (88 FR 21386), as the denominator. Thus, although a SNF 
may have lower proportions of residents with Medicare overall, the 
proportion of DES only takes into consideration the proportion of 
residents with Medicare who also have Medicaid. Additionally, we note 
that the HEA is intended to recognize and reward all SNFs for providing 
excellent care to higher proportions of residents with DES.
    We also solicited public comments on utilizing a measure 
performance scaler, assigning a point value of 2 for each measure for 
which a SNF is a top tier performing SNF, and defining a top tier 
performing SNF as a SNF whose performance for the program year is in 
the top third of the performance of all SNFs on the measure for the 
same program year. We received public comments on these proposals. The 
following is a summary of the comments we received and our responses.
    Comment: One commenter supported this proposal to recognize SNFs 
that perform in the top third.
    Response: We agree that recognizing performance in the top third is 
appropriate because it strikes a balance between rewarding high quality 
performance and providing an appropriate payment adjustment to those 
who perform well and serve a high proportion of residents with DES 
while incentivizing lower performing SNFs to improve.
    Comment: A few commenters suggested CMS limit those receiving a 
bonus to SNFs in the top 20 percent of performance instead of the top 
third.
    Response: We thank the commenters for their recommendation but 
believe recognizing performance in the top third strikes a balance 
between rewarding high quality performance and providing an appropriate 
payment adjustment to those who perform well and serve a high 
proportion of residents with DES while still incentivizing lower 
performing SNFs to improve. Further, as explained in the proposed rule 
(88 FR 21385) based on our calculation of measure data from FY 2018 to 
2021, the average SNF Performance Score for SNFs in the top third of 
performance that care for high proportions of residents with DES (SNFs 
with proportions of residents with DES in the top third) is 8.4 points 
lower than the SNF Performance Score for SNFs in the top third of 
performance that do not care for high proportions of residents with DES 
(40.8 for high performing SNFs with high proportions of residents with 
DES and 49.2 for all other high performing SNFs). Because of these 
existing performance disparities between SNFs that serve a high 
proportion of residents with DES and those that do not, setting the 
performance threshold too high may inadvertently exclude SNFs that 
serve a high proportion of residents with DES from the HEA. In the 
future, we may consider raising the performance threshold for the HEA 
based on ongoing monitoring of SNF performance, especially among those 
in the top tier.
    Comment: One commenter expressed concern that if there is low 
variability in a measure score between the top and bottom third, there 
may not be a clinically meaningful difference.
    Response: Although we recognize that some measures may have low 
variability in performance, we aim to reward high performing SNFs and 
incentivize lower performing SNFs to improve, even if those are small 
improvements. We believe setting the high-performance threshold at the 
top third strikes this balance regardless of variability in the 
measure.
    Comment: A few commenters expressed their support for assigning a 
point value of 2 for each measure and noted their interest in 
commenting on future rulemaking if this changes as the program expands.
    Response: We thank the commenters for their support. We agree that 
assigning a point value of 2 is appropriate at this time and would use 
rulemaking to propose any revisions to this policy.
    We also solicited public comments on using an underserved 
multiplier to calculate the HEA, utilizing a logistic exchange function 
to calculate the underserved multiplier, and setting a floor of 20 
percent for a SNF to be eligible for any HEA bonus points. We received 
public comments on these proposals. The following is a summary of the 
comments we received and our responses.
    Comment: One commenter supported the use of a logistic exchange 
function to calculate the underserved multiplier.
    Response: We thank the commenter for their support.
    Comment: A few commenters supported the proposal that a SNF's 
population must include at least 20 percent of residents with DES in 
order to be eligible for the underserved multiplier especially since 
those who do not meet this floor will not be penalized.
    Response: We thank commenters for their support of the 20 percent 
floor.
    Comment: One commenter expressed concerns about the 20 percent 
floor noting that they would prefer for there to be no floor.
    Response: We disagree that it would be preferable to not have a 20 
percent floor. As noted in the proposed rule (88 FR 21388), we strongly 
believe a floor of 20 percent allows us to accomplish our goals of this 
adjustment. Specifically, the 20 percent floor reinforces that the 
adjustment is intended to appropriately measure performance by 
rewarding SNFs that are serving higher proportions of SNF residents 
with DES while also achieving high performance. We believe this 
approach is necessary to remain consistent with the goal to reward high 
quality care specifically among SNFs that care for higher proportions 
of SNF residents with DES. We anticipate the vast majority of SNFs will 
be able to earn HEA bonus points despite this floor. We also believe 
that the challenges associated with caring for residents with DES, a 
complex resident population, would be negligible if greater than 80 
percent of a SNF's resident population is not underserved because 
residents with DES are more likely to have disabilities or functional 
impairments, more likely to be medically complex, more likely to have 
greater social needs, and have a greater risk of negative health 
outcomes compared to those without DES.\417\
---------------------------------------------------------------------------

    \417\ Johnston, K.J., & Joynt Maddox, K.E. (2019). The Role of 
Social, Cognitive, And Functional Risk Factors In Medicare Spending 
For Dual And Nondual Enrollees. Health Affairs (Project Hope), 
38(4), 569-576. https://doi.org/10.1377/hlthaff.2018.05032.
---------------------------------------------------------------------------

    After consideration of public comments, we are finalizing the 
Health Equity Adjustment for the SNF VBP Program beginning with the FY 
2027 program year.
    We are also finalizing our definition of ``underserved multiplier'' 
as the mathematical result of applying a logistic function to the 
number of SNF residents who are members of the underserved population 
out of the SNF's total Medicare population, as identified from the 
SNF's Part A claims, during the performance period that applies to the 
1-year measures for the applicable program year. We are also finalizing 
our definition of ``underserved population'' as Medicare beneficiaries 
who are SNF residents in a Medicare Part A stay who are also dually 
eligible, both partial and full, for Medicaid.
    Further, in an effort to minimize burden on providers, we aim to 
align our Health Equity Adjustment to a

[[Page 53316]]

similar adjustment proposed for inclusion in the Hospital Value Based 
Purchasing Program as is feasible and appropriate. As part of this 
alignment, we are making a technical change to our definition of the 
health equity adjustment bonus points so the definition is as follows: 
the points that a SNF can earn for a program year based on its 
performance and proportion of SNF residents who are members of the 
underserved population.
    We are also finalizing the updates to our regulations at Sec.  
413.338 to reflect this Health Equity Adjustment, including the 
clarified definitions of the ``underserved multiplier,'' ``underserved 
population,'' and ``health equity adjustment bonus points.''
e. Increasing the Payback Percentage To Support the HEA
    We previously adopted 60 percent as the SNF VBP Program's payback 
percentage for FY 2019 and subsequent fiscal years, subject to 
increases as needed to implement the Program's Low-Volume Adjustment 
policy for SNFs without sufficient data on which to base measure 
scores. We based this decision on numerous considerations, including 
our estimates of the number of SNFs that receive a positive payment 
adjustment under the Program, the marginal incentives for all SNFs to 
reduce hospital readmissions and make quality improvements, and the 
Medicare Program's long-term sustainability. We also stated that we 
intended to monitor the effects of the payback percentage policy on 
Medicare beneficiaries, on participating SNFs, and on their measured 
performance, and we stated that we intended to consider any adjustments 
to the payback percentage in future rulemaking.
    In previous rules, we have received many public comments urging us 
to increase the payback percentage. For example, in the FY 2018 SNF PPS 
final rule (82 FR 36620), we responded to comments urging us to 
finalize a 70 percent payback percentage. We stated at that time that 
we did not believe that a 70 percent payback percentage appropriately 
balanced the policies that we considered when we proposed the 60 
percent policy. We responded to similar comments in the FY 2019 SNF PPS 
final rule (83 FR 39281), where commenters urged us to revisit the 
payback percentage policy and adopt 70 percent as the Program's policy. 
We reiterated that we did not believe it was appropriate to revisit the 
payback percentage at that time, which was prior to the Program's first 
incentive payments taking effect on October 1, 2018.
    As part of our ongoing monitoring and evaluation efforts associated 
with the SNF VBP Program, we considered whether to update the Program's 
payback percentage policy to support the proposed HEA. After our 
consideration, and in conjunction with the HEA bonus points, we 
proposed to increase the total amount available for a fiscal year to 
fund the value-based incentive payment amounts beginning with the FY 
2027 program year.
    We proposed this update to our payback percentage policy both to 
increase SNFs' incentives under the Program to undertake quality 
improvement efforts and to minimize the impact of the proposed HEA on 
the distribution of value-based incentive payments to SNFs that do not 
earn the HEA. Because the SNF VBP Program's value-based incentive 
payment amounts depend on the distribution of SNF Performance Scores in 
each SNF VBP program year, providing additional incentives to SNFs 
serving higher proportions of SNF residents with DES without increasing 
the payback percentage could reduce other SNFs' value-based incentive 
payment amounts. While we do not believe that those reductions would be 
significant, we view that a change to the payback percentage will 
further increase SNFs' incentivizes to implement effective quality 
improvement programs.
    In determining how to modify the payback percentage, we considered 
the maximum number of HEA bonus points that would be awarded, as it is 
important that those points translate into meaningful enough rewards 
for SNFs to meet our goals of this adjustment to appropriately measure 
performance by rewarding SNFs that overcome the challenges of caring 
for higher proportions of SNF residents with DES and to incentivize 
SNFs who have not achieved such high-quality care to work towards 
improvement. However, we also have to ensure that the additional HEA 
bonus points available do not lead to value-based incentive payments 
that exceed the maximum 70 percent payback percentage authorized under 
section 1888(h)(5)(C)(ii)(III) of the Act. Additionally, we considered 
the maximum number of HEA bonus points that would be awarded in 
comparison to the average SNF Performance Score as we believe providing 
more HEA bonus points for the HEA relative to the average a SNF 
receives for their performance on the Program measures could undermine 
the incentives for SNFs to perform in the SNF VBP Program.
    We conducted an analysis utilizing FY 2018 through FY 2021 measure 
data for our previously finalized and new measures, including a 
simulation of performance from all 8 measures for the FY 2027 Program, 
to determine what would be the greatest amount we could increase the 
payback percentage by for the HEA while not exceeding the 70 percent 
maximum or allowing for too many HEA bonus points. We examined the 
interaction of the two factors that directly impact the size of the 
incentives, the assigned point value for each measure and the payback 
percentage. For the first factor, as stated previously, we proposed to 
assign 2 points per measure to each SNF that is a top tier performing 
SNF for that measure. This assigned point value would be used to 
calculate the measure performance scaler and resulting HEA bonus 
points. In this analysis, we also tested alternatives of assigning a 
point value of 1 or 3 per measure to determine how each option would 
impact the payback percentage and resulting value-based incentive 
payment amounts. For the payback percentage factor, we tested 
increasing the payback percentage to a fixed amount of 65 percent. We 
also tested an option in which we allow the payback percentage to vary 
based on performance data such that SNFs that do receive the HEA would 
not experience a decrease in their value-based incentive payment 
amount, to the greatest extent possible, relative to no HEA in the 
Program and maintaining a payback percentage of 60 percent.
    Table 22 has three columns representing possible point values 
assigned to each measure that are then used to calculate the measure 
performance scaler. As shown in Table 22, regardless of the assigned 
points per measure, 78 percent of SNFs would receive the HEA in this 
analysis. This means that 78 percent of SNFs were top tier performing 
SNFs for at least 1 measure and had at least 20 percent of their 
residents with DES, and therefore would have received some HEA bonus 
points. Table 22 also shows the mean number of HEA bonus points per SNF 
receiving the HEA, as well as the HEA bonus points at the 90th 
percentile and the maximum HEA bonus points that would have been 
received for the HEA. Table 22 then provides an estimate of the payback 
percentage that would have been required such that SNFs that do receive 
the HEA would not experience a decrease in their value-based incentive 
payment amount, to the greatest extent possible, relative to no HEA in 
the Program and maintaining a payback percentage of 60 percent. This 
analysis

[[Page 53317]]

also identified that the average SNF, prior to the implementation of 
the HEA, would have received a SNF Performance Score of 31.6 and that 
the 90th percentile SNF Performance Score was 49.7.
    As stated previously, we proposed to assign a point value of 2 for 
each measure in which a SNF is a top tier performing SNF. Table 22 
shows that assigning a point value of 2 per measure would have resulted 
in a 66 percent payback percentage, meaning once all SNFs have been 
awarded HEA bonus points, the value-based incentive payment amounts 
would result in a payback percentage of 66 percent. Assigning a point 
value of any higher number, such as 3 points per measure could result 
in the payback percentage exceeding the 70 percent maximum. This is 
because the amount of HEA bonus points would vary with performance, and 
so we expect the HEA bonus points to vary from year to year, creating a 
significant risk that assigning a point value of 3 for each measure 
would result in a payback percentage above the 70 percent maximum. 
Further, assigning a point value of 3 for each measure would result in 
HEA bonus points as high as 20. Considering the average SNF Performance 
Score during this same time period would have been 31.6, the addition 
of 20 bonus points puts far too much weight on the HEA compared to each 
of the Program measures.

  Table 22--Estimated HEA Bonus Points and Payment Adjustments Resulting From Scoring Options Based on FY 2018-
                                                    2021 Data
----------------------------------------------------------------------------------------------------------------
                                                                    1 assigned      2 assigned      3 assigned
                                                                    point value     point value     point value
                                                                    per measure     per measure     per measure
----------------------------------------------------------------------------------------------------------------
                                               SNFs receiving HEA
----------------------------------------------------------------------------------------------------------------
Total Number of SNFs receiving HEA..............................          10,668          10,668          10,668
Percentage of SNFs receiving HEA................................             78%             78%             78%
----------------------------------------------------------------------------------------------------------------
                                   HEA bonus points (among SNFs receiving HEA)
----------------------------------------------------------------------------------------------------------------
Mean............................................................            0.89            1.78            2.68
90th percentile.................................................            2.25            4.50            6.76
Max.............................................................            6.67           13.33           20.00
----------------------------------------------------------------------------------------------------------------
                          Assume payback will vary based on assigned points per measure
----------------------------------------------------------------------------------------------------------------
Estimate of percent payback required such that SNFs not                      63%             66%             69%
 receiving the HEA would not experience a decrease in their
 value-based incentive payment amount *.........................
Amount to SNFs receiving HEA ($MM)..............................           $14.3           $29.6           $45.3
----------------------------------------------------------------------------------------------------------------
Notes:
* Relative to no HEA in the Program and maintaining a payback percentage of 60 percent.

    Because we proposed to assign a point value of 2 for each measure 
in the Program and based on this analysis, we proposed that the payback 
percentage would vary by program year to account for the application of 
the HEA such that SNFs that do receive the HEA would not experience a 
decrease in their value-based incentive payment amount, to the greatest 
extent possible, relative to no HEA in the Program and maintaining a 
payback percentage of 60 percent. Utilizing a variable approach ensures 
a very limited number of SNFs (if any) that do not receive HEA bonus 
points will experience a downward payment adjustment. For a given 
program year, we proposed to calculate the final payback percentage 
using the following steps. First, we will calculate SNF value-based 
incentive payment amounts with a payback percentage of 60 percent and 
without the application of the proposed HEA. Second, we will identify 
which SNFs receive the HEA, and which do not based on their proportion 
of residents with DES and individual measure performance. Third, while 
maintaining the value-based incentive payment amounts calculated in the 
first step for those SNFs that do not receive the HEA, we will 
calculate the payback percentage needed to apply the HEA as described 
in section VIII.E.4.d. of this final rule. As shown in Table 23, 
through our analysis, we estimated that assigning 2 points per measure 
would require an increase in the 60 percent payback percentage of 6.02 
percentage points for the FY 2027 program year and 5.40 percentage 
points for the FY 2028 program year. These are estimates and we would 
expect some variation that could be the result of SNFs with high 
proportions of residents with DES significantly changing their 
performance, changes in Medicaid eligibility requirements such that the 
proportions of residents with DES changes, changes to the Program such 
as adding additional measures which could add additional points 
available for the HEA, and other possible factors. For the last factor, 
increasing the points available could result in an increased payback 
percentage beyond the 70 percent maximum; however, we intend to adjust 
the number of points available through the rulemaking process if we add 
measures to the Program. With our current proposal of assigning a point 
value of 2 for each measure, we do not anticipate that any factors will 
result in an increase in payback beyond the 70 percent maximum. 
However, we will continue to monitor the data closely and intend to 
make further proposals if necessary, in future rulemaking. Thus, as 
shown in Table 23, a variable payback percentage will allow all SNFs 
that receive the HEA to also receive increased value-based incentive 
payment amounts, and also means that SNFs that do not receive the HEA 
will not experience a decrease in their value-based incentive payment 
amount, to the greatest extent possible, relative to no HEA in the 
Program and maintaining a payback percentage of 60 percent.
    We also explored setting a fixed payback percentage of 65 percent. 
This would mean that despite assigning higher point values for each 
measure, the resulting value-based incentive payment amounts would be 
capped to ensure the payback percentage would not exceed 65 percent. 
This would ensure that the payback percentage is below the 70 percent 
maximum. However, as shown in Table 23,

[[Page 53318]]

including a fixed percentage point payback would result in some SNFs, 
including SNFs that care for the highest quintile of residents with DES 
and almost one-third of rural SNFs, receiving reduced value-based 
incentive payment amounts compared to the absence of the HEA in the 
Program. This would be a significant negative consequence of this 
proposal, and our proposal is structured to avoid this outcome. We do 
not want SNFs that provide high quality care and that serve large 
proportions of residents who are underserved to be disadvantaged by 
this HEA.

Table 23--Estimated Differences for the FY 2027 and 2028 Program Years Between a Variable Payback Percentage and
                             a Fixed Payback Percentage Based on FY 2018-2021 Data *
----------------------------------------------------------------------------------------------------------------
                                                          FY 2027 program                 FY 2028 program
                                                 ---------------------------------------------------------------
                                                    Variable **        Fixed        Variable **        Fixed
----------------------------------------------------------------------------------------------------------------
Payback percentage..............................          66.02%             65%          65.40%             65%
# (%) SNFs worse off *** among . . .
    All SNFs....................................          0 (0%)     5,233 (38%)          0 (0%)     4,105 (29%)
    Rural SNFs..................................          0 (0%)     1,146 (32%)          0 (0%)       853 (23%)
    SNFs in the highest quintile of proportion            0 (0%)       372 (14%)          0 (0%)       409 (15%)
     of their residents with DES................
Mean value-based incentive payment amount change
 per SNF among . . .
    All SNFs....................................          $2,162          $1,796          $1,901          $1,759
    SNFs that are worse off ***.................              $0          ($366)              $0          ($162)
    SNFs that are better off ***................          $2,771          $3,136          $2,433          $2,552
    Rural SNFs..................................            $969            $808            $940            $877
    SNFs in the highest quintile of proportion            $5,997          $5,691          $4,949          $4,846
     of their residents with DES................
Value-based incentive payment amounts
    Amount of value-based incentive payments             $324.18         $319.17         $323.23         $321.24
     with HEA ($MM).............................
    Amount of value-based incentive payments             $294.62         $294.62         $296.53         $296.53
     without HEA (60% of withhold) ($MM)........
    Amount of increase due to HEA ($MM).........          $29.56          $24.55          $26.70          $24.71
----------------------------------------------------------------------------------------------------------------
Notes:
* Based on assigning a point value of 2 for each measure in which the SNF is a top tier performing SNF.
** Actual payback percentage may change from what was modeled based on final Program data.
*** Payment changes, ``worse off'', and ``better off'' all compare to the absence of the HEA in the Program and
  a payback percentage of 60 percent.

    We proposed to adopt a variable payback percentage and proposed to 
amend our regulations at Sec.  413.338(c)(2)(i) to reflect this change 
to the payback percentage for FY 2027 and subsequent fiscal years. We 
solicited public comment on these proposals.
    We received public comments on these proposals. The following is a 
summary of the comments we received and our responses.
    Comment: Many commenters supported the proposal to increase the 
payback percentage. A few of these commenters also urged CMS to pay out 
the full 70 percent allowable by statute.
    Response: We thank commenters for their support. As noted in the FY 
2018 rule (82 FR 36619 through 36620), the 60 percent payback 
percentage was set to appropriately balance the number of SNFs that 
receive a positive payment adjustment, the marginal incentives for all 
SNFs to reduce hospital readmissions and make broad-based care quality 
improvements, and the Medicare Program's long-term sustainability 
through the additional estimated Medicare trust fund savings. We 
continue to hold those goals for the payback percentage as we have 
expanded the Program. We believe it is appropriate to utilize the 
additional payback to specifically target the HEA, but we continue to 
balance each of the considerations listed above and do not believe it 
is appropriate to increase the payback percentage beyond what will be 
used to fund the HEA at this time.
    Comment: A few commenters supported the use of a variable payback 
percentage as long as it stays under the 70 percent threshold allowable 
by statute.
    Response: We thank the commenters for their support of the variable 
payback percentage and agree that we do not intend to allow the payback 
percentage to increase beyond the 70 percent threshold. We reiterate we 
will continue to monitor the data closely and intend to make further 
proposals if necessary, in future rulemaking.
    After consideration of public comments, we are finalizing the 
updates to the payback percentage and codifying those updates in our 
regulations.
5. Health Equity Approaches Under Consideration for Future Program 
Years: Request for Information (RFI)
    We are committed to achieving equity in health outcomes for 
residents by promoting SNF accountability for health disparities, 
supporting SNFs' quality improvement activities to reduce these 
disparities, and incentivizing better care for all residents. The 
Health Equity Adjustment, as described previously, will revise the SNF 
VBP scoring methodology to reward SNFs that provide high quality care 
to residents with DES and create an incentive for all SNFs to treat 
residents with DES. We also aim to incentivize the achievement of 
health equity in the SNF VBP Program in other ways, including focusing 
specifically on reducing disparities to ensure we are incentivizing 
improving care for all populations, including residents who may be 
underserved. In order to do so, we solicited public comment on possible 
health equity advancement approaches to incorporate into the Program in 
future program years that could supplement the Health Equity Adjustment 
described in section VIII.E.4 of this final rule. We are also seeking 
input on potential ways to assess improvements in health equity in 
SNFs.

