[Federal Register Volume 87, Number 73 (Friday, April 15, 2022)]
[Proposed Rules]
[Pages 22720-22809]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2022-07906]



[[Page 22719]]

Vol. 87

Friday,

No. 73

April 15, 2022

Part III





Department of Health and Human Services





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Centers for Medicare & Medicaid Services





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42 CFR Part 413





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 
2023; Request for Information on Revising the Requirements for Long-
Term Care Facilities To Establish Mandatory Minimum Staffing Levels; 
Proposed Rule

  Federal Register / Vol. 87 , No. 73 / Friday, April 15, 2022 / 
Proposed Rules  

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DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Medicare & Medicaid Services

42 CFR Part 413

[CMS-1765-P]
RIN 0938-AU76


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 2023; Request for Information on Revising the Requirements for 
Long-Term Care Facilities To Establish Mandatory Minimum Staffing 
Levels

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

ACTION: Proposed rule; request for comments.

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SUMMARY: This proposed rule would update: Payment rates; forecast error 
adjustment; diagnosis code mappings; the Patient Driven Payment Model 
(PDPM) parity adjustment, the SNF Quality Reporting Program (QRP), SNF 
Value-Based Purchasing (VBP) Program. It also proposes to establish a 
permanent cap policy. This proposed rule also includes a request for 
information related to long-term care (LTC) facilities. CMS requests 
comments on these proposals as well as on related subjects and 
announces the application of a risk adjustment for the SNF Readmission 
Measure for COVID-19 beginning in FY 2023.

DATES: To be assured consideration, comments must be received at one of 
the addresses provided below, by June 10, 2022.

ADDRESSES: In commenting, please refer to file code CMS-1765-P.
    Comments, including mass comment submissions, must be submitted in 
one of the following three ways (please choose only one of the ways 
listed):
    1. Electronically. You may submit electronic comments on this 
regulation to https://www.regulations.gov. Follow the ``Submit a 
comment'' instructions.
    2. By regular mail. You may mail written comments to the following 
address ONLY: Centers for Medicare & Medicaid Services, Department of 
Health and Human Services, Attention: CMS-1765-P, P.O. Box 8016, 
Baltimore, MD 21244-8016.
    Please allow sufficient time for mailed comments to be received 
before the close of the comment period.
    3. By express or overnight mail. You may send written comments to 
the following address ONLY: Centers for Medicare & Medicaid Services, 
Department of Health and Human Services, Attention: CMS-1765-P, Mail 
Stop C4-26-05, 7500 Security Boulevard, Baltimore, MD 21244-1850.
    For information on viewing public comments, see the beginning of 
the SUPPLEMENTARY INFORMATION section.

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.

SUPPLEMENTARY INFORMATION: 
    Inspection of Public Comments: All comments received before the 
close of the comment period are available for viewing by the public, 
including any personally identifiable or confidential business 
information that is included in a comment. We post all comments 
received before the close of the comment period on the following 
website as soon as possible after they have been received: https://www.regulations.gov. Follow the search instructions on that website to 
view public comments. CMS will not post on Regulations.gov public 
comments that make threats to individuals or institutions or suggest 
that the individual will take actions to harm the individual. CMS 
continues to encourage individuals not to submit duplicative comments. 
We will post acceptable comments from multiple unique commenters even 
if the content is identical or nearly identical to other comments.

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 proposed 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. Proposed SNF PPS Rate Setting Methodology and FY 2023 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
IV. 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
V. Other SNF PPS Issues
    A. Proposed Permanent Cap on Wage Index Decreases
    B. Technical Updates to PDPM ICD-10 Mappings
    C. Recalibrating the PDPM Parity Adjustment
    D. Request for Information: Infection Isolation
VI. 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 Measure Proposals Beginning With the FY 2025 
SNF QRP
    D. SNF QRP Quality Measures Under Consideration for Future 
Years: Request for Information (RFI)
    E. Overarching Principles for Measuring Equity and Healthcare 
Quality Disparities Across CMS Quality Programs--Request for 
Information (RFI)
    F. Inclusion of the CoreQ: Short Stay Discharge Measure in a 
Future SNF QRP Program Year--Request for Information (RFI)
    G. Form, Manner, and Timing of Data Submission Under the SNF QRP
    H. Policies Regarding Public Display of Measure Data for the SNF 
QRP
VII. Skilled Nursing Facility Value-Based Purchasing Program (SNF 
VBP)
    A. Statutory Background
    B. SNF VBP Program Measures
    C. SNF VBP Performance Period and Baseline Period Proposals
    D. Performance Standards
    E. SNF VBP Performance Scoring
    F. Proposal To Adopt a Validation Process for the SNF VBP 
Program Beginning With the FY 2023 Program Year
    G. Proposed SNF Value-Based Incentive Payments for FY 2023

[[Page 22721]]

    H. Public Reporting on the Provider Data Catalog Website
    I. Requests for Comment on Additional SNF VBP Program Measure 
Considerations for Future Years
VIII. Request for Information: Revising the Requirements for Long-
Term Care (LTC) Facilities To Establish Mandatory Minimum Staffing 
Levels
IX. Collection of Information Requirements
X. Response to Comments
XI. 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 proposed rule would update the SNF prospective payment rates 
for fiscal year (FY) 2023, 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 this proposed rule) in the Federal 
Register, before the August 1 that precedes the start of each FY. In 
addition, this proposed rule proposes requirements for the Skilled 
Nursing Facility Quality Reporting Program (SNF QRP) and the Skilled 
Nursing Facility Value-Based Purchasing Program (SNF VBP), including 
proposals to adopt new quality measures for the SNF VBP Program. The 
SNF QRP includes proposals to adopt one new measure to promote patient 
safety, begin collection of information which is expected to improve 
quality of care for all SNF patients, and revise associated regulation 
text. The proposal also seeks comment on several subjects related to 
the SNF QRP including principles for measuring healthcare quality 
disparities and developing measures of healthcare equity in the SNF 
QRP. This proposed rule also seeks comment on numerous issues related 
to the SNF VBP Program, including additional measures on staffing 
turnover and COVID-19 vaccination for healthcare personnel, the 
Program's exchange function, validation, and the SNF VBP Program's 
approach to health equity. This proposed rule also includes a request 
for information on revising the requirements for long-term care (LTC) 
facilities to establish mandatory minimum staffing levels.

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 proposed rule would reflect an update to 
the rates that we published in the SNF PPS final rule for FY 2022 (86 
FR 42424, August 4, 2021). In addition, the proposed rule includes a 
proposed forecast error adjustment for FY 2023, proposes updates to the 
diagnosis code mappings used under the Patient Driven Payment Model 
(PDPM), and includes a proposed recalibration of the PDPM parity 
adjustment. Additionally, this proposed rule solicits comments on 
criteria related to patient isolation for active infection in a SNF. 
This proposed rule also proposes to establish a permanent cap policy to 
smooth the impact of year-to-year changes in SNF payments related to 
changes in the SNF wage index.
    This proposed rule proposes requirements for the SNF QRP, including 
the adoption of one new measure beginning with the FY 2025 SNF QRP: The 
Influenza Vaccination Coverage among Healthcare Personnel (HCP) (NQF 
#0431) measure. We are also proposing to revise the compliance date for 
the Transfer of Health Information measures and certain standardized 
patient assessment data elements. In addition, we are proposing to 
revise regulation text that pertains to data submission requirements 
for the SNF QRP. Finally, we are seeking comment on three subjects: 
Future measure concepts for the SNF QRP, overarching principles for 
measuring equity and healthcare disparities across CMS programs, 
including the SNF QRP, and the inclusion of the CoreQ: Short Stay 
Discharge Measure in the SNF QRP.
    Additionally, we are proposing several updates for the SNF VBP 
Program, including a policy to suppress the Skilled Nursing Facility 
30-Day All-Cause Readmission Measure (SNFRM) for the FY 2023 SNF VBP 
Program Year for scoring and payment adjustment purposes. We are also 
proposing to add two new measures to the SNF VBP Program beginning with 
the FY 2026 SNF VBP program year and one new measure beginning with the 
FY 2027 program year. We are also proposing several updates to the 
scoring methodology beginning with the FY 2026 program year and 
requesting public comments on several other measures we are considering 
for future rulemaking including a measure of staff turnover, whether we 
should update the exchange function, issues related to validation of 
SNF VBP data, and issues related to health equity. We are also 
proposing to revise our regulation text in accordance with our 
proposals.

C. Summary of Cost and Benefits
[GRAPHIC] [TIFF OMITTED] TP15AP22.008

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

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Information Technology (ONC) participate in the Post-Acute Care 
Interoperability Workgroup (PACIO) to facilitate collaboration with 
industry stakeholders 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 2022 ISA is available at https://www.healthit.gov/isa/sites/isa/files/inline-files/2022-ISA-Reference-Edition.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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    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.\5\ 
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 a 
universal floor of interoperability across the country. On January 18, 
2022, ONC announced a significant milestone by releasing the Trusted 
Exchange Framework \6\ and Common Agreement Version 1.\7\ 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 
Trusted Exchange Framework and 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.\8\ For more information, we refer readers to https://www.healthit.gov/topic/interoperability/trusted-exchange-framework-and-common-agreement.
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    \5\ Sections 4001 through 4008 of Public Law 114-255. Available 
at https://www.govinfo.gov/content/pkg/PLAW-114publ255/html/PLAW-114publ255.htm.
    \6\ 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.
    \7\ 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.
    \8\ 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.
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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 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 IMPACT Act 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) 
updated section 1888(h) of the Act, authorizing the Secretary to apply 
up to nine additional measures to the VBP program for SNFs.

[[Page 22723]]

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 
2022 (86 FR 42424, August 4, 2021).
    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 
proposed rule provides the required annual updates to the per diem 
payment rates for SNFs for FY 2023.

III. Proposed SNF PPS Rate Setting Methodology and FY 2023 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 index, 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 Index
    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. 
Accordingly, we have developed a SNF market basket index that 
encompasses the most commonly used 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 market basket index, 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 market basket index, 
which included updating the base year from 2014 to 2018.
    The SNF market basket index is used to compute the market basket 
percentage change 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 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 and described in section III.B. of this proposed rule.
    For this proposed rule, we propose a FY 2023 SNF market basket 
percentage of 2.8 percent based on IHS Global Inc.'s (IGI's) fourth 
quarter 2021 forecast of the 2018-based SNF market basket (before 
application of the forecast error adjustment and productivity 
adjustment). We also propose that if more recent data subsequently 
become 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 2023 SNF market basket percentage 
change, labor-related share relative importance, forecast error 
adjustment, or productivity adjustment in the SNF PPS final rule.
    In section III.B.5. of this proposed rule, we discuss the 2 percent 
reduction applied to the market basket update for those SNFs that fail 
to submit measures data as required by section 1888(e)(6)(A) of the 
Act.
2. Use of the SNF Market Basket Percentage
    Section 1888(e)(5)(B) of the Act defines the SNF market basket 
percentage as the percentage change in the SNF market basket index from 
the midpoint of the previous FY to the midpoint of the current FY. For 
the Federal rates set forth in this proposed rule, we use the 
percentage change in the SNF market basket index to compute the update 
factor for FY 2023. This factor is based on the FY 2023 percentage 
increase in the 2018-based SNF market basket index reflecting routine, 
ancillary, and capital-related expenses. As stated previously, in this 
proposed rule, the SNF market basket percentage update is estimated to 
be 2.8 percent for FY 2023 based on IGI's fourth quarter 2021 forecast.
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

[[Page 22724]]

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 2021 (the most recently available FY for which there is 
final data), the forecasted or estimated increase in the SNF market 
basket index was 2.2 percent, and the actual increase for FY 2021 is 
3.7 percent, resulting in the actual increase being 1.5 percentage 
point higher than the estimated increase. Accordingly, as the 
difference between the estimated and actual amount of change in the 
market basket index exceeds the 0.5 percentage point threshold, under 
the policy previously described (comparing the forecasted and actual 
increase in the market basket), the FY 2023 market basket percentage 
change of 2.8 percent, would be adjusted upward to account for the 
forecast error correction of 1.5 percentage point, resulting in a SNF 
market basket percentage change of 3.9 percent after reducing the 
market basket update by the productivity adjustment of 0.4 percentage 
point, discussed later in this section of the preamble.
    Table 2 shows the forecasted and actual market basket increases for 
FY 2021.
[GRAPHIC] [TIFF OMITTED] TP15AP22.009

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 in 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 multifactor productivity (MFP) with total factor 
productivity (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. However, as mentioned 
above, the data and methods are unchanged. 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 are changing the name of this adjustment to refer to it 
as the ``productivity adjustment,'' rather than the ``MFP adjustment.''
a. Incorporating the Productivity Adjustment Into the Market Basket 
Update
    Per section 1888(e)(5)(A) of the Act, the Secretary shall 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. 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 this FY 2023 SNF PPS proposed rule, 
the current proposed productivity adjustment (the 10-year moving 
average of TFP for the period ending September 30, 2023) is projected 
to be 0.4 percentage point.
    Consistent with section 1888(e)(5)(B)(i) of the Act and Sec.  
413.337(d)(2), as discussed previously in this section of the preamble, 
the market basket percentage for FY 2023 for the SNF PPS is based on 
IGI's fourth quarter 2021 forecast of the SNF market basket percentage, 
which is estimated to be 2.8 percent. This market basket percentage is 
then increased by 1.5 percentage point, due to application of the 
forecast error adjustment discussed earlier in this section of the 
preamble. Finally, as discussed earlier in this section of the 
preamble, we are applying a 0.4 percentage point productivity 
adjustment to the FY 2023 SNF market basket percentage. The resulting

[[Page 22725]]

productivity-adjusted FY 2023 SNF market basket update is, therefore, 
equal to 3.9 percent, or 2.8 percent plus 1.5 percentage point to 
account for forecast error and less 0.4 percentage point to account for 
the productivity adjustment.
5. Market Basket Update Factor for FY 2023
    Sections 1888(e)(4)(E)(ii)(IV) and (e)(5)(i) of the Act require 
that the update factor used to establish the FY 2023 unadjusted Federal 
rates be at a level equal to the market basket index percentage change. 
Accordingly, we determined the total growth from the average market 
basket level for the period of October 1, 2021 through September 30, 
2022 to the average market basket level for the period of October 1, 
2022 through September 30, 2023. This process yields a percentage 
change in the 2018-based SNF market basket of 2.8 percent.
    As further explained in section III.B.3. of this proposed rule, as 
applicable, we adjust the market basket percentage change by the 
forecast error 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 change in the market basket 
exceeds a 0.5 percentage point threshold in absolute terms. Since the 
actual FY 2021 SNF market basket percentage change exceeded the 
forecasted FY 2021 SNF market basket percentage change (FY 2021 is the 
most recently available FY for which there is historical data) by more 
than the 0.5 percentage point threshold, we propose to adjust the FY 
2023 market basket percentage change upward by the forecast error 
correction. Applying the 1.5 percentage point forecast error correction 
results in an adjusted FY 2023 SNF market basket percentage change of 
4.3 percent (2.8 percent market basket update plus 1.5 percentage point 
forecast error adjustment).
    Section 1888(e)(5)(B)(ii) of the Act requires us to reduce the 
market basket percentage change by the productivity adjustment (10-year 
moving average of changes in TFP for the period ending September 30, 
2023) which is estimated to be 0.4 percentage point, as described in 
section III.B.4. of this proposed rule. Thus, we apply a net SNF market 
basket update factor of 3.9 percent in our determination of the FY 2022 
SNF PPS unadjusted Federal per diem rates, which reflects a market 
basket increase factor of 2.8 percent, plus the 1.5 percentage point 
forecast error correction and less the 0.4 percentage point 
productivity adjustment.
    We note that if more recent data become available (for example, a 
more recent estimate of the SNF market basket and/or productivity 
adjustment), we would use such data, if appropriate, to determine the 
FY 2023 SNF market basket percentage change, labor-related share 
relative importance, forecast error adjustment, or productivity 
adjustment in the FY 2023 SNF PPS 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 
1 percent market basket increase for FY 2018). 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 
index 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.
6. Unadjusted Federal Per Diem Rates for FY 2023
    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, to adjust each per 
diem component of the Federal rates forward to reflect the change in 
the average prices for FY 2023 from the average prices for FY 2022. We 
propose to further adjust the rates by a wage index budget neutrality 
factor, described later in this section. 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 
2023, prior to adjustment for case-mix.
[GRAPHIC] [TIFF OMITTED] TP15AP22.010


[[Page 22726]]


[GRAPHIC] [TIFF OMITTED] TP15AP22.011

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 IV.A. of this 
proposed 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 2023 payment rates set forth in this 
proposed rule reflect the use of the PDPM case-mix classification 
system from October 1, 2022, through September 30, 2023. The case-mix 
adjusted PDPM payment rates for FY 2023 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 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 proposed 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.
    As we noted in the FY 2022 SNF PPS final rule (86 FR 42434), we 
continue to monitor the impact of PDPM implementation on patient 
outcomes and program outlays. Because of this analysis, in section V.C. 
of this

[[Page 22727]]

proposed rule, we propose to recalibrate the PDPM parity adjustment 
discussed in the FY 2020 SNF PPS final rule (84 FR 38734). Following 
the methodology of this proposed change, Tables 5 and 6 incorporate the 
proposed recalibration of the PDPM parity adjustment.
BILLING CODE 4120-01-P
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[[Page 22728]]


[GRAPHIC] [TIFF OMITTED] TP15AP22.013

BILLING CODE 4120-01-C

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 propose to continue 
this practice for FY 2023, 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 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 2023, the updated wage data are for hospital cost reporting 
periods beginning on or after October 1, 2018 and before October 1, 
2019 (FY 2019 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) authorized us to establish a geographic 
reclassification procedure that is 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. However, 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. In addition, adopting such an approach would require 
a significant commitment of resources by CMS and the Medicare 
Administrative Contractors, 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 in 
such a manner as to permit us to establish a SNF-specific wage index, 
we do not believe this undertaking is feasible at this time. Therefore, 
as discussed above in this section, 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.

[[Page 22729]]

    In addition, we propose to 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 proposed to continue using the average wage 
index from all contiguous Core-Based Statistical Areas (CBSAs) as a 
reasonable proxy. For FY 2023, there are no rural geographic areas that 
do not have hospitals, and thus, this methodology will not be applied. 
For rural Puerto Rico, we proposed not to apply this methodology due to 
the distinct economic circumstances that exist there (for example, due 
to the close proximity to one another of almost all of Puerto Rico's 
various urban and non-urban areas, this methodology would produce a 
wage index for rural Puerto Rico that is higher than that in half of 
its urban areas); instead, we would continue using the most recent wage 
index previously available for that area. For urban areas without 
specific hospital wage index data, we proposed that we would use the 
average wage indexes of all of the urban areas within the State to 
serve as a reasonable proxy for the wage index of that urban CBSA. For 
FY 2023, the only urban area without wage index data available is CBSA 
25980, Hinesville-Fort Stewart, GA.
    The wage index applicable to FY 2023 is set forth in Tables A and B 
available on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/WageIndex.html.
    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. For FY 2023 and subsequent years, we are 
proposing 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, which is further discussed in 
section V.A. of this proposed rule.
    As we previously stated in the FY 2008 SNF PPS proposed and final 
rules (72 FR 25538 through 25539, and 72 FR 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. We note that on March 6, 2020, OMB issued Bulletin No. 20-
01, which provided updates to and superseded OMB Bulletin No. 18-04 
that was issued on September 14, 2018. The attachments to OMB Bulletin 
No. 20-01 provided detailed information on the updates (available on 
the web at https://www.whitehouse.gov/wp-content/uploads/2020/03/Bulletin-20-01.pdf). In the FY 2021 SNF PPS final rule (85 FR 47611), 
we stated that we intended to propose any updates from OMB Bulletin No. 
20-01 in the FY 2022 SNF PPS proposed rule. After reviewing OMB 
Bulletin No. 20-01, we have determined that the changes in OMB Bulletin 
20-01 encompassed delineation changes that do not impact the CBSA-based 
labor market area delineations adopted in FY 2021. Therefore, while we 
proposed to adopt the updates set forth in OMB Bulletin No. 20-01 
consistent with our longstanding policy of adopting OMB delineation 
updates, we noted that specific wage index updates would not be 
necessary for FY 2022 as a result of adopting these OMB updates and for 
these reasons CMS is likewise not making such a proposal for FY 2023.
    The proposed wage index applicable to FY 2023 is set forth in 
Tables A and B and is available on the CMS website at http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/WageIndex.html.
    Once calculated, we would apply the wage index adjustment to the 
labor-related portion of the Federal rate. Each year, we calculate a 
revised 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 2018 (82 FR 36548 through 
36566), we finalized a proposal to revise the labor-related share to 
reflect the relative importance of the 2014-based SNF market basket 
cost weights for the following cost categories: Wages and Salaries; 
Employee Benefits; Professional Fees: Labor-Related; Administrative and 
Facilities Support

[[Page 22730]]

Services; Installation, Maintenance, and Repair Services; All Other: 
Labor-Related Services; and a proportion of Capital-Related expenses. 
Effective beginning FY 2022 (86 FR 42437), we rebased and revised 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 42424).
    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 2023. 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 2023 than the base year weights 
from the SNF market basket. We calculate the labor-related relative 
importance for FY 2023 in four steps. First, we compute the FY 2023 
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 2023 price index level for that cost category by the 
total market basket price index level. Third, we determine the FY 2023 
relative importance for each cost category by multiplying this ratio by 
the base year (2018) weight. Finally, we add the FY 2023 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 2023 
labor-related relative importance.
    Table 7 summarizes the proposed labor-related share for FY 2023, 
based on IGI's fourth quarter 2021 forecast of the 2018-based SNF 
market basket, compared to the labor-related share that was used for 
the FY 2022 SNF PPS final rule.
[GRAPHIC] [TIFF OMITTED] TP15AP22.014

    To calculate the labor portion of the case-mix adjusted per diem 
rate, we would 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 2023 
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 stakeholders in understanding the effect of the 
wage 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 2023 (Federal rates 
effective October 1, 2022), 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 2022 to the weighted average wage adjustment 
factor for FY 2023. For this calculation, we would use the same FY 2021 
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 proposed budget

[[Page 22731]]

neutrality factor for FY 2023 as set forth in this proposed rule is 
1.0011.
    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.

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 VII. of this proposed rule for further 
discussion of our policies and proposals for the SNF VBP Program.

F. Adjusted Rate Computation Example

    Tables 8 through 10 provide examples generally illustrating payment 
calculations during FY 2023 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 proposed parity adjustment discussed in section 
V.C. of this proposed rule) to compute the provider's case-mix adjusted 
per diem rate for FY 2023, based on the patient's PDPM classification, 
as well as how the variable per diem (VPD) adjustment 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 2023 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 8, 
SNF XYZ's total PPS payment for this particular patient's stay would 
equal $20,112.27.
BILLING CODE 4120-01-P
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[GRAPHIC] [TIFF OMITTED] TP15AP22.017

BILLING CODE 4120-01-C

IV. 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 this 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/

[[Page 22733]]

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).
    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. Rep. No. 106-479 at 854 (1999) (Conf. Rep.)) 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 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, 
new 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 described in that section.
    In this proposed rule, we specifically invite 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 request that commenters identify in their comments the 
specific HCPCS code that is associated with the service in question, as 
well as their rationale for

[[Page 22734]]

requesting that the identified HCPCS code(s) be excluded.
    We note that the original BBRA amendment and the CAA 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, July 1, 2020), as subsequently modified by the 
Secretary. In addition, as noted above 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 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 would actually represent a 
substantive change in the scope of the exclusions from SNF consolidated 
billing, we would 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, 2022). By making any new exclusions in this manner, we 
could 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.

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 proposed 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 propose to make certain revisions in the regulation text itself. 
Specifically, we propose to revise Sec.  413.337(b)(4) and add new 
paragraphs (b)(4)(i) through (iii). These proposed revisions reflect 
that the application of the wage index would be made on the basis of 
the location of the facility in an urban or rural area as defined in 
Sec.  413.333, and that starting on October 1, 2022, we would apply a 
cap on decreases to the wage index such that the wage index applied to 
a SNF is not less than 95 percent of the wage index applied to that SNF 
in the prior FY, as discussed in section V.A. of this proposed rule.

V. Other SNF PPS Issues

A. Proposed Permanent Cap on Wage Index Decreases

    As discussed above in section III.D. of this rule, we have proposed 
and finalized temporary transition policies in the past to mitigate 
significant changes to payments due to changes to the SNF PPS wage 
index. Specifically, for FY 2015 (79 FR 45644 through 45646), we 
implemented a 50/50 blend for all geographic areas consisting of the 
wage index values computed using the then-current OMB area delineations 
and the wage index values computed using new area delineations based on 
OMB Bulletin No. 13-01. In FY 2021 (85 FR 47594, 47617), we implemented 
a 1-year transition to mitigate any negative effects of wage index 
changes by applying a 5 percent cap on any decrease in a SNF's wage 
index from the final wage index from FY 2020. We explained that we 
believed the 5-percent cap would provide greater transparency and would 
be administratively less complex than the prior methodology of applying 
a 50/50 blended wage index. We indicated that no cap would be applied 
to the reduction in the wage index for FY 2022, and we noted that this 
transition approach struck an appropriate balance by providing a 
transition period to mitigate the resulting short-term instability and 
negative impacts on providers and time for them to adjust to their new 
labor market area delineations and wage index values.
    In the FY 2022 final rule (86 FR 42424, 42439), commenters 
recommended CMS extend the transition period adopted in the FY 2021 SNF 
PPS final rule so that SNFs could offset the enormous cuts scheduled 
for FY 2022. Because we did not propose to modify the transition policy 
that was finalized in the FY 2021 SNF PPS final rule, we did not extend 
the transition period for FY 2022. However, we acknowledged that 
certain changes to wage index policy may significantly affect Medicare 
payment. In addition, we reiterated that our policy principles with 
regard to the wage index include generally using the most current data 
and information available and providing that data and information, as 
well as any approaches to addressing any significant effects on 
Medicare payments resulting from these potential scenarios, in notice 
and comment rulemaking. With these policy principles in mind for this 
FY 2023 proposed rule, we considered how best to address the potential 
scenarios about which commenters raised concerns in the FY 2022 final 
rule around SNF payment volatility; that is, scenarios in which changes 
to wage index policy may significantly affect Medicare payments.
    In the past, we have established transition policies of limited 
duration to phase in significant changes to labor market. In taking 
this approach in the past, we have sought to strike an appropriate 
balance between maintaining the accuracy of the overall labor market 
area wage index system and mitigating short-term instability and 
negative impacts on providers due to

[[Page 22735]]

wage index changes. In accordance with the requirements of the SNF PPS 
wage index regulations at Sec.  413.337(a)(1), we use an appropriate 
wage index based on the best available data, including the best 
available labor market area delineations, to adjust SNF PPS payments 
for wage differences. We have previously stated that, because the wage 
index is a relative measure of the value of labor in prescribed labor 
market areas, we believe it is important to implement new labor market 
area delineations with as minimal a transition as is reasonably 
possible. However, we recognize that changes to the wage index have the 
potential to create instability and significant negative impacts on 
certain providers even when labor market areas do not change. In 
addition, year-to-year fluctuations in an area's wage index can occur 
due to external factors beyond a provider's control, such as the COVID-
19 public health emergency (PHE). For an individual provider, these 
fluctuations can be difficult to predict. So, we also recognize that 
predictability in Medicare payments is important to enable providers to 
budget and plan their operations.
    In light of these considerations, we are proposing a permanent 
approach to smooth year-to-year changes in providers' wage indexes. We 
are proposing a policy that we believe increases the predictability of 
SNF PPS payments for providers, and mitigates instability and 
significant negative impacts to providers resulting from changes to the 
wage index.
    As previously discussed, we believed applying a 5-percent cap on 
wage index decreases for FY 2021 provided greater transparency and was 
administratively less complex than prior transition methodologies. In 
addition, we believed this methodology mitigated short-term instability 
and fluctuations that can negatively impact providers due to wage index 
changes. Lastly, we have noted that we believed the 5-percent cap we 
applied to all wage index decreases for FY 2021 provided an adequate 
safeguard against significant payment reductions related to the 
adoption of the revised CBSAs. However, we recognize there are 
circumstances that a one-year mitigation policy, like the one adopted 
for FY 2021, would not effectively address future years where providers 
continue to be negatively affected by significant wage index decreases.
    Typical year-to-year variation in the SNF PPS wage index has 
historically been within 5 percent, and we expect this will continue to 
be the case in future years. For FY 2023, the provider level impact 
analysis indicates that approximately 97 percent of SNFs will 
experience a wage index change within 5 percent. Because providers are 
usually experienced with this level of wage index fluctuation, we 
believe applying a 5-percent cap on all wage index decreases each year, 
regardless of the reason for the decrease, would effectively mitigate 
instability in SNF PPS payments due to any significant wage index 
decreases that may affect providers in any year. We believe this 
approach would address concerns about instability that commenters 
raised in the FY 2022 SNF PPS rule. Additionally, we believe that 
applying a 5-percent cap on all wage index decreases would support 
increased predictability about SNF PPS payments for providers, enabling 
them to more effectively budget and plan their operations. Lastly, 
because applying a 5-percent cap on all wage index decreases would 
represent a small overall impact on the labor market area wage index 
system we believe it would ensure the wage index is a relative measure 
of the value of labor in prescribed labor market wage areas. As 
discussed in further detail in section XI.A.4. of this proposed rule, 
we estimate that applying a 5-percent cap on all wage index decreases 
will have a very small effect on the wage index budget neutrality 
factor for FY 2023. Because the wage index is a measure of the value of 
labor (wage and wage-related costs) in a prescribed labor market area 
relative to the national average, we anticipate that in the absence of 
proposed policy changes most providers will not experience year-to-year 
wage index declines greater than 5 percent in any given year. We also 
believe that when the 5-percent cap would be applied under this 
proposal, it is likely that it would be applied similarly to all SNFs 
in the same labor market area, as the hospital average hourly wage data 
in the CBSA (and any relative decreases compared to the national 
average hourly wage) would be similar. While this policy may result in 
SNFs in a CBSA receiving a higher wage index than others in the same 
area (such as situations when delineations change), we believe the 
impact would be temporary. Therefore, we anticipate that the impact to 
the wage index budget neutrality factor in future years would continue 
to be minimal.
    The Secretary has broad authority to establish appropriate payment 
adjustments under the SNF PPS, including the wage index adjustment. As 
discussed earlier in this section, the SNF PPS regulations require us 
to use an appropriate wage index based on the best available data. For 
the reasons discussed earlier in this section, we believe that a 5-
percent cap on wage index decreases would be appropriate for the SNF 
PPS. Therefore, for FY 2023 and subsequent years, we are proposing to 
apply a permanent 5-percent cap on any decrease to a provider's wage 
index from its wage index in the prior year, regardless of the 
circumstances causing the decline. That is, we are proposing that a 
SNF's wage index for FY 2023 would not be less than 95 percent of its 
final wage index for FY 2022, regardless of whether the SNF is part of 
an updated CBSA, and that for subsequent years, a provider's wage index 
would not be less than 95 percent of its wage index calculated in the 
prior FY. This means, if a SNF's prior FY wage index is calculated with 
the application of the 5-percent cap, then the following year's wage 
index would not be less than 95 percent of the SNF's capped wage index 
in the prior FY. For example, if a SNF's wage index for FY 2023 is 
calculated with the application of the 5-percent cap, then its wage 
index for FY 2024 would not be less than 95 percent of its capped wage 
index in FY 2023. Lastly, we propose 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. As we have discussed in this 
proposed rule, we believe this proposed methodology would maintain the 
SNF PPS wage index as a relative measure of the value of labor in 
prescribed labor market areas, increase the predictability of SNF PPS 
payments for providers, and mitigate instability and significant 
negative impacts to providers resulting from significant changes to the 
wage index. In section XI. of this proposed rule, we estimate the 
impact to payments for providers in FY 2023 based on this proposed 
policy. We also note that we would examine the effects of this policy 
on an ongoing basis in the future in order to assess its continued 
appropriateness.
    Subject to the aforementioned proposal becoming final, we are also 
proposing to revise the regulation text at Sec.  413.337(a)(1) to 
provide that starting October 1, 2022, we will apply a cap on decreases 
to the wage index such that the wage index applied is not less than 95 
percent of the wage index applied to that SNF in the prior year.
    We invite public comments on this proposal.

B. Technical Updates to PDPM ICD-10 Mappings

    In the FY 2019 SNF PPS final rule (83 FR 39162), we finalized the

[[Page 22736]]

implementation of the Patient Driven Payment Model (PDPM), effective 
October 1, 2019. The PDPM utilizes International Classification of 
Diseases, Version 10 (ICD-10) codes in several ways, including to 
assign patients to clinical categories under several PDPM components, 
specifically the PT, OT, SLP and NTA components. The ICD-10 code 
mappings and lists used under PDPM are available on the PDPM website at 
https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/PDPM.
    Each year, the ICD-10 Coordination and Maintenance Committee, a 
Federal interdepartmental committee that is chaired by representatives 
from the National Center for Health Statistics (NCHS) and by 
representatives from CMS, meets biannually and publishes updates to the 
ICD-10 medical code data sets in June of each year. These changes 
become effective October 1 of the year in which these updates are 
issued by the committee. The ICD-10 Coordination and Maintenance 
Committee also has the ability to make changes to the ICD-10 medical 
code data sets effective on April 1 of each year.
    In the FY 2020 SNF PPS final rule (84 FR 38750), we outlined the 
process by which we maintain and update the ICD-10 code mappings and 
lists associated with the PDPM, as well as the SNF Grouper software and 
other such products related to patient classification and billing, so 
as to ensure that they reflect the most up to date codes possible. 
Beginning with the updates for FY 2020, we apply nonsubstantive changes 
to the ICD-10 codes included on the PDPM code mappings and lists 
through a subregulatory process consisting of posting updated code 
mappings and lists on the PDPM 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 ICD-10 medical code data 
set. On the other hand, substantive changes, or those that go beyond 
the intention of maintaining consistency with the most current ICD-10 
medical code data set, will be proposed through notice and comment 
rulemaking. For instance, changes to the assignment of a code to a 
comorbidity list or other changes that amount to changes in policy are 
considered substantive changes for which we would undergo notice and 
comment rulemaking.
    We are proposing several changes to the PDPM ICD-10 code mappings 
and lists. We would note that, in the case of any diagnoses that are 
either currently mapped to ``Return to Provider'' or that we are 
proposing to classify into this category, this is not intended to 
reflect any judgment on the importance of recognizing and treating 
these conditions, but merely that there are more specific diagnoses 
than those mapped to ``Return to Provider'' or that we do not believe 
that the diagnosis should serve as the primary diagnosis for a Part-A 
covered SNF stay. Our proposed changes are as follows:
    On October 1, 2021, D75.839 ``Thrombocytosis, unspecified,'' took 
effect and was mapped to the clinical category of ``Cardiovascular and 
Coagulations.'' However, there are more specific codes to indicate why 
a patient with thrombocytosis would require SNF care. If the cause is 
unknown, the SNF could use D47.3, ``Essential (hemorrhagic) 
thrombocythemia'' or D75.838, ``other thrombocytosis'' which is a new 
code that took effect on October 1, 2021. Further, elevated platelet 
count without other symptoms is not reason enough for SNF skilled care 
so this would not be used as a primary diagnosis. For this reason, we 
proposed to change the assignment of D75.839 to ``Return to Provider.''
    On October 1, 2021, D89.44, ``Hereditary alpha tryptasemia'' went 
into effect and was mapped to the clinical category, ``Medical 
Management.'' However, this is not a diagnosis that would be treated as 
a primary condition in the SNF, rather it would be treated in the 
outpatient setting. Therefore, we propose to change the assignment of 
D89.44 to ``Return to Provider.''
    On October 1, 2021, F32.A, ``Depression, unspecified'' went into 
effect and was mapped to ``Medical Management.'' However, there are 
more specific codes that would more adequately capture the diagnosis of 
depression. Further, 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 
appropriate to treat a patient whose primary diagnosis is depression. 
For this reason, we propose to change the assignment of F32.A to 
``Return to Provider.''
    On October 1, 2021, G92.9, ``Unspecified toxic encephalopathy'' 
took effect and was mapped to the clinical category of ``Acute 
Neurologic.'' However, there are more specific codes that should be 
used to describe encephalopathy treated in a SNF. Therefore, we propose 
to change the assignment of G92.9 to ``Return to Provider.''
    On October 1, 2021, M54.50, ``Low back pain, unspecified'' went 
into effect and was mapped to the clinical category of ``Non-surgical 
Orthopedic/Musculoskeletal.'' However, if low back pain were the 
primary diagnosis, the SNF should have a greater understanding of what 
is causing the pain. There are more specific codes to address this 
condition. Therefore, we propose to change the assignment of M54.50 to 
``Return to Provider.''
    In the FY 2022 proposed rule (86 FR 19984 through 19985), we 
proposed to reclassify K20.81, ``Other esophagitis with bleeding,'' 
K20.91, ``Esophagitis, unspecified with bleeding,'' and K21.01, 
``Gastro-esophageal reflux disease with esophagitis, with bleeding'' 
from ``Return to Provider'' to ``Medical Management.'' Our rationale 
for the change was a recognition that these codes represent these 
esophageal conditions with more specificity than originally considered 
because of the bleeding that is part of the conditions and that they 
would more likely be found in SNF patients. We received one comment 
suggesting additional changes to similar ICD-10 code mappings and 
comorbidity lists that at the time were outside the scope of 
rulemaking. This commenter suggested that we consider remapping the 
following similar diagnosis codes that frequently require SNF skilled 
care, from ``Return to Provider'' to ``Medical Management'': K22.11, 
``Ulcer of esophagus with bleeding;'' K25.0, ``Acute gastric ulcer with 
hemorrhage;'' K25.1, ``Acute gastric ulcer with perforation;'' K25.2, 
``Acute gastric ulcer with both hemorrhage and perforation;'' K26.0, 
``Acute duodenal ulcer with hemorrhage;'' K26.1, ``Acute duodenal ulcer 
with perforation;'' K26.2, ``Acute duodenal ulcer with both hemorrhage 
and perforation;'' K27.0 ``Acute peptic ulcer, site unspecified with 
hemorrhage;'' K27.1, ``Acute peptic ulcer, site unspecified with 
perforation;'' K27.2, ``Acute peptic ulcer, site unspecified with both 
hemorrhage and perforation;'' K28.0, ``Acute gastrojejunal ulcer with 
hemorrhage;'' K28.1, ``Acute gastrojejunal ulcer with perforation;'' 
K28.2, ``Acute gastrojejunal ulcer with both hemorrhage and 
perforation;'' and K29.01, ``Acute gastritis with bleeding.'' Upon 
review of these codes, we recognize that they represent conditions with 
more specificity than originally considered because of the bleeding (or 
perforation) that is part of the conditions and that they would more 
likely be found in SNF patients.'' Therefore, we propose to remap these

[[Page 22737]]

ICD-10 codes to ``Medical Management.''
    We also received a comment requesting we consider remapping M62.81, 
``Muscle weakness (generalized)'' from ``Return to Provider'' to ``Non-
orthopedic Surgery'' with the rationale that there is currently no 
sequela or late-effects ICD-10 code available when patients require 
skilled nursing and therapy due to late effects of resolved infections 
such as pneumonia or urinary tract infections. We considered the 
request and determined that muscle weakness (generalized) is 
nonspecific and if the original condition is resolved, but the 
resulting muscle weakness persists as a result of the known original 
diagnosis, there are more specific codes that exist that would account 
for why the muscle weakness is on-going, such as muscle wasting or 
atrophy. Therefore, we are not proposing this specific remapping. This 
commenter also requested that that we consider remapping R62.7, ``Adult 
failure to thrive'' from ``Return to Provider'' to ``Medical 
Management.'' According to this commenter, physicians often diagnose 
adult failure to thrive when a resident has been unable to have oral 
intake sufficient for survival. Typically, this diagnosis is appended 
when the physician has determined that a feeding tube should be 
considered to provide sufficient intake for survival. According to the 
commenter, it would then appropriately become the primary diagnosis for 
a skilled stay. We considered this request and believe that R6.2 is a 
nonspecific code and SNF primary diagnoses should be coded to the 
highest level of specificity. If the patient has been unable to have 
oral intake, the primary diagnosis (for example, Ulcerative Colitis) 
for admission to a SNF should explain why the patient is unable to have 
oral intake sufficient for survival. Therefore, we are not proposing 
this specific remapping.
    We invite comments on the proposed substantive changes to the ICD-
10 code mappings discussed previously in this section, as well as 
comments on additional substantive and non-substantive changes that 
commenters believe are necessary.

C. Recalibrating the PDPM Parity Adjustment

1. Background
    On October 1, 2019, we implemented the Patient Driven Payment Model 
(PDPM) under the SNF PPS, a new case-mix classification model that 
replaced the prior case-mix classification model, the Resource 
Utilization Groups, Version IV (RUG-IV). As discussed in the FY 2019 
SNF PPS final rule (83 FR 39256), as with prior system transitions, we 
proposed and finalized implementing PDPM in a budget neutral manner. 
This means that the transition to PDPM, along with the related policies 
finalized in the FY 2019 SNF PPS final rule, were not intended to 
result in an increase or decrease in the aggregate amount of Medicare 
Part A payment to SNFs. We believe ensuring parity is integral to the 
process of providing ``for an appropriate adjustment to account for 
case mix'' that is based on appropriate data in accordance with section 
1888(e)(4)(G)(i) of the Act. Section V.I. of the FY 2019 SNF PPS final 
rule (83 FR 39255 through 39256) discusses the methodology that we used 
to implement PDPM in a budget neutral manner. Specifically, we 
multiplied each of the PDPM case-mix indexes (CMIs) by an adjustment 
factor that was calculated by comparing total payments under RUG-IV 
using FY 2017 claims and assessment data (the most recent final claims 
data available at the time) to what we expected total payments would be 
under PDPM based on that same FY 2017 claims and assessment data. In 
the FY 2020 SNF PPS final rule (84 FR 38734 through 38735), we 
finalized an updated standardization multiplier and parity adjustment 
based on FY 2018 claims and assessment data. This analysis resulted in 
an adjustment factor of 1.46, by which all the PDPM CMIs were 
multiplied so that total estimated payments under PDPM would be equal 
to total actual payments under RUG-IV, assuming no changes in the 
population, provider behavior, and coding. By multiplying each CMI by 
1.46, the CMIs were inflated by 46 percent to achieve budget 
neutrality.
    We used a similar type of parity adjustment in FY 2011 when we 
transitioned from RUG-III to RUG-IV. As discussed in the FY 2012 SNF 
PPS final rule (76 FR 48492 through 48500), we observed that once 
actual RUG-IV utilization data became available, the actual RUG-IV 
utilization patterns differed significantly from those we had projected 
using the historical data that grounded the RUG-IV parity adjustment. 
We then used actual FY 2011 RUG-IV utilization data to recalibrate the 
RUG-IV parity adjustment and decreased the nursing CMIs for all RUG-IV 
therapy groups from an adjustment factor of 61 percent to an adjustment 
factor of 19.84 percent, while maintaining the original 61 percent 
total nursing CMI increase for all non-therapy RUG-IV groups. As a 
result of this recalibration, FY 2012 SNF PPS rates were reduced by 
12.5 percent, or $4.47 billion, in order to achieve budget neutrality 
under RUG-IV prospectively.
    Since PDPM implementation, we have closely monitored SNF 
utilization data to determine if the parity adjustment finalized in the 
FY 2020 SNF PPS final rule (84 FR 38734 through 38735) provided for a 
budget neutral transition between RUG-IV and PDPM as intended. Similar 
to what occurred in FY 2011 with RUG-IV implementation, we have 
observed significant differences between the expected SNF PPS payments 
and case-mix utilization based on historical data, and the actual SNF 
PPS payments and case-mix utilization under PDPM, based on FY 2020 and 
FY 2021 utilization data. As discussed in the FY 2022 SNF PPS final 
rule (86 FR 42466 through 42469), it appears that PDPM may have 
inadvertently triggered a significant increase in overall payment 
levels under the SNF PPS of approximately 5 percent and that 
recalibration of the parity adjustment may be warranted.
    Following the methodology utilized in calculating the initial PDPM 
parity adjustment, we would typically use claims and assessment data 
for a given year to classify patients under both the current system and 
the prior system to compare aggregate payments and determine an 
appropriate adjustment factor to achieve parity. However, we 
acknowledge that the typical methodology for recalibrating the parity 
adjustment may not provide an accurate recalibration under PDPM for a 
number of reasons. First, the ongoing COVID-19 PHE has had impacts on 
nursing home care protocols and many other aspects of SNF operations 
that affected utilization data in FY 2020 and FY 2021. Second, given 
the significant differences in payment incentives and patient 
assessment requirements between RUG-IV and PDPM, using the same 
methodology that we have used in the past to calculate a recalibrated 
PDPM parity adjustment could lead to a potential overcorrection in the 
recalibration.
    In the FY 2022 SNF PPS proposed rule (86 FR 19987 through 19989), 
we solicited comments from stakeholders on a potential methodology for 
recalibrating the PDPM parity adjustment to account for these potential 
effects without compromising the accuracy of the adjustment. After 
considering the feedback and recommendations received, summarized in 
the FY 2022 SNF PPS final rule (86 FR 42469 through 42471), we are 
proposing an updated recalibration methodology. We also present results

[[Page 22738]]

from our data monitoring efforts to provide transparency on our efforts 
to parse out the effects of PDPM implementation from the effects of the 
COVID-19 PHE. We invite comments on this proposal for recalibrating the 
PDPM parity adjustment, that is discussed throughout the subsequent 
sections of this proposed rule, to ensure that PDPM is implemented in a 
budget neutral manner, as originally intended.
2. Methodology for Recalibrating the PDPM Parity Adjustment
a. Effect of COVID-19 Public Health Emergency
    FY 2020 was a year of significant change under the SNF PPS. In 
addition to implementing PDPM on October 1, 2019, a national COVID-19 
PHE was declared beginning January 27, 2020. With the announcement of 
the COVID-19 PHE, and under authority granted us by section 1812(f) of 
the Act, we issued two temporary modifications to the limitations of 
section 1861(i) of the Act beginning March 1, 2020 that affected SNF 
coverage. The 3-day prior hospitalization modification allows a SNF to 
furnish Medicare Part A services without requiring a 3-day qualifying 
hospital stay, and the benefit period exhaustion modification allows a 
one-time renewal of benefits for an additional 100 days of Part A SNF 
coverage without a 60-day break in spell of illness. These COVID-19 
PHE-related modifications allowed coverage for beneficiaries who would 
not typically be able to access the Part A SNF benefit, such as 
community and long-term care nursing home patients without a prior 
qualifying hospitalization.
    We acknowledge that the COVID-19 PHE had significant impacts on 
nursing home care protocols and many other aspects of SNF operations. 
For months, infection and mortality rates were high among nursing home 
residents. Additionally, facilities were often unable to access testing 
and affordable personal protective equipment (PPE), and were required 
to be closed to visitors and barred from conducting communal events to 
help control infections (March 2021 MedPAC Report to Congress, 204, 
available at https://www.medpac.gov/wp-content/uploads/2021/10/mar21_medpac_report_ch7_sec.pdf). As described in the FY 2022 SNF PPS 
final rule (86 FR 42427), many commenters voiced concerns about 
additional costs due to the COVID-19 PHE that could be permanent due to 
changes in patient care, infection control staff and equipment, 
personal protective equipment, reporting requirements, increased wages, 
increased food prices, and other necessary costs. Some commenters who 
received CARES Act Provider Relief funds indicated that those funds 
were not enough to cover these additional costs. Additionally, a few 
commenters from rural areas stated that their facilities were heavily 
impacted from the additional costs, particularly the need to raise 
wages, and that this could affect patients' access to care.
    However, we note that the relevant issue for a recalibration of the 
PDPM parity adjustment is whether or not the COVID-19 PHE caused 
changes in the SNF case-mix distribution. In other words, the issue is 
whether patient classification, or the relative percentages of 
beneficiaries in each PDPM group, was different than what it would have 
been if not for the COVID-19 PHE. We remind commenters that the parity 
adjustment refers only to the transition between case-mix 
classification models (in this case, from RUG-IV to PDPM) and is not 
intended to include other unrelated SNF policies such as the market 
basket increase, which is intended to address such issues as the 
additional costs described previously. A key aspect of our 
recalibration methodology, described in further detail later in this 
section, involves parsing out the impacts of the COVID-19 PHE and the 
PHE-related modifications from those which occurred solely, or at least 
principally, due to the implementation of PDPM.
b. Effect of PDPM Implementation
    As discussed in the FY 2022 SNF PPS final rule (86 FR 42467), we 
presented evidence that the transition to PDPM impacted certain aspects 
of SNF patient classification and care provision prior to the beginning 
of the COVID-19 PHE. For example, according to the latest data 
available, SNF patients received an average of approximately 93 therapy 
minutes per utilization day in FY 2019. Between October 2019 and 
December 2019, the 3 months after PDPM implementation and before the 
onset of the COVID-19 PHE, the average number of therapy minutes SNF 
patients received per day dropped to approximately 68 minutes per 
utilization day, a decrease of approximately 27 percent. Given this 
reduction in therapy provision since PDPM implementation, we found that 
using patient assessment data collected under PDPM would lead to a 
significant underestimation of what RUG-IV case-mix and payments would 
have been (for example, the Ultra-High and Very-High Rehabilitation 
assignments are not nearly as prevalent using PDPM-reported data), 
which would in turn lead to an overcorrection in the parity adjustment. 
Additionally, there were significant changes in the patient assessment 
schedule such as the removal of the Change of Therapy Other Medicare 
Required Assessment. Without having an interim assessment between the 
5-day assessment and the patient's discharge from the facility, we are 
unable to determine if the RUG-IV group into which the patient 
classified on the 5-day assessment changed during the stay, or if the 
patient continued to receive an amount of therapy services consistent 
with the initial RUG-IV classification.
    Therefore, given the significant differences in payment incentives 
and patient assessment requirements between RUG-IV and PDPM, using the 
same methodology that we have used in the past to calculate a 
recalibrated PDPM parity adjustment could lead to a potential 
overcorrection in the recalibration. In the FY 2022 SNF PPS proposed 
rule (86 FR 19988), we described an alternative recalibration 
methodology that used FY 2019 RUG-IV case-mix distribution as a proxy 
for what total RUG-IV payments would have been absent PDPM 
implementation. We believed that this methodology provides a more 
accurate representation of what RUG-IV payments would have been, were 
it not for the changes precipitated by PDPM implementation, than using 
data reported under PDPM to reclassify these patients under RUG-IV. We 
solicited comments from stakeholders on this aspect of our potential 
methodology for recalibrating the PDPM parity adjustment and they were 
generally receptive to our approach.
c. FY 2022 SNF PPS Proposed Rule Potential Parity Adjustment 
Methodology and Comments
    In the FY 2022 SNF PPS proposed rule (86 FR 19986 through 19987), 
we presented a potential methodology that attempted to account for the 
effects of the COVID-19 PHE by removing those stays with a COVID-19 
diagnosis and those stays using a PHE-related modification from our 
data set, and we solicited comment on how stakeholders believed the 
COVID-19 PHE affected the distribution of patient case-mix in ways that 
were not sufficiently captured by our subset population methodology. 
According to the latest data available, 10 percent of SNF stays in FY 
2020 and 17 percent of SNF stays in FY 2021 included a COVID-19 ICD-10 
diagnosis code either as a primary or secondary diagnosis, while 17 
percent of SNF stays in FY 2020 and 27 percent of SNF stays in FY 2021 
utilized a PHE-related

[[Page 22739]]

modification (with the majority of these cases using the prior 
hospitalization modification), as identified by the presence of a 
``Disaster Relief (DR)'' condition code on the SNF claim. As compared 
to prior years, when approximately 98 percent of SNF beneficiaries had 
a qualifying prior hospital stay, approximately 86 percent and 81 
percent of SNF beneficiaries had a qualifying prior hospitalization in 
FY 2020 and FY 2021, respectively. These general statistics are 
important, as they highlight that while the PHE for COVID-19 certainly 
impacted many aspects of nursing home operations, the large majority of 
SNF beneficiaries entered into Part A SNF stays in FY 2020 and FY 2021 
as they would have in any other year; that is, without using a PHE-
related modification, with a prior hospitalization, and without a 
COVID-19 diagnosis.
    Moreover, as discussed FY 2022 SNF PPS proposed rule (86 FR 19988), 
we found that even after removing those using a PHE-related 
modification and those with a COVID-19 diagnosis from our data set, the 
observed inadvertent increase in SNF payments since PDPM was 
implemented was approximately the same. To calculate expected total 
payments under RUG-IV, we used the percentage of stays in each RUG-IV 
group in FY 2019 and multiplied these percentages by the total number 
of FY 2020 days of service. We then multiplied the number of days for 
each RUG-IV group by the RUG-IV per diem rate, which we obtained by 
inflating the FY 2019 SNF PPS RUG-IV rates by the FY 2020 market basket 
update factor. The total payments under RUG-IV also accounted for the 
human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/
AIDS) add-on of a 128 percent increase in the PPS per diem payment 
under RUG-IV, and a provider's FY 2020 urban or rural status. To 
calculate the actual total payments under PDPM, we used data reported 
on FY 2020 claims. Specifically, we used the Health Insurance 
Prospective Payment System (HIPPS) code on the SNF claim to identify 
the patient's case-mix assignment and associated CMIs, utilization days 
on the claim to calculate stay payments and the variable per diem 
adjustment, the presence of an HIV diagnosis on the claim to account 
for the PDPM AIDS add-on of 18 percent to the nursing component, and 
the highest point value (8 points) to the NTA component, and a 
provider's urban or rural status. Using this approach, and as described 
in the FY 2022 SNF PPS proposed rule (86 FR 19988), we identified a 5.3 
percent increase in aggregate spending under PDPM as compared to 
expected total payments under RUG-IV for FY 2020 when considering the 
full SNF population, and a 5 percent increase in aggregate spending 
under PDPM for FY 2020 when considering the subset population. This 
finding suggests that a large portion of the changes observed in SNF 
utilization are due to PDPM and not the PHE for COVID-19, as the 
``new'' population of SNF beneficiaries (that is, COVID-19 patients and 
those using a PHE-related modification) did not appear to be the main 
cause of the increase in SNF payments after implementation of PDPM. 
Although these results are similar, we believed it would be more 
appropriate to pursue a potential recalibration using the subset 
population.
    Some commenters agreed with our approach, stating that our subset 
population was a reasonable method to account for the effect of the 
COVID-19 PHE, and made a few suggestions for improvements. They stated 
that our analysis may have undercounted COVID-19 patients because there 
was no COVID-19 specific diagnosis code available before April 2020 and 
a shortage of tests at the beginning of the PHE led to SNFs being 
unable to report COVID-19 cases. To address these issues, commenters 
suggested that CMS consider using non-specific respiratory diagnoses or 
depression as proxies for COVID-19 cases. We considered this option, 
though we believe that such a change would overestimate the population 
to be excluded due to the non-specific nature of those diagnoses. 
Additionally, because we did not provide our COVID-19 population 
definition in the FY 2022 SNF PPS proposed or final rules, commenters 
were concerned that our methodology did not include COVID-19 diagnoses 
from the Minimum Data Set (MDS) patient assessments in addition to SNF 
claims. Commenters were also concerned that we did not exclude 
transitional stays resulting from CMS' instruction to assess all 
patients anew in October 2019 using the PDPM MDS assessment, even 
though some patients were in the middle or end of their Medicare Part A 
coverage. We address these concerns by sharing a revised COVID-19 
population definition in section V.C.2.d. of this rule.
    However, many commenters expressed concern that our subset 
population methodology would not accurately represent what the SNF 
patient case-mix would look like outside of the COVID-19 PHE 
environment, stating that data collected during the PHE was entirely 
too laden with COVID-19 related effects on the entire SNF population to 
be utilized and pointing to multiple reasons for greater clinical 
acuity even among our subset population. For example, because elective 
surgeries were halted, those admitted were the most compromised who 
could not be cared for at home. Additionally, limitations regarding 
visitation and other infection control protocols led to higher levels 
of mood distress, cognitive decline, functional decline, compromised 
skin integrity, change in appetite, and weight loss requiring diet 
modifications. In response to these comments, we have conducted 
comprehensive data analysis and monitoring to identify changes in 
provider behavior and payments since implementing PDPM, and present a 
revised parity adjustment methodology in section V.C.2.d. of this rule 
that we believe more accurately accounts for these changes while 
excluding the effect of the COVID-19 PHE on the SNF population.
d. FY 2023 SNF PPS Proposed Parity Adjustment Methodology
    In this section, we propose a revised methodology for the 
calculating the parity adjustment that takes into account the comments 
received in response to the potential methodology described in the FY 
2022 SNF PPS proposed rule (86 FR 19986 through 19987). In response to 
the comments received about the subset population methodology, we 
modified our definition of COVID-19, which we derived from the Centers 
for Disease Control and Prevention (CDC) coding guidelines, to align 
with the definition used by publicly available datasets from CMS's 
Office of Enterprise Data and Analytics (OEDA) and found no significant 
impact on our calculations. For the FY 2022 SNF proposed rule, we 
defined the COVID-19 population to include stays that have either the 
interim COVID-19 code B97.29 recorded as a primary or secondary 
diagnosis in addition to one of the symptom codes J12.89, J20.8, J22, 
or J80, or the new COVID-19 code U07.1 recorded as a primary or 
secondary diagnosis on their SNF claims or MDS 5-day admission 
assessments. For the FY 2023 SNF proposed rule, we define the COVID-19 
population to include stays that have the interim COVID-19 code B97.29 
from January 1, 2020 to March 31, 2020 or the new COVID-19 code U07.1 
from April 1, 2020 onward recorded as a primary or secondary diagnosis 
on their SNF claims, MDS 5-day admission assessments, or MDS interim 
payment assessments. Both FY

