20218 Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 

DEPARTMENT OF HEALTH AND 

HUMAN SERVICES 

Centers for Medicare & Medicaid 

Services 

42 CFR Part 412 

[CMS–1767–P] 

RIN 0938–AU78 

Medicare Program; Inpatient 

Rehabilitation Facility Prospective 

Payment System for Federal Fiscal 

Year 2023 and Updates to the IRF 

Quality Reporting Program 

AGENCY: Centers for Medicare & 

Medicaid Services (CMS), HHS. 

ACTION: Proposed rule. 

SUMMARY: This rulemaking proposes 

updating the prospective payment rates 

for inpatient rehabilitation facilities 

(IRFs) for Federal fiscal year (FY) 2023. 

As required by statute, this proposed 

rule includes the classification and 

weighting factors for the IRF prospective 

payment system’s case-mix groups and 

a description of the methodologies and 

data used in computing the prospective 

payment rates for FY 2023. In addition, 

we are proposing to codify CMS’ 

existing teaching status adjustment 

policy through proposed amendments to 

the regulation text and proposing to 

update and clarify the IRF teaching 

policy with respect to IRF hospital 

closures and displaced residents. In this 

proposed rule, we are also soliciting 

comments on the methodology for 

updating the facility level adjustment 

factors. Additionally, we are soliciting 

comments regarding the IRF transfer 

payment policy. This rule proposes to 

establish a permanent cap policy to 

smooth the impact of year-to-year 

changes in IRF payments related to 

changes in the IRF wage index. This 

proposed rule also includes updates for 

the IRF Quality Reporting Program 

(QRP). 

DATES: To be assured consideration, 

comments must be received at one of 

the addresses provided below, no later 

than 5 p.m. on May 31, 2022. 

ADDRESSES: In commenting, please refer 

to file code CMS–1767–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 http://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– 

1767–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– 

1767–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: 

Gwendolyn Johnson, (410) 786–6954, 

for general information. 

Catie Cooksey, (410) 786–0179, for 

information about the IRF payment 

policies and payment rates. 

Kim Schwartz, (410) 786–2571 and 

Gwendolyn Johnson, (410) 786–6954, 

for information about the IRF coverage 

policies. 

Ariel Cress, (410) 786–8571, for 

information about the IRF quality 

reporting 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: http://

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 Information 

Through the Internet on the CMS 

Website 

The IRF prospective payment system 

(IRF PPS) Addenda along with other 

supporting documents and tables 

referenced in this proposed rule are 

available through the internet on the 

CMS website at https://www.cms.gov/ 

Medicare/Medicare-Fee-for-ServicePayment/InpatientRehabFacPPS. 

We note that prior to 2020, each rule 

or notice issued under the IRF PPS has 

included a detailed reiteration of the 

various regulatory provisions that have 

affected the IRF PPS over the years. That 

discussion, along with detailed 

background information for various 

other aspects of the IRF PPS, is now 

available on the CMS website at https:// 

www.cms.gov/Medicare/Medicare-Feefor-Service-Payment/Inpatient

RehabFacPPS. 

I. Executive Summary 

A. Purpose 

This rulemaking proposes updating 

the prospective payment rates for IRFs 

for FY 2023 (that is, for discharges 

occurring on or after October 1, 2022, 

and on or before September 30, 2023) as 

required under section 1886(j)(3)(C) of 

the Social Security Act (the Act). As 

required by section 1886(j)(5) of the Act, 

this proposed rule includes the 

classification and weighting factors for 

the IRF PPS’s case-mix groups (CMGs) 

and a description of the methodologies 

and data used in computing the 

prospective payment rates for FY 2023. 

This proposed rule proposes to codify 

CMS’ existing teaching status 

adjustment policy through proposed 

amendments to the regulation text and 

proposes to update and clarify the IRF 

teaching policy with respect to IRF 

hospital closures and displaced 

residents. We are also soliciting 

comments on the methodology for 

updating the facility level adjustment 

factors. Additionally, we are soliciting 

comments regarding the IRF transfer 

payment policy. We are also proposing 

to establish a permanent cap policy to 

smooth the impact of year-to-year 

changes in IRF payments related to 

changes in the IRF wage index. This 

rule also proposes to require quality 

data reporting on all IRF patients 

beginning with the FY 2025 IRF QRP 

and amend the regulations consistent 

with the proposed requirements. This 

rule also proposes to correct an error in 

the regulations text at § 412.614(d)(2). 

Finally, we are seeking comment on 

three issues: (1) Future measure 

concepts under consideration for the 

IRF QRP; (2) a future dQM for the IRF 

QRP; and (3) overarching principles for 

measuring equity and health quality 

disparities across CMS Quality 

Programs, including the IRF QRP. 

B. Summary of Major Provisions 

In this proposed rule, we use the 

methods described in the FY 2022 IRF 


lotter on DSK11XQN23PROD with PROPOSALS3
Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 20219 

PPS final rule (86 FR 42362) to update 

the prospective payment rates for FY 

2023 using updated FY 2021 IRF claims 

and the most recent available IRF cost 

report data, which is FY 2020 IRF cost 

report data. This proposed rule proposes 

to codify CMS’ existing teaching status 

adjustment policy through proposed 

amendments to the regulation text and 

proposes to update and clarify the IRF 

teaching status adjustment policy with 

respect to IRF hospital closures and 

displaced residents. We are also 

soliciting comments on the 

methodology for updating the facility 

level adjustment factors. Additionally, 

we are soliciting comments regarding 

the IRF transfer payment policy. 

We are also proposing to establish a 

permanent cap policy to smooth the 

impact of year-to-year changes in IRF 

payments related to changes in the IRF 

wage index. This rule also proposes to 

collect quality reporting data for all IRF 

patients beginning with the FY 2025 IRF 

QRP and revise the regulations. Finally, 

we are seeking comment on three issues: 

(1) Future measure concepts for the IRF 

QRP; (2) a future digital quality measure 

(dQM) for the IRF QRP; and (3) 

overarching principles for measuring 

equity and health quality disparities 

across CMS Quality Programs, including 

the IRF QRP. 

C. Summary of Impact 

II. Background 

A. Statutory Basis and Scope for IRF 

PPS Provisions 

Section 1886(j) of the Act provides for 

the implementation of a per-discharge 

PPS for inpatient rehabilitation 

hospitals and inpatient rehabilitation 

units of a hospital (collectively, 

hereinafter referred to as IRFs). 

Payments under the IRF PPS encompass 

inpatient operating and capital costs of 

furnishing covered rehabilitation 

services (that is, routine, ancillary, and 

capital costs), but not direct graduate 

medical education costs, costs of 

approved nursing and allied health 

education activities, bad debts, and 

other services or items outside the scope 

of the IRF PPS. A complete discussion 

of the IRF PPS provisions appears in the 

original FY 2002 IRF PPS final rule (66 

FR 41316) and the FY 2006 IRF PPS 

final rule (70 FR 47880) and we 

provided a general description of the 

IRF PPS for FYs 2007 through 2019 in 

the FY 2020 IRF PPS final rule (84 FR 

39055 through 39057). A general 

description of the IRF PPS for FYs 2020 

through 2022, along with detailed 

background information for various 

other aspects of the IRF PPS, is now 

available on the CMS website at https:// 

www.cms.gov/Medicare/Medicare-Feefor-Service-Payment/Inpatient

RehabFacPPS. 

Under the IRF PPS from FY 2002 

through FY 2005, the prospective 

payment rates were computed across 

100 distinct CMGs, as described in the 

FY 2002 IRF PPS final rule (66 FR 

41316). We constructed 95 CMGs using 

rehabilitation impairment categories 

(RICs), functional status (both motor and 

cognitive), and age (in some cases, 

cognitive status and age may not be a 

factor in defining a CMG). In addition, 

we constructed five special CMGs to 

account for very short stays and for 

patients who expire in the IRF. 

For each of the CMGs, we developed 

relative weighting factors to account for 

a patient’s clinical characteristics and 

expected resource needs. Thus, the 

weighting factors accounted for the 

relative difference in resource use across 

all CMGs. Within each CMG, we created 

tiers based on the estimated effects that 

certain comorbidities would have on 

resource use. 

We established the Federal PPS rates 

using a standardized payment 

conversion factor (formerly referred to 

as the budget-neutral conversion factor). 

For a detailed discussion of the budgetneutral conversion factor, please refer to 

our FY 2004 IRF PPS final rule (68 FR 

45684 through 45685). In the FY 2006 

IRF PPS final rule (70 FR 47880), we 

discussed in detail the methodology for 

determining the standard payment 

conversion factor. 

We applied the relative weighting 

factors to the standard payment 

conversion factor to compute the 

unadjusted prospective payment rates 

under the IRF PPS from FYs 2002 

through 2005. Within the structure of 

the payment system, we then made 

adjustments to account for interrupted 

stays, transfers, short stays, and deaths. 

Finally, we applied the applicable 

adjustments to account for geographic 

variations in wages (wage index), the 

percentage of low-income patients, 

location in a rural area (if applicable), 

and outlier payments (if applicable) to 

the IRFs’ unadjusted prospective 

payment rates. 

For cost reporting periods that began 

on or after January 1, 2002, and before 

October 1, 2002, we determined the 

final prospective payment amounts 

using the transition methodology 

prescribed in section 1886(j)(1) of the 

Act. Under this provision, IRFs 

transitioning into the PPS were paid a 

blend of the Federal IRF PPS rate and 

the payment that the IRFs would have 

received had the IRF PPS not been 

implemented. This provision also 

allowed IRFs to elect to bypass this 

blended payment and immediately be 

paid 100 percent of the Federal IRF PPS 

rate. The transition methodology 

expired as of cost reporting periods 

beginning on or after October 1, 2002 

(FY 2003), and payments for all IRFs 

now consist of 100 percent of the 

Federal IRF PPS rate. 

Section 1886(j) of the Act confers 

broad statutory authority upon the 

Secretary to propose refinements to the 

IRF PPS. In the FY 2006 IRF PPS final 

rule (70 FR 47880) and in correcting 

amendments to the FY 2006 IRF PPS 

final rule (70 FR 57166), we finalized a 

number of refinements to the IRF PPS 

case-mix classification system (the 

CMGs and the corresponding relative 

weights) and the case-level and facilitylevel adjustments. These refinements 

included the adoption of the Office of 

Management and Budget’s (OMB’s) 

Core-Based Statistical Area (CBSA) 

market definitions; modifications to the 

CMGs, tier comorbidities; and CMG 

relative weights, implementation of a 

new teaching status adjustment for IRFs; 

rebasing and revising the market basket 

index used to update IRF payments, and 

updates to the rural, low-income 

percentage (LIP), and high-cost outlier 

adjustments. Beginning with the FY 


lotter on DSK11XQN23PROD with PROPOSALS3EP06AP22.009</GPH>

TABLE 1: Cost and Benefit 

Provision Description Transfers/Costs 

[FY 2023 IRF PPS payment rate The overall economic impact of this proposed rule is an estimated $170 million in 

Update increased payments from the Federal Government to IRFs during FY 2023. 