[[Page 53319]]

As is the case across healthcare settings, significant disparities 
persist in the skilled nursing environment.418 419 420 421 
The goal of explicitly incorporating health equity-focused components 
into the Program is to both measure and incentivize equitable care in 
SNFs. By doing so, we not only aim to encourage SNFs to focus on 
achieving equity for all residents, but also to afford individuals and 
families the opportunity to make more informed decisions about their 
healthcare.
---------------------------------------------------------------------------

    \418\ Li, Y., Glance, L.G., Yin, J., & Mukamel, D.B. (2011). 
Racial Disparities in Rehospitalization Among Medicare Patients in 
Skilled Nursing Facilities. American Journal of Public Health, 
101(5), 875-882. https://doi.org/10.2105/AJPH.2010.300055.
    \419\ Rahman, M., Grabowski, D.C., Gozalo, P.L., Thomas, K.S., & 
Mor, V. (2014). Are Dual Eligibles Admitted to Poorer Quality 
Skilled Nursing Facilities? Health Services Research, 49(3), 798-
817. https://doi.org/10.1111/1475-6773.12142.
    \420\ Rivera-Hernandez, M., Rahman, M., Mukamel, D., Mor, V., & 
Trivedi, A. (2019). Quality of Post-Acute Care in Skilled Nursing 
Facilities That Disproportionately Serve Black and Hispanic 
Patients. The Journals of Gerontology. Series A, Biological Sciences 
and Medical Sciences, 74(5). https://doi.org/10.1093/gerona/gly089.
    \421\ Zuckerman, R.B., Wu, S., Chen, L.M., Joynt Maddox, K.E., 
Sheingold, S.H., & Epstein, A.M. (2019). The Five-Star Skilled 
Nursing Facility Rating System and Care of Disadvantaged 
Populations. Journal of the American Geriatrics Society, 67(1), 108-
114. https://doi.org/10.1111/jgs.15629.
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    The RFI consists of four main sections. The first section requested 
input on resident-level demographic and social risk indicators, as well 
as geographic-level indices that could be used to assess health equity 
gaps. The second section requested input on possible health equity 
advancement approaches that could be added to the Program and describes 
questions that should be considered for each. The third section 
requested input on other approaches that could be considered for 
inclusion in the SNF VBP Program in conjunction with the approaches 
described in the second section. Finally, the fourth section requested 
input on adopting domains that could incorporate health equity.
a. Resident-Level Indicators and Geographic-Level Indices To Assess 
Disparities in Healthcare Quality
    To identify SNFs that care for residents who are underserved and 
determine their performance among these populations, we need to select 
an appropriate indicator of such. Identifying and prioritizing social 
risk or demographic variables to consider for measuring equity can be 
challenging. This is due to the high number of variables that have been 
identified in the literature as risk factors for poorer health outcomes 
and the limited availability or quality of standardized data. Each 
source of data has advantages and disadvantages in identifying 
populations to assess the presence of underlying disparities. Income-
based indicators are a frequently used measure for assessing 
disparities,\422\ but other social risk indicators can also provide 
important insights. As described in section VIII.E.4. of this final 
rule, we proposed to utilize dual eligibility status (DES) to measure 
the underserved population in SNFs, as this data is readily available 
and DES as a metric has been used extensively to study the SNF 
population.423 424 However, as additional data and research 
becomes available, we may be able to utilize other social risk factors 
to define the underserved population. We refer readers to the ASPE 
Report to Congress on Social Risk Factors and Performance Under 
Medicare's Value-Based Purchasing Programs for additional indicators we 
could consider for use in the Program, including the LIS Program, ADI, 
and others.\425\ We solicited comment on which demographic variables, 
social risk indicators, or combination of indicators would be most 
appropriate for assessing disparities and measuring improvements in 
health equity in the SNF VBP Program for the health equity approaches 
described in this RFI. We provide a summary of the comments we 
received, and our responses, later in this section.
---------------------------------------------------------------------------

    \422\ National Academies of Sciences, Engineering, and Medicine. 
2016. Accounting for Social Risk Factors in Medicare Payment: 
Identifying Social Risk Factors. Washington, DC: The National 
Academies Press. https://doi.org/10.17226/21858.
    \423\ Rahman, M., Grabowski, D.C., Gozalo, P.L., Thomas, K.S., & 
Mor, V. (2014). Are Dual Eligibles Admitted to Poorer Quality 
Skilled Nursing Facilities? Health Services Research, 49(3), 798-
817. https://doi.org/10.1111/1475-6773.12142.
    \424\ Zuckerman, R.B., Wu, S., Chen, L.M., Joynt Maddox, K.E., 
Sheingold, S.H., & Epstein, A.M. (2019). The Five-Star Skilled 
Nursing Facility Rating System and Care of Disadvantaged 
Populations. Journal of the American Geriatrics Society, 67(1), 108-
114. https://doi.org/10.1111/jgs.15629.
    \425\ Office of the Assistant Secretary for Planning and 
Evaluation, U.S. Department of Health & Human Services. First Report 
to Congress on Social Risk Factors and Performance in Medicare's 
Value-Based Purchasing Program. 2016. https://aspe.hhs.gov/sites/default/files/migrated_legacy_files/171041/ASPESESRTCfull.pdf.
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b. Approaches To Assessing Health Equity Advancement in the SNF VBP 
Program
    We are interested in developing approaches that would incentivize 
the advancement of health equity for all SNFs, focusing on improving 
care for all residents, including those who may currently face 
disparities in their care. Such an approach would aim to include as 
many SNFs as possible and would not be restricted to those serving 20 
percent or more of residents with DES like the Health Equity Adjustment 
we discuss in section VIII.E.4. of this final rule. There are many 
different ways to add a health equity-focused component or adjustment 
to the Program to meet these objectives. In the FY 2023 SNF PPS 
proposed rule (87 FR 22789), we requested commenters' views on which 
adjustments would be most effective for the SNF VBP Program to account 
for any equity gaps that we may observe in the SNF setting. Although 
many commenters were supportive of incorporating health equity-focused 
adjustments into the Program, there was no clear consensus on the type 
of adjustment that would be most effective. Therefore, we requested 
additional comments on potential approaches to assessing health equity 
advancement in the Program. We have outlined approaches to assess 
underlying equity gaps or designed to promote health equity, which may 
be considered for use in the Program and grouped them into three broad 
categories for assessment: applying points to current measures, equity-
focused measures, and composite measures. The remainder of this section 
discusses these categories and relevant questions to consider for each. 
We also highlight two methods used for calculating disparities.
    We identified four key considerations that we should consider when 
employing quality measurement as a tool to address health disparities 
and advance health equity. When considering which equity-focused 
measures could be prioritized for development for SNF VBP, we examined 
past reports that assess such measures and encouraged commenters to 
review each category against the following considerations: 
426 427
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    \426\ Office of the Assistant Secretary for Planning and 
Evaluation, U.S. Department of Health & Human Services. Second 
Report to Congress on Social Risk Factors and Performance in 
Medicare's Value-Based Purchasing Program. 2020. https://aspe.hhs.gov/reports/second-report-congress-social-risk-medicares-value-based-purchasing-programs.
    \427\ RAND Health Care. 2021. Developing Health Equity Measures. 
Washington, DC: U.S. Department of Health and Human Services, Office 
of the Assistant Secretary for Planning and Evaluation, and RAND 
Health Care.

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[[Page 53320]]

     To what extent does the approach support consumer choice? 
It is essential that quality measures reflect consumer needs and allow 
consumers to make informed choices about their care.428 429 
In the Program, measure data is available on the Provider Data Catalog 
website. Having access to and understanding this data would empower 
consumers with more information in selecting their optimal SNF, 
including one that demonstrates greater performance in advancing 
equity.
---------------------------------------------------------------------------

    \428\ Heenan, M.A., Randall, G.E., & Evans, J.M. (2022). 
Selecting Performance Indicators and Targets in Health Care: An 
International Scoping Review and Standardized Process Framework. 
Risk Management and Healthcare Policy, 15, 747-764. https://doi.org/10.2147/RMHP.S357561.
    \429\ Meyer, G.S., Nelson, E.C., Pryor, D.B., James, B., 
Swensen, S.J., Kaplan, G.S., Weissberg, J.I., Bisognano, M., Yates, 
G.R., & Hunt, G.C. (2012). More quality measures versus measuring 
what matters: A call for balance and parsimony. BMJ Quality & 
Safety, 21(11), 964-968. https://doi.org/10.1136/bmjqs-2012-001081.
---------------------------------------------------------------------------

     How long would it take to include this approach in the 
program? Some approaches may take considerably longer than others to 
include in the Program. For instance, we intend to consult the 
consensus-based entity for any new measures we proposed to ensure to 
have appropriate feedback, which would add additional time to their 
development. Although we do not want this time to deter interested 
parties from recommending measures for inclusion in the program, we are 
interested in understanding commenters' prioritization of measures as 
it relates to the amount of time they may take to implement when 
deciding on the best approach for the Program.
     Is this approach aligned with other Medicare quality 
reporting and VBP programs? Implementing quality initiatives requires 
time and resources.\430\ It is one of our top priorities to ensure 
alignment between quality programs to limit the burden of quality 
reporting and implementation. Thus, it is important for us to consider 
when developing a health equity component, if and how other programs 
are incorporating health equity to align and standardize measures 
wherever possible.
---------------------------------------------------------------------------

    \430\ Blanchfield, B.B., Demehin, A.A., Cummings, C.T., Ferris, 
T.G., & Meyer, G.S. (2018). The Cost of Quality: An Academic Health 
Center's Annual Costs for Its Quality and Patient Safety 
Infrastructure. Joint Commission Journal on Quality and Patient 
Safety, 44(10), 583-589. https://doi.org/10.1016/j.jcjq.2018.03.012.
---------------------------------------------------------------------------

     What is the impact on populations that are underserved or 
the SNFs that serve these populations? Although the goal of a health 
equity-focused adjustment to the Program would be to decrease 
disparities and incentivize high-quality care for all populations 
including those who are underserved, we also want to create appropriate 
guardrails that protect SNFs against potential unintended consequences. 
It is important for us to understand if any proposed approach may 
create potential negative consequences for residents who are 
underserved or the SNFs that treat these individuals and any steps we 
can take to mitigate that.
(1) Applying Points to Current Measures To Assess Health Equity
    The first category of health equity advancement approaches we 
requested comments on are mechanisms that apply points to current 
measures to assess health equity, rewarding SNFs based on the extent to 
which they provide equitable care. This category affords each SNF the 
ability to score additional points for all measures where they 
demonstrate a high level of equity or a reduction in disparities over 
time. An approach that applies points to current measures to assess 
health equity could include, but is not limited to, the following:
     Points applied to one, some, or all measures for SNFs that 
achieve higher health equity performance on those measures. This would 
include measuring a SNF's performance on each measure for residents who 
are undeserved and comparing that to the same SNF's performance among 
all other residents on the same measures effectively assessing health 
equity gaps. This approach would utilize a Within-Facility Disparity 
method for assessing disparities, as described in more detail later in 
this section.
     Points applied to one, some, or all measures for SNFs that 
have better performance among residents who are underserved. This would 
include only measuring performance among residents who are underserved 
and comparing that performance across all SNFs. This approach would 
utilize an Across-Facility Disparity method for assessing disparities, 
as described in more detail later in this section.
     Points applied to one, some, or all measures based on a 
weighted average of each SNF's performance among resident groups with 
the worst and best outcomes for each measure. We could define resident 
groups by any social risk indicator, for example DES. This approach 
measures performance among all residents in the SNF and places greater 
weight on the performance of the worst performing group, with the goal 
of raising the quality floor at every SNF.
    We note that any social risk indicator could be used to assess 
health equity gaps. We welcomed comments on any approach outlined in 
this section or any other approach that applies additional points to 
current measures to assess health equity that should be considered for 
inclusion in the SNF VBP Program.
(2) New Measure Approach
    The second category of health equity advancement approaches we 
requested comments on is a new health equity-focused measure, which 
would be included as one of the 10 allowable measures in the Program. 
This category includes the development of a new measure that assesses 
health equity and could include a structural, process, or outcome 
measure. A health equity-focused measure would be included as one of 
the measures in the program and thus would be included in the scoring 
calculations like other measures. A health equity-focused measure could 
include, but is not limited to, the following:
     A structural measure. For example, a facility commitment 
to health equity measure, in which SNFs are assessed on factors like 
leadership engagement, data collection, and improvement activities that 
support addressing disparities in quality outcomes. This measure could 
be similar to the ``Hospital Commitment to Health Equity'' measure that 
was finalized in the FY 2023 Inpatient Prospective Payment System/Long 
Term Care Hospital Prospective Payment System final rule (87 FR 48785).
     A process measure. For example, a drivers of health 
measure, in which residents are screened for specific health-related 
social needs (HRSNs) to ensure a successful transition home, like 
transportation or food insecurity. This measure could be similar to the 
``Screening for Social Drivers of Health'' measure that was finalized 
in the FY 2023 Inpatient Prospective Payment System/Long Term Care 
Hospital Prospective Payment System final rule (87 FR 48785).
     An outcome measure. For example, a measure that is 
calculated using data stratified for specific populations that are 
underserved, such as residents with DES.
    We note that each of these possible measures are only suggestions 
for what might be included in the Program. We welcomed comments on any 
measures that should be considered for inclusion in the SNF VBP Program 
including the ones described in this section and what data sources 
should be considered to construct those measures.
(3) Composite Measure Approach
    The third category of health equity advancement approaches we 
requested comments on is the development and

[[Page 53321]]

implementation of a new health equity-focused composite measure. An 
equity-focused composite measure would be included as one of the 10 
allowable measures in the program and thus would be included in the 
scoring calculations like other measures. Generally, a composite 
measure can provide a simplified view of a rather complex topic by 
combining multiple factors into one measure. A composite measure could 
include, but is not limited to, the following:
     A composite of all measure scores for residents who are 
underserved to compare across all SNFs. This could utilize an Across-
Facility Disparity method for assessing disparities, as described in 
more detail later in this section.
     A composite of the health disparity performance within 
each SNF for some or all measures. This approach could utilize a 
Within-Facility Disparity method for assessing disparities, as 
described in more detail later in this section.
    We noted that any social risk indicator could be used to assess 
health equity gaps. We welcomed comments on each of the composite 
measures described in this section. We also welcomed comments on the 
specific factors or measures that should be included in a composite 
measure.
    In considering whether to include in the Program any of the 
approaches described in this section, points applied to current 
measures based on equity, new measures, or composite measures, we 
encouraged commenters to consider the following questions:
     To what extent do these approaches support 
consumer choice? What approaches described in this section best support 
consumer choice? Would any approach be easier to interpret than others? 
Would any of the approaches described in this section provide 
information that other approaches would not that would aid consumer 
choice? Are there other factors we should consider in developing any of 
the approaches described in this section that are easiest for consumers 
to utilize and understand? How should any of the approaches described 
in this section be displayed and shared with consumers to facilitate 
understanding of how to interpret the approach?
     How long would it take to include this approach 
in the program? If some approaches would take longer to implement, 
should they still be considered for inclusion in the Program or should 
a different approach be prioritized? For instance, a measure that is 
already being utilized by another program could be implemented sooner 
than a measure that still needs to be developed. Should any of the 
approaches described in this section be considered regardless of the 
time it would take to include the approach in the Program?
     Is this approach aligned with other Medicare 
quality reporting and VBP programs? Are there similar approaches to 
those described in this section that are aligned with other programs 
that we should consider for SNF VBP? If any of the approaches described 
in this section are not aligned with other programs, should they still 
be considered for inclusion in the Program? If these approaches are 
only aligned somewhat with other programs, should they still be 
considered for inclusion in the Program? Several other programs, 
including the End-Stage Renal Disease Quality Incentive Program, the 
Merit-based Incentive Payment System, the Hospital Inpatient Quality 
Reporting Program, the Inpatient Psychiatric Facility Quality Reporting 
Program, and the PPS-Exempt Cancer Hospital Quality Reporting Program 
also submitted equity-focused measures to the 2022 MUC List that could 
be considered for the Program.\431\ Further, we are in the process of 
developing a Hospital Equity Index. Should any of these measures be 
considered for SNF VBP?
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    \431\ https://mmshub.cms.gov/measure-lifecycle/measure-implementation/pre-rulemaking/lists-and-reports.
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     What is the impact on populations that are 
underserved or the SNFs that serve these populations? Are there any 
potential impacts, including negative or positive unintended 
consequences, that could occur when implementing the approaches 
described in this section? Are there steps we should take to mitigate 
any potential negative unintended consequences? How can we ensure these 
approaches provide a strong enough incentive to improve care for all 
populations by identifying areas of inequities? We are interested in 
all perspectives and particularly of those living in and serving 
underserved communities.
(4) Disparity Method Approaches
    Many of the approaches described previously in this section would 
rely on calculating disparities. There are several different conceptual 
approaches to calculating disparities to assess health equity gaps. 
Currently in the acute care setting, two complementary approaches are 
used to confidentially provide disparity information to hospitals for a 
subset of existing measures. The first approach, referred to as the 
Within-Facility Disparity method, compares measure performance results 
for a single measure between subgroups of patients with and without a 
given factor. This type of comparison directly estimates disparities in 
outcomes between subgroups and can be helpful to identify potential 
disparities in care. This type of approach can be used with most 
measures that include patient-level data. The second approach, referred 
to as the Across-Facility Disparity method, provides performance on 
measures for only the subgroup of patients with a particular social 
risk factor. These approaches can be used by a SNF to compare their own 
measure performance on a particular subgroup of patients against 
subgroup-specific State and national benchmarks. Alone, each approach 
may provide an incomplete picture of disparities in care for a 
particular measure, but when reported together with overall quality 
performance, these approaches may provide detailed information about 
where differences in care may exist or where additional scrutiny may be 
appropriate. For example, the Across-Facility Disparity method 
indicates that a SNF underperformed (when compared to other SNFs on 
average) for patients with a given social risk indicator, which would 
signal the need to improve care for this population. However, if the 
SNF also underperformed for patients without that social risk indicator 
(the Within-Facility Disparity method, as described earlier in this 
section), the measured difference, or disparity in care, could be 
negligible even though performance for the group that particular social 
risk factor remains poor. We refer readers to the technical report 
describing the CMS Disparity Methods in detail, as well as the FY 2018 
IPPS/LTCH PPS final rule (82 FR 38405 through 38407) and the posted 
Disparity Methods Updates and Specifications Report posted on the 
QualityNet website at https://qualitynet.cms.gov/inpatient/measures/disparity-methods.
    We solicited comments on all of the approaches to assessing health 
equity advancement described above, as well as whether similar 
approaches to the two discussed in the previous paragraph could be used 
for calculating disparities to assess health equity in a SNF. These 
calculations would then be used for scoring purposes for each of the 
approaches described previously in this section, either to calculate a 
SNF's performance on a new measure or a composite measure, or to 
determine the amount of points that should be applied to current 
measures to assess heath

[[Page 53322]]

equity. We provide a summary of the comments we received, and our 
responses, later in this section.
c. Other Approaches To Assessing Health Equity Advancement in the SNF 
VBP Program
    There are also many other health equity approaches that could be 
considered for inclusion in the Program. In particular, we explored 
risk adjustment, stratification/peer grouping, and adding improvement 
points when developing the Health Equity Adjustment discussed in 
section VIII.E.4. of this final rule. We have specific concerns when 
applying each of those approaches to the SNF VBP Program independently; 
however, we solicited comment on the potential of incorporating these 
approaches. We provide a summary of the comments we received, and our 
responses, later in this section.
d. Development of Domains and Domain Weighting for Inclusion in the SNF 
VBP Program
    As we expand the number of measures on which we assess performance 
under the SNF VBP Program, we are considering whether we should group 
the measures into measure domains. Creating domains would align the SNF 
VBP Program with other CMS programs such as the Hospital Value-Based 
Purchasing (VBP) Program. The Hospital VBP Program currently groups its 
measures into four domains that are defined based on measure type, and 
then weights the sum of a hospital's performance score on each measure 
in the domain such that the domain is weighted at 25 percent of the 
hospital's total performance score. Although the Hospital VBP Program 
uses four domains, each with a 25 percent weight, we could consider for 
the SNF VBP Program, grouping measures into a different number of 
domains and then weighting each domain by different amounts.
    We solicited comments on whether we should consider proposing the 
addition of quality domains for future program years. We also solicited 
comments on if those domains should be utilized to advance health 
equity in the Program.
    The following is a summary of all the comments we received on this 
health equity RFI including resident-level indicators and geographic-
level indices to assess disparities in healthcare quality, approaches 
to assessing health equity, other approaches to assessing health 
equity, and the development of domains and domain weighting.
    Comment: A few commenters supported CMS implementing policies in 
the SNF VBP Program to address health equity. One commenter recommended 
that CMS make facility level data on race and ethnicity available to 
help SNFs address inequities. One commenter suggested CMS align SDOH 
data across all care settings for future health equity measures to ease 
reporting burden. One commenter suggested CMS prioritize measures that 
address recurring resident and caregiver complaints as a way to address 
health inequities. A few commenters expressed concerns about the 
Program utilizing these types of indices to assess disparities as 
current measure designs may mask regional and individual disparities. 
One commenter supported CMS applying points to the Program measures to 
incentivize improving health equity. One commenter recommended CMS 
expand the scope of practice for advanced practice providers to help 
support health equity efforts. A few commenters recommended CMS create 
domain weights to address health equity as they believe that some 
measures and data are more impacted by inequity than others.
    Response: We will take this feedback into consideration as we 
develop potential future health equity-related policies.

F. Updates to the Extraordinary Circumstances Exception Policy 
Regulation Text

    In the FY 2019 SNF PPS final rule (83 FR 39280 through 39281), we 
adopted an Extraordinary Circumstances Exception (ECE) policy for the 
SNF VBP Program. We have also codified this policy in our regulations 
at Sec.  413.338(d)(4).
    To accommodate the SNF VBP Program's expansion to additional 
quality measures and apply the ECE policy to those measures, we 
proposed to update our regulations at Sec.  413.338(d)(4)(v) to remove 
the specific reference to the SNF Readmission Measure. We proposed that 
the new language will specify, in part, that we would calculate a SNF 
performance score for a program year that does not include the SNF's 
``performance during the calendar months affected by the extraordinary 
circumstance.''
    We solicited public comment on this proposal.
    We did not receive public comments on this provision and therefore, 
we are finalizing as proposed.