[[Page 22740]]

2022 and FY 2023 definitions of the COVID-19 population exclude 
transitional stays. We note that we found no significant impact on our 
calculations, as the COVID-19 population definition change only 
increased the stay count of our subset population by less than 1 
percent.
    In response to the comments described previously and based on 
additional data collection through FY 2021, we have identified a 
recalibration methodology that we believe better accounts for COVID-19 
related effects. We propose to use the same type of subset population 
discussed earlier in section V.C.2.c.of this proposed rule, which 
excludes stays that either used a section 1812(f) of the Act 
modification or that included a COVID-19 diagnosis, with a 1-year 
``control period'' derived from both FY 2020 and FY 2021 data. 
Specifically, we used 6 months of FY 2020 data from October 2019 
through March 2020 and 6 months of FY 2021 data from April 2021 through 
September 2021 (which our data suggests were periods with relatively 
low COVID-19 prevalence) to create a full 1-year period with no 
repeated months to account for seasonality effects. As shown in Table 
11, we believe this combined approach provides the most accurate 
representation of what the SNF case-mix distribution would look like 
under PDPM outside of a COVID-19 PHE environment. While using the 
subset population method alone for FY 2020 and FY 2021 data results in 
differences of 0.31 percent and 0.40 percent between the full and 
subset populations, respectively, introducing the control period closes 
the gap between the full and subset population adjustment factors to 
0.02 percent, suggesting that the control period captures additional 
COVID-19 related effects on patient acuity that the subset population 
method alone does not. Accordingly, the combined methodology of using 
the subset population with data from the control period results in the 
lowest parity adjustment factor. Table 12 shows that while using the 
subset population method would lead to a 4.9 percent adjustment factor 
($1.8 billion) using FY 2020 data and a 5.3 percent adjustment factor 
($1.9 billion) using FY 2021 data, introducing the control period 
reduces the adjustment factor to 4.6 percent ($1.7 billion). The 
robustness of the control period approach is further demonstrated by 
the fact that using data from the control period, with either the full 
or subset population, would lead to approximately the same parity 
adjustment factor of 4.58 percent as compared to 4.6 percent. We invite 
comments on our proposed combined methodology of using the subset 
population and data from the control period for the purposes of 
calculating the recalibrated parity adjustment factor.
[GRAPHIC] [TIFF OMITTED] TP15AP22.018

[GRAPHIC] [TIFF OMITTED] TP15AP22.019

    Our data analysis and monitoring efforts provides further support 
for the accuracy and appropriateness of a 4.6 percent parity adjustment 
factor, as we have identified numerous changes that demonstrate the 
different impacts of PDPM implementation and the COVID-19 PHE on 
reported patient clinical acuity. As described earlier, commenters 
stated that limitations regarding visitation and other infection 
control protocols due to the PHE led to higher levels of mood distress, 
cognitive decline, functional decline, compromised skin integrity, 
change in appetite, and weight loss requiring diet modifications among 
the non-COVID population. However, our data shows that most of these 
metrics, with the exception of functional decline and compromised skin 
integrity, had already exhibited clear changes concurrent with PDPM 
implementation and well before the start of the COVID-19 PHE. For 
example, in regard to higher levels of mood distress and cognitive 
decline, we observed an average of 4 percent of stays with depression 
and 40 percent of stays with cognitive impairment, with an average mood 
score of 1.9, in the fiscal year prior to PDPM implementation (FY 
2019). In the 3 months directly following PDPM implementation and 
before the start of the COVID-19 PHE (October 2019 to December 2019), 
these averages increased to 11 percent of stays with depression and 44 
percent of stays with cognitive impairment, with an average mood scale 
of 2.9. As for change in appetite and weight loss requiring diet 
modifications, we observed an average of 15 percent of stays with any 
SLP comorbidity, 5 percent of stays with a swallowing disorder, and 22 
percent of stays with a mechanically altered diet in FY 2019. In the 3 
months directly following PDPM implementation, these averages increased 
to 19 percent of stays with any SLP comorbidity, 17 percent of stays 
with a swallowing disorder, and 25 percent of stays with a mechanically 
altered diet. Notably, we also observed that the percentage of stays 
with a swallowing disorder that did not also receive a mechanically 
altered diet increased from 1 percent in FY 2019 to 5 percent in the 3 
months directly

[[Page 22741]]

following PDPM implementation. While many of these metrics increased 
further after the start of the COVID-19 PHE, they remained elevated at 
around their post-PDPM implementation levels even during periods of low 
COVID-19 prevalence. As a result, our parity adjustment calculations 
remained much the same even during months when rates of COVID-19 cases 
were quite low, suggesting that patient case mix classification has 
stabilized independent of the ongoing COVID-19 PHE.
    Another reason that commenters cited to explain the greater 
clinical acuity among the subset population is that, because elective 
surgeries were halted, patients who were admitted were more severely 
ill and could not be treated at home. We acknowledge that the subset 
population methodology, or any method predicated on data from the 
COVID-19 PHE period, may not accurately represent what SNF patient 
case-mix would look like outside of the COVID-19 PHE environment 
because while we can remove data that we believe are due to COVID 
impacts, it is more difficult to add data back in that was missing due 
to the COVID-19 PHE.
    However, we believe that the addition of the control period to the 
subset population methodology helps to resolve this issue. For example, 
there likely would have been more joint replacements were it not for 
the COVID-19 PHE. Our data show that the rate of major joint 
replacement or spinal surgery decreased from 7.6 percent of stays in FY 
2019, to 5.5 percent of stays in FY 2021, to 5.2 percent of stays in FY 
2022. Similarly, rates of orthopedic surgery decreased from 9.1 percent 
of stays in FY 2019, to 9.0 percent of stays in FY 2021, to 8.8 percent 
of stays in FY 2022. Using the control period, which excludes the 
periods of highest COVID-19 prevalence and lowest rates of elective 
surgeries, we arrive at rates of 6.4 percent of stays with major joint 
replacement or spinal surgery, and 9.5 percent of stays with orthopedic 
surgery. Therefore, we believe that using the control period is a 
closer representation of SNF patient case-mix outside of a COVID-19 PHE 
environment than using either FY 2021 or FY 2022 data alone.
    Given the results of our data analyses, we propose adopting the 
methodology based upon the subset population during the control period, 
and lowering the PDPM parity adjustment factor from 46 percent to 38 
percent for each of the PDPM case-mix adjusted components. If we 
applied this methodology for FY 2023, we estimated a reduction in 
aggregate SNF spending of 4.6 percent, or approximately $1.7 billion. 
We note that the parity adjustment is calculated and applied at a 
systemic level to all facilities paid under the SNF PPS, and there may 
be variation between facilities based on their unique patient 
population, share of non-case-mix component payment, and urban or rural 
status. We invite comments on the methodology described in this section 
of the proposed rule for recalibrating the PDPM parity adjustment, as 
well as the findings of our analysis described throughout this section. 
To assist commenters in providing comments on this issue, we have also 
posted a file on the CMS website, at https://www.cms.gov/medicare/medicare-fee-for-service-payment/snfpps, which provides the FY 2019 RUG 
IV case-mix distribution and calculation of total payments under RUG-
IV, as well as PDPM case-mix utilization data at the case mix group and 
component level to demonstrate the calculation of total payments under 
PDPM.
3. Methodology for Applying the Recalibrated PDPM Parity Adjustment
    As discussed in the FY 2022 SNF PPS proposed rule (86 FR 19988), we 
believe it would be appropriate to apply the recalibrated parity 
adjustment across all PDPM CMIs in equal measure, as the initial 
increase to the PDPM CMIs to achieve budget neutrality was applied 
equally, and therefore, this method would properly implement and 
maintain the integrity of the PDPM classification methodology as it was 
originally designed. Tables 5 and 6 in section III.C. of this proposed 
rule set forth what the PDPM CMIs and case-mix adjusted rates would be 
if we apply the recalibration methodology in equal measure in FY 2023.
    We acknowledge that we received several comments in response to 
last year's rule objecting to this approach given that our data 
analysis, presented in Table 23 of the FY 2022 SNF PPS proposed rule 
(86 FR 19987), showed significant increases in the average CMI for the 
SLP, Nursing, and NTA components for both the full and subset FY 2020 
populations as compared to what was expected, with increases of 22.6 
percent, 16.8 percent, and 5.6 percent, respectively, for the full FY 
2020 SNF population. As described in the FY 2022 SNF PPS final rule (86 
FR 42471), some commenters disagreed with adjusting the CMIs across all 
case-mix adjusted components in equal measure, suggesting that this 
approach would harm patient care by further reducing PT and OT therapy 
minutes. Instead, the commenters recommended a targeted approach that 
focuses the parity adjustment on the SLP, Nursing, and NTA components 
in proportion to how they are driving the unintended increase observed 
under PDPM.
    We considered these comments but believe that it would be most 
appropriate to propose applying the parity adjustment across all 
components equally. First, as described earlier, the initial increase 
to the PDPM CMIs to achieve budget neutrality was applied across all 
components, and therefore, it would be appropriate to implement a 
revision to the CMIs in the same way. Second, the reason we do not 
observe the same magnitude of change in the PT and OT components is 
that, in designing the PDPM payment system, the data used to help 
determine what payment groups SNF patients would classify into under 
PDPM was collected under the prior payment model (RUG-IV), which 
included incentives that encouraged significant amounts of PT and OT. 
Given that PT and OT were furnished in such high amounts under RUG-IV, 
we had already assumed that a significant portion of patients would be 
classified into the higher paying PT and OT groups corresponding to 
having a Section GG function score of 10 to 23. Therefore, this left 
little room for additional increases in PT and OT classification after 
PDPM implementation. In other words, the PT and OT components results 
were as expected according to the original design of PDPM, while the 
SLP, Nursing, and NTA results were not.
    However, to fully explore the alternative targeted approach that 
commenters suggested, we have updated our analysis of the average CMI 
by PDPM component from Table 23 of the FY 2022 SNF PPS proposed rule 
(86 FR 19987) and found that a similar pattern still holds when 
comparing the expected average CMIs for FY 2019 and the expected actual 
CMIs for the subset population during the control period. Table 13 
shows significant increases in average case-mix of 18.6 percent for the 
SLP component and the 10.8 percent for the Nursing component, a 
moderate increase of 3.0 percent for the NTA component, and a slight 
increase of 0.4 percent for the PT and OT components, respectively. We 
also provide Table 14 to show the potential impact of applying the 
recalibrated PDPM parity adjustment to the PDPM CMIs in a targeted 
manner, instead of an equal approach as presented in Tables 5 and 6 in 
section III.C. of this proposed rule. We invite comments on whether 
stakeholders believe a targeted approach is preferable to our proposed 
equal approach.
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4. Delayed and Phased Implementation
    As we noted in the FY 2012 SNF PPS final rule (76 FR 48493), we 
believe it is imperative that we act in a well-considered but expedient 
manner once excess payments are identified, as we did in FY 2012. 
However, we acknowledged that applying a reduction in payments without 
time to prepare could create a financial burden for providers, 
particularly considering the ongoing COVID-19 PHE. Therefore, in the FY 
2022 SNF PPS proposed rule (86 FR 19988 through 19990), we solicited 
comments on two potential mitigation strategies to ease the transition 
to prospective budget neutrality: Delayed implementation and phased 
implementation, both of which are described later in this section. We 
noted that for either of these options, the adjustment would be applied 
prospectively, and the CMIs would not be adjusted to account for 
deviations from budget neutrality in years before the payment 
adjustments are implemented.
    A delayed implementation strategy would mean that we would 
implement the reduction in payment in a later year than the year the 
reduction is finalized. For example, considering the 4.6 percent 
reduction discussed previously in this preamble, if this reduction is 
finalized in FY 2023 with a 1-year delayed implementation, this would 
mean that the full 4.6 percent reduction

[[Page 22743]]

will be applied prospectively applied to the PDPM CMIs in FY 2024. By 
comparison, a phased implementation strategy would mean that the amount 
of the reduction would be spread out over some number of years. For 
example, if we were to use a 2-year phased implementation approach to 
the 4.6 percent reduction discussed previously in this proposed rule 
with no delayed implementation, this would mean that the PDPM CMIs 
would be reduced by 2.3 percent in the first year of implementation in 
FY 2023 and then reduced by the remaining 2.3 percent in the second and 
final year of implementation in FY 2024. We could also use a 
combination of both mitigation strategies, such as a 1-year delayed 
implementation with a 2-year phased approach, would mean that the PDPM 
CMIs would be reduced by 2.3 percent in the first year of 
implementation in FY 2024 and then reduced by the remaining 2.3 percent 
in the second and final year of implementation in FY 2025.
    In the FY 2022 SNF PPS proposed rule (86 FR 19988 through 19990), 
we solicited comments on the possibility of combining the delayed and 
phased implementation approaches and what stakeholders believe would be 
appropriate to appropriately mitigate the impact of the reduction in 
SNF PPS payments. As described in the FY 2022 SNF PPS final rule (86 FR 
42470 through 42471), the majority of commenters supported combining 
both mitigation strategies of delayed implementation of 2 years and a 
gradual phase-in of no more than 1 percent per year. In its comments to 
the FY 2022 SNF PPS proposed rule, MedPAC supported delayed 
implementation, but did not believe a phased-in approach was warranted 
given the high level of aggregate payment to SNFs.
    As stated in the FY 2022 SNF PPS proposed rule (86 FR 19989) and FY 
2022 SNF PPS final rule (86 FR 42471), we believe it is imperative that 
we act in a well-considered but expedient manner once excess payments 
are identified. Additionally, we stated that we would consider whether 
the delayed and phased implementation approaches were warranted to 
mitigate potential negative impacts on providers resulting from 
implementation of such a reduction in the SNF PPS rates entirely within 
a single year. After careful consideration, we are proposing to 
recalibrate the parity adjustment in FY 2023 with no delayed 
implementation or phase-in period, particularly after considering that 
we have already granted a 1-year delayed implementation by not 
proposing or finalizing the parity adjustment in the FY 2022 SNF PPS 
proposed and final rules. This proposal would lead to a prospective 
reduction in Medicare Part A SNF payments of approximately 4.6 percent 
(-$1.7 billion) in FY 2023. We would note that this reduction would be 
substantially mitigated by the proposed FY 2023 net SNF market basket 
update factor of 3.9 percent, which reflects a market basket increase 
factor of 2.8 percent, adjusted upward to account for the 1.5 
percentage point forecast error correction and adjusted downward to 
account for the 0.4 percentage point productivity adjustment, as 
discussed in section III.B. of this proposed rule. Taken together, the 
preliminary net budget impact in FY 2023 would be an estimated decrease 
of $320 million in aggregate payment to SNFs if the parity adjustment 
is implemented in one year.
    While we note many commenters supported both mitigation strategies 
of delayed implementation and phased implementation, we emphasize that 
we have already granted a 1-year delayed implementation by not 
proposing or finalizing the parity adjustment in the FY 2022 SNF PPS 
proposed and final rules, and instead taking a year to solicit and 
consider comments on our parity adjustment methodology. As stated in 
the FY 2022 final rule, we estimated a reduction in SNF spending of 5 
percent, or approximately $1.7 billion, if we had implemented the 
parity adjustment in FY 2022 (86 FR 42471). Moreover, in light of the 
potential reduction in payments associated with each possible option 
outlined in Table 2, the SNF PPS has been paying in excess of budget 
neutrality at a rate of approximately $1.7 billion per year since PDPM 
was implemented in FY 2020. We therefore believe that delaying the 
implementation of the proposed recalibration or phasing the 
recalibration in over some amount of time would only serve to prolong 
these payments in excess of the intended policy.
    Further, MedPAC's March 2022 Report to Congress (available at 
https://www.medpac.gov/wp-content/uploads/2022/03/Mar22_MedPAC_ReportToCongress_Ch7_SEC.pdf) has found that since 2000, 
the aggregate Medicare margin for freestanding SNFs has consistently 
been above 10 percent each year. In 2020, the aggregate Medicare margin 
was 16.5 percent, a sizable increase from 11.9 percent in 2019. 
Additionally, the aggregate Medicare margin in 2020 increased to an 
estimated 19.2 percent when including Federal relief funds for the 
COVID-19 PHE (March 2022 MedPAC Report to Congress, 251-252). Given 
these high Medicare margins, we do not believe that a delayed 
implementation or a phase-in approach is needed. Rather, these 
mitigation strategies would continue to pay facilities at levels that 
significantly exceed intended SNF payments, had PDPM been implemented 
in a budget neutral manner as finalized by CMS in the FY 2019 SNF PPS 
final rule (83 FR 39256). It is also important to note that the parity 
adjustment recalibration would serve to remove an unintended increase 
in payments from moving to a new case mix classification system, rather 
than decreasing an otherwise appropriate payment amount. Thus, we do 
not believe that the recalibration should negatively affect facilities, 
beneficiaries, and quality of care, or create an undue hardship on 
providers.
    We continue to believe that in implementing PDPM, it is essential 
that we stabilize the baseline as quickly as possible without creating 
a significant adverse effect on the industry or to beneficiaries. We 
invite comments on our proposal to recalibrate the parity adjustment by 
4.6 percent in FY 2023, and whether stakeholders believe delayed 
implementation or phase-in period is warranted or not, in light of the 
data analysis and policy considerations presented previously.

D. Request for Information: Infection Isolation

    Under the SNF PPS, various patient characteristics are used to 
classify patients in Medicare-covered SNF stays into payment groups. 
One of these characteristics is isolation due to an active infection. 
In order for a patient to qualify to be coded as being isolated for an 
active infectious disease, the patient must meet all of the following 
criteria:
    1. The patient has active infection with highly transmissible or 
epidemiologically significant pathogens that have been acquired by 
physical contact or airborne or droplet transmission.
    2. Precautions are over and above standard precautions. That is, 
transmission-based precautions (contact, droplet, and/or airborne) must 
be in effect.
    3. The patient is in a room alone because of active infection and 
cannot have a roommate. This means that the resident must be in the 
room alone and not cohorted with a roommate regardless of whether the 
roommate has a similar active infection that requires isolation.
    4. The patient must remain in his or her room. This requires that 
all services be brought to the resident (for example, rehabilitation, 
activities, dining, etc.).

[[Page 22744]]

    Being coded for infection isolation can have a significant impact 
on the Medicare payment rate for a patient's SNF stay. The increase in 
a SNF patient's payment rate as a result of being coded under infection 
isolation is driven by the increase in the relative costliness of 
treating a patient who must be isolated due to an infection. More 
specifically, in 2005, we initiated a national nursing home staff time 
measurement (STM) study, the Staff Time and Resource Intensity 
Verification (STRIVE) Project. The STRIVE project was the first 
nationwide time study for nursing homes in the United States to be 
conducted since 1997, and the data collected were used to establish 
payment systems for Medicare skilled nursing facilities (SNFs) as well 
as Medicaid nursing facilities (NFs).
    In the STRIVE project final report, titled ``Staff Time and 
Resource Intensity Verification Project Phase II'' section 4.8 
(available at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/TimeStudy), we discussed how infection isolation was 
categorized into the Extensive Services RUG-III category based on the 
high resource intensity that was required for treating patients for 
whom facilities would code this category on the MDS. The significant 
increase in payment associated with this item is intended to account 
for the increase in relative resource utilization and costs associated 
with treating a patient isolated due to an active infection, as well as 
the PPE and additional protocols which must be followed treating such a 
patient, which are significantly greater than treating patients outside 
of such an environment.
    During the COVID-19 PHE, a number of stakeholders raised concerns 
with the definition of ``infection isolation'', as it relates to the 
treatment of SNF patients being cohorted due to either the diagnosis or 
suspected diagnosis of COVID-19. Specifically, stakeholders took issue 
with criterion 1, which requires that the patient have an active 
infection, rather than suspicion of an active infection, and criterion 
3, which requires that the patient be in the room alone, rather than 
being cohorted with other patients. To this point, we have maintained 
that the definition of ``infection isolation'' is appropriate and 
should not be changed in response to the circumstances of the COVID-19 
PHE. Due to the ubiquitous nature of the PHE and precautions that are 
being taken throughout SNFs with regard to PPE and other COVID-19 
related needs, we understand that the general costs for treating all 
SNF patients may have increased. However, as the case-mix 
classification model is intended to adjust payments based on relative 
differences in the cost of treating different SNF patients, we are 
unclear on if the relative increase in resource intensity for each 
patient being treated within a cohorted environment is the same 
relative increase as it would be for treating a single patient isolated 
due to an active infection.
    We would like to take this opportunity to invite the public to 
submit their comments about isolation due to active infection and how 
the PHE has affected the relative staff time resources necessary for 
treating these patients. Specifically, we invite comments on whether or 
not the relative increase in resource utilization for each of the 
patients within a cohorted room, all with an active infection, is the 
same or comparable to that of the relative increase in resource 
utilization associated with a patient that is isolated due to an active 
infection.

VI. 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 update described 
in section 1888(e)(5)(B)(i) of the Act applicable to a SNF for a fiscal 
year, 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 fiscal year. For 
more information on the requirements we have adopted for the SNF QRP, 
we refer readers to the FY 2016 SNF PPS final rule (80 FR 46427 through 
46429), FY 2017 SNF PPS final rule (81 FR 52009 through 52010), FY 2018 
SNF PPS final rule (82 FR 36566 through 36605), FY 2019 SNF PPS final 
rule (83 FR 39162 through 39272), and FY 2020 SNF PPS final rule (84 FR 
38728 through 38820).

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 2023 SNF QRP
    The SNF QRP currently has 15 measures for the FY 2023 SNF QRP, 
which are outlined in Table 15. 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)(3).
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C. SNF QRP Quality Measure Proposals Beginning With the FY 2025 SNF QRP

    Section 1899B(h)(1) of the Act permits the Secretary to remove, 
suspend, or add quality measures or resource use or other measures 
described in sections 1899B(c)(1) and (d)(1) of the Act, respectively, 
so long as the Secretary publishes in the Federal Register (with a 
notice and comment period) a justification for such removal, suspension 
or addition. Section 1899B(a)(1)(B) of the Act requires that all of the 
data that must be reported in accordance with section 1899B(a)(1)(A) of 
the Act (including resource use or other measure data under section 
1899B(d)(1) of the Act) be standardized and interoperable to allow for 
the exchange of the information among post-acute care (PAC) providers 
and other providers and the use by such providers of such data to 
enable access to longitudinal information and to facilitate coordinated 
care.
    We propose to adopt one new measure for the SNF QRP beginning with 
the FY 2025 SNF QRP: The Influenza Vaccination Coverage among 
Healthcare Personnel (HCP) (NQF #0431) measure as an ``other measure'' 
under section 1899B(d)(1) of the Act. In accordance with section 
1899B(a)(1)(B) of the Act, the data used to calculate this measure are 
standardized and interoperable. The proposed measure supports the 
``Preventive Care'' Meaningful Measure area and the ``Promote Effective 
Prevention and Treatment of Chronic Disease'' healthcare priority.\9\ 
The Influenza Vaccination Coverage among HCP measure is a process 
measure, developed by the Centers for Disease Control and Prevention 
(CDC), and reports on the percentage of HCP who receive the influenza 
vaccination. This measure is currently used in other post-acute care 
(PAC) Quality Reporting Programs (QRPs), including the Inpatient 
Rehabilitation Facility (IRF) QRP and the Long-Term Care Hospital 
(LTCH) QRP. The measure is described in more detail in section VI.C.1. 
of this proposed rule.
---------------------------------------------------------------------------

    \9\ CMS Measures Inventory Tool. (2022). Influenza Vaccination 
Coverage among Healthcare Personnel. Retrieved from https://cmit.cms.gov/CMIT_public/ReportMeasure?measureId=854.
---------------------------------------------------------------------------

    In addition, we propose to revise the compliance date for the 
collection of the Transfer of Health (TOH) Information to the Provider-
PAC measure, the TOH Information to the Patient-PAC measure, and 
certain standardized patient assessment data elements from October 1st 
of the year that is at least 2 full fiscal years after the end of the 
COVID-19

[[Page 22746]]

PHE to October 1, 2023. We believe the COVID-19 PHE revealed why the 
TOH Information measures and standardized patient assessment data 
elements are important to the SNF QRP. The new data elements will 
facilitate communication and coordination across care settings as well 
as provide information to support our mission of analyzing the impact 
of the COVID-19 PHE on patients to improve the quality of care in SNFs. 
We describe this proposal in more detail in section VI.C.2. of this 
proposed rule.
    Finally, we propose to make certain revisions to regulation text at 
Sec.  413.360 to include a new paragraph to reflect all the data 
completion thresholds required for SNFs to meet the compliance 
threshold for the annual payment update, as well as certain conforming 
revisions. We describe this proposal in more detail in section VI.C.3. 
of this proposed rule.
1. Influenza Vaccination Coverage Among Healthcare Personnel (NQF 
#0431) Measure Beginning With the FY 2025 SNF QRP
a. Background
    The CDC Advisory Committee on Immunization Practices (ACIP) 
recommends that all persons 6 months of age and older, including HCP 
and persons training for professions in health care, should be 
vaccinated annually against influenza.\10\ The basis of this 
recommendation stems from the spells of illness, hospitalizations, and 
mortality associated with the influenza virus. Between 2010 and 2020, 
the influenza virus resulted in 12,000 to 52,000 deaths in the United 
States each year, depending on the severity of the 
strain.11 12 Preliminary estimates from the CDC revealed 35 
million cases, 380,000 hospitalizations, and 20,000 deaths linked to 
influenza in the United States during the 2019 to 2020 influenza 
season.\13\ Persons aged 65 years and older are at higher risk for 
experiencing burdens related to severe influenza due to the changes in 
immune defenses that come with increasing age.14 15 The CDC 
estimates that 70 to 85 percent of seasonal influenza-related deaths 
occur among people aged 65 years and older, and 50 to 70 percent of 
influenza-related hospitalizations occur among this age group.\16\ 
Residents of long-term care facilities, who are often of older age, 
have greater susceptibility for acquiring influenza due to general 
frailty and comorbidities, close contact with other residents, 
interactions with visitors, and exposure to staff who rotate between 
multiple facilities.17 18 19 Therefore, monitoring and 
reporting influenza vaccination rates among HCP is important as HCP are 
at risk for acquiring influenza from residents and exposing influenza 
to residents.\20\ For example, one early report of HCP influenza 
infections during the 2009 H1N1 influenza pandemic estimated 50 percent 
of HCP had contracted the influenza virus from patients or coworkers 
within the health care setting.\21\
---------------------------------------------------------------------------

    \10\ Grohskopf, L.A., Alyanak, E., Broder, K.R., Walter, E.B., 
Fry, A.M., & Jernigan, D.B. (2019). Prevention and Control of 
Seasonal Influenza with Vaccines: Recommendations of the Advisory 
Committee on Immunization Practices--United States, 2019-20 
Influenza Season. MMWR Recomm Rep, 68(No. RR-3), 1-21. https://www.cdc.gov/mmwr/volumes/68/rr/rr6803a1.htm?s_cid=rr6803a1_w.
    \11\ Centers for Disease Control and Prevention (CDC). (2021). 
Disease Burden of Flu. Retrieved from https://www.cdc.gov/flu/about/burden/index.html?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fflu%2Fabout%2Fdisease%2Fus_flu-related_deaths.htm.
    \12\ Frentzel, E., Jump, R., Archbald-Pannone, L., Nace, D.A., 
Schweon, S.J., Gaur, S., Naqvi, F., Pandya, N., Mercer, W., & 
Infection Advisory Subcommittee of AMDA, The Society for Post-Acute 
and Long-Term Care Medicine (2020). Recommendations for Mandatory 
Influenza Vaccinations for Health Care Personnel From AMDA's 
Infection Advisory Subcommittee. Journal of the American Medical 
Directors Association, 21(1), 25-28.e2. https://doi.org/10.1016/j.jamda.2019.11.008.
    \13\ Centers for Disease Control and Prevention (CDC). (2021). 
Estimated Flu-Related Illnesses, Medical visits, Hospitalizations, 
and Deaths in the United States--2019-2020 Flu Season. Retrieved 
from https://www.cdc.gov/flu/about/burden/2019-2020.html.
    \14\ Centers for Disease Control and Prevention (CDC). (2021). 
Retrieved from Flu & People 65 Years and Older: https://www.cdc.gov/flu/highrisk/65over.htm?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fflu%2Fabout%2Fdi
sease%2F65over.htm.
    \15\ Frentzel, E., Jump, R., Archbald-Pannone, L., Nace, D.A., 
Schweon, S.J., Gaur, S., Naqvi, F., Pandya, N., Mercer, W., & 
Infection Advisory Subcommittee of AMDA, The Society for Post-Acute 
and Long-Term Care Medicine (2020). Recommendations for Mandatory 
Influenza Vaccinations for Health Care Personnel From AMDA's 
Infection Advisory Subcommittee. Journal of the American Medical 
Directors Association, 21(1), 25-28.e2. https://doi.org/10.1016/j.jamda.2019.11.008.
    \16\ Centers for Disease Control and Prevention (CDC). (2021). 
Retrieved from Flu & People 65 Years and Older: https://www.cdc.gov/flu/highrisk/65over.htm?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fflu%2Fabout%2Fdi
sease%2F65over.htm.
    \17\ Lansbury, L.E., Brown, C.S., & 
Nguyen[hyphen]Van[hyphen]Tam, J.S. (2017). Influenza in 
long[hyphen]term care facilities. Influenza Other Respir Viruses, 
11(5), 356-366. https://dx.doi.org/10.1111%2Firv.12464.
    \18\ Pop-Vicas, A., & Gravenstein, S. (2011). Influenza in the 
elderly: A mini-review. Gerontology, 57(5), 397-404. https://doi.org/10.1159/000319033.
    \19\ Strausbaugh, L.J., Sukumar, S.R., & Joseph, C.L. (2003). 
Infectious disease outbreaks in nursing homes: an unappreciated 
hazard for frail elderly persons. Clinical infectious diseases: an 
official publication of the Infectious Diseases Society of America, 
36(7), 870-876. https://doi.org/10.1086/368197.
    \20\ Wilde, J.A., McMillan, J.A., Serwint, J., Butta, J., 
O'Riordan, M.A., & Steinhoff, M.C. (1999). Effectiveness of 
influenza vaccine in health care professionals: a randomized trial. 
JAMA, 281(10), 908-913. https://doi.org/10.1001/jama.281.10.908.
    \21\ Harriman K, Rosenberg J, Robinson S, et al. (2009). Novel 
influenza A (H1N1) virus infections among health-care personnel--
United States, April-May 2009. MMWR Morb Mortal Wkly Rep, 58(23), 
641-645. Retrieved from https://www.cdc.gov/mmwr/preview/mmwrhtml/mm5823a2.htm.
---------------------------------------------------------------------------

    Despite the fact that influenza commonly spreads between HCP and 
SNF residents, vaccine hesitancy and organizational barriers often 
prevent influenza vaccination. For example, although the CDC emphasizes 
the importance for HCP to receive the influenza vaccine, the 2017 to 
2018 influenza season shows higher influenza vaccination coverage among 
HCP working in hospitals (approximately 92 percent) and lower coverage 
among those working in long-term care facilities (approximately 68 
percent).22 23 HCP working in long-term care facilities, 
including SNFs, have expressed concerns about the influenza vaccine's 
effectiveness and safety, fearing potential side effects and adverse 
reactions.\24\ Other HCP believe healthy individuals are not 
susceptible to infection and therefore find vaccination 
unnecessary.\25\ In addition, many HCP do not prioritize influenza 
vaccination, expressing a lack of time to get vaccinated.\26\ Lower HCP 
influenza vaccination in long-term care facilities also stems from 
organizational barriers, such as inadequate vaccine record keeping, 
frequent staff turnover, an

[[Page 22747]]

absence of influenza vaccine mandates, a lack of communication about 
vaccination rates, and a lack of incentives encouraging HCP flu 
vaccination.\27\ Given the fact that influenza vaccination coverage 
among HCP is typically lower in long-term care settings, such as SNFs, 
when compared to other care settings, we believe the proposed measure 
has the potential to increase influenza vaccination coverage in SNFs, 
promote patient safety, and increase the transparency of quality of 
care in the SNF setting.
---------------------------------------------------------------------------

    \22\ Black, C.L., Yue, X., Ball, S.W., Fink, R.V., de Perio, 
M.A., Laney, A.S., Williams, W.W., Graitcer, S.B., Fiebelkorn, A.P., 
Lu, P.J., & Devlin, R. (2018). Influenza Vaccination Coverage Among 
Health Care Personnel--United States, 2017-18 Influenza Season. 
MMWR. Morbidity and mortality weekly report, 67(38), 1050-1054. 
https://doi.org/10.15585/mmwr.mm6738a2.
    \23\ Jaklevic M.C. (2020). Flu Vaccination Urged During COVID-19 
Pandemic. JAMA. 324(10),926-927. https://doi.org/10.1001/jama.2020.15444.
    \24\ Frentzel, E., Jump, R., Archbald-Pannone, L., Nace, D.A., 
Schweon, S.J., Gaur, S., Naqvi, F., Pandya, N., Mercer, W., & 
Infection Advisory Subcommittee of AMDA, The Society for Post-Acute 
and Long-Term Care Medicine (2020). Recommendations for Mandatory 
Influenza Vaccinations for Health Care Personnel From AMDA's 
Infection Advisory Subcommittee. Journal of the American Medical 
Directors Association, 21(1), 25-28.e2. https://doi.org/10.1016/j.jamda.2019.11.008.
    \25\ Kenny, E., McNamara, [Aacute]., Noone, C., & Byrne, M. 
(2020). Barriers to seasonal influenza vaccine uptake among health 
care workers in long-term care facilities: A cross-sectional 
analysis. British journal of health psychology, 25(3), 519-539. 
https://doi.org/10.1111/bjhp.12419.
    \26\ Kose, S., Mandiracioglu, A., Sahin, S., Kaynar, T., Karbus, 
O., & Ozbel, Y. (2020). Vaccine hesitancy of the COVID-19 by health 
care personnel. Int J Clin Pract, 75(5), e13917. https://doi.org/10.1111/ijcp.13917.
    \27\ Ofstead, C.L., Amelang, M.R., Wetzler, H.P., & Tan, L. 
(2017). Moving the needle on nursing staff influenza vaccination in 
long-term care: Results of an evidence-based intervention. Vaccine, 
35(18), 2390-2395. https://doi.org/10.1016/j.vaccine.2017.03.041.
---------------------------------------------------------------------------

    Although concerns about vaccine effectiveness often prevent some 
HCP from getting the influenza vaccine, the CDC notes that higher 
influenza vaccination rates reduce the risk of influenza-related 
illness between 40 to 60 percent among the overall population during 
seasons when the circulating influenza virus is well-matched to viruses 
used to make influenza vaccines.\28\ During the 2019 to 2020 influenza 
season, vaccinations prevented 7.5 million influenza-related illnesses, 
105,000 influenza-related hospitalizations, and 6,300 deaths.\29\ 
Additionally, among adults with influenza-associated hospitalization, 
influenza vaccination is also associated with a 26 percent lower risk 
of intensive care unit admission, and 31 percent lower risk of 
influenza-related deaths compared to individuals who were unvaccinated 
against influenza.\30\ Several cluster-randomized trials comparing HCP 
influenza vaccination groups to control groups demonstrate reductions 
in long-term care resident mortality rates as related to HCP influenza 
vaccination.31 32 33 34 To reduce vaccine hesitancy and 
organizational barriers to influenza vaccination, several strategies 
can be used to increase influenza vaccination among HCP. These include 
availability of on-site influenza vaccinations and educational 
campaigns about influenza risks and vaccination 
benefits.35 36 37
---------------------------------------------------------------------------

    \28\ Centers for Disease Control and Prevention (CDC). (2021). 
Retrieved from Vaccine Effectiveness: How Well Do Flu Vaccines 
Work?: https://www.cdc.gov/flu/vaccines-work/vaccineeffect.htm.
    \29\ Centers for Disease Control and Prevention (CDC). (2021). 
Retrieved from Vaccine Effectiveness: How Well Do Flu Vaccines 
Work?: https://www.cdc.gov/flu/vaccines-work/vaccineeffect.htm.
    \30\ Ferdinands, J.M., Thompson, M.G., Blanton, L., Spencer, S., 
Grant, L., & Fry, A.M. (2021). Does influenza vaccination attenuate 
the severity of breakthrough infections? A narrative review and 
recommendations for further research. Vaccine, 39(28), 3678-3695. 
https://doi.org/10.1016/j.vaccine.2021.05.011.
    \31\ Carman, W.F., Elder, A.G., Wallace, L.A., McAulay, K., 
Walker, A., Murray, G.D., & Stott, D.J. (2000). Effects of influenza 
vaccination of health-care workers on mortality of elderly people in 
long-term care: a randomised controlled trial. Lancet (London, 
England), 355(9198), 93-97. https://doi.org/10.1016/S0140-6736(99)05190-9.
    \32\ Hayward, A.C., Harling, R., Wetten, S., Johnson, A.M., 
Munro, S., Smedley, J., Murad, S., & Watson, J.M. (2006). 
Effectiveness of an influenza vaccine programme for care home staff 
to prevent death, morbidity, and health service use among residents: 
cluster randomised controlled trial. BMJ (Clinical research ed.), 
333(7581), 1241. https://doi.org/10.1136/bmj.39010.581354.55.
    \33\ Lemaitre, M., Meret, T., Rothan-Tondeur, M., Belmin, J., 
Lejonc, J.L., Luquel, L., Piette, F., Salom, M., Verny, M., Vetel, 
J.M., Veyssier, P., & Carrat, F. (2009). Effect of influenza 
vaccination of nursing home staff on mortality of residents: a 
cluster-randomized trial. Journal of the American Geriatrics 
Society, 57(9), 1580-1586. https://doi.org/10.1111/j.1532-5415.2009.02402.x.
    \34\ Potter, J., Stott, D.J., Roberts, M.A., Elder, A.G., 
O'Donnell, B., Knight, P.V., & Carman, W.F. (1997). Influenza 
vaccination of health care workers in long-term-care hospitals 
reduces the mortality of elderly patients. The Journal of infectious 
diseases, 175(1), 1-6. https://doi.org/10.1093/infdis/175.1.1.
    \35\ Bechini, A., Lorini, C., Zanobini, P., Mand[ograve] 
Tacconi, F., Boccalini, S., Grazzini, M., Bonanni, P., & Bonaccorsi, 
G. (2020). Utility of Healthcare System-Based Interventions in 
Improving the Uptake of Influenza Vaccination in Healthcare Workers 
at Long-Term Care Facilities: A Systematic Review. Vaccines, 8(2), 
165. https://doi.org/10.3390/vaccines8020165.
    \36\ Ofstead, C.L., Amelang, M.R., Wetzler, H.P., & Tan, L. 
(2017). Moving the needle on nursing staff influenza vaccination in 
long-term care: Results of an evidence-based intervention. Vaccine, 
35(18), 2390-2395. https://doi.org/10.1016/j.vaccine.2017.03.041.
    \37\ Yue, X., Black, C., Ball, S., Donahue, S., de Perio, M.A., 
Laney, A.S., & Greby, S. (2019). Workplace Interventions and 
Vaccination-Related Attitudes Associated With Influenza Vaccination 
Coverage Among Healthcare Personnel Working in Long-Term Care 
Facilities, 2015-2016 Influenza Season. Journal of the American 
Medical Directors Association, 20(6), 718-724. https://doi.org/10.1016/j.jamda.2018.11.029.
---------------------------------------------------------------------------

    Addressing HCP influenza vaccination in SNFs is particularly 
important as vulnerable populations often reside in SNFs. Vulnerable 
populations are less likely to receive the influenza vaccine, and thus, 
are susceptible to contracting the virus. For example, not only are 
Black residents more likely to receive care from facilities with lower 
overall influenza vaccination rates, but Black residents are also less 
likely to be offered and receive influenza vaccinations in comparison 
to White residents.38 39 40 41 Racial and ethnic disparities 
in influenza vaccination, specifically among Black and Hispanic 
populations, are also higher among short-stay residents receiving care 
for less than 100 days in the nursing home.\42\ Additionally, Medicare 
fee-for-service beneficiaries of Black, Hispanic, rural, and lower-
income populations are less likely to receive inactivated influenza 
vaccines, and non-White beneficiaries are generally less likely to 
receive high-dose influenza vaccines in comparison to White 
beneficiaries.43 44 45 Therefore, the proposed measure has 
the potential to increase influenza vaccination coverage of HCP in 
SNFs, as well as prevent the spread of the influenza virus to 
vulnerable populations who are less likely to receive influenza 
vaccinations.
---------------------------------------------------------------------------

    \38\ Cai, S., Feng, Z., Fennell, M.L., & Mor, V. (2011). Despite 
small improvement, black nursing home residents remain less likely 
than whites to receive flu vaccine. Health affairs (Project Hope), 
30(10), 1939-1946. https://doi.org/10.1377/hlthaff.2011.0029.
    \39\ Luo, H., Zhang, X., Cook, B., Wu, B., & Wilson, M.R. 
(2014). Racial/Ethnic Disparities in Preventive Care Practice Among 
U.S. Nursing Home Residents. Journal of Aging and Health, 26(4), 
519-539. https://doi.org/10.1177/0898264314524436.
    \40\ Mauldin, R.L., Sledge, S.L., Kinney, E.K., Herrera, S., & 
Lee, K. (2021). Addressing Systemic Factors Related to Racial and 
Ethnic Disparities among Older Adults in Long-Term Care Facilities. 
IntechOpen.
    \41\ Travers, J.L., Dick, A.W., & Stone, P.W. (2018). Racial/
Ethnic Differences in Receipt of Influenza and Pneumococcal 
Vaccination among Long-Stay Nursing Home Residents. Health services 
research, 53(4), 2203-2226. https://doi.org/10.1111/1475-6773.12759.
    \42\ Riester, M.R., Bosco, E., Bardenheier, B.H., Moyo, P., 
Baier, R.R., Eliot, M., Silva, J.B., Gravenstein, S., van Aalst, R., 
Chit, A., Loiacono, M.M., & Zullo, A.R. (2021). Decomposing Racial 
and Ethnic Disparities in Nursing Home Influenza Vaccination. 
Journal of the American Medical Directors Association, 22(6), 1271-
1278.e3. https://doi.org/10.1016/j.jamda.2021.03.003.
    \43\ Hall, L.L., Xu, L., Mahmud, S.M., Puckrein, G.A., Thommes, 
E.W., & Chit, A. (2020). A Map of Racial and Ethnic Disparities in 
Influenza Vaccine Uptake in the Medicare Fee-for-Service Program. 
Advances in therapy, 37(5), 2224-2235. https://doi.org/10.1007/s12325-020-01324-y.
    \44\ Inactivated vaccines use the killed version of the germ 
that causes a disease. Inactivated vaccines usually don't provide 
immunity (protection) that is as strong as the live vaccines. For 
more information regarding inactivated vaccines we refer readers to 
the following web page: https://hhs.gov/immunization/basics/types/index.html.
    \45\ High dose flu vaccines contain four times the amount of 
antigen (the inactivated virus that promotes a protective immune 
response) as a regular flu shot. It is associated with a stronger 
immune response following vaccination. For more information 
regarding high dose flu vaccines, we refer readers to the following 
web page: https://www.cdc.gov/flu/highrisk/65over.htm.
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    The COVID-19 pandemic has exposed the importance of implementing 
infection prevention strategies, including the promotion of HCP 
influenza vaccination. Activity of the influenza virus has been lower 
during the COVID-19 pandemic as several strategies to reduce the spread 
of COVID-19 have also reduced the spread of influenza, including mask 
mandates, social distancing, and increased hand hygiene.\46\ However, 
even though more

[[Page 22748]]

people are receiving COVID-19 vaccines, it is still important to 
encourage annual HCP influenza vaccination to prevent health care 
systems from getting overwhelmed by the co-circulation of COVID-19 and 
influenza viruses. A 2020 literature search revealed several studies in 
which those with severe cases of COVID-19, requiring hospitalization, 
were less likely to be vaccinated against influenza.\47\ HCP 
vaccinations against influenza may prevent the spread of illness 
between HCP and residents, thus reducing resident morbidities 
associated with influenza and pressure on already stressed health care 
systems. In fact, several thousand nursing homes voluntarily reported 
weekly influenza vaccination coverage through an NHSN module based on 
the NQF #0431 measure during the overlapping 2020 to 2021 influenza 
season and COVID-19 pandemic. Even after the COVID-19 pandemic ends, 
promoting HCP influenza vaccination is important in preventing 
morbidity and mortality associated with influenza.
---------------------------------------------------------------------------

    \46\ Wang, X., Kulkarni, D., Dozier, M., Hartnup, K., Paget, J., 
Campbell, H., Nair, H., & Usher Network for COVID-19 Evidence 
Reviews (UNCOVER) group (2020). Influenza vaccination strategies for 
2020-21 in the context of COVID-19. Journal of global health, 10(2), 
021102. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7719353/.
    \47\ Del Riccio, M., Lorini, C., Bonaccorsi, G., Paget, J., & 
Caini, S. (2020). The Association between Influenza Vaccination and 
the Risk of SARS-CoV-2 Infection, Severe Illness, and Death: A 
Systematic Review of the Literature. International journal of 
environmental research and public health, 17(21), 7870. https://doi.org/10.3390/ijerph17217870.
---------------------------------------------------------------------------

    Variation in influenza vaccination coverage rates indicate the 
proposed measure's usability and use. A CDC analysis during the 2020 to 
2021 influenza season revealed that among 16,535 active, CMS-certified 
nursing homes, 17.3 percent voluntarily submitted data for the proposed 
measure through the National Healthcare Safety Network (NHSN). Average 
staff influenza vaccination coverage was approximately 64 percent, 
ranging from 0.3 percent to 100 percent with an interquartile range of 
40 to 93.9 percent. Variation in influenza vaccination coverage rates 
by facility demonstrates the utility of the measure for resident choice 
of facility. Variation in influenza vaccination rates by type of HCP 
demonstrates the utility of the proposed measure for targeted quality 
improvement efforts.
    For these reasons, we propose to adopt the CDC developed Influenza 
Vaccination Coverage among Healthcare Personnel (NQF #0431) measure for 
the SNF QRP, as collected through the CDC's NHSN, to report the 
percentage of HCP who receive the influenza vaccine. We believe this 
measure will encourage HCP to receive the influenza vaccine, resulting 
in fewer cases, less hospitalizations, and lower mortality associated 
with the virus.
b. Stakeholder Input and Pilot Testing
    In the development and specification of this measure, a transparent 
process was employed to seek input from stakeholders and national 
experts and engage in a process that allows for pre-rulemaking input in 
accordance with section 1890A of the Act. To meet this requirement, 
opportunities were provided for stakeholder input by a Delphi panel and 
Steering Committee through the measure's pilot testing. The measure's 
pilot testing assessed reliability and validity among 234 facilities 
and five facility types (that is, long-term care facilities, acute care 
hospitals, ambulatory surgery centers, physician practices, and 
dialysis centers) across four jurisdictions (that is, California, New 
Mexico, New York City, and western Pennsylvania) between 2010 and 
2011.48 49
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    \48\ Libby T.E., Lindley M.C., Lorick S.A., MacCannell T., Lee 
S.J., Smith C, Geevarughese A., Makvandi M., Nace D.A., Ahmed F. 
(2013). Reliability and validity of a standardized measure of 
influenza vaccination coverage among healthcare personnel. Infect 
Control Hosp Epidemiol, 34(4),335-45. https://doi.org/10.1086/669859.
    \49\ The Libby et al. (2013) article (preceding footnote) is 
referenced throughout the entirety of section VI.C.1.b. of this 
rule.
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    Two methods were used to conduct reliability testing, including 
interrater reliability testing and the use of case studies. Interrater 
reliability was assessed among 96 facilities, including 19 long-term 
care facilities, by comparing agreement between two raters: Facility 
staff and project staff. Project staff reviewed individual-level 
records from randomly selected facilities to assess agreement with how 
facility staff classified HCP into numerator and denominator 
categories. For more information regarding numerator and denominator 
definitions, refer to section VI.C.1.e. of this proposed rule. 
Interrater reliability results demonstrated high adjusted agreement 
between facility and project staff for numerator data (91 percent) and 
denominator data (96 percent). Most numerator disagreements resulted 
from health care facilities reporting verbal declinations in the 
``declined vaccination'' numerator rather than categorizing verbal 
declinations as ``missing/unknown'' as there was no written 
documentation of the declination. There was also numerator disagreement 
related to contraindications as HCP did not properly cite true medical 
contraindications. Adhering to true medical contraindications and 
tracking declinations of the influenza vaccine among HCP should 
additionally improve reliability.
    Case studies were also used to assess reliability. Facilities 
received a series of 23 vignettes, in which they were instructed to 
select appropriate numerator and denominator categories for the 
hypothetical cases described in each vignette. Most numerator and 
denominator elements were categorized correctly. For example, 95.6 
percent of facility staff correctly categorized employees that were 
vaccinated at the facility, 88.6 percent correctly categorized 
employees vaccinated elsewhere, etc.\50\ However, problematic 
denominator elements included poor facility understanding of how to 
classify physician-owners of health care facilities who work part-time 
and physicians who were credentialed by a facility but had not admitted 
patients in the past 12 months. Problematic numerator elements were 
related to confusion about reporting persistent deferrals of 
vaccination and verbal vaccine declinations for non-medical reasons.
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    \50\ For a full list of case study categorization results, 
please refer to the following study: Libby T.E., Lindley M.C., 
Lorick S.A., MacCannell T., Lee S.J., Smith C., Geevarughese A., 
Makvandi M., Nace D.A., Ahmed F. (2013). Reliability and validity of 
a standardized measure of influenza vaccination coverage among 
healthcare personnel. Infect Control Hosp Epidemiol, 34(4),335-45. 
https://doi.org/10.1086/669859.
---------------------------------------------------------------------------

    Two methods were also used for validity testing: Convergent 
validity assessments and face validity assessment. Convergent validity 
examined the association between the number of evidence-based 
strategies used by a health care facility to promote influenza 
vaccination and the facility's reported vaccination rate among each HCP 
denominator group. The association between employee vaccination rates 
and the number of strategies used was borderline significant. The 
association between credentialed non-employee vaccination rates and the 
number of strategies used was significant, and the association between 
other non-employee vaccination rates and the number of strategies used 
was also significant, demonstrating convergent validity.
    Face validity was assessed through a Delphi panel, which convened 
in June 2011 and provided stakeholder input on the proposed measure. 
The Delphi panel, comprised of nine experts in influenza vaccination 
measurement and quality improvement from several public and private 
organizations, rated elements of the proposed measure using a Likert 
scale. The Delphi panel

[[Page 22749]]

discussed pilot testing results from the first round of ratings during 
a one-hour moderated telephone conference. After the conference 
concluded, panelists individually rated a revised set of elements. 
Ultimately, the Delphi panel reached a consensus that the majority of 
the proposed measure's numerator definitions had strong face validity. 
However, the panel raised concerns regarding the accuracy of self-
reported data and deemed validity lowest for denominator categories of 
credentialed and other nonemployees of the facility.
    After the conclusion of measure testing, the proposed measure's 
specifications were revised in alignment with the Delphi panel's 
ratings and with guidance from a Steering Committee. The CDC-convened 
Steering Committee was comprised of representatives from several 
institutions, including CMS, the Joint Commission, the Federation of 
American Hospitals, the American Osteopathic Association, the American 
Medical Association, and others. To address concerns raised through 
pilot testing and to reduce institutional barriers to reporting, 
denominator specifications were revised to include a more limited 
number of HCP among whom vaccination could be measured with greater 
reliability and accuracy: Employees, licensed independent 
practitioners, and adult students/trainees and volunteers. The measure 
was also revised to require vaccinations received outside of the 
facility to be documented, but allow for self-report of declinations 
and medical contraindications. Verbal declinations were assigned to the 
``declined'' numerator category, and an ``unknown'' category was added 
to give facilities actionable data on unvaccinated HCP who may not have 
purposefully declined. For more information regarding pilot testing 
results and measure input from the Delphi panel and Steering Committee, 
refer to the article published in the Infection Control & Hospital 
Epidemiology journal by the measure developer.\51\
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    \51\ Libby T.E,. Lindley M.C., Lorick S.A., MacCannell T, Lee 
S.J., Smith C., Geevarughese A., Makvandi M., Nace D.A., Ahmed F. 
(2013). Reliability and validity of a standardized measure of 
influenza vaccination coverage among healthcare personnel. Infect 
Control Hosp Epidemiol, 34(4),335-45. https://doi.org/10.1086/669859.
---------------------------------------------------------------------------

c. Measure Applications Partnership (MAP) Review
    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 
through the Federal rulemaking process for use in Medicare programs. 
This allows multi-stakeholder groups to provide recommendations to the 
Secretary on the measures included in the list.
    We included the Influenza Vaccination Coverage among HCP measure 
under the SNF QRP Program in the publicly available ``List of Measures 
Under Consideration for December 1, 2021'' (MUC List).\52\ Shortly 
after, several National Quality Forum (NQF)-convened Measures 
Applications Partnership (MAP) workgroups met virtually to provide 
input on the proposed measure. First, the MAP Rural Health Workgroup 
convened on December 8, 2021. Members generally agreed that the 
proposed measure would be suitable for use by rural providers within 
the SNF QRP program, noting the measure's rural relevance. Likewise, 
the MAP Health Equity workgroup met on December 9, 2021, in which the 
majority of voting members agreed that the proposed measure has 
potential for decreasing health disparities. The MAP Post-Acute Care/
Long-Term Care (PAC/LTC) workgroup met on December 16, 2021, in which 
the majority of voting workgroup members supported rulemaking of the 
proposed measure. Finally, the MAP Coordinating Committee convened on 
January 19, 2022, in which the committee agreed with the MAP's 
preliminary measure recommendation of support for rulemaking.
---------------------------------------------------------------------------