[FY 2025 IRF QRP changes The overall economic impact of this proposed rule is an estimated increase in cost to IRFs 

of $31,783,532.15 beginning with FY 2025. 
20220 Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 

1Patel A, Jernigan DB. Initial Public Health 

Response and Interim Clinical Guidance for the 

2019 Novel Coronavirus Outbreak—United States, 

December 31, 2019–February 4, 2020. MMWR Morb 

Mortal Wkly Rep 2020;69:140–146. DOI http://

dx.doi.org/10.15585/mmwr.mm6905e1. 

2CMS, ‘‘COVID–19 Emergency Declaration 

Blanket Waivers for Health Care Providers,’’ 

(updated Feb. 19 2021) (available at https://

www.cms.gov/files/document/summary-covid-19- 

emergency-declaration-waivers.pdf). 3CMS, ‘‘COVID–19 Frequently Asked Questions 

(FAQs) on Medicare Fee-for-Service (FFS) Billing,’’ 

(updated March 5, 2021) (available at https://

www.cms.gov/files/document/03092020-covid-19- 

faqs-508.pdf). 

2006 IRF PPS final rule (70 FR 47908 

through 47917), the market basket index 

used to update IRF payments was a 

market basket reflecting the operating 

and capital cost structures for 

freestanding IRFs, freestanding inpatient 

psychiatric facilities (IPFs), and longterm care hospitals (LTCHs) (hereinafter 

referred to as the rehabilitation, 

psychiatric, and long-term care (RPL) 

market basket). Any reference to the FY 

2006 IRF PPS final rule in this proposed 

rule also includes the provisions 

effective in the correcting amendments. 

For a detailed discussion of the final key 

policy changes for FY 2006, please refer 

to the FY 2006 IRF PPS final rule. 

The regulatory history previously 

included in each rule or notice issued 

under the IRF PPS, including a general 

description of the IRF PPS for FYs 2007 

through 2020, is available on the CMS 

website at https://www.cms.gov/ 

Medicare/Medicare-Fee-for-ServicePayment/InpatientRehabFacPPS. 

In late 2019,1 the United States began 

responding to an outbreak of a virus 

named ‘‘SARS–CoV–2’’ and the disease 

it causes, which is named ‘‘coronavirus 

disease 2019’’ (abbreviated ‘‘COVID– 

19’’). Due to our prioritizing efforts in 

support of containing and combatting 

the PHE for COVID–19, and devoting 

significant resources to that end, we 

published two interim final rules with 

comment period affecting IRF payment 

and conditions for participation. The 

interim final rule with comment period 

(IFC) entitled, ‘‘Medicare and Medicaid 

Programs; Policy and Regulatory 

Revisions in Response to the COVID–19 

Public Health Emergency’’, published 

on April 6, 2020 (85 FR 19230) 

(hereinafter referred to as the April 6, 

2020 IFC), included certain changes to 

the IRF PPS medical supervision 

requirements at 42 CFR 412.622(a)(3)(iv) 

and 412.29(e) during the PHE for 

COVID–19. In addition, in the April 6, 

2020 IFC, we removed the postadmission physician evaluation 

requirement at § 412.622(a)(4)(ii) for all 

IRFs during the PHE for COVID–19. In 

the FY 2021 IRF PPS final rule, to ease 

documentation and administrative 

burden, we also removed the postadmission physician evaluation 

documentation requirement at 42 CFR 

412.622(a)(4)(ii) permanently beginning 

in FY 2021. 

A second IFC entitled, ‘‘Medicare and 

Medicaid Programs, Basic Health 

Program, and Exchanges; Additional 

Policy and Regulatory Revisions in 

Response to the COVID–19 Public 

Health Emergency and Delay of Certain 

Reporting Requirements for the Skilled 

Nursing Facility Quality Reporting 

Program’’ was published on May 8, 2020 

(85 FR 27550) (hereinafter referred to as 

the May 8, 2020 IFC). Among other 

changes, the May 8, 2020 IFC included 

a waiver of the ‘‘3-hour rule’’ at 

§ 412.622(a)(3)(ii) to reflect the waiver 

required by section 3711(a) of the 

Coronavirus Aid, Relief, and Economic 

Security Act (CARES Act) (Pub. L. 116– 

136, enacted on March 27, 2020). In the 

May 8, 2020 IFC, we also modified 

certain IRF coverage and classification 

requirements for freestanding IRF 

hospitals to relieve acute care hospital 

capacity concerns in States (or regions, 

as applicable) experiencing a surge 

during the PHE for COVID–19. In 

addition to the policies adopted in our 

IFCs, we responded to the PHE with 

numerous blanket waivers 2 and other 

flexibilities,3 some of which are 

applicable to the IRF PPS. 

B. Provisions of the PPACA and the 

Medicare Access and CHIP 

Reauthorization Act of 2015 (MACRA) 

Affecting the IRF PPS in FY 2012 and 

Beyond 

The Patient Protection and Affordable 

Care Act (PPACA) (Pub. L. 111–148) 

was enacted on March 23, 2010. The 

Health Care and Education 

Reconciliation Act of 2010 (Pub. L. 111– 

152), which amended and revised 

several provisions of the PPACA, was 

enacted on March 30, 2010. In this 

proposed rule, we refer to the two 

statutes collectively as the ‘‘Patient 

Protection and Affordable Care Act’’ or 

‘‘PPACA’’. 

The PPACA included several 

provisions that affect the IRF PPS in FYs 

2012 and beyond. In addition to what 

was previously discussed, section 

3401(d) of the PPACA also added 

section 1886(j)(3)(C)(ii)(I) of the Act 

(providing for a ‘‘productivity 

adjustment’’ for FY 2012 and each 

subsequent FY). The productivity 

adjustment for FY 2023 is discussed in 

section V.B. of this proposed rule. 

Section 1886(j)(3)(C)(ii)(II) of the Act 

provides that the application of the 

productivity adjustment to the market 

basket update may result in an update 

that is less than 0.0 for a FY and in 

payment rates for a FY being less than 

such payment rates for the preceding 

FY. 

Sections 3004(b) of the PPACA and 

section 411(b) of the MACRA (Pub. L. 

114–10, enacted on April 16, 2015) also 

addressed the IRF PPS. Section 3004(b) 

of PPACA reassigned the previously 

designated section 1886(j)(7) of the Act 

to section 1886(j)(8) of the Act and 

inserted a new section 1886(j)(7) of the 

Act, which contains requirements for 

the Secretary to establish a QRP for 

IRFs. Under that program, data must be 

submitted in a form and manner and at 

a time specified by the Secretary. 

Beginning in FY 2014, section 

1886(j)(7)(A)(i) of the Act requires the 

application of a 2-percentage point 

reduction to the market basket increase 

factor otherwise applicable to an IRF 

(after application of paragraphs (C)(iii) 

and (D) of section 1886(j)(3) of the Act) 

for a FY if the IRF does not comply with 

the requirements of the IRF QRP for that 

FY. Application of the 2-percentage 

point reduction may result in an update 

that is less than 0.0 for a FY and in 

payment rates for a FY being less than 

such payment rates for the preceding 

FY. Reporting-based reductions to the 

market basket increase factor are not 

cumulative; they only apply for the FY 

involved. Section 411(b) of the MACRA 

amended section 1886(j)(3)(C) of the Act 

by adding paragraph (iii), which 

required us to apply for FY 2018, after 

the application of section 

1886(j)(3)(C)(ii) of the Act, an increase 

factor of 1.0 percent to update the IRF 

prospective payment rates. 

C. Operational Overview of the Current 

IRF PPS 

As described in the FY 2002 IRF PPS 

final rule (66 FR 41316), upon the 

admission and discharge of a Medicare 

Part A fee-for-service (FFS) patient, the 

IRF is required to complete the 

appropriate sections of a Patient 

Assessment Instrument (PAI), 

designated as the IRF–PAI. In addition, 

beginning with IRF discharges occurring 

on or after October 1, 2009, the IRF is 

also required to complete the 

appropriate sections of the IRF–PAI 

upon the admission and discharge of 

each Medicare Advantage (MA) patient, 

as described in the FY 2010 IRF PPS 

final rule (74 FR 39762 and 74 FR 

50712). All required data must be 

electronically encoded into the IRF–PAI 

software product. Generally, the 

software product includes patient 

classification programming called the 

Grouper software. The Grouper software 

uses specific IRF–PAI data elements to 

classify (or group) patients into distinct 


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Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 20221 

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

www.hl7.org/fhir/. 

5HL7 FHIR. PACIO Functional Status 

Implementation Guide. Available at https://

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

6The IMPACT Act (Pub. L. 113–185) requires the 

reporting of standardized patient assessment data 

with regard to quality measures and standardized 

patient assessment data elements. The Act also 

requires the submission of data pertaining to 

measure domains of resource use, and other 

domains. In addition, the IMPACT Act requires 

assessment data to be standardized and 

interoperable to allow for exchange of the data 

among post-acute providers and other providers. 

The Act intends for standardized post-acute care 

data to improve Medicare beneficiary outcomes 

through shared-decision making, care coordination, 

and enhanced discharge planning. 

7Centers 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. 

8Centers for Medicare & Medicaid Services. 

Health Informatics and Interoperability Group. 

Policies and Technology for Interoperability and 

Burden Reduction. Available at https://

www.cms.gov/Regulations-and-Guidance/ 

Guidance/Interoperability/index. 

9Bates, David W, and Lipika Samal. 

‘‘Interoperability: What Is It, How Can We Make It 

Work for Clinicians, and How Should We Measure 

Continued 

CMGs and account for the existence of 

any relevant comorbidities. 

The Grouper software produces a fivecharacter CMG number. The first 

character is an alphabetic character that 

indicates the comorbidity tier. The last 

four characters are numeric characters 

that represent the distinct CMG number. 

A free download of the Grouper 

software is available on the CMS 

website at http://www.cms.gov/ 

Medicare/Medicare-Fee-for-ServicePayment/InpatientRehabFacPPS/ 

Software.html. The Grouper software is 

also embedded in the internet Quality 

Improvement and Evaluation System 

(iQIES) User tool available in iQIES at 

https://www.cms.gov/medicare/qualitysafety-oversight-general-information/ 

iqies. 

Once a Medicare Part A FFS patient 

is discharged, the IRF submits a 

Medicare claim as a Health Insurance 

Portability and Accountability Act of 

1996 (HIPAA) (Pub. L. 104–191, enacted 

on August 21, 1996) -compliant 

electronic claim or, if the 

Administrative Simplification 

Compliance Act of 2002 (ASCA) (Pub. L. 

107–105, enacted on December 27, 

2002) permits, a paper claim (a UB–04 

or a CMS–1450 as appropriate) using the 

five-character CMG number and sends it 

to the appropriate Medicare 

Administrative Contractor (MAC). In 

addition, once a MA patient is 

discharged, in accordance with the 

Medicare Claims Processing Manual, 

chapter 3, section 20.3 (Pub. 100–04), 

hospitals (including IRFs) must submit 

an informational-only bill (type of bill 

(TOB) 111), which includes Condition 

Code 04 to their MAC. This will ensure 

that the MA days are included in the 

hospital’s Supplemental Security 

Income (SSI) ratio (used in calculating 

the IRF LIP adjustment) for FY 2007 and 

beyond. Claims submitted to Medicare 

must comply with both ASCA and 

HIPAA. 

