22720 Federal Register / Vol. 87, No. 73 / Friday, April 15, 2022 / Proposed Rules 

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. 

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: 

PDPM@cms.hhs.gov 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-ServicePayment/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 


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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 yearto-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 AllCause 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 

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 postacute care settings, CMS and the Office 

of the National Coordinator for Health 


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1HL7 FHIR Release 4. Available at https://

www.hl7.org/fhir/. 

2HL7 FHIR. PACIO Functional Status 

Implementation Guide. Available at https://

paciowg.github.io/functional-status-ig/. 

3PACIO Project. Available at http://

pacioproject.org/about/. 

4Centers 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-dataelement-library-fact-sheet. 

5Sections 4001 through 4008 of Public Law 114– 

255. Available at https://www.govinfo.gov/content/ 

pkg/PLAW-114publ255/html/PLAW114publ255.htm. 

6The 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. 

7Common 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. 

8The 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. 

Information Technology (ONC) 

participate in the Post-Acute Care 

Interoperability Workgroup (PACIO) to 

facilitate collaboration with industry 

stakeholders to develop Health Level 

Seven International® (HL7) Fast 

Healthcare Interoperability Resource®

(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, reassessment 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. 

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-ISAReference-Edition.pdf. 

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-andcommon-agreement. 

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-Feefor-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 allcause all-condition hospital readmission 

measure and an all-condition riskadjusted 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. 


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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 wagerelated 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), § 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 


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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. 

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, costreporting 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 

§ 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 


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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), SpeechLanguage 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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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-AssessmentInstruments/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 threecharacter RUG–IV group into which the 

patient classified as well as a twocharacter 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/wpcontent/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 


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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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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 SNFspecific 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 SNFspecific 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. 


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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 nonurban 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-Feefor-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/wpcontent/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/wpcontent/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 laborrelated portion of the Federal rate. Each 

year, we calculate a revised laborrelated 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 laborrelated 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 


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Services; Installation, Maintenance, and 

Repair Services; All Other: LaborRelated 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. 

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 laborrelated 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 


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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 

§ 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 casemix 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-Feefor-Service-Payment/SNFPPS/ 

WageIndex.html. Finally, Table 10 

provides the case-mix and wage index 

adjusted per-diem rate for this patient 

for each day of the 30-day stay, as well 

as the total payment for this stay. Table 

10 also includes the VPD adjustment 

factors for each day of the patient’s stay, 

to clarify why the patient’s per diem 

rate changes for certain days of the stay. 

As illustrated in Table 8, SNF XYZ’s 

total PPS payment for this particular 

patient’s stay would equal $20,112.27. 

BILLING CODE 4120–01–P 


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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 casemix 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 § 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 casemix 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://

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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-Feefor-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 speechlanguage 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-ServicePayment/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 


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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 SNFlevel 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, SNFlevel 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-ServicePayment/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 § 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 § 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 


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wage index changes. In accordance with 

the requirements of the SNF PPS wage 

index regulations at § 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 wagerelated 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 yearto-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 

§ 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 


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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-ServicePayment/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-ServicePayment/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 


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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 nonsubstantive 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 casemix 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 


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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 onetime 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 casemix and payments would have been (for 

example, the Ultra-High and Very-High 

Rehabilitation assignments are not 

nearly as prevalent using PDPMreported 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 


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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 


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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. 

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 


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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-feefor-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 casemix 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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BILLING CODE 4120–01–C 

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 wellconsidered 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 


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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.). 


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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-Feefor-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 § 413.360(b)(3). 

BILLING CODE 4120–01–P 


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9CMS Measures Inventory Tool. (2022). Influenza 

Vaccination Coverage among Healthcare Personnel. 

Retrieved from https://cmit.cms.gov/CMIT_public/ 

ReportMeasure?measureId=854. 

BILLING CODE 4120–01–C 

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 postacute 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. 

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 


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22746 Federal Register / Vol. 87, No. 73 / Friday, April 15, 2022 / Proposed Rules 

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. 

11Centers 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. 

12Frentzel, 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 PostAcute 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. 

13Centers 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. 

14Centers 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

%2Fdisease%2F65over.htm. 

15Frentzel, 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 PostAcute 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. 

16Centers 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

%2Fdisease%2F65over.htm. 

17Lansbury, L.E., Brown, C.S., & 

Nguyen-Van-Tam, J.S. (2017). Influenza in 

long-term care facilities. Influenza Other Respir 

Viruses, 11(5), 356–366. https://dx.doi.org/ 

10.1111%2Firv.12464. 

18Pop-Vicas, A., & Gravenstein, S. (2011). 

Influenza in the elderly: A mini-review. 