    \52\ Centers for Medicare and Medicaid Services. (2021). List of 
Measures Under Consideration for December 1, 2021. CMS.gov. https://www.cms.gov/files/document/measures-under-consideration-list-2020-report.pdf.
---------------------------------------------------------------------------

    In addition to receiving feedback from MAP workgroup and committee 
members, NQF received four comments by industry stakeholders during the 
proposed measure's MAP pre-rulemaking process. Commenters were 
generally supportive of the measure as SNF QRP adoption would promote 
measure interoperability, encourage vaccination, and likely decrease 
the spread of infection. One commenter was not supportive of the 
measure due to burdens of NHSN data submission.
    Overall, the MAP offered support for rulemaking, noting that the 
measure aligns with the IRF and LTCH PAC QRPs and adds value to the 
current SNF QRP measure set since influenza vaccination among HCP is 
not currently addressed within the SNF QRP program. The MAP noted the 
importance of vaccination coverage among HCP as an actionable strategy 
that can decrease viral transmission, morbidity, and mortality within 
SNFs. The final MAP report is available at https://www.qualityforum.org/Publications/2022/03/MAP_2021-2022_Considerations_for_Implementing_Measures_Final_Report_-_Clinicians,_Hospitals,_and_PAC-LTC.aspx.
d. 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 the entity with 
a contract under section 1890(a) of the Act, currently the NQF. 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 
consensus organization identified by the Secretary.
    The proposed Influenza Vaccination Coverage among HCP measure 
initially received NQF endorsement in 2008 as NQF #0431. Measure 
endorsement was renewed in 2017, and the measure is due for maintenance 
in the spring 2022 cycle. The measure was originally tested in nursing 
homes and has been endorsed by NQF for use in nursing home settings 
since the measure was first endorsed. No additional modifications were 
made to the proposed measure for the spring 2022 measure maintenance 
cycle, but as noted in section VI.C.1.a. of this proposed rule that 
several thousand nursing homes voluntarily reported weekly influenza 
vaccination coverage through an NHSN module based on the NQF #0431 
measure during the overlapping 2020 to 2021 influenza season and COVID-
19 pandemic. The measure is currently used in several of our programs, 
including the Hospital Inpatient and Prospective Payment System (PPS)-
Exempt Cancer Hospital QRPs. Among PAC programs, the proposed measure 
is also reported in the IRF and LTCH QRPs as adopted in the FY 2014 IRF 
PPS final rule (78 FR 47905 through 47906) and the FY 2013 Inpatient 
Prospective Payment System (IPPS)/LTCH PPS final rule (77 FR 53630 
through 53631), respectively.
    After review of the NQF's consensus-endorsed measures, we were 
unable to identify any NQF-endorsed measures for SNFs focused on 
capturing influenza vaccinations among HCP. For example, although the 
Percent of Residents or Patients Who Were Assessed and Appropriately 
Given the Seasonal Influenza Vaccine (Short Stay) (NQF #0680) and the 
Percent of Residents

[[Page 22750]]

Assessed and Appropriately Given the Seasonal Influenza Vaccine (Long 
Stay) (NQF #0681) measures are both NQF-endorsed and assess rates of 
influenza vaccination, they assess vaccination rates among residents in 
the nursing home rather than HCP in the SNF. Additionally, the Percent 
of Programs of All-Inclusive Care for the Elderly (PACE) Healthcare 
Personnel with Influenza Immunization measure resembles the proposed 
measure since it assesses influenza vaccination among HCP; however, it 
is not NQF endorsed and is not specific to the SNF setting.
    Therefore, after consideration of other available measures, we find 
the NQF endorsed Influenza Vaccination Coverage among HCP measure 
appropriate for the SNF QRP, and are proposing the measure beginning 
with the FY 2025 SNF QRP. Application of the Influenza Vaccination 
Coverage among HCP measure within the SNF QRP promotes measure 
harmonization across quality reporting programs that also report this 
measure. This proposed measure has the potential to generate actionable 
data on vaccination rates that can be used to target quality 
improvement among SNF providers.
e. Quality Measure Calculation
    The Influenza Vaccination Coverage among HCP measure is a process 
measure developed by the CDC to track influenza vaccination coverage 
among HCP in facilities such as SNFs. The measure reports on the 
percentage of HCP who receive influenza vaccination. The term 
``healthcare personnel'' refers to all paid and unpaid persons working 
in a health care setting, contractual staff not employed by the health 
care facility, and persons not directly involved in patient care but 
potentially exposed to infectious agents that can be transmitted to and 
from HCP. Since the proposed measure is a process measure, rather than 
an outcome measure, it does not require risk-adjustment.
    The proposed measure's denominator is the number of HCP who are 
physically present in the health care facility for at least 1 working 
day between October 1 and March 31 of the following year, regardless of 
clinical responsibility or patient contact. The proposed measure's 
reporting period is October 1 through March 31; this reporting period 
refers to the proposed measure's denominator only. The denominator 
would be calculated separately for three required categories: 
Employees, meaning all persons who receive a direct paycheck from the 
reporting facility (that is, on the SNF's payroll); Licensed 
independent practitioners,\53\ such as physicians, advanced practice 
nurses, and physician assistants who are affiliated with the reporting 
facility, who do not receive a direct paycheck from the reporting 
facility; and Adult students/trainees and volunteers who do not receive 
a direct paycheck from the reporting facility. A denominator can be 
calculated for an optional category as well: Other contract personnel, 
defined as persons providing care, treatment, or services at the 
facility through a contract who do not fall into any of the three 
required denominator categories.
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    \53\ Refer to the proposed measure's specifications in The 
National Healthcare Safety Network (NSHN) Manual Healthcare 
Personnel Safety Component Protocol--Healthcare Personnel 
Vaccination Module: Influenza Vaccination Summary linked at https://www.cdc.gov/nhsn/pdfs/hps-manual/vaccination/hps-flu-vaccine-protocol.pdf for an exhaustive list of those included in the 
licensed independent practitioners definition.
---------------------------------------------------------------------------

    The proposed measure's numerator consists of all HCP included in 
the denominator population who received an influenza vaccine any time 
from when it first became available (such as August or September) 
through March 31 of the following year and who fall into one of the 
following categories: (a) Received an influenza vaccination 
administered at the health care facility; (b) reported in writing 
(paper or electronic) or provided documentation that an influenza 
vaccination was received elsewhere, (c) were determined to have a 
medical contraindication/condition of severe allergic reaction to eggs 
or other component(s) of the vaccine, or a history of Guillain-Barre 
(GBS) within 6 weeks after a previous influenza vaccination; (d) were 
offered but declined the influenza vaccination; or (e) had an unknown 
vaccination status or did not meet any of the definitions of the other 
numerator categories (a through d). As described in the FY 2014 IRF PPS 
final rule, measure numerator data is required based on data collected 
from October 1st or whenever the vaccine becomes available.\54\ 
Therefore, if the vaccine is available prior to October 1st, any 
vaccine given before October 1st is credited towards vaccination 
coverage. Likewise, if the vaccine becomes available after October 1st, 
the vaccination counts are to begin as soon as possible after October 
1st.
---------------------------------------------------------------------------

    \54\ 78 FR 47906.
---------------------------------------------------------------------------

    We propose that SNFs submit data for the measure through the CDC/
NHSN data collection and submission framework.\55\ In alignment with 
the data submission frameworks utilized for this measure in the IRF and 
LTCH QRPs, SNFs would use the HCP influenza data reporting module in 
the NHSN HPS Component and complete two forms. SNFs would complete the 
first form (CDC 57.203) to indicate the type of data they plan on 
reporting to the NHSN by selecting the ``Influenza Vaccination 
Summary'' option under ``Healthcare Personnel Vaccination Module'' to 
create a reporting plan. SNFs would then complete a second form (CDC 
57.214) to report the number of HCP who have worked at the health care 
facility for at least 1 day between October 1 and March 31 
(denominator) and the number of HCP who fall into each numerator 
category. To meet the minimum data submission requirements, SNFs would 
enter a single influenza vaccination summary report at the conclusion 
of the measure reporting period. If SNFs submit data more frequently, 
such as on a monthly basis, the information would be used to calculate 
one summary score for the proposed measure which would be publicly 
reported on Care Compare. For more information regarding proposed data 
submission requirements for this measure and its public reporting plan, 
we refer readers to sections VI.G.2. and VI.H.2. of this proposed rule. 
Details related to the use of NHSN for data submission can be found at 
the CDC's NHSN Healthcare Personnel Safety (HPS) Component web page at 
https://www.cdc.gov/nhsn/hps/vaccination/index.html?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fnhsn%2Finpatient-rehab%2Fvaccination%2Findex.html.
---------------------------------------------------------------------------

    \55\ Centers for Disease Control and Prevention (CDC). (2021) 
https://www.cdc.gov/nhsn/hps/weekly-covid-vac/index.html. Healthcare 
Personnel Safety Component (HPS). CDC.gov.
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    We invite public comment on our proposal to add a new measure, 
Influenza Vaccination Coverage among Healthcare Personnel (NQF #0431), 
to the SNF QRP beginning with the FY 2025 SNF QRP.
2. Revised Compliance Date for Certain Skilled Nursing Facility Quality 
Reporting Program Requirements Beginning With the FY 2024 SNF QRP
a. Background
    Section 1888(d)(6)(B)(i)(III) of the Act requires that, for FY 2019 
and each subsequent year, SNFs must report standardized patient 
assessment data required under section 1899B(b)(1) of the Act. Section 
1899B(a)(1)(C) of the Act requires, in part, the Secretary to modify 
the PAC assessment instruments in order for PAC providers, including 
SNFs, to submit standardized patient assessment data under the Medicare 
program. In the FY 2020 SNF PPS final rule (84 FR 38755 through 38817), 
we

[[Page 22751]]

adopted two TOH Information quality measures as well as standardized 
patient assessment data that would satisfy five categories defined by 
section 1899B(c)(1). The TOH Information to the Provider--Post-Acute 
Care (PAC) measure and the TOH Information to the Patient--PAC measure 
are process-based measures that assess whether or not a current 
reconciled medication list is given to the subsequent provider when a 
patient is discharged or transferred from his or her current PAC 
setting or is given to the patient, family, or caregiver when the 
patient is discharged from a PAC setting to a private home/apartment, a 
board and care home, assisted living, a group home, or transitional 
living. Section 1899B(b)(1)(B) of the Act defines standardized patient 
assessment data as data required for at least the quality measures 
described in section 1899B(c)(1) of the Act and that is with respect to 
the following categories: (1) Functional status; (2) cognitive 
function; (3) special services, treatments, and interventions; (4) 
medical conditions and comorbidities; (5) impairments, and (6) other 
categories deemed necessary and appropriate by the Secretary.
    The interim final rule with comment period that appeared in the May 
8, 2020 Federal Register (85 FR 27550) (hereafter referred to as the 
``May 8th COVID-19 IFC''), delayed the compliance date for certain 
reporting requirements under the SNF QRP (85 FR 27596 through 27597). 
Specifically, we delayed the requirement for SNFs to begin reporting 
the TOH Information to Provider--PAC and the TOH Information to 
Patient--PAC measures and the requirement for SNFs to begin reporting 
certain standardized patient assessment data elements from October 1, 
2020, to October 1st of the year that is at least 2 full fiscal years 
after the end of the COVID-19 PHE. We also delayed the adoption of the 
updated version of the Minimum Data Set (MDS) 3.0 v1.18.1 \56\ which 
SNFs would have used to report the TOH Information measures and certain 
standardized patient assessment data elements.
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    \56\ The MDS version referred to in IFC-2 was MDS 3.0 v1.18.1. 
This version number, MDS 3.0 v1.18.11, reflects the version which 
would be implemented if the proposal is finalized.
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    Currently, SNFs must use the MDS 3.0 v1.18.11 to begin collecting 
data on the two TOH Information measures beginning with discharges on 
October 1st of the year that is at least 2 full fiscal years after the 
end of the COVID-19 PHE. SNFs must also begin collecting data on 
certain standardized patient assessment data elements on the MDS 3.0 
v1.18.11, beginning with admissions and discharges (except for the 
preferred language, need for interpreter services, hearing, vision, 
race, and ethnicity standardized patient assessment data elements, 
which would be collected at admission only) on October 1st of the year 
that is at least 2 full fiscal years after the end of the COVID-19 PHE. 
This delay to begin collecting data for these measures was intended to 
provide relief to SNFs from the added burden of implementing an updated 
instrument during the COVID-19 PHE. We wanted to provide maximum 
flexibilities for SNFs to respond to the public health threats posed by 
the COVID-19 PHE, and to reduce the burden in administrative efforts 
associated with attending trainings, training their staff, and working 
with their vendors to incorporate the updated assessment instruments 
into their operations.
    At the time the May 8th COVID-19 IFC was published, we believed 
this delay would not have a significant impact on the SNF QRP. However, 
we were in the initial months of the COVID-19 PHE, and very little was 
known about the SARS-CoV-2 virus. Additionally, we believed the delay 
in the collection of the TOH Information measures and standardized 
patient assessment data elements were necessary to allow SNFs to focus 
on patient care and staff safety. However, the COVID-19 PHE has 
illustrated the important need for these TOH Information measures and 
standardized patient assessment data elements under the SNF QRP. The 
PHE's disproportionate impact among non-Hispanic Black, or Hispanic or 
Latino persons 57 58 59 60 61 62 63 demonstrates the 
importance of analyzing this impact in order to improve quality of care 
within SNFs especially during a crisis. One important strategy for 
addressing these important inequities is by improving data collection 
to allow for better measurement and reporting on equity across post-
acute care programs and policies. The information will inform our 
Meaningful Measures framework.
---------------------------------------------------------------------------

    \57\ Bhumbra S, Malin S, Kirkpatrick L, et al. Clinical Features 
of Critical Coronavirus Disease 2019 in Children. Pediatric Critical 
Care Medicine. 2020;02:02. DOI: https://doi.org/10.1097/PCC.0000000000002511.
    \58\ Ebinger JE, Achamallah N, Ji H, Claggett BL, Sun N, Botting 
P, et al. Pre-existing Traits Associated with Covid-19 Illness 
Severity. PLoS ONE [Electronic Resource]. 2020;15(7):e0236240. DOI: 
https://doi.org/10.1101/2020.04.29.20084533.
    \59\ Gold JAW, Wong KK, Szablewski CM, Patel PR, Rossow J, da 
Silva J, et al. Characteristics and Clinical Outcomes of Adult 
Patients Hospitalized with COVID-19--Georgia, March 2020. MMWR Morb 
Mortal Wkly Rep. 2020;69(18):545-50. DOI: http://dx.doi.org/10.15585/mmwr.mm6918e1.
    \60\ Hsu HE, Ashe EM, Silverstein M, Hofman M, Lange SJ, 
Razzaghi H, et al. Race/Ethnicity, Underlying Medical Conditions, 
Homelessness, and Hospitalization Status of Adult Patients with 
COVID-19 at an Urban Safety-Net Medical Center--Boston, 
Massachusetts, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(27):864-9. 
DOI: http://dx.doi.org/10.15585/mmwr.mm6927a3.
    \61\ Kim L, Whitaker M, O'Hallaran A, et al. Hospitalization 
Rates and Characteristics of Children Aged <18 Years Hospitalized 
with Laboratory-confirmed COVID-19--COVID-NET, 14 states, March 1-
July 25, 2020. MMWR Morb Mortal Wkly Rep 2020;69:1081-1088. DOI: 
http://dx.doi.org/10.15585/mmwr.mm6932e3.
    \62\ Killerby ME, Link-Gelles R, Haight SC, Schrodt CA, England 
L, Gomes DJ, et al. Characteristics Associated with Hospitalization 
Among Patients with COVID-19--Metropolitan Atlanta, Georgia, March-
April 2020. MMWR Morb Mortal Wkly Rep. 2020;69(25):790-4. DOI.
    \63\ Price-Haywood EG, Burton J, Fort D, Seoane L. 
Hospitalization and Mortality among Black Patients and White 
Patients with Covid-19. New England Journal of Medicine. 
2020;382(26):2534-43. DOI: https://doi.org/10.1056/NEJMsa2011686.
---------------------------------------------------------------------------

b. Current Assessment of SNFs' Capabilities
    To accommodate the COVID-19 PHE, we provided additional guidance 
and flexibilities, and as a result SNFs have had the opportunity to 
adopt new processes and modify existing processes to accommodate the 
significant health crisis presented by the COVID-19 PHE. For example, 
we held regular ``Office Hours'' conference calls to provide SNFs 
regular updates on the availability of supplies, as well as answer 
questions about delivery of care, reporting, and billing. We also 
supported PAC providers, including SNFs, by providing flexibilities in 
the delivery of care in response to the PHE,\64\ such as waiving the 
requirements at Sec.  483.30 for physician and non-physician 
practitioners to perform in-person visits, allowing them to use 
telehealth methods where deemed appropriate. We also waived the nurse 
aide training and certification requirements Sec.  483.35(d) (with the 
exception of Sec.  483.35(d)(1)(i)), allowing SNFs to employ nurse 
aides for longer than 4 months even when they have yet not met the 
standard training and certification requirements, and we waived the 
requirement at Sec.  483.95(g)(1) for nursing aides to receive at least 
12 hours of in-service training annually. To reduce provider burden, we 
waived the Pre-Admission Screening and Annual Resident Review (PASARR) 
at Sec.  483.20(k), allowing SNFs more flexibility in scheduling Level 
1 assessments. We narrowed the scope of requirements for a SNF's 
Quality Assurance and Performance

[[Page 22752]]

Improvement (QAPI) program to the aspects of care most associated with 
COVID-19 (Sec.  483.75), that is infection control and adverse events. 
Additionally, we waived timeframe requirements on MDS assessments and 
transmission at Sec.  483.20, along with waiving requirements for 
submitting staffing data through the Payroll-Based Journal (PBJ) system 
at Sec.  483.70(q), to grant SNFs the greater flexibility needed to 
adapt to the rapidly evolving burdens of the PHE. While the MDS and PBJ 
requirements have since been terminated, many of these waivers for SNFs 
are still in effect today.
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    \64\ Centers for Medicare and Medicaid Services. COVID-19 
Emergency Declaration Blanket waivers for Health Care Providers. 
Accessed 11/23/2021. Retrieved from https://www.cms.gov/files/document/covid-19-emergency-declaration-waivers.pdf.
---------------------------------------------------------------------------

    In addition, as of March 1, 2022, 86.2 percent of the population 
aged 12 and older (81.3 percent of those 5 and older) has received at 
least one vaccination.\65\ Further, although there is a recent increase 
in COVID-19 cases, vaccinated individuals aged 18 years and older 
through March 4, 2022 were 3.2 times less likely to test positive, over 
9 times less likely to be hospitalized, and experience 41 times lower 
risk of death, compared to unvaccinated individuals.\66\ We also 
believe that SNFs have more information and interventions to deploy to 
effectively prevent and treat COVID-19 than they had at the time the 
May 8th COVID-19 IFC was finalized,67 68 69 70 including 
three vaccines that are either approved or authorized in the United 
States to prevent COVID-19, and antiviral drugs that are approved or 
authorized to treat COVID-19.71 72 73 74 75 Also, recent 
reports suggest that the rollout of COVID-19 vaccines have alleviated 
some of the burden on SNFs imposed by the PHE.76 77
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    \65\ CDC COVID Data Tracker. Accessed 3/4/2022. Retrieved from 
https://covid.cdc.gov/covid-data-tracker/#vaccinations_vacc-people-onedose-pop-5yr.
    \66\ CDC COVID Data Tracker. Accessed 3/4/2022. Retrieved from 
https://covid.cdc.gov/covid-data-tracker/#rates-by-vaccine-status.
    \67\ COVID research: a year of scientific milestones. Nature. 
May 5, 2021. Retrieved from https://www.nature.com/articles/d41586-020-00502-w.
    \68\ CDC COVID Data Tracker. Accessed 2/10/2022. Retrieved from 
https://covid.cdc.gov/covid-data-tracker/#datatracker-home.
    \69\ Clinical trial of therapeutics for severely ill 
hospitalized COVID-19 patients begins. National Institutes of Health 
News Releases. April 22, 2021. Retrieved from https://www.nih.gov/news-events/news-releases/clinical-trialtherapeutics-severely-ill-hospitalized-covid-19-patients-begins.
    \70\ COVID-19 Treatment Guidelines. National Institutes of 
Health. Updated October 27, 2021. Retrieved from https://www.covid19treatmentguidelines.nih.gov/whats-new/.
    \71\ Here's Exactly Where We are with Vaccine and Treatments for 
COVID-19. Healthline. November 9, 2021. Retrieved from https://www.healthline.com/health-news/heres-exactly-where-were-at-with-vaccines-and-treatments-forcovid-19.
    \72\ U.S. Food and Drug Administration (2021). Janssen Biotech, 
Inc. COVID-19 Vaccine EUA Letter of Authorization. Available at 
https://www.fda.gov/media/146303/download. Accessed 9/9/2021.
    \73\ On January 31, 2021, FDA approved a second COVID-19 
vaccine. Available at https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-takes-key-action-approving-second-covid-19-vaccine. Accessed 3/02/22. The Moderna 
COVID-19 Vaccine also continues to be available under EUA. U.S. Food 
and Drug Administration (2022). Spikevax and Moderna COVID-19 
Vaccine. https://www.fda.gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/spikevax-and-moderna-covid-19-vaccineAccessed3/02/22.
    \74\ FDA Approves First COVID-19 Vaccine  FDA, 
available at https://www.fda.gov/news-events/pressannouncements/fda-approves-first-covid-19-vaccine. Accessed 9/03/21. The Pfizer-
BioNTech vaccine also continues to be available under EUA. U.S. Food 
and Drug Administration (2021). Comirnaty and Pfizer-BioNTech COVID-
19 Vaccine. Accessed 9/28/2021.
    \75\ FDA Approves First Treatment for COVID-19. October 22, 
2020. Available at https://www.fda.gov/newsevents/press-announcements/fda-approves-first-treatment-covid-19. Accessed 9/9/
2021.
    \76\ M. Domi, M. Leitson, D. Gifford, A. Nicolaou, K. Sreenivas, 
C. Bishnoi. The BNT162b2 vaccine is associated with lower new COVID-
19 cases in nursing home residents and staff. Journal of the 
American Geriatrics Society (2021), 10.1111/jgs.17224.
    \77\ American Health Care Association and National Center for 
Assisted Living. COVID-19 Vaccines Helping Long Term Care Facilities 
Rebound From The Pandemic. May 25, 2021. Retrieved from https://www.ahcancal.org/News-and-Communications/Press-Releases/Pages/COVID-19-Vaccines-Helping-Long-Term-Care-Facilities-Rebound-From-The-Pandemic.aspx.
---------------------------------------------------------------------------

    Despite the COVID-19 PHE, we must maintain our commitment to the 
quality of care for all patients, and we continue to believe that the 
collection of the standardized patient assessment data elements and TOH 
Information measures will contribute to this effort. That includes an 
ongoing commitment to achieving health equity by improving data 
collection to better measure and analyze disparities across programs 
and policies.78 79 80 81 82 83 84 85 We also 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 
stakeholders that it is important to gather additional information 
about race, ethnicity, gender, language and other social determinants 
of health (SDOH). Some SNFs noted they had already begun to collect 
some of this information for use in their operations. Our commitment to 
the quality of care for all patients also includes improving the 
quality of care in SNFs through a reduction in preventable adverse 
events. Health information, such as medication information, that is 
incomplete or missing increases the likelihood of a patient or resident 
safety risk, and is often life-threatening.86 87 88 89 90 91 
Poor communication and coordination across health care settings 
contributes to patient complications, hospital readmissions, emergency 
department visits and medication 
errors.92 93 94 95 96 97 98 99 100 101 Further

[[Page 22753]]

delaying the data collection has the potential to further exacerbate 
these issues. We believe the benefit of having this information 
available in a standardized format outweighs the potential burden of 
collecting this data, as data availability is a necessary step in 
addressing health disparities in SNFs.
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    \78\ COVID-19 Health Equity Interactive Dashboard. Emory 
University. Accessed January 12, 2022. Retrieved from https://covid19.emory.edu/.
    \79\ COVID-19 is affecting Black, Indigenous, Latinx, and other 
people of color the most. The COVID Tracking Project. March 7, 2021. 
Accessed January 12, 2022. Retrieved from https://covidtracking.com/race.
    \80\ Centers for Medicare & Medicaid Services. CMS Quality 
Strategy. 2016. Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-Quality-Strategy.pdf.
    \81\ Report to Congress: Improving Medicare Post-Acute Care 
Transformation (IMPACT) Act of 2014 Strategic Plan for Accessing 
Race and Ethnicity Data. January 5, 2017. Available at https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Research-Reports-2017-Report-to-Congress-IMPACT-ACT-of-2014.pdf.
    \82\ Rural Health Research Gateway. Rural Communities: Age, 
Income, and Health Status. Rural Health Research Recap. November 
2018.
    \83\ https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf.
    \84\ www.cdc.gov/mmwr/volumes/70/wr/mm7005a1.htm.
    \85\ Poteat TC, Reisner SL, Miller M, Wirtz AL. 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. Published 2020 Jul 24. doi:10.1101/
2020.07.21.20159327.
    \86\ Kwan, J.L., Lo, L., Sampson, M., & Shojania, K.G., 
``Medication reconciliation during transitions of care as a patient 
safety strategy: a systematic review,'' Annals of Internal Medicine, 
2013, Vol. 158(5), pp. 397-403.
    \87\ Boockvar, K.S., Blum, S., Kugler, A., Livote, E., 
Mergenhagen, K.A., Nebeker, J.R., & Yeh, J., ``Effect of admission 
medication reconciliation on adverse drug events from admission 
medication changes,'' Archives of Internal Medicine, 2011, Vol. 
171(9), pp. 860-861.
    \88\ Bell, C.M., Brener, S.S., Gunraj, N., Huo, C., Bierman, 
A.S., Scales, D.C., & Urbach, D.R., ``Association of ICU or hospital 
admission with unintentional discontinuation of medications for 
chronic diseases,'' JAMA, 2011, Vol. 306(8), pp. 840-847.
    \89\ Basey, A.J., Krska, J., Kennedy, T.D., & Mackridge, A.J., 
``Prescribing errors on admission to hospital and their potential 
impact: a mixed-methods study,'' BMJ Quality & Safety, 2014, Vol. 
23(1), pp. 17-25.
    \90\ Desai, R., Williams, C.E., Greene, S.B., Pierson, S., & 
Hansen, R.A., ``Medication errors during patient transitions into 
nursing homes: characteristics and association with patient harm,'' 
The American Journal of Geriatric Pharmacotherapy, 2011, Vol. 9(6), 
pp. 413-422.
    \91\ Boling, P.A., ``Care transitions and home health care,'' 
Clinical Geriatric Medicine, 2009, Vol. 25(1), pp. 135-48.
    \92\ Barnsteiner, J.H., ``Medication Reconciliation: Transfer of 
medication information across settings--keeping it free from 
error,''
    \93\ Arbaje, A.I., Kansagara, D.L., Salanitro, A.H., Englander, 
H.L., Kripalani, S., Jencks, S.F., & Lindquist, L.A., ``Regardless 
of age: incorporating principles from geriatric medicine to improve 
care transitions for patients with complex needs,'' Journal of 
General Internal Medicine, 2014, Vol. 29(6), pp. 932-939.
    \94\ Jencks, S.F., Williams, M.V., & Coleman, E.A., 
``Rehospitalizations among patients in the Medicare fee-for-service 
program,'' New England Journal of Medicine, 2009, Vol. 360(14), pp. 
1418-1428.
    \95\ Institute of Medicine. ``Preventing medication errors: 
quality chasm series,'' Washington, DC: The National Academies Press 
2007. Available at https://www.nap.edu/read/11623/chapter/1.
    \96\ Kitson, N.A., Price, M., Lau, F.Y., & Showler, G., 
``Developing a medication communication framework across continuums 
of care using the Circle of Care Modeling approach,'' BMC Health 
Services Research, 2013, Vol. 13(1), pp. 1-10.
    \97\ Mor, V., Intrator, O., Feng, Z., & Grabowski, D.C., ``The 
revolving door of rehospitalization from skilled nursing 
facilities,'' Health Affairs, 2010, Vol. 29(1), pp. 57-64.
    \98\ Institute of Medicine. ``Preventing medication errors: 
quality chasm series,'' Washington, DC: The National Academies Press 
2007. Available at https://www.nap.edu/read/11623/chapter/1.
    \99\ Kitson, N.A., Price, M., Lau, F.Y., & Showler, G., 
``Developing a medication communication framework across continuums 
of care using the Circle of Care Modeling approach,'' BMC Health 
Services Research, 2013, Vol. 13(1), pp. 1-10.
    \100\ Forster, A.J., Murff, H.J., Peterson, J.F., Gandhi, T.K., 
& Bates, D.W., ``The incidence and severity of adverse events 
affecting patients after discharge from the hospital.'' Annals of 
Internal Medicine, 2003,138(3), pp. 161-167.
    \101\ King, B.J., Gilmore[hyphen] Bykovsky, A.L., Roiland, R.A., 
Polnaszek, B.E., Bowers, B.J., & Kind, A.J. ``The consequences of 
poor communication during transitions from hospital to skilled 
nursing facility: a qualitative study,'' Journal of the American 
Geriatrics Society, 2013, Vol. 61(7), 1095-1102.
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    Given the flexibilities described earlier in this section, SNFs' 
increased knowledge and interventions to deploy to effectively prevent 
and treat COVID-19, and the trending data on COVID-19, we believe that 
SNFs are in a better position to accommodate the reporting of the TOH 
Information measures and certain standardized patient assessment data 
elements. Specifically, we believe SNFs have learned how to adapt and 
now have the administrative capacity to attend training, train their 
staff, and work with their vendors to incorporate the updated 
assessment instruments into their operations. Moreover, these 
standardized patient assessment data elements are reflective of patient 
characteristic that providers may already be recording for their own 
purposes, such as preferred language, race, ethnicity, hearing, vision, 
health literacy, and cognitive function. It is also important to align 
the collection of this data with the IRFs and LTCHs who will begin 
collecting this information on October 1, 2022, and home health 
agencies (HHAs) who will begin collecting this information on January 
1, 2023.\102\
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    \102\ Calendar Year 2020 Home Health final rule (86 FR 62385 
through 62390).
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c. Collection of the Transfer of Health (TOH) Information to Provider-
PAC Measure, the Transfer of Health (TOH) Information to Patient-PAC 
Measure and Certain Standardized Patient Assessment Data Elements 
Beginning October 1, 2023
    We propose to revise the compliance date from the May 8th COVID-19 
IFC from October 1st of the year that is at least 2 full FYs after the 
end of the COVID-19 PHE to October 1, 2023. This revised date would 
begin the collection of data on the TOH Information to Provider-PAC 
measure and TOH Information to Patient-PAC measure, and certain 
standardized patient assessment data elements on the updated version of 
the MDS assessment instrument referred to as MDS 3.0 v1.18.11. We 
believe this revised date of October 1, 2023, which is a 3-year delay 
from the original compliance date finalized in the FY 2020 SNF PPS 
final rule (84 FR 38755 through 38764), balances the support that SNFs 
have needed during much of the COVID-19 PHE, the flexibilities we 
provided to support SNFs, and the time necessary to develop preventive 
and treatment options along with the need to collect this important 
data. We believe this date is sufficiently far in advance for SNFs to 
make the necessary preparations to begin reporting these data elements 
and the TOH Information measures. As described in the previous sections 
of this proposed rule, the need for the standardized patient assessment 
data elements and TOH Information measures have been shown to be even 
more pressing with issues of health inequities, exacerbated by the 
COVID-19 PHE. This data, which includes information on SDOH, provides 
information that is expected to improve quality of care for all, and is 
not already found in assessment or claims data currently available. 
Consequently, we propose to revise the compliance date to reflect this 
balance and assure that data collection begins on October 1, 2023.
    As stated in the FY 2020 SNF PPS final rule (84 FR 38774), we will 
provide the training and education for SNFs to be prepared for this 
implementation date. In addition, if we adopt an October 1, 2023 
compliance date, we would release a draft of the updated version of the 
MDS 3.0 v1.18.11 in early 2023 with sufficient lead time to prepare for 
the October 1, 2023 start date.
    Based upon our evaluation, we propose that SNFs collect the TOH 
Information to Provider-PAC measure, the TOH Information to the 
Patient-PAC measure, and certain standardized patient assessment data 
elements beginning October 1, 2023. Accordingly, we propose that SNFs 
begin collecting data on the two TOH Information measures beginning 
with discharges on October 1, 2023. We also propose that SNFs begin 
collecting data on the six categories of standardized patient 
assessment data elements on the MDS 3.0 v1.18.11, beginning with 
admissions and discharges (except for the preferred language, need for 
interpreter services, hearing, vision, race, and ethnicity standardized 
patient assessment data elements, which would be collected at admission 
only) on October 1, 2023. We invite public comment on this proposal.
3. Proposed Revisions to the Regulation Text (Sec.  413.360)
    The FY 2022 SNF PPS final rule (86 FR 42480 through 42489) added 
the COVID-19 Vaccination Coverage among Healthcare Personnel (HCP 
COVID-19 Vaccine) measure to the SNF QRP beginning with the FY 2024 
QRP. The data submission method for the HCP COVID-19 Vaccine is the 
NHSN. The NHSN is a system maintained by the CDC, whose mission it is 
to protect the health security of the nation. The NHSN is used to 
collect and report on healthcare acquired infections, such as catheter 
associated urinary tract infections and central-line associated 
bloodstream infections. The NHSN also collects vaccination information 
since vaccines play a major role in preventing the spread of harmful 
infections. Healthcare acquired infections are a threat to 
beneficiaries, SNFs, and the public. Given the significance of the 
information collected through the NHSN, and the fact that infection 
prevention affects all beneficiaries, 100 percent of the information 
required to calculate the HCP COVID-19 Vaccine must be submitted to the 
NHSN. The HCP COVID-19 Vaccine measure is an important part of the 
nation's response to the COVID 19 public health emergency, and 
therefore 100 percent of the information is necessary to monitor the 
health and safety of beneficiaries.
    For consistency in our regulations, we are proposing conforming 
revisions to the Requirements under the SNF QRP at Sec.  413.360. 
Specifically, we propose to redesignate Sec.  413.360(b)(2) to Sec.  
413.360(f)(2) and add a new paragraph (f) for the SNF QRP data 
completeness thresholds. The new paragraph would reflect all data 
completion thresholds required for SNFs to meet or exceed in order to 
avoid receiving a 2-percentage point reduction to their annual payment 
update for a given fiscal year.
    At Sec.  413.360(b), Data submission requirement, we propose to 
remove

[[Page 22754]]

paragraph (b)(2) and redesignate paragraph (b)(3) as paragraph (b)(2). 
At Sec.  413.360, we propose to add a new paragraph (f), Data 
completion thresholds.
    At Sec.  413.360(f)(1), we propose to add new language to state 
that SNFs must meet or exceed two separate data completeness 
thresholds: One threshold set at 80 percent for completion of required 
quality measures data and standardized patient assessment data 
collected using the MDS submitted through the CMS-designated data 
submission system, beginning with FY 2018 and for all subsequent 
payment updates; and a second threshold set at 100 percent for measures 
data collected and submitted using the CDC NHSN, beginning with FY 2023 
and for all subsequent payment updates.
    At Sec.  413.360(f)(2), we propose to add new language to state 
that these thresholds (80 percent for completion of required quality 
measures data and standardized patient assessment data on the MDS; 100 
percent for CDC NHSN data) will apply to all measures and standardized 
patient assessment data requirements adopted into the SNF QRP.
    At Sec.  413.360(f)(3), we propose to add new language to state 
that a SNF must meet or exceed both thresholds to avoid receiving a 2-
percentage point reduction to their annual payment update for a given 
fiscal year.
    We invite public comment on this proposal.

D. SNF QRP Quality Measures Under Consideration for Future Years: 
Request for Information (RFI)

    We are seeking input on the importance, relevance, and 
applicability of the concepts under consideration listed in Table 16 in 
the SNF QRP. More specifically, we are seeking input on a cross-setting 
functional measure that would incorporate the domains of self-care and 
mobility. Our measure development contractor for the cross-setting 
functional outcome measure convened a Technical Expert Panel (TEP) on 
June 15 and June 16, 2021 to obtain expert input on the development of 
a functional outcome measure for PAC. During this meeting, the 
possibility of creating one measure to capture both self-care and 
mobility was discussed. We are also seeking input on measures of health 
equity, such as structural measures that assess an organization's 
leadership in advancing equity goals or assess progress towards 
achieving equity priorities. Finally, we are seeking input on the value 
of a COVID-19 Vaccination Coverage measure that would assess whether 
SNF patients were up to date on their COVID-19 vaccine.
[GRAPHIC] [TIFF OMITTED] TP15AP22.023

    While we will not be responding to specific comments submitted in 
response to this RFI in the FY 2023 SNF PPS final rule, we intend to 
use this input to inform our future measure development efforts.

E. Overarching Principles for Measuring Equity and Healthcare Quality 
Disparities Across CMS Quality Programs--Request for Information (RFI)

    Significant and persistent disparities in healthcare outcomes exist 
in the United States. Belonging to an underserved community is often 
associated with worse health outcomes.\103\ \104\ \105\ \106\ \107\ 
\108\ \109\ \110\ \111\ With this in mind, we aim to advance health 
equity, by which we mean 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. 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, 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.\112\
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    \103\ Joynt KE, Orav E, Jha AK. (2011). Thirty-day readmission 
rates for Medicare beneficiaries by race and site of care. JAMA, 
305(7):675-681.
    \104\ 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.
    \105\ 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.
    \106\ 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.
    \107\ Rural Health Research Gateway. (2018). Rural communities: 
Age, Income, and Health status. Rural Health Research Recap. 
Available at https://www.ruralhealthresearch.org/assets/2200-8536/rural-communities-age-income-health-status-recap.pdf. Accessed 
February 3, 2022.
    \108\ U.S. Department of Health and Human Services. Office of 
the Secretary. Progress Report to Congress. HHS Office of Minority 
Health. 2020 Update on the Action Plan to Reduce Racial and Ethnic 
Health Disparities. FY 2020. Available at https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf. Accessed February 3, 2022.
    \109\ Centers for Disease Control and Prevention. Morbidity and 
Mortality Weekly Report (MMWR). Heslin, KC, Hall JE. Sexual 
Orientation Disparities in Risk Factors for Adverse COVID-19-Related 
Outcomes, by Race/Ethnicity--Behavioral Risk Factor Surveillance 
System, United States, 2017-2019. February 5, 2021/70(5); 149-154. 
Available at https://www.cdc.gov/mmwr/volumes/70/wr/mm7005a1.htm?s_cid=mm7005a1_w. Accessed February 3, 2022.
    \110\ 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.
    \111\ Milkie Vu et al. Predictors of Delayed Healthcare Seeking 
Among American Muslim Women, Journal of Women's Health 26(6) (2016) 
at 58; S.B. Nadimpalli, et al., The Association between 
Discrimination and the Health of Sikh Asian Indians.
    \112\ Centers for Medicare and Medicaid Services. Available at 
https://www.cms.gov/pillar/health-equity. Accessed February 9, 2022.
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    We are committed to achieving equity in healthcare outcomes for our 
enrollees by supporting healthcare providers' quality improvement 
activities to reduce health disparities, enabling them to make more 
informed decisions, and promoting healthcare provider accountability 
for healthcare

[[Page 22755]]

disparities.\113\ Measuring healthcare disparities in quality measures 
is a cornerstone of our approach to advancing healthcare equity. 
Hospital performance results that illustrate differences in outcomes 
between patient populations have been reported to hospitals 
confidentially since 2015. We provide additional information about this 
program in section XI.E.1.a. of this proposed rule.
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    \113\ CMS Quality Strategy. 2016. Available at https://
www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-
Instruments/Qualityinitiativesgeninfo/downloads/cms-quality-
strategy.pdf. Accessed February 3, 2022.
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    This RFI consists of three sections. The first section discusses a 
general framework that could be utilized across CMS quality programs to 
assess disparities in healthcare quality. The next section outlines the 
approaches that could be used in the SNF QRP to assess drivers of 
healthcare quality disparities in the SNF QRP. Additionally, this 
section discusses measures of health equity that could be adapted for 
use in the SNF QRP. Finally, the third section solicits public comment 
on the principles and approaches listed in the first two sections, as 
well as seeking other thoughts about disparity measurement guidelines 
for the SNF QRP.
1. Cross-Setting Framework To Assess Healthcare Quality Disparities
    We have identified five key considerations that we could apply 
consistently across our programs when advancing the use of measurement 
and stratification as tools to address health care disparities and 
advance health equity. The remainder of this section describes each of 
these considerations.
a. Identification of Goals and Approaches for Measuring Healthcare 
Disparities and Using Measure Stratification Across CMS Quality 
Programs
    By quantifying healthcare disparities through quality measure 
stratification (that is, measuring performance differences among 
subgroups of beneficiaries), we aim to provide useful tools for 
healthcare providers to drive improvement based on data. We hope that 
these results support healthcare provider efforts in examining the 
underlying drivers of disparities in their patients' care and to 
develop their own innovative and targeted quality improvement 
interventions. Quantification of health disparities can also support 
communities in prioritizing and engaging with healthcare providers to 
execute such interventions, as well as providing additional tools for 
accountability and decision-making.
    There are several different conceptual approaches to reporting 
health disparities. In the acute care setting, two complementary 
approaches are already used to confidentially provide disparity 
information to hospitals for a subset of existing measures. The first 
approach, referred to as the ``within-hospital 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 ``between-hospital 
disparity methodology,'' provides performance on measures for only the 
subgroup of patients with a particular social risk factor (SRF). These 
approaches can be used by a healthcare provider 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 ``between-provider'' disparity method may 
indicate that a SNF underperformed (when compared to other facilities 
on average) for patients with a given SRF, which would signal the need 
to improve care for this population. However, if the SNF also 
underperformed for patients without that SRF (the ``within-hospital'' 
disparity, as described earlier in this section), the measured 
difference, or disparity in care, could be negligible even though 
performance for the group that has been historically marginalized 
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.\114\
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    \114\ Centers for Medicare & Medicaid Services (CMS), HHS. 
Disparity Methods Confidential Reporting. Available at https://qualitynet.cms.gov/inpatient/measures/disparity-methods. Accessed 
February 3, 2022.
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    We are interested in whether similar approaches to the two 
discussed in the previous paragraph could be used to produce 
confidential stratified measure results for selected SNF QRP measures, 
as appropriate and feasible. However, final decisions regarding 
disparity reporting will be made at the program-level, as we intend to 
tailor the approach used in each setting to achieve the greatest 
benefit and avoid unintentional consequences or biases in measurement 
that may exacerbate disparities in care.
b. Guiding Principles for Selecting and Prioritizing Measures for 
Disparity Reporting
    We intend to expand our efforts to provide stratified reporting for 
additional clinical quality measures, provided they offer meaningful, 
actionable, and valid feedback to healthcare providers on their care 
for populations that may face social disadvantage or other forms of 
discrimination or bias. We are mindful, however, that it may not be 
possible to calculate stratified results for all quality measures, and 
that there may be situations where stratified reporting is not desired. 
To help inform prioritization of candidate measures for stratified 
reporting, we aim to receive feedback on several systematic principles 
under consideration that we believe will help us prioritize measures 
for disparity reporting across programs:
    (1) Programs may consider stratification, among existing clinical 
quality measures for further disparity reporting, prioritizing 
recognized measures which have met industry standards for measure 
reliability and validity.
    (2) Programs may consider measures for prioritization that show 
evidence that a treatment or outcome being measured is affected by 
underlying healthcare disparities for a specific social or demographic 
factor. Literature related to the measure or outcome should be reviewed 
to identify disparities related to the treatment or outcome, and should 
carefully consider both SRFs and patient demographics. In addition, 
analysis of Medicare-specific data should be done in order to 
demonstrate evidence of disparity in care for some or most healthcare 
providers that treat Medicare patients.
    (3) Programs may consider establishing statistical reliability and 
representation standards (for example, the percent of patients with a 
SRF included in reporting facilities) prior to reporting results. They 
may also consider prioritizing measures that reflect performance on 
greater numbers of patients to ensure that the reported results of the 
disparity calculation are reliable and representative.

[[Page 22756]]

    (4) After completing stratification, programs may consider 
prioritizing the reporting of measures that show differences in measure 
performance between subgroups across healthcare providers.
c. Principles for Social Risk Factor (SRF) and Demographic Data 
Selection and Use
    SRFs are the wide array of non-clinical drivers of health known to 
negatively impact patient outcomes. These include factors such as 
socioeconomic status, housing availability, and nutrition (among 
others), often inequitably affecting historically marginalized 
communities on the basis of race and ethnicity, rurality, sexual 
orientation and gender identity, religion, and 
disability.115 116 117 118 119 120 121 122
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    \115\ Joynt KE, Orav E, Jha AK. Thirty-day readmission rates for 
Medicare beneficiaries by race and site of care. JAMA. 
2011;305(7):675-681.
    \116\ Lindenauer PK, Lagu T, Rothberg MB, et al. Income 
inequality and 30 day outcomes after acute myocardial infarction, 
heart failure, and pneumonia: Retrospective cohort study. BMJ. 2013 
Feb 14;346:f521.
    \117\ Trivedi AN, Nsa W, Hausmann LRM, et al. Quality and equity 
of care in U.S. hospitals. N Engl J Med. 2014;371(24):2298-2308.
    \118\ Polyakova M, Udalova V, Kocks G, et al. Racial disparities 
in excess all-cause mortality during the early COVID-19 pandemic 
varied substantially across states. Health Affairs. 2021;40(2): 307-
316.
    \119\ Rural Health Research Gateway. (2018). Rural communities: 
Age, Income, and Health status. Rural Health Research Recap. 
Available at https://www.ruralhealthresearch.org/assets/2200-8536/rural-communities-age-income-health-status-recap.pdf. Accessed 
February 3, 2022.
    \120\ HHS Office of Minority Health (2020). 2020 Update on the 
Action Plan to Reduce Racial and Ethnic Health Disparities. 
Available at https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf Accessed February 3, 2022.
    \121\ Poteat TC, Reisner SL, Miller M, Wirtz AL. COVID-19 
vulnerability of transgender women with and without HIV infection in 
the Eastern and Southern U.S. medRxiv [Preprint]. 
2020.07.21.20159327. doi: 10.1101/2020.07.21.20159327. PMID: 
32743608; PMCID: PMC7386532.
    \122\ Vu M, Azmat A, Radejko T, Padela AI. Predictors of Delayed 
Healthcare Seeking Among American Muslim Women. Journal of Women's 
Health. 2016 Jun;25(6):586-593; Nadimpalli SB, Cleland CM, 
Hutchinson MK, et al. The Association between Discrimination and the 
Health of Sikh Asian Indians. Health Psychol. 2016 Apr;35(4):351-
355.
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    Identifying and prioritizing social risk or demographic variables 
to consider for disparity reporting 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 of many self-reported SRFs and demographic factors across 
the healthcare sector. Several proxy data sources, such as area-based 
indicators of social risk and imputation methods, may be used if 
individual patient-level data are not available. Each source of data 
has advantages and disadvantages for disparity reporting.
     Patient-reported data are considered to be the gold 
standard for evaluating quality of care for patients with SRFs.\123\ 
While data sources for many SRFs and demographic variables are still 
developing among several CMS settings, demographic data elements 
collected through assessments already exist in SNFs. Beginning October 
1, 2022, other PAC settings (86 FR 62345 through 62347, 62381 through 
62390) will begin collecting additional standardized patient data 
elements about race, ethnicity, preferred language, transportation, 
health literacy, and social isolation. Data collection for these items 
in SNF has been proposed for October 1, 2023 (See section VI.C.2. of 
this proposed rule).
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    \123\ Jarr[iacute]n OF, Nyandege AN, Grafova IB, Dong X, Lin H. 
Validity of race and ethnicity codes in Medicare administrative data 
compared with gold-standard self-reported race collected during 
routine home health care visits. Med Care. 2020;58(1):e1-e8. doi: 
10.1097/MLR.0000000000001216. PMID: 31688554; PMCID: PMC6904433.
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     CMS Administrative Claims data have long been used for 
quality measurement due to their availability and will continue to be 
evaluated for usability in measure development and or stratification. 
Using these existing data allows for high impact analyses with 
negligible healthcare provider burden. For example, dual eligibility 
for Medicare and Medicaid has been found to be an effective indicator 
of social risk in beneficiary populations.\124\ There are, however, 
limitations in these data's usability for stratification analysis.
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    \124\ Office of the Assistant Secretary for Planning and 
Evaluation. Report to Congress: Social Risk Factors and Performance 
Under Medicare's Value-Based Purchasing Program. December 20, 2016. 
Available at https://www.aspe.hhs.gov/reports/report-congress-social-risk-factors-performance-under-medicares-value-based-purchasing-programs. Accessed February 3, 2022.
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     Area-based indicators of social risk create approximations 
of patient risk based on neighborhood context. Several indexes, such as 
Agency for Healthcare Research and Quality (AHRQ) Socioeconomic Status 
(SES) Index,\125\ the Centers for Disease Control and Prevention/Agency 
for Toxic Substances and Disease Registry (CDC/ATSDR) Social 
Vulnerability Index (SVI),\126\ and the Health Resources and Services 
Administration (HRSA) Area Deprivation Index (ADI),\127\ provide 
multifaceted contextual information about an area and may be considered 
as an efficient way to stratify measures that include many SRFs.
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    \125\ Bonito A., Bann C., Eicheldinger C., Carpenter L. Creation 
of New Race-Ethnicity Codes and Socioeconomic Status (SES) 
Indicators for Medicare Beneficiaries. Final Report, Sub-Task 2. 
(Prepared by RTI International for the Centers for Medicare & 
Medicaid Services through an interagency agreement with the Agency 
for Healthcare Research and Policy, under Contract No. 500-00-0024, 
Task No. 21) AHRQ Publication No. 08-0029-EF. Rockville, MD, Agency 
for Healthcare Research and Quality. January 2008. Available at 
https://archive.ahrq.gov/research/findings/final-reports/medicareindicators/medicareindicators1.html. Accessed February 7, 
2022.
    \126\ Flanagan, B.E., Gregory, E.W., Hallisey, E.J., Heitgerd, 
J.L., Lewis, B. A social vulnerability index for disaster 
management. Journal of Homeland Security and Emergency Management. 
2011;8(1):1-22. Available at https://www.atsdr.cdc.gov/placeandhealth/svi/img/pdf/Flanagan_2011_SVIforDisasterManagement-508.pdf. Accessed February 3, 2022.
    \127\ Center for Health Disparities Research. University of 
Wisconsin School of Medicine and Public Health. Neighborhood Atlas. 
Available at https://www.neighborhoodatlas.medicine.wisc.edu/. 
Accessed February 3, 2022.
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     Imputed data sources use statistical techniques to 
estimate patient-reported factors, including race and ethnicity. One 
such tool is the Medicare Bayesian Improved Surname Geocoding (MBISG) 
method (currently in version 2.1), which combines information from 
administrative data, surname, and residential location to estimate race 
and ethnicity of patients at a population level.\128\
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    \128\ Haas A., Elliott MN, Dembosky JW, et al. Imputation of 
race/ethnicity to enable measurement of HEDIS performance by race/
ethnicity. Health Serv Res. 2019;54(1):13-23. doi: 10.1111/1475-
6773.13099. Epub 2018 Dec 3. PMID: 30506674; PMCID: PMC6338295. 
Available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338295/pdf/HESR-54-13.pdf. Accessed February 3, 2022.
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d. Identifying Meaningful Performance Differences
    While we aim to use standardized approaches where possible, 
differences in performance on stratified results will be identified at 
the program level due to contextual variations across programs and 
settings. We look forward to feedback on the benefits and limitations 
of the possible reporting approaches described in this section:
     Statistical approaches could be used to reliably group 
results, such as using confidence intervals, creating cut points based 
on standard deviations, or using a clustering algorithm.
     Programs could use a ranked ordering and percentile 
approach, ordering healthcare providers in a ranked system based on 
their performance on disparity measures to quickly allow them to 
compare their performance to other similar providers.
     SNFs could be categorized into groups based on their 
performance using defined thresholds, such as fixed intervals of 
results of disparity