Section 3 of the ASCA amended 

section 1862(a) of the Act by adding 

paragraph (22), which requires the 

Medicare program, subject to section 

1862(h) of the Act, to deny payment 

under Part A or Part B for any expenses 

for items or services for which a claim 

is submitted other than in an electronic 

form specified by the Secretary. Section 

1862(h) of the Act, in turn, provides that 

the Secretary shall waive such denial in 

situations in which there is no method 

available for the submission of claims in 

an electronic form or the entity 

submitting the claim is a small provider. 

In addition, the Secretary also has the 

authority to waive such denial in such 

unusual cases as the Secretary finds 

appropriate. For more information, see 

the ‘‘Medicare Program; Electronic 

Submission of Medicare Claims’’ final 

rule (70 FR 71008). Our instructions for 

the limited number of Medicare claims 

submitted on paper are available at 

http://www.cms.gov/manuals/ 

downloads/clm104c25.pdf. 

Section 3 of the ASCA operates in the 

context of the administrative 

simplification provisions of HIPAA, 

which include, among others, the 

requirements for transaction standards 

and code sets codified in 45 CFR part 

160 and part 162, subparts A and I 

through R (generally known as the 

Transactions Rule). The Transactions 

Rule requires covered entities, including 

covered healthcare providers, to 

conduct covered electronic transactions 

according to the applicable transaction 

standards. (See the CMS program claim 

memoranda at http://www.cms.gov/ 

ElectronicBillingEDITrans/ and listed in 

the addenda to the Medicare 

Intermediary Manual, Part 3, section 

3600). 

The MAC processes the claim through 

its software system. This software 

system includes pricing programming 

called the ‘‘Pricer’’ software. The Pricer 

software uses the CMG number, along 

with other specific claim data elements 

and provider-specific data, to adjust the 

IRF’s prospective payment for 

interrupted stays, transfers, short stays, 

and deaths, and then applies the 

applicable adjustments to account for 

the IRF’s wage index, percentage of lowincome patients, rural location, and 

outlier payments. For discharges 

occurring on or after October 1, 2005, 

the IRF PPS payment also reflects the 

teaching status adjustment that became 

effective as of FY 2006, as discussed in 

the FY 2006 IRF PPS final rule (70 FR 

47880). 

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 electronic health 

information. 

To further interoperability in postacute care settings, CMS and the Office 

of the National Coordinator for Health 

Information Technology (ONC) 

participate in the Post-Acute Care 

Interoperability Workgroup (PACIO) to 

facilitate collaboration with industry 

stakeholders to develop Fast Healthcare 

Interoperability Resources® (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.4 5 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.6 We encourage 

PAC provider and health information 

technology (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).7 The DEL furthers 

CMS’ goal of data standardization and 

interoperability. These interoperable 

data elements can reduce provider 

burden by allowing the use and 

exchange of healthcare data; supporting 

provider exchange of electronic health 

information for care coordination, 

person-centered care; and supporting 

real-time, data driven, clinical decisionmaking.8 9 Standards in the DEL can be 


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It in the Future?.’’ Health services research vol. 53,5 

(2018): 3270–3277. doi:10.1111/1475–6773.12852. 

10The 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. 

referenced on the CMS website (https:// 

del.cms.gov/DELWeb/pubHome) 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) requires HHS to 

take new steps to enable the electronic 

sharing of health information and to 

further interoperability for providers 

and settings across the care continuum. 

Section 4003 of the Cures Act required 

HHS 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 and 

Common Agreement Version 1. 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 incorporated by 

reference Qualified Health Information 

Network Technical Framework Version 

1 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 

Common Agreement follows a networkof-networks structure, which allows for 

connection at different levels and is 

inclusive of many different types of 

entities, such as health information 

networks, healthcare practices, 

hospitals, public health agencies, and 

Individual Access Services (IAS) 

Providers.10 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 IRFs. 

III. Summary of Provisions of the 

Proposed Rule 

In this proposed rule, we are 

proposing to update the IRF PPS for FY 

2023 and the IRF QRP for FY 2025. 

The proposed policy changes and 

updates to the IRF prospective payment 

rates for FY 2023 are as follows: 

• Update the CMG relative weights 

and average length of stay values for FY 

2023, in a budget neutral manner, as 

discussed in section IV. of this proposed 

rule. 

• Update the IRF PPS payment rates 

for FY 2023 by the market basket 

increase factor, based upon the most 

current data available, with a 

productivity adjustment required by 

section 1886(j)(3)(C)(ii)(I) of the Act, as 

described in section V. of this proposed 

rule. 

• Describe the establishment of a 

permanent cap policy in order to 

smooth the impact of year-to-year 

changes in IRF payments related to 

certain changes to the IRF wage index, 

as discussed in section V. of this 

proposed rule. 

• Update the FY 2023 IRF PPS 

payment rates by the FY 2023 wage 

index and the labor-related share in a 

budget-neutral manner, as discussed in 

section V. of this proposed rule. 

• Describe the calculation of the IRF 

standard payment conversion factor for 

FY 2023, as discussed in section V. of 

this proposed rule. 

• Update the outlier threshold 

amount for FY 2023, as discussed in 

section VI. of this proposed rule. 

• Update the cost-to-charge ratio 

(CCR) ceiling and urban/rural average 

CCRs for FY 2023, as discussed in 

section VI. of this proposed rule. 

• Describe the proposed codification 

of CMS’ existing teaching status 

adjustment policy and proposed 

clarifications and updates of the IRF 

teaching status adjustment policy with 

respect to IRF hospital closures and 

displaced residents, as discussed in 

section VII. of this proposed rule. 

• Solicit comments on the 

methodology used to update the facilitylevel adjustment factors, as discussed in 

section VIII. of this proposed rule. 

• Solicit comments on the IRF 

transfer payment policy, as discussed in 

section IX. of this proposed rule. 

We also propose updates to the IRF 

QRP and request information in section 

VII. of this proposed rule as follows: 

• Update data reporting requirements 

under the IRF QRP beginning with FY 

2025. 

• Request information on (1) future 

measure concepts under consideration 

for the IRF QRP; (2) inclusion of a future 

dQM for the IRF QRP; and (3) CMS’ 

overarching principles for measuring 

healthcare disparities across CMS 

Quality Programs, including the IRF 

QRP. 

IV. Proposed Update to the Case-Mix 

Group (CMG) Relative Weights and 

Average Length of Stay (ALOS) Values 

for FY 2023 

As specified in § 412.620(b)(1), we 

calculate a relative weight for each CMG 

that is proportional to the resources 

needed by an average inpatient 

rehabilitation case in that CMG. For 

example, cases in a CMG with a relative 

weight of 2, on average, will cost twice 

as much as cases in a CMG with a 

relative weight of 1. Relative weights 

account for the variance in cost per 

discharge due to the variance in 

resource utilization among the payment 

groups, and their use helps to ensure 

that IRF PPS payments support 

beneficiary access to care, as well as 

provider efficiency. 

In this proposed rule, we propose to 

update the CMG relative weights and 

ALOS values for FY 2023. Typically, we 

use the most recent available data to 

update the CMG relative weights and 

average lengths of stay. For FY 2023, we 

are proposing to use the FY 2021 IRF 

claims and FY 2020 IRF cost report data. 

These data are the most current and 

complete data available at this time. 

Currently, only a small portion of the 

FY 2021 IRF cost report data are 

available for analysis, but the majority 

of the FY 2021 IRF claims data are 

available for analysis. We are proposing 

that if more recent data become 

available after the publication of this 

proposed rule and before the 

publication of the final rule, we would 

use such data to determine the FY 2023 

CMG relative weights and ALOS values 

in the final rule. 

We are proposing to apply these data 

using the same methodologies that we 

have used to update the CMG relative 

weights and ALOS values each FY since 

we implemented an update to the 

methodology. The detailed CCR data 

from the cost reports of IRF provider 

units of primary acute care hospitals is 

used for this methodology, instead of 

CCR data from the associated primary 

care hospitals, to calculate IRFs’ average 

costs per case, as discussed in the FY 

2009 IRF PPS final rule (73 FR 46372). 

In calculating the CMG relative weights, 

we use a hospital-specific relative value 


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method to estimate operating (routine 

and ancillary services) and capital costs 

of IRFs. The process to calculate the 

CMG relative weights for this proposed 

rule is as follows: 

Step 1. We estimate the effects that 

comorbidities have on costs. 

Step 2. We adjust the cost of each 

Medicare discharge (case) to reflect the 

effects found in the first step. 

Step 3. We use the adjusted costs from 

the second step to calculate CMG 

relative weights, using the hospitalspecific relative value method. 

Step 4. We normalize the FY 2023 

CMG relative weights to the same 

average CMG relative weight from the 

CMG relative weights implemented in 

the FY 2022 IRF PPS final rule (86 FR 

42362). 

Consistent with the methodology that 

we have used to update the IRF 

classification system in each instance in 

the past, we propose to update the CMG 

relative weights for FY 2023 in such a 

way that total estimated aggregate 

payments to IRFs for FY 2023 are the 

same with or without the changes (that 

is, in a budget-neutral manner) by 

applying a budget neutrality factor to 

the standard payment amount. To 

calculate the appropriate budget 

neutrality factor for use in updating the 

FY 2023 CMG relative weights, we use 

the following steps: 

Step 1. Calculate the estimated total 

amount of IRF PPS payments for FY 

2023 (with no changes to the CMG 

relative weights). 

Step 2. Calculate the estimated total 

amount of IRF PPS payments for FY 

2023 by applying the proposed changes 

to the CMG relative weights (as 

discussed in this proposed rule). 

Step 3. Divide the amount calculated 

in step 1 by the amount calculated in 

step 2 to determine the budget 

neutrality factor of 0.9979 that would 

maintain the same total estimated 

aggregate payments in FY 2023 with and 

without the proposed changes to the 

CMG relative weights. 

Step 4. Apply the budget neutrality 

factor from step 3 to the FY 2023 IRF 

PPS standard payment amount after the 

application of the budget-neutral wage 

adjustment factor. 

In section V.E. of this proposed rule, 

we discuss the proposed use of the 

existing methodology to calculate the 

proposed standard payment conversion 

factor for FY 2023. 

In Table 2, ‘‘Proposed Relative 

Weights and Average Length of Stay 

Values for Case-Mix Groups,’’ we 

present the proposed CMGs, the 

comorbidity tiers, the corresponding 

relative weights, and the ALOS values 

for each CMG and tier for FY 2023. The 

ALOS for each CMG is used to 

determine when an IRF discharge meets 

the definition of a short-stay transfer, 

which results in a per diem case level 

adjustment. 