Gerontology, 57(5), 397–404. https://doi.org/ 

10.1159/000319033. 

19Strausbaugh, 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. 

20Wilde, 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. 

21Harriman 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. 

22Black, 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. 

24Frentzel, 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 PostAcute 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, A ́ ., 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. 

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 § 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

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 


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27Ofstead, 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. 

28Centers 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. 

29Centers 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. 

30Ferdinands, 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. 

31Carman, W.F., Elder, A.G., Wallace, L.A., 

McAulay, K., Walker, A., Murray, G.D., & Stott, D.J. 

(2000). Effects of influenza vaccination of healthcare workers on mortality of elderly people in longterm care: a randomised controlled trial. Lancet 

(London, England), 355(9198), 93–97. https://

doi.org/10.1016/S0140-6736(99)05190-9. 

32Hayward, 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. 

33Lemaitre, 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 clusterrandomized trial. Journal of the American Geriatrics 

Society, 57(9), 1580–1586. https://doi.org/10.1111/ 

j.1532-5415.2009.02402.x. 

34Potter, 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. 

35Bechini, A., Lorini, C., Zanobini, P., Mando`

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. 

36Ofstead, 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. 

38Cai, 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. 

39Luo, 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. 

40Mauldin, 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. 

41Travers, 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. 

42Riester, 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. 

43Hall, 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. 

45High 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. 

46Wang, X., Kulkarni, D., Dozier, M., Hartnup, K., 

Paget, J., Campbell, H., Nair, H., & Usher Network 

for COVID–19 Evidence Reviews (UNCOVER) group 

Continued 

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. 

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

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 highdose 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. 

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 


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(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. 

48Libby 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. 49The Libby et al. (2013) article (preceding 

footnote) is referenced throughout the entirety of 

section VI.C.1.b. of this rule. 

50For 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. 

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. 

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 prerulemaking 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

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. 

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 


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51Libby 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. 

52Centers 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-list2020-report.pdf. 

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

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 multistakeholder 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. 

In addition to receiving feedback from 

MAP workgroup and committee 

members, NQF received four comments 

by industry stakeholders during the 

proposed measure’s MAP prerulemaking 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 consensusendorsed 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 


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53Refer 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. 

54 78 FR 47906. 

55Centers 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. 

Assessed and Appropriately Given the 

Seasonal Influenza Vaccine (Long Stay) 

(NQF #0681) measures are both NQFendorsed 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. 

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. 

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%2Finpatientrehab%2Fvaccination%2Findex.html. 

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 


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56The 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. 

57Bhumbra 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. 

58Ebinger 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. 

60Hsu 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. 

63Price-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. 

64Centers 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-declarationwaivers.pdf. 

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 processbased 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. 

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 nonHispanic 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. 

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 § 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 § 483.35(d) 

(with the exception of § 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 § 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 

§ 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 


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65CDC COVID Data Tracker. Accessed 3/4/2022. 

Retrieved from https://covid.cdc.gov/covid-datatracker/#vaccinations_vacc-people-onedose-pop5yr. 

66CDC COVID Data Tracker. Accessed 3/4/2022. 

Retrieved from https://covid.cdc.gov/covid-datatracker/#rates-by-vaccine-status. 

67COVID research: a year of scientific milestones. 

Nature. May 5, 2021. Retrieved from https://

www.nature.com/articles/d41586-020-00502-w. 

68CDC COVID Data Tracker. Accessed 2/10/2022. 

Retrieved from https://covid.cdc.gov/covid-datatracker/#datatracker-home. 

69Clinical 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-illhospitalized-covid-19-patients-begins. 

70COVID–19 Treatment Guidelines. National 

Institutes of Health. Updated October 27, 2021. 

Retrieved from https://

www.covid19treatmentguidelines.nih.gov/whatsnew/. 

71Here’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-withvaccines-and-treatments-forcovid-19. 

72U.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. 

73On 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-actionapproving-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-andmoderna-covid-19-vaccineAccessed3/02/22. 

74FDA 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. 

75FDA Approves First Treatment for COVID–19. 

October 22, 2020. Available at https://www.fda.gov/ 

newsevents/press-announcements/fda-approvesfirst-treatment-covid-19. Accessed 9/9/2021. 

76M. 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. 

77American 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/Newsand-Communications/Press-Releases/Pages/COVID19-Vaccines-Helping-Long-Term-Care-FacilitiesRebound-From-The-Pandemic.aspx. 

78COVID–19 Health Equity Interactive 

Dashboard. Emory University. Accessed January 12, 

2022. Retrieved from https://covid19.emory.edu/. 

79COVID–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. 