[[Page 22757]]

measures, indicating different levels of performance.
     Benchmarking or comparing individual results to State or 
national average, is another potential reporting strategy.
     Finally, a ranking system is not appropriate for all 
programs and healthcare settings, and some programs may only report 
disparity results.
e. Guiding Principles for Reporting Disparity Measures
    Reporting of the results as discussed previously in this section 
can be employed in several ways to drive improvements in quality. 
Confidential reporting, or reporting results privately to healthcare 
providers, is generally used for new programs or new measures recently 
adopted for programs through notice and comment rulemaking to give 
healthcare providers an opportunity to become more familiar with 
calculation methods and to improve before other forms of reporting are 
used. In addition, many results are reported publicly, in accordance 
with the statute. This method provides all stakeholders with important 
information on healthcare provider quality, and in turn, relies on 
market forces to incentivize healthcare providers to improve and become 
more competitive in their markets without directly influencing payment 
from us. One important consideration is to assess differential impact 
on SNFs, such as those located in rural or critical access areas, to 
ensure that reporting does not disadvantage already resource-limited 
settings. The type of reporting chosen by programs will depend on the 
program context.
    Regardless of the methods used to report results, it is important 
to report stratified measure data alongside overall measure results. 
Review of both measures results along with stratified results can 
illuminate greater levels of detail about quality of care for subgroups 
of patients, providing important information to drive quality 
improvement. Unstratified quality measure results address general 
differences in quality of care between healthcare providers and promote 
improvement for all patients, but unless stratified results are 
available, it is unclear if there are subgroups of patients that 
benefit most from initiatives. Notably, even if overall quality measure 
scores improve, without identifying and measuring differences in 
outcomes between groups of patients, it is impossible to track progress 
in reducing disparity for patients with heightened risk of poor 
outcomes.
2. Approaches to Assessing Drivers of Healthcare Quality Disparities 
and Developing Measures of Healthcare Equity in the SNF QRP
    This section presents information on two approaches for the SNF 
QRP. The first section presents information about a method that could 
be used to assist SNFs in identifying potential drivers of healthcare 
quality disparities. The second section describes measures of 
healthcare equity that might be appropriate for inclusion in the SNF 
QRP.
a. Performance Disparity Decomposition
    In response to the FY 2022 SNF PPS proposed rule's RFI (86 FR 20000 
through 20001), ``Closing the Health Equity Gap in Post-Acute Care 
Quality Reporting Programs,'' some stakeholders noted that, while 
stratified results provide more information about disparities compared 
to overall measure scores, they provide limited information towards 
understanding the drivers of these disparities. As a result, it is up 
to the SNFs to determine which factors are leading to performance gaps 
so that they can be addressed. Unfortunately, identifying which factors 
are contributing to the performance gaps may not always be 
straightforward, especially if the SNF has limited information or 
resources to determine the extent to which a patient's SDOH or other 
mediating factors (for example, health histories) explain a given 
disparity. An additional complicating factor is the reality that there 
are likely multiple SDOH and other mediating factors responsible for a 
given disparity, and it may not be obvious to the SNF which of these 
factors are the primary drivers.
    Consequently, we may consider methods to use the data already 
available in enrollment, claims, and assessment data to estimate the 
extent to which various SDOH (for example, transportation, health 
literacy) and other mediating factors drive disparities in an effort to 
provide more actionable information. Researchers have utilized 
decomposition techniques to examine inequality in health care and, 
specifically, as a way to understand and explain the underlying causes 
of inequality.\129\ At a high level, regression decomposition is a 
method that allows one to estimate the extent to which disparities 
(that is, differences) in measure performance between subgroups of 
patient populations are due to specific factors. These factors can be 
either non-clinical (for example, SDOH) or clinical. Similarly, we may 
utilize regression decomposition to identify and calculate the specific 
contribution of SDOHs and other mediating factors to observed 
disparities. This approach may better inform our understanding of the 
extent to which providers and policy-makers may be able to narrow the 
gap in health care outcomes. Additionally, provider-specific 
decomposition results could be shared through confidential feedback so 
that SNFs can see the disparities within their facility with more 
granularity, allowing them to set priority targets in some performance 
areas while knowing which areas of their care are already relatively 
equitable. Importantly, these results could help SNFs identify reasons 
for disparities that might not be obvious without having access to 
additional data sources (for example, the ability to link data across 
providers).
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    \129\ Rahimi E, Hashemi Nazari S. A detailed explanation and 
graphical representation of the Blinder-Oaxaca decomposition method 
with its application in health inequalities. Emerg Themes Epidemiol. 
2021;18:12. https://doi.org/10.1186/s12982-021-00100-9. Accessed 
February 24, 2022.
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    To more explicitly demonstrate the types of information that could 
be provided through decomposition of a measure disparity, consider the 
following example for a given SNF. Figures 1 through 3 depict an 
example (using hypothetical data) of how a disparity in a measure of 
Medicare Spending Per Beneficiary (MSPB) between dually eligible 
beneficiaries (that is, those enrolled in Medicare and Medicaid) and 
non-dually eligible beneficiaries (that is, those with Medicare only) 
could be decomposed among two mediating factors, one SDOH and one 
clinical factor: (1) Low health literacy; and (2) high-volume of 
emergency department (ED) use. These examples were selected because if 
they were shown to be drivers of disparity in their SNF, the healthcare 
provider could mitigate their effects. Additionally, high-volume ED use 
is used as a potential mediating factor that could be difficult for 
SNFs to determine on their own, as it would require having longitudinal 
data for patients across multiple facilities.
BILLING CODE 4120-01-P

[[Page 22758]]

[GRAPHIC] [TIFF OMITTED] TP15AP22.024

BILLING CODE 4120-01-C
    In this example (Figure 1), the overall Medicare spending disparity 
is $1,000: Spending, on average, is $5,000 per non-dual beneficiary and 
$6,000 per dual beneficiary. We can also see from Figure 2 that in this 
SNF, the dual population has twice the prevalence of beneficiaries with 
low health literacy and high ED use compared to the non-dual 
population. Using regression techniques, the difference in overall 
spending between non-dual and dual beneficiaries can be divided into 
three causes: (1) A difference in the prevalence of mediating factors 
(for example, low health literacy and high ED use) between the two 
groups; (2) a difference in how much spending is observed for 
beneficiaries with these mediating factors between the two groups; and 
(3) differences in baseline spending that are not due to either (1) or 
(2). In Figure 3, the `Non-Dual beneficiaries' column breaks down the 
overall spending per non-dual beneficiary, $5,000, into a baseline

[[Page 22759]]

spending of $4,600 plus the effects of the higher spending for the 10 
percent of non-dual beneficiaries with low health literacy ($300) and 
the 5 percent with high ED use ($100). The `Dual beneficiaries' column 
similarly decomposes the overall spending per dual beneficiary ($6,000) 
into a baseline spending of $5,000, plus the amounts due to dual 
beneficiaries' 20 percent prevalence of low health literacy ($600, 
twice as large as the figure for non-dual beneficiaries because the 
prevalence is twice as high), and dual beneficiaries' 10 percent 
prevalence of high-volume ED use ($200, similarly twice as high as for 
non-dual beneficiaries due to higher prevalence). This column also 
includes an additional $100 per risk factor because dual beneficiaries 
experience a higher cost than non-dual beneficiaries within the low 
health literacy risk factor, and similarly within the high ED use risk 
factor. Based on this information, a SNF can determine that the overall 
$1,000 disparity can be divided into differences simply due to risk 
factor prevalence ($300 + $100 = $400 or 40 percent of the total 
disparity), disparities in costs for beneficiaries with risk factors 
($100 + $100 = $200 or 20 percent) and disparities that remain 
unexplained (differences in baseline costs: $400 or 40 percent).
    In particular, the SNF can see that simply having more patients 
with low health literacy and high ED use accounts for a disparity of 
$400. In addition, there is still a $200 disparity stemming from 
differences in costs between non-dual and dual patients for a given 
risk factor, and another $400 that is not explained by either low 
health literacy or high ED use. These differences may instead be 
explained by other SDOH that have not yet been included in this 
breakdown, or by the distinctive pattern of care decisions made by 
providers for dual and non-dual beneficiaries. These cost estimates 
would provide additional information that facilities could use when 
determining where to devote resources aimed at achieving equitable 
health outcomes (for example, facilities may choose to focus efforts on 
the largest drivers of a disparity).
b. Measures Related to Health Equity
    Beyond identifying disparities in individual health outcomes and by 
individual risk factors, there is interest in developing more 
comprehensive measures of health equity that reflect organizational 
performance. When determining which equity measures could be 
prioritized for development for SNF QRP, we will draw from its 
experience with the CMS Measures Management System (MMS) Blueprint 
\130\ and may consider the following:
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    \130\ Centers for Medicare & Medicaid Services. CMS Measures 
Management System Blueprint. Version 17.0. September 2021. Available 
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/MMS/Downloads/Blueprint.pdf.
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     Measures should be actionable in terms of quality 
improvement.
     Measures should help beneficiaries and their caregivers 
make informed healthcare decisions.
     Measures should not create incentives to lower the quality 
of care.
     Measures should adhere to high scientific acceptability 
standards.
    We have developed measures assessing health equity, or designed to 
promote health equity, in other settings outside of the SNF. As a 
result, there may be measures that could be adapted for use in the SNF 
QRP. The remainder of this section discusses two such measures, 
beginning with the Health Equity Summary Score (HESS), and then a 
structural measure assessing the degree of hospital leadership 
engagement in health equity performance data.
(1) Health Equity Summary Score
    The HESS measure was developed by the CMS Office of Minority Health 
(OMH) \131\ to identify and to reward healthcare providers (that is, 
Medicare Advantage [MA] plans) that perform relatively well on measures 
of care provided to beneficiaries with SRFs, as well as to discourage 
the non-treatment of patients who are potentially high-risk, in the 
context of value-based purchasing. Additionally, a version of the HESS 
is in development for the Hospital Inpatient Quality Reporting (HIQR) 
program.\132\ This composite measure provides a summary of equity of 
care delivery by combining performance and improvement across multiple 
measures and multiple at-risk groups. The HESS was developed with the 
following goals: Allow for ``multiple grouping variables, not all of 
which will be measurable for all plans;'' allow for ``disaggregation by 
grouping variable for nuanced insights;'' and allow for the future 
usage of additional and different SRFs for grouping.\133\
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    \131\ Agniel D, Martino SC, Burkhart Q, et al. Incentivizing 
excellent care to at-risk groups with a health equity summary score. 
J Gen Intern Med. 2021;36(7):1847-1857. doi: 10.1007/s11606-019-
05473-x. Epub 2019 Nov 11. PMID: 31713030; PMCID: PMC8298664. 
Available at https://link.springer.com/content/pdf/10.1007/s11606-019-05473-x.pdf. Accessed February 3, 2022.
    \132\ Centers for Medicare & Medicaid Services, FY 2022 IPPS/
LTCH PPS Proposed Rule. 88 FR 25560. May 10, 2021.
    \133\ Centers for Medicare & Medicaid Services Office of 
Minority Health (CMS OMH). 2021b. ``Health Equity as a `New Normal': 
CMS Efforts to Address the Causes of Health Disparities.'' Presented 
at CMS Quality Conference, March 2 to 3, 2021.
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    The HESS computes across-provider disparity in performance, as well 
as within-provider and across-provider disparity improvement in 
performance. Calculation starts with a cross-sectional score and an 
overall improvement score for each SRF of race/ethnicity and dual 
eligibility, for each plan. The overall improvement score is based on 
two separate improvement metrics: Within-plan improvement and 
nationally benchmarked improvement. Within-plan improvement is defined 
as how that plan improves the care of patients with SRFs relative to 
higher-performing patients between the baseline period and performance 
period, and is targeted at eliminating within-plan disparities. 
Nationally benchmarked improvement is improvement of care for 
beneficiaries with SRFs served by that MA plan, relative to the 
improvement of care for similar beneficiaries across all MA plans, and 
is targeted at improving the overall care of populations with SRFs. 
Within-plan improvement and nationally benchmarked improvement are then 
combined into an overall improvement score. Meanwhile, the cross-
sectional score measures overall measure performance among 
beneficiaries with SRFs during the performance period, regardless of 
improvement.
    To calculate a provider's overall score, the HESS uses a composite 
of five clinical quality measures based on Healthcare Effectiveness 
Data and Information Set (HEDIS) data and seven MA Consumer Assessment 
of Healthcare Providers and Systems (CAHPS) patient experience 
measures. A provider's overall HESS score is calculated once using only 
CAHPS-based measures and once using only HEDIS-based measures, due to 
incompatibility between the two data sources. The HESS uses a composite 
of these measures to form a cross-sectional score, a nationally 
benchmarked improvement score, and a within-plan improvement score, one 
for each SRF. These scores are combined to produce a SRF-specific 
blended score, which is then combined with the blended score for 
another SRF to produce the overall HESS.

[[Page 22760]]

(2) Degree of Hospital Leadership Engagement in Health Equity 
Performance Data
    We have developed a structural measure for use in acute care 
hospitals assessing the degree to which hospital leadership is engaged 
in the collection of health equity performance data, with the 
motivation that that organizational leadership and culture can play an 
essential role in advancing equity goals. This structural measure, 
entitled the Hospital Commitment to Health Equity measure (MUC 2021-
106), was included on the CMS List of Measures Under Consideration (MUC 
List) \134\ and assesses hospital commitment to health equity using a 
suite of equity-focused organizational competencies aimed at achieving 
health equity for racial and ethnic minorities, people with 
disabilities, sexual and gender minorities, individuals with limited 
English proficiency, rural populations, religious minorities, and 
people facing socioeconomic challenges. The measure will include five 
attestation-based questions, each representing a separate domain of 
commitment. A hospital will receive a point for each domain where it 
attests to the corresponding statement (for a total of 5 points). At a 
high level, the five domains cover the following: (1) Strategic plan to 
reduce health disparities; (2) approach to collecting valid and 
reliable demographic and SDOH data; (3) analyses performed to assess 
disparities; (4) engagement in quality improvement activities; \135\ 
and (5) leadership involvement in activities designed to reduce 
disparities. The specific questions asked within each domain, as well 
as the detailed measure specification are found in the CMS MUC List for 
December 2021 at https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf. A SNF could receive a point for 
each domain where data are submitted through a CMS portal to reflect 
actions taken by the SNF for each corresponding domain (for a point 
total).
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    \134\ Centers for Medicare & Medicaid Services. List of Measures 
Under Consideration for December 1, 2021. Available at https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf. Accessed March 1, 2022.
    \135\ Quality is defined by the National Academy of Medicine as 
the degree to which health services for individuals and populations 
increase the likelihood of desired health outcomes and are 
consistent with current professional knowledge. Quality improvement 
is the framework used to systematically improve care. Quality 
improvement seeks to standardize processes and structure to reduce 
variation, achieve predictable results, and improve outcomes for 
patients, healthcare systems, and organizations. Structure includes 
things like technology, culture, leadership, and physical capital; 
process includes knowledge capital (for example, standard operating 
procedures) or human capital (for example, education and training). 
Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/MMS/Quality-Measure-and-Quality-Improvement-. Accessed March 1, 2022.
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    We believe this type of organizational commitment structural 
measure may complement the health disparities approach described in 
previous sections, and support SNFs in quality improvement, efficient, 
effective use of resources, and leveraging available data. As defined 
by AHRQ, structural measures aim to ``give consumers a sense of a 
healthcare provider's capacity, systems, and processes to provide high-
quality care.'' \136\ We acknowledge that collection of this structural 
measure may impose administrative and/or reporting requirements for 
SNFs.
---------------------------------------------------------------------------

    \136\ Agency for Healthcare Research and Quality. Types of 
Health Care Quality Measures. 2015. Available at https://www.ahrq.gov/talkingquality/measures/types.html. Accessed February 
3, 2022.
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    We are interested in obtaining feedback from stakeholders on 
conceptual and measurement priorities for the SNF QRP to better 
illuminate organizational commitment to health equity.
3. Solicitation of Public Comment
    The goal of this request for information is to describe some key 
principles and approaches that we will consider when advancing the use 
of quality measure development and stratification to address health 
care disparities and advance health equity across our programs.
    We invite general comments on the principles and approaches 
described previously in this section of the rule, as well as additional 
thoughts about disparity measurement guidelines suitable for 
overarching consideration across CMS' QRP programs. Specifically, we 
invite comment on:
     Identification of Goals and Approaches for Measuring 
Healthcare Disparities and Using Measure Stratification Across CMS 
Quality Reporting Programs:
    ++ The use of the within- and between-provider disparity methods in 
SNFs to present stratified measure results.
    ++ The use of decomposition approaches to explain possible causes 
of measure performance disparities.
    ++ Alternative methods to identify disparities and the drivers of 
disparities.
     Guiding Principles for Selecting and Prioritizing Measures 
for Disparity Reporting:
    ++ Principles to consider for prioritization of health equity 
measures and measures for disparity reporting, including prioritizing 
stratification for validated clinical quality measures, those measures 
with established disparities in care, measures that have adequate 
sample size and representation among healthcare providers and outcomes, 
and measures of appropriate access and care.
     Principles for SRF and Demographic Data Selection and Use:
    ++ Principles to be considered for the selection of SRFs and 
demographic data for use in collecting disparity data including the 
importance of expanding variables used in measure stratification to 
consider a wide range of SRFs, demographic variables, and other markers 
of historic disadvantage. In the absence of patient-reported data we 
will consider use of administrative data, area-based indicators, and 
imputed variables as appropriate.
     Identification of Meaningful Performance Differences:
    ++ Ways that meaningful difference in disparity results should be 
considered.
     Guiding Principles for Reporting Disparity Measures:
    ++ Guiding principles for the use and application of the results of 
disparity measurement.
     Measures Related to Health Equity:
    ++ The usefulness of a HESS score for SNFs, both in terms of 
provider actionability to improve health equity, and in terms of 
whether this information would support Care Compare website users in 
making informed healthcare decisions.
    ++ The potential for a structural measure assessing a SNF's 
commitment to health equity, the specific domains that should be 
captured, and options for reporting these data in a manner that would 
minimize burden.
    ++ Options to collect facility-level information that could be used 
to support the calculation of a structural measure of health equity.
    ++ Other options for measures that address health equity.
    While we will not be responding to specific comments submitted in 
response to this RFI in the FY 2023 SNF PPS final rule, we will 
actively consider all input as we develop future regulatory proposals 
or future subregulatory policy guidance. 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.

[[Page 22761]]

F. Inclusion of the CoreQ: Short Stay Discharge Measure in a Future SNF 
QRP Program Year--Request for Information (RFI)

1. Background
    The SNF QRP furthers our mission to improve the quality of health 
care for beneficiaries through measurement, transparency, and public 
reporting of data. The SNF QRP and CMS' other quality programs are 
foundational for contributing to improvements in health care, enhancing 
patient outcomes, and informing consumer choice. In October 2017, we 
launched the Meaningful Measures Framework. This framework captures our 
vision to address health care quality priorities and gaps, including 
emphasizing digital quality measurement, reducing measurement burden, 
and promoting patient perspectives, while also focusing on 
modernization and innovation.\137\ Meaningful Measures 2.0 builds on 
the initial framework by establishing a goal of increasing Patient 
Reported Outcomes Measures (PROMs) by 50 percent.\138\ Ensuring that 
patients and families are engaged as partners in their care can be an 
effective way to measure the quality of patient care.
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    \137\ Meaningful Measures 2.0: Moving from Measure Reduction to 
Modernization. Available at https://www.cms.gov/meaningful-measures-20-moving-measure-reduction-modernization.
    \138\ 2021 CMS Quality Conference. CMS Quality Measurement 
Action Plan. March 2021. Available at https://www.cms.gov/files/document/2021-cms-quality-conference-cms-quality-measurement-action-plan-march-2021.pdf.
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2. Potential Future Inclusion of the CoreQ: Short Stay Discharge 
Measure
    Collecting satisfaction information from SNF patients is more 
important now than ever. There has been a philosophical change in 
healthcare that now includes the patient and their preferences as an 
integral part of the system of care. The Institute of Medicine (IOM) 
endorsed this change by putting the patient as central to the care 
system (IOM, 2001).\139\ To achieve the goal of patient-centered care, 
there must be a way to measure patient satisfaction since it is 
necessary to understand patient preferences. Measuring patients' 
satisfaction can also help organizations identify deficiencies that 
other quality metrics may struggle to identify, such as communication 
between a patient and the healthcare provider.
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    \139\ Institute of Medicine (US) Committee on Quality of Health 
Care in America. Crossing the Quality Chasm: A New Health System for 
the 21st Century. Washington (DC): National Academies Press (US); 
2001. ISBN-10: 0-309-07280-8.
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    We define a Patient Reported Outcome (PRO) as any report of the 
status of a patient's health condition or health behavior that comes 
directly from the patient, without interpretation of the patient's 
response by a clinician or anyone else.\140\ Therefore, they are an 
important component of assessing whether healthcare providers are 
improving the health and well-being of patients. We have demonstrated 
interest in consumers' perspective on quality of care in nursing homes 
by supporting the development of the CAHPS survey for patients in 
nursing facilities,\141\ and adding provisions for comprehensive 
person-centered care planning and quality of life to the nursing home 
requirements of participation at Sec. Sec.  483.21 and 483.24 
respectively effective November 28, 2017.
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    \140\ Patient Reported Outcome Measures. Supplemental Material 
to the CMS MMS Blueprint. Available at https://www.cms.gov/files/document/blueprint-patient-reported-outcome-measures.pdf.
    \141\ Sangl, J., Buchanan, J., Cosenza C., Bernard S., Keller, 
S., Mitchell, N., Brown, J., Castle, N., Sekscenski, E., Larwood, D. 
The Development of a CAHPS Instrument for Nursing Home Residents 
(NHCAHPS). J Aging Soc Policy. 2007;19(2):63-82. doi: 10.1300/
J031v19n02_04.
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    In the FY 2022 SNF PPS proposed rule (86 FR 19998), we sought 
comments on potential future PROMs for the SNF QRP. We summarized the 
comments received in the FY 2022 SNF PPS final rule (86 FR 42490 
through 42491). In this year's proposed rule, we are requesting 
stakeholder feedback specifically on the inclusion of the CoreQ: Short 
Stay Discharge measure in the SNF QRP in future program years, 
including whether there are any challenges or impacts we should 
consider for a potential future proposal.
    Collection of patient experience data aligns with the person-
centered care domain of CMS's Meaningful Measures 2.0 Framework,\142\ 
and addresses an aspect of patient experience that is not currently 
included in the SNF QRP. We believe collecting and assessing 
satisfaction data from SNF patients is important for understanding 
patient experiences and preferences, while ensuring the patient can 
easily and discretely share their information and provide information 
to help consumers choose a trusted SNF. PRO data could be incorporated 
into QAPI strategies to help facilities improve their quality of care.
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    \142\ Centers for Medicare & Medicaid Services. Meaningful 
Measures 2.0: Moving from Measure Reduction to Modernization. 
Available at https://www.cms.gov/meaningful-measures-20-moving-measure-reduction-modernization.
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3. Overview of the CoreQ: Short Stay Discharge Measure in a Future SNF 
QRP Program Year
    The CoreQ: Short Stay Discharge Measure calculates the percentage 
of individuals discharged in a 6-month period from a SNF, within 100 
days of admission, who are satisfied with their SNF stay. This patient-
reported outcome measure is based on the CoreQ: Short Stay Discharge 
questionnaire that utilizes four items: (1) In recommending this 
facility to your friends and family, how would you rate it overall; (2) 
Overall, how would you rate the staff; (3) How would you rate the care 
you receive; (4) How would you rate how well your discharge needs were 
met. The CoreQ questionnaire uses a 5-point Likert Scale: Poor (1); 
Average (2); Good (3); Very Good (4); and Excellent (5).
    The numerator is the sum of the individuals in the facility that 
have an average satisfaction score of greater than or equal to 3 for 
the four questions on the CoreQ: Short Stay Discharge questionnaire. 
The denominator includes all patients, regardless of payer, that are 
admitted to the SNF for post-acute care and are discharged within 100 
days, receive the survey and who respond to the CoreQ: Short Stay 
Discharge questionnaire within two months of receiving the 
questionnaire.
    The CoreQ: Short Stay Discharge Measure excludes certain patients 
from the denominator, such as patients who die during their SNF stay, 
patients discharged to another hospital, another SNF, psychiatric 
facility, IRF or LTCH, patients with court appointed legal guardians 
for all decisions, patients who have dementia impairing their ability 
to answer the questionnaire,\143\ patients discharged on hospice, and 
patients who left the SNF against medical device. For additional 
information about the CoreQ: Short Stay Discharge Measure, please visit 
https://cmit.cms.gov/CMIT_public/ViewMeasure?MeasureId=3436.
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    \143\ Patients who have dementia impairment their ability to 
answer the questionnaire are defined as having a Brief Interview of 
Mental Status (BIMS) score on the MDS 3.0 as 7 or lower. Available 
at https://cmit.cms.gov/CMIT_public/ViewMeasure?MeasureId=3436.
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4. Measure Application Partnership (MAP) Review
    The CoreQ: Short Stay Discharge Measure (NQF #2614) was endorsed by 
the National Quality Forum (NQF) in 2016 and achieved re-endorsement in 
2020. We included the CoreQ: Short Stay Discharge Measure (NQF #2614) 
under the SNF QRP Program in the publicly available ``List of Measures

[[Page 22762]]

Under Consideration for December 1, 2017'' (MUC List).\144\ The NQF-
convened Measure Applications Partnership (MAP) Post-Acute Care/Long-
Term Care (PACLTC) workgroup met on December 13, 2017 and provided 
input on the measure. The MAP offered support of the CoreQ Short Stay 
Discharge Measure (NQF #2614) for rulemaking, noting that it adds value 
by adding addressing a gap area for the QRP. The MAP reiterated the 
value of resident-reported outcomes and noted that this measure could 
reflect quality of care from the resident's perspective, but also noted 
the potential burden of collecting the data and cautioned the 
implementation of a new data collection requirement should be done with 
the least possible burden to the facility. We refer readers to the 
final MAP report available at https://www.qualityforum.org/Publications/2018/02/MAP_2018_Considerations_for_Implementing_Measures_in_Federal_Programs_-_PAC-LTC.aspx.
---------------------------------------------------------------------------

    \144\ Centers for Medicare & Medicaid Services. List of Measures 
Under Consideration for December 1, 2017. Available at https://www.cms.gov/files/document/2017amuc-listclearancerpt.pdf.
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5. Data Sources
    CoreQ is administered by customer satisfaction vendors that have 
added CoreQ to their questionnaires. Currently, nearly 40 customer 
satisfaction vendors have incorporated or will incorporate CoreQ into 
their surveys when asked by clients. For information on customer 
satisfaction vendors that have added CoreQ to their questionnaires, we 
refer readers to http://www.CoreQ.org. For more information about 
administering CoreQ, we encourage readers to visit http://www.CoreQ.org 
and review the CoreQ Satisfaction Questionnaire and User's Manual 
available at http://www.coreq.org/CoreQ%20Satisfaction%20Questionnaire%20and%20User%20Manual.pdf.
6. Solicitation of Public Comment
    In this proposed rule, we are requesting stakeholder feedback on 
future adoption and implementation of the CoreQ: Short Stay Discharge 
Measure into the SNF QRP.
    Specifically, we seek comment on the following:
     Would you support utilizing the CoreQ to collect PROs?
     Do SNFs believe the questions asked in the CoreQ would add 
value to their patient engagement and quality of care goals?
     Should CMS establish a minimum number of surveys to be 
collected per reporting period or a waiver for small providers?
     How long would facilities and customer satisfaction 
vendors need to accommodate data collection and reporting for all 
participating SNFs?
     What specific challenges do SNFs anticipate for collecting 
the CoreQ: Short Stay Discharge measure? What are potential solutions 
for those challenges?

G. Form, Manner, and Timing of Data Submission Under the SNF QRP

1. Background
    We refer readers to the regulatory text at Sec.  413.360(b) for 
information regarding the current policies for reporting SNF QRP data.
2. Proposed Schedule for Data Submission of the Influenza Vaccination 
Coverage Among Healthcare Personnel (NQF #0431) Measure Beginning With 
the FY 2025 SNF QRP
    As discussed in section VI.C.1. of this proposed rule, we are 
proposing to adopt the Influenza Vaccination Coverage among HCP quality 
measure beginning with the FY 2025 SNF QRP. The CDC has determined that 
the influenza vaccination season begins on October 1st (or when the 
vaccine becomes available) and ends on March 31st of the following 
year. Therefore, we propose an initial data submission period from 
October 1, 2022 through March 31, 2023. In subsequent years, data 
collection for this measure will be from October 1 through March 31 of 
the following year.
    This measure requires that the provider submit a minimum of one 
report to the NHSN by the data submission deadline of May 15 for each 
influenza season following the close of the data collection period each 
year to meet our requirements. Although facilities may edit their data 
after May 15, the revised data will not be shared with us.\145\ SNFs 
would submit data for the measure through the CDC/NHSN web-based 
surveillance system. SNFs would use the Influenza Vaccination Summary 
option under the NHSN HPS Component to report the number of HCP who 
receive the influenza vaccination (numerator) among the total number of 
HCP in the facility for at least 1 working day between October 1 and 
March 31 of the following year, regardless of clinical responsibility 
or patient contact (denominator).
---------------------------------------------------------------------------

    \145\ Centers for Disease Control and Prevention (CDC). (2021). 
HCP Influenza Vaccination Summary Reporting FAQs. Retrieved from 
https://www.cdc.gov/nhsn/faqs/vaccination/faq-influenza-vaccination-
summary-
reporting.html#:~:text=To%20meet%20CMS%20reporting%20requirements,not
%20be%20shared%20with%20CMS.
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    We invite public comment on this proposal.

H. 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. SNF 
QRP measure data are currently displayed on the Nursing homes including 
rehab services website within Care Compare and the Provider Data 
Catalog. Both Care Compare and the Provider Data Catalog replaced 
Nursing Home Compare and Data.Medicare.gov, which were retired in 
December 2020. For a more detailed discussion about our policies 
regarding public display of SNF QRP measure data and procedures for the 
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 Influenza Vaccination Coverage Among 
Healthcare Personnel (NQF #0431) Measure Beginning With the FY 2025 SNF 
QRP
    We propose to publicly report the Influenza Vaccination Coverage 
among HCP (NQF #0431) measure beginning with the October 2023 Care 
Compare refresh or as soon as technically feasible using data collected 
from October 1, 2022 through March 31, 2023. If finalized as proposed, 
a SNF's Influenza Vaccination Coverage among HCP rate would be 
displayed based on 6 months of data. Provider preview reports would be 
distributed in July 2023. Thereafter, Influenza Vaccination Coverage 
among HCP rates would be displayed based on 6 months of data, 
reflecting the reporting period of October 1 through March 31, updated 
annually. We invite public comment on this proposal for the public 
display of the Influenza Vaccination Coverage among Healthcare 
Personnel (NQF #0431) measure on Care Compare.

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

A. Statutory Background

    Section 215(b) of the Protecting Access to Medicare Act of 2014 
(Pub. L. 113-93) authorized the SNF VBP Program (the ``Program'') by 
adding section 1888(h) to the Act. Additionally, section 111 of the 
Consolidated

[[Page 22763]]

Appropriations Act, 2021 authorized the Secretary to apply additional 
measures to the SNF VBP Program for payments for services furnished on 
or after October 1, 2023. The SNF VBP Program 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 payment is made for care, moving increasingly towards 
rewarding better value, outcomes, and innovations instead of merely 
rewarding volume.
    As a prerequisite to implementing the SNF VBP Program, in the FY 
2016 SNF PPS final rule (80 FR 46409 through 46426), we adopted an all-
cause, all-condition hospital readmission measure, as required by 
section 1888(g)(1) of the Act and discussed other policies to implement 
the Program such as performance standards, the performance period and 
baseline period, and scoring. SNF VBP Program policies have been 
codified in our regulations at 42 CFR 413.338. For additional 
background information on the SNF VBP Program, including an overview of 
the SNF VBP Report to Congress and a summary of the Program's statutory 
requirements, we refer readers to the following prior final rules:
     In the FY 2017 SNF PPS final rule (81 FR 51986 through 
52009), we adopted an all-condition, risk-adjusted potentially 
preventable hospital readmission measure for SNFs, as required by 
section 1888(g)(2) of the Act, adopted policies on performance 
standards, performance scoring, and sought comment on an exchange 
function methodology to translate SNF performance scores into value-
based incentive payments, among other topics.
     In the FY 2018 SNF PPS final rule (82 FR 36608 through 
36623), we adopted additional policies for the Program, including an 
exchange function methodology for disbursing value-based incentive 
payments.
     In the FY 2019 SNF PPS final rule (83 FR 39272 through 
39282), we adopted more policies for the Program, including a scoring 
adjustment for low-volume facilities.
     In the FY 2020 SNF PPS final rule (84 FR 38820 through 
38825), we adopted additional policies for the Program, including a 
change to our public reporting policy and an update to the deadline for 
the Phase One Review and Correction process. We also adopted a data 
suppression policy for low-volume SNFs.
     In the FY 2021 SNF PPS final rule (85 FR 47624 through 
47627), we amended regulatory text definitions at Sec.  413.338(a)(9) 
and (11) to reflect the definition of Performance Standards and the 
updated Skilled Nursing Facility Potentially Preventable Readmissions 
after Hospital Discharge measure name, respectively. We also updated 
the Phase One Review and Correction deadline and codified that update 
at Sec.  413.338(e)(1). Additionally, we codified the data suppression 
policy for low-volume SNFs at Sec.  413.338(e)(3)(i) through (iii) and 
amended Sec.  413.338(e)(3) to reflect that SNF performance information 
will be publicly reported on the Nursing Home Compare website and/or 
successor website (84 FR 38823 through 38824), which since December 
2020 is the Provider Data Catalog website (https://data.cms.gov/provider-data/).
     In the September 2nd interim final rule with comment (IFC) 
(85 FR 54837), we revised the performance period for the FY 2022 SNF 
VBP Program to be April 1, 2019 through December 31, 2019 and July 1, 
2020 through September 30, 2020, in response to the COVID-19 Public 
Health Emergency (PHE).
     In the FY 2022 SNF PPS final rule (86 FR 42502 through 
42517), we adopted additional policies for the Program, including a 
measure suppression policy to offer flexibility in response to the 
COVID-19 PHE. We adopted policies to suppress the SNFRM for scoring and 
payment purposes for the FY 2022 SNF VBP program year, to revise the 
SNFRM risk adjustment lookback period for the FY 2023 SNF VBP program 
year, and to use FY 2019 data for the baseline period for the FY 2024 
SNF VBP program year. We also updated the Phase One Review and 
Correction process and updated the instructions for requesting an 
Extraordinary Circumstances Exception (ECE). Finally, we finalized a 
special scoring policy assigning all SNFs a performance score of zero, 
effectively ranking all SNFs equally in the FY 2022 SNF VBP program 
year. This policy was codified at Sec.  413.338(g) of our regulations.
    To improve the clarity of our regulations, we propose to update and 
renumber the ``Definitions'' used in Sec.  413.338 by revising 
paragraphs (a)(1) and (4) through (17). We seek public comment on these 
proposed updates.

B. SNF VBP Program Measures

    For background on the measures we have adopted for the SNF VBP 
Program, we refer readers to the FY 2016 SNF PPS final rule (80 FR 
46419), where we finalized the Skilled Nursing Facility 30-Day All-
Cause Readmission Measure (SNFRM) (NQF #2510) that we are currently 
using for the SNF VBP Program. We also refer readers to the FY 2017 SNF 
PPS final rule (81 FR 51987 through 51995), where we finalized the 
Skilled Nursing Facility 30-Day Potentially Preventable Readmission 
Measure (SNFPPR) that we will use for the SNF VBP Program instead of 
the SNFRM as soon as practicable, as required by statute. The SNFPPR 
measure's name is now ``Skilled Nursing Facility Potentially 
Preventable Readmissions after Hospital Discharge measure'' (Sec.  
413.338(a)(11)). We intend to submit the SNFPPR measure for NQF 
endorsement review as soon as practicable, and to assess transition 
timing of the SNFPPR measure to the SNF VBP Program after NQF 
endorsement review is complete.
1. Proposal To Suppress the SNFRM for the FY 2023 Program Year
a. Background
    We remain concerned about the effects of the PHE for COVID-19 on 
our ability to assess performance on the SNFRM in the SNF VBP Program. 
As of mid-December 2021, more than 50 million COVID-19 cases and 
800,000 COVID-19 deaths have been reported in the United States (U.S.) 
\146\ COVID-19 has overtaken the 1918 influenza pandemic as the 
deadliest disease in American history.\147\ Moreover, the individual 
and public health ramifications of COVID-19 extend beyond the direct 
effects of COVID-19 infections. Several studies have demonstrated 
significant mortality increases in 2020, beyond those attributable to 
COVID-19 deaths. One paper quantifies the net impact (direct and 
indirect effects) of the pandemic on the U.S. population during 2020 
using three metrics: Excess deaths, life expectancy, and total years of 
life lost. The findings indicate there were 375,235 excess deaths, with 
83 percent attributable to direct effects, and 17 percent attributable 
to indirect effects, of COVID-19. The decrease in life expectancy was 
1.67 years, translating to a reversion of 14 years in historical life 
expectancy gains. Total years of life lost in 2020 was 7,362,555 across 
the U.S. (73 percent directly attributable, 27 percent indirectly 
attributable to COVID-19), with considerable heterogeneity at the 
individual State level.\148\
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    \146\ https://covid.cdc.gov/covid-data-tracker/#datatracker-home.
    \147\ https://www.statnews.com/2021/09/20/covid-19-set-to-overtake-1918-spanish-flu-as-deadliest-disease-in-american-history/.
    \148\ Chan, E.Y.S., Cheng, D., & Martin, J. (2021). Impact of 
COVID-19 on excess mortality, life expectancy, and years of life 
lost in the United States. PloS one, 16(9), e0256835. https://pubmed.ncbi.nlm.nih.gov/34469474/.

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[[Page 22764]]

b. Proposed Suppression of the SNFRM for the FY 2023 SNF VBP Program 
Year
    In the FY 2022 SNF PPS final rule (86 FR 42503 through 42505), we 
adopted a quality measure suppression policy for the duration of the 
PHE for COVID-19 that would enable us to suppress the use of the SNFRM 
for purposes of scoring and payment adjustments in the SNF VBP Program 
if we determine that circumstances caused by the PHE for COVID-19 have 
affected the measure and the resulting performance scores 
significantly.
    We also adopted a series of Measure Suppression Factors to guide 
our determination of whether to propose to suppress the SNF readmission 
measure for one or more program years that overlap with the PHE for 
COVID-19. The Measure Suppression Factors that we adopted are:
     Measure Suppression Factor 1: Significant deviation in 
national performance on the measure during the PHE for COVID-19, which 
could be significantly better or significantly worse compared to 
historical performance during the immediately preceding program years.
     Measure Suppression Factor 2: Clinical proximity of the 
measure's focus to the relevant disease, pathogen, or health impacts of 
the PHE for COVID-19.
     Measure Suppression Factor 3: Rapid or unprecedented 
changes in:
    ++ Clinical guidelines, care delivery or practice, treatments, 
drugs, or related protocols, or equipment or diagnostic tools or 
materials; or
    ++ The generally accepted scientific understanding of the nature or 
biological pathway of the disease or pathogen, particularly for a novel 
disease or pathogen of unknown origin.
     Measure Suppression Factor 4: Significant national 
shortages or rapid or unprecedented changes in:
    ++ Healthcare personnel.
    ++ Medical supplies, equipment, or diagnostic tools or materials.
    ++ Patient case volumes or facility-level case mix.
    We refer readers to the FY 2022 SNF PPS final rule (86 FR 42503 
through 42505) for additional details on this policy, including 
summaries of the public comments that we received and our responses.
    Additionally, in the FY 2022 SNF PPS final rule (86 FR 42505 
through 42507), we suppressed the SNFRM for the FY 2022 SNF VBP program 
year under Measure Suppression Factor (4): Significant national 
shortages or rapid or unprecedented changes in healthcare personnel and 
patient case volumes or facility-level case mix. We refer readers to 
that final rule for additional discussion of the analyses we conducted 
of SNFRM performance during the PHE for COVID-19, how the measure's 
reliability changed, how its current risk-adjustment model does not 
factor in COVID-19, and how the PHE affected different regions of the 
country at different times, as well as summaries of the public comments 
that we received on that proposal and our responses.
    The PHE for COVID-19 has had direct, significant, and continuing 
effects on our ability to measure SNFs' performance on the SNFRM. SNFs 
are experiencing a significant downward trend in admissions compared 
with their pre-COVID-19 admission rates. For the FY 2021 program year, 
a total of 1,566,540 SNF admissions were eligible for inclusion in the 
SNFRM (based on FY 2019 data). We have estimated that approximately 
1,069,789 admissions would be eligible for inclusion for the FY 2023 
program year (based on currently available data, which ranged from July 
1, 2020 through June 30, 2021), representing a volume decrease of 
approximately 32 percent. Based on this lower number of eligible SNF 
admissions, we have estimated that only 75.2 percent of SNFs would be 
eligible to be scored on the SNFRM for FY 2021, compared with 82.4 
percent that were eligible to be scored for FY 2019. Given the 
significant decrease in SNF admissions during FY 2021, we are concerned 
that using FY 2021 data to calculate SNFRM rates for the FY 2023 
program year would have significant negative impacts on the measure's 
reliability. Our contractor's analysis using FY 2019 data showed that 
such changes may lead to a 15 percent decrease in the measure 
reliability, assessed by the intra-class correlation coefficient (ICC).
    We also remain concerned that the pandemic's disparate effects on 
different regions of the country throughout the PHE have presented 
challenges to our assessments of performance on the SNFRM. According to 
CDC data,\149\ for example, new COVID-19 cases at the beginning of FY 
2021 (October 1, 2020) were highest in Texas (3,534 cases), California 
(3,062 cases), and Wisconsin (3,000 cases). By April 1, 2021, however, 
new cases were highest in Michigan (6,669 cases), Florida (6,377 
cases), and New Jersey (5,606 cases). This variation in COVID-19 case 
rates throughout the PHE has also been demonstrated in several studies. 
For example, studies have found widespread geographic variation in 
county-level COVID-19 cases across the U.S.150 151 152 
Specifically, one study found that, across US census regions, counties 
in the Midwest had the greatest cumulative rate of COVID-19 cases.\153\ 
Another study found that U.S. counties with more immigrant residents, 
as well as more Central American or Black residents, have more COVID-19 
cases.\154\ These geographic variations in COVID-19 case rates are 
often linked to a wide range of county-level characteristics, including 
sociodemographic and health-related factors.\155\ In addition, these 
studies have found evidence of temporal variation in county-level 
COVID-19 cases. For example, one study found that while many county-
level factors show persistent effects on COVID-19 severity over time, 
some factors have varying effects on COVID-19 severity over time.\156\ 
The significant variation in COVID-19 case rates across the U.S. can 
affect the validity of performance data. Therefore, we do not believe 
it would be fair or equitable to assess SNFs' performance on the 
measure using FY

[[Page 22765]]

2021 data, which has been affected by these variations in COVID-19 case 
rates.
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    \149\ ``United States COVID-19 Cases and Deaths by State,'' 
Centers for Disease Control. Retrieved from https://data.cdc.gov/Case-Surveillance/United-States-COVID-19-Cases-and-Deaths-by-State-o/9mfq-cb36/data on March 22, 2022.
    \150\ Desmet, K., & Wacziarg, R. (2022). JUE Insight: 
Understanding spatial variation in COVID-19 across the United 
States. Journal of Urban Economics, 127, 103332. https://doi.org/10.1016/j.jue.2021.103332.
    \151\ Messner, W., & Payson, SE (2020). Variation in COVID-19 
outbreaks at the US State and county levels. Public Health, 187, 15-
18. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7396895/pdf/main.pdf.
    \152\ Khan, S.S., Krefman, A.E., McCabe, M.E., Petito, L.C., 
Yang, X., Kershaw, K.N., Pool, L.R., & Allen, N.B. (2022). 
Association between county-level risk groups and COVID-19 outcomes 
in the United States: a socioecological study. BMC Public Health, 
22, 81. https://doi.org/10.1186/s12889-021-12469-y.
    \153\ Khan, S.S., Krefman, A.E., McCabe, M.E., Petito, L.C., 
Yang, X., Kershaw, K.N., Pool, L.R., & Allen, N.B. (2022). 
Association between county-level risk groups and COVID-19 outcomes 
in the United States: A socioecological study. BMC Public Health, 
22, 81. https://doi.org/10.1186/s12889-021-12469-y.
    \154\ Strully, K., Yang, T-C., & Lui, H. (2021). Regional 
variation in COVID-19 disparities: connections with immigrant and 
Latinx communities in U.S. counties. Annals of Epidemiology, 53, 56-
62. https://doi.org/10.1016/j.annepidem.2020.08.016.
    \155\ CDC COVID-19 Response Team. (2020). Geographic Differences 
in COVID-19 Cases, Deaths, and Incidence--United States, February 
12-April 7, 2020. MMWR Morbidity and Mortality Weekly Report, 
69(15), 465-471. http://dx.doi.org/10.15585/mmwr.mm6915e4.
    \156\ Desmet, K., & Wacziarg, R. (2022). JUE Insight: 
Understanding spatial variation in COVID-19 across the United 
States. Journal of Urban Economics, 127, 103332. https://doi.org/10.1016/j.jue.2021.103332.
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    Increases in the number of COVID-19 cases are typically followed by 
an increase in the number of COVID-19 related hospitalizations, 
especially among the unvaccinated. Although COVID-19 vaccines began to 
come available in December of 2020, it was only readily available in 
early summer 2021 resulting in less than half of eligible Americans 
being fully vaccinated by the beginning of the fourth quarter of FY 
2021. In addition, the vaccination rates were not evenly distributed 
across the country. Regions with significantly lower vaccination rates 
experienced higher hospitalization and ICU rates making them more prone 
to capacity challenges. Hospital capacity challenges have the potential 
to influence decisions that impact their downstream post-acute 
partners. As a result, for the first three quarters of FY 2021 
performance year, low vaccinated regions' SNFs could have faced care 
coordination challenges with their partnering hospitals that regions 
with high vaccination rates did not experience. The continuation of the 
pandemic into 2021 did not necessarily impact all measures in the post-
acute space, but measures related to hospital care may be impacted 
because of how closely the surge in COVID-19 cases was related to the 
surge in COVID-19 related hospital cases. Unlike other value-based 
purchasing programs that have multiple measures, the SNF VBP Program's 
single-measure requirement, currently the SNFRM, means that suppression 
of the measure will directly impact the payment adjustment.
    The combination of fewer admissions to SNFs, regional differences 
in the prevalence of COVID-19 throughout the PHE and changes in 
hospitalization patterns in FY 2021 has impacted our ability to use the 
SNFRM to calculate payments for the FY 2023 program year.
    Based on the significant and continued decrease in the number of 
patients admitted to SNFs, which likely reflects shifts in utilization 
patterns due to the risk of spreading COVID-19 in SNFs, we are 
proposing to suppress the SNFRM for the FY 2023 SNF VBP program year 
under Measure Suppression Factor (4): Significant national shortages or 
rapid or unprecedented changes in: Healthcare personnel, and Patient 
case volumes or facility-level case mix.
    As with the suppression policy that we adopted for the FY 2022 SNF 
VBP Program, under this proposal for the FY 2023 SNF VBP Program we 
would use the previously finalized performance period (FY 2021) and 
baseline period (FY 2019) to calculate each SNF's RSRR for the SNFRM. 
Then, we would suppress the use of SNF readmission measure data for 
purposes of scoring and payment adjustments. We would assign all 
participating SNFs a performance score of zero in the FY 2023 SNF VBP 
Program Year. This assignment would result in all participating SNFs 
receiving an identical performance score, as well as an identical 
incentive payment multiplier.
    Under this proposed policy, we would reduce each participating 
SNF's adjusted Federal per diem rate for FY 2023 by 2 percentage points 
and award each participating SNF 60 percent of that 2 percent withhold, 
resulting in a 1.2 percent payback for the FY 2023 SNF VBP Program 
Year. We believe this continued application of the 2 percent withhold 
is required under section 1888(h)(5)(C)(ii)(III) of the Act and that a 
payback percentage that is spread evenly across all participating SNFs 
is the most equitable way to reduce the impact of the withhold in light 
of our proposal to award a performance score of zero to all SNFs.
    However, as discussed more fully in section VII.E.3.a. of this 
proposed rule, beginning with the FY 2023 program year, we are 
proposing to remove the low-volume adjustment policy from the SNF VBP 
Program and instead, implement case and measure minimums that SNFs must 
meet in order to be eligible to participate in the SNF VBP for a 
program year.
    Under this proposal, SNFs that do not report a minimum of 25 
eligible stays for the SNFRM for the FY 2023 program year would not be 
included in the SNF VBP for that program year. As a result, the payback 
percentage for FY 2023 would remain at 60.00 percent.
    For the FY 2023 program year, we are also proposing to provide 
quarterly confidential feedback reports to SNFs and to publicly report 
the SNFRM rates for the FY 2023 SNF VBP Program Year. However, we would 
make clear in the public presentation of those data that the measure 
has been suppressed for purposes of scoring and payment adjustments 
because of the effects of the PHE for COVID-19 on the data used to 
calculate the measure. The public presentation would be limited to SNFs 
that reported the minimum number of eligible stays. Finally, we are 
proposing to codify these proposals for the FY 2023 SNF VBP in our 
regulation text at Sec.  413.338(i).
    We continue to be concerned about effects of the COVID-19 PHE, but 
are encouraged by the rollout of COVID-19 vaccinations and treatment 
for those diagnosed with COVID-19 and believe that SNFs are better 
prepared to adapt to this virus. Our measure suppression policy focuses 
on a short-term, equitable approach during this unprecedented PHE, and 
it was not intended for indefinite application. Additionally, we want 
to emphasize the importance of value-based care and incentivizing 
quality care tied to payment. The SNF VBP Program is an example of our 
effort to link payments to healthcare quality in the SNF setting. We 
understand that the COVID-19 PHE is ongoing and unpredictable in 
nature; however, we believe that 2022 presents a more promising outlook 
in the fight against COVID-19. Over the course of the pandemic, 
providers have gained experience managing the disease, surges of COVID-
19 infection, and supply chain fluctuations.\157\ While COVID-19 cases 
among nursing home staff reached a recent peak in January of 2022, 
those case counts dropped significantly by the week ending February 6, 
2022, to 22,206.\158\ COVID-19 vaccinations and boosters have also been 
taken up by a significant majority of nursing home residents, and 
according to CDC, by February 6, 2022, more than 68 percent of 
completely vaccinated nursing home residents had received 
boosters.\159\ Finally, the Biden-Harris Administration has mobilized 
efforts to distribute home test kits,\160\ N-95 masks,\161\ and 
increase COVID-19 testing in schools.\162\ In light of this

[[Page 22766]]

more promising outlook, we intend to resume the use of the SNFRM for 
scoring and payment adjustment purposes beginning with the FY 2024 
program year. That is, for FY 2024, for each SNF, we would calculate 
measure scores in the SNF VBP Program. We would then calculate a SNF 
performance score for each SNF and convert the SNF performance scores 
to value-based incentive payments.
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    \157\ McKinsey and Company. (2021). How COVID-19 is Reshaping 
Supply Chains. Available at https://www.mckinsey.com/business-functions/operations/our-insights/how-covid-19-is-reshaping-supply-chains.
    \158\ ``Nursing Home Covid-19 Data Dashboard.'' Centers for 
Disease Control, retrieved from https://www.cdc.gov/nhsn/covid19/ltc-report-overview.html on February 14, 2022.
    \159\ ``Nursing Home Covid-19 Data Dashboard.'' Centers for 
Disease Control, retrieved from https://www.cdc.gov/nhsn/covid19/ltc-report-overview.html on February 14, 2022.
    \160\ The White House. (2022). Fact Sheet: The Biden 
Administration to Begin Distributing At-Home, Rapid COVID-19 Tests 
to Americans for Free. Available at https://www.whitehouse.gov/briefing-room/statements-releases/2022/01/14/fact-sheet-the-biden-administration-to-begin-distributing-at-home-rapid-covid-19-tests-to-americans-for-free/.
    \161\ Miller, Z. 2021. The Washington Post. Biden to give away 
400 million N95 masks starting next week. Available at https://www.washingtonpost.com/politics/biden-to-give-away-400-million-n95-masks-starting-next-week/2022/01/19/5095c050-7915-11ec-9dce-7313579de434_story.html.
    \162\ The White House. (2022). FACT SHEET: Biden-Harris 
Administration Increases COVID-19 Testing in Schools to Keep 
Students Safe and Schools Open. Available at https://www.whitehouse.gov/briefing-room/statements-releases/2022/01/12/fact-sheet-biden-harris-administration-increases-covid-19-testing-in-schools-to-keep-students-safe-and-schools-open/.
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    We invite public comment on this proposal to suppress the SNFRM for 
the FY 2023 program year and to codify our scoring and payment 
proposals for FY 2023 in our regulation text.
2. Technical Updates to the SNFRM to Risk Adjust for COVID-19 Patients 
Beginning With the FY 2023 Program Year
    The emergence of the COVID-19 PHE, along with the high prevalence 
of COVID-19 in patients admitted to SNFs, has prompted us to examine 
whether we should develop an adjustment to the SNFRM that would 
properly account for COVID-19 patients. As detailed in this section, we 
considered four options that such an adjustment could take. After 
careful examination of each of the four options, we are updating the 
technical specifications of the SNFRM such that COVID-19 patients 
(diagnosed at any time within 12 months prior to or during the prior 
proximal hospitalization [PPH]) will remain in the measure's cohort, 
but we will add a variable to the risk adjustment model that accounts 
for the clinical differences in outcomes for these patients. We believe 
this change is technical in nature and does not substantively change 
the SNFRM.
    In order to determine whether and how to update the SNFRM, we first 
sought to understand the frequency of COVID-19 diagnoses in patients 
admitted to a SNF between July 1, 2020 and June 30, 2021. Of the 
1,069,789 SNF stays included in the year of data, 134,674 (13 percent) 
had a primary or secondary diagnosis of COVID-19. Of those patients 
with COVID-19, 108,859 (81 percent) had a primary or secondary COVID-19 
diagnosis during the PPH and 25,815 (19 percent) had a COVID-19 
diagnosis in their history only (within 12 months of the SNF 
admission).
    We then compared clinical and demographic characteristics between 
patients with and without COVID-19 between July 1, 2020, and June 30, 
2021. When compared to the 30-day readmission rate for patients without 
COVID-19 (20.2 percent), the observed 30-day readmission rate was 
noticeably higher for patients with COVID-19 during the PPH (23.4 
percent) and patients with a history of COVID-19 (26.9 percent). Both 
groups also experienced higher 30-day mortality rates compared to 
patients without COVID-19 (14.9 percent versus 8.8 percent and 10.7 
percent versus 8.8 percent, respectively). Admissions for patients with 
COVID-19 during the PPH or a history of COVID-19 were also much more 
likely to be for patients who were dual-eligible (40.3 percent versus 
28.9 percent and 45.2 percent versus 28.9 percent, respectively) and 
for patients who were non-white (21.1 percent vs. 15.2 percent and 24.4 
percent versus 15.2 percent, respectively).
    Next, we compared readmission odds ratios for patients with COVID-
19 during the PPH and for patients with a history of COVID-19. Patients 
with COVID-19 during the PPH had significantly higher odds of 
readmission (1.18), while patients with a history of COVID-19 but no 
COVID-19 during the PPH had significantly lower odds of readmission 
(0.84), after adjusting for all other variables in the SNFRM risk-
adjustment model.
    Although patients with only a history of COVID-19 had higher 
observed readmission rates than patients with COVID-19 during the PPH 
(26.9 percent versus 23.4 percent), they experienced lower readmission 
odds ratios (0.84 versus 1.18). This is because patients with a history 
of COVID-19 during the 12 months prior to the SNF admission are 
generally much sicker and have a substantially higher number of average 
comorbidities (15) compared to patients with COVID-19 during the PPH 
(10). We expect unadjusted readmission rates for patients with a 
history of COVID-19 to be higher because they are suffering from many 
more comorbidities, making it more likely they will be readmitted to 
the hospital. After adjusting for all their other comorbidities, we 
concluded that COVID-19 is not a significant reason for why they return 
to the hospital. Instead, their other comorbidities are a more 
significant cause of their readmission; that is, patients with a 
history of COVID-19 but no COVID-19 during the PPH have lower odds of 
being readmitted to a hospital once they've been admitted to the SNF. 
However, we believed it was important to keep the history of COVID-19 
variable in the model for two reasons: (1) To address any potential 
concerns with the face validity of the measure if it did not adjust for 
history of COVID-19; and (2) to account for long COVID and other 
possible long-term effects of the virus. On the other hand, patients 
with a COVID-19 diagnosis during the PPH remain at higher odds of 
readmission even after accounting for their other comorbidities. Even 
when all other comorbidities are taken into account in the current risk 
adjustment model, a COVID-19 diagnosis during the PPH still raises a 
patient's odds of being readmitted compared to patients who did not 
have any COVID-19 diagnosis during the PPH.
    After having examined the prevalence of COVID-19 in SNF patients 
and the differences between patients with and without COVID-19, we then 
evaluated several options for how to account for COVID-19 in the 
measure. We evaluated four options.
     Under Option 1, we considered and tested whether to add a 
binary risk-adjustment variable for patients who had a primary or 
secondary diagnosis of COVID-19 during the PPH.
     Under Option 2, we considered and tested whether to add a 
binary risk-adjustment variable for patients who had a history of 
COVID-19 in the 12 months prior to the PPH.
     Under Option 3, we combined the first 2 options into a 
categorical risk-adjustment variable. The reference category is 
patients without a history of COVID-19 and no COVID-19 diagnosis during 
the PPH. The first comparison category is patients who had a history of 
COVID-19 in the 12 months prior to the PPH and no COVID-19 diagnosis 
during the PPH. The second comparison category is patients who had a 
primary or secondary diagnosis of COVID-19 during the PPH. If a patient 
had both a history of COVID-19 and a COVID-19 diagnosis during the PPH, 
they would be included in the second comparison category.
     Under Option 4, we considered and tested removing patients 
with a COVID-19 diagnosis during the PPH from the measure cohort.
    We compared how well the model predicted whether patients were 
readmitted or not (model fit and performance) for these four options to 
a reference period (FY 2019) that predated COVID-19. Ideally, whichever 
option we chose would perform as similarly as possible to the reference 
period, providing us with confidence that the emergence of COVID-19 has 
not caused the model to perform worse.
    The percentage of SNFs that would receive a measure score (75 
percent), measure reliability (0.45), and C-statistic (0.66) was 
identical for the first 3 risk-adjustment options. The percentage of 
SNFs with a measure score, measure reliability score, and C-statistic 
values was 71 percent, 0.41, and 0.67 for Option 4 (excluding COVID-19 
patients), respectively. The percentage

[[Page 22767]]

of SNFs with a measure score was lower for the first 3 options than the 
baseline period (75 percent versus 82 percent), but the measure 
reliability was nearly identical (0.45 versus 0.46), as was the C-
statistic (0.66 versus 0.68).
    We also considered removing readmissions from the outcome for 
patients with a primary or secondary diagnosis of COVID-19 during the 
readmission hospital stay, but decided it would not be appropriate for 
this measure. Community spread of COVID-19 in SNFs is a possible marker 
of poor infection control and patients who are admitted to a SNF 
without any COVID-19 diagnoses but then potentially acquire COVID-19 in 
a SNF should not be excluded from the readmission outcome.
    After careful examination, we are selecting Option 3 and modifying 
the SNFRM beginning with the FY 2023 SNF VBP program year by adding a 
risk-adjustment variable for both COVID-19 during the PPH and patients 
with a history of COVID-19. This option both maintains the integrity of 
the model (as demonstrated by nearly identical measure reliability and 
C-statistic values) and allows the measure to appropriately adjust for 
SNF patients with COVID-19. We believe this approach will continue to 
maintain the validity and reliability of the SNFRM. This approach will 
retain COVID-19 patients in the measure cohort and prevent a further 
decrease in the sample size, which would harm the measure's 
reliability.
    As discussed further in section VII.B.2.c. of this proposed rule, 
though we believe risk-adjusting the SNFRM for COVID-19 is an important 
step in maintaining the validity and reliability of the SNFRM, this 
risk-adjustment alone is not sufficient for ensuring a reliable SNF 
performance score in light of the overall decrease in SNF admissions in 
FY 2021. That is, the risk-adjustment is designed to maintain the 
scientific reliability of the measure, but it does not mitigate the 
effects of the PHE on patient case volumes and the resulting impact on 
the validity of the SNFRM.
3. Quality Measure Proposals 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 Consolidated Appropriations Act, 2021 (Pub. L. 116-
120)) allows the Secretary to add up to nine new measures to the SNF 
VBP Program 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.
    Currently, the SNF VBP Program includes only a single quality 
measure, the SNFRM, which we intend to transition to the SNFPPR as soon 
as practicable. Both the SNFRM and the SNFPPR assess the rate of 
hospital readmissions. In considering which measures might be 
appropriate to add to the SNF VBP Program, we requested public comment 
on potential future measures to include in the expanded SNF VBP Program 
in the FY 2022 SNF PPS proposed rule (86 FR 20009 through 20011). We 
refer readers to summaries of stakeholder input in the FY 2022 SNF PPS 
final rule (86 FR 42507 through 42511). We considered this input as we 
developed our quality measure proposals for this proposed rule.
    Based on the input we received, and for reasons discussed in 
sections VII.B.3.b. and VII.B.3.c. of this proposed rule, we are 
proposing to adopt two new quality measures for the SNF VBP Program 
beginning with the FY 2026 program year: (1) Skilled Nursing Facility 
(SNF) Healthcare Associated Infections (HAI) Requiring Hospitalization 
(SNF HAI) measure; and (2) Total Nursing Hours per Resident Day 
Staffing (Total Nurse Staffing) measure. We are also proposing to adopt 
an additional quality measure for the SNF VBP Program beginning with 
the FY 2027 program year: Discharge to Community (DTC)--Post-Acute Care 
(PAC) Measure for Skilled Nursing Facilities (NQF #3481), which we 
discuss in section VII.B.3.d. of this proposed rule.
    We note that although none of these quality measures have been 
specified under section 1899B(c)(1) of the Act, we determined after 
consideration of those measures that none are appropriate for adoption 
into the SNF VBP Program until, at a minimum, we have had sufficient 
time to review their specifications and conduct further analyses to 
ensure that they are suited for meeting the objectives of the SNF VBP 
Program. We are currently reviewing measures of patient falls and 
functional status, which are both specified under section 1899B(c)(1) 
of the Act, to determine whether any of them would be appropriate for 
the SNF VBP Program. We also believe it is important to cover the full 
range of SNF services in the SNF VBP Program, which includes measure 
topics beyond those specified under section 1899B(c)(1) of the Act. 
Since we have determined that the measures specified under section 
1899B(c)(1) of the Act are not yet appropriate for the SNF VBP Program, 
we are proposing to begin the Program expansion with measures that 
address other important indicators of SNF care quality, including 
measures that align with the topics listed under section 
1888(h)(2)(A)(ii) of the Act and align with HHS priorities.
    The proposed SNF HAI measure is a patient safety measure, and the 
proposed DTC PAC SNF measure is a care coordination measure. With 
regard to the proposed Total Nurse Staffing measure, many studies have 
found that the level of nurse staffing is associated with patient 
safety,\163\ patient functional status,164 165 and patient 
experience.166 167 Nursing home staffing, including SNF 
staffing, is also a high priority for the Department of Health and 
Human Services (HHS) and the Biden-Harris Administration because of its 
central role in the quality of care for Medicare beneficiaries.\168\
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    \163\ Horn SD, Buerhaus P, Bergstrom N, et al. RN staffing time 
and outcomes of long-stay nursing home residents: Pressure ulcers 
and other adverse outcomes are less likely as RNs spend more time on 
direct patient care. Am J Nurs 2005 6:50-53. https://pubmed.ncbi.nlm.nih.gov/16264305/.
    \164\ 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.
    \165\ 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/.
    \166\ https://www.wolterskluwer.com/en/expert-insights/study-patient-satisfaction-grows-with-nurse-staffing.
    \167\ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8522577/.
    \168\ 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/.
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    We believe that adopting these measures to begin affecting SNF 
payments in the FY 2026 program year would provide SNFs with sufficient 
time to prepare and become familiar with the quality measures, as well 
as with the numerous other programmatic changes that would take effect 
in the FY 2023 program year, if our proposals in this proposed rule are 
finalized.
    As we discuss in section VII.H.1 of this proposed rule, we are also 
considering and requesting public comment on additional quality 
measures for potential adoption in the SNF VBP through future 
rulemaking.