BILLING CODE 4120–01–P 


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TABLE 2: Proposed Relative Weights and Average Length of Stay Values for the Case-Mix 

G roups 

Relative Wei2ht A vera2e Leni?th of Stay 

CMG Description No No 

CMG Comor- Tier Tier Tier Comor- (M=motor, A=age) Tier 1 Tier2 Tier3 bidity 1 2 3 bidity 

Tier Tier 

0101 Stroke M >=72.50 0.9925 0.8649 0.7867 0.7457 10 10 10 9 

0102 Stroke M >=63.50 and M <72.50 1.2547 1.0934 0.9946 0.9428 12 13 11 11 

0103 Stroke M >=50.50 and M <63.50 1.6297 1.4202 1.2918 1.2246 14 14 14 13 

0104 Stroke M >=41.50 and M <50.50 2.0846 1.8166 1.6524 1.5664 18 18 17 17 

0105 Stroke M <41.50 and A >=84.50 2.5116 2.1887 1.9908 1.8872 22 22 21 20 

0106 Stroke M <41.50 and A <84.50 2.8661 2.4977 2.2719 2.1537 25 26 23 23 

0201 Traumatic brain iniurv M >=73 .50 1.1188 0.9016 0.8174 0.7674 11 10 9 9 

0202 Traumatic brain injury M >=61.50 and 1.4040 1.1314 1.0257 0.9630 12 13 11 11 

M<73.50 

0203 Traumatic brain injury M >=49.50 and 1.7227 1.3882 1.2585 1.1816 14 15 13 13 

M <61.50 

0204 Traumatic brain injury M >=35.50 and 2.1283 1.7151 1.5548 1.4598 19 18 16 16 

M<49.50 

0205 Traumatic brain injury M <35.50 2.6967 2.1731 1.970 I 1.8496 28 23 20 18 

0301 Non-traumatic brnin iniurv M >=65.50 1.1%8 0.9648 0.8939 0.8329 11 10 10 9 

0302 Non-traumatic brain injury M >=52.50 1.5416 1.2427 1.1513 1.0728 13 13 12 12 

andM<65.50 

0303 Non-traumatic brain injury M >=42.50 1.8527 1.4935 1.3837 1.2894 15 15 14 14 

andM<52.50 

0304 Non-traumatic brain injury M <42.50 2.1557 1.7378 1.6100 1.5002 19 18 16 15 

and A >=78.50 

0305 Non-traumatic brain injury M <42.50 2.351'.l 1.8955 1.7561 1.6364 20 19 17 17 

and A <78.50 

0401 Traumatic spinal cord injury M 1.3351 1.0963 1.0476 0.9612 12 11 12 11 

>=56.50 

0402 Traumatic spinal cord injury M 1.7137 1.4071 1.3446 1.2337 17 15 15 14 

>=47.50 and M <56.50 

0403 Traumatic spinal cord injury M 2.1227 1.7430 1.6656 1.5282 17 19 17 17 

>=41.50 and M <47.50 

0404 Traumatic spinal cord injury M <31.50 3.1577 2.5928 2.4777 2.2733 22 27 26 22 

and A <61.50 

0405 Traumatic spinal cord injury M 2.6222 2.1531 2.0575 1.8878 23 23 21 20 

>=31.50 and M <41.50 

0406 Traumatic spinal cord injury M 3.4284 2.8151 2.6901 2.4682 37 29 25 27 

>=24.50 and M <31.50 and A >=61.50 

0407 Traumatic spinal cord injury M <24.50 4.3072 3.5367 3.3796 3.1008 47 36 33 32 

and A >=61.50 

0501 Non-traumatic spinal cord injury M 1.2513 0.9862 0.9303 0.8656 11 11 10 10 

>=60.50 

0502 Non-traumatic spinal cord i1tjury M 1.5504 1.2219 1.1527 1.0725 16 13 12 12 

>=53.50 and M <60.50 

0503 Non-tramnatic spinal cord irtjury M 1.7832 1.4054 1.3257 1.2335 15 14 14 14 

>=48.50 and M <53.50 

0504 Non-lramnalic spinal cord injmy M 2.1593 1.7019 1.6054 1.4937 19 18 17 16 

>=39.50 and M <48.50 

0505 Non-tramnatic spinal cord irtjury M 2.9652 2.3370 2.2046 2.0512 26 24 22 21 

<39.50 

0601 Neurological M >=64.50 1.3467 1.0065 0.9546 0.8514 11 10 10 10 

0602 Neurological M >=52.50 and M <64.50 1.6786 1.2546 1.1899 1.0613 13 13 12 12 

0603 Neurological M >=43.50 and M <52.50 2.0028 1.4968 1.4196 1.2662 16 15 14 13 

0604 Neurological M <43.50 2.4823 1.8552 1.7596 1.5694 20 18 17 16 

0701 Fracture of lower extremitv M >=61.50 1.2411 0.9617 0.9179 0.8506 11 11 10 10 
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Relative W eie:ht Averae:e Len!!:th of Stav 

CMG Description No No 

CMG Comor- Tier Tier Tier Comor- (M=motor, A=age) Tier 1 Tier 2 Tier3 bidity 1 2 3 bidity 

Tier Tier 

0702 Fracture of lower extremity M >=52.50 1.5298 1.1853 1.1313 1.0484 13 13 12 12 

andM <61.50 

0703 Fracture oflower extremity M >=41.50 1.9047 1.4759 1.4086 1.3054 16 15 15 14 

andM <52.50 

0704 Fracture oflower extremitv M <41.50 2.2917 1.7757 1.6948 1.5706 19 18 17 16 

0801 Replacement of lower-extremity joint 1.1275 0.9613 0.8690 0.7954 10 10 9 9 

M>=63.50 

0802 Replacement of lower-extremity joint 1.2974 1.1061 1.0000 0.9153 11 11 10 10 

M >=57.50 and M <63.50 

0803 Replacement of lower-extremity joint 1.4361 1.2244 1.1069 1.0131 12 13 12 11 

M >=51.50 and M <57.50 

0804 Replacement of lower-extremity joint 1.6466 1.4038 1.2691 1.1616 14 14 13 12 

M >=42.50 and M <51.50 

0805 Replacement of lower-extremity joint 1.9673 1.6772 1.5163 1.3878 16 16 15 14 

M<42.50 

0901 Other orthopedic M >=63 .50 1.2057 0.9636 0.8944 0.8246 11 11 10 9 

0902 Other orthopedic M >=51.50 and M 1.5217 1.2162 1.1288 1.0408 13 13 12 11 

<63.50 

0903 Other orthopedic M >=44.50 and M 1.8095 1.4462 1.3423 1.2376 15 15 14 13 

<51.50 

0904 Other orthopedic M <44.5 2.1120 1.6879 1.5667 1.4445 17 17 16 15 

1001 Amputation lower extremity M 1.2249 1.0603 0.9236 0.8475 11 12 10 10 

>=64.50 

1002 Amputation lower extremity M 1.5178 1.3139 1.1444 1.0502 14 13 12 12 

>=55.50 and M <64.50 

1003 Amputation lower extremity M 1.7988 1.5571 1.3563 1.2446 15 16 14 14 

>=4 7.50 and M <55.50 

1004 Amputation lower extremitv M <47.50 2.2548 1.9519 1.7001 1.5601 18 20 17 16 

1101 Amputation non-lower extremity M 1.3654 1.3654 1.0059 0.7976 13 13 11 11 

>=58.50 

1102 Amputation non-lower extremity M 1.6779 1.6779 1.2361 0.9801 14 15 13 12 

>=52.50 and M <58.50 

1103 Amputation non-lower extremity M 2.1932 2.1932 1.6158 1.2812 19 17 16 14 

<52.50 

1201 Osteoarthritis M >=61.50 1.3177 1.0415 0.9341 0.8331 10 10 11 9 

1202 Osteoarthritis M >=49.50 and M 1.7152 1.3557 1.2158 1.0845 14 13 12 12 

<61.50 

1203 Osteoarthritis M <49.50 and A >=74.50 2.1200 1.6758 1.5028 1.3405 16 15 15 14 

1204 Osteoarthritis M <49.50 and A <74.50 2.2232 1.7573 1.5759 1.4057 16 15 16 16 

1301 Rheumatoid other arthritis M >=62.50 1.2188 0.9151 0.8690 0.8576 9 10 9 9 

1302 Rheumatoid other arthritis M >=51.50 1.6186 1.2153 1.1541 1.1389 12 12 11 12 

andM<62.50 

1303 Rheumatoid other arthritis M >=44.50 1.8950 1.4227 1.3511 1.3333 14 14 14 14 

and M <51.50 and A >=64.50 

1304 Rheumatoid other arthritis M <44.50 2.3349 1.7530 1.6647 1.6429 15 17 17 16 

and A >=64.50 

1305 Rheumatoid other arthritis M <51.50 2.0923 1.5709 1.4918 1.4722 16 15 15 15 

and A <64.50 

1401 Cardiac M >=68.'i0 11391 0 900'i 0 8301 0 7'i92 1[) 10 9 9 

1402 Cardiac M >=55.50 and M <68.50 1.4510 1.1471 1.0574 0.9671 13 12 11 11 

1403 Cardiac M >=45.50 and M <55.50 1.7577 1.3896 1.2808 1.1715 15 14 13 13 

1404 Cardiac M <45.50 2.1542 1.7030 1.5698 1.4358 18 17 16 15 

1501 Pulmonarv M >=68.50 1.3050 1.0215 0.9761 0.9439 11 10 10 10 

1502 Pulmonary M >=56.50 and M <68.50 1.5932 1.2471 1.1917 1.1523 13 12 12 12 
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Relative W eie:ht Averae:e Lene:th of Stav 

CMG Description No No 

CMG Comor- Tier Tier Tier Comor- (M=motor, A=age) Tier 1 Tier2 Tier3 hidity 1 2 3 hidity 