80Centers for Medicare & Medicaid Services. CMS 

Quality Strategy. 2016. Available at https://

www.cms.gov/Medicare/Quality-Initiatives-PatientAssessment-Instruments/QualityInitiativesGenInfo/ 

Downloads/CMS-Quality-Strategy.pdf. 

81Report to Congress: Improving Medicare PostAcute 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-toCongress-IMPACT-ACT-of-2014.pdf. 

82Rural Health Research Gateway. Rural 

Communities: Age, Income, and Health Status. 

Rural Health Research Recap. November 2018. 

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PDF/Update_HHS_Disparities_Dept-FY2020.pdf. 

84www.cdc.gov/mmwr/volumes/70/wr/ 

mm7005a1.htm. 

85Poteat 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. 

87Boockvar, 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. 

88Bell, 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. 

89Basey, A.J., Krska, J., Kennedy, T.D., & 

Mackridge, A.J., ‘‘Prescribing errors on admission to 

hospital and their potential impact: a mixedmethods 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. 

91Boling, P.A., ‘‘Care transitions and home health 

care,’’ Clinical Geriatric Medicine, 2009, Vol. 25(1), 

pp. 135–48. 

92Barnsteiner, J.H., ‘‘Medication Reconciliation: 

Transfer of medication information across 

settings—keeping it free from error,’’ 

93Arbaje, 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. 

Improvement (QAPI) program to the 

aspects of care most associated with 

COVID–19 (§ 483.75), that is infection 

control and adverse events. 

Additionally, we waived timeframe 

requirements on MDS assessments and 

transmission at § 483.20, along with 

waiving requirements for submitting 

staffing data through the Payroll-Based 

Journal (PBJ) system at § 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. 

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

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 


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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. 

97Mor, 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. 

100Forster, 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- 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. 

102Calendar Year 2020 Home Health final rule (86 

FR 62385 through 62390). 

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. 

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

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 (§ 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 

§ 413.360. Specifically, we propose to 

redesignate § 413.360(b)(2) to 

§ 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 § 413.360(b), Data submission 

requirement, we propose to remove 


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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. 

104Lindenauer 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. 

105Trivedi 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. 

106Polyakova, 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. 

107Rural 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-statusrecap.pdf. Accessed February 3, 2022. 

108U.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. 

109Centers 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. 

110Poteat 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. 

111Milkie 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. 

112Centers for Medicare and Medicaid Services. 

Available at https://www.cms.gov/pillar/healthequity. Accessed February 9, 2022. 

paragraph (b)(2) and redesignate 

paragraph (b)(3) as paragraph (b)(2). At 

§ 413.360, we propose to add a new 

paragraph (f), Data completion 

thresholds. 

At § 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 § 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 § 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 selfcare and mobility. Our measure 

development contractor for the crosssetting 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. 

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

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 


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113CMS Quality Strategy. 2016. Available at 

https://www.cms.gov/Medicare/Quality-InitiativesPatient-Assessment-Instruments/ 

Qualityinitiativesgeninfo/downloads/cms-qualitystrategy.pdf. Accessed February 3, 2022. 

114Centers for Medicare & Medicaid Services 

(CMS), HHS. Disparity Methods Confidential 

Reporting. Available at https://qualitynet.cms.gov/ 

inpatient/measures/disparity-methods. Accessed 

February 3, 2022. 

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. 

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 ‘‘betweenprovider’’ 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

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 programlevel, 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. 


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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. 

116Lindenauer 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. 

117Trivedi 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. 

118Polyakova 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. 

119Rural 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-statusrecap.pdf. Accessed February 3, 2022. 

120HHS 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. 

121Poteat 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. 

123 Jarrı ́n OF, Nyandege AN, Grafova IB, Dong X, 

Lin H. Validity of race and ethnicity codes in 

Medicare administrative data compared with goldstandard 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. 

124Office of the Assistant Secretary for Planning 

and Evaluation. Report to Congress: Social Risk 

Factors and Performance Under Medicare’s ValueBased Purchasing Program. December 20, 2016. 

Available at https://www.aspe.hhs.gov/reports/ 

report-congress-social-risk-factors-performanceunder-medicares-value-based-purchasingprograms. Accessed February 3, 2022. 

125Bonito 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. 

126Flanagan, 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. 

127Center 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. 

128Haas 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. 

(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 nonclinical 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

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). 

• 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. 

• 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. 

• 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

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 


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129Rahimi 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. 

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, providerspecific 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). 

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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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 nondual 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 


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130Centers for Medicare & Medicaid Services. 

CMS Measures Management System Blueprint. 