[[Page 22768]]

    We propose to update our regulations at Sec.  413.338(d)(5) to note 
that, for a given fiscal year, CMS will specify the measures for the 
SNF VBP Program.
b. Proposal To Adopt the Skilled Nursing Facility Healthcare-Associated 
Infections (HAI) Requiring Hospitalization Measure Beginning With the 
FY 2026 SNF VBP Program Year
    As part of the SNF VBP Program expansion authorized under the CAA, 
we are proposing to adopt the SNF HAI measure for the FY 2026 SNF VBP 
Program and subsequent years. The SNF HAI measure is an outcome measure 
that estimates the risk-standardized rate of HAIs that are acquired 
during SNF care and result in hospitalization using 1 year of Medicare 
fee-for-service (FFS) claims data. The proposed SNF HAI measure 
assesses SNF performance on infection prevention and management, which 
would align the Program with the Patient Safety domain of CMS's 
Meaningful Measures 2.0 Framework. In addition, the SNF HAI measure is 
currently part of the SNF Quality Reporting Program (QRP) measure set. 
For more information on this measure in the SNF QRP, please visit 
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 refer readers to the SNF HAI Measure Technical 
Report for the measure specifications, which we are proposing to adopt 
as the SNF HAI measure specifications for the SNF VBP Program.
(1) Background
    Healthcare-associated infections (HAIs) are defined as infections 
acquired while receiving care at a health care facility that were not 
present or incubating at the time of admission.\169\ HAIs are a 
particular concern in the SNF setting, and thus, monitoring the 
occurrence of HAIs among SNF residents can provide valuable information 
about a SNF's quality of care. A 2014 report from the Office of the 
Inspector General (OIG) estimated that one in four adverse events among 
SNF residents is due to HAIs, and approximately half of all HAIs are 
potentially preventable.\170\ In addition, analyses from FY 2019 found 
a wide variation in facility-level HAI rates among SNF providers with 
25 or more stays, which indicates a performance gap. Specifically, 
among the 14,102 SNFs included in the sample, the FY 2019 facility-
level, risk-adjusted rate of SNF HAIs requiring hospitalization ranged 
from 2.36 percent to 17.62 percent.\171\
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    \169\ World Health Organization. (2010). The burden of health 
care-associated infections worldwide. Retrieved from https://www.who.int/news-room/feature-stories/detail/the-burden-of-health-care-associated-infection-worldwide.
    \170\ Office of Inspector General. (2014). Adverse events in 
skilled nursing facilities: National incidence among Medicare 
beneficiaries. Retrieved from https://oig.hhs.gov/oei/reports/oei-06-11-00370.pdf.
    \171\ https://www.cms.gov/files/document/snf-hai-technical-report.pdf.
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    While HAIs are not considered ``never events,'' or serious adverse 
errors in the provision of health care services that should never 
occur, most are preventable.\172\ HAIs are most often the result of 
poor processes and structures of care. Specifically, evidence suggests 
that inadequate patient management following a medical intervention, 
such as surgery or device implantation, and poor adherence to infection 
control protocols and antibiotic stewardship guidelines contribute to 
the occurrence of HAIs.173 174 175 In addition, several 
provider characteristics relate to the occurrence of HAIs, including 
staffing levels (for example, low staff-to-resident ratios), facility 
structure characteristics (for example, high occupancy rates), and 
adoption, or lack thereof, of infection surveillance and prevention 
policies.176 177 178 179 180 181
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    \172\ CMS. (2006). Eliminating Serious Preventable, and Costly 
Medical Errors--Never Events. Retrieved from https://www.cms.gov/newsroom/fact-sheets/eliminating-serious-preventable-and-costly-medical-errors-never-events.
    \173\ Beganovic, M. and Laplante, K. (2018). Communicating with 
Facility Leadership; Metrics for Successful Antimicrobial 
Stewardship Programs (ASP) in Acute Care and Long-Term Care 
Facilities. Rhode Island Medical Journal, 101(5), 45-49. http://www.rimed.org/rimedicaljournal/2018/06/2018-06-45-antimicrobial-beganovic.pdf.
    \174\ Cooper, D., McFarland, M., Petrilli, F., & Shells, C. 
(2019). Reducing Inappropriate Antibiotics for Urinary Tract 
Infections in LongTerm Care: A Replication Stud-y. Journal of 
Nursing Care Quality, 34(1), 1621. https://doi.org/10.1097/NCQ.0000000000000343.
    \175\ Feldstein, D., Sloane, P.D., & Feltner, C. (2018). 
Antibiotic stewardship programs in nursing homes: A systematic 
review. Journal of the American Medical Directors Association, 
19(2), 110-116. http://dx.doi.org/10.1016/j.jamda.2017.06.019.
    \176\ Castle, N., Engberg, J.B., Wagner, L.M., & Handler, S. 
(2017). Resident and facility factors associated with the incidence 
of urinary tract infections identified in the Nursing Home Minimum 
Data Set. Journal of Applied Gerontology, 36(2), 173-194. http://dx.doi.org/10.1177/0733464815584666.
    \177\ Crnich, C.J., Jump, R., Trautner, B., Sloane, P.D., & 
Mody, L. (2015). Optimizing antibiotic stewardship in nursing homes: 
A narrative review and recommendations for improvement. Drugs & 
Aging, 32(9), 699-716. http://dx.doi.org/10.1007/s40266-015-0292-7.
    \178\ Dick, A.W., Bell, J.M., Stone, N.D., Chastain, A.M., 
Sorbero, M., & Stone, P.W. (2019). Nursing home adoption of the 
National Healthcare Safety Network Long-term Care Facility 
Component. American Journal of Infection Control, 47(1), 59-64. 
http://dx.doi.org/10.1016/j.ajic.2018.06.018.
    \179\ Cooper, D., McFarland, M., Petrilli, F., & Shells, C. 
(2019). Reducing inappropriate antibiotics for urinary tract 
infections in long-term care: A replication study. Journal of 
Nursing Care Quality, 34(1), 16-21. http://dx.doi.org/10.1097/NCQ.0000000000000343.
    \180\ Gucwa, A.L., Dolar, V., Ye, C., & Epstein, S. (2016). 
Correlations between quality ratings of skilled nursing facilities 
and multidrug-resistant urinary tract infections. American Journal 
of Infection Control, 44(11), 1256-1260. http://dx.doi.org/10.1016/j.ajic.2016.03.015.
    \181\ Travers, J.L., Stone, P.W., Bjarnadottir, R.I., 
Pogorzelska-Maziarz, M., Castle, N.G., & Herzig, C.T. (2016). 
Factors associated with resident influenza vaccination in a national 
sample of nursing homes. American Journal of Infection Control, 
44(9), 1055-1057. http://dx.doi.org/10.1016/j.ajic.2016.01.019.
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    Inadequate prevention and treatment of HAIs is likely to result in 
poor health care outcomes for SNF residents, as well as wasteful 
resource use. Specifically, studies find that HAIs are associated with 
longer lengths of stay, use of higher-intensity care (for example, 
critical care services and hospital readmissions), increased mortality, 
and higher health care costs.182 183 184 185 Addressing HAIs 
in SNFs is particularly important as several factors place SNF 
residents at increased risk for infections, including increased age, 
cognitive and functional decline, use of indwelling devices, frequent 
care transitions, and close contact with other residents and healthcare 
workers.186 187 Further, infection prevention and control

[[Page 22769]]

deficiencies are consistently among the most frequently cited 
deficiencies in surveys conducted to assess SNF compliance with Federal 
quality standards.\188\ Infection prevention and control deficiencies 
can include practices directly related to the occurrence and risks of 
HAIs, such as inconsistent use of hand hygiene practices or improper 
use of protective equipment or procedures during an infectious disease 
outbreak, which further underscores the importance of efforts to 
improve practices to reduce the prevalence of HAIs.
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    \182\ CMS. (2006). Eliminating Serious Preventable, and Costly 
Medical Errors--Never Events. Retrieved from https://www.cms.gov/newsroom/fact-sheets/eliminating-serious-preventable-and-costly-medical-errors-never-events.
    \183\ Centers for Disease Control and Prevention (2009). The 
Direct Medical Costs of Healthcare Associated Infections in U.S. 
Hospitals and the Benefits of Prevention. Retrieved from https://www.cdc.gov/hai/pdfs/hai/scott_costpaper.pdf.
    \184\ Ouslander, J.G., Diaz, S., Hain, D., & Tappen, R. (2011). 
Frequency and diagnoses associated with 7- and 30-day readmission of 
skilled nursing facility patients to a nonteaching community 
hospital. Journal of the American Medical Directors Association, 
12(3), 195-203. http://dx.doi.org/10.1016/j.jamda.2010.02.015.
    \185\ Zimlichman, E., Henderson, D., Tamir, O., Franz, C., Song, 
P., Yamin, C.K., Keohane, C., Denham, C.R., & Bates, D.W. (2013). 
Health Care-Associated Infections: A Meta-analysis of Costs and 
Financial Impact on the US Health Care System. JAMA Internal 
Medicine, 173(22), 2039-2046. https://doi.org/10.1001/jamainternmed.2013.9763.
    \186\ Montoya, A., & Mody, L. (2011). Common infections in 
nursing homes: A review of current issues and challenges. Aging 
Health, 7(6), 889-899. http://dx.doi.org/10.2217/ahe.11.80.
    \187\ U.S. Department of Health and Human Services, Office of 
Disease Prevention and Health Promotion. (2013). Chapter 8: Long-
Term Care Facilities (p. 194-239) in National Action Plan to Prevent 
Health Care-Associated Infections: Road Map to Elimination. 
Retrieved from https://health.gov/sites/default/files/2019-09/hai-action-plan-ltcf.pdf.
    \188\ Infection Control Deficiencies Were Widespread and 
Persistent in Nursing Homes Prior to COVID-19 Pandemic (GAO-20-
576R), May, 2020. https://www.gao.gov/products/gao-20-576r.
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    Given the effects of HAIs, preventing and reducing their occurrence 
in SNFs is critical to delivering safe and high-quality care. We 
believe the proposed SNF HAI measure aligns with this goal by 
monitoring the occurrence of HAIs and assessing SNFs on their 
performance on infection prevention and control efforts. In doing so, 
we believe the proposed measure would promote patient safety and 
increase the transparency of care quality in the SNF setting, which 
would align the SNF VBP Program with the Patient Safety domain of CMS's 
Meaningful Measures 2.0 Framework. Prevention and reduction of HAIs has 
also been a priority at Federal, State, and local levels. For example, 
the HHS Office of Disease Prevention and Health Promotion has created a 
National Action Plan to Prevent HAIs, with specific attention to HAIs 
in long-term care facilities. We refer readers to additional 
information on the National Action Plan available at https://www.hhs.gov/oidp/topics/healthcareassociatedinfections/haiactionplan/index.html.
    Evidence suggests there are several interventions that SNFs may 
utilize to effectively reduce HAI rates among their residents and thus, 
improve quality of care. These interventions include adoption of 
infection surveillance and prevention policies, safety procedures, 
antibiotic stewardship, and staff education and training 
programs.189 190 191 192 193 194 195 In addition, infection 
prevention and control programs with core components in education, 
monitoring, and feedback have been found to be successful in reducing 
HAI rates.\196\ The effectiveness of these interventions suggest 
improvement of HAI 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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    \189\ Office of Inspector General. (2014). Adverse events in 
skilled nursing facilities: National incidence among Medicare 
beneficiaries. Retrieved from https://oig.hhs.gov/oei/reports/oei-06-11-00370.pdf.
    \190\ Beganovic, M. and Laplante, K. (2018). Communicating with 
Facility Leadership; Metrics for Successful Antimicrobial 
Stewardship Programs (ASP) in Acute Care and Long-Term Care 
Facilities. Rhode Island Medical Journal, 101(5), 45-49. http://www.rimed.org/rimedicaljournal/2018/06/2018-06-45-antimicrobial-beganovic.pdf.
    \191\ Crnich, C.J., Jump, R., Trautner, B., Sloane, P.D., & 
Mody, L. (2015). Optimizing antibiotic stewardship in nursing homes: 
A narrative review and recommendations for improvement. Drugs & 
Aging, 32(9), 699-716. http://dx.doi.org/10.1007/s40266-015-0292-7.
    \192\ Freeman-Jobson, J.H., Rogers, J.L., & Ward-Smith, P. 
(2016). Effect of an Education Presentation On the Knowledge and 
Awareness of Urinary Tract Infection among Non-Licensed and Licensed 
Health Care Workers in Long-Term Care Facilities. Urologic Nursing, 
36(2), 67-71. Retrieved from https://pubmed.ncbi.nlm.nih.gov/27281862/.
    \193\ Hutton, D.W., Krein, S.L., Saint, S., Graves, N., Kolli, 
A., Lynem, R., & Mody, L. (2018). Economic Evaluation of a Catheter-
Associated Urinary Tract Infection Prevention Program in Nursing 
Homes. Journal of the American Geriatrics Society, 66(4), 742-747. 
http://dx.doi.org/10.1111/jgs.15316.
    \194\ Nguyen, H.Q., Tunney, M.M., & Hughes, C.M. (2019). 
Interventions to Improve Antimicrobial Stewardship for Older People 
in Care Homes: A Systematic Review. Drugs & aging, 36(4), 355-369. 
https://doi.org/10.1007/s40266-019-00637-0.
    \195\ Sloane, P.D., Zimmerman, S., Ward, K., Kistler, C.E., 
Paone, D., Weber, D.J., Wretman, C.J., & Preisser, J.S. (2020). A 2-
Year Pragmatic Trial of Antibiotic Stewardship in 27 Community 
Nursing Homes. Journal of the American Geriatrics Society, 68(1), 
46-54. https://doi.org/10.1111/jgs.16059.
    \196\ Lee, M.H., Lee GA, Lee S.H., & Park Y.H. (2019). 
Effectiveness and core components of infection prevention and 
control programs in long-term care facilities: A systematic review. 
https://www.journalofhospitalinfection.com/action/showPdf?pii=S0195-6701%2819%2930091-X.
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(2) Overview of Measure
    The proposed SNF HAI measure, which was finalized for adoption in 
the SNF QRP in the FY 2022 SNF PPS final rule (86 FR 42473 through 
42480), is an outcome measure that estimates the risk-standardized rate 
of HAIs that are acquired during SNF care and result in hospitalization 
using 1 year of Medicare FFS claims data. A HAI is defined, for the 
purposes of this measure, as an infection that is likely to be acquired 
during SNF care and severe enough to require hospitalization, or an 
infection related to invasive (not implanted) medical devices (for 
example, catheters, insulin pumps, and central lines). Several types of 
infections are excluded from the measure. We discuss those exclusions 
in detail in section VII.B.2.b.(5) of this proposed rule. In addition, 
all SNF stays with an admission date during the 1-year period are 
included in the measure cohort, except those meeting the exclusion 
criteria, which we also discuss in section VII.B.2.b.(5) of this 
proposed rule.
    Unlike other HAI measures that target specific infections, this 
proposed measure targets all HAIs serious enough to require admission 
to an acute care hospital.
    Validity and reliability testing has been conducted for this 
proposed measure. For example, split-half testing on the SNF HAI 
measure indicated moderate reliability. In addition, validity testing 
showed good model discrimination as the HAI model can accurately 
predict HAI cases while controlling for differences in resident case-
mix. We refer readers to the SNF HAI Measure Technical Report for 
further details on the measure testing results available at https://www.cms.gov/files/document/snf-hai-technical-report.pdf.
(a) Measure Applications Partnership (MAP) Review
    The SNF HAI measure was included as a SNF VBP measure under 
consideration in the publicly available ``List of Measures Under 
Consideration for December 1, 2021.'' \197\
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    \197\ https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf.
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    The MAP offered conditional support of the SNF HAI measure for 
rulemaking, contingent upon NQF endorsement, noting that the measure 
would add value to the Program due to the addition of an overall 
measurement of all HAIs acquired within SNFs requiring hospitalization. 
We refer readers to the final 2021-2022 MAP report available at https://www.qualityforum.org/Publications/2022/03/MAP_2021-2022_Considerations_for_Implementing_Measures_Final_Report_-_Clinicians,_Hospitals,_and_PAC-LTC.aspx. We intend to submit the SNF 
HAI measure for NQF endorsement, consistent with the MAP 
recommendation.
(3) Data Sources
    The proposed SNF HAI measure uses Medicare FFS claims data to 
estimate the risk-adjusted rate of HAIs that are acquired during SNF 
care and result in hospitalization. Specifically, this measure uses 
data from the Medicare Enrollment Database (EDB), as well as Medicare 
SNF and inpatient hospital claims from the CMS Common Working File 
(CWF). HAIs are identified using the principal diagnosis code and the 
Present on Admission (POA) indicators on the Medicare inpatient 
rehospitalization claim within a specified incubation window. We refer 
readers to the SNF HAI Measure Technical Report for further details on 
how these data components are utilized in calculating the SNF HAI 
measure

[[Page 22770]]

available at https://www.cms.gov/files/document/snfhaitechnicalreport.pdf. We note that the proposed SNF HAI measure is 
calculated entirely using administrative data and therefore, it would 
not impose any additional data collection or submission burden for SNF 
providers.
(4) Inclusion and Exclusion Criteria
    The measure's cohort includes all Part A FFS Medicare SNF residents 
18 years and older who have a SNF admission date during the 1-year 
measure period and who do not meet any of the exclusion criteria, which 
we describe next. Additionally, the hospital admission must occur 
during the time period which begins on day 4 after SNF admission and 
ends 3 days after SNF discharge. We note that residents who died during 
the SNF stay or during the post-discharge window (3 days after SNF 
discharge), and residents with a missing discharge date (or have 
``active'' SNF stays) are included in the measure's cohort.
    There are several scenarios in which a SNF stay is excluded from 
the measure cohort and thus, excluded from the measure denominator. 
Specifically, any SNF stay that meets one or more of the following 
criteria is excluded from the cohort and measure denominator:
     Resident is less than 18 years old at SNF admission.
     The SNF length of stay was shorter than 4 days.
     Residents who were not continuously enrolled in Part A FFS 
Medicare during the SNF stay, 12 months prior to the measure period, 
and 3 days after the end of the SNF stay.
     Residents who did not have a Part A short-term acute care 
hospital stay within 30 days prior to the SNF admission date. The 
short-term stay must have positive payment and positive length of stay.
     Residents who were transferred to a Federal hospital from 
a SNF as determined by the discharge status code on the SNF claim.
     Residents who received care from a provider located 
outside the U.S., Puerto Rico, or another U.S. territory as determined 
from the first two characters of the SNF CMS Certification Number.
     SNF stays in which data were missing on any variable used 
in the measure calculation or risk adjustment. This also included stays 
where Medicare did not pay for the stay, which is identified by non-
positive payment on the SNF claim.
    The measure numerator includes several HAI conditions. We refer 
readers to Appendix A of the SNF HAI Measure Technical Report, 
available at https://www.cms.gov/files/document/snf-hai-technical-report.pdf, for a complete list of the ICD-10 codes that correspond to 
the HAI conditions included in the measure numerator. There are also 
several types of HAIs that are excluded from the proposed measure 
numerator. For example, HAIs reported during emergency department 
visits and observations stays are excluded from the numerator. In 
addition, the HAI definition excludes infections that meet any of the 
following criteria:
     Chronic infections (for example, chronic viral hepatitis 
B).
     Infections that typically require a long period of time to 
present (for example, typhoid arthritis).
     Infections that are likely related to the prior hospital 
stay (for example, postprocedural retroperitoneal abscess).
     Sequela (a condition which is the consequence of a 
previous disease or injury) and subsequent encounter codes.
     Codes that include ``cause disease classified elsewhere.''
     Codes likely to represent secondary infection, where the 
primary infection would likely already be coded (for example, 
pericarditis, myocarditis, or cardiomyopathy).
     Infections likely to be community acquired.
     Infections common in other countries and/or acquired 
through animal contact.
     Preexisting infections that fall within the CDC's National 
Healthcare Safety Network (NHSN) Repeat Infection Timeframe (RIT) of 14 
days. We refer readers to the SNF HAI Measure Technical Report for 
additional information on the repeat infection timeframe (RIT) and 
conditions that are considered preexisting (https://www.cms.gov/files/document/snf-hai-technical-report.pdf).
(5) Risk Adjustment
    Risk adjustment is a statistical process used to account for risk 
factor differences across SNF residents. By controlling for these 
differences in resident case-mix, we can better isolate the proposed 
measure's outcome and its relationship to the quality of care delivered 
by SNFs. For the proposed SNF HAI measure, the measure's numerator and 
denominator are both risk-adjusted. Specifically, the denominator is 
risk-adjusted for resident characteristics excluding the SNF effect. 
The numerator is risk-adjusted for resident characteristics, as well as 
a statistical estimate of the SNF effect beyond resident case -mix. The 
SNF effect, or the provider-specific behaviors that influence a SNF's 
HAI rates, accounts for clustering of patients within the same SNF and 
captures variation in the measure outcome across SNFs, which helps 
isolate differences in measure performance. The risk adjustment model 
for this proposed measure includes the following resident 
characteristic variables:
     Age and sex category.
     Original reason for Medicare entitlement.
     Surgery or procedure category from the prior proximal 
inpatient (IP) stay.
     Dialysis treatment, but not end-stage renal disease (ESRD) 
on the prior proximal IP claim.
     Principal diagnosis on the prior proximal IP hospital 
claim.
     Hierarchical Condition Categories (HCC) comorbidities.
     Length of stay of the prior proximal IP stay.
     Prior intensive care or coronary care utilization during 
the prior proximal IP stay.
     The number of prior IP stays within a 1-year lookback 
period from SNF admission.
(6) Measure Calculation
(a) Numerator
    The risk-adjusted numerator is the estimated number of SNF stays 
predicted to have a HAI that is acquired during SNF care and results in 
hospitalization. This estimate begins with the unadjusted, observed 
count of the measure outcome, or the raw number of stays with a HAI 
acquired during SNF care and resulting in hospitalization. The 
unadjusted, observed count of the measure outcome is then risk-adjusted 
for resident characteristics and a statistical estimate of the SNF 
effect beyond resident case -mix, which we discussed in section 
VII.B.3.b.(5) of this proposed rule.
(b) Denominator
    The risk-adjusted denominator is the expected number of SNF stays 
with the measure outcome, which represents the predicted number of SNF 
stays with the measure outcome if the same SNF residents were treated 
at an ``average'' SNF. The calculation of the risk-adjusted denominator 
begins with the total eligible Medicare Part A FFS SNF stays during the 
measurement period and then applying risk adjustment for resident 
characteristics, excluding the SNF effect, as we discussed in section 
VII.B.3.b.(5) of this proposed rule.
    The SNF HAI measure rate, which is reported at the facility-level, 
is the risk-standardized rate of HAIs that are acquired during SNF care 
and result in hospitalization. This risk-adjusted HAI rate is 
calculated by multiplying the

[[Page 22771]]

standardized risk ratio (SRR) for a given SNF by the national average 
observed rate of HAIs for all SNFs. The SRR is a ratio that measures 
excess HAIs and is the predicted number of HAIs (adjusted numerator) 
divided by the expected number of HAIs (adjusted denominator). A lower 
measure score for the SNF HAI measure indicates better performance in 
prevention and management of HAIs. For technical information on the 
proposed measure's calculation, we refer readers to the SNF HAI Measure 
Technical Report available at https://www.cms.gov/files/document/snf-hai-technical-report.pdf.
    Because a ``lower is better'' rate could cause confusion among SNFs 
and the public, we propose to invert SNF HAI measure rates, similar to 
the approach used for the SNFRM, for scoring. Specifically, we propose 
to invert SNF HAI measure rates using the following calculation:

SNF HAI Inverted Rate = 1-Facility's SNF HAI rate.

    This calculation would invert SNFs' HAI measure rates such that 
higher SNF HAI measure rates would reflect better performance. We 
believe this inversion is important to incentivize improvement in a 
clear and understandable manner, so that ``higher is better'' for all 
measure rates included in the Program.
(7) Proposed Confidential Feedback Reports and Public Reporting
    We refer readers to the FY 2017 SNF PPS final rule (81 FR 52006 
through 52007) for discussion of our policy to provide quarterly 
confidential feedback reports to SNFs on their measure performance. We 
also refer readers to the FY 2022 SNF PPS final rule (86 FR 42516 
through 42517) for a summary of our two-phase review and corrections 
policy for SNFs' quality measure data. Furthermore, we refer readers to 
the FY 2018 SNF PPS final rule (82 FR 36622 through 36623) and the FY 
2021 SNF PPS final rule (85 FR 47626) where we finalized our policy to 
publicly report SNF measure performance information under the SNF VBP 
Program on the Provider Data Catalog website currently hosted by HHS 
and available at https://data.cms.gov/provider-data/. We are proposing 
to update and redesignate the confidential feedback report and public 
reporting policies, which are currently codified at Sec.  413.338(e)(1) 
through (3), to Sec.  413.338(f), to include the SNF HAI measure.
    We invite public comment on our proposal to adopt the SNF HAI 
measure beginning with the FY 2026 SNF VBP program year.
c. Proposal To Adopt the Total Nursing Hours per Resident Day Staffing 
Measure Beginning With the FY 2026 SNF VBP Program Year
    We are proposing to adopt the Total Nursing Hours per Resident Day 
Staffing (Total Nurse Staffing) measure for the FY 2026 program year 
and subsequent years. The Total Nurse Staffing measure is a structural 
measure that uses auditable electronic data reported to CMS's Payroll 
Based Journal (PBJ) system to calculate total nursing hours per 
resident day. Given the well-documented impact of nurse staffing on 
patient outcomes and quality of care, this proposed measure would align 
the Program with the Person-Centered Care domain of CMS's Meaningful 
Measures 2.0 Framework. In addition, the Total Nurse Staffing measure 
is currently included in the Five -Star Quality Rating System. For more 
information on the Five -Star Quality Rating System, see https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/CertificationandComplianc/FSQRS.
(1) Background
    Staffing is a crucial component of quality care for nursing home 
residents. Numerous studies have explored the relationship between 
nursing home staffing levels and quality of care. The findings and 
methods of these studies have varied, but most have found a strong, 
positive relationship between staffing and quality 
outcomes.198 199 200 201 202 Specifically, studies have 
shown an association between nurse staffing levels and 
hospitalizations,203 204 pressure 
ulcers,205 206 207 weight loss,208 209 functional 
status,210 211 and survey deficiencies,212 213 
among other quality and clinical outcomes. The strongest relationships 
have been identified for registered nurse (RN) staffing; several 
studies have found that higher RN staffing is associated with better 
care quality.214 215 We recognize that the relationship 
between nurse staffing and quality of care is multi-faceted, with 
elements such as staff turnover playing a critical role.\216\ We refer 
readers to additional discussion of staffing turnover in section 
VII.B.3. of this proposed rule.
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    \198\ 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/.
    \199\ Backhaus R, Verbeek H, van Rossum E, Capezuti E, Hamer 
JPH. Nursing staffing impact on quality of care innursing homes: A 
systemic review of longitudinal studies. J Am Med Dir Assoc. 
2014;15(6):383-393. https://pubmed.ncbi.nlm.nih.gov/24529872/.
    \200\ Spilsbury K, Hewitt C, Stirk L, Bowman C. The relationship 
between nurse staffing and quality of care innursing homes: A 
systematic review. Int J Nurs Stud. 2011; 48(6):732-750. https://pubmed.ncbi.nlm.nih.gov/21397229/.
    \201\ 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.
    \202\ Spilsbury et al.
    \203\ 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.
    \204\ Dorr DA, Horn SD, Smout RJ. Cost analysis of nursing home 
registered nurse staffing times. J Am Geriatr Soc. 2005 
May;53(5):840-5. doi: 10.1111/j.1532-5415.2005.53267.x. PMID: 
15877561. https://pubmed.ncbi.nlm.nih.gov/15877561/.
    \205\ Alexander, G.L. An analysis of nursing home quality 
measures and staffing. Qual Manag Health Care. 2008;17:242-251. 
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3006165/.
    \206\ Horn SD, Buerhaus P, Bergstrom N, et al. RN staffing time 
and outcomes of long-stay nursing home residents: Pressure ulcers 
and other adverse outcomes are less likely as RNs spend more time on 
direct patient care. Am J Nurs 2005 6:50-53. https://pubmed.ncbi.nlm.nih.gov/16264305/.
    \207\ Bostick et al.
    \208\ 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.
    \209\ Bostick et al.
    \210\ 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.
    \211\ Bostick et al.
    \212\ Castle NG, Wagner LM, Ferguson-Rome JC, Men A, Handler SM. 
Nursing home deficiency citations for infection control. Am J Infect 
Control. 2011 May;39(4):263-9. doi: 10.1016/j.ajic.2010.12.010. 
PMID: 21531271.
    \213\ Castle N, Wagner L, Ferguson J, Handler S. Hand hygiene 
deficiency citations in nursing homes. J Appl Gerontol. 2014 
Feb;33(1):24-50. doi: 10.1177/0733464812449903. Epub 2012 Aug 1. 
PMID: 24652942. https://pubmed.ncbi.nlm.nih.gov/24652942/.
    \214\ 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/.
    \215\ Dellefield ME, Castle NG, McGilton KS, Spilsbury K. The 
relationship between registered nurses and nursing home quality: An 
integrative review (2008-2014). Nurs Econ. 2015;33(2):95-108, 116. 
https://pubmed.ncbi.nlm.nih.gov/26281280/.
    \216\ Bostick et al.
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    The PHE due to COVID-19 has further underscored the critical 
importance of sufficient staffing to quality and clinical outcomes. 
Several recent studies have found that higher staffing is associated 
with lower COVID-19 incidence and fewer deaths.217 218 219
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    \217\ R. Tamara Konetzka, Elizabeth M. White, Alexander Pralea, 
David C. Grabowski, Vincent Mor, A systematic review of long-term 
care facility characteristics associated with COVID-19 outcomes, 
Journal of the American Geriatrics Society, 10.1111/jgs.17434, 69, 
10, (2766-2777), (2021). https://agsjournals.onlinelibrary.wiley.com/doi/10.1111/jgs.17434.
    \218\ Williams, CS, Zheng Q, White A, Bengtsson A, Shulman ET, 
Herzer KR, Fleisher LA. The association of nursing home quality 
ratings and spread of COVID-19. Journal of the American Geriatrics 
Society, 10.1111/jgs. 17309, 69, 8, (2070-2078), 2021. https://doi.org/10.1111/jgs.17309.
    \219\ Gorges, RJ and Konetzka, RT. Staffing Levels and COVID-19 
Cases and Outbreaks in U.S. Nursing Homes. Journal of the American 
Geriatrics Society, 10.1111/jgs. 16787, 68, 11, (2462-2466), 2020. 
https://agsjournals.onlinelibrary.wiley.com/doi/full/10.1111/jgs.16787.

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[[Page 22772]]

    Multiple Institute of Medicine (IOM) reports have examined the 
complex array of factors that influence care quality in nursing homes, 
including staffing variables such as staffing levels and 
turnover.220 221 In the 2004 report, ``Keeping Patients 
Safe: Transforming the Work Environment of Nurses,'' the IOM's 
Committee on the Work Environment for Nurses and Patient Safety 
highlighted the positive relationships between higher nursing staffing 
levels, particularly RN levels, and better patient outcomes, and 
recognized the need for minimum staffing standards to support 
appropriate levels of nursing staff in nursing homes.\222\
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    \220\ Institute of Medicine. 1996. Nursing Staff in Hospitals 
and Nursing Homes: Is It Adequate? Washington, DC: The National 
Academies Press. https://doi.org/10.17226/5151.
    \221\ Institute of Medicine 2004. Keeping Patients Safe: 
Transforming the Work Environment of Nurses. Washington, DC: The 
National Academies Press. https://doi.org/10.17226/10851.
    \222\ IOM, 2004.
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    Previously published Phase I and Phase II ``Reports to Congress on 
the Appropriateness of Minimum Staffing Ratios in Nursing Homes'' 
further studied the relationship between quality and nurse staffing 
levels and provided compelling evidence of the relationship between 
staffing ratios and quality of care.223 224 The Phase II 
report, completed in 2001, identified staffing thresholds that 
maximized quality outcomes, demonstrating a pattern of incremental 
benefits of increased nurse staffing until a threshold was reached. 
Specifically, the Phase II study used Medicaid Cost Report data from a 
representative sample of 10 states, including over 5,000 facilities, to 
identify staffing thresholds below which quality of care was 
compromised and above which there was no further benefit of additional 
staffing with respect to quality. The study found evidence of a 
relationship between higher staffing and better outcomes for total 
nurse staffing levels up to 4.08 hours per resident day and RN staffing 
levels up to 0.75 RN hours per resident day. In the 2001 study, minimum 
staffing levels at any level up to these thresholds were associated 
with incremental quality improvements, and no significant quality 
improvements were observed for staffing levels above these thresholds. 
The findings were also supported by case studies of individual 
facilities, units, and residents.
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    \223\ Centers for Medicare and Medicaid Services. Report to 
Congress: Appropriateness of Minimum Nurse Staffing Ratios in 
Nursing Homes, Phase I (2000). Baltimore, MD: Centers for Medicare 
and Medicaid Services. https://phinational.org/wp-content/uploads/legacy/clearinghouse/Phase_I_VOL_I.pdf.
    \224\ 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.
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    We have long identified staffing as one of the vital components of 
a nursing home's ability to provide quality care and used staffing data 
to gauge its impact on quality of care in nursing homes more accurately 
and effectively. In 2003, the National Quality Forum Nursing Home 
Steering Committee recommended that a nurse staffing quality measure be 
included in the set of nursing home quality measures that are publicly 
reported by CMS. The proposed Total Nurse Staffing measure is currently 
used in the Nursing Home Five-Star Quality Rating System, as one of two 
measures that comprise the staffing domain. For more information on the 
Five-Star Quality Rating System, we refer readers to https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/CertificationandComplianc/FSQRS.
    Current Federal requirements for nurse staffing are outlined in the 
long-term care facility requirements for participation 
(requirements).\225\ The regulations at 42 CFR 483.35 specify, in part, 
that every facility must have sufficient nursing staff with the 
appropriate competencies and skill sets to provide nursing and related 
services to assure resident safety and attain or maintain the highest 
practicable physical, mental, and psychosocial well-being of each 
resident, as determined by resident assessments and individual plans of 
care and considering the number, acuity and diagnoses of the facility's 
resident population in accordance with the facility assessment required 
at Sec.  483.70(e). We adopted this competency-based approach to 
sufficient staffing to ensure every nursing home provides the staffing 
levels needed to meet the specific needs of their resident population, 
including their person-centered care goals. We also note that current 
regulations require (unless these requirements are waived) facilities 
to have an RN onsite at least 8 consecutive hours a day, 7 days a week 
and around-the-clock services from licensed nursing staff under 
sections 1819(b)(4)(C) and 1919(b)(4)(C) of the Act, and Sec.  
483.35(a) and (b).
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    \225\ FY 2017 Consolidated Medicare and Medicaid Requirements 
for Participation for Long-Term Care Facilities Final Rule (81 FR 
68688 through 68872). https://www.govinfo.gov/content/pkg/FR-2016-10-04/pdf/2016-23503.pdf.
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    Section 1128I(g) of the Act requires facilities to electronically 
submit direct care staffing information (including agency and contract 
staff) based on payroll and other auditable data. In August 2015, we 
amended the requirements for long term care facilities at Sec.  
483.70(q) to require the electronic submission of payroll-based 
staffing data, which includes RNs, licensed practical nurses (LPNs) or 
vocational nurses, certified nursing assistants, and other types of 
medical personnel as specified by us, along with census data, data on 
agency and contract staff, and information on turnover, tenure and 
hours of care provided by each category of staff per resident day.\226\ 
We developed the PBJ system to enable facilities to submit the required 
staffing information in a format that is auditable to ensure accuracy. 
Development of the PBJ system built on several earlier studies that 
included extensive testing of payroll-based staffing measures. The 
first mandatory PBJ reporting period began July 1, 2016.
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    \226\ 80 FR 46390, Aug. 4, 2015 (https://www.govinfo.gov/content/pkg/FR-2015-08-04/pdf/2015-18950.pdf).
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    We post staffing information publicly to help consumers understand 
staffing levels and how they differ across nursing homes. See sections 
1819(i)(1)(A)(i) and 1919(i)(1)(A)(i) of the Act. However, there are 
currently no staffing measures in the SNF VBP Program.
    Given the strong evidence regarding the relationship between 
sufficient staffing levels and improved care for patients, inclusion of 
this measure in the SNF VBP Program adds an important new dimension to 
provide a more comprehensive assessment of and accountability for the 
quality of care provided to residents and serves to drive improvements 
in staffing that are likely to translate into better resident care. PBJ 
data show that there is variability across SNFs in performance on the 
proposed measure, and that there is an opportunity and potential for 
many SNFs to improve their staffing levels. For Q4 CY 2020, average 
total

[[Page 22773]]

nurse staffing was 4.09 hours per resident day for the case-mix 
adjusted Total Nurse Staffing measure, with considerable variability 
across facilities ranging from 2.81 hours per resident day to 5.93 
hours per resident day. Staffing levels increased after April 2018, 
when we first reported PBJ-based staffing measures on Nursing Home 
Compare and using them in the Five-Star Quality Rating System. Average 
nursing staffing hours per resident day increased from 3.85 in Q4 CY 
2017 (publicly reported in April 2018) to 4.08 for Q4 CY 2020 (publicly 
reported in April 2021).
    Inclusion of this measure in the SNF VBP Program also aligns with 
our current priorities and focus areas for the Program and optimizing 
the use of measures that SNFs are already reporting to CMS. Because the 
measure is currently used in the Nursing Home Five-Star Quality Rating 
System, inclusion of this measure in the Program does not add reporting 
or administrative burden to SNFs. Recognizing the importance of 
staffing to supporting and advancing person-centered care needs, this 
proposed measure would align the Program with the Person-Centered Care 
domain of CMS's Meaningful Measures 2.0 Framework.
(2) Overview of Measure
    The proposed measure is a structural measure that uses auditable 
electronic data reported to CMS's PBJ system to calculate total nursing 
hours, which includes RNs, LPNs, and certified nurse aides (CNA), per 
resident day. The measure uses a count of daily resident census derived 
from Minimum Data Set (MDS) resident assessments and is case-mix 
adjusted based on the distribution of MDS resident assessments by 
Resource Utilization Groups, version IV (RUG-IV groups). The proposed 
measure was specified and originally tested at the facility level with 
SNFs as the care setting. The proposed measure is not currently NQF 
endorsed; however, we plan to submit it for endorsement in the next 1 
to 2 years.
    Data on the proposed measure have been publicly reported on the 
Provider Data Catalog website currently hosted by HHS, available at 
https://data.cms.gov/provider-data/, for many years and have been used 
in the Nursing Home Five Star Quality Rating System since its inception 
in 2008. The data source for the measure changed in 2018, when we 
started collecting payroll-based staffing data through the PBJ system. 
Since April 2018, we have been using PBJ and the MDS as the data 
sources for this measure for public reporting and for use in the Five-
Star Quality Rating System. For more information, see the Proposed 
Specifications for the SNF VBP Program Total Nursing Hours per Resident 
Day Measure, at https://www.cms.gov/medicare/providerenrollmentandcertification/certificationandcomplianc/downloads/usersguide.pdf.
    The CMS report ``Appropriateness of Minimum Nurse Staffing Ratios 
in Nursing Homes, Phase II,'' described earlier in this section, showed 
the relationship between quality and nurse staffing levels using 
several methods, establishing the face validity of the Total Nurse 
Staffing measure. The study included an analysis of data from 10 states 
including over 5,000 facilities and found evidence of a relationship 
between staffing ratios and the quality of nursing home care.
    We note that payroll data are considered the gold standard for 
nurse staffing measures and 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.\227\ In 
contrast, PBJ staffing data are electronically submitted and are 
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.\228\
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    \227\ https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/SurveyCertificationGenInfo/Downloads/QSO18-17-NH.pdf.
    \228\ https://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=96520.
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(a) Stakeholder and TEP Input
    In considering whether the total nurse staffing measure would be 
appropriate for the SNF VBP program, we looked at the developmental 
history of the measure in which we employed a transparent process that 
provided stakeholders and national experts the opportunity to provide 
pre-rulemaking input. We convened stakeholder meetings and offered 
engagement opportunities at all phases of measure development, from 
2004 through 2019. Stakeholder calls and meetings have included 
patient/consumer advocates and a wide range of facilities throughout 
the country including large and small, rural and urban, independently 
owned facilities and national chains. In addition to input obtained 
through stakeholder meetings, we solicited input through a dedicated 
email address ([email protected]).
(b) MAP Review
    The Total Nurse Staffing measure was included in the publicly 
available ``List of Measures Under Consideration for December 1, 
2021.'' \229\ The MAP conditionally supported the Total Nurse Staffing 
measure for rulemaking, pending NQF endorsement. We refer readers to 
the final 2021-2022 MAP report available at https://www.qualityforum.org/Publications/2022/03/MAP_2021-2022_Considerations_for_Implementing_Measures_Final_Report_-_Clinicians,_Hospitals,_and_PAC-LTC.aspx.
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    \229\ https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf.
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(3) Data Sources
    The proposed measure is calculated using auditable, electronic 
staffing data submitted by each SNF for each quarter through the PBJ 
system, along with daily resident census information derived from 
Minimum Data Set, Version 3.0 (MDS 3.0) standardized patient 
assessments. We refer readers to the Proposed Specifications for the 
SNF VBP Program Total Nursing Hours per Resident Day Measure, at 
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/SNF-VBP/Measure. We note that the 
proposed Total Nurse Staffing measure is already reported on the 
Provider Data Catalog website and used as part of the Five-Star Quality 
Rating System and thus, there would be no additional data collection or 
submission burdens for SNF providers.
(4) Inclusion and Exclusion Criteria
    The target population for the measure is all SNFs to whom the SNF 
VBP applies and that are not excluded for the reasons listed below. A 
set of exclusion criteria are used to identify facilities with highly 
improbable staffing data and these facilities are excluded. The 
exclusion criteria are as follows:
     Total nurse staffing, aggregated over all days in the 
quarter that the facility reported both residents and staff is 
excessively low (<1.5 hours per resident day).
     Total nurse staffing, aggregated over all days in the 
quarter that the facility reported both residents and staff is 
excessively high (>12 hours per resident day).
     Nurse aide staffing, aggregated over all days in the 
quarter that the facility reported both residents and staff is 
excessively high (>5.25 hours per resident day).