Tier Tier 

1503 Pulmonarv M >=45.50 and M <56.50 1.8631 1.4584 1.3936 1.3476 16 14 13 13 

1504 PulmonaJV M <45.50 2.2211 1.7387 1.6614 1.6065 21 17 16 15 

1601 Pain svndrome M >=65.50 1.1344 0.8838 0.8577 0.7884 9 10 10 9 

1602 Pain syndrome M >=58.50 and M 1.3362 1.0409 1.0102 0.9286 10 11 11 10 

<65.50 

1603 Pain syndrome M >=43 .50 and M 1.6219 1.2635 1.2263 1.1271 14 13 13 13 

<58.50 

1604 Pain svndrome M <43 .50 1.9754 1.5389 1.4935 1.3728 13 14 16 14 

1701 Major multiple trauma without brain or 1.3007 1.0284 0.9660 0.8785 11 10 11 10 

spinal cord iniurv M >=57.50 

Major multiple trauma without brain or 1.6141 1.2762 1.1988 1.0902 13 14 13 12 

1702 spinal cord ittjury M >=50.50 and M 

<57.50 

Major multiple trauma without brain or 1.9052 1.5063 1.4150 1.2868 16 15 15 14 

1703 spinal cord ittjury M >=41.50 and M 

<50.50 

Major multiple trauma without brain or 2.1637 1.7107 1.6069 1.4614 17 18 17 15 

1704 spinal cord ittjury M >=36.50 and M 

<41.50 

1705 Major multiple lrauma williout brain or 2.4707 1.9534 1.8349 1.6687 23 19 19 17 

spinal cord iniurv M <36.50 

1801 Major multiple trauma with brain or 1.2112 0.9565 0.8907 0.8256 13 11 10 10 

spinal cord injury M >=67.50 

Major multiple trauma with brain or 1.4573 1.1509 1.0717 0.9934 15 13 11 12 

1802 spinal cord ittjury M >=55.50 and M 

<67.50 

Major multiple trauma with brain or 1.8392 1.4525 1.3526 1.2537 17 16 15 14 

1803 spinal cord ittjury M >=45.50 and M 

<55.50 

Major multiple trauma with brain or 2.1284 1.6809 1.5653 1.4509 18 17 16 15 

1804 spinal cord ittjury M >=40.50 and M 

<45.50 

Major multiple trauma with bnrin or 2.5424 2.0078 1.8697 1.7331 22 22 19 18 

1805 spinal cord injury M >=30.50 and M 

<40.50 

1806 Major multiple trauma with brain or 3.4682 2.7389 2.5505 2.3641 38 27 24 24 

spinal cord iniurv M <30.50 

1901 Guillain-Barre M >=66.50 1.1559 1.0349 0.9948 0.9308 11 13 12 10 

1902 Guillain-Barre M >=51.50 and M 1.4513 1.2994 1.2490 1.1686 14 13 14 13 

<66.50 

1903 Guillain-Barrc M >=38.50 and M 2.1262 1.9036 1.8298 1.7120 18 20 18 19 

<51.50 

1904 Guillain-Barre M <38.50 3.2810 2.9375 2.8237 2.6419 31 31 28 26 

2001 Miscellaneous M >=66.50 1.2012 0.9694 0.8922 0.8118 10 10 10 9 

2002 Miscellaneous M >=55.50 and M 1.4875 1.2005 1.1049 1.0053 13 12 12 11 

<66.50 

2003 Miscellaneous M >-46.50 and M 1.7674 1.4264 1.3128 1.1944 15 14 13 13 

<55.50 

2004 Miscellaneous M <46.50 and A 2.0809 1.6794 1.5457 1.4063 18 17 16 15 

>=77.50 

2005 Miscellaneous M <46.50 and A <77.50 2.2291 1.7990 1.6558 1.5064 19 18 16 15 

2101 Bums M >=52.50 1.5991 1.1452 1.1279 1.0538 14 13 12 11 

2102 Burns M <52.50 2.4689 1.7682 1.7415 1.6270 27 18 16 16 

5001 Short-stay cases, length of stay is 3 0.1700 3 

davs or fewer 
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Generally, updates to the CMG 

relative weights result in some increases 

and some decreases to the CMG relative 

weight values. Table 2 shows how we 

estimate that the application of the 

proposed revisions for FY 2023 would 

affect particular CMG relative weight 

values, which would affect the overall 

distribution of payments within CMGs 

and tiers. We note that, because we 

propose to implement the CMG relative 

weight revisions in a budget-neutral 

manner (as previously described), total 

estimated aggregate payments to IRFs 

for FY 2023 would not be affected as a 

result of the proposed CMG relative 

weight revisions. However, the 

proposed revisions would affect the 

distribution of payments within CMGs 

and tiers. 

BILLING CODE 4120–01–C 

As shown in Table 3, 99.3 percent of 

all IRF cases are in CMGs and tiers that 

would experience less than a 5 percent 

change (either increase or decrease) in 

the CMG relative weight value as a 

result of the proposed revisions for FY 

2023. The proposed changes in the 

ALOS values for FY 2023, compared 

with the FY 2022 ALOS values, are 

small and do not show any particular 

trends in IRF length of stay patterns. 

We invite public comment on our 

proposed updates to the CMG relative 

weights and ALOS values for FY 2023. 

V. Proposed FY 2023 IRF PPS Payment 

Update 

A. Background 

Section 1886(j)(3)(C) of the Act 

requires the Secretary to establish an 

increase factor that reflects changes over 

time in the prices of an appropriate mix 

of goods and services for which 

payment is made under the IRF PPS. 

According to section 1886(j)(3)(A)(i) of 

the Act, the increase factor shall be used 

to update the IRF prospective payment 

rates for each FY. Section 

1886(j)(3)(C)(ii)(I) of the Act requires the 

application of the productivity 

adjustment described in section 

1886(b)(3)(B)(xi)(II) of the Act. Thus, in 

this proposed rule, we are proposing to 

update the IRF PPS payments for FY 

2023 by a market basket increase factor 

as required by section 1886(j)(3)(C) of 

the Act based upon the most current 

data available, with a productivity 

adjustment as required by section 

1886(j)(3)(C)(ii)(I) of the Act. 

We have utilized various market 

baskets through the years in the IRF 

PPS. For a discussion of these market 

baskets, we refer readers to the FY 2016 

IRF PPS final rule (80 FR 47046). 

In FY 2016, we finalized the use of a 

2012-based IRF market basket, using 

Medicare cost report data for both 

freestanding and hospital-based IRFs (80 

FR 47049 through 47068). Beginning 

with FY 2020, we finalized a rebased 

and revised IRF market basket to reflect 

a 2016 base year. The FY 2020 IRF PPS 

final rule (84 FR 39071 through 39086) 

contains a complete discussion of the 

development of the 2016-based IRF 

market basket. 

B. Proposed FY 2023 Market Basket 

Update and Productivity Adjustment 

For FY 2023 (that is, beginning 

October 1, 2022 and ending September 

30, 2023), we are proposing to update 

the IRF PPS payments by a market 

basket increase factor as required by 

section 1886(j)(3)(C) of the Act, with a 

productivity adjustment as required by 

section 1886(j)(3)(C)(ii)(I) of the Act. For 

FY 2023, we are proposing to use the 

same methodology described in the FY 

2022 IRF PPS final rule (86 FR 42373 

through 42376). 

Consistent with historical practice, we 

are proposing to estimate the market 

basket update for the IRF PPS for FY 

2023 based on IHS Global Inc.’s (IGI’s) 

forecast using the most recent available 

data. Based on IGI’s fourth quarter 2021 

forecast with historical data through the 

third quarter of 2021, the proposed 

2016-based IRF market basket increase 

factor for FY 2023 is projected to be 3.2 

percent. We are also proposing that if 

more recent data become available after 

the publication of the proposed rule and 

before the publication of the final rule 

(for example, a more recent estimate of 

the market basket update or 

productivity adjustment), we would use 


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Relative Weight Average Length of Stay 

CMG Description No No 

CMG Comor- Tier Tier Tier Comor- (M=motor, A=age) Tier 1 Tier2 Tier3 bidity 1 2 3 bidity 

Tier Tier 

5101 Expired, orthopedic, length of stay is 13 0.7386 8 

davs or fewer 

5102 Expired, orthopedic, length of stay is 14 1.8869 17 

davsormore 

5103 Expired, not orthopedic, length of stay 0.8943 9 

is 15 davs or fewer 

5104 Expired, not orthopedic, length of stay 2.2732 21 

is 16 days or more 

. TABLE3 . »· IS t r1 U IODa ec so e an2es o e ea 1ve e121 s 0

b f IEff t fth Ch t th CMG R I f W . ht 

Percentage Change in CMG Relative Number of Cases Affected Percentage of Cases 

Weights Affected 

Increased by 15% or more 64 0.0% 

Increased bv between 5% and 15% 1,227 0.3% 

Changed by less than 5% 370,829 99.3% 

Decreased bv between 5% and 15% 1,320 0.4% 

Decreased by 15% or more 11 0.0% 
20228 Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 

11https://www.medpac.gov/wp-content/uploads/ 

2022/03/Mar22_MedPAC_ReportToCongress_

SEC.pdf. 

such data, if appropriate, to determine 

the FY 2023 market basket update in the 

final rule. 

According to section 1886(j)(3)(C)(i) of 

the Act, the Secretary shall establish an 

increase factor based on an appropriate 

percentage increase in a market basket 

of goods and services. Section 

1886(j)(3)(C)(ii) of the Act then requires 

that, after establishing the increase 

factor for a FY, the Secretary shall 

reduce such increase factor for FY 2012 

and each subsequent FY, 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 

sets forth the definition of this 

productivity adjustment. The statute 

defines the productivity adjustment to 

be equal to the 10-year moving average 

of changes in annual economy-wide, 

private nonfarm business multifactor 

productivity (as projected by the 

Secretary for the 10-year period ending 

with the applicable FY, year, cost 

reporting period, or other annual 

period) (the ‘‘productivity adjustment’’). 

The U.S. Department of Labor’s Bureau 

of Labor Statistics (BLS) publishes the 

official measures of productivity for the 

U.S. economy. 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) is now published by 

BLS as private nonfarm business total 

factor productivity. However, as 

mentioned above, the data and methods 

are unchanged. Please see www.bls.gov 

for the BLS historical published TFP 

data. A complete description of IGI’s 

TFP projection methodology is available 

on the CMS website at https://

www.cms.gov/Research-Statistics-Dataand-Systems/Statistics-Trends-andReports/MedicareProgramRatesStats/ 

MarketBasketResearch. In addition, in 

the FY 2022 IRF final rule (86 FR 

42374), we noted that effective with FY 

2022 and forward, CMS changed the 

name of this adjustment to refer to it as 

the productivity adjustment rather than 

the MFP adjustment. 

Using IGI’s fourth quarter 2021 

forecast, the 10-year moving average 

growth of TFP for FY 2023 is projected 

to be 0.4 percent. Thus, in accordance 

with section 1886(j)(3)(C) of the Act, we 

are proposing to base the FY 2023 

market basket update, which is used to 

determine the applicable percentage 

increase for the IRF payments, on IGI’s 

fourth quarter 2021 forecast of the 2016- 

based IRF market basket. We are 

proposing to then reduce this 

percentage increase by the estimated 

productivity adjustment for FY 2023 of 

0.4 percentage point (the 10-year 

moving average growth of TFP for the 

period ending FY 2023 based on IGI’s 

fourth quarter 2021 forecast). Therefore, 

the proposed FY 2023 IRF update is 

equal to 2.8 percent (3.2 percent market 

basket update reduced by the 0.4 

percentage point productivity 

adjustment). Furthermore, we are 

proposing that if more recent data 

become available after the publication of 

the proposed rule and before the 

publication of the final rule (for 

example, a more recent estimate of the 

market basket and/or productivity 

adjustment), we would use such data, if 

appropriate, to determine the FY 2023 

market basket update and productivity 

adjustment in the final rule. 

For FY 2023, the Medicare Payment 

Advisory Commission (MedPAC) 

recommends that we reduce IRF PPS 

payment rates by 5 percent.11 As 

discussed, and in accordance with 

sections 1886(j)(3)(C) and 1886(j)(3)(D) 

of the Act, the Secretary is proposing to 

update the IRF PPS payment rates for 

FY 2023 by a productivity-adjusted IRF 

market basket increase factor of 2.8 

percent. Section 1886(j)(3)(C) of the Act 

does not provide the Secretary with the 

authority to apply a different update 

factor to IRF PPS payment rates for FY 

2023. 