G. Updates to the Validation Processes for the SNF VBP Program

1. Background
    Section 1888(h)(12) of the Act requires the Secretary to apply a 
validation process to SNF VBP Program measures and ``the data submitted 
under [section 1888(e)(6)] [. . .] as appropriate[. . .].''
    We previously finalized a validation approach for the SNFRM and 
codified that approach at Sec.  413.338(j) of our regulations. In the 
FY 2023 SNF PPS proposed rule (87 FR 22788 through 22789), we requested 
comments on the validation of additional SNF measures and assessment 
data. In the FY 2023 SNF PPS final rule (87 FR 47595 through 47596), we 
summarized commenters' views and stated that we would take this 
feedback into consideration as we develop our policies for future 
rulemaking.
    Beginning with the FY 2026 program year, the SNFRM will no longer 
be the only measure in the SNF VBP Program. We adopted a second claims-
based measure, SNF HAI, beginning with that program year and proposed 
to replace the SNFRM with another claims-based measure, the SNF WS PPR 
measure, beginning with the FY 2028 program year. We also adopted the 
DTC PAC SNF measure, another claims-based measure, beginning with the 
FY 2027 program year and proposed a fourth claims-based measure, Long 
Stay Hospitalization, beginning with that program year. We adopted the 
Total Nurse Staffing measure, which is calculated using Payroll Based 
Journal (PBJ) data, beginning with the FY 2026 program year and 
proposed to adopt the Nursing Staff Turnover measure, which is also 
calculated using PBJ data, beginning with the FY 2026 program year. We 
also proposed to adopt the DC Function and the Falls with Major Injury 
(Long-Stay) measures calculated using Minimum Data Set (MDS) data 
beginning with the FY 2027 program year. The addition of measures 
calculated from these data sources has prompted us to consider the most 
feasible way to expand our validation program under the SNF VBP 
Program.
    After considering our existing validation process and the data 
sources for the new measures, and for the reasons discussed more fully 
below, we proposed to: (1) apply the validation process we previously 
adopted for the SNFRM to include all claims-based measures; (2) adopt a 
validation process that applies to SNF VBP measures for which the data 
source is PBJ data; and (3) adopt a validation process that applies to 
SNF VBP measures for which

[[Page 53323]]

the data source is MDS data. We believe these new validation policies 
will ensure that the data we use to calculate the SNF VBP measures are 
accurate for quality measurement purposes.
    We note that these new validation policies will apply only to the 
SNF VBP Program, and we intend to propose a validation process that 
would apply to the data SNFs report under the SNF QRP, in future 
rulemaking.
2. Application of the Existing Validation Process for the SNFRM to All 
Claims-Based Measures Reported in the SNF VBP Program
    Beginning with the FY 2026 program year, we will need to validate 
the SNF HAI measure and beginning with the FY 2027 program year, we 
will need to validate the Long Stay Hospitalization and DTC PAC SNF 
measures to meet our statutory requirements. Beginning with the FY 2028 
program year, we will also need to validate the SNF WS PPR measure. 
Therefore, we proposed to expand the previously adopted SNFRM 
validation process to include all claims-based measures, including the 
SNF HAI, Long Stay Hospitalization, DTC PAC SNF, and SNF WS PPR 
measures, as well as any other claims-based measures we may adopt for 
the SNF VBP Program in the future.
    The SNF HAI measure is calculated using Medicare SNF FFS claims 
data and Medicare inpatient hospital claims data. As discussed in the 
FY 2023 SNF PPS final rule (87 FR 47590), information reported through 
claims are validated for accuracy by Medicare Administrative 
Contractors (MACs) who use software to determine whether billed 
services are medically necessary and should be covered by Medicare, 
review claims to identify any ambiguities or irregularities, and use a 
quality assurance process to help ensure quality and consistency in 
claim review and processing. They conduct prepayment and post-payment 
audits of Medicare claims, using both random selection and targeted 
reviews based on analyses of claims data.
    Beginning with the FY 2027 program year, we proposed to adopt the 
Long Stay Hospitalization measure in the SNF VBP Program. This measure 
utilizes SNF FFS claims and inpatient hospital claims data. We believe 
that adopting the existing MAC's process of validating claims for 
medical necessity through targeted and random audits, as detailed in 
the prior paragraph, satisfies our statutory requirement to adopt a 
validation process for the Long Stay Hospitalization measure for the 
SNF VBP Program.
    The DTC PAC SNF measure also uses claims-based data, including data 
from the ``Patient Discharge Status Code.'' We refer readers to the FY 
2023 SNF PPS final rule (87 FR 47577 through 47578) for additional 
discussion of the data source for the DTC PAC SNF measure. We also 
refer readers to the FY 2017 SNF PPS final rule (81 FR 52021 through 
52029) for a thorough analysis on the accuracy of utilizing the 
discharge status field. We believe that adopting the existing MAC's 
process for validating the claims portion of the DTC PAC SNF measure 
for payment accuracy satisfies our statutory requirement to adopt a 
validation process for the SNF VBP Program because MACs review claims 
for medical necessity, ambiguities, and quality assurance through 
random and targeted reviews, as detailed in the second paragraph of 
this section.
    Beginning with the FY 2028 program year, we proposed to replace the 
SNFRM with the SNF WS PPR measure. The SNFRM and SNF WS PPR measure 
utilize the same claims-based data sources. Therefore, the SNFRM's 
validation process based on data that are validated for accuracy by 
MACs as detailed in the second paragraph of this section, satisfies the 
statutory requirement to adopt a validation process for the SNF WS PPR 
measure for the SNF VBP Program.
    We solicited public comment on the proposed application of our 
previously finalized validation process to all claim-based measures in 
the SNF VBP Program and also proposed to codify it at Sec.  413.338(j) 
of our regulations.
    We received public comments on this proposal. The following is a 
summary of the comments we received and our responses.
    Comment: A few commenters supported our proposal to apply our 
previously finalized validation process to all claim-based measures in 
the SNF VBP Program.
    Response: We thank commenters for their support.
    After consideration of public comments, we are finalizing the 
application of our previously finalized validation process to all 
claims-based measures in the SNF VBP Program.
3. Adoption of a Validation Process That Applies to SNF VBP Measures 
That Are Calculated Using PBJ Data
    Beginning with the FY 2026 program year, the Total Nurse Staffing 
measure, adopted in the FY 2023 SNF PPS final rule, and the Nursing 
Staff Turnover measure, are calculated using PBJ data that nursing 
facilities with SNF beds are already required to report to CMS. PBJ 
data includes direct care staffing information (including agency and 
contract staff) based on payroll and other auditable data.\432\ CMS 
conducts quarterly audits aimed at verifying that the staffing hours 
submitted by facilities are aligned with the hours staff were paid to 
work over the same timeframe. The PBJ audit process requires selected 
facilities to submit documentation, that may include payroll, invoice, 
or contractual obligation data, supporting the staffing hours reported 
in the PBJ data.\433\ This documentation of hours is compared against 
the reported PBJ staffing hours data and a facility whose audit 
identifies significant inaccuracies between the hours reported and the 
hours verified will be presumed to have low levels of staffing. We 
believe that this existing PBJ data audit process is sufficient to 
ensure that the PBJ data we use to calculate the Total Nurse Staffing 
and Nursing Staff Turnover measures are an accurate representation of a 
facility's staffing. Accordingly, we proposed to adopt that process for 
purposes of validating SNF VBP measures that are calculated using PBJ 
data. We also proposed to codify this policy at Sec.  413.338(j) of our 
regulations.
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    \432\ Centers for Medicare and Medicaid Services. (2022, October 
12). Staffing Data Submission Payroll Based Journal (PBJ). https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/nursinghomequalityinits/staffing-data-submission-pbj.
    \433\ Centers for Medicare and Medicaid (CMS). (2018). 
Transition to Payroll-Based Journal (PBJ) Staffing Measures on the 
Nursing Home Compare tool on Medicare.gov and the Five Star Quality 
Rating System. Center for Clinical Standards and Quality/Quality, 
Safety and Oversight Group. https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/SurveyCertificationGenInfo/Downloads/QSO18-17-NH.pdf.
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    We solicited public comment on our proposal to adopt the above 
validation process that applies to measures calculated using the PBJ 
data.
    We received public comments on this proposal. The following is a 
summary of the comments we received and our responses.
    Comment: A few commenters supported our proposed approach to 
validate PBJ-based measures with existing processes.
    Response: We thank commenters for their support.
    After consideration of public comments, we are finalizing the 
validation process for SNF VBP measures that are calculated using PBJ 
data as proposed.

[[Page 53324]]

4. Adoption of a Validation Process That Applies to SNF VBP Measures 
That Are Calculated Using MDS Data
    We proposed to adopt two MDS measures in the SNF VBP Program, the 
DC Function and Falls with Major Injury (Long-Stay) measures beginning 
with the FY 2027 program year/FY 2025 performance period. The MDS is a 
federally mandated resident assessment instrument that is required to 
be completed for all residents in a Medicare or Medicaid certified 
nursing facility, and for residents whose stay is covered under SNF PPS 
in a non-critical access hospital swing bed facility. The MDS 
``includes the resident in the assessment process, and uses standard 
protocols used in other settings . . . supporting the primary 
legislative intent that MDS be a tool to improve clinical assessment 
and supports the credibility of programs that rely on MDS.'' \434\ 
There is no current process to verify that the MDS data submitted by 
providers to CMS for quality measure calculations is accurate for use 
in our SNF quality reporting and value-based purchasing programs. While 
MDS data are audited to ensure accurate payments, we do not believe 
that this audit process focuses sufficiently on the Program's quality 
measurement data for use in a quality reporting or value-based 
purchasing program. While the update to MDS 3.0 was designed to improve 
the reliability, accuracy, and usefulness of reporting than prior 
versions,\435\ we believe we need to validate MDS data when those data 
are used for the purpose of a quality reporting or value-based 
purchasing program. Therefore, we proposed to adopt a new validation 
method that we will apply to the SNF VBP measures that are calculated 
using MDS data to meet our statutory requirement. This method is 
similar to the method we use to validate measures reported by hospitals 
under the Hospital Inpatient Quality Reporting Program.
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    \434\ Centers for Medicare and Medicaid Services (CMS). (2023, 
March 29). Minimum Data Set (MDS) 3.0 for Nursing Homes and Swing 
Bed Providers. https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/nursinghomequalityinits/nhqimds30.
    \435\ Centers for Medicare and Medicaid Services (CMS). (2023, 
March 29). Minimum Data Set (MDS) 3.0 for Nursing Homes and Swing 
Bed Providers. https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/nursinghomequalityinits/nhqimds30.
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    We proposed to validate the MDS data used to calculate these 
measures as follows:
     We proposed to randomly select, on an annual basis, up to 
1,500 active and current SNFs, including non-critical access hospital 
swing bed facilities providing SNF-level services, that submit at least 
one MDS record in the calendar year 3 years prior to the fiscal year of 
the relevant program year or were included in the SNF VBP Program in 
the year prior to the relevant program year. For example, for the FY 
2027 SNF VBP Program, we would choose up to 1,500 SNFs that submitted 
at least one MDS record in calendar year 2024 or were participating in 
the FY 2026 SNF VBP Program/FY 2024 performance period for validation 
in FY 2025.
     We proposed that the validation contractor will, for each 
quarter that applies to validation, request up to 10 randomly selected 
medical charts from each of the selected SNFs.
     We proposed that the validation contractor will request 
either digital or paper copies of the randomly selected medical charts 
from each SNF selected for audit. The SNF will have 45 days from the 
date of the request (as documented on the request) to submit the 
requested records to the validation contractor. If the SNF has not 
complied within 30 days, the validation contractor will send the SNF a 
reminder to inform the SNF that it must return digital or paper copies 
of the requested medical records within 45 calendar days following the 
date of the initial validation contractor medical record request.
    We believe the process will be minimally burdensome on SNFs 
selected to submit up to 10 charts.
    We intend to propose a penalty that applies to a SNF that either 
does not submit the requested number of charts or that we otherwise 
conclude has not achieved a certain validation threshold in future 
rulemaking. We also intend to propose in future rulemaking the process 
by which we would evaluate the submitted medical charts against the MDS 
to determine the validity of the MDS data used to calculate the measure 
results. We invited public comment on what that process could include.
    We solicited public comments on our proposal to adopt the above 
validation process for MDS measures beginning with the FY 2027 program 
year. The following is a summary of the comments we received and our 
responses.
    Comment: Several commenters supported the proposed approach to 
validate MDS-based measures through random audits. One commenter 
recommended CMS include family and caregiver feedback into the 
development of this process.
    Response: We thank the commenters for their support.
    Comment: A few commenters supported the proposal to validate MDS 
data for the SNF QRP to ensure data submitted is not erroneous or 
incomplete.
    Response: We thank the commenters for their support.
    Comment: A few commenters who supported validation of MDS data 
recommended that CMS implement validation of MDS data prior to using 
MDS-based measures in the SNF VBP Program.
    Response: We believe it is not feasible to begin validating MDS 
data submitted for program years before the FY 2027 SNF VBP program 
year. We do not believe that delaying the expansion of the SNF VBP 
Program until MDS data validation is in place is appropriate because 
MDS-based measures have been used within the SNF QRP for many years. 
Because SNFs have had extensive experience with MDS-based quality 
measurement through participation in the SNF QRP, we believe that SNFs 
have had ample time to ensure the data's accuracy prior to use in the 
SNF VBP Program and that it is appropriate to move forward with using 
these measure types in parallel with our implementation of new 
validation processes.
    Comment: A few commenters recommended that CMS not include a 
penalty for SNFs that fail validation of MDS-based measures because 
facilities are already penalized through the withholding of funds.
    Response: We will take this comment into consideration as we 
develop additional validation policies for the SNF VBP Program. 
However, we do not agree that we should hold SNFs harmless for failing 
validation. We believe that a robust validation program ensures that 
the most accurate quality data possible are scored for purposes of the 
SNF VBP Program.
    Comment: A few commenters did not support the proposal to validate 
MDS-based measures. One commenter recommended CMS phase out self-
reported measures instead of implementing a validation process. A few 
commenters expressed that MDS based data are extensively validated 
through other means (State audits and surveys) and that a new process 
is an inefficient use of funds. One commenter stated that they believed 
the rationale for validating MDS-based measures contradicts the 
rationale used to validate the claims-based measures.
    Response: We believe that prioritizing validation for those data 
submissions already required of SNFs represents a more practical, less 
burdensome policy for SNFs than adopting new measures to replace MDS-
based measurement. MDS data are statutorily required to be submitted to 
the SNF QRP by SNFs

[[Page 53325]]

under section 1888(e)(6) of the Act. Because SNFs already submit MDS 
data pursuant to other quality reporting requirements, we believe that 
MDS-based measures strike an appropriate balance between effective 
quality measurement and reporting burden.
    We recognize that MDS audits are being completed though other 
means. We believe that these audits, which are effective for their use 
cases, are insufficient to ensure the accuracy of MDS data elements 
used for the SNF VBP Program's current and future quality measures. For 
example, State surveyors may review MDS data to ensure that it meets 
State standards, which may not align with ensuring the data are 
accurate for use in the Program's quality measures. We believe that a 
validation process is needed for the SNF VBP Program that includes 
auditing the MDS data elements that are used in the measures to ensure 
the data are accurate. Additionally, we believe that ensuring the 
Program's data are an accurate representation of a SNFs quality of care 
is an effective use of funds. Ensuring accurate data means that our 
beneficiaries can trust the publicly available quality data and make 
better informed decisions about their care.
    We interpret the comment ``contradicting rationale'' to be 
questioning why the audit of MDS data for payment purposes does not 
focus sufficiently on the Program's quality measurement data for use in 
a quality reporting or value-based purchasing program as stated in the 
proposed rule (88 FR 21398). We note that PBJ measures must be 
auditable under 42 CFR 483.70 \436\ and SNF claims and other payment-
related information must be audited under section 1983 of the Act. 
Therefore, we believe that the claims and PBJ measure data elements 
that are audited for their respective purposes are sufficient with the 
SNF VBP Program's statutory requirement for validating claims-based and 
PBJ-based quality measures. For example, the hospitalizations and 
staffing hours data elements included in the SNF WS PPR, Total Nurse 
Staffing and Nursing Staff Turnover measures are the core tenets of 
both their respective measures, and ensuring that claims are valid for 
payment or ensuring that staffing is capture for regulatory oversite. 
Although MDS data is audited for other purposes, we feel that a more 
comprehensive validation process is required for MDS-based quality 
measures. We further clarify that these existing MDS data audits only 
review a portion of MDS elements used in the current measures and that 
the Program's MDS-based quality measures are calculated using data 
elements that are not consistently reviewed in these audits. We believe 
that a new validation process is necessary because exiting payment 
audits do not audit all the MDS data elements needed for the quality 
measures.
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    \436\ CMS. (June 2022). Electronic Staffing Data Submission 
Payroll-Based Journal. https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/nursinghomequalityinits/downloads/pbj-policy-manual-final-v25-11-19-2018.pdf.
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    Comment: A few commenters did not support CMS pulling up to 10 
charts per SNF as they do not believe it is minimally burdensome.
    Response: We proposed this 10-chart maximum because we believe that 
it strikes the appropriate balance between creating a relatively 
reliable annual validation estimate with a quantity of charts that are 
least burdensome to SNFs. The 10 chart maximum is also generally 
consistent with similar policies we have adopted for the Hospital IQR 
Program and HAC Reduction Program. For the FY 2026 program year, we 
request up to 8 charts per quarter for the clinical process of care 
category of measures and up to 8 charts per quarter for the eCQM 
category of measures, for a total of up to 16 charts per quarter for 
the Hospital IQR Program validation, and we request up to 10 charts per 
quarter for the Hospital-Acquired Condition Reduction Program 
validation (https://qualitynet.cms.gov/files/648726a004f753001cd0577b?filename=IP_FY26_ValFactSheet_05082023.pdf).
    After consideration of public comments, we are finalizing the 
validation process for MDS-based measures in the SNF VBP Program as 
proposed.

H. SNF Value-Based Incentive Payments for FY 2024

    We refer readers to the FY 2018 SNF PPS final rule (82 FR 36616 
through 36621) for discussion of the exchange function methodology that 
we have adopted for the Program, as well as the specific form of the 
exchange function (logistic, or S-shaped curve) that we finalized, and 
the payback percentage of 60 percent of the amounts withheld from SNFs' 
Medicare payments as required by the SNF VBP Program statute.
    We also discussed the process that we undertake for reducing SNFs' 
adjusted Federal per diem rates under the Medicare SNF PPS and awarding 
value-based incentive payments in the FY 2019 SNF PPS final rule (83 FR 
39281 through 39282).
    For the FY 2024 SNF VBP program year, we will reduce SNFs' adjusted 
Federal per diem rates for the fiscal year by the applicable percentage 
specified under section 1888(h)(6)(B) of the Act, 2 percent, and will 
remit value-based incentive payments to each SNF based on their SNF 
Performance Score, which is calculated based on their performance on 
the Program's quality measure.

I. Public Reporting on the Provider Data Catalog Website

    Section 1888(g)(6) of the Act requires the Secretary to establish 
procedures to make SNFs' performance information on the SNFRM and the 
SNF WS PPR available to the public on the Nursing Home Compare website 
or a successor website, and to provide SNFs an opportunity to review 
and submit corrections to that information prior to its publication. We 
began publishing SNFs' performance information on the SNFRM in 
accordance with this provision on October 1, 2017. In December 2020, we 
retired the Nursing Home Compare website and are now using the Provider 
Data Catalog website (https://data.cms.gov/provider-data/) to make 
quality data available to the public, including SNF VBP performance 
information. We will begin publishing performance information on the 
SNF WS PPR measure when that measure is implemented beginning in the FY 
2028 program year.
    Additionally, section 1888(h)(9)(A) of the Act requires the 
Secretary to make available to the public certain information on SNFs' 
performance under the SNF VBP Program, including their SNF Performance 
Scores and rankings. Section 1888(h)(9)(B) of the Act requires the 
Secretary to post aggregate information on the Program, including the 
range of SNF Performance Scores and the number of SNFs receiving value-
based incentive payments, and the range and total amount of those 
payments.
    In the FY 2017 SNF PPS final rule (81 FR 52006 through 52009), we 
discussed the statutory requirements governing confidential feedback 
reports and public reporting of SNFs' performance information under the 
SNF VBP Program and finalized our two-phased review and correction 
process. In the FY 2018 SNF PPS final rule (82 FR 36621 through 36623), 
we finalized additional requirements for phase two of our review and 
correction process, a policy to publish SNF VBP Program performance 
information on the Nursing Home Compare or a successor website after 
SNFs have had the opportunity to review and submit corrections to that 
information. In that final rule, we also

[[Page 53326]]

finalized the requirements to rank SNFs and adopted data elements that 
are included in the ranking to provide consumers and interested parties 
with the necessary information to evaluate SNF's performance under the 
Program. In the FY 2020 SNF PPS final rule (84 FR 38823 through 38825), 
we finalized a policy to suppress from public display SNF VBP 
performance information for low-volume SNFs and finalized updates to 
the phase one review and correction deadline. In the FY 2021 SNF PPS 
final rule (85 FR 47626 through 47627), we finalized additional updates 
to the phase one review and correction deadline. In the FY 2022 SNF PPS 
final rule (86 FR 42516 through 42517), we finalized a phase one review 
and correction claims ``snapshot'' policy. In the FY 2023 SNF PPS final 
rule (87 FR 47591 through 47592), we finalized updates to our data 
suppression policy for low-volume SNFs due to the addition of new 
measures and case and measure minimum policies.