[[Page 22774]]

(5) Measure Calculation and Case-Mix Adjustment
    We are proposing to calculate case-mix adjusted hours per resident 
day for each facility for each staff type using this formula:

Hours Adjusted = (Hours Reported/Hours 
CaseMix) * Hours National Average

    The reported hours are those reported by the facility through PBJ. 
National average hours for a given staff type represent the national 
mean of case-mix hours across all facilities active on the last day of 
the quarter that submitted valid nurse staffing data for the quarter.
    The measure is case-mix adjusted based on the distribution of MDS 
assessments by RUG-IV groups. The CMS Staff Time Resource Intensity 
Verification (STRIVE) Study measured the average number of RN, LPN, and 
NA minutes associated with each RUG-IV group (using the 66-group 
version of RUG-IV).\230\ We refer to these as ``case-mix hours.'' The 
case-mix values for each facility are based on the daily distribution 
of residents by RUG-IV group in the quarter covered by the PBJ reported 
staffing and estimates of daily RN, LPN, and NA hours from the CMS 
STRIVE Study. This adjustment is based on the distribution of MDS 
assessments by RUG-IV groups to account for differences in acuity, 
functional status, and care needs of residents, and therefore is 
appropriate for the SNF VBP program. For more information, see the 
Proposed Specifications for the SNF VBP Program Total Nursing Hours per 
Resident Day Measure, at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/SNF-VBP/Measure.
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    \230\ https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/TimeStudy.
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(a) Numerator
    The proposed numerator for the measure is total nursing hours (RN + 
LPN + NA hours). RN hours include the RN director of nursing, RNs with 
administrative duties, and RNs. LPN hours include licensed practical 
and licensed vocational nurses with administrative duties and licensed 
practical and licensed vocational nurses. NA hours include certified 
nurse aides (CNAs), aides in training, and medication aides/
technicians. We note that the proposed PBJ staffing data include both 
facility employees (full-time and part-time) and individuals under an 
organization (agency) contract or an individual contract. The proposed 
PBJ staffing data do not include ``private duty'' nursing staff 
reimbursed by a resident or his/her family. Also, hospice staff and 
feeding assistants are not included.
(b) Denominator
    The proposed denominator for the measure is a count of daily 
resident census derived from MDS resident assessments. It is calculated 
by: (1) Identifying the reporting period (quarter) for which the census 
will be calculated; (2) extracting MDS assessment data for all 
residents of a facility beginning one year prior to the reporting 
period to identify all residents that may reside in the facility (i.e., 
any resident with an MDS assessment); and (3) identifying discharged or 
deceased residents using specified criteria. For any date, residents 
whose assessments do not meet the criteria for being identified as 
discharged or deceased prior to that date are assumed to reside in the 
facility. The count of these residents is the census for that 
particular day. We refer readers to the Proposed Specifications for the 
SNF VBP Program Total Nursing Hours per Resident Day Measure for more 
information on the calculation of daily resident census used in the 
denominator of the reported nurse staffing ratios, at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/SNF-VBP/Measure.
    The currently publicly reported Total Nurse Staffing measure is 
reported on a quarterly basis. To align with other quality measures for 
the expanded SNF VBP Program, we are proposing to report the measure 
rate for the SNF VBP Program for each SNF as a simple average rate of 
total nurse staffing per resident day across available quarters in the 
1-year performance period.
(6) Proposed Confidential Feedback Reports and Public Reporting
    We refer readers to the FY 2017 SNF PPS final rule (81 FR 52006 
through 52007) for discussion of our policy to provide quarterly 
confidential feedback reports to SNFs on their measure performance. We 
also refer readers to the FY 2022 SNF PPS final rule (86 FR 42516 
through 42517) for a summary of our two-phase review and corrections 
policy for SNFs' quality measure data. Furthermore, we refer readers to 
the FY 2018 SNF PPS final rule (82 FR 36622 through 36623) and the FY 
2021 SNF PPS final rule (85 FR 47626) where we finalized our policy to 
publicly report SNF measure performance information under the SNF VBP 
Program on the Provider Data Catalog website currently hosted by HHS 
and available at https://data.cms.gov/provider-data/. We are proposing 
to update and redesignate the confidential feedback report and public 
reporting policies, which are currently codified at Sec.  413.338(e)(1) 
through (3) as Sec.  413.338(f), to include the Total Nurse Staffing 
measure.
    We invite public comment on our proposal to adopt the Total Nurse 
Staffing measure beginning with the FY 2026 SNF VBP program year.
d. Proposal To Adopt the DTC--PAC Measure for SNFs (NQF #3481) 
Beginning With the FY 2027 SNF VBP Program Year
    As part of the SNF VBP Program expansion authorized under the CAA, 
we are proposing to adopt the DTC PAC SNF measure for the FY 2027 SNF 
VBP Program and subsequent years. The DTC PAC SNF measure (NQF #3481) 
is an outcome measure that assesses the rate of successful discharges 
to community from a SNF setting, using 2 years of Medicare FFS claims 
data. This proposed measure addresses an important health care outcome 
for many SNF residents (returning to a previous living situation and 
avoiding further institutionalization) and would align the Program with 
the Seamless Care Coordination domain of CMS's Meaningful Measures 2.0 
Framework. In addition, the DTC PAC SNF measure is currently part of 
the SNF QRP measure set.\231\ For more information on this measure in 
the SNF QRP, see 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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    \231\ We note that the SNF QRP refers to this measure as the 
``Discharge to Community--PAC SNF QRP'' measure. Though we are using 
a different measure short name (``DTC PAC SNF''), we are proposing 
to adopt the same measure the SNF QRP uses for purposes of the SNF 
VBP program.
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(1) Background
    We believe it is an important goal in post-acute care settings to 
return patients to their previous levels of independence and 
functioning with discharge to community being one of the primary goals 
for post-acute patients. We also believe it is important to improve 
access to community discharge options for SNF residents. Discharge to 
community is considered a valuable outcome to measure because it 
provides important information about patient outcomes after being 
discharged from a SNF and is a multifaceted measure that captures the 
patient's functional status, cognitive capacity,

[[Page 22775]]

physical ability, and availability of social support at home.
    In 2019, 1.5 million of Medicare's FFS beneficiaries (4 percent of 
all Medicare FFS beneficiaries) utilized Medicare coverage for a SNF 
stay.\232\ However, almost half of the older adults that are admitted 
to SNFs are not discharged to the community, and for a significant 
proportion of those that are discharged back to the community, it may 
take up to 365 days.233 234 In 2017, the SNF QRP and other 
PAC QRP programs adopted this measure; however, there remains 
considerable variation in performance on this measure. In 2019, the 
lowest performing SNFs had risk-adjusted rates of successful discharge 
to the community at or below 39.5 percent, while the best performing 
SNFs had rates of 53.5 percent or higher, indicating considerable room 
for improvement.\235\
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    \232\ https://www.medpac.gov/wp-content/uploads/2021/10/mar21_medpac_report_ch7_sec.pdf.
    \233\ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3711511/.
    \234\ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4706779/.
    \235\ March 2021 MedPAC Report to Congress: https://www.medpac.gov/wp-content/uploads/import_data/scrape_files/docs/default-source/reports/mar21_medpac_report_to_the_congress_sec.pdf.
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    In addition to being an important outcome from a resident and 
family perspective, residents discharged to community settings, on 
average, incur lower costs over the recovery episode, compared with 
those discharged to institutional settings.236 237 We 
believe including this measure in the SNF VBP Program will further 
encourage SNFs to prepare residents for discharge to community, when 
clinically appropriate, which may have significant cost-saving 
implications for the Medicare program given the high costs of care in 
institutional settings. Also, providers have discovered that successful 
discharge to community is a key factor in their ability to achieve 
savings, where capitated payments for post-acute care were in 
place.\238\ For residents who require long-term care due to persistent 
disability, discharge to community could result in lower long-term care 
costs for Medicaid and for residents' out-of-pocket expenditures.\239\
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    \236\ Dobrez D, Heinemann AW, Deutsch A, Manheim L, Mallinson T. 
Impact of Medicare's prospective payment system for inpatient 
rehabilitation facilities on stroke patient outcomes. American 
Journal of Physical Medicine & Rehabilitation. 2010;89(3):198-204. 
https://doi.org/10.1097/PHM.0b013e3181c9fb40https://doi.org/10.1097/PHM.0b013e3181c9fb40.
    \237\ Gage B, Morley M, Spain P, Ingber M. Examining Post Acute 
Care Relationships in an Integrated Hospital System. Final Report. 
RTI International;2009. https://aspe.hhs.gov/sites/default/files/private/pdf/75761/report.pdf.
    \238\ Doran JP, Zabinski SJ. Bundled payment initiatives for 
Medicare and non-Medicare total joint arthroplasty patients at a 
community hospital: Bundles in the real world. The journal of 
arthroplasty. 2015;30(3):353-355. https://doi.org/10.1016/j.arth.2015.01.035.
    \239\ Newcomer RJ, Ko M, Kang T, Harrington C, Hulett D, Bindman 
AB. Health Care Expenditures After Initiating Long-term Services and 
Supports in the Community Versus in a Nursing Facility. Medical 
Care. 2016; 54(3):221-228. https://doi.org/10.1097/MLR.0000000000000491https://doi.org/10.1097/MLR.0000000000000491.
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    Discharge to community is also an actionable health care outcome, 
as targeted interventions have been shown to successfully increase 
discharge to community rates in a variety of post-acute settings. Many 
of these interventions involve discharge planning or specific 
rehabilitation strategies, such as addressing discharge barriers and 
improving medical and functional status.240 241 242 243 
Other factors that have shown positive associations with successful 
discharge to community include patient safety culture within the SNF 
and availability of home and community-based 
services.244 245 The effectiveness of these interventions 
suggests that improvement in discharge to community rates among post-
acute care residents is possible through modifying provider-led 
processes and interventions. Therefore, including the DTC PAC SNF 
measure in the SNF VBP Program may provide further incentive for 
providers to continue improving on current interventions or implement 
new interventions.
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    \240\ Kushner DS, Peters KM, Johnson-Greene D. Evaluating 
Siebens Domain Management Model for Inpatient Rehabilitation to 
Increase Functional Independence and Discharge Rate to Home in 
Geriatric Patients. Archives of physical medicine and 
rehabilitation. 2015;96(7):1310-1318. https://doi.org/10.1016/j.apmr.2015.03.011.
    \241\ Wodchis WP, Teare GF, Naglie G, et al. Skilled nursing 
facility rehabilitation and discharge to home after stroke. Archives 
of physical medicine and rehabilitation. 2005;86(3):442-448. https://doi.org/10.1016/j.apmr.2004.06.067.
    \242\ Berkowitz RE, Jones RN, Rieder R, et al. Improving 
disposition outcomes for patients in a geriatric skilled nursing 
facility. Journal of the American Geriatrics Society. 
2011;59(6):1130-1136. https://doi.org/10.1111/j.1532-5415.2011.03417.
    \243\ Kushner DS, Peters KM, Johnson-Greene D. Evaluating use of 
the Siebens Domain Management Model during inpatient rehabilitation 
to increase functional independence and discharge rate to home in 
stroke patients. PM & R: The journal of injury, function, and 
rehabilitation. 2015;7(4):354- 364. https://doi.org/10.1016/j.pmrj.2014.10.010.
    \244\ https://doi.org/10.1111/j.1532-5415.2011.03417 Wenhan Guo, 
Yue Li, Helena Temkin-Greener, Community Discharge Among Post-Acute 
Nursing Home Residents: An Association With Patient Safety Culture?, 
Journal of the American Medical Directors Association, Volume 22, 
Issue 11, 2021, Pages 2384-2388.e1, ISSN 1525-8610, https://doi.org/10.1016/j.jamda.2021.04.022.
    \245\ https://doi.org/10.1016/j.pmrj.2014.10.010 Wang, S., 
Temkin-Greener, H., Simning, A., Konetzka, R.T. and Cai, S. (2021), 
Outcomes after Community Discharge from Skilled Nursing Facilities: 
The Role of Medicaid Home and Community-Based Services. Health Serv 
Res, 56: 16-16. https://doi.org/10.1111/1475-6773.13737.
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(2) Overview of Measure
    This measure, which was finalized for adoption under the SNF QRP 
(81 FR 52021 through 52029), reports a SNF's risk-standardized rate of 
Medicare FFS residents who are discharged to the community following a 
SNF stay, do not have an unplanned readmission to an acute care 
hospital or LTCH in the 31 days following discharge to community, and 
remain alive during the 31 days following discharge to community. 
Community, for this measure, is defined as home or selfcare, with or 
without home health services. We are proposing to adopt this measure 
beginning with the FY 2027 program year. We note that including this 
measure in the FY 2027 program year would provide advanced notice for 
facilities to prepare for the inclusion of this measure in the SNF VBP 
program. This also provides the necessary time to incorporate the 
operational processes associated with including this two-year measure 
in the SNF VBP program.
(a) Stakeholder and TEP Input
    In considering the selection of this measure for the SNF VBP 
Program, we reviewed the developmental history of the measure, which 
employed a transparent process that provided stakeholders and national 
experts the opportunity to provide pre-rulemaking input. Our measure 
development contractor convened a TEP, which was strongly supportive of 
the importance of measuring discharge to community outcomes and 
implementing the measure, Discharge to Community PAC SNF QRP in the SNF 
QRP. The panel provided input on the technical specifications of this 
measure, including the feasibility of implementing the measure, as well 
as the overall measure reliability and validity. We refer readers to 
the FY 2017 SNF PPS final rule (81 FR 52023), as well as a summary of 
the TEP proceedings available on the PAC Quality Initiatives Downloads 
and Videos website available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos for 
additional information.
(b) MAP Review
    The DTC PAC SNF measure was included in the publicly available 
``List of Measures Under Consideration for

[[Page 22776]]

December 1, 2021,'' \246\ and the MAP supported the DTC PAC SNF measure 
for rulemaking for the SNF VBP Program. We refer readers to the final 
MAP report available at https://www.qualityforum.org/Publications/2022/03/MAP_2021-2022_Considerations_for_Implementing_Measures_Final_Report_-_Clinicians,_Hospitals,_and_PAC-LTC.aspx.
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    \246\ https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf.
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(3) Data Sources
    We are proposing to use data from the Medicare FFS claims and 
Medicare eligibility files to calculate this measure. We would use data 
from the ``Patient Discharge Status Code'' on Medicare FFS claims to 
determine whether a resident was discharged to a community setting for 
calculation of this measure. The eligibility files provide information 
such as date of birth, date of death, sex, reasons for Medicare 
eligibility, periods of Part A coverage, and periods in the Medicare 
FFS program. The data elements from the Medicare FFS claims are those 
basic to the operation of the Medicare payment systems and include data 
such as date of admission, date of discharge, diagnoses, procedures, 
indicators for use of dialysis services, and indicators of whether the 
Part A benefit was exhausted. The inpatient claims data files contain 
patient-level PAC and other hospital records. SNFs would not need to 
report additional data in order for us to calculate this measure.\247\
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    \247\ https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Downloads/Measure-Specifications-for-FY17-SNF-QRP-Final-Rule.pdf.
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    We refer readers to the FY 2017 SNF PPS final rule where we adopted 
the DTC measure for use in the SNF QRP (81 FR 52021 through 52029). In 
that rule, we provided an analysis related to the accuracy of using the 
``Patient Discharge Status Code'' in determining discharge to a 
community setting. Specifically, in all PAC settings, we tested the 
accuracy of determining discharge to a community setting using the 
``Patient Discharge Status Code'' on the PAC claim by examining whether 
discharge to community coding based on PAC claim data agreed with 
discharge to community coding based on PAC assessment data. We found 
agreement between the two data sources in all PAC settings, ranging 
from 94.6 percent to 98.8 percent. Specifically, in the SNF setting, 
using 2013 data, we found 94.6 percent agreement in discharge to 
community codes when comparing discharge status codes on claims and the 
Discharge Status (A2100) on the Minimum Data Set (MDS) 3.0 discharge 
assessment, when the claims and MDS assessment had the same discharge 
date. We further examined the accuracy of the ``Patient Discharge 
Status Code'' on the PAC claim by assessing how frequently discharges 
to an acute care hospital were confirmed by follow-up acute care 
claims. We discovered that 88 percent to 91 percent of IRF, LTCH, and 
SNF claims with acute care discharge status codes were followed by an 
acute care claim on the day of, or day after, PAC discharge. We believe 
these data support the use of the claims ``Patient Discharge Status 
Code'' for determining discharge to a community setting for this 
measure. In addition, this measure can feasibly be implemented in the 
SNF VBP Program because all data used for measure calculation are 
derived from Medicare FFS claims and eligibility files, which are 
already available to CMS.
(4) Inclusion and Exclusion Criteria
    We are proposing that the DTC PAC SNF measure would use the same 
specifications under the SNF VBP Program as the Discharge to 
Community--PAC SNF QRP measure used in the SNF QRP, which are available 
at https://www.cms.gov/files/zip/snf-qrp-measure-calculations-and-reporting-users-manual-v301-addendum-effective-10-01-2020.zip. The 
target population for the measure is the group of Medicare FFS 
residents who are admitted to a SNF and are not excluded for the 
reasons listed in this paragraph. The measure exclusion criteria are 
determined by processing Medicare claims and eligibility data to 
determine whether the individual exclusion criteria are met. All 
measure exclusion criteria are based on administrative data. Only SNF 
stays that are preceded by a short-term acute care stay in the 30 days 
prior to the SNF admission date are included in the measure. Stays 
ending in transfers to the same level of care are excluded. The measure 
excludes residents for which the following conditions are true:
     Age under 18 years;
     No short-term acute care stay within the 30 days preceding 
SNF admission;
     Discharges to a psychiatric hospital;
     Discharges against medical advice;
     Discharges to disaster alternative care sites or Federal 
hospitals;
     Discharges to court/law enforcement;
     Residents discharged to hospice and those with a hospice 
benefit in the post-discharge observation window;
     Residents not continuously enrolled in Part A FFS Medicare 
for the 12 months prior to the post-acute admission date, and at least 
31 days after post-acute discharge date;
     Residents whose prior short-term acute care stay was for 
non-surgical treatment of cancer;
     Post-acute stays that end in transfer to the same level of 
care;
     Post-acute stays with claims data that are problematic 
(e.g., anomalous records for stays that overlap wholly or in part, or 
are otherwise erroneous or contradictory);
     Planned discharges to an acute or LTCH setting;
     Medicare Part A benefits exhausted;
     Residents who received care from a facility located 
outside of the U.S., Puerto Rico or a U.S. territory; and
     Swing Bed Stays in Critical Access Hospitals.
    This measure also excludes residents who had a long-term nursing 
facility stay in the 180 days preceding their hospitalization and SNF 
stay, with no intervening community discharge between the long-term 
nursing facility stay and qualifying hospitalization.
(5) Risk Adjustment
    The measure is risk-adjusted for variables including demographic 
and eligibility characteristics, such as age and sex, principal 
diagnosis, types of surgery or procedures from the prior short-term 
acute care stay, comorbidities, length of stay and intensive care 
utilization from the prior short-term acute care stay, ventilator 
status, ESRD status, and dialysis, among other variables. For 
additional technical information about the proposed measure, including 
information about the measure calculation, risk adjustment, and 
denominator exclusions, we refer readers to the document titled, Final 
Specifications for SNF QRP Quality Measures and Standardized Patient 
Assessment Data Elements, available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Downloads/Final-Specifications-for-SNF-QRP-Quality-Measures-and-SPADEs.pdf. We note that we are proposing to use 
the technical information and specifications found in this document for 
purposes of calculating this measure in the SNF VBP Program.
(6) Measure Calculation
    We are proposing to adopt the DTC PAC SNF measure for the SNF VBP 
Program for FY 2027 and subsequent years. This measure is calculated 
using 2 years of data. Since Medicare FFS claims data are already 
reported to the

[[Page 22777]]

Medicare program for payment purposes, and Medicare eligibility files 
are also available, SNFs will not be required to report any additional 
data to us for calculation of this measure.
(a) Numerator
    The measure numerator is the risk-adjusted estimate of the number 
of residents who are discharged to the community, do not have an 
unplanned readmission to an acute care hospital or LTCH in the 31-day 
post-discharge observation window, and who remain alive during the 
post-discharge observation window. This estimate starts with the 
observed discharges to community and is risk-adjusted for patient/
resident characteristics and a statistical estimate of the facility 
effect beyond case mix. A patient/resident who is discharged to the 
community is considered to have an unfavorable outcome if they have a 
subsequent unplanned readmission to an acute care hospital or LTCH in 
the post-discharge observation window, which includes the day of 
discharge and the 31 days following day of discharge. Discharge to 
community is determined based on the ``Patient Discharge Status Code'' 
from the PAC claim. Discharge to community is defined as discharge to 
home or self-care with or without home health services, which includes 
the following Patient Discharge Status Codes: 01 Discharged to home or 
self-care (routine discharge); 06 Discharged/transferred to home under 
care of organized home health service organization; 81 Discharged to 
home or self-care with a planned acute care hospital readmission; and 
86 Discharged/transferred to home under care of organized home health 
service organization with a planned acute care hospital inpatient 
readmission. Residents who are discharged to the community are also 
considered to have an unfavorable outcome if they die in the post-
discharge window, which includes the day of discharge and the 31 days 
following day of discharge. Death in the post-discharge window is 
identified based on date of death from Medicare eligibility files.
(b) Denominator
    The denominator for the DTC PAC SNF measure is the risk-adjusted 
expected number of discharges to community. This estimate includes risk 
adjustment for patient/resident characteristics with the facility 
effect removed. The ``expected'' number of discharges to community is 
the predicted number of risk-adjusted discharges to community if the 
same residents were treated at the average facility appropriate to the 
measure.
(7) Proposed Confidential Feedback Reports and Public Reporting
    We refer readers to the FY 2017 SNF PPS final rule (81 FR 52006 
through 52007) for discussion of our policy to provide quarterly 
confidential feedback reports to SNFs on their measure performance. We 
also refer readers to the FY 2022 SNF PPS final rule (86 FR 42516 
through 42517) for a summary of our two-phase review and corrections 
policy for SNFs' quality measure data. Furthermore, we refer readers to 
the FY 2018 SNF PPS final rule (82 FR 36622 through 36623) and the FY 
2021 SNF PPS final rule (85 FR 47626) where we finalized our policy to 
publicly report SNF measure performance information under the SNF VBP 
Program on the Provider Data Catalog website currently hosted by HHS 
and available at https://data.cms.gov/provider-data/. We are proposing 
to update and redesignate the confidential feedback report and public 
reporting policies, which are currently codified at Sec.  413.338(e)(1) 
through (3) to Sec.  413.338(f), to include the DTC PAC SNF measure.
    We invite public comment on our proposal to adopt the DTC PAC SNF 
measure beginning with the FY 2027 SNF VBP program year.

C. SNF VBP Performance Period and Baseline Period Proposals

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 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. We also refer readers 
to the FY 2022 SNF PPS final rule, where we finalized our proposal to 
use FY 2019 data for the FY 2024 baseline period (86 FR 42512 through 
42513).
2. Proposal To Revise the Baseline Period for the FY 2025 SNF VBP 
Program
    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 2025 
program year would be FY 2021. However, as more fully described in 
section VII.B.1. of this proposed rule, we have determined that the 
significant decrease in SNF admissions and staffing shortages 
associated with the PHE for COVID-19 in FY 2021 has impacted SNFRM 
validity and reliability. Because the baseline period for this measure 
is used to calculate the performance standards under the SNF VBP 
Program, we are concerned about using COVID-19 impacted data for the FY 
2025 baseline period for scoring and payment purposes.
    Therefore, we are proposing to use a baseline period of FY 2019 for 
the FY 2025 program year. We believe using data from this period will 
provide sufficiently valid and reliable data for evaluating SNF 
performance that can be used for FY 2025 scoring. We are also proposing 
to select this revised data period because it would capture a full year 
of data, including any seasonal effects.
    We considered using FY 2020 as the baseline period for the FY 2025 
program. However, under the ECE, SNF qualifying claims for a 6-month 
period in FY 2020 (January 1, 2020, through June 30, 2020) are excepted 
from the calculation of the SNFRM, which means that we will not have a 
full year of data to calculate the SNFRM for a FY 2020 baseline period.
    We also considered using FY 2022 as the baseline period for the FY 
2025 program year, which will be the baseline period for the FY 2026 
program year for the SNFRM under the previously established policy for 
adopting baseline periods for future years (83 FR 39277). However, it 
is operationally infeasible for us to calculate performance standards 
using a FY 2022 baseline period for the FY 2025 program year because 
performance standards must be published at least 60 days prior to the 
start of the performance period, currently planned as FY 2023, as 
required under section 1888(h)(3)(C) of the Act. We invite public 
comment on our proposal to update the baseline period for the FY 2025 
SNF VBP Program.
3. Proposed Performance Periods and Baseline Periods for the SNF HAI 
Measure Beginning With the FY 2026 SNF VBP Program
a. Proposed Performance Period for the SNF HAI Measure for the FY 2026 
SNF VBP Program and Subsequent Years
    In considering the appropriate performance period for the SNF HAI 
measure for the FY 2026 SNF VBP Program, we recognize that we must 
balance the length of the performance period with our need to calculate 
valid and reliable performance scores and

[[Page 22778]]

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 our testing of the measure, we found that a 1-year 
performance period produced moderately reliable performance scores. We 
refer readers to the SNF HAI Measure Technical Report for further 
information on measure testing results, available at https://www.cms.gov/files/document/snfhaitechnicalreport.pdf. 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 a 1-year performance period for the SNF 
HAI measure would be operationally feasible for the SNF VBP Program and 
would provide sufficiently accurate and reliable SNF HAI measure rates 
and resulting performance scores.
    We also recognize that we must balance our desire to specify a 
performance period 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 the SNFRM, we believe that a performance period that 
occurs 2 fiscal years prior to the applicable fiscal program year is 
most appropriate for the SNF HAI measure.
    For these reasons, we are proposing to adopt a 1-year performance 
period for the SNF HAI measure. In addition, we are proposing to adopt 
FY 2024 (October 1, 2023 through September 30, 2024) as the performance 
period for the SNF HAI measure for the FY 2026 SNF VBP Program.
    In alignment with the current Program measure, we are also 
proposing that, for the SNF HAI measure, we would 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 invite public comment on our proposals related to the 
performance period for the SNF HAI measure for the FY 2026 program year 
and subsequent years.
b. Proposed Baseline Period for the SNF HAI Measure for the FY 2026 SNF 
VBP Program and Subsequent Years
    We discussed in the FY 2016 SNF PPS final rule (80 FR 46422) that, 
as with other Medicare quality programs, we generally adopt a baseline 
period for a fiscal year that occurs prior to the performance period 
for that fiscal year to establish measure performance standards. In the 
FY 2016 SNF PPS final rule (80 FR 46422), we also discussed our intent 
to adopt baseline periods that are as close as possible in duration as 
the performance period for a fiscal year as well as our intent to 
seasonally align baseline periods with the performance period 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 proposed performance period length for the SNF HAI measure, we 
believe a 1-year baseline period is most appropriate for the SNF HAI 
measure.
    We also recognize that we are required to calculate and announce 
performance standards no later than 60 days prior to the start of the 
performance period, as required by section 1888(h)(3)(C) of the Act. 
Therefore, in alignment with the SNFRM baseline period, we believe that 
a baseline period that occurs 4 fiscal years prior to the applicable 
fiscal program year, and 2 fiscal years prior to the performance 
period, is most appropriate for the SNF HAI measure and would provide 
sufficient time to calculate and announce performance standards prior 
to the start of the performance period.
    For these reasons, we are proposing to adopt a 1-year baseline 
period for the SNF HAI measure. In addition, we are proposing to adopt 
FY 2022 (October 1, 2021 through September 30, 2022) as the baseline 
period for the SNF HAI measure for the FY 2026 SNF VBP Program.
    In alignment with the current Program measure, we are also 
proposing that for the SNF HAI measure, we would 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 invite public comment on our proposals related to the baseline 
period for the SNF HAI measure for the FY 2026 program year and 
subsequent years.
4. Proposed Performance Period and Baseline Period for the Total 
Nursing Hours per Resident Day Staffing Measure Beginning With the FY 
2026 SNF VBP Program
a. Proposed Performance Period for the Total Nursing Hours per Resident 
Day Staffing Measure for the FY 2026 SNF VBP Program and Subsequent 
Years
    In considering the appropriate performance period for the Total 
Nurse Staffing measure for the FY 2026 SNF VBP Program, we recognize 
that we must balance the length of the performance period 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. The Total Nurse Staffing measure is currently reported on a 
quarterly basis for the Nursing Home Five-Star Quality Rating System. 
For purposes of inclusion in the SNF VBP Program, we are proposing that 
the measure rate would be calculated on an annual basis. To do so, we 
are proposing to aggregate the quarterly measure rates using a simple 
mean of the available quarterly case-mix adjusted scores in a 1-year 
performance period. We conducted testing of the measure and found that 
the quarterly measure rate and resident census are stable across 
quarters. Further, an unweighted yearly measure aligns the SNF VBP 
Program rates with rates reported on the Provider Data Catalog website 
currently hosted by HHS, available at https://data.cms.gov/provider-data/. It can also be easily understood by, and is transparent to, the 
public. In addition, we refer readers to the FY 2017 SNF PPS final rule 
(81 FR 51998 through 51999) for discussion of the factors we should 
consider when specifying performance periods for the SNF VBP Program as 
well as our preference for 1-year performance periods. Based on these 
considerations, we believe that a 1-year performance period for the 
Total Nurse Staffing measure would be operationally feasible under the 
SNF VBP Program and would provide sufficiently accurate and reliable 
Total Nurse Staffing measure rates and resulting performance scores.
    We also recognize that we must balance our desire to specify a 
performance period 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

[[Page 22779]]

the fiscal year involved, in accordance with section 1888(h)(7) of the 
Act. In considering these constraints, and in alignment with the SNFRM, 
we believe that a performance period that occurs 2 fiscal years prior 
to the applicable fiscal program year is most appropriate for the Total 
Nurse Staffing measure.
    For these reasons, we are proposing to adopt a 1-year performance 
period for the Total Nurse Staffing measure. In addition, we are 
proposing to adopt FY 2024 (October 1, 2023 through September 30, 2024) 
as the performance period for the Total Nurse Staffing measure for the 
FY 2026 SNF VBP program year.
    In alignment with the current Program measure, we are also 
proposing that for the Total Nurse Staffing measure, we would 
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 invite public comment on our proposals related to the 
performance period for the Total Nurse Staffing measure for the FY 2026 
program year and subsequent years.
b. Proposed Baseline Period for the Total Nursing Hours per Resident 
Day Staffing Measure for the FY 2026 SNF VBP Program and Subsequent 
Years
    We discussed in the FY 2016 SNF PPS final rule (80 FR 46422) that, 
as with other Medicare quality programs, we generally adopt a baseline 
period for a fiscal year that occurs prior to the performance period 
for that fiscal year to establish measure performance standards. In the 
FY 2016 SNF PPS final rule (80 FR 46422), we also discussed our intent 
to adopt baseline periods that are as close as possible in duration as 
the performance period for a fiscal year, as well as our intent to 
seasonally align baseline periods with the performance period 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 proposed performance period length for the Total Nurse Staffing 
measure, we believe a 1-year baseline period is most appropriate.
    We also recognize that we are required to calculate and announce 
performance standards no later than 60 days prior to the start of the 
performance period, as required by section 1888(h)(3)(C) of the Act. 
Therefore, in alignment with the SNFRM baseline period, we believe that 
a baseline period that occurs 4 fiscal years prior to the applicable 
fiscal program year, and 2 fiscal years prior to the performance 
period, is most appropriate for the Total Nurse Staffing measure and 
would provide sufficient time to calculate and announce performance 
standards prior to the start of the performance period.
    For these reasons, we are proposing to adopt a 1-year baseline 
period for the Total Nurse Staffing measure. In addition, we are 
proposing to adopt FY 2022 (October 1, 2021 through September 30, 2022) 
as the baseline period for the Total Nurse Staffing measure for the FY 
2026 SNF VBP Program.
    In alignment with the current Program measure, we are also 
proposing that for the Total Nurse Staffing measure, we would 
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 invite public comment on our proposals related to the baseline 
period for the Total Nurse Staffing measure for the FY 2026 program 
year and subsequent years.
5. Proposed Performance Periods and Baseline Periods for the DTC PAC 
Measure for SNFs for the FY 2027 SNF VBP Program and Subsequent Years
a. Proposed Performance Period for the DTC PAC SNF Measure for the FY 
2027 SNF VBP Program and Subsequent Years
    Under the SNF QRP, The Discharge to Community--PAC SNF QRP measure 
has a reporting period that uses 2 consecutive years to calculate the 
measure (83 FR 39217 through 39272). In alignment with the reporting 
period that applies to the measure under the SNF QRP, we are proposing 
to adopt a 2-year performance period for the DTC PAC SNF measure under 
the SNF VBP.
    We are proposing to align our performance period with the 
performance period for the measure used by the SNF QRP to maintain 
streamlined data requirements and reduce any confusion for 
participating SNFs. In addition, we are proposing to adopt FY 2024 
through FY 2025 (October 1, 2023 through September 30, 2025) as the 
performance period for the DTC PAC SNF measure for the FY 2027 SNF VBP 
Program.
    We are also proposing that for the DTC PAC SNF measure, we would 
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 invite public comment on our proposals related to the 
performance period for the DTC PAC SNF measure for FY 2027 program year 
and subsequent years.
b. Proposed Baseline Period for the DTC PAC SNF Measure for the FY 2027 
SNF VBP Program Year and Subsequent Years
    We discussed in the FY 2016 SNF PPS final rule (80 FR 46422) that, 
as with other Medicare quality programs, we generally adopt a baseline 
period for a fiscal year that occurs prior to the performance period 
for that fiscal year to establish measure performance standards. In the 
FY 2016 SNF PPS final rule (80 FR 46422), we also discussed our intent 
to adopt baseline periods that are as close as possible in duration as 
the performance period for a fiscal year, as well as our intent to 
seasonally align baseline periods with the performance period 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 proposed performance period length for the DTC PAC SNF measure, we 
believe a 2-year baseline period is most appropriate for this measure.
    We also recognize that we are required to calculate and announce 
performance standards no later than 60 days prior to the start of the 
performance period, as required by section 1888(h)(3)(C) of the Act. 
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 performance period, is most appropriate for the DTC PAC SNF 
measure and would provide sufficient time to calculate and announce 
performance standards prior to the start of the performance period.
    For these reasons, we are proposing to calculate the performance 
period for the DTC PAC SNF measure using two consecutive years of data. 
In addition, we are proposing to adopt FY 2021 through FY 2022 (October 
1, 2020 through September 30, 2022) as the baseline period for the DTC 
PAC SNF measure for the FY 2027 SNF VBP Program.
    In alignment with the current Program measure, we are also 
proposing that for the DTC PAC SNF measure, we would 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 invite public comment on our proposals related to the baseline 
period for the DTC PAC SNF measure for FY 2027 program year and 
subsequent years.

[[Page 22780]]

D. 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. We adopted the final numerical values for 
the FY 2023 performance standards in the FY 2021 SNF PPS final rule (85 
FR 47625) and adopted the final numerical values for the FY 2024 
performance standards in the FY 2022 SNF PPS final rule (86 FR 42513). 
We also adopted a policy allowing us to correct the numerical values of 
the performance standards in the FY 2019 SNF PPS final rule (83 FR 
39276 through 39277).
    We are not proposing any changes to these performance standard 
policies in this proposed rule.
2. SNF VBP Performance Standards Correction Policy
    In the FY 2019 SNF PPS final rule (83 FR 39276 through 39277), we 
finalized a policy to correct numerical values of performance standards 
for a program year in cases of errors. We also finalized that we will 
only update the numerical values for a program year one time, even if 
we identify a second error, because we believe that a one-time 
correction will allow us to incorporate new information into the 
calculations without subjecting SNFs to multiple updates. We stated 
that any update we make to the numerical values based on a calculation 
error will be announced via the CMS website, listservs, and other 
available channels to ensure that SNFs are made fully aware of the 
update. In the FY 2021 SNF PPS final rule (85 FR 47625), we amended the 
definition of ``Performance standards'' at Sec.  413.338(a)(9), 
consistent with these policies finalized in the FY 2019 SNF PPS final 
rule, to reflect our ability to update the numerical values of 
performance standards if we determine there is an error that affects 
the achievement threshold or benchmark. To improve the clarity of this 
policy, we are proposing to amend the definition of ``Performance 
standards'' and redesignate it as Sec.  413.338(a)(12), then add 
additional detail about the correction policy at Sec.  413.338(d)(6).
    We are not proposing any changes to the performance standards 
correction policy in this proposed rule. We seek public comment on our 
changes to the text at Sec.  413.338(a)(12) and (d)(6).
3. Proposed Performance Standards for the FY 2025 Program Year
    As discussed in section VII.C.2. of this proposed rule, we are 
proposing to use FY 2019 data as the baseline period for the FY 2025 
program year. Based on this proposed updated baseline period and our 
previously finalized methodology for calculating performance standards 
(81 FR 51996 through 51998), the proposed estimated numerical values 
for the FY 2025 program year performance standards are shown in Table 
18.
[GRAPHIC] [TIFF OMITTED] TP15AP22.025

E. SNF VBP Performance Scoring

1. Background
    We refer readers to the FY 2017 SNF PPS final rule (81 FR 52000 
through 52005) for a detailed discussion of the scoring methodology 
that we have finalized for the Program. We also refer readers to the FY 
2018 SNF PPS final rule (82 FR 36614 through 36616) for discussion of 
the rounding policy we adopted. We also refer readers to the FY 2019 
SNF PPS final rule (83 FR 39278 through 39281), where we adopted: (1) A 
scoring policy for SNFs without sufficient baseline period data, (2) a 
scoring adjustment for low-volume SNFs, and (3) an extraordinary 
circumstances exception policy. Finally, we refer readers to the FY 
2022 SNF PPS final rule (86 FR 42513 through 42515), where we adopted 
for FY 2022 a special scoring and payment policy due to the impact of 
the PHE for COVID-19.
2. Proposed Special Scoring Policy for the FY 2023 SNF VBP Program Due 
to the Impact of the PHE for COVID-19
    In section VII.B.1. of this proposed rule, we are proposing to 
suppress the SNFRM for the FY 2023 program year due to the impacts of 
the PHE for COVID-19. Specifically, for FY 2023 scoring, we are 
proposing that, for all SNFs participating in the FY 2023 SNF VBP 
Program, we would use data from the previously finalized performance 
period (FY 2021) and baseline period (FY 2019) to calculate each SNF's 
RSRR for the SNFRM. Then, we would assign all SNFs a performance score 
of zero. This would result in all participating SNFs receiving an 
identical performance score, as well as an identical incentive payment 
multiplier. We also propose that SNFs that do not meet the proposed 
case minimum for FY 2023 (see VII.E.3.b. of this proposed rule) will be 
excluded from the Program for FY 2023. SNFs would not be ranked for the 
FY 2023 SNF VBP Program. We are also proposing to update our regulation 
text at Sec.  413.338(i) to codify this scoring policy for FY 2023. As 
we noted in section VII.B.1. of this proposed rule, our goal is to 
continue the use of measure data for scoring and payment adjustment 
purposes beginning with the FY 2024 program year.
    We invite public comment on our proposal to use a special scoring 
policy for the FY 2023 Program year.
3. Proposed Case Minimum and Measure Minimum Policies
a. Background
    Section 111(a)(1) of Division CC of the CAA amended section 
1888(h)(1) of the Act by adding paragraph (h)(1)(C), which established 
criteria for excluding SNFs from the SNF VBP Program. Specifically, 
with respect to payments for services furnished on or after October 1, 
2022, paragraph (h)(1)(C) precludes the SNF VBP Program from applying 
to a SNF for which there are not a minimum number of cases (as 
determined by the Secretary) for the measures that apply to the SNF for 
the performance period for the applicable fiscal year, or a minimum 
number of measures (as determined by the Secretary) that apply to the 
SNF for the performance period for the applicable fiscal year.
    To implement this provision, we are proposing to establish case and 
measure minimums that SNFs must meet to be included in the Program for 
a given

[[Page 22781]]

program year. These proposed case and measure minimum requirements 
would serve as eligibility criteria for determining whether a SNF is 
included in, or excluded from, the Program for a given program year. 
Inclusion in the Program for a program year means that a SNF would 
receive a SNF performance score and would be eligible to receive a 
value-based incentive payment. Exclusion from the Program for a program 
year means that, for the applicable fiscal year, a SNF would not be 
subject to the requirements under Sec.  413.338 and would also not be 
subject to a payment reduction under Sec.  413.337(f). Instead, the SNF 
would receive its full Federal per diem rate under Sec.  413.337 for 
the applicable fiscal year.
    We are proposing to establish a case minimum for each SNF VBP 
measure that SNFs must have during the performance period for the 
program year. We are also proposing that SNFs must have a minimum 
number of measures during the performance period for the applicable 
program year in order to be eligible to participate in the SNF VBP 
Program for that program year. We propose to codify these changes to 
the applicability of the SNF VBP beginning with FY 2023 at Sec.  
413.338(b).
    We are proposing that the case and measure minimums would be based 
on statistical accuracy and reliability, such that only SNFs that have 
sufficient data would be included in the SNF VBP Program for a program 
year. We believe this would ensure that we apply program requirements 
only to SNFs for which we can calculate reliable measure rates and SNF 
performance scores.
    Because the proposed case and measure minimum policies would ensure 
that SNFs participate in the program for a program year only if they 
have sufficient data for calculating accurate and reliable measure 
rates and SNF performance scores, we do not believe there is a 
continuing need to apply the low-volume adjustment (LVA) policy 
beginning with FY 2023. Accordingly, we are proposing to remove the LVA 
policy from the Program beginning with the FY 2023 program year in 
section VII.E.5. of this proposed rule.
b. Proposed Case Minimum During a Performance Period for the SNFRM 
Beginning With the FY 2023 SNF VBP Program Year
    We are proposing that beginning with the FY 2023 program year, SNFs 
must have a minimum of 25 eligible stays for the SNFRM during the 
applicable 1-year performance period in order to be eligible to receive 
a score on that measure under the SNF VBP Program.
    We believe this case minimum requirement for the SNFRM is 
appropriate and consistent with the findings of reliability tests 
conducted for the SNFRM, and it is also consistent with the case 
threshold we have applied under the LVA policy. The reliability testing 
results, which combined 2014 and 2015 calendar year (CY) SNFRM files, 
indicated that a minimum of 25 eligible stays for the SNFRM produced 
sufficiently reliable measure rates. In addition, the testing results 
found that approximately 85 percent of all SNFs met the 25-eligible 
stay minimum during the CY 2015 testing period. While excluding 15 
percent of SNFs may seem high, we continue to believe that the 25-
eligible stay minimum for the SNFRM appropriately balances quality 
measure reliability with our desire to allow as many SNFs as possible 
to participate in the Program. For further details on the measure 
testing, we refer readers to the minimum eligible stay threshold 
analysis for the SNFRM available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/Other-VBPs/SNFRM-Reliability-Testing-Memo.pdf.
    We believe this proposed case minimum requirement for the SNFRM 
would ensure that those SNFs included in the Program would receive a 
sufficiently accurate and reliable SNF performance score. However, we 
are also proposing changes to our scoring and payment policies for the 
FY 2023 SNF VBP Program in this proposed rule. If finalized, beginning 
with the FY 2023 SNF VBP program year, any SNF that does not meet this 
proposed case minimum requirement for the SNFRM during the applicable 
performance period would be excluded from the Program for the affected 
program year provided there are no other measures specified for the 
affected program year. Those SNFs would not be subject to any payment 
reductions under the Program and instead would receive their full 
Federal per diem rate.
    We invite public comment on our proposal to adopt a case minimum 
requirement for the SNFRM beginning with the FY 2023 SNF VBP program 
year.
c. Proposed Case Minimums During a Performance Period for the SNF HAI, 
Total Nurse Staffing, and DTC PAC SNF Measures
    In this proposed rule, we are proposing to adopt the SNF HAI and 
Total Nurse Staffing measures beginning with the FY 2026 program year, 
as well as the DTC PAC SNF measure beginning with the FY 2027 program 
year.
    For the SNF HAI measure, we are proposing that SNFs must have a 
minimum of 25 eligible stays during the applicable 1-year performance 
period in order to be eligible to receive a score on the measure. We 
believe this case minimum requirement for the SNF HAI measure is 
appropriate and consistent with the findings of measure testing 
analyses. For example, testing results indicated that a 25-eligible 
stay minimum produced moderately reliable measure rates for purposes of 
public reporting under the SNF QRP. In addition, testing results found 
that 85 percent of SNFs met the 25-eligible stay minimum for public 
reporting under the SNF QRP. We believe these case minimum standards 
for public reporting purposes are also appropriate standards for 
establishing a case minimum for this measure under the SNF VBP Program. 
In addition, we believe these testing results for the 25-eligible stay 
minimum 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 this measure. For further 
details on SNF HAI measure testing for the SNF QRP, we refer readers to 
the SNF HAI Measure Technical Report available at https://www.cms.gov/files/document/snf-hai-technical-report.pdf.
    For the Total Nurse Staffing measure, we are proposing that SNFs 
must have a minimum of 25 residents, on average, across all available 
quarters during the applicable 1-year performance period in order to be 
eligible to receive a score on the measure. We tested three potential 
case minimums for this measure: a 25-resident minimum, a minimum of one 
quarter of PBJ data, and a minimum of two quarters of PBJ data. Among 
all SNFs eligible for the SNF VBP Program, over 94 percent of SNFs 
satisfied the case minimum under all three alternatives tested. There 
were very minimal differences observed between the case minimums 
tested, and this finding held for most subgroups tested as well, 
including rural SNFs, large SNFs, and those SNFs serving the highest 
proportion of dually eligible beneficiaries. The only notable observed 
difference occurred within small SNFs, defined as those with fewer than 
46 beds as a proxy for size. About 90 percent of small SNFs reported 
two quarters of PBJ data, and about 92 percent of small SNFs reported 
one quarter of PBJ data, but only about 63 percent of small SNFs 
satisfied the 25-resident minimum, indicating that even

[[Page 22782]]

after two quarters of successful PBJ reporting there was a substantial 
proportion of small SNFs (about 27 percent) reporting minimal numbers 
of residents, calling into question the utility of their limited 
staffing data. After considering these alternatives, we determined that 
the proposed 25-resident minimum best balances quality measure 
reliability with our desire to score as many SNFs as possible on this 
measure. We also note that the 25-resident minimum for this measure 
would align with the case minimums we are proposing for the other 
proposed measures.
    Further, for the DTC PAC SNF measure, we are proposing that SNFs 
must have a minimum of 25 eligible stays during the applicable 2-year 
performance period in order to be eligible to receive a score on the 
measure. We believe this case minimum requirement for the DTC PAC SNF 
measure (https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/Other-VBPs/SNFRM-Reliability-Testing-Memo.pdf) is appropriate and consistent with the 
findings of measure testing analyses. Analyses conducted by CMS 
contractors found that a 25 eligible stay minimum produced good to 
excellent measure score reliability. In addition, analyses using 2015 
through 2016 Medicare FFS claims data found that 94 percent of SNFs met 
the 25 eligible stay minimum during the 2-year performance period. We 
believe these testing results for the 25 eligible stay minimum 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 this measure. The complete measure testing 
results conducted by our contractors that we included as part of the 
documentation supporting our request for NQF to endorse the measure are 
available at https://www.qualityforum.org/QPS/3481.
    We invite public comment on our proposal to adopt case minimums for 
the SNF HAI, Total Nurse Staffing, and DTC PAC SNF measures.
d. Proposed Measure Minimums for the FY 2026 and FY 2027 Program Years
    We are proposing to adopt measure minimums for the FY 2026 and FY 
2027 program years. Under these policies, only SNFs that have the 
minimum number of measures applicable to the program year would be 
eligible for inclusion in the Program for that program year.
    In this proposed rule, we are proposing to adopt two new quality 
measures (SNF HAI and Total Nurse Staffing measures) beginning with the 
FY 2026 Program. If finalized, the SNF VBP Program would consist of 
three quality measures in FY 2026 (SNF Readmission Measure, SNF HAI, 
and Total Nurse Staffing measures). We are proposing that for FY 2026, 
SNFs must have the minimum number of cases for two of these three 
measures during the performance period to receive a performance score 
and value-based incentive payment. SNFs that do not meet these minimum 
requirements would be excluded from the FY 2026 program and would 
receive their full Federal per diem rate for that fiscal year. Under 
these proposed minimum requirements, we estimate that approximately 14 
percent of SNFs would be excluded from the FY 2026 Program. 
Alternatively, if we required SNFs to have the minimum number of cases 
for all three measures during the performance period, approximately 21 
percent of SNFs would be excluded from the FY 2026 Program. We also 
assessed the consistency of value-based incentive payment adjustment 
factors, or incentive payment multipliers (IPMs), between time periods 
as a proxy for performance score reliability under the different 
measure minimum options. The testing results indicated that the 
reliability of the SNF performance score would be relatively consistent 
across the different measure minimum requirements. Specifically, for 
the FY 2026 program year, we estimate that under the proposed minimum 
of two measures, 82 percent of SNFs receiving a net-negative IPM in the 
first testing period also received a net-negative IPM in the second 
testing period. Alternatively, under a minimum of three measures for 
the FY 2026 program year, we found that the consistency was 81 percent. 
Based on these testing results, we believe the proposed minimum of two 
out of three measures for FY 2026 best balances SNF performance score 
reliability with our desire to ensure that as many SNFs as possible can 
receive a performance score and value-based incentive payment.
    We are also proposing to adopt an additional quality measure (DTC 
PAC SNF measure) beginning with the FY 2027 Program. If finalized, the 
SNF VBP Program would consist of four quality measures in FY 2027 (SNF 
Readmission Measure, SNF HAI, Total Nurse Staffing, and DTC PAC SNF 
measures). We are proposing that for FY 2027, SNFs must have the 
minimum number of cases for three of the four measures during a 
performance period to receive a performance score and value-based 
incentive payment. SNFs that do not meet these minimum requirements 
would be excluded from the FY 2027 program and would receive their full 
Federal per diem rate for that fiscal year. Under these proposed 
minimum requirements, we estimate that approximately 16 percent of SNFs 
would be excluded from the FY 2027 Program. Alternatively, if we 
required SNFs to have the minimum number of cases for all four 
measures, we estimate that approximately 24 percent of SNFs would be 
excluded from the FY 2027 Program. We also assessed the consistency of 
incentive payment multipliers (IPMs) between time periods as a proxy 
for performance score reliability under the different measure minimum 
options. The testing results indicated that the reliability of the SNF 
performance score for the FY 2027 program year would be relatively 
consistent across the different measure minimum requirements. That is, 
among the different measure minimums for the FY 2027 program year, a 
strong majority (between 85 and 87 percent) of the SNFs receiving a 
net-negative IPM for the first testing period also received a net-
negative IPM for the second testing period. These findings indicate 
that increasing the measure minimum requirements does not meaningfully 
increase the consistency of the performance score. Based on these 
testing results, we believe the proposed minimum of three out of four 
measures for FY 2027 best balances SNF performance score reliability 
with our desire to ensure that as many SNFs as possible can receive a 
performance score and value-based incentive payment.
    Under these proposals, we also estimate that 14 percent of SNFs 
would be excluded from the Program for the FY 2026 program year, but 
that the excluded SNFs would, as a whole, provide care to approximately 
2 percent of the total number of eligible SNF stays. Similarly, for the 
FY 2027 Program, we estimate that 16 percent of SNFs would be excluded 
from the Program but that the excluded SNFs, as a whole, provide care 
to approximately 2 percent of the total number of eligible SNF stays.
    We invite public comment on our proposed measure minimums for the 
FY 2026 and FY 2027 SNF VBP program years.

[[Page 22783]]

4. Proposed Update to the Scoring Policy for SNFs Without Sufficient 
Baseline Period Data Beginning With the FY 2026 Program Year
    In the FY 2019 SNF PPS final rule (83 FR 39278), we finalized a 
policy to score SNFs based only on their achievement during the 
performance period for any program year for which they do not have 
sufficient baseline period data, which we defined as SNFs with fewer 
than 25 eligible stays during the baseline period for a fiscal year. We 
codified this policy at Sec.  413.338(d)(1)(iv) of our regulations.
    We continue to be concerned that measuring SNF performance on a 
given measure for which the SNF does not have sufficient baseline 
period data may result in unreliable improvement scores for that 
measure and, as a result, unreliable SNF performance scores. However, 
the current policy was designed for a SNF VBP Program with only one 
measure. As we continue to add measures to the Program, we aim to 
maintain the reliability of our SNF performance scoring. Therefore, we 
are proposing to update our policy beginning with the FY 2026 program 
year. Under the proposed update, we would not award improvement points 
to a SNF on a measure for a program year if the SNF has not met the 
case minimum for that measure during the baseline period that applies 
to the measure for the program year. That is, if a SNF does not meet a 
case minimum threshold for a given measure during the applicable 
baseline period, that SNF would only be eligible to be scored on 
achievement for that measure during the performance period for that 
measure for the applicable fiscal year.
    For example, if a SNF has fewer than the minimum of 25 eligible 
stays during the applicable 1-year baseline period for the SNF HAI 
measure for FY 2026, that SNF would only be scored on achievement 
during the performance period for the SNF HAI measure for FY 2026, so 
long as that SNF meets the case minimum for that measure during the 
applicable performance period.
    We are also proposing to codify this update in our regulation text 
at Sec.  413.338(e)(1)(iv).
    We invite public comment on this proposal to update the policy for 
scoring SNFs that do not have sufficient baseline period data.
5. Proposal To Remove the LVA Policy From the SNF VBP Program Beginning 
With the FY 2023 Program Year
    In the FY 2019 SNF PPS final rule (83 FR 39278 through 39280), we 
finalized our LVA policy, which provides an adjustment to the Program's 
scoring methodology to ensure low-volume SNFs receive sufficiently 
reliable performance scores for the SNF readmission measure. In that 
final rule, we also codified the LVA policy in Sec.  413.338(d)(3) of 
our regulations. As we discussed in the FY 2019 SNF PPS final rule, we 
found that the reliability of the SNFRM measure rates and resulting 
performance scores were adversely affected if SNFs had fewer than 25 
eligible stays during the performance period for a program year (83 FR 
39279). Therefore, we believed that assigning a performance score that 
would result in a value-based incentive payment amount that is equal to 
the adjusted Federal per diem rate that the SNF would have received in 
the absence of the Program, to any SNF with fewer than 25 eligible 
stays for the SNFRM during the performance period, was the most 
appropriate adjustment for ensuring reliable performance scores.
    However, we no longer believe the LVA policy is necessary because 
we are now required under the statute to have case and measure minimum 
policies for the SNF VBP Program, and those policies will achieve the 
same payment objective as the LVA policy. Therefore, we are proposing 
to remove the LVA Policy from the SNF VBP Program's scoring methodology 
beginning with the FY 2023 program year. With the removal of the LVA 
policy, the total amount available for a fiscal year would no longer be 
increased as appropriate for each fiscal year to account for the 
assignment of a performance score to low-volume SNFs. We are proposing 
to update the Total amount available for a fiscal year to 60 percent of 
the total amount of the reduction to the adjusted SNF PPS payments for 
that fiscal year, as estimated by CMS, in our regulations atSec.  
413.338(c)(2)(i). We are proposing to update the LVA policy at Sec.  
413.338(d)(3) to reflect its removal from the program.
    We invite public comment on our proposal to remove the LVA policy 
from the SNF VBP Program beginning with the FY 2023 program year.
6. Proposal To Update the SNF VBP Scoring Methodology Beginning in the 
FY 2026 Program Year
a. Background
    In the FY 2017 SNF PPS final rule (81 FR 52000 through 52005), we 
adopted a scoring methodology for the SNF VBP Program where we score 
SNFs on their performance on the SNFRM, award between 0 and 100 points 
to each SNF (with up to 90 points available for improvement), and award 
each SNF a SNF performance score consisting of the higher of its scores 
for achievement and improvement. The SNF performance score is then 
translated into a value-based incentive payment multiplier that can be 
applied to each SNF's Medicare claims during the SNF VBP Program year 
using an exchange function. Additionally, in the FY 2018 SNF PPS final 
rule (82 FR 36615), we adopted a clarification of our rounding policy 
in SNF VBP scoring to award SNF performance scores that are rounded to 
the nearest ten-thousandth of a point, or with no more than five 
significant digits to the right of the decimal point. We have also 
codified numerous aspects of the SNF VBP Program's policies in our 
regulations at Sec.  413.338, and our scoring policies appear in 
paragraph (d) of that section.
    We refer readers to the FY 2017 rule cited above for a detailed 
discussion of the SNF VBP Program's scoring methodology, public 
comments on the proposed policies, and examples of our scoring 
calculations.
b. Proposed Measure-Level Scoring Update
    We are proposing to update our achievement and improvement scoring 
methodology to allow a SNF to earn a maximum of 10 points on each 
measure for achievement, and a maximum of 9 points on each measure for 
improvement. For purposes of determining these points, we are proposing 
to define the benchmark as the mean of the top decile of SNF 
performance on the measure during the baseline period and the 
achievement threshold as the 25th percentile of national SNF 
performance on the measure during the baseline period.
    We are proposing to award achievement points to SNFs based on their 
performance period measure rate for each measure according to the 
following:
     If a SNF's performance period measure rate is equal to or 
greater than the benchmark, the SNF would be awarded 10 points for 
achievement.
     If a SNF's performance period measure rate is less than 
the achievement threshold, the SNF would receive 0 points for 
achievement.
     If a SNF's performance period measure rate is equal to or 
greater than the achievement threshold, but less than the benchmark, we 
will award between 0 and 10 points according to the following formula:

[[Page 22784]]

[GRAPHIC] [TIFF OMITTED] TP15AP22.026

    We are also proposing to award improvement points to SNFs based on 
their performance period measure rate according to the following:
     If a SNF's performance period measure rate is equal to or 
lower than its baseline period measure rate, the SNF would be awarded 0 
points for improvement.
     If a SNF's performance period measure rate was equal to or 
higher than the benchmark, the SNF would be awarded 9 points for 
improvement.
     If a SNF's performance period measure rate was greater 
than its baseline period measure rate but less than the benchmark, we 
will award between 0 and 9 points according to the following formula:
[GRAPHIC] [TIFF OMITTED] TP15AP22.027

    Under this proposal, we will score SNFs' performance on achievement 
and improvement for each measure and award them the higher of the two 
scores for each measure to be included in the SNF performance score, 
except in the instance that the SNF does not meet the case minimum 
threshold for the measure during the applicable baseline period, in 
which case we propose in section VII.E.4. that the SNF would only be 
scored on achievement. As discussed in the following subsection of this 
proposed rule, we will then sum each SNFs' measure points and normalize 
them to arrive at a SNF performance score that ranges between 0 and 100 
points. We believe that this policy appropriately recognizes the best 
performers on each measure and reserves the maximum points for their 
performance levels while also recognizing that improvement over time is 
important and should also be rewarded.
    We further propose that this change would apply beginning with the 
FY 2026 SNF VBP program year. Under this proposal, all measures in the 
expanded SNF VBP Program would be weighted equally, as we believe that 
an equal weighting approach is simple for participating SNFs to 
understand and assigns significant scoring weight (that is, 33.33 
percentage points if a SNF has sufficient data on all three measures 
proposed for FY 2026) to each measure topic covered by the expanded SNF 
VBP Program. However, as we consider whether we should propose to adopt 
additional measures, we also intend to consider whether we should group 
the measures into domains and weight them, similar to what we do under 
the Hospital VBP Program scoring methodology.
    We view this proposed change to measure-level scoring as a 
necessary update to the SNF VBP Program's scoring methodology to 
incorporate additional quality measures and to allow us to add more 
measures in the future. We are also proposing to codify these updates 
to our scoring methodology in our regulation text by revising the 
heading for paragraph (d) and adding paragraph (e)(1) at Sec.  413.338.
    We invite public comment on this proposal.
c. Proposed Normalization Policy
    We continue to believe that awarding SNF performance scores out of 
a total of 100 points helps stakeholders more easily understand the 
performance evaluation that we provide through the SNF VBP Program. We 
therefore believe that continuing to award SNF performance scores out 
of 100 points would help stakeholders understand the revised scoring 
methodology and would allow the scoring methodology to accommodate 
additional measures in the future without more methodological changes.
    Therefore, we considered how we could construct the SNF performance 
score such that the scores continue to range between 0 and 100 points. 
We considered our past experience in our VBP programs, specifically 
including our experience with the Hospital VBP Program, where we award 
between 0 and 10 points to participating providers for their 
performance on each measure, and to arrive at a Total Performance Score 
that ranges between 0 and 100 points regardless of the number of 
measures on which the hospital has sufficient data, we normalize 
hospitals' scores. We believe the Hospital VBP Program's success in 
comprehensible measure-level scoring provides a strong model for the 
expanded SNF VBP Program.
    We are therefore proposing to adopt a ``normalization'' policy for 
SNF performance scores under the expanded SNF VBP Program, effective in 
the FY 2026 program year. Under this policy, we would calculate a raw 
point total for each SNF by adding up the SNF's score on each of the 
measures. For example, a SNF that met the case minimum to receive a 
score on three quality measures would receive a score between 0 to 30 
points, while a SNF that met the case minimum to receive a score on two 
quality measures would receive a score between 0 to 20 points. We would 
then normalize the raw point totals by converting them to a 100-point 
scale, with the normalized values being awarded as the SNF performance 
score. For example, we would normalize a SNF's raw point total of 27 
points out of 30 by converting that total to a 100-point scale, with 
the result that the SNF would receive a SNF performance score of 90.
    In addition to allowing us to maintain a 100-point total 
performance score scale, this policy would enable us to adopt 
additional quality measures for the program without making further 
changes to the scoring methodology. If, for example, we proposed to 
adopt a total of seven quality measures in the future, the 
normalization policy would enable us to continue to award SNF 
performance scores on a 100-point scale, even though the maximum raw 
point total would be 70 points.