We invite public comment on our 

proposals for the FY 2023 market basket 

update and productivity adjustment. 

C. Proposed Labor-Related Share for FY 

2023 

Section 1886(j)(6) of the Act specifies 

that the Secretary is to adjust the 

proportion (as estimated by the 

Secretary from time to time) of IRFs’ 

costs that are attributable to wages and 

wage-related costs, of the prospective 

payment rates computed under section 

1886(j)(3) of the Act, for area differences 

in wage levels by a factor (established 

by the Secretary) reflecting the relative 

hospital wage level in the geographic 

area of the rehabilitation facility 

compared to the national average wage 

level for such facilities. The laborrelated share is determined by 

identifying the national average 

proportion of total costs that are related 

to, influenced by, or vary with the local 

labor market. We are proposing to 

continue to classify a cost category as 

labor-related if the costs are laborintensive and vary with the local labor 

market. 

Based on our definition of the laborrelated share and the cost categories in 

the 2016-based IRF market basket, we 

are proposing to calculate the laborrelated share for FY 2023 as the sum of 

the FY 2023 relative importance of 

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 the CapitalRelated relative importance from the 

2016-based IRF market basket. For more 

details regarding the methodology for 

determining specific cost categories for 

inclusion in the 2016-based IRF laborrelated share, see the FY 2020 IRF PPS 

final rule (84 FR 39087 through 39089). 

The relative importance reflects the 

different rates of price change for these 

cost categories between the base year 

(2016) and FY 2023. Based on IGI’s 

fourth quarter 2021 forecast of the 2016- 

based IRF market basket, the sum of the 

FY 2023 relative importance for Wages 

and Salaries, Employee Benefits, 

Professional Fees: Labor-related, 

Administrative and Facilities Support 

Services, Installation Maintenance & 

Repair Services, and All Other: Laborrelated Services is 69.4 percent. We are 

proposing that the portion of CapitalRelated costs that are influenced by the 

local labor market is 46 percent. Since 

the relative importance for CapitalRelated costs is 8.2 percent of the 2016- 

based IRF market basket for FY 2023, we 

are proposing to take 46 percent of 8.2 

percent to determine the labor-related 

share of Capital-Related costs for FY 

2022 of 3.8 percent. Therefore, we are 

proposing a total labor-related share for 

FY 2023 of 73.2 percent (the sum of 69.4 

percent for the proposed labor-related 

share of operating costs and 3.8 percent 

for the proposed labor-related share of 

Capital-Related costs). We are proposing 

that if more recent data become 

available after publication of the 

proposed rule and before the 

publication of the final rule (for 

example, a more recent estimate of the 

labor-related share), we would use such 

data, if appropriate, to determine the FY 

2023 IRF labor-related share in the final 

rule. 

Table 4 shows the current estimate of 

the proposed FY 2023 labor-related 

share and the FY 2022 final laborrelated share using the 2016-based IRF 

market basket relative importance. 


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Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 20229 

We invite public comments on the 

proposed labor-related share for FY 

2023. 

D. Proposed Wage Adjustment for FY 

2023 

1. Background 

Section 1886(j)(6) of the Act requires 

the Secretary to adjust the proportion of 

rehabilitation facilities’ costs 

attributable to wages and wage-related 

costs (as estimated by the Secretary from 

time to time) by a factor (established by 

the Secretary) reflecting the relative 

hospital wage level in the geographic 

area of the rehabilitation facility 

compared to the national average wage 

level for those facilities. The Secretary 

is required to update the IRF PPS wage 

index on the basis of information 

available to the Secretary on the wages 

and wage-related costs to furnish 

rehabilitation services. Any adjustment 

or updates made under section 

1886(j)(6) of the Act for a FY are made 

in a budget-neutral manner. 

For FY 2023, we propose to maintain 

the policies and methodologies 

described in the FY 2022 IRF PPS final 

rule (86 FR 42377) related to the labor 

market area definitions and the wage 

index methodology for areas with wage 

data. Thus, we propose to use the core 

based statistical areas (CBSAs) labor 

market area definitions and the FY 2023 

pre-reclassification and pre-floor 

hospital wage index data. In accordance 

with section 1886(d)(3)(E) of the Act, 

the FY 2023 pre-reclassification and 

pre-floor hospital wage index is based 

on data submitted for hospital cost 

reporting periods beginning on or after 

October 1, 2018, and before October 1, 

2019 (that is, FY 2019 cost report data). 

The labor market designations made 

by the OMB include some geographic 

areas where there are no hospitals and, 

thus, no hospital wage index data on 

which to base the calculation of the IRF 

PPS wage index. We propose to 

continue to use the same methodology 

discussed in the FY 2008 IRF PPS final 

rule (72 FR 44299) to address those 

geographic areas where there are no 

hospitals and, thus, no hospital wage 

index data on which to base the 

calculation for the FY 2023 IRF PPS 

wage index. 

We invite public comment on our 

proposals regarding the Wage 

Adjustment for FY 2023. 

2. Core-Based Statistical Areas (CBSAs) 

for the FY 2023 IRF Wage Index 

The wage index used for the IRF PPS 

is calculated using the prereclassification and pre-floor inpatient 

PPS (IPPS) wage index data and is 

assigned to the IRF on the basis of the 

labor market area in which the IRF is 

geographically located. IRF labor market 

areas are delineated based on the CBSAs 

established by the OMB. The CBSA 

delineations (which were implemented 

for the IRF PPS beginning with FY 2016) 

are based on revised OMB delineations 

issued on February 28, 2013, in OMB 

Bulletin No. 13–01. 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). 

We refer readers to the FY 2016 IRF PPS 

final rule (80 FR 47068 through 47076) 

for a full discussion of our 

implementation of the OMB labor 

market area delineations beginning with 

the FY 2016 wage index. 

Generally, OMB issues major 

revisions to statistical areas every 10 

years, based on the results of the 

decennial census. Additionally, OMB 

occasionally issues updates and 

revisions to the statistical areas in 

between decennial censuses to reflect 

the recognition of new areas or the 

addition of counties to existing areas. In 

some instances, these updates merge 

formerly separate areas, transfer 

components of an area from one area to 

another, or drop components from an 

area. On July 15, 2015, OMB issued 

OMB Bulletin No. 15–01, which 

provides minor updates to and 

supersedes OMB Bulletin No. 13–01 

that was issued on February 28, 2013. 

The attachment to OMB Bulletin No. 

15–01 provides detailed information on 

the update to statistical areas since 

February 28, 2013. The updates 

provided in OMB Bulletin No. 15–01 are 

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. 

In the FY 2018 IRF PPS final rule (82 

FR 36250 through 36251), we adopted 

the updates set forth in OMB Bulletin 

No. 15–01 effective October 1, 2017, 

beginning with the FY 2018 IRF wage 

index. For a complete discussion of the 

adoption of the updates set forth in 

OMB Bulletin No. 15–01, we refer 

readers to the FY 2018 IRF PPS final 

rule. In the FY 2019 IRF PPS final rule 

(83 FR 38527), we continued to use the 

OMB delineations that were adopted 


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TABLE 4: FY 2023 Proposed IRF Labor-Related Share and FY 2022 IRF Labor-Related 

Share 

FY 2023 Proposed FY 2022 Final Labor 

Labor-Related Share 1 Related Share 2 

Wages and Salaries 48.8 48.3 

Employee Benefits 11.3 11.4 

Professional Fees: Labor-Related 3 5.0 5.0 

Administrative and Facilities Suooort Services 0.8 0.8 

Installation, Maintenance, and Repair Services 1.6 1.6 

All Other: Labor-Related Services 1.9 1.9 

Subtotal 69.4 69.0 

Labor-related portion of Capital-Related ( 46%) 3.8 3.9 

Total Labor-Related Share 73.2 72.9 

1 Based on the 2016-based IRF market basket relative importance, IGI 4th quarter 2021 forecast. 

2 Based on the 2016-based IRF market basket relative importance as published in the Federal Register 

(86 FR 42377). 

3 Includes all contract advertising and marketing costs and a portion of accounting, architectural, engineering, 

legal, management consulting, and home office contract labor costs. 
20230 Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 

beginning with FY 2016 to calculate the 

area wage indexes, with updates set 

forth in OMB Bulletin No. 15–01 that 

we adopted beginning with the FY 2018 

wage index. 

On August 15, 2017, OMB issued 

OMB Bulletin No. 17–01, which 

provided updates to and superseded 

OMB Bulletin No. 15–01 that was issued 

on July 15, 2015. The attachments to 

OMB Bulletin No. 17–01 provide 

detailed information on the update to 

statistical areas since July 15, 2015, and 

are based on the application of the 2010 

Standards for Delineating Metropolitan 

and Micropolitan Statistical Areas to 

Census Bureau population estimates for 

July 1, 2014 and July 1, 2015. In the FY 

2020 IRF PPS final rule (84 FR 39090 

through 39091), we adopted the updates 

set forth in OMB Bulletin No. 17–01 

effective October 1, 2019, beginning 

with the FY 2020 IRF wage index. 

On April 10, 2018, OMB issued OMB 

Bulletin No. 18–03, which superseded 

the August 15, 2017 OMB Bulletin No. 

17–01, and on September 14, 2018, 

OMB issued OMB Bulletin No. 18–04, 

which superseded the April 10, 2018 

OMB Bulletin No. 18–03. These 

bulletins established revised 

delineations for Metropolitan Statistical 

Areas, Micropolitan Statistical Areas, 

and Combined Statistical Areas, and 

provided guidance on the use of the 

delineations of these statistical areas. A 

copy of this bulletin may be obtained at 

https://www.whitehouse.gov/wpcontent/uploads/2018/09/Bulletin-18- 

04.pdf. 

To this end, as discussed in the FY 

2021 IRF PPS proposed (85 FR 22075 

through 22079) and final (85 FR 48434 

through 48440) rules, 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 an IRF’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 

IRF PPS. OMB issued further revised 

CBSA delineations in OMB Bulletin No. 

20–01, on March 6, 2020 (available on 

the web at https://www.whitehouse.gov/ 

wp-content/uploads/2020/03/Bulletin20-01.pdf). However, we determined 

that the changes in OMB Bulletin No. 

20–01 do not impact the CBSA-based 

labor market area delineations adopted 

in FY 2021. Therefore, CMS did not 

propose to adopt the revised OMB 

delineations identified in OMB Bulletin 

No. 20–01 for FY 2022, and for these 

reasons CMS is likewise not making 

such a proposal for FY 2023. 