IX. Civil Money Penalties: Waiver of Hearing, Automatic Reduction of 
Penalty Amount

    Section 488.436 provides a facility the option to waive its right 
to a hearing in writing and receive a 35 percent reduction in the 
amount of civil money penalties (CMPs) owed in lieu of contesting the 
enforcement action. This regulation was first adopted in a 1994 final 
rule (59 FR 56116, 56243), with minor corrections made to the 
regulation text in 1997 (62 FR 44221) and in 2011 (76 FR 15127) to 
implement section 6111 of the Affordable Care Act of 2010. Over the 
years, we have observed that most facilities who have been imposed CMPs 
do not request a hearing to appeal the survey findings of noncompliance 
on which their CMPs are based.
    In CY 2016, 81 percent of LTC facilities submitted a written waiver 
of a hearing and an additional 15 percent of facilities did not submit 
a waiver although they did not contest the penalty and its basis. Only 
4 percent of facilities availed themselves of the full hearing process. 
The data from CY 2018 and CY 2019 stayed fairly consistent with 80 
percent of facilities submitting a written waiver of a hearing and 14 
percent of facilities not submitting the waiver nor contesting the 
penalty and its basis. Only 6 percent of facilities availed themselves 
of the full hearing process. In CY 2020, 81 percent of facilities 
submitted a written waiver of the hearing, 15 percent of facilities did 
not submit a waiver nor contest the penalty and its basis, and only 4 
percent of facilities availed themselves of the full hearing process. 
In CY 2021, 91 percent of facilities submitted a written waiver of the 
hearing, 7 percent of facilities did not submit the waiver nor contest 
the penalty and its basis, and only 2 percent of facilities utilized 
the full hearing process. Data from CY 2022 continues this trend 
showing that 81 percent of LTC facilities submitted a written waiver of 
their hearing rights and 17 percent of facilities did not submit a 
waiver of appeal rights but did not contest the penalty nor its basis. 
Again, only 2 percent of facilities availed themselves of the full 
hearing process in CY 2022. Therefore, based on our experience with LTC 
facilities with imposed CMPs and the input provided by our CMS 
Locations (formerly referred to as Regional Offices) that impose and 
collect CMPs, we proposed to revise these requirements at Sec.  488.436 
by creating a constructive waiver process.
    Specifically, we proposed to revise the current written waiver 
process to allow a constructive waiver that retains the accompanying 35 
percent penalty reduction, however, we will revisit the appropriateness 
of that penalty reduction in a future rulemaking, if warranted, as 
discussed further below. Removal of the facility's requirement to 
submit a separate written request to waive their right to appeal would 
result in a cost and time savings for CMS, which currently receives and 
processes these waivers. This will allow CMS to reallocate this time 
and funding currently spent processing these waivers to bolstering 
other oversight and enforcement activities, including providing 
additional focus on nursing home compliance, as well as to cases 
involving facilities that choose to contest our findings through the 
Departmental Appeals Board. Current budgetary constraints have 
tightened oversight and enforcement resources, in addition to the 
survey and enforcement backlog resulting from the COVID-19 PHE.
    We proposed to amend the language at Sec.  488.436(a) by 
eliminating the requirement to submit a written waiver and create in 
its place a constructive waiver process that would operate by default 
when a timely request for a hearing has not been received. Facilities 
that wish to request a hearing to contest the noncompliance leading to 
the imposition of the CMP would continue to follow all applicable 
appeals process requirements, including those at Sec.  498.40, as 
currently referenced at Sec.  488.431(d).
    Specifically, we proposed to revise Sec.  488.436(a) to state that 
a facility is deemed to have waived its rights to a hearing if the time 
period for requesting a hearing has expired and request for a hearing 
has not been received within the requisite submission time. We have 
observed that many facilities submitting a request for a waiver of 
hearing wait until close to the end of the 60-day timeframe within 
which a waiver must be submitted, thus delaying the ultimate due date 
of the CMP amount. Under this proposed process, the 35 percent 
reduction would be applied after the 60-day timeframe.
    Given our finalized policy of removing the requirement to actively 
waive their right to a hearing, we will revisit the appropriateness of 
that penalty reduction, if warranted by the review, in a future 
rulemaking. The move to a constructive waiver process in this rule 
purely reflects the need to reduce costs and paperwork burden for CMS 
to prioritize current limited Survey and Certification resources for 
enforcement actions, and we will consider whether the existing penalty 
reduction is appropriate given this final policy.
    We also note that we continue to have the opportunity under Sec.  
488.444, to settle CMP cases at any time prior to a final 
administrative decision for Medicare-only SNFs, State-operated 
facilities, or other facilities for which our enforcement action 
prevails, in accordance with Sec.  488.30. This provides the 
opportunity to settle a case, when warranted, even if the facility's 
hearing right was not previously waived. Even if a hearing had been 
requested, if all parties can reach an agreement over deficiencies to 
be corrected and the CMP to be paid until corrections are made (for 
example, CMS agrees to lower a CMP amount based on actions the facility 
has taken to protect resident health and safety), then costly hearing 
procedures could be avoided. We believe that eliminating the current 
requirements for a written waiver at Sec.  488.436 will not negatively 
impact facilities.
    In addition to the changes to Sec.  488.436(a), we proposed 
corresponding changes to Sec. Sec.  488.432 and 488.442 which currently 
reference only the written waiver process. We proposed to make 
conforming changes that establish that a facility is considered to have 
waived its rights to a hearing if the time period for requesting a 
hearing has expired, in lieu of a written waiver of appeal rights. 
Finally, we note that the current requirements at Sec.  488.436(b) 
would remain unchanged. At the same time, CMS commits to studying its 
procedures for reviewing and processing waivers and as necessary 
modernizing those procedures to reduce the amount of time

[[Page 53327]]

required for documentation review of CMPs.
    The proposed revisions were previously proposed and published in 
the July 18, 2019 proposed rule entitled, ``Medicare and Medicaid 
Programs; Requirements for Long-Term Care Facilities: Regulatory 
Provisions to Promote Efficiency, and Transparency'' (84 FR 34737, 
34751). Although on July 14, 2022, we announced an extension of the 
timeline for publication of the final rule for the 2019 proposals (see 
87 FR 42137), we are withdrawing that proposal revising Sec.  488.436 
and we re-proposed the revisions for a facility to waive its hearing 
rights in an effort to gather additional feedback from interested 
parties (see FY 2024 SNF PPS proposed rule (88 FR 21316)). While this 
regulatory action is administrative in nature, in the future, we may 
assess whether the 35 percent penalty reduction is functioning as 
intended to make the civil money penalties administrative process more 
efficient, or whether a lesser penalty reduction is warranted.
    We solicited comments from the public addressing any potential 
circumstances in which facilities' needs or the public interest could 
best be met or only be met by the use of a written waiver. We received 
public comments on these proposals. The following is a summary of the 
comments we received and our responses.
    Comment: While the majority of comments received supported the 
constructive waiver, we did receive several comments opposing the 
constructive waiver provision. One commenter was concerned that if 
facilities are no longer required to proactively request a waiver to 
receive the reduction, there is no longer any corporate acknowledgement 
that a wrong has occurred that resulted in the penalty. The commenter 
stated that the reduced penalties would become a cost of doing 
business. Another commenter stated that the Federal nursing home 
regulations are the minimum standards LTC facilities agree to meet. The 
commenter stated that when a facility is issued a deficiency for a 
violation of those minimum standards, they should not automatically be 
given a 35 percent reduction solely because they decided to not appeal 
the deficiency finding, as CMPs are meant to be a deterrent and 
penalize LTC facilities who have violated the minimum requirements for 
participation. The commenter stated that an automatic 35 percent 
reduction serves as a reward to those facilities who flout the minimum 
standards and have actually been cited at actual harm or immediate 
jeopardy. Many commented that CMS already imposes comparatively few 
CMPs because, as a matter of policy, it generally limits CMPs to 
deficiencies that are cited for causing actual harm or putting 
residents in immediate jeopardy classifications of severity applied to 
less than 4 percent of all deficiencies observed in facility surveys. 
Some commenters stated that most deficiencies have no financial 
consequence, no matter how serious the harm to residents. They further 
stated that CMS provides no real rationale for the proposed rule, which 
creates a financial windfall of millions of dollars for LTC facilities. 
They were concerned that this is a signal to SNFs that compliance with 
regulations is not mandatory and effectively reduces the enforcement 
efforts of CMS. Another commenter stated that the financial 
repercussions facilities may face for violating regulations incentivize 
better care. Eliminating the requirement that facilities waive their 
rights to challenge CMS findings removes an incentive for facilities to 
comply with the regulations.
    Response: We appreciate the comments raised, but we believe 
clarification and modernization to improve efficiencies are warranted 
on the current waivers process. In CY 2022, 81 percent of LTC 
facilities submitted a written waiver of the hearing and 17 percent of 
facilities did not submit a waiver but did not contest the penalty and 
its basis. Only 2 percent of facilities actually contested the imposed 
penalty and its basis. The majority of facilities are already 
submitting a waiver, as is currently required, and receiving the 
reduction; consequently, the revision to the regulation would not have 
a significant effect on the amount of CMPs being collected. The 
constructive waiver process would not affect the frequency of CMPs 
being imposed, CMS' ability to penalize facilities for infractions, or 
the publication of facility infractions through Care Compare. We 
believe that by improving program efficiencies we will be able to 
divert these resources to strengthening other oversight and enforcement 
activities. We also note that facilities that waive their right to a 
hearing may have many reasons for doing so, and the removal of this 
active waiver requirement is in no way an indication that we are 
reducing necessary oversight and enforcement activities. We note that 
the penalty, and the citation that led to the imposition of the 
penalty, will continue to be posted on Care Compare and indicate that 
the facility was not in compliance. This will remain the case 
irrespective of whether the appeal is waived affirmatively or 
constructively.
    Moreover, as stated previously in this section of the final rule, 
we believe that the subsequent administrative savings from not 
processing written waivers would allow us to reallocate those resources 
to activities ensuring the health and safety of residents. However, in 
light of the comments submitted around the constructive waiver and the 
changes to the waiver process, we plan to review the appropriateness of 
the 35 percent penalty reduction in future rulemaking. After 
consideration of public comments, we are finalizing our proposed 
changes to the civil money penalty reduction process without 
modifications.

X. Waiver of Proposed Rulemaking

    We ordinarily publish a notice of proposed rulemaking in the 
Federal Register and invite public comment on the proposed rule. The 
notice of proposed rulemaking includes a reference to the legal 
authority under which the rule is proposed, and the terms and 
substances of the proposed rule or a description of the subjects and 
issues involved. This procedure can be waived, however, if an agency 
finds good cause that a notice-and-comment procedure is impracticable, 
unnecessary, or contrary to the public interest, and incorporates a 
statement of the finding and its reasons in the rule issued.
    In this case, we identified the need for additional conforming 
changes to the regulatory text after this rule was already proposed, as 
described in section V.D. of this proposed rule. The conforming changes 
are minor and necessary to implement the statute. Specifically, in the 
proposed rule, we revised the regulation text to implement the 
requirement under section 4121(a)(4) of Division FF of the CAA, 2023 to 
exclude marriage and family therapist (MFT) services and mental health 
counselor services (MHC) from SNF consolidated billing for services 
furnished on or after January 1, 2024. Subsequently, we identified the 
need for additional conforming changes to the regulatory text. In 
addition to adding the two new exclusions themselves to the regulation 
text as set forth in the proposed rule (and as described in section 
V.D. of this final rule), the existing exclusion for certain telehealth 
services needs to be revised as well, because it cross-refers to 
subparagraphs that are now being renumbered as a result of adding the 
new exclusions. Specifically, a conforming change is needed in the 
consolidated billing exclusion provision on telehealth services at 
existing Sec.  411.15(p)(2)(xii) (which, as a result of the other

[[Page 53328]]

regulation text changes finalized in this rule, will be redesignated 
Sec.  411.15(p)(2)(xiv)) and in the parallel provider agreement 
provision on telehealth services at existing Sec.  489.20(s)(12) 
(which, as a result of the other regulation text changes finalized in 
this rule, will be redesignated Sec.  489.20(s)(14)). Because these 
inadvertently omitted additional provisions implement statutory 
language without any exercise of discretion by the Secretary, we have 
determined that it would be unnecessary and contrary to public interest 
to rely on another notice-and-comment period to issue them. We are 
simply correcting oversights to reflect the policies that we previously 
proposed, received public comment on, and subsequently finalized in the 
final rule. For these reasons, we believe there is good cause to waive 
the requirements for notice and comment.

XI. Collection of Information Requirements

    Under the Paperwork Reduction Act of 1995 (PRA) (44 U.S.C. 3501 et 
seq.), we are required to provide 60-day notice in the Federal Register 
and solicit public comment before a ``collection of information'' 
requirement is submitted to the Office of Management and Budget (OMB) 
for review and approval. For the purpose of the PRA and this section of 
the preamble, collection of information is defined under 5 CFR 
1320.3(c) of the PRA's implementing regulations.
    To fairly evaluate whether an information collection should be 
approved by OMB, section 3506(c)(2)(A) of the PRA requires that we 
solicit comment on the following issues:
     The need for the information collection and its usefulness 
in carrying out the proper functions of our agency.
     The accuracy of our estimate of the information collection 
burden.
     The quality, utility, and clarity of the information to be 
collected.
     Recommendations to minimize the information collection 
burden on the affected public, including automated collection 
techniques.
    We solicited public comment (see section IX.D. of the FY 2024 SNF 
PPS proposed rule) on each of the aforementioned issues for the 
following sections of the rule that contained information collection 
requirements.

A. Wage Estimates

    To derive average private sector costs, we used data from the U.S. 
Bureau of Labor Statistics' (BLS') May 2021 National Occupational 
Employment and Wage Estimates for all salary estimates (http://www.bls.gov/oes/current/oes_nat.htm). In this regard, Table 24 presents 
BLS' mean hourly wage, our estimated cost of fringe benefits and other 
indirect costs (calculated at 100 percent of salary), and our adjusted 
hourly wage. See Table 25 for an estimate of the composite wage 
associated with removing the Application of Functional Assessment/Care 
Plan measure. See Table 26 for an estimate of the composite wage 
associated with adopting the Patient/Resident COVID 19 Vaccine measure.

                          Table 24--National Occupational Employment and Wage Estimates
----------------------------------------------------------------------------------------------------------------
                                                                                Fringe benefits
                                                 Occupation      Mean hourly       and other         Adjusted
               Occupation title                     code         wage ($/hr)   indirect costs ($/ hourly wage ($/
                                                                                      hr)               hr)
----------------------------------------------------------------------------------------------------------------
Licensed Vocational Nurse (LVN)..............         29-2061           24.93              24.93           49.86
Occupational Therapist (OT)..................         29-1122           43.02              43.02           86.04
Physical Therapist (PT)......................         29-1123           44.67              44.67           89.34
Registered Nurse (RN)........................         29-1141           39.78              39.78           79.56
Speech Language Pathologist (SLP)............         29-1127           41.26              41.26           82.52
----------------------------------------------------------------------------------------------------------------

    As mentioned, we have adjusted the private sector's employee hourly 
wage by a factor of 100 percent. This is necessarily a rough 
adjustment, both because fringe benefits and other indirect costs vary 
significantly across employers, and because methods of estimating these 
costs vary widely across studies. Nonetheless, we believe that doubling 
the hourly wage to estimate total cost is a reasonably accurate 
estimation method.

B. Information Collection Requirements (ICRs)

1. ICRs Regarding the Skilled Nursing Facility Quality Reporting 
Program (SNF QRP)
    When ready, we intend to account for the following changes under 
the standard non-rule PRA process that consists of publishing 60- and 
30-day Federal Register notices that solicit comment from the public. 
Consistent with this final rule, the notices will be associated with 
OMB control number 0938-1140 (CMS-10387). The notices will account for 
the changes identified in Tables 28 and 29 and changes to MDS (the 
minimum data set).
    In accordance with section 1888(e)(6)(A)(i) of the Act, the 
Secretary must reduce by 2-percentage points the otherwise applicable 
annual payment update to a SNF for a fiscal year if the SNF does not 
comply with the requirements of the SNF QRP for that fiscal year.
    In the SNF FY 2024 PPS proposed rule (88 FR 21332 through 21354), 
we proposed to modify one measure, adopt three new measures, and remove 
three measures from the SNF QRP. In the SNF FY 2024 PPS proposed rule 
(88 FR 21360), we also proposed to increase the data completion 
thresholds for the MDS items. We discussed in detail these information 
collections in the SNF FY 2024 PPS proposed rule (88 FR 21400). As 
discussed in section VI.C.2.a.(5) of this final rule, we are not 
finalizing the CoreQ: SS DC measure for the SNF QRP. Consequently, the 
ICRs related to the CoreQ: SS DC measure proposal are omitted from this 
final rule.
    As stated in section VII.C.1.a. of this final rule, we proposed to 
modify the COVID-19 Vaccination Coverage Among Healthcare Personnel 
(HCP COVID-19 Vaccine) measure beginning with the FY 2025 SNF QRP. 
While we are not making any changes to the data submission process for 
the HCP COVID-19 Vaccine measure, we are requiring that for purposes of 
meeting FY 2025 SNF QRP compliance, SNFs will report data on the 
measure using the modified numerator definition for at least one self-
selected week during each month of the reporting quarter beginning with 
reporting period of the 4th quarter of CY 2023. Under this requirement, 
SNFs will continue to report data for the HCP COVID-19 Vaccine measure 
to the CDC's National Healthcare Safety Network (NHSN) for at least one 
self-selected week during each month of the

[[Page 53329]]

reporting quarter. The burden associated with the HCP COVID-19 Vaccine 
measure is accounted for under OMB control number 0920-1317, entitled 
``[NCEZID] National Healthcare Safety Network (NHSN) Coronavirus 
(COVID-19) Surveillance in Healthcare Facilities.'' Because we are not 
making any updates to the form, manner, and timing of data submission 
for this measure, we are not making any changes to the currently 
approved (active) requirements or burden estimates under control number 
0920-1317. See the FY 2022 SNF PPS final rule (86 FR 42480 through 
42489) for a discussion of the form, manner, and timing of data 
submission of this measure.
    As a result of our decision to not adopt the CoreQ: SS DC measure, 
in this final rule, we are adopting two (instead of three) new measures 
and removing three measures from the SNF QRP. We present the burden 
associated with these proposals in the same order they were proposed in 
the SNF FY 2024 PPS proposed rule (88 FR 21332 through 21354).
    As stated in section VII.C.1.b. of this final rule, we proposed to 
adopt the Discharge Function Score (DC Function) measure beginning with 
the FY 2025 SNF QRP. This assessment-based quality measure will be 
calculated using data from the minimum data set (MDS) that are already 
reported to the Medicare program for payment and quality reporting 
purposes. The burden is currently approved by OMB under control number 
0938-1140 (CMS-10387). Under this requirement, there will be no 
additional burden for SNFs since it does not require the collection of 
new or revised data elements.
    As stated in section VII.C.1.c. of this final rule, we proposed to 
remove the Application of Percent of Long-Term Care Hospital (LTCH) 
Patients with an Admission and Discharge Functional Assessment and a 
Care Plan that Addresses Function (Application of Functional 
Assessment/Care Plan) measure beginning with the FY 2025 SNF QRP. We 
believe that the removal of the measure will result in a decrease of 18 
seconds (0.3 minutes or 0.005 hrs) of clinical staff time at admission 
beginning with the FY 2025 SNF QRP. We believe that the MDS item 
affected by the removal of the Application of Functional Assessment/
Care Plan measure is completed by Occupational Therapists (OT), 
Physical Therapists (PT), Registered Nurses (RN), Licensed Practical 
and Licensed Vocational Nurses (LVN), and/or Speech-Language 
Pathologists (SLP) depending on the functional goal selected. We 
identified the staff type per MDS item based on past SNF burden 
calculations. Our assumptions for staff type were based on the 
categories generally necessary to perform an assessment, however, 
individual SNFs determine the staffing resources necessary. Therefore, 
we averaged BLS' National Occupational Employment and Wage Estimates 
(See Table 25) for these labor types and established a composite cost 
estimate using our adjusted wage estimates. The composite estimate of 
$86.21/hr was calculated by weighting each hourly wage based on the 
following breakdown regarding provider types most likely to collect 
this data: OT 45 percent at $86.04/hr; PT 45 percent at $89.34/hr; RN 5 
percent at $79.56/hr; LVN 2.5 percent at $49.86/hr; and SLP 2.5 percent 
at $82.52/hr.
    For the purpose of deriving the composite wage we also estimated 
2,406,401 admission assessments from 15,471 SNFs annually.

                  Table 25--Estimated Composite Wage and Burden for Removing the Application of Functional Assessment/Care Plan Measure
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                             Adjusted       Percent of       Number of
                    Occupation title                        Occupation    hourly wage ($/   assessments     assessments     Total time    Total cost ($)
                                                               code             hr)          collected      collected *       (hours)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Occupational Therapist (OT).............................         29-1122           86.04              45     1,082,880.5           5,414         465,855
Physical Therapist (PT).................................         29-1123           89.34              45     1,082,880.5           5,414         483,723
Registered Nurse (RN)...................................         29-1141           79.56               5         120,320             602          47,863
Licensed Vocational Nurse (LVN).........................         29-2061           49.86             2.5          60,160             301          14,998
Speech Language Pathologist (SLP).......................         29-1127           82.52             2.5          60,160             301          24,822
                                                         -----------------------------------------------------------------------------------------------
    Total...............................................             n/a             n/a             100       2,406,401          12,032       1,037,261
                                                         -----------------------------------------------------------------------------------------------
    Composite Wage......................................                                $1,037,261/12,032 hrs = $86.2085/hr
--------------------------------------------------------------------------------------------------------------------------------------------------------

    For removing the Application of Functional Assessment/Care Plan 
measure, we estimate an annual decrease of minus 12,032 hours (0.005 hr 
x 2,406,401 admission assessments) and minus $1,037,261 (12,032 hours x 
$86.2085/hr) for all SNFs.
    As stated in section VII.C.1.d. of this final rule, we proposed to 
remove the Application of IRF Functional Outcome Measure: Change in 
Self-Care Score for Medical Rehabilitation Patients (Change in Self-
Care Score) measure as well as the Application of IRF Functional 
Outcome Measure: Change in Mobility Score for Medical Rehabilitation 
Patients (Change in Mobility Score) measure beginning with the FY 2025 
SNF QRP. While these assessment-based quality measures were proposed 
for removal, the data elements used to calculate the measures will 
still be reported by SNFs for other payment and quality reporting 
purposes. Therefore, we believe that the removal of the Change in Self-
Care Score and Change in Mobility Score measures will not have any 
impact on our currently approved reporting burden for SNFs.
    As stated in section VII.C.2.b. of this final rule, we proposed to 
adopt the COVID-19 Vaccine: Percent of Patients/Residents Who Are Up to 
Date (Patient/Resident COVID-19 Vaccine) measure beginning with the FY 
2026 SNF QRP. This assessment-based quality measure will be collected 
using the MDS. One data element will be added to the MDS at discharge 
to allow for the collection of the Patient/Resident COVID-19 Vaccine 
measure. We believe this will result in an increase of 18 seconds (0.3 
minutes or 0.005 hrs) of clinical staff time at discharge beginning 
with the FY 2026 SNF QRP. We believe that the added data element for 
the Patient/Resident COVID-19 Vaccine measure will be completed equally 
by an RN (0.0025 hr = 0.005 hr/2) and LVN (0.0025 hr = 0.005/2), 
however, individual SNFs determine the staffing resources necessary. 
Therefore, we averaged BLS' National Occupational Employment and Wage 
Estimates (see Table 26) for these labor types and established a 
composite cost estimate using our adjusted wage estimates. The

[[Page 53330]]

composite estimate of $64.71/hr (see Table 26) was calculated by 
weighting each hourly wage based on the following breakdown regarding 
provider types most likely to collect this data: RN 0.0025 hr at 
$79.56/hr and LVN 0.0025 hr at $49.86/hr.
    For purposes of deriving the burden impact, we estimated a total of 
2,406,401 discharges from 15,471 SNFs annually.

                              Table 26--Estimated Composite Wage for Adopting the Patient/Resident COVID-19 Vaccine Measure
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                             Adjusted       Percent of       Number of
                    Occupation title                        Occupation    hourly wage ($/   assessments     assessments     Total time    Total cost ($)
                                                               code             hr)          collected      collected *       (hours)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Registered Nurse (RN)...................................         29-1141           79.56              50     1,203,200.5           6,016         478,633
Licensed Vocational Nurse (LVN).........................         29-2061           49.86              50     1,203,200.5           6,016         299,958
                                                         -----------------------------------------------------------------------------------------------
    Total...............................................             n/a             n/a             100       2,406,401          12,032         778,591
                                                         -----------------------------------------------------------------------------------------------
    Composite Wage......................................                                 $778,591/12,032 hours = $64.71/hr
--------------------------------------------------------------------------------------------------------------------------------------------------------

    We estimate the total burden for complying with the SNF QRP 
requirements will increase by 12,032 hours (0.005 hr x 2,406,401 
discharge assessments) and $778,591 (12,032 hrs x $64.71/hr) for all 
SNFs annually based on the adoption of the Patient/Resident COVID-19 
Vaccine measure.
    In summary, we estimate the updated SNF QRP changes associated with 
the removal of the Application of Functional Assessment/Care Plan 
measure and the adoption of Patient/Resident COVID-19 measure will have 
a net zero effect on the total time to complete an MDS but will result 
in a decrease of $258,670 for all SNFs annually (see Table 27).