Version 17.0. September 2021. Available at https:// 

www.cms.gov/Medicare/Quality-Initiatives-PatientAssessment-Instruments/MMS/Downloads/ 

Blueprint.pdf. 

131Agniel 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. 

132Centers for Medicare & Medicaid Services, FY 

2022 IPPS/LTCH PPS Proposed Rule. 88 FR 25560. 

May 10, 2021. 

133Centers 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. 

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 nondual 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: 

• 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 highrisk, 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

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: Withinplan improvement and nationally 

benchmarked improvement. Withinplan 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 CAHPSbased 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. 


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134Centers 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. 

135Quality 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. 

136Agency 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. 

(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/measuresunder-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). 

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. 

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. 


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137Meaningful 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-cmsquality-conference-cms-quality-measurementaction-plan-march-2021.pdf. 

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. 

140Patient Reported Outcome Measures. 

Supplemental Material to the CMS MMS Blueprint. 

Available at https://www.cms.gov/files/document/ 

blueprint-patient-reported-outcome-measures.pdf. 

141Sangl, 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. 

142Centers for Medicare & Medicaid Services. 

Meaningful Measures 2.0: Moving from Measure 

Reduction to Modernization. Available at https://

www.cms.gov/meaningful-measures-20-movingmeasure-reduction-modernization. 

143Patients 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. 

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. 

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. 

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 

§§ 483.21 and 483.24 respectively 

effective November 28, 2017. 

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. 

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. 

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 


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144Centers for Medicare & Medicaid Services. List 

of Measures Under Consideration for December 1, 

2017. Available at https://www.cms.gov/files/ 

document/2017amuc-listclearancerpt.pdf. 

145Centers for Disease Control and Prevention 

(CDC). (2021). HCP Influenza Vaccination Summary 

Reporting FAQs. Retrieved from https://

www.cdc.gov/nhsn/faqs/vaccination/faq-influenzavaccination-summary-reporting.html#:∼

:text=To%20meet%20CMS%20reporting

%20requirements,not%20be%20shared

%20with%20CMS. 

Under Consideration for December 1, 

2017’’ (MUC List).144 The NQFconvened 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. 

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 § 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). 

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 ValueBased 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 


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146https://covid.cdc.gov/covid-data-tracker/ 

#datatracker-home. 

147https://www.statnews.com/2021/09/20/covid19-set-to-overtake-1918-spanish-flu-as-deadliestdisease-in-american-history/. 

148Chan, 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 

Continued 

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, allcondition 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 lowvolume 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 

§ 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 

§ 413.338(e)(1). Additionally, we 

codified the data suppression policy for 

low-volume SNFs at § 413.338(e)(3)(i) 

through (iii) and amended 

§ 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 § 413.338(g) of our 

regulations. 

To improve the clarity of our 

regulations, we propose to update and 

renumber the ‘‘Definitions’’ used in 

§ 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’’ (§ 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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States. PloS one, 16(9), e0256835. https://

pubmed.ncbi.nlm.nih.gov/34469474/. 

149 ‘‘United States COVID–19 Cases and Deaths by 

State,’’ Centers for Disease Control. Retrieved from 

https://data.cdc.gov/Case-Surveillance/UnitedStates-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. 

151Messner, 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 countylevel 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 countylevel 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. 

154Strully, 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. 

155CDC 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. 

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 facilitylevel 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 riskadjustment 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 


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157McKinsey 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-supplychains. 

158 ‘‘Nursing Home Covid-19 Data Dashboard.’’ 

Centers for Disease Control, retrieved from https:// 

www.cdc.gov/nhsn/covid19/ltc-reportoverview.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-reportoverview.html on February 14, 2022. 

160The White House. (2022). Fact Sheet: The 

Biden Administration to Begin Distributing AtHome, Rapid COVID-19 Tests to Americans for 

Free. Available at https://www.whitehouse.gov/ 

briefing-room/statements-releases/2022/01/14/factsheet-the-biden-administration-to-begindistributing-at-home-rapid-covid-19-tests-toamericans-for-free/. 

161Miller, 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-giveaway-400-million-n95-masks-starting-next-week/ 

2022/01/19/5095c050-7915-11ec-9dce7313579de434_story.html. 

162The 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/statementsreleases/2022/01/12/fact-sheet-biden-harrisadministration-increases-covid-19-testing-inschools-to-keep-students-safe-and-schools-open/. 

2021 data, which has been affected by 

these variations in COVID–19 case rates. 

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 

§ 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 


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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. 

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 riskadjustment 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 riskadjustment 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 riskadjustment 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 riskadjustment 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 riskadjustment 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 


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163Horn 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/. 

