[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
[[Page 22720]]
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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
[[Page 22722]]
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.
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[[Page 22726]]
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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.
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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.
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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.
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[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.
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\9\ CMS Measures Inventory Tool. (2022). Influenza Vaccination
Coverage among Healthcare Personnel. Retrieved from https://cmit.cms.gov/CMIT_public/ReportMeasure?measureId=854.
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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\
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\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.
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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.
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\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.
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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
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\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.
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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.
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\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.
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\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.
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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.
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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.
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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.
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\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.
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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.
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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.
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\54\ 78 FR 47906.
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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.
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\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.
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\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.
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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.
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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.
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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
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[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.
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\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.
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\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).
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\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\
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\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.
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\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.
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\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]]
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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.
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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]]
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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.
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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