[[Page 22785]]

    We view this proposed normalization policy as a useful update to 
the SNF VBP Program's scoring methodology to accommodate additional 
quality measures and to ensure that the public understands the SNF 
performance scores that we award. We are also proposing to codify these 
updates to our scoring methodology by adding paragraph (e)(2) to our 
regulation text at Sec.  413.338.
    We invite public comment on our proposal.

F. Proposal To Adopt a Validation Process for the SNF VBP Program 
Beginning With the FY 2023 Program Year

    Section 1888(h)(12) of the Act (as added by Division CC, section 
111(a)(4) of the Consolidated Appropriations Act, 2021 (Pub. L. 116-
120)), requires the Secretary to apply a process to validate SNF VBP 
program measures and data, as appropriate. We are proposing to adopt a 
validation process for the Program beginning with the FY 2023 Program 
year.
    For the SNFRM measure, we are proposing that the process we 
currently use to ensure the accuracy of the SNFRM satisfies this 
statutory requirement. Information reported through claims for the 
SNFRM measure are validated for accuracy by Medicare Administrative 
Contractors (MACs) to ensure accurate Medicare payments. MACs 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. We are proposing to codify these proposals for the FY 2023 
SNF VBP in our regulation text at Sec.  413.338(j).
    We are considering additional validation methods that may be 
appropriate to include in the future for the proposed SNF HAI, DTC PAC 
SNF, and Total Nurse Staffing measures, as well as for other new 
measures we may consider for the program, and for other SNF quality 
measures and assessment data. For more information, see section 
VII.I.c.3. of this proposed rule, Request for Comment on the SNF VBP 
Program Approach to Validation.
    We invite public comment on our proposal to adopt a validation 
process for the SNF VBP Program beginning with the FY 2023 program 
year.

G. Proposed SNF Value-Based Incentive Payments for FY 2023

    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. We adopted these policies for FY 
2019 and subsequent fiscal years.
    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).
    As discussed in section VII.B.1. of this proposed rule, we are 
proposing to suppress the SNFRM for the FY 2023 program year and 
assigning all SNFs a performance score of zero, which would result in 
all participating SNFs receiving an identical performance score, as 
well as an identical incentive payment multiplier. Under this proposal, 
we are proposing to not rank SNFs for FY 2023. We are also proposing to 
reduce each participating SNF's adjusted Federal per diem rate for FY 
2023 by 2 percentage points and to award each participating SNF 60 
percent of that 2 percent withhold, resulting in a 1.2 percent payback 
for the FY 2023 program year. We believe this continued application of 
the 2 percent withhold is spread evenly across all SNFs is the most 
equitable way to reduce the impact of the withhold considering our 
proposal to award a performance score of zero to all SNFs. We are also 
proposing that those SNFs that do not meet the proposed case minimum 
for the SNFRM for FY 2023 would be excluded from the Program for FY 
2023. We are proposing to update Sec.  413.338(i) to reflect that this 
special scoring and payment policy will apply for FY 2023 in addition 
to FY 2022. As noted in section VII.B.1. of this proposed rule, our 
goal is to resume use of the scoring methodology we finalized for the 
program prior to the PHE beginning with the FY 2024 program year.
    We invite public comment on this proposed change to the SNF VBP 
payment policy for the FY 2023 program year.

H. Public Reporting on the Provider Data Catalog Website

1. Background
    Section 1888(g)(6) of the Act requires the Secretary to establish 
procedures to make SNFs' performance information on SNF VBP Program 
measures 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 directive and the statutory deadline of 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.
    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 SNF performance scores 
and their ranking. 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 52009), we discussed the 
statutory requirements governing public reporting of SNFs' performance 
information under the SNF VBP Program. In the FY 2018 SNF PPS final 
rule (82 FR 36622 through 36623), we finalized our policy to publish 
SNF VBP Program performance information on the Nursing Home Compare or 
successor website after SNFs have had an opportunity to review and 
submit corrections to that information under the two-phase Review and 
Correction process that we adopted in the FY 2017 SNF PPS final rule 
(81 FR 52007 through 52009) and for which we adopted additional 
requirements in the FY 2018 SNF PPS final rule. In the FY 2018 SNF PPS 
final rule, we also adopted requirements to rank SNFs and adopted data 
elements that we will include in the ranking to provide consumers and 
stakeholders with the necessary information to evaluate SNF's 
performance under the Program (82 FR 36623).
    As discussed in section VII.B.1. of this proposed rule, we are 
proposing to suppress the SNFRM for the FY 2023 program year due to the 
impacts of the PHE for COVID-19. If that proposal is finalized, for all 
SNFs participating in the FY 2023 SNF VBP Program, we would use the 
performance period (FY 2021, October 1, 2020 through September 30, 
2021) we adopted in the FY 2021 SNF PPS final rule (85 FR 47624), as 
well as the previously finalized baseline period (FY 2019,

[[Page 22786]]

October 1, 2018 through September 30, 2019) to calculate each SNF's 
RSRR for the SNFRM. We are also proposing in section VII.E.2. of this 
proposed rule to assign all SNFs a performance score of zero. This will 
result in all participating SNFs receiving an identical performance 
score, as well as an identical incentive payment multiplier.
    While we would publicly report the SNFRM rates for the FY 2023 
program year, we would make clear in the public presentation of those 
data that we are suppressing the use of those data for purposes of 
scoring and payment adjustments in the FY 2023 SNF VBP Program given 
the significant changes in SNF patient case volume and facility-level 
case-mix described earlier.
2. Proposed Changes to the Data Suppression Policy for Low-Volume SNFs 
Beginning With the FY 2023 SNF VBP Program Year
    In the FY 2020 SNF PPS final rule (84 FR 38823 through 38824), we 
adopted a data suppression policy for low-volume SNF performance 
information. Specifically, we finalized that we will suppress the SNF 
performance information available to display as follows: (1) If a SNF 
has fewer than 25 eligible stays during the baseline period for a 
program year, we will not display the baseline risk-standardized 
readmission rate (RSRR) or improvement score, although we will still 
display the performance period RSRR, achievement score, and total 
performance score if the SNF had sufficient data during the performance 
period; (2) if a SNF has fewer than 25 eligible stays during the 
performance period for a program year and receives an assigned SNF 
performance score as a result, we will report the assigned SNF 
performance score and we will not display the performance period RSRR, 
the achievement score, or improvement score; and (3) if a SNF has zero 
eligible cases during the performance period for a program year, we 
will not display any information for that SNF. We codified this policy 
in the FY 2021 SNF PPS final rule (85 FR 47626) at Sec.  
413.338(e)(3)(i) through (iii).
    As discussed in section VII.B.1. of this proposed rule, we are 
proposing to suppress the SNFRM for the FY 2023 program year, and we 
are proposing special scoring and payment policies for FY 2023. In 
section VII.E.3.b of this proposed rule, we are proposing to adopt a 
new case minimum that would apply to the SNFRM beginning with FY 2023, 
new case minimums that would apply to the SNF HAI and Total Nurse 
Staffing measures and a measure minimum that would apply beginning with 
FY 2026, a new case minimum that would apply to the DTC PAC SNF measure 
and a new measure minimum that would apply beginning with FY 2027. As a 
result of these proposed policies, and in order to implement them for 
purposes of clarity and transparency in our public reporting, we 
propose revising the data suppression policy as follows:
    (1) If a SNF does not have the minimum number of cases during the 
baseline period that applies to a measure for a program year, we would 
publicly report the SNF's measure rate and achievement score if the SNF 
had minimum number of cases for the measure during the performance 
period for the program year;
    (2) If a SNF does not have the minimum number of cases during the 
performance period that applies to a measure for a program year, we 
would not publicly report any information on the SNF's performance on 
that measure for the program year;
    (3) If a SNF does not have the minimum number of measures during 
the performance period for a program year, we would not publicly report 
any data for that SNF for the program year.
    We are proposing to codify this policy at Sec.  413.338(f)(4).
    We invite public comment on these proposals.

I. Requests for Comment Related to Future SNF VBP Program Expansion 
Policies

1. Requests for Comment on Additional SNF VBP Program Measure 
Considerations for Future Years
(a) Request for Comment on Including a Staffing Turnover Measures in a 
Future SNF VBP Program Year
    In the FY 2022 SNF PPS final rule (86 FR 42507 through 42511), we 
summarized stakeholder feedback on our request for comments related to 
potential future measures for the SNF VBP Program, including a specific 
request for comment on measures that focus on staffing turnover. 
Specifically, we noted that we have been developing measures of staff 
turnover with data that are required to be submitted under section 
1128I(g)(4) of the Act, with the goal of making the information 
publicly available. We stated that, through our implementation of the 
PBJ staffing data collection program, we indicated that we will be 
reporting rates of employee turnover in the future (for more 
information on this program, see CMS memorandum QSO-18-17-NH).\248\ We 
refer readers to the FY 2022 SNF PPS final rule for additional details 
on this request for public comments and a summary of the public 
comments we received (86 FR 42507 through 42511).
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    \248\ https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/SurveyCertificationGenInfo/Downloads/QSO18-17-NH.pdf.
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    Nursing staff turnover has long been identified as a meaningful 
factor in nursing home quality of care.\249\ Studies have shown a 
relationship between staff turnover and quality outcomes; for example, 
higher staff turnover is associated with an increased likelihood of 
receiving an infection control citation.\250\ The collection of 
auditable payroll-based daily staffing data through the PBJ system has 
provided an opportunity to calculate, compare, and publicly report 
turnover rates; examine facility characteristics associated with higher 
or lower turnover rates; and further measure the relationship between 
turnover and quality outcomes. For example, a recent study using PBJ 
data found that nursing staff turnover is higher than previously 
understood, variable across facilities, and correlated with 
organizational characteristics such as for-profit status, chain 
ownership, and higher Medicaid census.\251\ In addition, we have found 
that higher overall star ratings are associated with lower average 
staff turnover rates, suggesting that lower staff turnover rates are 
associated with higher overall nursing home quality.\252\
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    \249\ 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.
    \250\ Lacey Loomer, David C. Grabowski, Ashvin Gandhi, 
Association between Nursing Home Staff Turnover and Infection 
Control Citations, SSRN Electronic Journal, 10.2139/ssrn.3766377, 
(2020). https://onlinelibrary.wiley.com/doi/abs/10.1111/1475-6773.13877.
    \251\ Gandhi, A., Yu, H., & Grabowski, D., ``High Nursing Staff 
Turnover in Nursing Homes Offers Important Quality Information'' 
(2021) Health Affairs, 40(3), 384-391. doi:10.1377/
hlthaff.2020.00957. https://www.healthaffairs.org/doi/full/10.1377/hlthaff.2020.00957.
    \252\ https://www.cms.gov/files/document/qso-22-08-nh.pdf.
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    In January of 2022, we began publicly reporting a staffing turnover 
measure on the Compare tool currently hosted by HHS, available at 
https://www.medicare.gov/care-compare, and this information will be 
included in the Nursing Home Five Star Quality Rating System in July 
2022. We refer readers to the Nursing Home Staff Turnover and Weekend 
Staffing Levels Memo for additional information related to this measure 
at https://www.cms.gov/files/document/qso-22-08-nh.pdf. We believe 
staffing turnover is an important indicator of quality of care provided 
in

[[Page 22787]]

nursing homes and SNFs. Additionally, in response to our request for 
comment on a staffing turnover measure, stakeholders strongly 
recommended that we consider measures of staffing turnover to assess 
patterns and consistency in staffing levels. As a part of our goals to 
build a robust and comprehensive measure set for the SNF VBP Program 
and in alignment with stakeholder recommendations, we intend to propose 
to adopt a staffing turnover measure in the SNF VBP Program in the FY 
2024 SNF PPS proposed rule. Specifically, the measure we intend to 
include in the SNF VBP program is the percent of total nurse staff that 
have left the facility over the last year. Total nurse staff include 
RNs, LPNs, and nurse aides. More information on this measure, can be 
found in the Five Star Rating Technical Users' Guide at https://www.cms.gov/medicare/provider-enrollment-and-certification/certificationandcomplianc/downloads/usersguide.pdf.
    The Biden-Harris Administration is committed to improving the 
quality of care in nursing homes. As stated in a fact sheet entitled 
``Protecting Seniors by Improving Safety and Quality of Care in the 
Nation's Nursing Homes,'' we are committed to strengthening the SNF VBP 
Program and have begun to measure and publish staff turnover and 
weekend staffing levels, metrics which closely align with the quality 
of care provided in a nursing home. We intend to propose new measures 
based on staffing adequacy, the resident experience, as well as how 
well facilities retain staff. Accordingly, we seek commenters' feedback 
on including the staff turnover measure that captures the percent of 
total nurse staff that have left the facility over the last year for 
the SNF VBP Program as currently specified or whether the measure 
should be revised before being proposed for inclusion in the SNF VBP 
program.
    In addition, we are interested in whether we should explore the 
development of a composite measure that would capture multiple aspects 
of staffing, including both total nurse hours and the staff turnover 
measure rather than having separate but related measures related to 
nursing home staffing, such a measure could potentially replace the 
initial measure we intend to propose to include in SNF VBP for FY 2024. 
Preliminary analyses using the staff turnover data on the Medicare.gov 
Care Compare website have indicated that as the lower average staff 
turnover decreases, the overall star ratings for facilities increases, 
suggesting that lower turnover is associated with higher overall 
quality,\253\ and research has indicated that staff turnover has been 
linked with increased infection control issues.\254\ We believe it is 
important to capture and tie aspects of both staffing levels and 
staffing turnover to quality payment and welcome commenter's feedback 
for how to balance those goals under the SNF VBP program. We are also 
interested to hear about actions SNFs may take or have taken to reduce 
staff turnover in their facilities, and for SNFs that did reduce staff 
turnover, the reduction's observed impact on quality of care. In 
particular, we are interested in best practices for maintaining 
continuity of staffing among both nursing and nurse aide staff. 
Finally, we are interested in commenters feedback on any considerations 
we should take into account related to the impact that including a 
Nursing Home Staff Turnover measure may have on health equity. Before 
proposing to include this measure in the SNF VBP Program in the FY 2024 
SNF PPS proposed rule, we would include the measure on a list of 
measures under consideration, as described in section 1890A of the Act.
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    \253\ To Advance Information on Quality of Care, CMS Makes 
Nursing Home Staffing Data Available, available at: https://www.cms.gov/newsroom/press-releases/advance-information-quality-care-cms-makes-nursing-home-staffing-data-available.
    \254\ Lacey Loomer, David C. Grabowski, Ashvin Gandhi, 
Association between Nursing Home Staff Turnover and Infection 
Control Citations, SSRN Electronic Journal, 10.2139/ssrn.3766377, 
(2020). https://onlinelibrary.wiley.com/doi/abs/10.1111/1475-6773.13877.
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(b) Request for Comment on Including the National Healthcare Safety 
Network (NHSN) COVID-19 Vaccination Coverage Among Healthcare Personnel 
Measure in a Future SNF VBP Program Year
    In addition to the staffing turnover measure and the other 
potential future measures listed in the FY 2022 SNF PPS final rule, we 
are also considering the inclusion of the National Healthcare Safety 
Network (NHSN) COVID-19 Vaccination Coverage among Healthcare Personnel 
measure, which measures the percentage of healthcare personnel who 
receive a complete COVID-19 vaccination course. This measure data is 
collected by the CDC NHSN and the measure was finalized for use in the 
SNF QRP in the FY 2022 SNF PPS final rule (86 FR 42480 through 42489). 
We seek commenters' feedback on whether to propose to include this 
measure in a future SNF VBP program year. Before proposing to include 
any such measure, we would include the measure on a list of measures 
under consideration, as required by section 1890A of the Act.
(c) Request for Comment on Updating the SNF VBP Program Exchange 
Function
    In the FY 2018 SNF PPS final rule (82 FR 36616 through 36619), we 
adopted an exchange function methodology for translating SNFs' 
performance scores into value-based incentive payments. We illustrated 
four possibilities for the functional forms that we considered--linear, 
cube, cube root, and logistic--and discussed how we assessed how each 
of the four possible exchange function forms would affect SNFs' 
incentive payments under the Program. We also discussed several 
important factors that we considered when adopting an exchange 
function, including the numbers of SNFs that receive more in value-
based incentive payments in each scenario compared to the number of 
SNFs for which a reduction is applied to their Medicare payments, as 
well as the resulting incentives for SNFs to reduce hospital 
readmissions. We also evaluated the distributions of value-based 
incentive payment adjustments and the functions' results for compliance 
with the Program's statutory requirements. We found that the logistic 
function maximized the number of SNFs with positive payment adjustments 
among SNFs measured using the SNFRM. We also found that the logistic 
function best fulfilled the requirement that SNFs in the lowest 40 
percent of the Program's ranking receive a lower payment rate than 
would otherwise apply, resulted in an appropriate distribution of 
value-based incentive payment percentages, and otherwise fulfilled the 
Program's requirements specified in statute.
    Additionally, we published a technical paper describing the 
analyses of the SNF VBP Program exchange function forms and payback 
percentages that informed the policies that we adopted in the FY 2018 
SNF PPS final rule. The paper is available on our website at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/Other-VBPs/SNF-VBP-exchange-function-analysis.pdf.
    As discussed earlier, we are proposing numerous policy changes to 
expand the SNF VBP Program's measure set based on authority provided by 
the Consolidated Appropriations Act, 2021, including additional quality 
measures and adjustments to the Program's scoring methodology to 
accommodate the presence of more than one quality measure. We are also 
considering whether we should propose a new form for the exchange 
function or modify the

[[Page 22788]]

logistic exchange function in future years.
    When we adopted the logistic function for the SNF VBP Program, we 
focused on that function's ability, coupled with the 60 percent payback 
percentage, to provide net-positive value-based incentive payments to 
as many top-performing SNFs as possible. We believed that structuring 
the Program's incentive payments in this manner enabled us to reward 
the Program's top-performing participants and provide significant 
incentives for SNFs that were not performing as well to improve over 
time.
    We continue to believe that these considerations are important and 
that net-positive incentive payments help drive quality improvement in 
the SNF VBP Program. However, in the context of a value-based 
purchasing program employing multiple measures, we are considering 
whether a new functional form or modifications to the existing logistic 
exchange function may provide the best incentives to SNFs to improve on 
the Program's measures.
    If finalized, the additional measures that we are proposing for the 
SNF VBP Program would align the Program more closely with the Hospital 
VBP Program, on which some of SNF VBP's policies, like the exchange 
function methodology, are based. The Hospital VBP Program employs a 
linear exchange function to translate its Total Performance Scores into 
value-based incentive payment percentages that can be applied to 
hospitals' Medicare claims. A linear exchange function is somewhat 
simpler for stakeholders to understand but presents less of an 
opportunity to reward top performers than the logistic form that we 
currently employ in the SNF VBP Program at https://data.cms.gov/provider-data/ or https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/SNF-VBP/SNF-VBP-Page.
    We request stakeholders' feedback on whether we should consider 
proposing either a new functional form or modified logistic exchange 
function for the SNF VBP Program. Specifically, we request comments on 
whether the proposed addition of new quality measures in the Program 
should weigh in favor of a new exchange function form, a modified 
logistic exchange function, or no change to the existing exchange 
function, whether stakeholders believe that the increased incentive 
payment percentages for top performers offered by the logistic function 
should outweigh the simplicity of the linear function, and whether we 
should further consider either the cube, cube root, or other functional 
forms.
3. Request for Comment on the Validation of SNF Measures and Assessment 
Data
    We have proposed to adopt measures for the SNF VBP Program that are 
calculated using data from a variety of sources, including Medicare FFS 
claims, the minimum data set (MDS), and the PBJ system, and we are 
seeking feedback on the adoption of additional validation procedures. 
In addition, section 1888(h)(12) of the Act requires the Secretary to 
apply a process to validate SNF VBP program measures, quality measure 
data, and assessment data as appropriate. MDS information is 
transmitted electronically by nursing homes to the national MDS 
database at CMS. The data set was updated in 2010 from MDS 2.0 to MDS 
3.0 to address concerns about the quality and validity of the MDS 2.0 
data. Final testing of MDS 3.0 showed strong results, with the updated 
database outperforming MDS 2.0 in terms of accuracy, validity for 
cognitive and mood items, and clinical relevance.\255\ Research has 
also shown that MDS 3.0 discharge data match Medicare enrollment and 
hospitalization claims data with a high degree of accuracy.\256\
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    \255\ RAND MDS 3.0 Final Study Report: https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Downloads/MDS30FinalReport-Appendices.zip.
    \256\ Rahman M, Tyler D, Acquah JK, Lima J, Mor V. Sensitivity 
and specificity of the Minimum Data Set 3.0 discharge data relative 
to Medicare claims. J Am Med Dir Assoc. 2014;15(11):819-824. 
doi:10.1016/j.jamda.2014.06.017: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4731611/.
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    Although The MDS data sets are assessed for accuracy, as described 
above, we are interested in ensuring the validity of the data reported 
by skilled nursing facilities because use of this data would have 
payment implications under the SNF VBP Program. Accordingly, we are 
requesting stakeholder feedback on the feasibility and need to select 
SNFs for validation via a chart review to determine the accuracy of 
elements entered into MDS 3.0 and PBJ. Additionally, we request 
feedback on data validation methods and procedures that could be 
utilized to ensure data element validity and accuracy.
    We note that other programs, including the Hospital IQR (85 FR 
58946) and Hospital OQR programs (76 FR 74485), have developed 
validation processes for chart-abstracted measures and electronic 
clinical quality measures (eCQMs), data sources not utilized for the 
SNF VBP Program. However, there are other elements of existing 
programs' validation procedures that may be considered for a future SNF 
VBP Program validation effort. For example, we request feedback on the 
volume of facilities to select for validation under the SNF VBP 
Program. We estimate that 3,300 hospitals report data under the 
Hospital OQR (86 FR 63961) and Hospital IQR (86 FR 45508) Programs. We 
estimate that over 15,000 SNFs are eligible for the SNF VBP Program. 
The Hospital OQR Program randomly selects the majority of hospitals 
(450 hospitals) for validation and additionally select a subset of 
targeted hospitals (50 hospitals) (86 FR 63872). Under the Hospital IQR 
Program, 400 hospitals are selected randomly and up to 200 hospitals 
are targeted for chart-abstracted data validation and up to 200 
hospitals are randomly selected for eCQM data validation (86 FR 45424). 
We sample approximately 10 records from 300 randomly selected 
facilities under the ESRD QIP Program (82 FR 50766).
    We also request stakeholder's feedback on the use of both random 
and targeted selection of facilities for validation. The Hospital OQR 
program identifies hospitals for targeted validation based on whether 
they have previously failed validation or have reported an outlier 
value deviating markedly from the measure values for other hospitals 
(more than 3 standard deviations of the mean) (76 FR 74485). Validation 
targeting criteria utilized by the Hospital IQR Program include factors 
such as: (1) Abnormal, conflicting or rapidly changing data patterns; 
(2) facilities which have joined the program within the previous 3 
years, and which have not been previously validated or facilities which 
have not been randomly selected for validation in any of the previous 3 
years; and (3) any hospital that passed validation in the previous 
year, but had a two-tailed confidence interval that included 75 percent 
(85 FR 58946).
    Finally, we request stakeholder feedback on the implementation 
timeline for additional SNF VBP Program validation processes, as well 
as validation processes for other quality measures and assessment data. 
We believe it may be feasible to implement additional validation 
procedures beginning with data from the FY 2026 program year, at the 
earliest. Additionally, we may consider the adoption of a pilot of 
additional data validation processes; such an approach would be 
consistent with the implementation of the ESRD QIP data validation 
procedures, which began with a pilot in CY 2014 (82 FR 50766).

[[Page 22789]]

    We request stakeholder's feedback on the data validation 
considerations for the SNF VBP Program discussed previously in this 
section.
4. Request for Comment on a SNF VBP Program Approach To Measuring and 
Improving Health Equity
    Significant and persistent inequities in healthcare 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, and queer (LGBTQ+) community; living in a rural area; 
being a member of a religious minority; or being near or below the 
poverty level, is often associated with worse health 
outcomes.257 258 259 260 261 262 263 264 265 In accordance 
with Executive Order 13985 of January 20, 2021 on Advancing Racial 
Equity and Support for Underserved Communities Through the Federal 
Government, equity is defined as 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, 
and queer (LGBTQ+) persons; persons with disabilities; persons who live 
in rural areas; and persons otherwise adversely affected by persistent 
poverty or inequality (86 FR 7009). In February 2022, we further 
expanded on this definition by defining 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. 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, eliminating avoidable differences in health outcomes 
experienced by people who are disadvantaged or underserved, and 
providing the care and support that our enrollees need to thrive. Over 
the past decade we have enacted a suite of programs and policies aimed 
at reducing health care disparities including the CMS Mapping Medicare 
Disparities Tool,\266\ the CMS Innovation Center's Accountable Health 
Communities Model,\267\ the CMS Disparity Methods stratified reporting 
program,\268\ and efforts to expand social risk factor data collection, 
such as the collection of Standardized Patient Assessment Data Elements 
in the post-acute care setting.\269\
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    \257\ Joynt KE, Orav E, Jha AK. (2011). Thirty-day readmission 
rates for Medicare beneficiaries by race and site of care. JAMA, 
305(7):675-681.
    \258\ 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.
    \259\ 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.
    \260\ 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.
    \261\ 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-incomehealth-status-recap.pdf.
    \262\ https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf.
    \263\ www.cdc.gov/mmwr/volumes/70/wr/mm7005a1.htm.
    \264\ Milkie Vu et al. Predictors of Delayed Healthcare Seeking 
Among American Muslim Women, Journal of Women's Health 26(6) (2016) 
at 58; S.B. Nadimpalli, et al., The Association between 
Discrimination and the Health of Sikh Asian Indians Health Psychol. 
2016 Apr; 35(4): 351-355.
    \265\ 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.
    \266\ https://www.cms.gov/About-CMS/Agency-Information/OMH/OMH-Mapping-Medicare-Disparities.
    \267\ https://innovation.cms.gov/innovation-models/ahcm.
    \268\ https://qualitynet.cms.gov/inpatient/measures/disparity-methods.
    \269\ 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.
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    As we continue to leverage our value-based purchasing programs to 
improve quality of care across settings, we are interested in exploring 
the role of health equity in creating better health outcomes for all 
populations in these programs. As the March 2020 ASPE Report to 
Congress on Social Risk Factors and Performance in Medicare's VBP 
Program notes, it is important to implement strategies that cut across 
all programs and health care settings to create aligned incentives that 
drive providers to improve health outcomes for all beneficiaries.\270\ 
Therefore, in this proposed rule, we are requesting stakeholder 
feedback on guiding principles for a general framework that could be 
utilized across our quality programs to assess disparities in 
healthcare quality in a broader Request for Information (RFI) in 
section VI.E. of this proposed rule. We refer readers to this RFI 
titled, ``Overarching Principles for Measuring Healthcare Quality 
Disparities Across CMS Quality Programs--A Request for Information,'' 
which includes a complete discussion on the key considerations that we 
intend to take into account when determining how to address healthcare 
disparities and advance health equity across all of our quality 
programs. Additionally, we are interested in stakeholder feedback on 
specific actions the SNF VBP Program can take to align with other 
value-based purchasing and quality programs to address healthcare 
disparities and advance health equity.
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    \270\ 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/social-risk-factors-and-medicares-value-basedpurchasing-programs.
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    As we continue assessing the SNF VBP Program's policies in light of 
its operation and its expansion as directed by the CAA, we request 
public comments on policy changes that we should consider on the topic 
of health equity. We specifically request comments on whether we should 
consider incorporating adjustments into the SNF VBP Program to reflect 
the varied patient populations that SNFs serve around the country and 
tie health equity outcomes to SNF payments under the Program. These 
adjustments could occur at the measure level in forms such as 
stratification (for example, based on dual status or other metrics) or 
including measures of social determinants of health (SDOH). These 
adjustments could also be incorporated at the scoring or incentive 
payment level in forms such as modified benchmarks, points adjustments, 
or modified incentive payment multipliers (for example, peer comparison 
groups based on whether the facility includes a high proportion of dual 
eligible beneficiaries or other metrics). We request commenters' views 
on which of these adjustments, if any, would be most effective for the 
SNF VBP Program at accounting for any health equity issues that we may 
observe in the SNF population.

VIII. Request for Information: Revising the Requirements for Long-Term 
Care (LTC) Facilities To Establish Mandatory Minimum Staffing Levels

    The COVID-19 Public Health Emergency has highlighted and 
exacerbated long-standing concerns

[[Page 22790]]

with inadequate staffing in long-term care (LTC) facilities. The Biden-
Harris Administration is committed to improving the quality of U.S. 
nursing homes so that seniors and others living in nursing homes get 
the reliable, high-quality care they deserve.\271\ As a result, we 
intend to propose minimum standards for staffing adequacy that nursing 
homes would be required to meet. We will conduct a new research study 
to help inform policy decisions related to determining the level and 
type of staffing needed to ensure safe and quality care and expect to 
issue proposed rules within 1 year. We are seeking opportunities to 
improve our health and safety standards to promote thoughtful, informed 
staffing plans and decisions within LTC facilities that aim to meet 
resident needs, including maintaining or improving resident function 
and quality of life. Such an approach is essential to effective person-
centered care. Therefore, we are considering policy options for future 
rulemaking to establish specific minimum direct care staffing standards 
and are seeking stakeholder input to inform our policy decisions.
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    \271\ 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/.
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A. Background

    The requirements for participation for LTC facilities are the 
baseline health and safety standards that Medicare-certified providers 
and suppliers must meet to receive Medicare and Medicaid payment. We 
have broad statutory authority to establish health and safety 
regulations for several types of health care providers and suppliers, 
which include Conditions of Participation (CoPs), Conditions for 
Coverage (CfCs), and Requirements for LTC facilities. Section 1102 of 
the Act grants the Secretary authority to make and publish such rules 
and regulations, not inconsistent with the Act, as may be necessary to 
the efficient administration of the functions with which the Secretary 
is charged under the Act. Section 1871 of the Act grants the Secretary 
authority to prescribe regulations as may be necessary to carry out the 
administration of the Medicare program. Finally, section 1819 of the 
Act establishes requirements specifically with respect to skilled 
nursing facilities (SNFs), including, among other requirements, section 
1819(b)(1)(A) of the Act, which requires that a SNF must care for its 
residents in such a manner and in such an environment as will promote 
maintenance or enhancement of the quality of life of each resident, 
section 1819(b)(4)(C)(i) of the Act, which requires that a SNF must 
provide 24-hour licensed nursing service sufficient to meet nursing 
needs of its residents, and must use the services of a registered 
professional nurse at least 8 consecutive hours a day. Section 
1819(d)(4)(B) of the Act further states that a SNF must meet such other 
requirements relating to the health, safety, and well-being of 
residents or relating to the physical facilities thereof as the 
Secretary may find necessary. These provisions are largely paralleled 
in section 1919 of the Act for nursing facilities (NFs).
    The regulatory requirements for SNFs and NFs, collectively referred 
to as LTC facilities and colloquially known as nursing homes, are 
codified at 42 CFR part 483. In this request for information, we are 
seeking public input on addressing direct care staffing requirements, 
especially those for registered nurses (RNs), licensed practical nurses 
(LPNs), or, in California and Texas, licensed vocational nurses (LVNs), 
and certified nursing assistants (CNAs), colloquially known as nurse 
aides, through the requirements for participation for LTC facilities. 
We also welcome input on which individuals should also be considered 
direct care staff, beyond nurses and CNAs.
    Existing regulations at Sec.  483.35 require that LTC facilities 
have sufficient nursing staff with the appropriate competencies and 
skill sets to provide nursing and related services to assure resident 
safety and attain or maintain the highest practicable physical, mental, 
and psychosocial well-being of each resident, as determined by resident 
assessments and individual plans of care and considering the number, 
acuity and diagnoses of the facility's resident population in 
accordance with a required facility assessment. Requirements at Sec.  
483.35(a) for sufficient staff mirror the statutory language at 
sections 1819(b)(4)(C)(i) and 1919(b)(4)(C)(i) of the Act, requiring 
(with certain exceptions) an RN to provide services in a facility 8 
consecutive hours a day, 7 days a week as well as ``sufficient 
numbers'' of licensed nurses and other nursing personnel 24 hours a day 
to meet residents' needs. Certain nurse staffing requirements may be 
waived in accordance with the statute, under specific circumstances.
1. Prior Staffing Studies
    As indicated later in this section, there is research that 
associates increased RN staffing with improved quality of care. We have 
conducted prior studies that have been noted as potential sources for 
helping us assess minimum staffing levels, including the STM (1995 to 
1997) and STRIVE (2006 to 2007) studies,\272\ which determined the 
amount of nursing (RN, LVN, and nurse aide) time dedicated to residents 
classified under each RUG group. Both these studies measured the direct 
care time that was actually provided by the facilities and not nurse 
staffing levels necessary to provide adequate quality of care. Other 
studies as discussed later in this section, focus on the number of 
hours of nursing care a resident must receive to achieve certain 
quality objectives. At least one study noted that the relationship is 
not necessarily linear; that is, it takes more labor resources to 
achieve a certain level of improvement, but beyond that improvement 
slows.\273\ Our own 2001 study conducted by Abt Associates reported 
that facilities with staffing levels below 4.1 hours per resident day 
(HPRD) for long stay residents (that is, those residents in the 
facility at least 90 days) may provide care that results in harm and 
jeopardy to residents.\274\ A 2004 study by Schnelle and colleagues 
found that the highest-staffed nursing homes reported significantly 
lower resident care loads on all staffing reports and provided better 
care than all other homes.\275\ In a more recent study involving 13,500 
nursing homes, Schnelle et al. used a mathematical model to determine 
the CNA staffing necessary to provide activities of daily living (ADL) 
care to residents in accordance with their needs as identified in 
Minimum Data Set (MDS) data.\276\ Based on their model, CNA staffing 
required for ADL care that would result in a rate of care omissions 
below 10 percent ranged from 2.8 HPRD to 3.6 HPRD. However, the nursing 
homes participating in the study reported actual CNA staffing that 
ranged

[[Page 22791]]

from 2.3 HPRD to 2.5 HPRD. The rate of care omissions reported by the 
authors was intended for illustrative purposes, not necessarily as a 
desirable or acceptable level of staffing.
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    \272\ https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/TimeStudy.
    \273\ Zhang, Unruh, Liu, and Wan, 2006. ``Minimum Nurse Staffing 
Ratios for Nursing Homes''.
    \274\ Appropriateness of Minimum Nurse Staffing Ratios in 
Nursing Homes, Phase II Final Report, 2001, Abt Associates. https://theconsumervoice.org/uploads/files/issues/CMS-Staffing-Study-Phase-II.pdf.
    \275\ Schnelle JF, Simmons SF, Harrington C, Cadogan M, Garcia 
E, M Bates-Jensen B. Relationship of nursing home staffing to 
quality of care. Health Serv Res. 2004 Apr;39(2):225-50. doi: 
10.1111/j.1475-6773.2004.00225.x. PMID: 15032952; PMCID: PMC1361005.
    \276\ Schnelle, J.F., Schroyer, L.D., Saraf, A.A., Simmons, S.F. 
Determining nurse aide staffing requirements to provide care based 
on resident workload: A discrete event simulation model. JAMDA. 
2016; 17:970-977. https://www.jamda.com/article/S1525-8610(16)30358-
9/fulltext.
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    Despite these requirements and general understanding of the impacts 
of staffing on resident health and safety, understaffing continues to 
be an area of concern. We are aware of ongoing quality concerns and the 
association of RN staffing with quality of care. A staffing level of 
4.1 HPRD is currently the most common number put forward as a potential 
minimum standard to ensure the adequacy of nursing staff, largely 
attributed to the 2001 Abt Associates study. As noted below, the care 
needs of, and the type of care provided to, LTC facility residents have 
changed. Therefore we are now reevaluating the evidence and conducting 
a new study.
2. Trends in Resident Composition and Care Needs in LTC Facilities
    Based on existing data analyses from Centers for Disease Control 
and Prevention's National Center for Health Statistics Vital and Health 
Statistics, Series 3, Number 43 (February 2019), the average hours of 
nursing care per resident per day for LTC facilities is 3 hours and 48 
minutes 0.54 RN hours (up 0.02 hours from 2013), 0.85 LPN or LVN hours 
(same as 2013), and 2.41 Aide hours (down 0.05 hours from 2013), plus 
an additional 0.08 hours of Social Worker time and 0.19 hours 
activities staff time. This does not include therapist time, although 
virtually all LTC facilities (99.5 percent) offer at least some 
therapeutic services as therapeutic services are critical to helping 
residents ``attain or maintain the highest practicable physical, 
mental, and psychosocial well-being'' in order for a facility to 
achieve its statutory mandate that a nursing facility provide services 
and activities to attain or maintain the highest practicable physical, 
mental, and psychosocial well-being of each resident (see sections 
1819(b)(2) and 1919(b)(2) of the Act). Very few LTC facilities (0.4 
percent) were exclusive to dementia patients, who often require more 
care than the general LTC resident population; and only 14.9 percent 
offered a dedicated dementia care unit within the larger facility.\277\
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    \277\ https://www.cdc.gov/nchs/data/series/sr_03/sr03_43-508.pdf.
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    A study of trends in LTC facilities from 1985 to 2015 revealed 
changes in resident composition and increased acuity and care 
needs.\278\ The percentage of residents with dementia increased from 39 
to 45 percent. Prevalence of psychiatric diagnoses among residents 
almost tripled from 11 to 31 percent. The number of residents admitted 
from the hospital increased from 67 percent in 2000 to 85 percent in 
2015 reflecting an increased percentage of residents being admitted for 
post-acute care with higher levels of acuity and functional 
impairments. Physical abilities decreased among residents from 1995 to 
2015 with increased assistance among residents needed for bathing (89 
to 96 percent), dressing (74 to 92 percent), transferring (60 to 85 
percent), toileting (49 to 88 percent), and eating (38 to 56 percent). 
The study also found an overall decrease in the number of facilities 
nationwide by over 3,000, declining occupancy rates which fell from 87 
to 81 percent, and overall increased staffing levels. Although the 
study found that overall direct care HPRD increased from 3.39 to 3.79, 
a breakdown by job title or discipline revealed that the increase was 
largely attributed to CNAs. CNA HPRD increased from 2.26 to 2.42 hours 
while nursing hours remained relatively stable for LPN/LVN hours (0.87 
to 0.88) and decreased for RN hours (0.66 to 0.58).
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    \278\ https://www.sciencedirect.com/science/article/pii/S1525861019305274?via%3Dihub.
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    An Issue Brief published by the Office of the Assistant Secretary 
for Planning and Evaluation (ASPE) in October 2020 revealed similar 
findings.\279\ From 2002 to 2015, the proportion of older adults 
residing in LTC facilities declined. The age-standardized prevalence of 
dementia among older adults in the United States (U.S.) increased; 
however, the largest increase occurred among LTC facility residents. 
Moreover, the proportion of LTC facility residents with limitations in 
three or more activities of daily living was significantly higher than 
older adults living in other settings (that is, private home, 
apartment, or assisted living facility). Both of these studies suggest 
an overall decrease in census of LTC facilities occurred simultaneously 
with an increase in resident acuity and care needs while direct care 
responsibilities shifted from nursing personnel to CNAs. We welcome 
comment on these trends and their implications for staffing level 
requirements.
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    \279\ https://aspe.hhs.gov/reports/trends-use-residential-settings-among-older-adults-issue-brief-0.
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3. Existing Data on Staffing in LTC Facilities
    To ensure the availability of reliable and auditable data on LTC 
facility staffing, we developed a system to collect staffing 
information that is auditable back to payroll data, known as the 
Payroll Based Journal (PBJ). The Affordable Care Act (Pub. L. 111-148, 
March 23, 2010) added a new section 1128I to the Act to promote greater 
accountability for LTC facilities (defined under section 1128I(a) of 
the Act as SNFs). As added by the Affordable Care Act, section 1128I(g) 
of the Act pertains to the submission of staffing data by LTC 
facilities, and specifies that the Secretary, after consulting with 
State LTC ombudsman programs, consumer advocacy groups, provider 
stakeholder groups, employees and their representatives and other 
parties the Secretary deems appropriate, shall require a facility to 
electronically submit to the Secretary direct care staffing 
information, including information for agency and contract staff, based 
on payroll and other verifiable and auditable data in a uniform format 
according to specifications established by the Secretary in 
consultation with such programs, groups, and parties. Since July 2016, 
nursing homes have been submitting data electronically through the PBJ 
system as required under section 1128I(g) of the Act and Sec.  
483.70(q). The data submitted by facilities are the number of hours 
direct care staff are paid to work each day. All data submitted is 
auditable back to payroll and other verifiable sources.
    In April 2018, we began using PBJ data to calculate staffing 
measures posted on Nursing Home Compare, and used in the Five Star 
Quality System. Staffing data is submitted quarterly and facilities are 
downgraded to a one-star staffing rating for a quarter if they meet 
either of the following criteria:
     Facilities fail to submit any staffing data for the 
reporting quarter.
     Facilities report four or more days in a quarter with zero 
registered nurse hours.\280\
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    \280\ https://cmsintranet.share.cms.gov/ER/Pages/DetailOpportunities.aspx#; https://www.cms.gov/medicare/provider-enrollment-and-certification/certificationandcomplianc/downloads/usersguide.pdf.
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    Facilities that report staffing below established thresholds are 
downgraded. LTC facilities with significant inaccuracies between the 
hours reported and the hours verified, or facilities who failed to 
submit any data by the required deadline would be presumed to have low 
levels of staffing. This results in these facilities being downgraded 
to a one-star rating in the staffing domain, which drops their overall 
(composite) star rating by one-star for a quarter.
    In April 2019, we established new thresholds for staffing ratings 
and

[[Page 22792]]

adjusted the staffing rating's grid to increase the weight RN staffing 
has on the staffing rating. We also reduced the number of days without 
an RN onsite that triggers an automatic downgrade to one-star from 7 
days to 4 days.
    In January 2022, we began posting on Care Compare the level of 
total nurse and RN staffing on weekends provided by each facility over 
a quarter and the percent of nursing staff and number of administrators 
that stopped working at the nursing home over a 12-month period. This 
data will be used in the Nursing Home Five Star Quality Rating System 
beginning in July 2022. We further anticipate using PBJ data to analyze 
the effects of LTC facility staffing on resident health and safety as 
we consider regulatory action. We are also considering a range of 
initiatives to further improve Care Compare.
4. Considerations and Approaches To Address Staffing Concerns
    States have implemented a variety of methods to attempt to address 
concerns about adequate staffing and care in LTC facilities. Some 
States have implemented a CNA hour-per-resident day model, with some 
including part or all of the hours of licensed nurses into this 
calculation). For example, the District of Columbia requires a minimum 
daily average of 4.1 hours of direct nursing care per resident per day 
(with opportunity to adjust the requirements above or below this level, 
as determined by the Director of Department of Health), an RN on site 
24 hours a day 7 days a week, plus additional nursing and medical 
staffing requirements.\281\ Some States have implemented a ratio of 
numbers of full-time equivalent CNAs per resident. For example, Maine 
requires 3.58 HPRD with at least 0.508 of those hours provided by an 
RN.\282\ Arkansas requires at least 3.36 average HPRD each month to 
include licensed nurses; nurse aides; medication assistants; 
physicians; physician assistants; licensed physical or occupational 
therapists or licensed therapy assistants; registered respiratory 
therapists; licensed speech-language pathologists; infection 
preventionists; and other healthcare professionals licensed or 
certified in the State, plus requirements for minimum numbers of 
licensed nurses per residents per shift.\283\
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    \281\ https://doh.dc.gov/sites/default/files/dc/sites/doh/publication/attachments/Nursing_Facility_Regulations_Health_Care_Facilities_Improvement_2012.pdf.
    \282\ https://theconsumervoice.org/uploads/files/issues/CV_StaffingReport.pdf.
    \283\ https://theconsumervoice.org/uploads/files/issues/CV_StaffingReport.pdf.
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    Research reporting on the outcomes of these State requirements is 
limited. A 2009 study that examined the impact of State staffing 
requirements in 16 States concluded that ``[m]andated staffing 
standards affect only low-staff facilities facing potential for 
penalties, and effects are small. Selected facility-level outcomes may 
show improvement at all facilities due to a general response to 
increased standards or to other quality initiatives implemented at the 
same time as staffing standards.\284\ However, Florida reported 
improved resident care outcomes and decreased deficiencies after 
increasing its nurse staffing levels. Specifically, Florida found 
``evidence that quality of care has substantially improved in Florida 
nursing homes since the introduction of increased nurse staffing levels 
and other quality standards since 2001. Average deficiencies per 
facility have decreased. Importantly, the citations for the more 
serious deficiencies have decreased dramatically and remain lower than 
the national average. Measures of resident care outcomes have improved 
in 2007 after the new staffing standards of 2.9 HPRD were instituted.'' 
\285\
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    \284\ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2669632/pdf/hesr0044-0056.pdf.
    \285\ Hyer, K. et al., (2009) University of South Florida, 
Analyses on Outcomes of Increased Nurse Staffing Policies in Florida 
Nursing Homes: Staffing Levels, Quality and Costs (2002-2007).
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    An alternative or supplementary approach to mandating a specific 
number of direct care HPRD is to mandate the presence of an RN in a 
nursing home for more hours per day than is currently required, 
potentially 24 hours a day 7 days a week, subject to the statutory 
waiver. We note that a number of States already require this. Increased 
presence of RNs in nursing facilities would help address several 
issues. First, greater RN presence has been associated in research 
literature with higher quality of care and fewer deficiencies. Second, 
it has been reported in the literature that LPNs or LVNs may find 
themselves practicing outside of their scope of practice because, at 
least in part, there are not enough RNs providing direct patient 
care.\286\ Increasing the number of hours per day that a LTC facility 
must have RNs in the nursing home would alleviate concerns about LPNs 
engaging in activities outside their scope of practice in the face of 
resident need during times when no RN is on site.
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    \286\ https://www.journalofnursingregulation.com/article/S2155-8256(15)30229-5/fulltext.
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    We recognize that RN presence alone would not address all these 
concerns. In addition to their clinical responsibilities, many RNs in 
LTC facilities appropriately carry out administrative duties as part or 
most of their routine work responsibilities. Further, that there are 
times of the day when nursing care demands may be less (such as during 
the night when most residents are sleeping); however, nursing care 
needs may occur at any time of the day and cannot be predicted or 
anticipated. Increases in resident acuity worsen this problem and 
safety should be maintained at all times.
    With regard to whether there is an adequate supply of RNs, a 
December 2017 HRSA report on the future of the nursing workforce 
suggested that growth in RN supply would actually outpace demand in the 
period between 2012 and 2030.\287\ The report noted that the national 
projections mask a distributional imbalance of RNs at the State level 
and that there is considerable variation in the geographic distribution 
of the growth in RN supply. Seven States were projected to have a 
shortage by 2030. Four States, California, Texas, New Jersey, and South 
Carolina, were projected to have the most significant deficiencies 
(>10,000 or more full-time employees), while South Dakota, Georgia, 
South Carolina, and Alaska were also projected to have shortages.\288\
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    \287\ https://bhw.hrsa.gov/sites/default/files/bureau-health-workforce/data-research/nchwa-hrsa-nursing-report.pdf.
    \288\ https://bhw.hrsa.gov/sites/default/files/bureau-health-workforce/data-research/nchwa-hrsa-nursing-report.pdf.
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    In looking at the employment of RNs in LTC facilities, the BLS 
reported in its May 2020 Occupational Employment and Wage Statistics 
\289\ that 143,250 RNs were employed in nursing care facilities (SNFs); 
down from 151,300 in the May 2019 Occupational Employment Statistics 
148,970.\290\ At the same time, the number of LTC facilities has 
decreased somewhat from 15,844 based on FY 2012 to 15,691 in 2015, 
based on CASPER data. For CNAs, BLS reported in its May 2020 
Occupational Employment and Wage Statistics \291\ that 527,480 CNAs 
were employed in SNFs, down from 566,240 in the May 2019 Occupational 
Employment and Wage Statistics.\292\
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    \289\ https://www.bls.gov/oes/current/oes291141.htm.
    \290\ https://www.bls.gov/oes/2019/may/oes291141.htm.
    \291\ https://www.bls.gov/oes/current/oes311131.htm.
    \292\ https://www.bls.gov/oes/2019/may/oes311131.htm.
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    A 2022 analysis by Buerhaus et al. suggests that there is a 
tightening labor market for RNs, LPNs, and CNAs, marked by falling 
employment and rising wages through June 2021. Unemployment rates 
remained higher in

[[Page 22793]]

nonhospital settings, including LTC facilities, and among RNs and CNAs 
who are members of racial and ethnic minority groups. The study notes 
that overall employment in LTC facilities has fallen more than in other 
nonhospital sectors.\293\ In short, data indicate that there may be 
skilled direct care workers with experience in the LTC setting 
available.
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    \293\ Nurse Employment During The First Fifteen Months Of The 
COVID-19 Pandemic, Peter I. Buerhaus, Douglas O. Staiger, David I. 
Auerbach, Max C. Yates, and Karen Donelan, Health Affairs 2022 41:1, 
79-85.
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    There is concern that a facility can have sufficient numbers of 
staff, but if those staff do not have the skills and competencies to do 
the necessary work, quality will not improve. A 2011 review of the 
literature on nurse staffing and quality of care raises questions about 
the need to address issues beyond simply the numbers of nurses.\294\ 
Specifically, the authors concluded that ``[a] focus on numbers of 
nurses fails to address the influence of other staffing factors (for 
example, turnover and agency staff use), training and experience of 
staff, and care organization and management.'' They note that the 
studies they reviewed presented 42 measures of quality and 52 ways of 
measuring staffing. They also note that it is ``difficult to offer 
conclusions and recommendations about nurse staffing based on the 
existing research evidence.'' An October 2011 research article by John 
R. Bowblis concluded that minimum direct care staffing requirements for 
LTC facilities ``change staffing levels and skill mix, improve certain 
aspects of quality, but can lead to use of care practices associated 
with lower quality.'' \295\
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    \294\ Spilsbury, Hewitt, Stirk and Bowman ``The relationship 
between nurse staffing and quality of care in nursing homes: A 
systematic review'' The International Journal of Nursing Studies 
48(2011)732-750.
    \295\ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3207189/.
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    The American Nurses Association (ANA), in its 2020 Principles for 
Nurse Staffing, describe appropriate nurse staffing as ``a match of 
registered nurse expertise with the needs of the recipient of nursing 
care services in the context of the practice setting and situation.'' 
\296\ The ANA further notes that ``staffing needs must be determined 
based on an analysis of healthcare consumer status (for example, degree 
of stability, intensity, and acuity), and the environment in which the 
care is provided. Other considerations to be included are: Professional 
characteristics, skill set, and mix of the staff and previous staffing 
patterns that have been shown to improve outcomes.'' The International 
Council of Nurses (ICN) included similar considerations in its 2018 
statement of principles of safe staffing levels.\297\ The ICN policy 
statement notes that ``Safe nurse staffing means that an appropriate 
number of nurses is available at all times across the continuum of 
care, with a suitable mix of education, skills and experience to ensure 
that patient care needs are met and that the working environment and 
conditions support staff to deliver quality care. This requires having 
an appropriate base staffing that includes a range of competencies 
which can be deployed to meet changing and fluctuating patient acuity 
in real time.'' Nurses are not the only skilled workers who provide 
regular direct care to LTC facility residents. By a wide margin, the 
numbers of LPNs, home and personal care aides, CNAs, and other support 
staff working in SNFs far exceeded the numbers of registered nurses 
over the 5-year period 2014 to 2018.\298\
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    \296\ https://patientcarelink.org/wp-content/uploads/2021/02/2-ANA-Principles-for-Nurse-Staffing-3rd-Edition.pdf.
    \297\ https://www.icn.ch/sites/default/files/inline-files/PS_C_%20Evidence%20based%20safe%20nurse%20staffing_1.pdf.
    \298\ https://www.nmnec.org/wp-content/uploads/2021/05/Future-of-Nursing-2020-2030.pdf.
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5. The Impact of the COVID-19 Pandemic on Staffing in LTC Facilities
    While the adequacy of LTC staffing has been a topic of national 
interest for many years, the COVID-19 pandemic and associated Public 
Health Emergency (PHE) have had unprecedented impacts on staff and 
residents of LTC facilities, with evolving effects on staffing. A 2019 
study by Geng et al.\299\ assessed LTC facility staffing prior to the 
spread of COVID-19 using various data available from us. The study 
found that staffing levels for LPNs, CNAs especially RNs were stable 
during weekdays but dropped on weekends. On average, weekend RN 
staffing in terms of time spent per resident was 17 minutes (42 
percent) less than weekday staffing, LPN staffing 9 minutes (17 
percent) less, and nurse aide staffing 12 minutes (9 percent) less. 
Larger facilities, on average, had a larger decrease in staffing time 
per resident during weekends. Decreases were smaller among facilities 
with higher five-star overall ratings and with lower shares of Medicaid 
residents (who are more likely to be long-term residents without 
skilled care needs, thereby impacting nurse staffing needs to a lesser 
degree).
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    \299\ Geng F, Stevenson DG, Grabowski DC. Daily Nursing Home 
Staffing Levels Highly Variable, Often Below CMS Expectations. 
Health Aff (Millwood). 2019 Jul;38(7):1095-1100. doi: 10.1377/
hlthaff.2018.05322. Erratum in: Health Aff (Millwood). 2019 
Sep;38(9):1598. PMID: 31260368.
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    A 2020 study by McGarry et al.\300\ examined access to personal 
protective equipment (PPE), staffing, and facility characteristics 
associated with shortages of PPE and staffing from May through the end 
of July 2020. Findings included the following:
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    \300\ https://www.healthaffairs.org/doi/10.1377/hlthaff.2020.01269.
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     One in five LTC facilities reported facing a severe 
shortage of PPE or staff shortage in early July 2020. Rates of both PPE 
shortages and staff did not meaningfully improve from May to July 2020.
     PPE shortages were magnified in LTC facilities with COVID-
19 cases among staff or residents and those with low quality scores.
     Staff shortages were greater in LTC facilities with COVID-
19 cases, particularly among those serving a high proportion of 
disadvantaged patients on Medicaid and those with lower quality scores, 
including pre-pandemic staffing score.
     Most prominent staff shortages were for nurses and nursing 
aides as opposed to other providers or staff.
    More recent research, using PBJ data, shows that LTC facility 
staffing (nurse staff HPRD) remained steady or increased slightly 
during the COVID-19 pandemic when adjusted for declining resident 
census.\301\ Slight increases in staffing were concentrated in counties 
with high COVID-19 prevalence, low Medicaid census, and not-for profit 
facilities. Furthermore, an analysis of the incidence of COVID-19 among 
facilities with different staffing ratings found that facilities with 1 
to 3 stars for nurse staffing had 18 to 22 percent more weeks with high 
COVID-19 incidence than 5-star staffed nursing homes.\302\
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    \301\ https://www.healthaffairs.org/doi/10.1377/hlthaff.2020.02351.
    \302\ https://agsjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/jgs.17309.
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    The 2021 National Academy of Medicine Report, ``The Future of 
Nursing 2020 to 2030: Charting a Path to Achieve Health Equity'' 
specifically addressed nurse staffing in nursing homes since the onset 
of COVID-19.\303\ As of 2020, there were 15,417 LTC facilities in the 
U.S.,\304\ and in 2017, these facilities housed just over 1.3

[[Page 22794]]

million residents.\305\ As of the end of May 2020, there had been 
95,515 cumulative confirmed cases of COVID-19 among LTC facility 
residents in the U.S. and 30.2 deaths per 1,000 residents. At that 
time, almost one-third (31,782) of the 103,700 people who had died from 
COVID-19 in the U.S. through the end of May were residents of LTC 
facilities.\306\ As of mid-February 2022, approximately 150,000 deaths 
have occurred among U.S. LTC facility residents, and close to 2,300 
staff have died.\307\
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    \303\ https://nap.nationalacademies.org/catalog/25982/the-future-of-nursing-2020-2030-charting-a-path-to.
    \304\ CMS (Centers for Medicare & Medicaid Services). 2020. Long 
term care facility reporting on COVID-19. https://www.cms.gov/files/document/covid-nursing-home-reporting-numbers-5-31-20.pdf.
    \305\ https://www.kff.org/coronavirus-covid-19/issue-brief/data-note-how-might-coronavirus-affect-residents-in-nursing-facilities/.
    \306\ https://data.cms.gov/covid-19/covid-19-nursing-home-data.
    \307\ https://data.cms.gov/covid-19/covid-19-nursing-home-data.
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    A recent study of 4,254 LTC facilities across eight States found 
that those that were high-performing with respect to nurse staffing had 
fewer COVID-19 cases relative to their low-performing 
counterparts.\308\ These findings suggest that poorly resourced LTC 
facilities with nurse staffing shortages may have been more susceptible 
to the spread of COVID-19. A 2020 study involving all 215 nursing homes 
in Connecticut revealed that a 20-minute increase in RN staffing HPRD 
was associated with 22 percent fewer confirmed cases of COVID-19 and 26 
percent fewer COVID-19 deaths.\309\
---------------------------------------------------------------------------

    \308\ Figueroa JF, Wadhera RK, Papanicolas I, et al. Association 
of Nursing Home Ratings on Health Inspections, Quality of Care, and 
Nurse Staffing With COVID-19 Cases. JAMA. 2020;324(11):1103-1105. 
doi:10.1001/jama.2020.14709.
    \309\ https://agsjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/jgs.16689.
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    Evidence suggests that in addition to staffing quantity and 
composition, consistent staffing is an important consideration. A 2021 
study by McGarry et al. examined the relationship between the number of 
unique staff members entering a facility daily, including direct care 
staff and staff members not involved resident care, direct care staff-
to-resident ratios and skills mix, and the number of COVID-19 cases and 
deaths in the facility.\310\ The study concluded that ``[c]onventional 
staffing quality measures, including direct care staff-to-resident 
ratios and skills mix, were not significant predictors of COVID-19 
cases or deaths.'' The authors suggest that, moving forward, policy 
makers should encourage policies that not only maintain sufficient 
direct caregivers to provide safe and effective care for residents, but 
also promote the use of full-time and more consistent staff.
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    \310\ Larger Nursing Home Staff Size Linked To Higher Number Of 
COVID-19 Cases In 2020 Brian E. McGarry, Ashvin D. Gandhi, David C. 
Grabowski, and Michael Lawrence Barnett Health Affairs 2021 40:8, 
1261-1269.
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    In considering resident health and safety issues associated with 
facility staffing, we must consider different levels of risk and 
benefit. We have reviewed the recommendations of the Institute of 
Medicine (IOM) in its 2004 report ``Keeping Patients Safe: Transforming 
the Work Environment of Nurses.'' \311\ That report reiterates prior 
recommendations for a mandatory RN presence in LTC facilities and 
mandatory minimum staffing requirements, although it does not recommend 
a specific ratio. The report states, in part, that ``[p]atient safety 
requires staff resources that are sufficient to prevent an 
inappropriately high rate of untoward events that could be avoided with 
adequate staffing levels. For such a standard to be reasonable, it must 
at least be based on the number of residents in the LTC facility and 
address NAs, who provide most of the care to LTC facility residents. 
Such minimum staffing standards are not a precise statement of how many 
staff are required to fully meet the needs of each specific group of 
residents on each unit, nor are they a quality improvement tool to 
optimize quality in each LTC facility. Rather, a minimum staffing level 
is one that avoids placing individual residents unnecessarily at risk 
because of insufficient numbers of staff to provide even the most basic 
care.'' The report discusses our 2001 Report to Congress 
``Appropriateness of Minimum Nurse Staffing Ratios in Nursing Homes-
Phase II Final Report'' \312\ and states: ``With respect to the 
recommendation that DHHS specify staffing standards in regulations that 
would increase with the number of patients and be based on the findings 
and recommendations of the Phase II DHHS report to Congress on the 
appropriateness of minimum staffing ratios in nursing homes, the 
committee notes that the thresholds identified in that study above 
which no further benefit from staffing ratios could be identified are 
above the staffing levels of 75 to 90 percent of facilities, depending 
on the type of staff. However, a minimum standard set by DHHS need not 
approach the threshold level above which there is no further benefit. 
In fact, such a standard would go beyond the expectation for a minimum, 
which is intended to identify situations in which facilities 
unequivocally place residents at an unacceptable level of risk. The 
challenge is that there is no absolute minimum level of risk for 
untoward events that is considered acceptable.'' The IOM report further 
states: ``The study does not propose a specific minimum standard for 
RNs, licensed nurses, and NAs because agreement must first be reached 
about what is an unacceptable level of risk.''
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    \311\ https://www.ncbi.nlm.nih.gov/books/NBK216190/.
    \312\ https://www.justice.gov/sites/default/files/elderjustice/legacy/2015/07/12/Appropriateness_of_Minimum_Nurse_Staffing_Ratios_in_Nursing_Homes.pdf
.
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    A successor report \313\ discussed that, ultimately, adequate 
staffing should involve direct care nurses in administrative decision 
making and consider both their levels of competence and unique 
organizational factors. The report asserts that nurse-staffing 
legislation is not a panacea for improving quality and safety.
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    \313\ https://www.rwjf.org/en/library/research/2014/03/cnf-ten-years-after-keeping-patients-safe.html.
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    Despite ongoing concern about LTC facility staffing, we have not 
yet directly addressed this issue in regulation. As discussed earlier 
in this section, while many studies indicate that consistent, adequate 
direct care facility staffing is vital to resident health and safety, 
we seek additional information to make fully informed policy proposals. 
We welcome your input on the topics addressed here, and others that you 
believe are relevant.