164Centers 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/wpcontent/uploads/legacy/clearinghouse/ 

PhaseIIVolumeIofIII.pdf. 

165Bostick 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/. 

166https://www.wolterskluwer.com/en/expertinsights/study-patient-satisfaction-grows-withnurse-staffing. 

167https://www.ncbi.nlm.nih.gov/pmc/articles/ 

PMC8522577/. 

168https://www.whitehouse.gov/briefing-room/ 

statements-releases/2022/02/28/fact-sheetprotecting-seniors-and-people-with-disabilities-byimproving-safety-and-quality-of-care-in-the-nationsnursing-homes/. 

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 riskadjustment 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 riskadjustment 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

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. 


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169World Health Organization. (2010). The 

burden of health care-associated infections 

worldwide. Retrieved from https://www.who.int/ 

news-room/feature-stories/detail/the-burden-ofhealth-care-associated-infection-worldwide. 

170Office 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. 171https://www.cms.gov/files/document/snf-haitechnical-report.pdf. 172CMS. (2006). Eliminating Serious Preventable, 

and Costly Medical Errors—Never Events. Retrieved 

from https://www.cms.gov/newsroom/fact-sheets/ 

eliminating-serious-preventable-and-costlymedical-errors-never-events. 173Beganovic, 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. 

174Cooper, 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. 

175Feldstein, 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. 

176Castle, 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. 

177Crnich, 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. 

179Cooper, 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. 

181Travers, 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. 

182CMS. (2006). Eliminating Serious Preventable, 

and Costly Medical Errors—Never Events. Retrieved 

from https://www.cms.gov/newsroom/fact-sheets/ 

eliminating-serious-preventable-and-costlymedical-errors-never-events. 

183Centers 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. 

184Ouslander, 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. 

185Zimlichman, E., Henderson, D., Tamir, O., 

Franz, C., Song, P., Yamin, C.K., Keohane, C., 

Denham, C.R., & Bates, D.W. (2013). Health CareAssociated 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. 

186Montoya, 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. 

187U.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-actionplan-ltcf.pdf. 

We propose to update our regulations 

at § 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/qualityinitiatives-patient-assessmentinstruments/nursinghomequalityinits/ 

skilled-nursing-facility-qualityreporting-program/snf-quality-reportingprogram-measures-and-technicalinformation. 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

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

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 


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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. 

189Office 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. 

190Beganovic, 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. 

191Crnich, 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. 

192Freeman-Jobson, J.H., Rogers, J.L., & WardSmith, 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/. 

193Hutton, 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. 

194Nguyen, 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. 

195Sloane, 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. 

196Lee, M.H., Lee GA, Lee S.H., & Park Y.H. 

(2019). Effectiveness and core components of 

infection prevention and control programs in longterm care facilities: A systematic review. https://

www.journalofhospitalinfection.com/action/ 

showPdf?pii=S0195-6701%2819%2930091-X. 

197https://www.cms.gov/files/document/ 

measures-under-consideration-list-2021-report.pdf. 

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. 

Given the effects of HAIs, preventing 

and reducing their occurrence in SNFs 

is critical to delivering safe and highquality 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. 

(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-haitechnical-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

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 


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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-haitechnical-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-haitechnical-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 riskadjusted 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 endstage 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 riskadjusted 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 riskstandardized rate of HAIs that are 

acquired during SNF care and result in 

hospitalization. This risk-adjusted HAI 

rate is calculated by multiplying the 


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216Bostick et al. 

217R. Tamara Konetzka, Elizabeth M. White, 

Alexander Pralea, David C. Grabowski, Vincent 

Continued 

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-haitechnical-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 § 413.338(e)(1) through (3), 

to § 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 welldocumented 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. 

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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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. 

218Williams, 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. 

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. 

223Centers 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/wpcontent/uploads/legacy/clearinghouse/Phase_I_

VOL_I.pdf. 

224Centers 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/wpcontent/uploads/legacy/clearinghouse/ 

PhaseIIVolumeIofIII.pdf. 

225FY 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. 

226 80 FR 46390, Aug. 4, 2015 (https://

www.govinfo.gov/content/pkg/FR-2015-08-04/pdf/ 

2015-18950.pdf). 

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

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. 

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/ProviderEnrollment-and-Certification/ 

CertificationandComplianc/FSQRS. 

Current Federal requirements for 

nurse staffing are outlined in the longterm 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 § 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 § 483.35(a) 

and (b). 

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 § 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. 

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 


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227https://www.cms.gov/Medicare/ProviderEnrollment-and-Certification/ 

SurveyCertificationGenInfo/Downloads/QSO18-17- 

NH.pdf. 