3. Proposed Permanent Cap on Wage 

Index Decreases 

As discussed above in this section of 

the rule, we have proposed and 

finalized temporary transition policies 

in the past to mitigate significant 

changes to payments due to changes to 

the IRF PPS wage index. Specifically, 

for FY 2016 (80 FR 47068), we 

implemented a 50/50 blend for all 

geographic areas consisting of the wage 

index values computed using the thencurrent 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 

48434), 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 an IRF’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 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 

42378), commenters recommended CMS 

extend the transition period adopted in 

the FY 2021 IRF PPS final rule so that 

wage index values do not change by 

more than 5 percent from year-to-year to 

protect IRFs from large payment 

volatility. Because we did not propose 

to modify the transition policy that was 

finalized in the FY 2021 IRF 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 payments. 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 IRF 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 areas. In taking this approach in 

the past, we sought to mitigate shortterm instability and fluctuations that 

can negatively impact providers due to 

wage index changes. In accordance with 

the requirements of the IRF PPS wage 

index regulations at § 412.624(a)(2), we 

use an appropriate wage index based on 

the best available data, including the 

best available labor market area 

delineations, to adjust IRF 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 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 IRF 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 believed the 

5-percent cap applied to all wage index 

decreases for FY 2021 provided an 


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Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 20231 

adequate safeguard against significant 

payment reductions related to the 

adoption of the revised CBSAs. 

However, as discussed earlier in this 

section of the proposed rule, we 

recognize there are circumstances that a 

1-year mitigation policy, like the one 

adopted for FY 2021, would not 

effectively address future years in which 

providers continue to be negatively 

affected by significant wage index 

decreases. 

Typical year-to-year variation in the 

IRF PPS wage index has historically 

been within 5 percent, and we expect 

this will continue to be the case in 

future years. 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 IRF 

PPS payments due to any significant 

wage index decreases that may affect 

providers in a year. We believe this 

approach would address concerns about 

instability that commenters raised in the 

FY 2022 IRF PPS rule. Additionally, we 

believe that applying a 5-percent cap on 

all wage index decreases would support 

increased predictability about IRF 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 areas. As 

discussed in further detail in section 

XIII.C.2. 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 IRFs 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 

IRFs 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 IRF PPS, 

including the wage index adjustment. 

As discussed earlier in this section, the 

IRF PPS regulations require us to use an 

appropriate wage index based on the 

best available data. For the reasons 

discussed in this section, we believe a 

5-percent cap on wage index decreases 

would be appropriate for the IRF PPS. 

Therefore, for FY 2023 and subsequent 

years, we are proposing to apply a 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 an IRF’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 

IRF 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 also means that if an 

IRF’s prior FY wage index is calculated 

with the application of the 5-percent 

cap, the following year’s wage index 

would not be less than 95 percent of the 

IRF’s capped wage index in the prior 

FY. For example, if an IRF’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 IRF 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 IRF 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 IRF PPS wage index as a relative 

measure of the value of labor in 

prescribed labor market areas, increase 

the predictability of IRF PPS payments 

for providers, and mitigate instability 

and significant negative impacts to 

providers resulting from significant 

changes to the wage index. In section 

XIII.C.2. 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 appropriateness. 

Subject to the aforementioned 

proposal becoming final, we are also 

proposing to revise the regulation text at 

§ 412.624(e)(1) to provide that starting 

October 1, 2022, CMS would 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 IRF in the prior year. 

We invite public comments on this 

proposal. 

4. Proposed Wage Adjustment 

To calculate the wage-adjusted facility 

payment for the proposed payment rates 

set forth in this proposed rule, we 

multiply the proposed unadjusted 

Federal payment rate for IRFs by the FY 

2023 labor-related share based on the 

2016-based IRF market basket relative 

importance (73.2 percent) to determine 

the labor-related portion of the standard 

payment amount. A full discussion of 

the calculation of the labor-related share 

is located in section V.C. of this 

proposed rule. We would then multiply 

the labor-related portion by the 

applicable IRF wage index. The wage 

index tables are available on the CMS 

website at https://www.cms.gov/ 

Medicare/Medicare-Fee-for-ServicePayment/InpatientRehabFacPPS/IRFRules-and-Related-Files.html. 

Adjustments or updates to the IRF 

wage index made under section 

1886(j)(6) of the Act must be made in a 

budget-neutral manner. We propose to 

calculate a budget-neutral wage 

adjustment factor as established in the 

FY 2004 IRF PPS final rule (68 FR 

45689) and codified at § 412.624(e)(1), 

as described in the steps below. We 

propose to use the listed steps to ensure 

that the FY 2023 IRF standard payment 

conversion factor reflects the proposed 

update to the wage indexes (based on 

the FY 2019 hospital cost report data) 

and the proposed update to the laborrelated share, in a budget-neutral 

manner: 

Step 1. Calculate the total amount of 

estimated IRF PPS payments using the 

labor-related share and the wage 

indexes from FY 2022 (as published in 

the FY 2022 IRF PPS final rule (86 FR 

42362)). 

Step 2. Calculate the total amount of 

estimated IRF PPS payments using the 

proposed FY 2023 wage index values 

(based on updated hospital wage data 

and taking into account the proposed 

permanent cap on wage index decreases 

policy) and the FY 2023 labor-related 

share of 73.2 percent. 

Step 3. Divide the amount calculated 

in step 1 by the amount calculated in 

step 2. The resulting quotient is the 

proposed FY 2023 budget-neutral wage 

adjustment factor of 1.0007. 

Step 4. Apply the budget neutrality 

factor from step 3 to the FY 2023 IRF 

PPS standard payment amount after the 


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20232 Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 

application of the increase factor to 

determine the proposed FY 2023 

standard payment conversion factor. 

We discuss the calculation of the 

proposed standard payment conversion 

factor for FY 2023 in section V.E. of this 

proposed rule. 

We invite public comments on the 

proposed IRF wage adjustment for FY 

2023 (and the proposed permanent cap 

on wage index decreases policy). 

E. Description of the Proposed IRF 

Standard Payment Conversion Factor 

and Payment Rates for FY 2023 

To calculate the proposed standard 

payment conversion factor for FY 2023, 

as illustrated in Table 5, we begin by 

applying the proposed increase factor 

for FY 2023, as adjusted in accordance 

with sections 1886(j)(3)(C) of the Act, to 

the standard payment conversion factor 

for FY 2022 ($17,240). Applying the 

proposed 2.8 percent increase factor for 

FY 2023 to the standard payment 

conversion factor for FY 2022 of $17,240 

yields a standard payment amount of 

$17,723. Then, we apply the proposed 

budget neutrality factor for the FY 2023 

wage index (taking into account the 

proposed permanent cap on wage index 

decreases policy), and labor-related 

share of 1.0007, which results in a 

standard payment amount of $17,735. 

We next apply the proposed budget 

neutrality factor for the CMG relative 

weights of 0.9979, which results in the 

standard payment conversion factor of 

$17,698 for FY 2023. 

We invite public comments on the 

proposed FY 2023 standard payment 

conversion factor. 

BILLING CODE 4120–01–P 

After the application of the proposed 

CMG relative weights described in 

section IV. of this proposed rule to the 

proposed FY 2023 standard payment 

conversion factor ($17,698), the 

resulting unadjusted IRF prospective 

payment rates for FY 2023 are shown in 

Table 6. 


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TABLE 5: Calculations to Determine the Proposed FY 2023 Standard Payment 

Conversion Factor 

Explanation for Adjustment Calculations 

Standard Pavment Conversion Factor for FY 2022 $17.240 

Proposed Matket Basket Increase Factor for FY 2023 (3.2%), reduced by 0.4 percentage 

point for the productivity adjustment as reQuired bv section 1886(i)(3)(C)(ii)(I) of the Act X 1.028 

Budget Neutrality Factor for the Uodates to the Wage Index and Labor-Related Share X 1.0007 

Budget Neutrality Factor for the Revisions to the CMG Relative Weights X 0.9979 

Prooosed FY 2023 Standard Pavment Conversion Factor = $17 698 
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TABLE 6 : FY 2023 P aymen t Rat es 

CMG Payment Rate Tier 1 Payment Rate Tier 2 Payment Rate Tier 3 Pavment Rate No Comorbidity 