                                                       Table 27--Summary of SNF QRP Burden Changes
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                      Total         Time per       Total  time
             Requirement                 Number of respondents      responses     response (hr)       (hr)             Wage ($/hr)        Total cost ($)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Removal of the Application of          15,471 SNFs.............     (2,406,401)         (0.005)        (12,032)  Varies.................     (1,037,261)
 Functional Assessment/Care Plan
 measure beginning with the FY 2025
 SNF QRP.
Adoption of the Patient/Resident       15,471 SNFs.............       2,406,401           0.005          12,032  Varies.................         778,591
 COVID-19 Vaccine measure beginning
 with the FY 2026 SNF QRP.
                                      ------------------------------------------------------------------------------------------------------------------
    Total Change.....................  n/a.....................               0               0               0  n/a....................       (258,670)
--------------------------------------------------------------------------------------------------------------------------------------------------------

    As stated in section VII.F.5. of this final rule, we proposed to 
increase the SNF QRP data completion thresholds for MDS data items 
beginning with the FY 2026 SNF QRP. SNFs will be required to report 100 
percent of the required quality measures data and standardized patient 
assessment data collected using the MDS on at least 90 percent of the 
assessments they submit through the CMS designated submission system. 
SNFs have been required to submit MDS quality measures data and 
standardized patient assessment data for the SNF QRP since October 1, 
2016. Since our data indicates that the majority of SNFs are already in 
compliance with, or exceeding this threshold, we are not making any 
changes to the burden that is currently approved by OMB under control 
number 0938-1140 (CMS-10387).
2. ICRs Regarding the Skilled Nursing Facility Value-Based Purchasing 
Program
    In section VIII.B.3. of this final rule, we are replacing the SNFRM 
with the SNF WS PPR measure beginning with the FY 2028 SNF VBP program 
year. The measure is calculated using Medicare FFS claims data, which 
are the same data we use to calculate the SNFRM, and therefore, this 
measure will not create any new or revised burden for SNFs.
    We are also adopting four new quality measures in the SNF VBP 
Program as discussed in section VIII.B.4. of this final rule. One of 
the measures is the Total Nursing Staff Turnover Measure beginning with 
the FY 2026 SNF VBP program year. This measure is calculated using PBJ 
data that nursing facilities with SNF beds currently report to us as 
part of the Five Star Quality Rating System, and therefore, this 
measure will not create new or revised burden for SNFs. We are also 
adopting three additional quality measures beginning with the FY 2027 
SNF VBP program year: (1) Percent of Residents Experiencing One or More 
Falls with Major Injury (Long-Stay) Measure (``Falls with Major Injury 
(Long-Stay) measure''), (2) Skilled Nursing Facility Cross-Setting 
Discharge Function Score Measure (``DC Function measure''), and (3) 
Number of Hospitalizations per 1,000 Long-Stay Resident Days Measure 
(``Long-Stay Hospitalization measure''). The Falls with Major Injury 
(Long-Stay) measure and the DC Function measure are calculated using 
MDS 3.0 data and are calculated by us under the Nursing Home Quality 
Initiative and SNF QRP Program, respectively. The Long-Stay 
Hospitalization measure is calculated using Medicare FFS claims data. 
Therefore, these three measures will not create new or revised burden 
for SNFs.
    Furthermore, in section VIII.G. of this final rule, we are updating 
the validation process for the SNF VBP Program, including adopting a 
new process for the Minimum Data Set (MDS) measures beginning with the 
FY 2027 SNF VBP program year. As finalized, we will validate data used 
to calculate the measures used in the SNF VBP Program, and 1,500 
randomly selected SNFs a year would be required to submit up to 10 
charts that would be used to validate the MDS measures.
    Finally, in section VIII.E.4. of this final rule, we are adopting a 
Health Equity Adjustment beginning with the

[[Page 53331]]

FY 2027 SNF VBP program year. The source of data we would use to 
calculate this adjustment is the State Medicare Modernization Act (MMA) 
file of dual eligibility, and therefore our calculation of this 
adjustment would not create any additional reporting burden for SNFs.
    The aforementioned FFS-related claims submission requirements and 
burden, which are previously mentioned in the preceding paragraphs, are 
active and approved by OMB under control number 0938-1140 (CMS-10387). 
The aforementioned MDS submission requirements and burden are active 
and approved by OMB under control number 0938-1140 and the burden 
associated with the items used to calculate the measures is already 
accounted for in the currently approved information collection since it 
is used for the SNF QRP. The aforementioned PBJ submission requirements 
and burden are PRA exempt (as are all nursing home requirements for 
participation). The increase in burden for the SNFs would be accounted 
for in the submission of up to 10 charts for review, and the proposed 
process would not begin until FY 2025. The required 60-day and 30-day 
notices would be published in the Federal Register and the comment 
periods would be separate from those associated with this rulemaking. 
This rule's changes will have no impact on any of the requirements and 
burden that are currently approved under these control numbers.
3. ICRs Regarding Civil Money Penalties: Waiver of Hearing, Automatic 
Reduction of Penalty Amount
    This rule finalizes our proposal to eliminate the requirement for 
facilities facing a civil money penalty to actively waive their right 
to a hearing in writing to receive a penalty reduction. We are 
creating, in its place, a constructive waiver process that will operate 
by default when CMS has not received a timely request for a hearing. 
While OBRA '87 exempts the waiver requirements and burden from the PRA, 
the requirements and burden are scored under the RIA section of this 
preamble.''

C. Summary of Finalized Requirements and Associated Burden Estimates

                                                    Table 28--Summary of Burden Estimates for FY 2025
--------------------------------------------------------------------------------------------------------------------------------------------------------
   Regulatory section(s) under      OMB control No.        Number of       Total number      Time per       Total time    Labor cost ($/
       Title 42 of the CFR           (CMS ID No.)         respondents      of responses    response (hr)       (hr)             hr)       Total cost ($)
--------------------------------------------------------------------------------------------------------------------------------------------------------
413.360(b)(1)...................  0938-1140.........  15,471 SNFs.......     (2,406,401)           0.005        (12,032)           86.21     (1,037,261)
                                  CMS-10387.........
--------------------------------------------------------------------------------------------------------------------------------------------------------


                                                    Table 29--Summary of Burden Estimates for FY 2026
--------------------------------------------------------------------------------------------------------------------------------------------------------
   Regulatory section(s) under      OMB control No.        Number of       Total number      Time per       Total time    Labor cost ($/
       Title 42 of the CFR           (CMS ID No.)         respondents      of responses    response (hr)       (hr)             hr)       Total cost ($)
--------------------------------------------------------------------------------------------------------------------------------------------------------
413.360.........................  0938-1140 CMS-      15,471 SNFs.......       2,406,401           0.005          12,032           79.56         778,591
                                   10387
--------------------------------------------------------------------------------------------------------------------------------------------------------

XII. Economic Analyses

A. Regulatory Impact Analysis

1. Statement of Need
a. Statutory Provisions
    This rule updates the FY 2024 SNF prospective payment rates as 
required under section 1888(e)(4)(E) of the Act. It also responds to 
section 1888(e)(4)(H) of the Act, which requires the Secretary to 
provide for publication in the Federal Register before the August 1 
that precedes the start of each FY, the unadjusted Federal per diem 
rates, the case-mix classification system, and the factors to be 
applied in making the area wage adjustment. These are statutory 
provisions that prescribe a detailed methodology for calculating and 
disseminating payment rates under the SNF PPS, and we do not have the 
discretion to adopt an alternative approach on these issues.
    With respect to the SNF QRP, this final rule finalizes updates 
beginning with the FY 2025 and FY 2026 SNF QRP. Specifically, we adopt 
a modification to a current measure in the SNF QRP beginning with the 
FY 2025 SNF QRP, which we believe will encourage healthcare personnel 
to remain up to date with the COVID-19 vaccine, resulting in fewer 
cases, less hospitalizations, and lower mortality associated with the 
virus. We adopt two new measures: (1) one to satisfy the requirement 
set forth in sections 1888(e)(6)(B)(i)(II) and 1899B(c)(1)(A) of the 
Act which would replace the current cross-setting process measure with 
one more strongly associated with desired patient functional outcomes 
beginning with the FY 2025 SNF QRP; and (2) one that supports the goals 
of CMS Meaningful Measures Initiative 2.0 to empower consumers, as well 
as assist SNFs leverage their care processes to increase vaccination 
coverage in their settings to protect residents and prevent negative 
outcomes beginning with the FY 2026 SNF QRP. We finalize the removal of 
three measures from the SNF QRP, beginning with the FY 2025 SNF QRP, as 
they meet the criteria specified at Sec.  413.360(b)(2) for measure 
removal. We further finalize an increase to the data completion 
threshold for Minimum Data Set (MDS) data items, beginning with the FY 
2026 SNF QRP, which we believe will improve our ability to 
appropriately analyze quality measure data for the purposes of 
monitoring SNF outcomes. For consistency in our regulations, we also 
finalize conforming revisions to the requirements related to these 
proposals under the SNF QRP at Sec.  413.360.
    With respect to the SNF VBP Program, this final rule updates the 
SNF VBP Program requirements for FY 2024 and subsequent years. Section 
1888(h)(2)(A)(ii) of the Act (as amended by section 111(a)(2)(C) of the 
CAA 2021) allows the Secretary to add up to nine new measures to the 
SNF VBP Program. We are finalizing four new measures for the SNF VBP 
Program. We are finalizing one new measure beginning with the FY 2026 
SNF VBP program year and three new measures beginning with the FY 2027 
program year. We are also replacing the SNFRM with the SNF WS PPR 
measure beginning with the FY

[[Page 53332]]

2028 SNF VBP Program year. Additionally, to better address health 
disparities and achieve health equity, we are finalizing a Health 
Equity Adjustment (HEA) beginning with the FY 2027 program year. As 
part of the HEA, we are finalizing a variable payback percentage (for 
additional information on the HEA and the fluctuating payback 
percentage see section VII.E.4. of the proposed rule). Section 
1888(h)(3) of the Act requires the Secretary to establish and announce 
performance standards for SNF VBP Program measures no later than 60 
days before the performance period, and this final rule includes 
numerical values of the performance standards for the SNFRM, the SNF 
Healthcare-Associated Infection Requiring Hospitalization (SNF HAI), 
Total Nurse Staffing, Nursing Staff Turnover, and the Discharge to 
Community--Post-Acute Care (DTC PAC SNF) measures. Section 
1888(h)(12)(A) of the Act requires the Secretary to apply a validation 
process to SNF VBP Program measures and ``the data submitted under 
[section 1888(e)(6)] [. . .] as appropriate[. . .].'' We are finalizing 
a new validation process for measures beginning in the FY 2027 program 
year.
b. Discretionary Provisions
    In addition, this final rule includes the following discretionary 
provisions:
(1) PDPM Parity Adjustment Recalibration
    In the FY 2023 SNF final rule (87 FR 47502), we finalized a 
recalibration of the PDPM parity adjustment with a 2-year phase-in 
period, resulting in a reduction of 2.3 percent, or $780 million, in FY 
2023 and a planned reduction in FY 2024 of 2.3 percent. We finalized 
the phased-in approach to implementing this adjustment based on a 
significant number of comments supporting this approach. Accordingly, 
we are implementing the second phase of the 2-year phase-in period, 
resulting in a reduction of 2.3 percent, or approximately $789 million, 
in FY 2024.
(2) SNF Forecast Error Adjustment
    Each year, we evaluate the SNF market basket forecast error for the 
most recent year for which historical data is available. The forecast 
error is determined by comparing the projected SNF market basket 
increase in a given year with the actual SNF market basket increase in 
that year. In evaluating the data for FY 2022, we found that the 
forecast error for FY 2022 was 3.6 percentage points, exceeding the 0.5 
percentage point threshold we established in regulation for proposing 
adjustments to correct for forecast error. Given that the forecast 
error exceeds the 0.5 percentage point threshold, current regulations 
require that the SNF market basket percentage increase for FY 2024 be 
adjusted upward by 3.6 percentage points to account for forecasting 
error in the FY 2022 SNF market basket update.
(3) Technical Updates to ICD-10 Mappings
    In the FY 2019 SNF PPS final rule (83 FR 39162), we finalized the 
implementation of the PDPM, effective October 1, 2019. The PDPM 
utilizes ICD-10 codes in several ways, including using the patient's 
primary diagnosis to assign patients to clinical categories under 
several PDPM components, specifically the PT, OT, SLP and NTA 
components. In this rule, we finalize several substantive changes to 
the PDPM ICD-10 code mapping.
(4) Civil Money Penalties: Waiver of Hearing, Automatic Reduction of 
Penalty Amount
    We are finalizing our proposal to eliminate the requirement for 
facilities to actively waive their right to a hearing in writing and 
create in its place a constructive waiver process that would operate 
automatically when CMS has not received a timely request for a hearing. 
At this time, the accompanying 35 percent penalty reduction will 
remain, but we will review the appropriateness of this reduction and, 
if warranted by the review, adjust it in a future rulemaking. The 
accompanying 35 percent penalty reduction will remain. This revision 
eliminating the LTC requirement to submit a written request for a 
reduced penalty amount when a hearing has been waived will simplify and 
streamline the current requirement, while maintaining a focus on 
providing high quality care to residents. This provision will also ease 
the administrative burden for facilities that are currently submitting 
waiver requests to CMS locations. In CY 2022, 81 percent of facilities 
facing CMPs filed an appeal waiver while only 2 percent of facilities 
filed an appeal of their CMP with the Departmental Appeals Board. The 
remaining 17 percent of facilities neither waived nor timely filed an 
appeal. We estimate that moving to a constructive waiver process will 
eliminate the time and paperwork necessary to complete and send in a 
written waiver and will thereby result, as detailed below, in a total 
annual savings of $2,299,716 in administrative costs for LTC facilities 
facing CMPs ($861,678 + $1,438,038 = $2,299,716). Ultimately, this 
provision will reduce administrative burden for facilities and for CMS.
2. Introduction
    We have examined the impacts of this final rule as required by 
Executive Order 12866 on Regulatory Planning and Review (September 30, 
1993), Executive Order 13563 on Improving Regulation and Regulatory 
Review (January 18, 2011), Executive Order 14094 entitled ``Modernizing 
Regulatory Review'' (April 6, 2023), the Regulatory Flexibility Act 
(RFA, September 19, 1980, Pub. L. 96-354), section 1102(b) of the Act, 
section 202 of the Unfunded Mandates Reform Act of 1995 (UMRA, March 
22, 1995; Pub. L. 104-4), Executive Order 13132 on Federalism (August 
4, 1999) and the Congressional Review Act (5 U.S.C. 804(2)).
    Executive Orders 12866 and 13563 direct agencies to assess all 
costs and benefits of available regulatory alternatives and, if 
regulation is necessary, to select regulatory approaches that maximize 
net benefits (including potential economic, environmental, public 
health and safety effects, distributive impacts, and equity). The 
Executive Order 14094 entitled ``Modernizing Regulatory Review'' 
(hereinafter, the Modernizing E.O.) amends section 3(f)(1) of Executive 
Order 12866 (Regulatory Planning and Review). The amended section 3(f) 
of Executive Order 12866 defines a ``significant regulatory action'' as 
an action that is likely to result in a rule: (1) having an annual 
effect on the economy of $200 million or more in any 1 year (adjusted 
every 3 years by the Administrator of OIRA for changes in gross 
domestic product), or adversely affect in a material way the economy, a 
sector of the economy, productivity, competition, jobs, the 
environment, public health or safety, or State, local, territorial, or 
tribal governments or communities; (2) creating a serious inconsistency 
or otherwise interfering with an action taken or planned by another 
agency; (3) materially altering the budgetary impacts of entitlement 
grants, user fees, or loan programs or the rights and obligations of 
recipients thereof; or (4) raise legal or policy issues for which 
centralized review would meaningfully further the President's 
priorities or the principles set forth in this Executive order, as 
specifically authorized in a timely manner by the Administrator of OIRA 
in each case.
    A regulatory impact analysis (RIA) must be prepared for major rules 
with significant regulatory action/s and/or with significant effects as 
per section 3(f)(1) ($200 million or more in any 1 year). Based on our 
estimates, OMB's

[[Page 53333]]

Office of Information and Regulatory Affairs has determined this 
rulemaking is significant per section 3(f)(1) as measured by the $200 
million or more in any 1 year, and hence also a major rule under 
Subtitle E of the Small Business Regulatory Enforcement Fairness Act of 
1996 (also known as the Congressional Review Act). Accordingly, we have 
prepared a Regulatory Impact Analysis that to the best of our ability 
presents the costs and benefits of the rulemaking. Therefore, OMB has 
reviewed these proposed regulations, and the Departments have provided 
the following assessment of their impact.
3. Overall Impacts
    This rule updates the SNF PPS rates contained in the SNF PPS final 
rule for FY 2023 (87 FR 47502). We estimate that the aggregate impact 
will be an increase of approximately $1.4 billion (4.0 percent) in Part 
A payments to SNFs in FY 2024. This reflects a $2.2 billion (6.4 
percent) increase from the update to the payment rates and a $789 
million (2.3 percent) decrease as a result of the second phase of the 
parity adjustment recalibration. We note in this final rule that these 
impact numbers do not incorporate the SNF VBP Program reductions that 
we estimate would total $184.85 million in FY 2024. We note that events 
may occur to limit the scope or accuracy of our impact analysis, as 
this analysis is future-oriented, and thus, very susceptible to 
forecasting errors due to events that may occur within the assessed 
impact time period.
    In accordance with sections 1888(e)(4)(E) and (e)(5) of the Act and 
implementing regulations at Sec.  413.337(d), we are updating the FY 
2023 payment rates by a factor equal to the market basket percentage 
increase adjusted for the forecast error adjustment and reduced by the 
productivity adjustment to determine the payment rates for FY 2024. The 
impact to Medicare is included in the total column of Table 30. The 
annual update in this rule applies to SNF PPS payments in FY 2024. 
Accordingly, the analysis of the impact of the annual update that 
follows only describes the impact of this single year. Furthermore, in 
accordance with the requirements of the Act, we will publish a rule or 
notice for each subsequent FY that will provide for an update to the 
payment rates and include an associated impact analysis.
4. Detailed Economic Analysis
    The FY 2024 SNF PPS payment impacts appear in Table 30. Using the 
most recently available data, in this case FY 2022 we apply the current 
FY 2023 CMIs, wage index and labor-related share value to the number of 
payment days to simulate FY 2023 payments. Then, using the same FY 2022 
data, we apply the FY 2024 CMIs, wage index and labor-related share 
value to simulate FY 2024 payments. We tabulate the resulting payments 
according to the classifications in Table 30 (for example, facility 
type, geographic region, facility ownership), and compare the simulated 
FY 2023 payments to the simulated FY 2024 payments to determine the 
overall impact. The breakdown of the various categories of data in 
Table 30 is as follows:
     The first column shows the breakdown of all SNFs by urban 
or rural status, hospital-based or freestanding status, census region, 
and ownership.
     The first row of figures describes the estimated effects 
of the various changes contained in this final rule on all facilities. 
The next six rows show the effects on facilities split by hospital-
based, freestanding, urban, and rural categories. The next nineteen 
rows show the effects on facilities by urban versus rural status by 
census region. The last three rows show the effects on facilities by 
ownership (that is, government, profit, and non-profit status).
     The second column shows the number of facilities in the 
impact database.
     The third column shows the effect of the second phase of 
the parity adjustment recalibration discussed in section IV.C. of this 
rule.
     The fourth column shows the effect of the annual update to 
the wage index. This represents the effect of using the most recent 
wage data available as well as accounts for the 5 percent cap on wage 
index transitions. The total impact of this change is 0.0 percent; 
however, there are distributional effects of the change.
     The fifth column shows the effect of all of the changes on 
the FY 2024 payments. The update of 6.4 percent is constant for all 
providers and, though not shown individually, is included in the total 
column. It is projected that aggregate payments would increase by 6.4 
percent, assuming facilities do not change their care delivery and 
billing practices in response.
    As illustrated in Table 30, the combined effects of all of the 
changes vary by specific types of providers and by location. For 
example, due to changes in this final rule, rural providers would 
experience a 3.0 percent increase in FY 2024 total payments.
    In this chart and throughout the rule, we use a multiplicative 
formula to derive total percentage change. This formula is:

(1 + Parity Adjustment Percentage) * (1 + Wage Index Update Percentage) 
* (1 + Payment Rate Update Percentage) - 1 = Total Percentage Change

    For example, the figures shown in Column 5 of Table 30 are 
calculated by multiplying the percentage changes using this formula. 
Thus, the Total Change figure for the Total Group Category is 4.0 
percent, which is (1 - 2.3%) * (1 + 0.0%) * (1 + 6.4%) -1.
    As a result of rounding and the use of this multiplicative formula 
based on percentages, derived dollar estimates may not sum.

                                   Table 30--Impact to the SNF PPS for FY 2024
----------------------------------------------------------------------------------------------------------------
                                                                      Parity
                                                     Number of      adjustment      Update wage    Total change
                Impact categories                   facilities     recalibration     data (%)           (%)
                                                                        (%)
----------------------------------------------------------------------------------------------------------------
                                                      Group
----------------------------------------------------------------------------------------------------------------
Total...........................................          15,503            -2.3             0.0             4.0
Urban...........................................          11,254            -2.3             0.1             4.1
Rural...........................................           4,249            -2.2            -0.7             3.3
Hospital-based urban............................             366            -2.3             0.0             4.0
Freestanding urban..............................          10,888            -2.3             0.1             4.1
Hospital-based rural............................             378            -2.2            -0.3             3.7

[[Page 53334]]

 
Freestanding rural..............................           3,871            -2.2            -0.7             3.3
----------------------------------------------------------------------------------------------------------------
                                                 Urban by region
----------------------------------------------------------------------------------------------------------------
New England.....................................             734            -2.3            -0.7             3.2
Middle Atlantic.................................           1,471            -2.4             1.4             5.3
South Atlantic..................................           1,945            -2.3             0.1             4.1
East North Central..............................           2,181            -2.3            -0.7             3.2
East South Central..............................             555            -2.2             0.0             4.0
West North Central..............................             958            -2.3            -0.4             3.6
West South Central..............................           1,454            -2.3             0.0             4.0
Mountain........................................             546            -2.3            -0.9             3.0
Pacific.........................................           1,404            -2.4             0.1             4.0
Outlying........................................               6            -2.0            -2.6             1.6
----------------------------------------------------------------------------------------------------------------
                                                 Rural by region
----------------------------------------------------------------------------------------------------------------
New England.....................................             117            -2.3            -1.1             2.8
Middle Atlantic.................................             205            -2.2            -0.3             3.7
South Atlantic..................................             489            -2.2             0.1             4.1
East North Central..............................             907            -2.2            -0.9             3.1
East South Central..............................             491            -2.2            -0.8             3.2
West North Central..............................           1,011            -2.2            -0.9             3.1
West South Central..............................             738            -2.2            -0.5             3.5
Mountain........................................             199            -2.3            -0.6             3.3
Pacific.........................................              91            -2.3            -2.0             1.9
Outlying........................................               1            -2.3             0.0             3.9
----------------------------------------------------------------------------------------------------------------
                                                    Ownership
----------------------------------------------------------------------------------------------------------------
For profit......................................          10,912            -2.3             0.0             4.0
Non-profit......................................           3,573            -2.3             0.0             3.9
Government......................................           1,018            -2.3            -0.4             3.6
----------------------------------------------------------------------------------------------------------------
Note: The Total column includes the FY 2024 6.4 percent market basket update. The values presented in Table 30
  may not sum due to rounding.