228https://www.qualityforum.org/WorkArea/ 

linkit.aspx?LinkIdentifier=id&ItemID=96520. 

229https://www.cms.gov/files/document/ 

measures-under-consideration-list-2021-report.pdf. 

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 FiveStar 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 casemix 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 payrollbased 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 PBJbased 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

(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 

(NHStaffing@cms.hhs.gov). 

(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. 

(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/QualityInitiatives-Patient-AssessmentInstruments/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). 


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230https://www.cms.gov/Medicare/Medicare-Feefor-Service-Payment/SNFPPS/TimeStudy. 

231We 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. 

(5) Measure Calculation and Case-Mix 

Adjustment 

We are proposing to calculate casemix 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 ‘‘casemix 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/QualityInitiatives-Patient-AssessmentInstruments/Value-Based-Programs/ 

SNF-VBP/Measure. 

(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 (fulltime 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/QualityInitiatives-Patient-AssessmentInstruments/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 § 413.338(e)(1) through (3) as 

§ 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/QualityInitiatives-Patient-AssessmentInstruments/NursingHomeQualityInits/ 

Skilled-Nursing-Facility-QualityReporting-Program/SNF-QualityReporting-Program-Measures-andTechnical-Information. 

(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, 


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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. 

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. 

241Wodchis 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. 

242Berkowitz 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 

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244https://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 

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Volume 22, Issue 11, 2021, Pages 2384–2388.e1, 

ISSN 1525–8610, https://doi.org/10.1016/ 

j.jamda.2021.04.022. 

245https://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 

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16–16. https://doi.org/10.1111/1475-6773.13737. 

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

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 longterm care costs for Medicaid and for 

residents’ out-of-pocket expenditures.239

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 postacute 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. 

(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/QualityInitiatives-Patient-AssessmentInstruments/Post-Acute-Care-QualityInitiatives/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 


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246https://www.cms.gov/files/document/ 

measures-under-consideration-list-2021-report.pdf. 

247https://www.cms.gov/Medicare/QualityInitiatives-Patient-Assessment-Instruments/ 

NursingHomeQualityInits/Downloads/MeasureSpecifications-for-FY17-SNF-QRP-Final-Rule.pdf. 

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. 

(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

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-andreporting-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/QualityInitiatives-Patient-AssessmentInstruments/NursingHomeQualityInits/ 

Downloads/Final-Specifications-forSNF-QRP-Quality-Measures-andSPADEs.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 


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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 riskadjusted 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 selfcare 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 § 413.338(e)(1) through (3) to 

§ 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 


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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 


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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. 


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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 

§ 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 § 413.338(a)(12), then 

add additional detail about the 

correction policy at § 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 § 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. 

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 § 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 


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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 

§ 413.338 and would also not be subject 

to a payment reduction under 

§ 413.337(f). Instead, the SNF would 

receive its full Federal per diem rate 

under § 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 § 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/QualityInitiatives-Patient-AssessmentInstruments/Value-Based-Programs/ 

Other-VBPs/SNFRM-Reliability-TestingMemo.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-haitechnical-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 


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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-PatientAssessment-Instruments/Value-BasedPrograms/Other-VBPs/SNFRMReliability-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 netnegative 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. 


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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 

§ 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 

§ 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 

§ 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 

at§ 413.338(c)(2)(i). We are proposing to 

update the LVA policy at § 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 valuebased 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 § 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: 


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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: 

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 

§ 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. 


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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 

§ 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 § 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 valuebased 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 § 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, 


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248https://www.cms.gov/Medicare/ProviderEnrollment-and-Certification/SurveyCertification

GenInfo/Downloads/QSO18-17-NH.pdf. 

249Centers 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/wpcontent/uploads/legacy/clearinghouse/PhaseII

VolumeIofIII.pdf. 

250Lacey 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. 

252https://www.cms.gov/files/document/qso-22- 

08-nh.pdf. 

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 facilitylevel 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 

§ 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 § 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). 

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

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 


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253To Advance Information on Quality of Care, 

CMS Makes Nursing Home Staffing Data Available, 

available at: https://www.cms.gov/newsroom/pressreleases/advance-information-quality-care-cmsmakes-nursing-home-staffing-data-available. 

254Lacey 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. 

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-andcertification/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. 

(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 valuebased 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-PatientAssessment-Instruments/Value-BasedPrograms/Other-VBPs/SNF-VBPexchange-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 


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255RAND MDS 3.0 Final Study Report: https://

www.cms.gov/Medicare/Quality-Initiatives-PatientAssessment-Instruments/NursingHomeQualityInits/ 

Downloads/MDS30FinalReport-Appendices.zip. 256Rahman 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/. 