B. Request for Information

    Given the ongoing concerns related to adequate staffing discussed 
prior, we are considering options for future rulemaking and are seeking 
stakeholder input. Specifically, we are interested in the issues 
provided later on in this section, but also welcome input on other 
aspects of staffing in LTC facilities that we should consider as we 
evaluate future policy options.
    1. Is there evidence (other than the evidence reviewed in this RFI) 
that establishes appropriate minimum threshold staffing requirements 
for both nurses and other direct care workers? To what extent do older 
studies remain relevant? What are the benefits of adequate staffing in 
LTC facilities to residents and quality of care?
    2. What resident and facility factors should be considered in 
establishing a minimum staffing requirement for LTC facilities? How 
should the facility assessment of resident needs and acuity impact the 
minimum staffing requirement?
    3. Is there evidence of the actual cost of implementing recommended 
thresholds, that accounts for current staffing levels as well as 
projected savings from reduced hospitalizations and other adverse 
events?
    4. Is there evidence that resources that could be spent on staffing 
are instead

[[Page 22795]]

being used on expenses that are not necessary to quality patient care?
    5. What factors impact a facility's capability to successfully 
recruit and retain nursing staff? What strategies could facilities 
employ to increase nurse staffing levels, including successful 
strategies for recruiting and retaining staff? What risks are 
associated with these strategies, and how could nursing homes mitigate 
these risks?
    6. What should CMS do if there are facilities that are unable to 
obtain adequate staffing despite good faith efforts to recruit workers? 
How would CMS define and assess what constitutes a good faith effort to 
recruit workers? How would CMS account for job quality, pay and 
benefits, and labor protections in assessing whether recruitment 
efforts were adequate and in good faith?
    7. How should nursing staff turnover be considered in establishing 
a staffing standard? How should CMS consider the use of short-term 
(that is, travelling or agency) nurses?
    8. What fields and professions should be considered to count 
towards a minimum staffing requirement? Should RNs, LPNs/LVAs, and CNAs 
be grouped together under a single nursing care expectation? How or 
when should they be separated out? Should mental health workers be 
counted as direct care staff?
    9. How should administrative nursing time be considered in 
establishing a staffing standard? Should a standard account for a 
minimum time for administrative nursing, in addition to direct care? If 
so, should it be separated out?
    10. What should a minimum staffing requirement look like, that is, 
how should it be measured? Should there be some combination of options? 
For example, options could include establishing minimum nurse HPRD, 
establishing minimum nurse to resident ratios, requiring that an RN be 
present in every facility either 24 hours a day or 16 hours a day, and 
requiring that an RN be on-call whenever an RN was not present in the 
facility. Should it include any non-nursing requirements? Is there data 
that supports a specific option?
    11. How should any new quantitative direct care staffing 
requirement interact with existing qualitative staffing requirements? 
We currently require that facilities have ``sufficient nursing staff'' 
based on a facility assessment and patient needs, including but not 
limited to the number of residents, resident acuity, range of 
diagnoses, and the content of care plans. We welcome comments on how 
facilities have implemented this qualitative requirement, including 
both successes and challenges and if or how this standard should work 
concurrently with a minimum staffing requirement. We would also welcome 
comments on how State laws limiting or otherwise restricting overtime 
for health care workers would interact with minimum staffing 
requirements.
    12. Have minimum staffing requirements been effective at the State 
level? What were facilities' experiences transitioning to these 
requirements? We note that States have implemented a variety of these 
options, discussed in section VIII.A. of this proposed rule, and would 
welcome comment on experiences with State minimum staffing 
requirements.
    13. Are any of the existing State approaches particularly 
successful? Should CMS consider adopting one of the existing successful 
State approaches or specific parts of successful State approaches? Are 
there other approaches to consider in determining adequate direct care 
staffing? We invite information regarding research on these approaches 
which indicate an association of a particular approach or approaches 
and the quality of care and/or quality of life outcomes experienced by 
resident, as well as any efficiencies that might be realized through 
such approaches.
    14. The IOM has recommended in several reports that we require the 
presence of at least one RN within every facility at all times. Should 
CMS concurrently require the presence of an RN 24 hours a day 7 days a 
week? We also invite comment on the costs and benefits of a mandatory 
24-hour RN presence, including savings from improved resident outcomes, 
as well as any unintended consequences of implementing this 
requirement.
    15. Are there unintended consequences we should consider in 
implementing a minimum staffing ratio? How could these be mitigated? 
For example, how would a minimum staffing ratio impact and/or account 
for the development of innovative care options, particularly in 
smaller, more home-like settings, for a subset of residents who might 
benefit from and be appropriate for such a setting? Are there concerns 
about shifting non-nursing tasks to nursing staff in order to offset 
additions to nursing staff by reducing other categories of staff?
    16. Does geographic disparity in workforce numbers make a minimum 
staffing requirement challenging in rural and underserved areas? If 
yes, how can that be mitigated?
    17. What constitutes ``an unacceptable level of risk of harm?'' 
What outcomes and care processes should be considered in determining 
the level of staffing needed?
    We welcome public input from a broad range of commenters including, 
but not limited to nursing home residents and caretakers, nursing 
staff, nurse aides, physicians, nursing home administrators, owners and 
operators, and researchers. We are particularly interested in data, 
evidence, and experience on the issues identified above and any others 
that are relevant to defining and ensuring adequate staffing in LTC 
facilities.

VIII. Collection of Information Requirements

    As explained below, this proposed rule would not impose any new or 
revised ``collection of information'' requirements or burden. 
Consequently, this proposed rule is not subject to the requirements of 
the Paperwork Reduction Act of 1995 (PRA) (44 U.S.C. 3501 et seq.). For 
the purpose of this section, collection of information is defined under 
5 CFR 1320.3(c) of the PRA's implementing regulations.
    With regard to the SNF QRP, in section VI.C.1. of this proposed 
rule, we propose that SNFs submit data on the Influenza Vaccination 
Coverage among HCP measure beginning with the FY 2025 SNF QRP. We note 
that the CDC has a PRA waiver for the collection and reporting of 
vaccination data under section 321 of the National Childhood Vaccine 
Injury Act (NCVIA) (Pub. L. 99-660, enacted November 14, 1986).\314\ 
Since the burden is waived from the requirements of the PRA, we have 
set out such burden under the economic analysis section (see section 
X.A.5.) of this proposed rule. While the waiver is specific to the 
PRA's requirements (``Chapter 35 of Title 44, United States Code''), 
our economic analysis requirements are not waived by any such statutes. 
We refer readers to section X.A.5. of this proposed rule, where we have 
provided an estimate of the burden to SNFs.
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    \314\ Section 321 of the NCVIA provides the PRA waiver for 
activities that come under the NCVIA, including those in the NCVIA 
at section 2102 of the Public Health Service Act (42 U.S.C. 300aa-
2). Section 321 is not codified in the U.S.C., but can be found in a 
note at 42 U.S.C. 300aa-1.
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    In section VI.C.2. of this proposed rule, we propose to revise the 
compliance date for certain SNF QRP reporting requirements including 
the Transfer of Health information measures and certain standardized 
patient assessment data elements (including race, ethnicity, preferred 
language, need for interpreter, health literacy, and social isolation). 
The proposed change in compliance date would have no

[[Page 22796]]

impact on any requirements or burden estimates; both proposals are 
active and accounted for under OMB control number 0938-1140 (CMS-
10387). Consequently, we are not proposing any changes under that 
control number.
    In section VI.C.3. of this proposed rule, we discuss our proposed 
revisions to the regulatory text. The proposed revisions have no 
collection of information implications.
    With regard to the SNF VBP Program, in section VII.B.1.b. of this 
proposed rule, we propose to suppress the SNFRM for scoring and payment 
purposes for the FY 2023 SNF VBP program year. This measure is 
calculated using Medicare FFS claims data, and our proposal to suppress 
data on this measure for the FY 2023 program year would not create any 
new reporting burden for SNFs. We note that, if our proposals described 
in section VII.B.1.b. of this proposed rule are finalized, we would 
publicly report the SNFRM rates for the FY 2023 program year, and we 
would make clear in the public presentation of those data that we are 
suppressing the use of those data for purposes of scoring and payment 
adjustments in the FY 2023 SNF VBP Program given the significant 
changes in SNF patient case volume and facility-level case mix 
described in that section of this proposed rule. In addition, as we 
describe in sections VII.B.3.b. and VII.B.3.c. of this proposed rule, 
we are proposing to adopt two additional measures (the SNF Healthcare-
Associated Infections (HAI) Requiring Hospitalization and the Total 
Nursing Hours per Resident Day/Payroll-Based Journal (PBJ) measures) 
beginning with the FY 2026 Program. The SNF HAI measure would be 
calculated using Medicare FFS claims data, therefore, our proposal to 
add the measure to the SNF VBP measure set would not create any new 
reporting burden for SNFs. The PBJ measure would be calculated using 
data that SNFs currently report to CMS under the Nursing Home Five-Star 
Quality Rating System, and therefore, our proposal to add the measure 
to the SNF VBP measure set would not create new reporting burden for 
SNFs.
    In section VII.B.3.d. of this proposed rule, we are proposing to 
adopt the DTC PAC Measure for SNFs beginning with the FY 2027 Program. 
The DTC PAC SNF measure would be calculated using Medicare FFS claims 
data; therefore, our proposal to add the measure to the SNF VBP measure 
set would not create a new reporting burden for SNFs.
    The aforementioned FFS-related claims submission requirements and 
burden are active and approved by OMB under control number 0938-1140 
(CMS-10387). This rule's proposed changes would have no impact on the 
requirements and burden that are currently approved under that control 
number.

IX. Response to Comments

    Because of the large number of public comments we normally receive 
on Federal Register documents, we are not able to acknowledge or 
respond to them individually. We will consider all comments we receive 
by the date and time specified in the DATES section of this preamble, 
and, when we proceed with a subsequent document, we will respond to the 
comments in the preamble to that document.

X. Economic Analyses

A. Regulatory Impact Analysis

1. Statement of Need
a. Statutory Provisions
    This proposed rule updates the FY 2023 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, the proposed rule updates the FY 2025 
SNF QRP requirements. Section 1888(e)(6) of the Act authorizes the SNF 
QRP and applies to freestanding SNFs, SNFs affiliated with acute care 
facilities, and all non-critical access hospital (CAH) swing-bed rural 
hospitals. We propose one new measure which we believe will encourage 
healthcare personnel to receive the influenza vaccine, resulting in 
fewer cases, less hospitalizations, and lower mortality associated with 
the virus. We propose to revise the compliance date for certain SNF QRP 
reporting requirements to improve data collection to allow for better 
measurement and reporting on equity across post-acute care programs and 
policies. For consistency in our regulations, we are also proposing 
conforming revisions to the Requirements under the SNF QRP at Sec.  
413.360.
    With respect to the SNF VBP Program, the proposed rule updates SNF 
VBP Program requirements for FY 2023 and subsequent years. 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 rule proposes numerical 
values of the performance standards for the all-cause, all-condition 
hospital readmission measure required by section 1888(g)(1) of the Act.
b. Discretionary Provisions
    In addition, this proposed rule proposes the following 
discretionary provisions:
(1) Recalibrating the Patient Driven Payment Model (PDPM) Parity 
Adjustment
    As a policy decision to ensure on-going budget neutral 
implementation of the new case mix system, the PDPM, we recommend 
proposing a recalibration of the PDPM parity adjustment. Since October 
1, 2019, we have been monitoring the implementation of PDPM and our 
analysis of FY 2020 and FY 2021 data reveals that the PDPM 
implementation led to an increase in Medicare Part A SNF spending, even 
after accounting for the effects of the COVID-19 PHE. We believe that 
proposing recalibration and reducing SNF spending by 4.6 percent, or 
$1.7 billion, in FY 2023 with no delayed implementation or phase-in 
period would allow for the most rapid establishment of payments at the 
appropriate level. This would work to ensure that PDPM will be budget-
neutral as intended and prevent continuing accumulation of excess SNF 
payments, which we cannot recoup.
(2) SNF Forecast Error Adjustment
    Each year, we evaluate the market basket forecast error for the 
most recent year for which historical data is available. The forecast 
error is determined by comparing the projected market basket increase 
in a given year with the actual market basket increase in that year. In 
evaluating the data for FY 2021, we found that the forecast error for 
that year was 1.5 percentage point, 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 threshold, current regulations require that the SNF 
market basket for FY 2022 be increased by 1.5 percentage point.

[[Page 22797]]

(3) Proposed Permanent Cap on Wage Index Decreases
    The Secretary has broad authority to establish appropriate payment 
adjustments under the SNF PPS, including the wage index adjustment. As 
discussed earlier in this section, the SNF PPS regulations require us 
to use an appropriate wage index based on the best available data. For 
the reasons discussed earlier in this section, we believe that a 5-
percent cap on wage index decreases would be appropriate for the SNF 
PPS. Therefore, for FY 2023 and subsequent years, we are proposing to 
apply a permanent 5-percent cap on any decrease to a provider's wage 
index from its wage index in the prior year, regardless of the 
circumstances causing the decline.
(4) Technical Updates to ICD-10 Mappings
    Each year, the ICD-10 Coordination and Maintenance Committee, a 
Federal interdepartmental committee that is chaired by representatives 
from the National Center for Health Statistics (NCHS) and by 
representatives from CMS, meets biannually and publishes updates to the 
ICD-10 medical code data sets in June of each year. These changes 
become effective October 1 of the year in which these updates are 
issued by the committee. The ICD-10 Coordination and Maintenance 
Committee also has the ability to make changes to the ICD-10 medical 
code data sets effective on April 1 of each year. In this proposed 
rule, we are proposing several changes to the ICD-10 code mappings and 
lists.
2. Introduction
    We have examined the impacts of this proposed 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), 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). Executive 
Order 13563 emphasizes the importance of quantifying both costs and 
benefits, of reducing costs, of harmonizing rules, and of promoting 
flexibility. Based on our estimates, OMB's Office of Information and 
Regulatory Affairs has determined this rulemaking is ``economically 
significant'' as measured by the $100 million threshold. Accordingly, 
we have prepared a regulatory impact analysis (RIA) as further 
discussed below. Also, the rule has been reviewed by OMB.
3. Overall Impacts
    This rule updates the SNF PPS rates contained in the SNF PPS final 
rule for FY 2022 (86 FR 42424). We estimate that the aggregate impact 
would be a decrease of approximately $320 million (0.9 percent) in Part 
A payments to SNFs in FY 2023. This reflects a $1.4 billion (3.9 
percent) increase from the proposed update to the payment rates and a 
$1.7 billion (4.6 percent) decrease from the proposed reduction to the 
SNF payment rates to account for the recalibrated parity adjustment. We 
note that these impact numbers do not incorporate the SNF VBP Program 
reductions that we estimate would total $185.55 million in FY 2023. We 
would 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 proposing to 
update the FY 2022 payment rates by a factor equal to the market basket 
index percentage change increased by the forecast error adjustment and 
reduced by the productivity adjustment to determine the payment rates 
for FY 2023. The impact to Medicare is included in the total column of 
Table 19. When proposing the SNF PPS rates for FY 2023, we proposed a 
number of standard annual revisions and clarifications mentioned 
elsewhere in this proposed rule.
    The annual update in this rule applies to SNF PPS payments in FY 
2023. 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 2023 SNF PPS payment impacts appear in Table 19. Using the 
most recently available data, in this case FY 2021 we apply the current 
FY 2022 CMIs, wage index and labor-related share value to the number of 
payment days to simulate FY 2022 payments. Then, using the same FY 2021 
data, we apply the FY 2023 CMIs, wage index and labor-related share 
value to simulate FY 2023 payments. We would note that, given that this 
same data is being used for both parts of this calculation, as compared 
to other analyses discussed in this proposed rule which compare data 
from FY 2020 to data from other fiscal years, any issues discussed 
throughout this proposed rule with regard to data collected in FY 2020 
will not cause any difference in this economic analysis. We tabulate 
the resulting payments according to the classifications in Table 19 
(for example, facility type, geographic region, facility ownership), 
and compare the simulated FY 2022 payments to the simulated FY 2023 
payments to determine the overall impact. The breakdown of the various 
categories of data in Table 19 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 proposed changes 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 proposed parity 
adjustment recalibration discussed in section V.C. of this proposed 
rule.
     The fourth column shows the effect of the proposed annual 
update to the wage index. This represents the effect of using the most 
recent wage data available as well as accounts for the proposed 5 
percent cap on wage index transitions, discussed in section V.A of this 
proposed rule. The total impact of this change is 0.0 percent; however, 
there are distributional effects of the proposed change.
     The fifth column shows the effect of all of the changes on 
the FY 2023 payments. The update of 3.9 percent is

[[Page 22798]]

constant for all providers and, though not shown individually, is 
included in the total column. It is projected that aggregate payments 
would increase by 3.9 percent, assuming facilities do not change their 
care delivery and billing practices in response. The figures in this 
column are calculated by multiplying the percentage change. For 
example, the Total Change figure for the Total Group Category is -0.9%, 
which is (1-4.6%) * (1 + 0.0%) * (1 + 3.9%).
    As illustrated in Table 19, the combined effects of all of the 
changes vary by specific types of providers and by location. For 
example, due to changes in this proposed rule, rural providers would 
experience a 1.0 percent decrease in FY 2023 total payments.
BILLING CODE 4120-01-P
[GRAPHIC] [TIFF OMITTED] TP15AP22.028

BILLING CODE 4120-01-C
5. Impacts for the Skilled Nursing Facility Quality Reporting Program 
(SNF QRP) for FY 2023
    Estimated impacts for the SNF QRP are based on analysis discussed 
in section IX.B. of this proposed 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. In section VI.A. 
of this proposed rule, we discuss the method for applying the 2-
percentage point reduction to SNFs that fail to meet the SNF QRP 
requirements.
    As discussed in section VI.C.1. of this proposed rule, we are 
proposing the adoption of one new measure to the SNF QRP beginning with 
the FY 2025 SNF QRP, the Influenza Vaccination Coverage among HCP (NQF 
#0431) 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. Although the burden 
associated with the Influenza Vaccination Coverage among HCP (NQF 
#0431) measure is not accounted for under the Centers for Diseases 
Control and Prevention Paperwork Reduction Act (CDC PRA) package due to 
the NCVIA waiver discussed in section IX. of this proposed

[[Page 22799]]

rule, the cost and burden is discussed here.
    Consistent with the CDC's experience of collecting data using the 
NHSN, we estimate that it would take each SNF an average of 15 minutes 
per month to collect data for the Influenza Vaccination Coverage among 
HCP (NQF #0431) measure and enter it into NHSN. We do not estimate that 
it will take SNFs additional time to input their data into NHSN, once 
they have logged onto the system for the purpose of submitting their 
monthly COVID-19 vaccine report. We believe it would take an 
administrative assistant 15 minutes to enter this data into NHSN. For 
the purposes of calculating the costs associated with the collection of 
information requirements, we obtained mean hourly wages from the U.S. 
Bureau of Labor Statistics' May 2020 National Occupational Employment 
and Wage Estimates.\315\ To account for overhead and fringe benefits, 
we have doubled the hourly wage. These amounts are detailed in Table 
20.
---------------------------------------------------------------------------

    \315\ https://www.bls.gov/oes/current/oes_nat.htm. Accessed 
February 1, 2022.
[GRAPHIC] [TIFF OMITTED] TP15AP22.029

    Based on this time range, it would cost each SNF an average cost of 
$9.38 each year. We believe the data submission for the Influenza 
Vaccination Coverage among HCP (NQF #0431) measure would cause SNFs to 
incur additional average burden of 15 minutes per year for each SNF and 
a total annual burden of 3,868 hours across all SNFs. The estimated 
annual cost across all 15,472 SNFs in the U.S. for the submission of 
the Influenza Vaccination Coverage among HCP (NQF #0431) measure would 
be an average of $145,127.36.
    As discussed in section VII.C.2. of this proposed rule, we are 
proposing that SNFs would begin collecting data on two quality measures 
and certain standardized patient assessment data elements beginning 
with discharges on October 1, 2023. CMS estimated the impacts for 
collecting the new data elements in the FY 2020 SNF PPS final rule (84 
FR 38829). When we delayed the compliance date for certain reporting 
requirements under the SNF QRP in the May 8th COVID-19 IFC, we did not 
remove the impacts for the new reporting requirements. However, we are 
providing updated impact information.
    For these two quality measures, we are adding 4 data elements on 
discharge which would require an additional 1.2 minutes of nursing 
staff time per discharge. We estimate these data elements for these 
quality measures would be completed by registered nurses (25 percent of 
the time or 0.30 minutes) and by licensed practical and vocational 
nurses (75 percent of the time or 0.90 minutes). For the purposes of 
calculating the costs associated with the collection of information 
requirements, we obtained mean hourly wages from the U.S. Bureau of 
Labor Statistics' May 2020 National Occupational Employment and Wage 
Estimates.\316\ To account for overhead and fringe benefits, we have 
doubled the hourly wage. These amounts are detailed in Table 21.
---------------------------------------------------------------------------

    \316\ https://www.bls.gov/oes/current/oes_nat.htm. Accessed 
February 1, 2022.
[GRAPHIC] [TIFF OMITTED] TP15AP22.030

    With 2,406,401 discharges from 15,472 SNFs annually, we estimate an 
annual burden of 48,128 additional hours (2,406,401 discharges x 1.2 
min/60) at a cost of $2,664,127 (2,406,401 x [(0.30/60 x $76.94/hr) + 
(0.90/60 x $48.16/hr)]). For each SNF we estimate an annual burden of 
3.11 hours (48,128 hr/15,472 SNFs) at a cost of $172.19 ($2,664,127/
15,472 SNFs).
    We are also proposing SNFs would begin collecting data on certain 
standardized patient assessment data elements, beginning with 
admissions and discharges (except for the preferred language, need for 
interpreter services, hearing, vision, race, and ethnicity standardized 
patient assessment data elements, which would be collected at admission 
only) on October 1, 2023. If finalized as proposed, SNFs would use the 
MDS 3.0 V1.18.11 to submit SNF QRP data. We are finalizing requirements 
to collect 55.5 standardized patient assessment data elements 
consisting of 8 data elements on admission and 47.5 data elements on 
discharge beginning with the FY 2025 SNF QRP. We estimate that the data 
elements would take an additional 12.675 minutes of nursing staff time 
consisting of 1.725 minutes to report on each admission and 10.95 
minutes to report on each discharge. We assume the added data elements 
would be performed by both registered nurses (25 percent of the time or 
3.169 minutes) and licensed practical and vocational (75 percent of the 
time or 9.506

[[Page 22800]]

minutes). We estimate the reporting of these assessment items will 
impose an annual burden of 508,352 total hours (2,406,401 discharges x 
12.675 min/60) at a cost of $28,139,825 ((508,352 hr x 0.25 x $76.94/
hr) + (508,352 hr x 0.75 x $48.16/hr)). For each SNF the annual burden 
is 32.86 hours (508,352 hr/15,472 SNFs) at a cost of $1,818.76 
($28,139,825/15,472 SNFs). The overall annual cost of the finalized 
changes associated with the newly added 59.5 assessment items is 
estimated at $1,990.95 per SNF annually ($172.19 + $1,818.76), or 
$30,803,952 ($2,664,127 + $28,139,825) for all 15,472 SNFs annually.
    We propose in section VI.C.3. of this proposed rule to make certain 
revisions in the regulation text itself at Sec.  413.360 to include new 
paragraph (f) to reflect all the data completion thresholds required 
for SNFs to meet the compliance threshold for the annual payment 
update, as well as certain conforming revisions. As discussed in 
section IX. of this proposed rule, this proposal would not affect the 
information collection burden for the SNF QRP.
    We welcome comments on the estimated time to collect influenza 
vaccination data and enter it into NHSN.
6. Impacts for the SNF VBP Program
    The estimated impacts of the FY 2023 SNF VBP Program are based on 
historical data and appear in Table 22. 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).
    However, in section VII.B.1 of this proposed rule, we are proposing 
to suppress the SNFRM for the FY 2023 program year. If finalized, we 
will award each participating SNF 60 percent of their 2 percent 
withhold. Additionally, we are proposing to apply a case minimum 
requirement for the SNFRM in section VII.E.3.b. of this proposed rule. 
In section VII.E.5. of this proposed rule, we are proposing to remove 
the Low-Volume Adjustment policy beginning with the FY 2023 Program 
year. As a result of these provisions, SNFs that do not meet the case 
minimum specified for the FY 2023 program year would be excluded from 
the program and would receive their full Federal per diem rate for that 
fiscal year. If finalized, this policy would maintain the overall 
payback percentage at 60 percent.
    Based on the 60 percent payback percentage, we estimated that we 
will redistribute approximately $278.32 million (of the estimated 
$463.87 million in withheld funds) in value-based incentive payments to 
SNFs in FY 2023, which means that the SNF VBP Program is estimated to 
result in approximately $185.55 million in savings to the Medicare 
Program in FY 2023.
    Our detailed analysis of the impacts of the FY 2023 SNF VBP Program 
is shown in Table 22.
BILLING CODE 4120-01-P

[[Page 22801]]

[GRAPHIC] [TIFF OMITTED] TP15AP22.031

    In section VII.B.2. of this proposed rule, we are also proposing to 
adopt two additional measures (the SNF HAI and Total Nurse Staffing 
measures) beginning with the FY 2026 program year. Additionally, we are 
proposing to apply a case minimum requirement for the SNF HAI and Total 
Nurse Staffing measures in section VII.E.3.c. of this proposed rule. In 
section VII.E.3.d. of this proposed rule, we are proposing to adopt a 
measure minimum policy for the FY 2026 program year. Therefore, we are 
providing estimated impacts of the FY 2026 SNF VBP Program, which are 
based on historical data and appear in Table 23. We modeled SNF 
performance in the Program using measure data from FY 2018 as the 
baseline period and FY 2019 as the performance period for the SNFRM, 
SNF HAI, and Total Nurse Staffing measures. 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), though we note that the logistic exchange function and payback 
percentage policies could be reconsidered in a future rulemaking. Based 
on the 60 percent payback percentage, we estimate that we will 
redistribute approximately $296.44 million (of the estimated $494.07 
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

[[Page 22802]]

approximately $197.63 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 Table 23.
[GRAPHIC] [TIFF OMITTED] TP15AP22.032

    In section VII.B.2. of this proposed rule, we are also proposing to 
adopt one additional measure (the DTC PAC SNF measure) beginning with 
the FY 2027 program year. Additionally, we are proposing to apply a 
case minimum requirement for the DTC PAC SNF measure in section 
VII.E.3.c. of this proposed rule. In section VII.E.3.d, of this 
proposed rule, we are proposing to adopt a measure minimum policy for 
the FY 2027 program year. Therefore, we are providing estimated impacts 
of the FY 2027 SNF VBP Program, which are based on historical data and 
appear

[[Page 22803]]

in Table 24. We modeled SNF performance in the Program using measure 
data from FY 2018 (the SNFRM, SNF HAI, and Total Nurse Staffing 
measures) and FY 2017-FY 2018 (the DTC PAC SNF measure) as the baseline 
period and FY 2019 (the SNFRM, SNF HAI, and Total Nurse Staffing 
measures) and FY 2019-FY 2020 (the DTC PAC SNF measure) 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), though we 
note that the logistic exchange function and payback percentage 
policies could be reconsidered in a future rule. Based on the 60 
percent payback percentage, we estimate that we will redistribute 
approximately $294.67 million (of the estimated $491.12 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 $196.45 million in savings to the Medicare Program in FY 
2027.
    Our detailed analysis of the impacts of the FY 2027 SNF VBP Program 
is shown in Table 24.

[[Page 22804]]

[GRAPHIC] [TIFF OMITTED] TP15AP22.033

BILLING CODE 4120-01-C
7. Alternatives Considered
    As described in this section, we estimate that the provisions in 
this proposed rule would result in an estimated net decrease in SNF 
payments of $320 million for FY 2023. This reflects a $1.4 billion 
increase from the proposed update to the payment rates of 3.9 percent 
and a $1.7 billion decrease from the proposed reduction to the SNF 
payment rates to account for the recalibrated parity adjustment.
    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

[[Page 22805]]

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 
index, 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 alternatives considered related to the 
methodology for calculating the proposed parity adjustment to the 
rates, we considered numerous alternative approaches to the 
methodology, including alternative data sets, applying the parity 
adjustment to targeted components of the payment system, and delaying 
or phasing-in the parity adjustment. These alternatives are described 
in full detail in section V.C. of this proposed rule.
    With regard to the proposal to add the HCP Influenza Vaccine 
measure to the SNF QRP Program, the COVID-19 pandemic has exposed the 
importance of implementing infection prevention strategies, including 
the promotion of HCP influenza vaccination. We believe this measure 
will encourage healthcare personnel to receive the influenza vaccine, 
resulting in fewer cases, less hospitalizations, and lower mortality 
associated with the virus, but were unable to identify any alternative 
methods for collecting the data. A compelling public need exists to 
target quality improvement among SNF providers and this proposed 
measure has the potential to generate actionable data on HCP 
vaccination rates.
    With regard to the proposal to revise the compliance date for the 
MDS v1.18.11, section 1888(d)(6)(B)(i)(III) of the Act requires that, 
for fiscal years 2019 and each subsequent year, SNFs must report 
standardized patient assessment data required under section 1899B(b)(1) 
of the Act. Section 1899(a)(1)(C) of the Act requires, in part, the 
Secretary to modify the PAC assessment instruments in order for PAC 
providers, including SNFs, to submit standardized patient assessment 
data under the Medicare program. Further delay of collecting this data 
would delay compliance with the current regulations.
    As discussed previously the burden for these proposals is minimal, 
and we believe the importance of the information necessitates these 
provisions.
    With regard to the proposals for the SNF VBP Program, we discuss 
alternatives considered within those sections. In section VII.B.2. of 
this proposed rule, we considered 4 options to adjust for COVID-19 in a 
technical update to the SNFRM. None of the alternatives would change 
the analysis of the impacts of the FY 2023 SNF VBP Program described in 
section X.A.6. of this proposed rule. In section VII.C.2. of this 
proposed rule, we propose to revise the baseline period for the FY 2025 
SNF VBP Program to FY 2019. We considered using alternative baseline 
periods, including FY 2020 and FY 2022, but these options are 
operationally infeasible. We will provide estimated impacts of the FY 
2025 SNF VBP Program in future rulemaking. In section
    In section VII.E.3.c. of this proposed rule, we are proposing that 
SNFs must have a minimum of 25 residents, on average, across all 
available quarters during the applicable 1-year performance period in 
order to be eligible to receive a score on the Total Nurse Staffing 
measure. We tested three alternative case minimums for this measure: A 
25-resident minimum, a minimum of one quarter of PBJ data, and a 
minimum of two quarters of PBJ data. After considering these 
alternatives, we determined that the proposed 25-resident minimum best 
balances quality measure reliability with our desire to score as many 
SNFs as possible on this measure.
    In section VII.E.3.d. of this proposed rule, we proposed measure 
minimums for the FY 2026 and FY 2027 SNF VBP programs. SNFs that do not 
meet these minimum requirements would be excluded from the program and 
would receive their full Federal per diem rate for that fiscal year. We 
also discussed alternatives, which are detailed below, that would 
result in more SNFs being excluded from the program.
    We are proposing that for FY 2026, SNFs must have the minimum 
number of cases for two of these three measures during the performance 
period to receive a performance score and value-based incentive 
payment. Under these proposed minimum requirements for the FY 2026 
program year, we estimate that approximately 14 percent of SNFs would 
be excluded from the FY 2026 Program. Specifically, if we required SNFs 
to have the minimum number of cases for all three measures during the 
performance period, approximately 21 percent of SNFs would be excluded 
from the FY 2026 Program. We also assessed the consistency of incentive 
payment multipliers (IPMs) between time periods as a proxy for 
performance score reliability under the different measure minimum 
options. The testing results indicated that the reliability of the SNF 
performance score would be relatively consistent across the different 
measure minimum requirements. Specifically, for the FY 2026 program 
year, we estimate that under the proposed minimum of two measures, 82 
percent of SNFs receiving a net-negative IPM in the first testing 
period also received a net-negative IPM in the second testing period. 
Alternatively, under a minimum of three measures for the FY 2026 
program year, we found that the consistency was 81 percent. Based on 
these testing results, we believe the proposed minimum of two out of 
three measures for FY 2026 best balances SNF performance score 
reliability with our desire to ensure that as many SNFs as possible can 
receive a performance score and value-based incentive payment.
    We are proposing that for FY 2027, SNFs must have the minimum 
number of cases for three of the four measures during a performance 
period to receive a performance score and value-based incentive 
payment. Under these proposed minimum requirements, we estimate that 
approximately 16 percent of SNFs would be excluded from the FY 2027 
Program. Alternatively, if we required SNFs to report the minimum 
number of cases for all four measures, we estimate that approximately 
24 percent of SNFs would be excluded from the FY 2027 Program. We also 
assessed the consistency of incentive payment multipliers (IPMs) 
between time periods as a proxy for performance score reliability under 
the different measure minimum options. The testing results indicated 
that the reliability of the SNF performance score for the FY 2027 
program year would be relatively consistent across the different 
measure minimum requirements. That is, among the different measure 
minimums for the FY 2027 program year, a strong majority (between 85 
and 87 percent) of the SNFs receiving a net-negative IPM for the first 
testing period also received a net-negative IPM for the second testing 
period. These findings indicate that increasing the measure minimum 
requirements does not meaningfully increase the consistency of the 
performance score. Based on these testing results, we believe the 
propose minimum of three out of four measures for FY 2027 best balances 
SNF performance score reliability with our desire to ensure that as 
many SNFs as possible can receive a performance

[[Page 22806]]

score and value-based incentive payment.
8. Accounting Statement
    As required by OMB Circular A-4 (available online at https://obamawhitehouse.archives.gov/omb/circulars_a004_a-4/), in Tables 25 
through 27, we have prepared an accounting statement showing the 
classification of the expenditures associated with the provisions of 
this proposed rule for FY 2023. Tables 19 and 25 provide our best 
estimate of the possible changes in Medicare payments under the SNF PPS 
as a result of the policies in this proposed rule, based on the data 
for 15,472 SNFs in our database. Table 26 provides our best estimate of 
the possible changes in Medicare payments under the SNF VBP as a result 
of the policies we have proposed for this program. Tables 20 and 27 
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.
[GRAPHIC] [TIFF OMITTED] TP15AP22.034

[GRAPHIC] [TIFF OMITTED] TP15AP22.035

[GRAPHIC] [TIFF OMITTED] TP15AP22.036

9. Conclusion
    This rule updates the SNF PPS rates contained in the SNF PPS final 
rule for FY 2022 (86 FR 42424). Based on the above, we estimate that 
the overall payments for SNFs under the SNF PPS in FY 2023 are 
projected to decrease by approximately $320 million, or 0.9 percent, 
compared with those in FY 2022. We estimate that in FY 2023, SNFs in 
urban and rural areas would experience, on average, a 0.9 percent 
decrease and 1.0 percent decrease, respectively, in estimated payments 
compared with FY 2022. Providers in the rural Pacific region would 
experience the largest estimated decrease in payments of approximately 
2.3 percent. Providers in the urban Pacific region would experience the 
smallest estimated decrease in payments of 0.1 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

[[Page 22807]]

included in the definition of a small entity.
    This rule would update the SNF PPS rates contained in the SNF PPS 
final rule for FY 2022 (86 FR 42424). Based on the above, we estimate 
that the aggregate impact for FY 2023 would be a decrease of $320 
million in payments to SNFs, resulting from the SNF market basket 
update to the payment rates, reduced by the proposed parity adjustment 
discussed in section IV.D. While it is projected in Table 19 that all 
providers would experience a net decrease 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 2023 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 2022 Report to Congress 
(available at https://www.medpac.gov/wp-content/uploads/2022/03/Mar22_MedPAC_ReportToCongress_Ch7_SEC.pdf), MedPAC states that Medicare 
covers approximately 10 percent of total patient days in freestanding 
facilities and 17 percent of facility revenue (March 2022 MedPAC Report 
to Congress, 238). As indicated in Table 19, the effect on facilities 
is projected to be an aggregate negative impact of 0.9 percent for FY 
2023. As the overall impact on the industry as a whole, and thus on 
small entities specifically, is less than the 3 to 5 percent threshold 
discussed previously, the Secretary has determined that this proposed 
rule will not have a significant impact on a substantial number of 
small entities for FY 2023.
    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 603 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 proposed 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 2022 (86 FR 42424)), the category of small rural hospitals 
is included within the analysis of the impact of this proposed rule on 
small entities in general. As indicated in Table 19, the effect on 
facilities for FY 2023 is projected to be an aggregate negative impact 
of 0.9 percent. As the overall impact on the industry as a whole is 
less than the 3 to 5 percent threshold discussed above, the Secretary 
has determined that this proposed rule will not have a significant 
impact on a substantial number of small rural hospitals for FY 2023.

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 2022, that 
threshold is approximately $165 million. This proposed 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 proposed 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 proposed 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 last year's proposed 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 last year's proposed rule is a fair estimate of 
the number of reviewers of this year's proposed rule.
    We also recognize that different types of entities are in many 
cases affected by mutually exclusive sections of this proposed rule, 
and therefore, for the purposes of our estimate we assume that each 
reviewer reads approximately 50 percent of the rule.
    Using the national mean hourly wage data from the May 2020 BLS 
Occupational Employment Statistics (OES) for medical and health service 
managers (SOC 11-9111), we estimate that the cost of reviewing this 
rule is $114.24 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 $456.96 (4 hours x $114.24). 
Therefore, we estimate that the total cost of reviewing this regulation 
is $156,280.32 ($442.96 x 342 reviewers).
    In accordance with the provisions of Executive Order 12866, this 
proposed 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 March 22, 2022.

List of Subjects in 42 CFR Part 413

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

    For the reasons set forth in the preamble, the Centers for Medicare 
& Medicaid Services proposes to amend 42 CFR chapter IV as set forth 
below:

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

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

0
2. Amend Sec.  413.337 by revising paragraph (b)(4) to read as follows:


Sec.  413.337  Methodology for calculating the prospective payment 
rates.

* * * * *
    (b) * * *
    (4) Standardization of data for variation in area wage levels and 
case-

[[Page 22808]]

mix. The cost data described in paragraph (b)(2) of this section are 
standardized to remove the effects of geographic variation in wage 
levels and facility variation in case-mix.
    (i) The cost data are standardized for geographic variation in wage 
levels using the wage index. The application of the wage index is made 
on the basis of the location of the facility in an urban or rural area 
as defined in Sec.  413.333.
    (ii) Starting on October 1, 2022, CMS applies a cap on decreases to 
the wage index such that the wage index applied to a SNF is not less 
than 95 percent of the wage index applied to that SNF in the prior FY.
    (iii) The cost data are standardized for facility variation in 
case-mix using the case-mix indices and other data that indicate 
facility case-mix.
* * * * *
0
3. Amend Sec.  413.338 by--
0
a. Revising paragraphs (a)(1) and (4) through (17);
0
b. Revising paragraphs (b) and (c)(2)(i), the paragraph (d) heading, 
and paragraph (d)(3);
0
c. Adding paragraphs (d)(5) and (6);
0
d. Redesignating paragraphs (e) through (g) as paragraphs (f) through 
(h);
0
e. Adding a new paragraph (e);
0
f. Revising newly redesignated paragraph (f)(1) and paragraph (f)(3) 
introductory text; and
0
g. Adding paragraphs (f)(4), (i), and (j).
    The revisions and additions read as follows:


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

    (a) * * *
    (1) Achievement threshold (or achievement performance standard) 
means the 25th percentile of SNF performance on a measure during the 
baseline period for a fiscal year.
* * * * *
    (4) Baseline period means the time period used to calculate the 
achievement threshold, benchmark, and improvement threshold that apply 
to a measure for a fiscal year.
    (5) Benchmark means, for a fiscal year, the arithmetic mean of the 
top decile of SNF performance on a measure during the baseline period 
for that fiscal year.
    (6) Eligible stay means, for purposes of the SNF readmission 
measure, an index SNF admission that would be included in the 
denominator of that measure.
    (7) Improvement threshold (or improvement performance standard) 
means an individual SNF's performance on a measure during the 
applicable baseline period for that fiscal year.
    (8) Logistic exchange function means the function used to translate 
a SNF's performance score into a value-based incentive payment 
percentage.
    (9) Low-volume SNF means a SNF with fewer than 25 eligible stays 
included in the SNF readmission measure denominator during the 
performance period for each of fiscal years 2019 through 2022.
    (10) Performance period means the time period during which SNF 
performance on a measure is calculated for a fiscal year.
    (11) Performance score means the numeric score ranging from 0 to 
100 awarded to each SNF based on its performance under the SNF VBP 
Program for a fiscal year.
    (12) Performance standards are the levels of performance that SNFs 
must meet or exceed to earn points on a measure under the SNF VBP 
Program for a fiscal year.
    (13) Ranking means the ordering of SNFs based on each SNF's 
Performance score under the SNF VBP Program for a fiscal year.
    (14) SNF readmission measure means, prior to October 1, 2019, the 
all-cause all-condition hospital readmission measure (SNFRM) or the 
all-condition risk-adjusted potentially preventable hospital 
readmission rate (SNFPPR) specified by CMS for application in the SNF 
Value-Based Purchasing Program. Beginning October 1, 2019, the term SNF 
readmission measure means the all-cause all-condition hospital 
readmission measure (SNFRM) or the all-condition risk-adjusted 
potentially preventable hospital readmission rate (Skilled Nursing 
Facility Potentially Preventable Readmissions after Hospital Discharge 
measure) specified by CMS for application in the SNF VBP Program.
    (15) SNF Value-Based Purchasing (VBP) Program means the program 
required under section 1888(h) of the Social Security Act.
    (16) Value-based incentive payment adjustment factor is the number 
that will be multiplied by the adjusted Federal per diem rate for 
services furnished by a SNF during a fiscal year, based on its 
performance score for that fiscal year, and after such rate is reduced 
by the applicable percent.
    (17) Value-based incentive payment amount is the portion of a SNF's 
adjusted Federal per diem rate that is attributable to the SNF VBP 
Program.
    (b) Applicability of the SNF VBP Program. The SNF VBP Program 
applies to SNFs, including facilities described in section 
1888(e)(7)(B) of the Act. Beginning with fiscal year 2023, the SNF VBP 
Program does not include a SNF, with respect to a fiscal year, if:
    (1) The SNF does not have the minimum number of cases that applies 
to each measure for the fiscal year, as specified by CMS; or
    (2) The SNF does not have the minimum number of measures for the 
fiscal year, as specified by CMS.
    (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 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.
* * * * *
    (d) Performance scoring under the SNF VBP Program (applicable, as 
described in this paragraph, to fiscal year 2019 through and including 
fiscal year 2025). * * *
    (3) If, with respect to a fiscal year beginning with fiscal year 
2019 through and including fiscal year 2022, CMS determines that a SNF 
is a low-volume SNF, CMS will assign a performance score to the SNF for 
the fiscal year that, when used to calculate the value-based incentive 
payment amount (as defined in paragraph (a)(17) of this section), 
results in a value-based incentive payment amount that is equal to the 
adjusted Federal per diem rate (as defined in paragraph (a)(2) of this 
section) that would apply to the SNF for the fiscal year without 
application of Sec.  413.337(f).
* * * * *
    (5) CMS will specify the measures for application in the SNF VBP 
Program for a given fiscal year.
    (6)(i) Performance standards are announced no later than 60 days 
prior to the start of the performance period that applies to that 
measure for that fiscal year.
    (ii) Beginning with the performance standards that apply to FY 
2021, if CMS discovers an error in the performance standard 
calculations subsequent to publishing their numerical values for a 
fiscal year, CMS will update the numerical values to correct the error. 
If CMS subsequently discovers one or more other errors with respect to 
the same fiscal year, CMS will not further update the numerical values 
for that fiscal year.

[[Page 22809]]

    (e) Performance scoring under the SNF VBP Program beginning with 
fiscal year 2026--(1) Points awarded based on SNF performance. CMS will 
award points to SNFs based on their performance on each measure for 
which the SNF reports the applicable minimum number of cases during the 
performance period applicable to that fiscal year as follows:
    (i) CMS will award from 1 to 9 points for achievement to each SNF 
whose performance on a measure during the applicable performance period 
meets or exceeds the achievement threshold for that measure but is less 
than the benchmark for that measure.
    (ii) CMS will award 10 points for achievement to a SNF whose 
performance on a measure during the applicable performance period meets 
or exceeds the benchmark for that measure.
    (iii) CMS will award from 0 to 9 points for improvement to each SNF 
whose performance on a measure during the applicable performance period 
exceeds the improvement threshold but is less than the benchmark for 
that measure.
    (iv) CMS will not award points for improvement to a SNF that does 
not meet the case minimum for a measure for the applicable baseline 
period.
    (v) The highest of the SNF's achievement and improvement score for 
a given measure will be the SNF's score on that measure for the 
applicable fiscal year.
    (2) Calculation of the SNF performance score. 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) of this section for each measure applicable to a fiscal 
year to calculate the SNF's point total.
    (ii) CMS will normalize the point total such that the resulting SNF 
performance score is expressed as a number of points earned out of a 
total of 100.
    (f) * * *
    (1) CMS will provide quarterly confidential feedback reports to 
SNFs on their performance on each measure specified for the fiscal 
year. Beginning with the baseline period and performance period quality 
measure quarterly reports issued on or after October 1, 2021, which 
contain the baseline period and performance period measure rates, 
respectively, SNFs will have 30 days following the date CMS provides 
each of these reports to review and submit corrections to the measure 
rates contained in that report. The administrative claims data used to 
calculate measure rates are not subject to review and correction under 
paragraph (e)(1) of this section. All correction requests must be 
accompanied by appropriate evidence showing the basis for the 
correction to each of the applicable measure rates.
* * * * *
    (3) CMS will publicly report the information described in 
paragraphs (f)(1) and (2) of this section on the Nursing Home Compare 
website or a successor website. Beginning with information publicly 
reported on or after October 1, 2019, and ending with information 
publicly reported on September 30, 2022 the following exceptions apply:
* * * * *
    (4) Beginning with the information publicly reported on or after 
October 1, 2022, the following exceptions apply:
    (i) If a SNF does not have the minimum number of cases during the 
baseline period that applies to a measure for a fiscal year, CMS will 
not publicly report the SNF's baseline period measure rate for that 
particular measure, although CMS will publicly report the SNF's 
performance period measure rate and achievement score if the SNF had 
the minimum number of cases for the measure during the performance 
period of the same program year;
    (ii) If a SNF does not have the minimum number of cases during the 
performance period that applies to a measure for a fiscal year, CMS 
will not publicly report any information with respect to the SNF's 
performance on that measure for the fiscal year;
    (iii) If a SNF does not have the minimum number of measures during 
the performance period for a fiscal year, CMS will not publicly report 
any data for that SNF for the fiscal year.
* * * * *
    (i) Special rules for the FY 2023 SNF VBP Program. (1) CMS will 
calculate a SNF readmission measure rate for each SNF based on its 
performance on the SNF readmission measure during the performance 
period specified by CMS for fiscal year 2023, but CMS will not 
calculate a performance score for any SNF using the methodology 
described in paragraphs (d)(1) and (2) of this section. CMS will 
instead assign a performance score of zero to each SNF.
    (2) CMS will calculate the value-based incentive payment adjustment 
factor for each SNF using a performance score of zero and will then 
calculate the value-based incentive payment amount for each SNF using 
the methodology described in paragraph (c)(2)(ii) of this section.
    (3) CMS will provide confidential feedback reports to SNFs on their 
performance on the SNF readmission measure in accordance with 
paragraphs (e)(1) and (2) of this section.
    (4) CMS will publicly report SNF performance on the SNF readmission 
measure in accordance with paragraph (f)(3) of this section.
    (j) Validation. (1) Beginning with the FY 2023 Program year, for 
the SNFRM measure, information reported through claims for the SNFRM 
measure are validated for accuracy by Medicare Administrative 
Contractors (MACs) to ensure accurate Medicare payments.
    (2) [Reserved]
0
4. Amend Sec.  413.360 by--
0
a. Removing paragraph (b)(2) and redesignating paragraph (b)(3) as 
paragraph (b)(2); and
0
b. Adding paragraph (f).
    The addition reads as follows:


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

* * * * *
    (f) Data completion threshold. (1) SNFs must meet or exceed two 
separate data completeness thresholds: One threshold set at 80 percent 
for completion of required quality measures data and standardized 
patient assessment data collected using the MDS submitted through the 
CMS designated data submission system; beginning with FY 2018 and for 
all subsequent payment updates; and a second threshold set at 100 
percent for measures data collected and submitted using the CDC NHSN, 
beginning with FY 2023 and for all subsequent payment updates.
    (2) These thresholds (80 percent for completion of required quality 
measures data and standardized patient assessment data on the MDS; 100 
percent for CDC NHSN data) will apply to all measures and standardized 
patient assessment data requirements adopted into the SNF QRP.
    (3) A SNF must meet or exceed both thresholds to avoid receiving a 
2-percentage point reduction to their annual payment update for a given 
fiscal year.

    Dated: April 8, 2022.
Xavier Becerra,
Secretary, Department of Health and Human Services.
[FR Doc. 2022-07906 Filed 4-11-22; 4:15 pm]
BILLING CODE 4120-01-P