5. Impacts for the Skilled Nursing Facility Quality Reporting Program 
(SNF QRP) for FY 2025 Through FY 2026
    Estimated impacts for the SNF QRP are based on analysis discussed 
in section VII.C. of this final rule. In accordance with section 
1888(e)(6)(A)(i) of the Act, the Secretary must reduce by 2 percentage 
points the annual payment update applicable to a SNF for a fiscal year 
if the SNF does not comply with the requirements of the SNF QRP for 
that fiscal year.
    As discussed in section VII.C.1.a. of this final rule, we proposed 
to modify one measure in the SNF QRP beginning with the FY 2025 SNF 
QRP, the COVID-19 Vaccination Coverage among Healthcare Personnel (HCP 
COVID-19 Vaccine) measure. We believe that the burden associated with 
the SNF QRP is the time and effort associated with complying with the 
non-claims-based measures requirements of the SNF QRP. The burden 
associated with the HCP COVID-19 Vaccine measure is accounted for under 
the CDC PRA package currently approved under OMB control number 0938-
1317 (expiration January 31, 2024).
    As discussed in section VII.C.1.b. of this final rule, we proposed 
that SNFs would collect data on one new quality measure, the Discharge 
Function Score (DC Function) measure, beginning with resident 
assessments completed on October 1, 2023. However, the DC Function 
measure utilizes data items that SNFs already report to CMS for payment 
and quality reporting purposes, and therefore, the burden is accounted 
for in the PRA package approved under OMB control number 0938-1140 
(expiration November 30, 2025).
    As discussed in section VII.C.1.c. of this final rule, we proposed 
to remove a measure from the SNF QRP, the Application of Percent of 
Long-Term Care Hospital Patients with an Admission and Discharge 
Functional Assessment and a Care Plan That Addresses Function 
(Application of Functional Assessment/Care Plan) measure, beginning 
with admission assessments completed on October 1, 2023. Although the 
proposed decrease in burden will be accounted for in a revised 
information collection request under OMB control number (0938-1140), we 
are providing impact information.
    With 2,406,401 admissions from 15,471 SNFs annually, we estimated 
an annual burden decrease of 12,032 fewer hours (2,406,401 admissions x 
0.005 hr) and a decrease of $1,037,261 (12,038 hrs x $86.2085/hr). For 
each SNF we estimate an annual burden decrease of 0.78 hours [(12,032 
hrs/15,471 SNFs) at a savings of $67.05 ($1,037,261 total burden/15,471 
SNFs).
    As discussed in section VII.C.1.d. of this final rule, we proposed 
to remove two measures from the SNF QRP, the Application of IRF 
Functional Outcome Measure: Change in Self-Care Score for Medical 
Rehabilitation Patients (Change in Self-Care Score) and Application of 
IRF Functional Outcome Measure: Change in Mobility Score for Medical 
Rehabilitation Patients (Change in Mobility Score) measures, beginning 
with assessments completed on October 1, 2023. However, the data items 
used

[[Page 53335]]

in the calculation of the Change in Self-Care Score and Change in 
Mobility Score measures are used for other payment and quality 
reporting purposes, and therefore there is no change in burden 
associated with this proposal.
    As discussed in section VII.C.3.a. of this final rule, we proposed 
to add a second measure to the SNF QRP, the COVID-19 Vaccine: Percent 
of Patients/Residents Who are Up to Date (Patient/Resident COVID-19 
Vaccine) measure, which would result in an increase of 0.005 hours of 
clinical staff time beginning with discharge assessments completed on 
October 1, 2024. Although the increase in burden will be accounted for 
in a revised information collection request under OMB control number 
(0938-1140), we provided impact information. With 2,406,401 discharges 
from 15,471 SNFs annually, we estimate an annual burden increase of 
12,032 hours (2,406,401 discharges x 0.005 hr) and an increase of 
$778,5914 (12,032 hrs x $64.71/hr). For each SNF we estimate an annual 
burden increase of 0.78 hours (12,032 hrs/15,471 SNFs) at an additional 
cost of $50.33 ($778,591 total burden/15,471 SNFs).
    We also proposed in section VII.F.5. of this final rule that SNFs 
would begin reporting 100 percent of the required quality measures data 
and standardized patient assessment data collected using the MDS on at 
least 90 percent of the assessments they submit through the CMS 
designated submission system beginning January 1, 2024. As discussed in 
section IX.B.1. of this final rule, this change will not affect the 
information collection burden for the SNF QRP.

                     Table 31--Estimated SNF QRP Program Impacts for FY 2025 Through FY 2027
----------------------------------------------------------------------------------------------------------------
                                                            Per SNF                          All SNFs
                                               -----------------------------------------------------------------
     Total benefit for the FY2025 SNF QRP          Change in                        Change in
                                                 annual burden      Change in     annual burden      Change in
                                                     hours         annual cost        hours         annual cost
----------------------------------------------------------------------------------------------------------------
Decrease in burden from the removal of the               (0.78)           ($67)         (12,032)    ($1,037,261)
 Functional Assessment/Care Plan measure......
----------------------------------------------------------------------------------------------------------------
                                       Total burden for the FY2026 SNF QRP
----------------------------------------------------------------------------------------------------------------
Increase in burden for the Patient/Resident                0.78             $50           12,032        $778,591
 COVID-19 Vaccine measure.....................
----------------------------------------------------------------------------------------------------------------

    We solicited public comments on the overall impact of the SNF QRP 
proposals for FY 2025 and 2026.
    We did not receive public comments on this provision and therefore, 
we are finalizing as proposed.
6. Impacts for the SNF VBP Program
    The estimated impacts of the FY 2024 SNF VBP Program are based on 
historical data and appear in Table 32. We modeled SNF performance in 
the Program using SNFRM data from FY 2019 as the baseline period and FY 
2021 as the performance period. Additionally, we modeled a logistic 
exchange function with a payback percentage of 60 percent, as we 
finalized in the FY 2018 SNF PPS final rule (82 FR 36619 through 
36621).
    For the FY 2024 program year, we will award each participating SNF 
60 percent of their 2 percent withhold. Additionally, in the FY 2023 
SNF PPS final rule (87 FR 47585 through 47587), we finalized our 
proposal to apply a case minimum requirement for the SNFRM. As a result 
of these provisions, SNFs that do not meet the case minimum specified 
for the SNFRM for the FY 2024 program year will be excluded from the 
Program and will receive their adjusted Federal per diem rate for that 
fiscal year. As previously finalized, this policy will maintain the 
overall payback percentage at 60 percent for the FY 2024 program year. 
Based on the 60 percent payback percentage, we estimated that we would 
redistribute approximately $277.27 million (of the estimated $462.12 
million in withheld funds) in value-based incentive payments to SNFs in 
FY 2024, which means that the SNF VBP Program is estimated to result in 
approximately $184.85 million in savings to the Medicare Program in FY 
2024.
    Our detailed analysis of the impacts of the FY 2024 SNF VBP Program 
is shown in Table 32.

                             Table 32--Estimated SNF VBP Program Impacts for FY 2024
----------------------------------------------------------------------------------------------------------------
                                                    Mean risk-
                                                   standardized        Mean       Mean incentive
         Characteristic              Number of      readmission     performance       payment       Percent of
                                    facilities     rate (SNFRM)        score        multiplier     total payment
                                                        (%)
----------------------------------------------------------------------------------------------------------------
Group:
    Total *.....................          11,176           20.47         28.3029         0.99140          100.00
    Urban.......................           8,710           20.58         27.1026         0.99084           87.12
    Rural.......................           2,436           20.07         32.7202         0.99346           12.88
    Hospital-based urban **.....             196           19.92         36.8240         0.99531            1.72
    Freestanding urban **.......           8,501           20.60         26.8949         0.99074           85.38
    Hospital-based rural **.....              87           19.58         39.2697         0.99636            0.36
    Freestanding rural **.......           2,275           20.08         32.6780         0.99347           12.38
Urban by region:
    New England.................             627           20.62         27.4602         0.99121            5.45
    Middle Atlantic.............           1,287           20.35         30.2740         0.99220           18.03
    South Atlantic..............           1,691           20.83         25.4855         0.99011           17.75
    East North Central..........           1,593           20.88         22.3914         0.98856           12.69
    East South Central..........             468           20.83         24.1778         0.98938            3.55
    West North Central..........             620           20.24         29.7294         0.99207            3.87

[[Page 53336]]

 
    West South Central..........             912           21.11         18.7872         0.98700            6.75
    Mountain....................             384           19.95         34.9771         0.99429            3.79
    Pacific.....................           1,125           19.93         36.2085         0.99528           15.24
    Outlying....................               3           20.46         23.6945         0.98431            0.00
Rural by region:
    New England.................              75           19.51         40.6317         0.99752            0.55
    Middle Atlantic.............             164           19.56         39.1621         0.99692            0.91
    South Atlantic..............             340           20.37         29.6459         0.99162            2.06
    East North Central..........             602           19.94         33.4406         0.99376            3.07
    East South Central..........             383           20.48         28.5196         0.99167            2.14
    West North Central..........             364           19.81         34.7097         0.99451            1.29
    West South Central..........             345           20.74         24.3765         0.98937            1.68
    Mountain....................              92           19.34         42.4305         0.99792            0.53
    Pacific.....................              71           18.48         58.5164         1.00597            0.64
    Outlying....................               0  ..............  ..............  ..............  ..............
Ownership:
    Government..................             464           19.98         34.5948         0.99435            2.86
    Profit......................           8,101           20.60         26.4146         0.99049           75.05
    Non-Profit..................           2,581           20.16         33.2172         0.99378           22.08
----------------------------------------------------------------------------------------------------------------
* The total group category excludes 3,721 SNFs that failed to meet the finalized measure minimum policy. The
  total group category includes 30 SNFs that did not have facility characteristics in the CMS Provider of
  Services (POS) file or historical payment data used for this analysis.
** The group category which includes hospital-based/freestanding by urban/rural excludes 87 swing bed SNFs that
  satisfied the current measure minimum policy.

    In section VIII.B.4.b. of this final rule, we are adopting one 
additional measure (Nursing Staff Turnover measure) beginning with the 
FY 2026 program year. Additionally, in section VIII.E.2.b. of this 
final rule, we are adopting a case minimum requirement for the Nursing 
Staff Turnover measure. In section VIII.E.2.c. of this final rule, we 
are maintaining the previously finalized measure minimum for FY 2026. 
Therefore, we provided estimated impacts of the FY 2026 SNF VBP 
Program, which are based on historical data and appear in Tables 33 and 
34. We modeled SNF performance in the Program using measure data from 
FY 2019 as the baseline period and FY 2021 as the performance period 
for the SNFRM, SNF HAI, Total Nurse Staffing, and Nursing Staff 
Turnover measures. Additionally, we modeled a logistic exchange 
function with a payback percentage of 60 percent. Based on the 60 
percent payback percentage, we estimated that we will redistribute 
approximately $294.75 million (of the estimated $491.24 million in 
withheld funds) in value-based incentive payments to SNFs in FY 2026, 
which means that the SNF VBP Program is estimated to result in 
approximately $196.50 million in savings to the Medicare Program in FY 
2026.
    Our detailed analysis of the impacts of the FY 2026 SNF VBP Program 
is shown in Tables 33 and 34.

                             Table 33--Estimated SNF VBP Program Impacts for FY 2026
----------------------------------------------------------------------------------------------------------------
                                                                                    Mean risk-
                                                    Mean risk-      Mean total     standardized     Mean total
                                                   standardized    nursing hours      rate of      nursing staff
         Characteristic              Number of      readmission    per resident      hospital-     turnover rate
                                    facilities     rate  (SNFRM)    day  (total      acquired     (nursing staff
                                                        (%)            nurse        infections     turnover) (%)
                                                                     staffing)     (SNF HAI) (%)
----------------------------------------------------------------------------------------------------------------
Group:
    Total *.....................          13,879           20.39            3.91            7.67           52.74
    Urban.......................          10,266           20.52            3.93            7.69           52.43
    Rural.......................           3,613           20.04            3.87            7.61           53.62
    Hospital-based urban **.....             239           20.01            5.22            6.52           45.90
    Freestanding urban **.......          10,018           20.53            3.90            7.72           52.57
    Hospital-based rural **.....             143           19.75            4.82            6.88           45.57
    Freestanding rural **.......           3,399           20.04            3.83            7.68           53.93
Urban by region:
    New England.................             706           20.54            4.04            7.09           45.50
    Middle Atlantic.............           1,408           20.31            3.68            7.55           46.06
    South Atlantic..............           1,810           20.77            4.01            7.86           51.79
    East North Central..........           1,956           20.74            3.59            7.72           55.47
    East South Central..........             538           20.73            3.96            8.02           55.78
    West North Central..........             839           20.18            4.19            7.41           57.73
    West South Central..........           1,207           20.97            3.74            8.02           59.10
    Mountain....................             490           19.94            4.15            7.15           56.54
    Pacific.....................           1,309           19.98            4.45            7.84           46.97

[[Page 53337]]

 
    Outlying....................               3           20.46            3.30            6.20             N/A
Rural by region:
    New England.................             106           19.55            4.30            6.63           54.74
    Middle Atlantic.............             192           19.60            3.42            7.17           53.04
    South Atlantic..............             432           20.24            3.72            7.79           52.83
    East North Central..........             802           19.94            3.63            7.46           53.02
    East South Central..........             451           20.43            3.93            8.18           51.90
    West North Central..........             802           19.85            4.12            7.50           53.49
    West South Central..........             577           20.58            3.82            7.99           55.76
    Mountain....................             168           19.54            4.18            7.16           55.96
    Pacific.....................              83           18.64            4.34            6.73           53.75
    Outlying....................               0               -               -               -               -
Ownership:
    Government..................             735           20.00            4.34            7.36           48.93
    Profit......................           9,975           20.51            3.72            7.89           54.29
    Non-Profit..................           3,169           20.11            4.43            7.04           48.74
----------------------------------------------------------------------------------------------------------------
* The total group category excludes 1,028 SNFs that failed to meet the finalized measure minimum policy.
** The group category that includes hospital-based/freestanding by urban/rural excludes 80 swing bed SNFs that
  satisfied the finalized measure minimum policy.
N/A = Not available because no facilities in this group received a measure result.


                             Table 34--Estimated SNF VBP Program Impacts for FY 2026
----------------------------------------------------------------------------------------------------------------
                                                                       Mean       Mean incentive
                 Characteristic                      Number of      performance       payment       Percent of
                                                    facilities         score        multiplier     total payment
----------------------------------------------------------------------------------------------------------------
Group:
    Total *.....................................          13,879         24.5877         0.99108          100.00
    Urban.......................................          10,266         24.4964         0.99106           85.88
    Rural.......................................           3,613         24.8470         0.99112           14.12
    Hospital-based urban **.....................             239         40.2184         1.00671            1.60
    Freestanding urban **.......................          10,018         24.1217         0.99069           84.26
    Hospital-based rural **.....................             143         41.0606         1.00583            0.38
    Freestanding rural **.......................           3,399         24.0807         0.99041           13.62
Urban by region:
    New England.................................             706         30.1328         0.99463            5.31
    Middle Atlantic.............................           1,408         26.0014         0.99182           17.27
    South Atlantic..............................           1,810         24.1128         0.99014           17.07
    East North Central..........................           1,956         18.8610         0.98737           12.69
    East South Central..........................             538         21.3335         0.98858            3.49
    West North Central..........................             839         26.4267         0.99302            3.99
    West South Central..........................           1,207         16.8688         0.98557            7.20
    Mountain....................................             490         27.4320         0.99295            3.81
    Pacific.....................................           1,309         34.7925         0.99925           15.02
    Outlying....................................               3         21.6999         0.98682            0.00
Rural by region:
    New England.................................             106         33.4096         0.99729            0.59
    Middle Atlantic.............................             192         22.9268         0.98939            0.91
    South Atlantic..............................             432         21.3377         0.98797            2.10
    East North Central..........................             802         22.3282         0.98960            3.20
    East South Central..........................             451         24.1187         0.99020            2.17
    West North Central..........................             802         29.2268         0.99485            1.80
    West South Central..........................             577         21.1394         0.98792            2.10
    Mountain....................................             168         30.0191         0.99532            0.63
    Pacific.....................................              83         37.8989         1.00119            0.62
    Outlying....................................               0               -               -            0.00
Ownership:
    Government..................................             735         33.4591         0.99976            3.20
    Profit......................................           9,975         21.0738         0.98806           75.04
    Non-Profit..................................           3,169         33.5907         0.99856           21.76
----------------------------------------------------------------------------------------------------------------
* The total group category excludes 1,028 SNFs that failed to meet the finalized measure minimum policy.
** The group category that includes hospital-based/freestanding by urban/rural excludes 80 swing bed SNFs that
  satisfied the finalized measure minimum policy.
N/A = Not available because no facilities in this group received a measure result.


[[Page 53338]]

    In section VIII.B.4. of this final rule, we are adopting three 
additional measures (Falls with Major Injury (Long-Stay), DC Function, 
and Long Stay Hospitalization measures) beginning with the FY 2027 
program year. Additionally, in section VIII.E.2.b. of this final rule, 
we are adopting case minimum requirements for the Falls with Major 
Injury (Long-Stay), DC Function, and Long Stay Hospitalization 
measures. In section VIII.E.2.d. of this final rule, we are also 
finalizing an update to our previously finalized measure minimum for 
the FY 2027 program year. Therefore, we provided estimated impacts of 
the FY 2027 SNF VBP Program, which are based on historical data and 
appear in Tables 35 and 36. We modeled SNF performance in the Program 
using measure data from FY 2019 (SNFRM, SNF HAI, Total Nurse Staffing, 
Nursing Staff Turnover, Falls with Major Injury (Long-Stay), and DC 
Function measures), CY 2019 (Long Stay Hospitalization measure), and FY 
2018 through FY 2019 (DTC PAC SNF measure) as the baseline period and 
FY 2021 (SNFRM, SNF HAI, Total Nurse Staffing, Nursing Staff Turnover, 
Falls with Major Injury (Long-Stay), and DC Function measures), CY 2021 
(Long Stay Hospitalization measure), and FY 2020 through FY 2021 (DTC 
PAC SNF measure) as the performance period. Additionally, we modeled a 
logistic exchange function with an approximate payback percentage of 
66.02 percent for the Health Equity Adjustment, as we finalized in 
section VIII.E.4.e. of this final rule. Based on the increase in 
payback percentage, we estimated that we will redistribute 
approximately $324.18 million (of the estimated $491.03 million in 
withheld funds) in value-based incentive payments to SNFs in FY 2027, 
which means that the SNF VBP Program is estimated to result in 
approximately $166.86 million in savings to the Medicare Program in FY 
2027. Of the $324.18 million, $29.56 million is due to the Health 
Equity Adjustment, as indicated in Table 23 in section VIII.E.4.e. of 
this final rule.
    Our detailed analysis of the impacts of the FY 2027 SNF VBP Program 
is shown in Tables 35 and 36.

[[Page 53339]]



                                                                     Table 35--Estimated SNF VBP Program Impacts for FY 2027
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                                                                                                                Mean percentage
                                                                             Mean case-mix                                                    Mean number of        of stays     Mean percentage
                                                              Mean risk-    adjusted total    Mean risk-      Mean total      Mean risk-       risk-adjusted       meeting or    of stays with a
                                                             standardized    nursing hours   standardized    nursing staff   standardized    hospitalizations      exceeding     fall with major
              Characteristic                   Number of      readmission    per resident      hospital-     turnover rate   discharge to     per 1,000 long-       expected      injury (falls
                                              facilities     rate  (SNFRM)    day (total       acquired     (nursing staff  community rate  stay resident days     discharge        with major
                                                                  (%)            nurse      infection rate   turnover) (%)   (DTC PAC) (%)      (long stay       function score   injury (long-
                                                                               staffing)     (SNF HAI) (%)                                   hospitalization)    (DC Function)      stay)) (%)
                                                                                                                                             (Hosp. per 1,000)        (%)
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Group:
    Total *...............................          13,672           20.39            3.92            7.68           52.64           51.28                1.47            51.96             3.36
    Urban.................................          10,083           20.52            3.94            7.69           52.30           52.03                1.50            51.72             3.07
    Rural.................................           3,589           20.03            3.86            7.63           53.58           49.18                1.39            52.61             4.16
    Hospital-based urban **...............             227           20.00            5.26            6.47           46.33           60.97                1.10            46.90             2.17
    Freestanding urban **.................           9,852           20.53            3.91            7.72           52.42           51.82                1.51            51.84             3.09
    Hospital-based rural **...............             138           19.72            4.84            6.86           45.96           52.78                1.07            49.82             4.22
    Freestanding rural **.................           3,409           20.04            3.82            7.68           53.87           48.80                1.40            52.85             4.16
Urban by region:
    New England...........................             706           20.54            4.05            7.09           45.51           55.47                1.41            56.04             3.67
    Middle Atlantic.......................           1,397           20.31            3.67            7.56           45.98           49.63                1.40            54.87             2.95
    South Atlantic........................           1,805           20.76            4.02            7.86           51.79           52.38                1.52            50.96             3.10
    East North Central....................           1,871           20.76            3.62            7.72           55.11           52.56                1.52            48.29             3.23
    East South Central....................             533           20.75            3.97            8.04           55.79           50.89                1.49            48.03             3.37
    West North Central....................             827           20.17            4.19            7.41           57.62           51.24                1.51            55.00             3.82
    West South Central....................           1,183           20.98            3.74            8.03           58.96           49.37                1.73            52.38             3.24
    Mountain..............................             472           19.93            4.16            7.13           56.75           57.52                1.17            55.02             2.96
    Pacific...............................           1,286           19.97            4.44            7.84           47.08           52.86                1.52            49.62             1.89
    Outlying..............................               3           20.46            3.30            6.20             N/A           66.54                 N/A            50.77             0.00
Rural by region:
    New England...........................             108           19.54            4.32            6.65           54.60           53.27                1.04            57.92             4.18
    Middle Atlantic.......................             191           19.57            3.41            7.13           52.89           47.82                1.13            53.15             3.99
    South Atlantic........................             421           20.24            3.73            7.79           52.89           48.10                1.42            49.41             3.84
    East North Central....................             799           19.94            3.63            7.47           52.80           51.48                1.30            49.59             4.14
    East South Central....................             439           20.42            3.92            8.25           51.98           48.11                1.57            48.57             3.65
    West North Central....................             800           19.84            4.10            7.51           53.61           47.74                1.35            56.70             4.77
    West South Central....................             577           20.55            3.82            8.02           55.64           47.69                1.73            53.31             4.17
    Mountain..............................             173           19.55            4.17            7.16           55.65           51.94                1.02            58.19             4.22
    Pacific...............................              81           18.63            4.32            6.76           54.33           54.64                0.96            55.69             3.11
    Outlying:.............................               0  ..............  ..............  ..............  ..............  ..............  ..................  ...............  ...............
Rural by region:
    Government............................             717           19.96            4.34            7.38           49.01           50.37                1.41            51.75             3.80
    Profit................................           9,825           20.52            3.73            7.90           54.16           50.32                1.53            51.24             3.17
    Non-Profit............................           3,130           20.10            4.44            7.04           48.71           54.49                1.33            54.25             3.85
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
* The total group category excludes 1,235 SNFs that failed to meet the finalized four out of eight measure minimum policy.
** The group category that includes hospital-based/freestanding by urban/rural excludes 46 swing bed SNFs that satisfied the finalized measure minimum policy.
N/A = Not available because no facilities in this group received a measure result.