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/QualityInitiatives-Patient-AssessmentInstruments/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

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 chartabstracted 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). 


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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. 

258Lindenauer 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. 

259Trivedi 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. 

260Polyakova, 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. 

261Rural 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-statusrecap.pdf. 

262https://www.minorityhealth.hhs.gov/assets/ 

PDF/Update_HHS_Disparities_Dept-FY2020.pdf. 

263www.cdc.gov/mmwr/volumes/70/wr/ 

mm7005a1.htm. 

264Milkie 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. 

265Poteat 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. 

266https://www.cms.gov/About-CMS/AgencyInformation/OMH/OMH-Mapping-MedicareDisparities. 

267https://innovation.cms.gov/innovationmodels/ahcm. 

268https://qualitynet.cms.gov/inpatient/ 

measures/disparity-methods. 

269https://www.cms.gov/Medicare/QualityInitiatives-Patient-Assessment-Instruments/PostAcute-Care-Quality-Initiatives/IMPACT-Act-of2014/-IMPACT-Act-Standardized-PatientAssessment-Data-Elements. 

270Office 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-medicaresvalue-basedpurchasing-programs. 

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 postacute care setting.269

As we continue to leverage our valuebased 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. 

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 


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271https://www.whitehouse.gov/briefing-room/ 

statements-releases/2022/02/28/fact-sheetprotecting-seniors-and-people-with-disabilities-byimproving-safety-and-quality-of-care-in-the-nationsnursing-homes/. 

272https://www.cms.gov/Medicare/Medicare-Feefor-Service-Payment/SNFPPS/TimeStudy. 273Zhang, Unruh, Liu, and Wan, 2006. 

‘‘Minimum Nurse Staffing Ratios for Nursing 

Homes’’. 274Appropriateness of Minimum Nurse Staffing 

Ratios in Nursing Homes, Phase II Final Report, 

2001, Abt Associates. https://theconsumervoice.org/ 

uploads/files/issues/CMS-Staffing-Study-PhaseII.pdf. 

275Schnelle 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. 

276Schnelle, 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. 

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, highquality 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 personcentered 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. 

A. Background 

The requirements for participation for 

LTC facilities are the baseline health 

and safety standards that Medicarecertified 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 § 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 § 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 


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277https://www.cdc.gov/nchs/data/series/sr_03/ 

sr03_43-508.pdf. 

278https://www.sciencedirect.com/science/ 

article/pii/S1525861019305274?via%3Dihub. 

279https://aspe.hhs.gov/reports/trends-useresidential-settings-among-older-adults-issue-brief0. 

280https://cmsintranet.share.cms.gov/ER/Pages/ 

DetailOpportunities.aspx#; https://www.cms.gov/ 

medicare/provider-enrollment-and-certification/ 

certificationandcomplianc/downloads/ 

usersguide.pdf. 

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. 

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

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). 

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. 

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 

§ 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

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 


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281https://doh.dc.gov/sites/default/files/dc/sites/ 

doh/publication/attachments/Nursing_Facility_

Regulations_Health_Care_Facilities_Improvement_

2012.pdf. 

282https://theconsumervoice.org/uploads/files/ 

issues/CV_StaffingReport.pdf. 

283https://theconsumervoice.org/uploads/files/ 

issues/CV_StaffingReport.pdf. 

284https://www.ncbi.nlm.nih.gov/pmc/articles/ 

PMC2669632/pdf/hesr0044-0056.pdf. 

285Hyer, 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). 

286https://www.journalofnursingregulation.com/ 

article/S2155-8256(15)30229-5/fulltext. 

287https://bhw.hrsa.gov/sites/default/files/ 

bureau-health-workforce/data-research/nchwahrsa-nursing-report.pdf. 

288https://bhw.hrsa.gov/sites/default/files/ 

bureau-health-workforce/data-research/nchwahrsa-nursing-report.pdf. 

289https://www.bls.gov/oes/current/ 

oes291141.htm. 

290https://www.bls.gov/oes/2019/may/

oes291141.htm. 

291https://www.bls.gov/oes/current/ 

oes311131.htm. 

292https://www.bls.gov/oes/2019/may/ 

oes311131.htm. 

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 fulltime 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

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

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. 

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

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

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 


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293Nurse 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. 

294Spilsbury, 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. 

295https://www.ncbi.nlm.nih.gov/pmc/articles/ 

PMC3207189/. 

296https://patientcarelink.org/wp-content/

uploads/2021/02/2-ANA-Principles-for-NurseStaffing-3rd-Edition.pdf. 

297https://www.icn.ch/sites/default/files/inlinefiles/PS_C_%20Evidence%20based%20safe%20

nurse%20staffing_1.pdf. 298https://www.nmnec.org/wp-content/uploads/ 

2021/05/Future-of-Nursing-2020-2030.pdf. 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. 

300https://www.healthaffairs.org/doi/10.1377/ 

hlthaff.2020.01269. 

301https://www.healthaffairs.org/doi/10.1377/ 

hlthaff.2020.02351. 

302https://agsjournals.onlinelibrary.wiley.com/ 

doi/epdf/10.1111/jgs.17309. 

303https://nap.nationalacademies.org/catalog/ 

25982/the-future-of-nursing-2020-2030-charting-apath-to. 

304CMS (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. 

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. 

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

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

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). 

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: 

• 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

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 


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305https://www.kff.org/coronavirus-covid-19/ 

issue-brief/data-note-how-might-coronavirus-affectresidents-in-nursing-facilities/. 

306https://data.cms.gov/covid-19/covid-19- 

nursing-home-data. 

307https://data.cms.gov/covid-19/covid-19- 

nursing-home-data. 

308Figueroa 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. 

309https://agsjournals.onlinelibrary.wiley.com/ 

doi/epdf/10.1111/jgs.16689. 

310Larger 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. 

311https://www.ncbi.nlm.nih.gov/books/ 

NBK216190/. 312https://www.justice.gov/sites/default/files/ 

elderjustice/legacy/2015/07/12/Appropriateness_

of_Minimum_Nurse_Staffing_Ratios_in_Nursing_

Homes.pdf. 

313https://www.rwjf.org/en/library/research/ 

2014/03/cnf-ten-years-after-keeping-patientssafe.html. 

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

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

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 staffto-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-toresident 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. 

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.’’ 

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. 

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 


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314Section 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. 

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. 

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 


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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 

§ 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 ongoing 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 budgetneutral 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. 


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(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 futureoriented, 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 

§ 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 


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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 

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 


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315https://www.bls.gov/oes/current/oes_nat.htm. 

Accessed February 1, 2022. 

316https://www.bls.gov/oes/current/oes_nat.htm. 

Accessed February 1, 2022. 

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. 

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. 

With 2,406,401 discharges from 

15,472 SNFs annually, we estimate an 

annual burden of 48,128 additional 

hours (2,406,401 discharges × 1.2 min/ 

60) at a cost of $2,664,127 (2,406,401 × 

[(0.30/60 × $76.94/hr) + (0.90/60 × 

$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 


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minutes). We estimate the reporting of 

these assessment items will impose an 

annual burden of 508,352 total hours 

(2,406,401 discharges × 12.675 min/60) 

at a cost of $28,139,825 ((508,352 hr × 

0.25 × $76.94/hr) + (508,352 hr × 0.75 

× $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 § 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 valuebased 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 


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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 valuebased incentive payments to SNFs in FY 

2026, which means that the SNF VBP 

Program is estimated to result in 


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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. 

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 


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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. 


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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 


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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 valuebased 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 netnegative 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 


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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. 

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, nonprofit 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-sizestandards) In addition, approximately 

20 percent of SNFs classified as small 

entities are non-profit organizations. 

Finally, individuals and states are not 


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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 × 

$114.24). Therefore, we estimate that 

the total cost of reviewing this 

regulation is $156,280.32 ($442.96 × 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 

■ 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. 

■ 2. Amend § 413.337 by revising 

paragraph (b)(4) to read as follows: 

§ 413.337 Methodology for calculating the 

prospective payment rates. 

* * * * * 

(b) * * * 

(4) Standardization of data for 

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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 § 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. 

* * * * * 

■ 3. Amend § 413.338 by— 

■ a. Revising paragraphs (a)(1) and (4) 

through (17); 

■ b. Revising paragraphs (b) and 

(c)(2)(i), the paragraph (d) heading, and 

paragraph (d)(3); 

■ c. Adding paragraphs (d)(5) and (6); 

■ d. Redesignating paragraphs (e) 

through (g) as paragraphs (f) through (h); 

■ e. Adding a new paragraph (e); 

■ f. Revising newly redesignated 

paragraph (f)(1) and paragraph (f)(3) 

introductory text; and 

■ g. Adding paragraphs (f)(4), (i), and (j). 

The revisions and additions read as 

follows: 

§ 413.338 Skilled nursing facility valuebased 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 valuebased 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 

§ 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. 


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

■ 4. Amend § 413.360 by— 

■ a. Removing paragraph (b)(2) and 

redesignating paragraph (b)(3) as 

paragraph (b)(2); and 

■ b. Adding paragraph (f). 

The addition reads as follows: 

§ 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 


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