[[Page 53340]]


                             Table 36--Estimated SNF VBP Program Impacts for FY 2027
----------------------------------------------------------------------------------------------------------------
                                                    Mean health        Mean       Mean incentive
         Characteristic              Number of     equity bonus     performance       payment       Percent of
                                    facilities      points ***      score ****      multiplier     total payment
----------------------------------------------------------------------------------------------------------------
Group:
    Total *.....................          13,672          1.3922         32.9455         0.99185          100.00
    Urban.......................          10,083          1.4065         33.2266         0.99208           85.82
    Rural.......................           3,589          1.3522         32.1558         0.99119           14.18
    Hospital-based urban **.....             227          1.0527         45.8943         1.00332            1.59
    Freestanding urban **.......           9,852          1.4151         32.9329         0.99182           84.23
    Hospital-based rural **.....             138          1.0851         43.4161         1.00072            0.38
    Freestanding rural **.......           3,409          1.3752         31.5523         0.99069           13.70
Urban by region:
    New England.................             706          1.6512         37.2281         0.99477            5.32
    Middle Atlantic.............           1,397          1.5283         34.0874         0.99249           17.29
    South Atlantic..............           1,805          1.2317         32.5500         0.99129           17.10
    East North Central..........           1,871          0.9931         28.9562         0.98911           12.59
    East South Central..........             533          0.9183         29.0674         0.98909            3.49
    West North Central..........             827          0.7315         32.7553         0.99175            3.98
    West South Central..........           1,183          1.3010         27.3676         0.98777            7.18
    Mountain....................             472          1.0725         39.2626         0.99648            3.82
    Pacific.....................           1,286          2.8460         42.4505         0.99940           15.04
    Outlying....................               3          0.0000         36.5564         0.99256            0.00
Rural by region:
    New England.................             108          1.9869         42.3485         0.99953            0.61
    Middle Atlantic.............             191          1.7348         31.4130         0.99020            0.91
    South Atlantic..............             421          1.6187         29.0528         0.98846            2.09
    East North Central..........             799          1.1916         31.2626         0.99059            3.22
    East South Central..........             439          1.6169         29.8730         0.98945            2.16
    West North Central..........             800          0.6760         33.9294         0.99251            1.81
    West South Central..........             577          1.7368         29.1213         0.98892            2.12
    Mountain....................             173          1.3443         39.8837         0.99746            0.64
    Pacific.....................              81          2.3226         45.2226         1.00188            0.62
    Outlying....................               0  ..............  ..............  ..............            0.00
Ownership:
    Government..................             717          1.5059         37.5369         0.99586            3.17
    Profit......................           9,825          1.5991         30.8612         0.99018           75.10
    Non-Profit..................           3,130          0.7168         38.4361         0.99618           21.72
----------------------------------------------------------------------------------------------------------------
* The total group category excludes 1,235 SNFs that failed to meet the finalized four out of eight measure
  minimum policy.
** The group category that includes hospital-based/freestanding by urban/rural excludes 46 swing bed SNFs that
  satisfied the finalized measure minimum policy.
*** Because performance scores are capped at 100 points, SNFs may not receive all health equity bonus points
  they earn.
**** The mean total performance score is calculated by adding the finalized Health Equity Adjustment bonus
  points to the normalized sum of individual measure scores. N/A = Not available because no facilities in this
  group received a measure result.

    In section VIII.B.3. of this final rule, we are replacing the SNFRM 
with the SNF WS PPR measure beginning with the FY 2028 program year. 
Additionally, in section VIII.E.2.b. of this final rule, we are 
adopting a case minimum requirement for the SNF WS PPR measure. 
Therefore, we provided estimated impacts of the FY 2028 SNF VBP 
Program, which are based on historical data and appear in Tables 37 and 
38. We modeled SNF performance in the Program using measure data from 
FY 2019 (SNF HAI, Total Nurse Staffing, Nursing Staff Turnover, Falls 
with Major Injury (Long-Stay), and DC Function measures), CY 2019 (Long 
Stay Hospitalization measure), FY 2018 through FY 2019 (DTC PAC SNF 
measure), and FY 2019 through FY 2020 (SNF WS PPR measure) as the 
baseline period and FY 2021 (SNF HAI, Total Nurse Staffing, Nursing 
Staff Turnover, Falls with Major Injury (Long-Stay), and DC Function 
measures), CY 2021 (Long Stay Hospitalization measure), FY 2020 through 
FY 2021 (DTC PAC SNF measure), and FY 2020 through FY 2021 (SNF WS PPR 
measure) as the performance period. Additionally, we modeled a logistic 
exchange function with an approximate payback percentage of 65.4 
percent, as we finalized in section VIII.E.4.e. of this final rule. 
Based on the increase in payback percentage, we estimated that we will 
redistribute approximately $323.23 million (of the estimated $494.21 
million in withheld funds) in value-based incentive payments to SNFs in 
FY 2028, which means that the SNF VBP Program is estimated to result in 
approximately $170.98 million in savings to the Medicare Program in FY 
2028.
    Our detailed analysis of the impacts of the FY 2028 SNF VBP Program 
is shown in Tables 37 and 38.

[[Page 53341]]



                                                                     Table 37--Estimated SNF VBP Program Impacts for FY 2028
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                                                                                                                       Mean            Mean
                                                                 Mean SNF                                                                       Mean number of     percentage of   percentage of
                                                                within-stay     Mean total      Mean risk-      Mean total      Mean risk-      risk- adjusted     stays meeting   stays with a
                                                                potentially    nursing hours   standardized    nursing staff   standardized    hospitalizations    or exceeding      fall with
               Characteristic                    Number of      preventable    per resident      hospital-     turnover rate   discharge to     per 1,000 long-      expected      major injury
                                                facilities      readmission     day (total       acquired     (nursing staff  community rate  stay resident days     discharge      (falls with
                                                               rate (SNF WS        nurse      infection rate   turnover) (%)   (DTC PAC) (%)      (long stay      function score   major injury
                                                                 PPR) (%)        staffing)     (SNF HAI) (%)                                   hospitalization)    (DC Function)   (long-stay))
                                                                                                                                               (hosp. per 1,000)        (%)             (%)
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Group:
    Total *.................................          14,048           11.57            3.92            7.67           52.74           51.18                1.47           51.96            3.36
    Urban...................................          10,313           11.71            3.94            7.69           52.41           51.94                1.51           51.75            3.07
    Rural...................................           3,735           11.18            3.87            7.62           53.66           49.10                1.39           52.53            4.15
    Hospital-based urban **.................             230            9.07            5.26            6.48           46.22           60.88                1.10           46.91            2.27
    Freestanding urban **...................          10,079           11.77            3.91            7.72           52.53           51.73                1.51           51.87            3.09
    Hospital-based rural **.................             142            9.44            4.84            6.88           45.96           52.54                1.06           49.90            4.19
    Freestanding rural **...................           3,548           11.30            3.83            7.67           53.95           48.71                1.40           52.75            4.14
Urban by region:
    New England.............................             712           10.70            4.05            7.09           45.49           55.47                1.41           55.98            3.67
    Middle Atlantic.........................           1,411           11.66            3.67            7.56           46.02           49.60                1.40           54.80            2.95
    South Atlantic..........................           1,827           11.86            4.04            7.85           51.78           52.34                1.53           51.03            3.11
    East North Central......................           1,935           11.88            3.61            7.73           55.28           52.39                1.52           48.33            3.22
    East South Central......................             539           11.77            3.96            8.03           55.87           50.88                1.49           48.20            3.34
    West North Central......................             858           11.27            4.17            7.41           57.92           51.11                1.51           55.12            3.83
    West South Central......................           1,235           12.75            3.73            8.02           59.06           49.27                1.73           52.68            3.21
    Mountain................................             482           10.17            4.17            7.14           56.57           57.32                1.17           54.76            2.98
    Pacific.................................           1,310           11.70            4.45            7.84           47.13           52.81                1.53           49.52            1.90
    Outlying................................               4            8.14            4.70            6.52             N/A           64.89                 N/A           47.36            0.00
Rural by region:
    New England.............................             112            9.98            4.33            6.67           54.86           52.92                1.05           57.56            4.20
    Middle Atlantic.........................             195           10.38            3.41            7.16           53.05           47.85                1.14           52.95            3.94
    South Atlantic..........................             436           11.43            3.72            7.76           53.00           48.14                1.42           49.32            3.79
    East North Central......................             824           10.90            3.63            7.48           53.03           51.45                1.30           49.40            4.12
    East South Central......................             451           12.06            3.93            8.23           51.93           48.13                1.57           48.54            3.64
    West North Central......................             854           10.77            4.12            7.50           53.54           47.56                1.34           56.37            4.72
    West South Central......................             603           12.40            3.83            8.02           55.74           47.62                1.72           53.46            4.16
    Mountain................................             178           10.02            4.17            7.15           55.81           51.79                1.03           58.21            4.25
    Pacific.................................              82            9.32            4.37            6.76           54.33           54.46                0.97           56.23            3.12
    Outlying................................               0  ..............  ..............  ..............  ..............  ..............  ..................  ..............  ..............
Ownership:
    Government..............................             737           10.84            4.36            7.38           48.97           50.33                1.42           51.79            3.85
    Profit..................................          10,119           11.98            3.72            7.90           54.28           50.25                1.52           51.27            3.17
    Non-Profit..............................           3,192           10.45            4.45            7.04           48.74           54.35                1.32           54.19            3.85
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
* The total group category excludes 859 SNFs that failed to meet the finalized four of eight measure minimum policy.
** The group category that includes hospital-based/freestanding by urban/rural excludes 49 swing bed SNFs that satisfied the finalized measure minimum policy.
N/A = Not available because no facilities in this group received a measure result.


[[Page 53342]]


                             Table 38--Estimated SNF VBP Program Impacts for FY 2028
----------------------------------------------------------------------------------------------------------------
                                                    Mean health        Mean       Mean incentive
         Characteristic              Number of     equity bonus     performance       payment       Percent of
                                    facilities      points ***      score ****      multiplier     total payment
----------------------------------------------------------------------------------------------------------------
Group:
    Total *.....................          14,048          1.3866         33.7117         0.99216          100.00
    Urban.......................          10,313          1.3834         33.8699         0.99229           85.72
    Rural.......................           3,735          1.3952         33.2749         0.99180           14.28
    Hospital-based urban **.....             230          1.0999         50.6699         1.00718            1.59
    Freestanding urban **.......          10,079          1.3903         33.4786         0.99194           84.13
    Hospital-based rural **.....             142          1.1789         46.3840         1.00274            0.38
    Freestanding rural **.......           3,548          1.4162         32.4459         0.99108           13.80
Urban by region:
    New England.................             712          1.6450         38.8562         0.99580            5.30
    Middle Atlantic.............           1,411          1.4441         34.5592         0.99248           17.19
    South Atlantic..............           1,827          1.2259         33.1678         0.99158           17.04
    East North Central..........           1,935          1.0242         29.8652         0.98953           12.61
    East South Central..........             539          0.9089         30.1968         0.98983            3.48
    West North Central..........             858          0.7433         33.4543         0.99206            4.01
    West South Central..........           1,235          1.2998         28.0800         0.98804            7.28
    Mountain....................             482          1.1398         41.1899         0.99784            3.83
    Pacific.....................           1,310          2.7134         41.8142         0.99832           14.99
    Outlying....................               4          0.0000         49.0903         1.00665            0.00
Rural by region:
    New England.................             112          2.1095         43.5189         1.00029            0.61
    Middle Atlantic.............             195          1.6914         32.6276         0.99092            0.91
    South Atlantic..............             436          1.6562         30.1287         0.98926            2.10
    East North Central..........             824          1.2515         32.2562         0.99102            3.24
    East South Central..........             451          1.6207         30.7335         0.99007            2.16
    West North Central..........             854          0.7418         35.6622         0.99352            1.85
    West South Central..........             603          1.7832         29.8043         0.98910            2.14
    Mountain....................             178          1.4983         41.1638         0.99796            0.64
    Pacific.....................              82          2.2569         45.2986         1.00159            0.62
    Outlying....................               0  ..............  ..............  ..............            0.00
Ownership:
    Government..................             737          1.5601         38.6989         0.99642            3.18
    Profit......................          10,119          1.5762         31.3261         0.99022           75.13
    Non-Profit..................           3,192          0.7454         40.1229         0.99730           21.69
----------------------------------------------------------------------------------------------------------------
* The total group category excludes 859 SNFs that failed to meet the finalized four out of eight measure minimum
  policy.
** The group category that includes hospital-based/freestanding by urban/rural excludes 49 swing bed SNFs that
  satisfied the finalized measure minimum policy.
*** Because performance scores are capped at 100 points, SNFs may not receive all health equity bonus points
  they earn.
**** The mean total performance score is calculated by adding the finalized Health Equity Adjustment bonus
  points to the normalized sum of individual measure scores.
N/A = Not available because no facilities in this group received a measure result.

7. Impacts for Civil Money Penalties (CMP): Waiver Process Changes
    Current requirements at Sec.  488.436(a) set forth a process for 
submitting a written waiver of a hearing to appeal deficiencies that 
lead to the imposition of a CMP which, when properly filed, results in 
the reduction by CMS or the State of a facility's CMP by 35 percent, as 
long as the CMP has not also been reduced by 50 percent under Sec.  
488.438. We proposed to restructure the waiver process by establishing 
a constructive waiver at Sec.  488.436(a) that would operate only when 
CMS has not received a timely request for a hearing. Since a large 
majority of facilities facing CMPs typically submit the currently 
required written waiver, this change to provide for a constructive 
waiver (after the 60-day timeframe in which to file an appeal following 
notice of CMP imposition) will reduce the costs and paperwork burden 
for CMS and will also ease the administrative burden for CMS in 
processing these waiver requests.
    This provision will generate operational efficiencies and savings 
by reallocating staff resources from current responsibilities of 
tracking and managing the receipt of documentation from facilities 
requesting a waiver in writing (accounting for approximately one hour 
per CMP case). For example, in CY 2022, we imposed a total of 11,475 
CMPs on 5,319 facilities, with an average of 2.16 CMPs per facility, 
resulting in a total of 9,191 hours each year (0.80 hours per CMP x 
5,319 facilities x 2.16 CMPs per facility) to manage the waiver-related 
review and processing. In CY 2022, 81 percent (4,308) of the 5,319 
facilities with imposed CMPs submitted written waivers. If a 
constructive waiver were introduced, we estimate that CMS would save 
roughly $625,315 per year ($84.00 per hour x 7,444 hours per year). Our 
estimate on the average rate of $84.00 per hour is based on a GS-12, 
step 5 salary rate of $42.00 per hour, with 100 percent benefits and an 
overhead package.
    Although our focus is on the prioritization of CMS resources for 
oversight and enforcement activities, finalizing this proposal will 
also ease the administrative burden for facilities that are currently 
submitting waiver requests to CMS locations. In CY 2022, 81 percent of 
facilities facing CMPs filed a waiver; while only 2 percent of 
facilities filed an appeal of their CMP with the Departmental Appeals 
Board. The remaining 17 percent of facilities neither waived nor timely 
filed an appeal. We estimate that moving to a constructive waiver 
process would

[[Page 53343]]

eliminate the time and paperwork necessary to complete and send in a 
written waiver and would thereby result, as detailed below, in a total 
annual savings of $2,299,716 in administrative costs for LTC facilities 
facing CMPs ($861,678 + $1,438,038 = $2,299,716).
    We estimate that, at a minimum, facilities will save the routine 
cost of preparing and filing a letter (estimated at $200 per letter 
based on the hourly rate of the employee(s) and the time required to 
prepare and file the letter) to waive their hearing rights. In CY 2022, 
there were 5,319 facilities who were imposed CMPs. Roughly 81 percent 
(4,308) of these facilities filed written waivers, therefore, we 
estimate an annual savings of $861,678 (4,308 x $200) since such 
letters would no longer be required to receive a 35 percent penalty 
reduction when a facility is not appealing the CMP.
    In addition, we believe that nationally some 17 percent of 
facilities fail to submit a waiver even though they had no intention of 
contesting the penalty and its basis. Under the change to offer a 
constructive waiver automatically, this 17 percent of facilities will 
now be eligible for the 35 percent CMP amount cost reduction. We note 
that in CY 2022, CMS imposed a combined total of $190,967,833 in per 
day and per instance CMPs, with a median total amount due of $4,545. 
Since CMS imposed CMPs on 5,319 facilities in CY 2022, we estimate a 
cost savings for 904 facilities (17 percent of 5,319), the typical 17 
percent who fail to submit a timely waiver request. We estimate the 
annual cost savings for these facilities at $1,438,038 ((35 percent x 
$4,545) x 904 facilities).
    Total annual savings from these reforms to facilities and the 
Federal government together will therefore be $2,925,031 ($2,299,716 
plus $625,315).
8. Alternatives Considered
    As described in this section, we estimate that the aggregate impact 
of the provisions in this final rule will result in an increase of 
approximately $1.4 billion (4.0 percent) in Part A payments to SNFs in 
FY 2024. This reflects a $2.2 billion (6.4 percent) increase from the 
update to the payment rates and a $789 million (2.3 percent) decrease 
as a result of the second phase of the parity adjustment recalibration, 
using the formula to multiply the percentage change described in 
section IV.A.4. of this final rule.
    Section 1888(e) of the Act establishes the SNF PPS for the payment 
of Medicare SNF services for cost reporting periods beginning on or 
after July 1, 1998. This section of the statute prescribes a detailed 
formula for calculating base payment rates under the SNF PPS, and does 
not provide for the use of any alternative methodology. It specifies 
that the base year cost data to be used for computing the SNF PPS 
payment rates must be from FY 1995 (October 1, 1994, through September 
30, 1995). In accordance with the statute, we also incorporated a 
number of elements into the SNF PPS (for example, case-mix 
classification methodology, a market basket update, a wage index, and 
the urban and rural distinction used in the development or adjustment 
of the Federal rates). Further, section 1888(e)(4)(H) of the Act 
specifically requires us to disseminate the payment rates for each new 
FY through the Federal Register, and to do so before the August 1 that 
precedes the start of the new FY; accordingly, we are not pursuing 
alternatives for this process.
    With regard to the proposals to modify the COVID-19 Vaccination 
Coverage Among Healthcare Personnel (HCP COVID-19 Vaccine) measure and 
to adopt the COVID-19 Vaccine: Percent of Patients/Residents Who are Up 
to Date (Patient/Resident COVID-19 Vaccine) measure to the SNF QRP 
Program, the COVID-19 pandemic has exposed the importance of 
implementing infection prevention strategies, including the promotion 
of COVID-19 vaccination for healthcare personnel (HCP) and residents. 
We believe these measures will encourage HCP and residents to be ``up 
to date'' with the COVID-19 vaccine, in accordance with current 
recommendations of the Centers for Disease Control and Prevention 
(CDC), and increase vaccine uptake in HCP and residents resulting in 
fewer cases, less hospitalizations, and lower mortality associated with 
the virus. We were unable to identify any alternative methods for 
collecting the data, and there is still an overwhelming public need to 
target infection control and related quality improvement activities 
among SNF providers as well as provide data to patients and caregivers 
about the rate of COVID-19 vaccination among SNFs' HCP and residents 
through transparency of data. Therefore, these measures have the 
potential to generate actionable data on COVID-19 vaccination rates for 
SNFs.
    While we proposed to remove the Application of Percent of Long-Term 
Care Hospital Patients with an Admission and Discharge Functional 
Assessment and a Care Plan That Addresses Function (Application of 
Functional Assessment/Care Plan) process measure, we also proposed to 
adopt the Discharge Function Score (DC Function) measure, which has 
strong scientific acceptability, and satisfies the requirement that 
there be at least one cross-setting function measure in the Post-Acute 
Care QRPs that uses standardized functional assessment data elements 
from standardized patient assessment instruments. We considered the 
alternative of delaying the proposal of the DC Function measure, but 
given its strong scientific acceptability, the fact that it provides an 
opportunity to replace the current cross-setting process measure with 
an outcome measure, and uses standardized functional assessment data 
elements that are already collected, we believe further delay is 
unwarranted. With regard to the proposal to remove the Application of 
Functional Assessment/Care Plan, the removal of this measure meets 
measure removal factors one and six set forth in Sec.  413.360(b)(2), 
and no longer provides meaningful distinctions in improvements in 
performance.
    The proposal to remove the Change in Self-Care Score and Change in 
Mobility Score measures meets measure removal factor eight set forth in 
Sec.  413.360(b)(2), and the costs associated with a measure outweigh 
the benefits of its use in the program. Therefore, no alternatives were 
considered.
    With regard to the proposal to increase the data completion 
threshold for the Minimum Data Set (MDS) items, the increased threshold 
of 100 percent completion of the required data elements on at least 90 
percent of assessments submitted, is based on the need for 
substantially complete records, which allows appropriate analysis of 
quality measure data for the purposes of updating quality measure 
specifications. These data are ultimately reported to the public, 
allowing our beneficiaries to gain a more complete understanding of SNF 
performance related to these quality metrics, and helping them to make 
informed healthcare choices. We considered the alternative of not 
increasing the data completion threshold, but our data suggest that 
SNFs are already in compliance with or exceeding this proposed 
threshold, and therefore, no additional burden is anticipated.
    With regard to the proposals for the SNF VBP Program, we discussed 
alternatives considered within those sections. In section VII.E.5. of 
the proposed rule, we discussed other approaches to incorporating 
health equity into the Program.
9. Accounting Statement
    As required by OMB Circular A-4 (available online at https://

[[Page 53344]]

obamawhitehouse.archives.gov/omb/circulars_a004_a-4/), in Tables 39 
through 43, we have prepared an accounting statement showing the 
classification of the expenditures associated with the provisions of 
this final rule for FY 2024. Tables 30 and 39 provide our best estimate 
of the possible changes in Medicare payments under the SNF PPS as a 
result of the policies in this final rule, based on the data for 15,503 
SNFs in our database. Tables 31 and 40 through 41 provide our best 
estimate of the additional cost to SNFs to submit the data for the SNF 
QRP as a result of the policies in this proposed rule. Table 42 
provides our best estimate of the possible changes in Medicare payments 
under the SNF VBP as a result of the policies for this program. Table 
43 provides our best estimate of the amount saved by LTC facilities and 
CMS by removing the requirement to submit a written request and 
establishing a constructive waiver process instead at Sec.  488.436(a) 
that will operate by default when CMS has not received notice of a 
facility's intention to submit a timely request for a hearing.

       Table 39--Accounting Statement: Classification of Estimated
   Expenditures, From the 2023 SNF PPS Fiscal Year to the 2024 SNF PPS
                               Fiscal Year
------------------------------------------------------------------------
                 Category                             Transfers
------------------------------------------------------------------------
Annualized Monetized Transfers............  $1.4 billion.*
From Whom To Whom?........................  Federal Government to SNF
                                             Medicare Providers.
------------------------------------------------------------------------
* The net increase of $1.4 billion in transfer payments reflects a 4.0
  percent increase, which is the product of the multiplicative formula
  described in section XII.A.4 of this rule. It reflects the 6.4 percent
  increase (approximately $2.2 billion) from the SNF market basket
  update to the payment rates, as well as a negative 2.3 percent
  decrease (approximately $789 million) from the second phase of the
  parity adjustment recalibration. Due to rounding and the nature of the
  multiplicative formula, dollar figures are approximations and may not
  sum.


Table 40--Accounting Statement: Classification of Estimated Expenditures
                       for the FY 2025 QRP Program
------------------------------------------------------------------------
                       Category                         Transfers/costs
------------------------------------------------------------------------
Savings to SNFs to Submit Data for QRP...............       ($1,037,261)
------------------------------------------------------------------------


Table 41--Accounting Statement: Classification of Estimated Expenditures
                     for the FY 2026 SNF QRP Program
------------------------------------------------------------------------
                       Category                         Transfers/costs
------------------------------------------------------------------------
Costs for SNFs to Submit Data for QRP................           $778,591
------------------------------------------------------------------------


Table 42--Accounting Statement: Classification of Estimated Expenditures
                     for the FY 2024 SNF VBP Program
------------------------------------------------------------------------
                 Category                             Transfers
------------------------------------------------------------------------
Annualized Monetized Transfers............  $277.27 million.*
From Whom To Whom?........................  Federal Government to SNF
                                             Medicare Providers.
------------------------------------------------------------------------
* This estimate does not include the 2 percent reduction to SNFs'
  Medicare payments (estimated to be $462.12 million) required by
  statute.


    Table 43--Accounting Statement: Civil Money Penalties: Waiver of
                  Hearing, Reduction of Penalty Amount
------------------------------------------------------------------------
                       Category                         Transfers/costs
------------------------------------------------------------------------
Cost Savings of Constructive Waiver..................         $2,925,031
------------------------------------------------------------------------
* The cost savings of $3 million is expected to occur in the first full
  year and be an ongoing savings for LTC Facilities and the Federal
  Government.

10. Conclusion
    This rule updates the SNF PPS rates contained in the SNF PPS final 
rule for FY 2023 (87 FR 47502). Based on the above, we estimate that 
the overall payments for SNFs under the SNF PPS in FY 2024 are 
projected to increase by approximately $1.4 billion, or 4.0 percent, 
compared with those in FY 2023. We estimate that in FY 2024, SNFs in 
urban and rural areas would experience, on average, a 4.1 percent 
increase and 3.3 percent increase, respectively, in estimated payments 
compared with FY 2023. Providers in the urban Middle Atlantic region 
would experience the largest estimated increase in payments of 
approximately 5.3 percent. Providers in the urban Outlying region would 
experience the smallest estimated increase in payments of 1.6 percent.

B. Regulatory Flexibility Act Analysis

    The RFA requires agencies to analyze options for regulatory relief 
of small entities, if a rule has a significant impact on a substantial 
number of small entities. For purposes of the RFA, small entities 
include small businesses, non-profit organizations, and small 
governmental jurisdictions. Most SNFs and most other providers and 
suppliers are small entities, either by reason of their non-profit 
status or by having revenues of $30 million or less in any 1 year. We 
utilized the revenues of individual SNF providers (from recent Medicare 
Cost Reports) to classify a small business, and not the revenue of a 
larger firm with which they may be affiliated. As a result, for the 
purposes of the RFA, we estimate that almost all SNFs are small 
entities as that term is used in the RFA, according to the Small 
Business Administration's latest size standards (NAICS 623110), with 
total revenues of $30 million or less in any 1 year. (For details, see 
the Small Business Administration's website at https://www.sba.gov/category/navigation-structure/contracting/contracting-officials/eligibility-size-standards) In addition, approximately 20 percent of 
SNFs classified as small entities are non-profit organizations. 
Finally, individuals and states are not included in the definition of a 
small entity.
    This rule updates the SNF PPS rates contained in the SNF PPS final 
rule for FY 2023 (87 FR 47502). Based on the above, we estimate that 
the aggregate impact for FY 2024 will be an increase of $1.4 billion in 
payments to SNFs, resulting from the SNF market basket update to the 
payment rates, reduced by the second phase of the parity adjustment 
recalibration discussed in section IV.C. of this final rule, using the 
formula described in section XII.A.4. of this rule. While it is 
projected in Table 30 that all providers would experience a net 
increase in payments, we note that some individual providers within the 
same region or group may experience different impacts on payments than 
others due to the distributional impact of the FY 2024 wage indexes and 
the degree of Medicare utilization.
    Guidance issued by the Department of Health and Human Services on 
the proper assessment of the impact on small entities in rulemakings, 
utilizes a cost or revenue impact of 3 to 5 percent as a significance 
threshold under the RFA. In their March 2023 Report to Congress 
(available at https://www.medpac.gov/wp-content/uploads/2023/03/Ch7_Mar23_MedPAC_Report_To_Congress_SEC.pdf), MedPAC states that 
Medicare covers approximately 10 percent of total patient days in 
freestanding facilities and 16 percent of facility revenue (March 2023 
MedPAC Report to Congress, 207). As indicated in Table 30, the effect 
on facilities is

[[Page 53345]]

projected to be an aggregate positive impact of 4.0 percent for FY 
2024. As the overall impact on the industry as a whole, and thus on 
small entities specifically, meets the 3 to 5 percent threshold 
discussed previously, the Secretary has determined that this final rule 
will have a significant impact on a substantial number of small 
entities for FY 2024.
    In addition, section 1102(b) of the Act requires us to prepare a 
regulatory impact analysis if a rule may have a significant impact on 
the operations of a substantial number of small rural hospitals. This 
analysis must conform to the provisions of section 604 of the RFA. For 
purposes of section 1102(b) of the Act, we define a small rural 
hospital as a hospital that is located outside of an MSA and has fewer 
than 100 beds. This final rule will affect small rural hospitals that: 
(1) furnish SNF services under a swing-bed agreement or (2) have a 
hospital-based SNF. We anticipate that the impact on small rural 
hospitals would be similar to the impact on SNF providers overall. 
Moreover, as noted in previous SNF PPS final rules (most recently, the 
one for FY 2023 (87 FR 47502)), the category of small rural hospitals 
is included within the analysis of the impact of this final rule on 
small entities in general. As indicated in Table 30, the effect on 
facilities for FY 2024 is projected to be an aggregate positive impact 
of 4.0 percent. As the overall impact on the industry as a whole meets 
the 3 to 5 percent threshold discussed above, the Secretary has 
determined that this final rule will have a significant impact on a 
substantial number of small rural hospitals for FY 2024.

C. Unfunded Mandates Reform Act Analysis

    Section 202 of the Unfunded Mandates Reform Act of 1995 also 
requires that agencies assess anticipated costs and benefits before 
issuing any rule whose mandates require spending in any 1 year of $100 
million in 1995 dollars, updated annually for inflation. In 2023, that 
threshold is approximately $177 million. This final rule will impose no 
mandates on State, local, or Tribal governments or on the private 
sector.

D. Federalism Analysis

    Executive Order 13132 establishes certain requirements that an 
agency must meet when it issues a proposed rule (and subsequent final 
rule) that imposes substantial direct requirement costs on State and 
local governments, preempts State law, or otherwise has federalism 
implications. This final rule will have no substantial direct effect on 
State and local governments, preempt State law, or otherwise have 
federalism implications.

E. Regulatory Review Costs

    If regulations impose administrative costs on private entities, 
such as the time needed to read and interpret this final rule, we 
should estimate the cost associated with regulatory review. Due to the 
uncertainty involved with accurately quantifying the number of entities 
that will review the rule, we assume that the total number of unique 
commenters on this year's final rule will be the number of reviewers of 
this year's proposed rule. We acknowledge that this assumption may 
understate or overstate the costs of reviewing this rule. It is 
possible that not all commenters reviewed this year's proposed rule in 
detail, and it is also possible that some reviewers chose not to 
comment on that proposed rule. For these reasons, we believe that the 
number of commenters on this year's proposed rule is a fair estimate of 
the number of reviewers of this year's final rule.
    We also recognize that different types of entities are in many 
cases affected by mutually exclusive sections of this final rule, and 
therefore, for the purposes of our estimate we assume that each 
reviewer reads approximately 50 percent of the rule.
    The mean wage rate for medical and health service manages (SOC 11-
9111) in BLS OEWS is $61.53, assuming benefits plus other overhead 
costs equal 100 percent of wage rate, we estimate that the cost of 
reviewing this rule is $123.06 per hour, including overhead and fringe 
benefits https://www.bls.gov/oes/current/oes_nat.htm. Assuming an 
average reading speed, we estimate that it would take approximately 4 
hours for the staff to review half of the proposed rule. For each SNF 
that reviews the rule, the estimated cost is $492.24 (4 hours x 
$123.06). Therefore, we estimate that the total cost of reviewing this 
regulation is $39,871.44 ($460.88 x 81 reviewers).
    In accordance with the provisions of Executive Order 12866, this 
final rule was reviewed by the Office of Management and Budget.
    Chiquita Brooks-LaSure, Administrator of the Centers for Medicare & 
Medicaid Services, approved this document on July 20, 2023.

List of Subjects

42 CFR Part 411

    Diseases, Medicare, Reporting and recordkeeping requirements.

42 CFR Part 413

    Diseases, Health facilities, Medicare, Puerto Rico, Reporting and 
recordkeeping.

42 CFR Part 488

    Administrative practice and procedure, Health facilities, Health 
professions, Medicare, Reporting and recordkeeping requirements.

42 CFR Part 489

    Health facilities, Medicare, Reporting and recordkeeping 
requirements.
    For the reasons set forth in the preamble, the Centers for Medicare 
& Medicaid Services amends 42 CFR chapter IV as set forth below:

PART 411--EXCLUSIONS FROM MEDICARE AND LIMITATIONS ON MEDICARE 
PAYMENT

0
1. The authority citation for part 411 continues to read as follows:

    Authority:  42 U.S.C. 1302, 1395w-101 through 1395w-152, 1395hh, 
and 1395nn.

0
2. Effective January 1, 2024, amend Sec.  411.15 by:
0
a. Redesignating paragraphs (p)(2)(vi) through (xviii) as (p)(2)(viii) 
through (xx);
0
b. Adding new paragraphs (p)(2)(vi) and (vii); and
0
c. Revising newly redesignated paragraph (p)(2)(xiv).
    The additions and revisions read as follows:


Sec.  411.15  Particular services excluded from coverage.

* * * * *
    (p) * * *
    (2) * * *
    (vi) Services performed by a marriage and family therapist, as 
defined in section 1861(lll)(2) of the Act.
    (vii) Services performed by a mental health counselor, as defined 
in section 1861(lll)(4) of the Act.
* * * * *
    (xiv) Services described in paragraphs (p)(2)(i) through (viii) of 
this section when furnished via telehealth under section 
1834(m)(4)(C)(ii)(VII) of the Act.
* * * * *

PART 413--PRINCIPLES OF REASONABLE COST REIMBURSEMENT; PAYMENT FOR 
END-STAGE RENAL DISEASE SERVICES; PROSPECTIVELY DETERMINED PAYMENT 
RATES FOR SKILLED NURSING FACILITIES; PAYMENT FOR ACUTE KIDNEY 
INJURY DIALYSIS

0
3. The authority citation for part 413 continues to read as follows:


[[Page 53346]]


    Authority:  42 U.S.C. 1302, 1395d(d), 1395f(b), 1395g, 1395l(a), 
(i), and (n), 1395m, 1395x(v), 1395x(kkk), 1395hh, 1395rr, 1395tt, 
and 1395ww.


0
4. Section 413.338 is amended by--
0
a. Removing the paragraph designations for paragraphs (a)(1) through 
(17);
0
b. In paragraph (a) adding definitions in alphabetical order for 
``Health equity adjustment bonus points'', ``Measure performance 
scaler'', ``Top tier performing SNF'', ``Underserved multiplier'', and 
``Underserved population'';
0
c. Revising paragraphs (c)(2)(i), (d)(4)(v), and (e)(2) introductory 
text;
0
d. Adding paragraph (e)(3);
0
e. Revising paragraph (j); and
0
f. Adding paragraph (k).
    The additions and revisions read as follows:


Sec.  413.338  Skilled nursing facility value-based purchasing program.

    (a) * * *
    Health equity adjustment (HEA) bonus points means the points that a 
SNF can earn for a program year based on its performance and proportion 
of SNF residents who are members of the underserved population.
* * * * *
    Measure performance scaler means, for a program year, the sum of 
the points assigned to a SNF for each measure on which the SNF is a top 
tier performing SNF.
* * * * *
    Top tier performing SNF means a SNF whose performance on a measure 
during the applicable program year meets or exceeds the 66.67th 
percentile of SNF performance on the measure during the same program 
year.
    Underserved multiplier means the mathematical result of applying a 
logistic function to the number of SNF residents who are members of the 
underserved population out of the SNF's total Medicare population, as 
identified from the SNF's Part A claims, during the performance period 
that applies to the 1-year measures for the applicable program year.
    Underserved population means Medicare beneficiaries who are SNF 
residents in a Medicare Part A stay who are also dually eligible, both 
partial and full, for Medicaid.
* * * * *
    (c) * * *
    (2) * * *
    (i) Total amount available for a fiscal year. The total amount 
available for value-based incentive payments for a fiscal year is at 
least 60 percent of the total amount of the reduction to the adjusted 
SNF PPS payments for that fiscal year, as estimated by CMS, and will be 
increased as appropriate for each fiscal year to account for the 
assignment of a performance score to low-volume SNFs under paragraph 
(d)(3) of this section. Beginning with the FY 2023 SNF VBP, the total 
amount available for value-based incentive payments for a fiscal year 
is 60 percent of the total amount of the reduction to the adjusted SNF 
PPS payments for that fiscal year, as estimated by CMS. Beginning with 
the FY 2027 SNF VBP, the total amount available for value-based 
incentive payments for a fiscal year is at least 60 percent of the 
total amount of the reduction to the adjusted SNF PPS payments for that 
fiscal year, as estimated by CMS, and will be increased as appropriate 
for each fiscal year to account for the application of the Health 
equity adjustment bonus points as calculated under paragraph (k) of 
this section.
* * * * *
    (d) * * *
    (4) * * *
    (v) CMS will calculate a SNF Performance Score for a fiscal year 
for a SNF for which it has granted an exception request that does not 
include its performance on a quality measure during the calendar months 
affected by the extraordinary circumstance.
* * * * *
    (e) * * *
    (2) Calculation of the SNF performance score for fiscal year 2026. 
The SNF performance score for FY 2026 is calculated as follows:
* * * * *
    (3) Calculation of the SNF performance score beginning with fiscal 
year 2027. The SNF performance score for a fiscal year is calculated as 
follows:
    (i) CMS will sum all points awarded to a SNF as described in 
paragraph (e)(1) of this section for each measure applicable to a 
fiscal year.
    (ii) CMS will normalize the SNF's point total such that the 
resulting point total is expressed as a number of points earned out of 
a total of 100.
    (iii) CMS will add to the SNF's point total under paragraph 
(e)(3)(ii) of this section any applicable health equity adjustment 
bonus points calculated under paragraph (k) of this section such that 
the resulting point total is the SNF Performance Score for the fiscal 
year, except that no SNF Performance Score may exceed 100 points.
* * * * *
    (j) Validation. (1) Beginning with the FY 2023 program year, for 
the SNFRM measure, and beginning with the FY 2026 program year for all 
other claims-based measures, the information reported through claims 
are validated for accuracy by Medicare Administrative Contractors 
(MACs).
    (2) Beginning with the FY 2026 program year, for all measures that 
are calculated using Payroll-Based Journal System data, information 
reported through the Payroll-Based Journal system is validated for 
accuracy by CMS and its contractors through quarterly audits.
    (3) Beginning with the FY 2027 program year, for all measures that 
are calculated using Minimum Data Set (MDS) information, such 
information is validated for accuracy by CMS and its contractors 
through periodic audits not to exceed 1,500 SNFs per calendar year.
    (k) Calculation of the Health equity adjustment (HEA) bonus points. 
CMS calculates the number of HEA bonus points that are added to a SNF's 
point total calculated under paragraph (e)(3)(iii) of this section by:
    (1) Determining for each measure whether the SNF is a top tier 
performing SNF and assigning two points to the SNF for each such 
measure;
    (2) Summing the points calculated under paragraph (k)(1) of this 
section to calculate the measure performance scaler;
    (3) Calculating the underserved multiplier for the SNF; and
    (4) Multiplying the measure performance scaler calculated under 
paragraph (k)(2) of this section by the underserved multiplier 
calculated under paragraph (k)(3) of this section.


0
5. Section 413.360 is amended by revising paragraphs (f)(1) and (2) to 
read as follows:


Sec.  413.360  Requirements under the Skilled Nursing Facility (SNF) 
Quality Reporting Program (QRP).

* * * * *
    (f) * * *
    (1) SNFs must meet or exceed the following data completeness 
thresholds with respect to a calendar year:
    (i) The threshold set at 100 percent completion of measures data 
and standardized patient assessment data collected using the Minimum 
Data Set (MDS) on at least 80 percent of the assessments SNFs submit 
through the CMS designated data submission system for FY 2018 through 
FY 2025 program years.
    (ii) The threshold set at 100 percent completion of measures data 
and standardized patient assessment data collected using the MDS on at 
least 90 percent of the assessments SNFs submit through the CMS 
designated data submission system for FY 2026 and for all subsequent 
payment updates.

[[Page 53347]]

    (iii) The threshold set at 100 percent for measures data collected 
and submitted through the Centers for Disease Control and Prevention's 
(CDC) National Healthcare Safety Network (NHSN) for FY 2023 and for all 
subsequent payment updates.
    (2) These thresholds apply to all measures and standardized patient 
assessment data requirements adopted into the SNF QRP.
* * * * *

PART 488--SURVEY, CERTIFICATION, AND ENFORCEMENT PROCEDURES

0
6. The authority citation for part 488 continues to read as follows:

    Authority: 42 U.S.C. 1302 and 1395hh.


0
7. Section 488.432 is amended by revising paragraph (c) to read as 
follows:


Sec.  488.432  Civil money penalties imposed by the State: NF-only.

* * * * *
    (c) When a facility waives a hearing. (1) If a facility waives its 
right to a hearing as specified in Sec.  488.436, the State initiates 
collection of civil money penalty imposed per day of noncompliance 
after 60 days from the date of the notice imposing the penalty and the 
State has not received a timely request for a hearing.
    (2) If a facility waives its right to a hearing as specified in 
Sec.  488.436, the State initiates collection of civil money penalty 
imposed per instance of noncompliance after 60 days from the date of 
the notice imposing the penalty and the State has not received a timely 
request for a hearing.
* * * * *

0
8. Section 488.436 is amended by revising paragraph (a) to read as 
follows:


Sec.  488.436  Civil money penalties: Waiver of hearing, reduction of 
penalty amount.

    (a) Constructive waiver of a hearing. A facility is considered to 
have waived its right to a hearing after 60 days from the date of the 
notice imposing the civil money penalty if CMS has not received a 
request for a hearing from the facility.
* * * * *

0
9. Section 488.442 is amended by revising paragraph (a)(2) introductory 
text to read as follows:


Sec.  488.442  Civil money penalties: Due date for payment of penalty.

    (a) * * *
    (2) After the facility waives its right to a hearing in accordance 
with Sec.  488.436(a). Except as provided for in Sec.  488.431, a civil 
money penalty is due 75 days after the notice of the penalty in 
accordance with Sec.  488.436 and a hearing request was not received 
when:
* * * * *

PART 489--PROVIDER AGREEMENTS AND SUPPLIER APPROVAL

0
10. The authority citation for part 489 continues to read as follows:

    Authority: 42 U.S.C. 1302, 1395i-3, 1395x, 1395aa(m), 1395cc, 
1395ff, and 1395hh.


0
11. Effective January 1, 2024, amend Sec.  489.20 by:
0
a. Redesignating paragraphs (s)(6) through (18) as paragraphs (s)(8) 
through (20), respectively;
0
b. Adding new paragraphs (s)(6) and (7); and
0
c. Revising newly redesignated paragraph (s)(14).
    The additions and revisions read as follows:


Sec.  489.20  Basis commitments.

* * * * *
    (s) * * *
    (6) Services performed by a marriage and family therapist, as 
defined in section 1861(lll)(2) of the Act.
    (7) Services performed by a mental health counselor, as defined in 
section 1861(lll)(4) of the Act.
* * * * *
    (14) Services described in paragraphs (s)(1) through (8) of this 
section when furnished via telehealth under section 
1834(m)(4)(C)(ii)(VII) of the Act.
* * * * *

Xavier Becerra,
Secretary, Department of Health and Human Services.
[FR Doc. 2023-16249 Filed 7-31-23; 4:15 pm]
BILLING CODE 4120-01-P