0101 $ 17,565.27 $ 15.307.00 $ 13 923.02 $ 13 197.40 

0102 $ 22,205.68 $ 19,350.99 $ 17 602.43 $ 16 685.67 

0103 $ 28.842.43 $ 25,134.70 $ 22 862.28 $ 21672.97 

0104 $ 36,893.25 $ 32,150.19 $ 29 244.18 $ 27 722.15 

0105 $ 44.450.30 $ 38,735.61 $ 35 233.18 $ 33 399.67 

0106 $ 50,724.24 $ 44,204.29 $ 40 208.09 $ 38 116.18 

0201 $ 19,800.52 $ 15,956.52 $ 14 466.35 $ 13 581.45 

0202 $ 24,847.99 $ 20,023.52 $ 18 152.84 $ 17 043.17 

0203 $ 30.488.34 $ 24,568.36 $ 22 272.93 $ 20 911.96 

0204 $ 37,666.65 $ 30,353.84 $ 27 516.85 $ 25 835.54 

0205 $ 47,726.20 $ 38,459.52 $ 34 866.83 $ 32 734.22 

0301 $ 21,180.97 $ 17,075.03 $ 15 820.24 $ 14 740.66 

0302 $ 27,283.24 $ 21,993.30 $ 20 375.71 $ 18 986.41 

0303 $ 32,789.08 $ 26,431.96 $ 24 488.72 $ 22 819.80 

0304 $ 38,151.58 $ 30,755.58 $ 28 493.78 $ 26 550.54 

0305 $ 41,613.31 $ 33,546.56 $ 31 079.46 $ 28 961.01 

0401 $ 23,628.60 $ 19,402.32 $ 18 540.42 $ 17 011.32 

0402 $ 30,329.06 $ 24,902.86 $ 23 796.73 $ 21834.02 

0403 $ 37.567.54 $ 30,847.61 $ 29 477.79 $ 27 046.08 

0404 $ 55,884.97 $ 45,887.37 $ 43 850.33 $ 40 232.86 

0405 $ 46.407.70 $ 38,105.56 $ 36 413.64 $ 33 410.28 

0406 $ 60,675.82 $ 49,821.64 $ 47 609.39 $ 43 682.20 

0407 $ 76,228.83 $ 62.592.52 $ 59 812.16 $ 54 877.96 

0501 $ 22,145.51 $ 17,453.77 $ 16 464.45 $ 15 319.39 

0502 $ 27.438.98 $ 21,625.19 $ 20 400.48 $ 18 981.11 

0503 $ 31,559.07 $ 24,872.77 $ 23 462.24 $ 21830.48 

0504 $ 38,215.29 $ 30,120.23 $ 28 412.37 $ 26 435.50 

0505 $ 52,478.11 $ 41,360.23 $ 39 017.01 $ 36 302.14 

0601 $ 23,833.90 $ 17,813.04 $ 16 894.51 $ 15 068.08 

0602 $ 29,707.86 $ 22,203.91 $ 21 058.85 $ 18 782.89 

0603 $ 35.445.55 $ 26.490.37 $ 25 124.08 $ 22 409.21 

0604 $ 43,931.75 $ 32,833.33 $ 31141.40 $ 27 775.24 

0701 $ 21,964.99 $ 17,020.17 $ 16 244.99 $ 15 053.92 

0702 $ 27,074.40 $ 20,977.44 $ 20 021.75 $ 18 554.58 

0703 $ 33,709.38 $ 26,120.48 $ 24 929.40 $ 23 102.97 

0704 $ 40,558.51 $ 31,426.34 $ 29 994.57 $ 27 796.48 

0801 $ 19,954.50 $ 17,013.09 $ 15 379.56 $ 14 076.99 

0802 $ 22,961.39 $ 19,575.76 $ 17 698.00 $ 16 198.98 

0803 $ 25.416.10 $ 21.669.43 $ 19 589.92 $ 17 929.84 

0804 $ 29,141.53 $ 24,844.45 $ 22 460.53 $ 20 558.00 

0805 $ 34.817.28 $ 29.683.09 $ 26 835.48 $ 24 561.28 

0901 $ 21,338.48 $ 17,053.79 $ 15 829.09 $ 14 593.77 

0902 $ 26.931.05 $ 21 524.31 $ 19 977.50 $ 18 420.08 

0903 $ 32,024.53 $ 25,594.85 $ 23 756.03 $ 21 903.04 

0904 $ 37.378.18 $ 29 872.45 $ 27 727.46 $ 25 564.76 

1001 $ 21,678.28 $ 18,765.19 $ 16 345.87 $ 14 999.06 

1002 $ 26,862.02 $ 23,253.40 $ 20,253.59 $ 18,586.44 

1003 $ 31,835.16 $ 27,557.56 $ 24,003.80 $ 22,026.93 

1004 $ 39,905.45 $ 34,544.73 $ 30,088.37 $ 27,610.65 

1101 $ 24,164.85 $ 24,164.85 $ 17,802.42 $ 14,115.92 

1102 $ 29,695.47 $ 29,695.47 $ 21,876.50 $ 17,345.81 

1103 $ 38,815.25 $ 38,815.25 $ 28,596.43 $ 22,674.68 

1201 $ 23,320.65 $ 18,432.47 $ 16,531.70 $ 14,744.20 

1202 $ 30,355.61 $ 23,993.18 $ 21,517.23 $ 19,193.48 

1203 $ 37,519.76 $ 29,658.31 $ 26,596.55 $ 23,724.17 

1204 $ 39,346.19 $ 31,100.70 $ 27,890.28 $ 24,878.08 
20234 Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 

F. Example of the Methodology for 

Adjusting the Proposed Prospective 

Payment Rates 

Table 7 illustrates the methodology 

for adjusting the proposed prospective 

payments (as described in section V. of 

this proposed rule). The following 

examples are based on two hypothetical 

Medicare beneficiaries, both classified 

into CMG 0104 (without comorbidities). 

The proposed unadjusted prospective 

payment rate for CMG 0104 (without 

comorbidities) appears in Table 7. 

Example: One beneficiary is in 

Facility A, an IRF located in rural 

Spencer County, Indiana, and another 

beneficiary is in Facility B, an IRF 

located in urban Harrison County, 

Indiana. Facility A, a rural non-teaching 

hospital has a Disproportionate Share 

Hospital (DSH) percentage of 5 percent 

(which would result in a LIP adjustment 

of 1.0156), a wage index of 0.8384, and 

a rural adjustment of 14.9 percent. 

Facility B, an urban teaching hospital, 

has a DSH percentage of 15 percent 

(which would result in a LIP adjustment 

of 1.0454 percent), a wage index of 

0.8763, and a teaching status adjustment 

of 0.0784. 

To calculate each IRF’s labor and nonlabor portion of the proposed 

prospective payment, we begin by 

taking the unadjusted prospective 

payment rate for CMG 0104 (without 


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CMG Payment Rate Tier 1 Payment Rate Tier 2 Payment Rate Tier 3 Payment Rate No Comorbidity 

1301 $ 21,570.32 $ 16,195.44 $ 15,379.56 $ 15,177.80 

1302 $ 28,645.98 $ 21,508.38 $ 20,425.26 $ 20,156.25 

1303 $ 33,537.71 $ 25,178.94 $ 23,911.77 $ 23,596.74 

1304 $ 41,323.06 $ 31,024.59 $ 29,461.86 $ 29,076.04 

1305 $ 37,029.53 $ 27,801.79 $ 26,401.88 $ 26,055.00 

1401 $ 20,159.79 $ 15,937.05 $ 14,691.11 $ 13,436.32 

1402 $ 25,679.80 $ 20,301.38 $ 18,713.87 $ 17,115.74 

1403 $ 31,107.77 $ 24,593.14 $ 22,667.60 $ 20,733.21 

1404 $ 38,125.03 $ 30,139.69 $ 27,782.32 $ 25,410.79 

1501 $ 23,095.89 $ 18,078.51 $ 17,275.02 $ 16,705.14 

1502 $ 28,196.45 $ 22,071.18 $ 21,090.71 $ 20,393.41 

1503 $ 32,973.14 $ 25,810.76 $ 24,663.93 $ 23,849.82 

1504 $ 39,309.03 $ 30,771.51 $ 29,403.46 $ 28,431.84 

1601 $ 20,076.61 $ 15,641.49 $ 15,179.57 $ 13,953.10 

1602 $ 23,648.07 $ 18,421.85 $ 17,878.52 $ 16,434.36 

1603 $ 28,704.39 $ 22,361.42 $ 21,703.06 $ 19,947.42 

1604 $ 34,960.63 $ 27,235.45 $ 26,431.96 $ 24,295.81 

1701 $ 23,019.79 $ 18,200.62 $ 17,096.27 $ 15,547.69 

1702 $ 28,566.34 $ 22,586.19 $ 21,216.36 $ 19,294.36 

1703 $ 33,718.23 $ 26,658.50 $ 25,042.67 $ 22,773.79 

1704 $ 38,293.16 $ 30,275.97 $ 28,438.92 $ 25,863.86 

1705 $ 43,726.45 $ 34,571.27 $ 32,474.06 $ 29,532.65 

1801 $ 21,435.82 $ 16,928.14 $ 15,763.61 $ 14,611.47 

1802 $ 25,791.30 $ 20,368.63 $ 18,966.95 $ 17,581.19 

1803 $ 32,550.16 $ 25,706.35 $ 23,938.31 $ 22,187.98 

1804 $ 37,668.42 $ 29,748.57 $ 27,702.68 $ 25,678.03 

1805 $ 44,995.40 $ 35,534.04 $ 33,089.95 $ 30,672.40 

1806 $ 61,380.20 $ 48,473.05 $ 45,138.75 $ 41,839.84 

1901 $ 20,457.12 $ 18,315.66 $ 17,605.97 $ 16,473.30 

1902 $ 25,685.11 $ 22,996.78 $ 22,104.80 $ 20,681.88 

1903 $ 37,629.49 $ 33,689.91 $ 32,383.80 $ 30,298.98 

1904 $ 58,067.14 $ 51,987.88 $ 49,973.84 $ 46,756.35 

2001 $ 21,258.84 $ 17,156.44 $ 15,790.16 $ 14,367.24 

2002 $ 26,325.78 $ 21,246.45 $ 19,554.52 $ 17,791.80 

2003 $ 31,279.45 $ 25,244.43 $ 23,233.93 $ 21,138.49 

2004 $ 36,827.77 $ 29,722.02 $ 27,355.80 $ 24,888.70 

2005 $ 39,450.61 $ 31,838.70 $ 29,304.35 $ 26,660.27 

2101 $ 28,300.87 $ 20,267.75 $ 19,961.57 $ 18,650.15 

2102 $ 43,694.59 $ 31,293.60 $ 30,821.07 $ 28,794.65 

5001 $ - $ - $ - $ 3,008.66 

5101 $ - $ - $ - $ 13,071.74 

5102 $ - $ - $ - $ 33,394.36 

5103 $ - $ - $ - $ 15,827.32 

5104 $ - $ - $ - $ 40,231.09 
Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 20235 

comorbidities) from Table 7. Then, we 

multiply the proposed labor-related 

share for FY 2023 (73.2 percent) 

described in section V.C. of this 

proposed rule by the proposed 

unadjusted prospective payment rate. 

To determine the non-labor portion of 

the proposed prospective payment rate, 

we subtract the labor portion of the 

Federal payment from the proposed 

unadjusted prospective payment. 

To compute the proposed wageadjusted prospective payment, we 

multiply the labor portion of the 

proposed Federal payment by the 

appropriate wage index located in the 

applicable wage index table. This table 

is available on the CMS website at 

https://www.cms.gov/Medicare/ 

Medicare-Fee-for-Service-Payment/ 

InpatientRehabFacPPS/IRF-Rules-andRelated-Files.html. 

The resulting figure is the wageadjusted labor amount. Next, we 

compute the proposed wage-adjusted 

Federal payment by adding the wageadjusted labor amount to the non-labor 

portion of the proposed Federal 

payment. 

Adjusting the proposed wage-adjusted 

Federal payment by the facility-level 

adjustments involves several steps. 

First, we take the wage-adjusted 

prospective payment and multiply it by 

the appropriate rural and LIP 

adjustments (if applicable). Second, to 

determine the appropriate amount of 

additional payment for the teaching 

status adjustment (if applicable), we 

multiply the teaching status adjustment 

(0.0784, in this example) by the wageadjusted and rural-adjusted amount (if 

applicable). Finally, we add the 

additional teaching status payments (if 

applicable) to the wage, rural, and LIPadjusted prospective payment rates. 

Table 7 illustrates the components of 

the adjusted payment calculation. 

BILLING CODE 4120–01–C 

Thus, the proposed adjusted payment 

for Facility A would be $28,522.97, and 

the adjusted payment for Facility B 

would be $28,333.19. 

VI. Proposed Update to Payments for 

High-Cost Outliers Under the IRF PPS 

for FY 2023 

A. Proposed Update to the Outlier 

Threshold Amount for FY 2023 

Section 1886(j)(4) of the Act provides 

the Secretary with the authority to make 

payments in addition to the basic IRF 

prospective payments for cases 

incurring extraordinarily high costs. A 

case qualifies for an outlier payment if 

the estimated cost of the case exceeds 

the adjusted outlier threshold. We 

calculate the adjusted outlier threshold 

by adding the IRF PPS payment for the 

case (that is, the CMG payment adjusted 

by all of the relevant facility-level 

adjustments) and the adjusted threshold 

amount (also adjusted by all of the 

relevant facility-level adjustments). 

Then, we calculate the estimated cost of 

a case by multiplying the IRF’s overall 

CCR by the Medicare allowable covered 

charge. If the estimated cost of the case 

is higher than the adjusted outlier 

threshold, we make an outlier payment 

for the case equal to 80 percent of the 

difference between the estimated cost of 

the case and the outlier threshold. 

In the FY 2002 IRF PPS final rule (66 

FR 41362 through 41363), we discussed 

our rationale for setting the outlier 

threshold amount for the IRF PPS so 

that estimated outlier payments would 

equal 3 percent of total estimated 

payments. For the FY 2002 IRF PPS 

final rule, we analyzed various outlier 

policies using 3, 4, and 5 percent of the 

total estimated payments, and we 

concluded that an outlier policy set at 

3 percent of total estimated payments 

would optimize the extent to which we 

could reduce the financial risk to IRFs 

of caring for high-cost patients, while 

still providing for adequate payments 

for all other (non-high cost outlier) 

cases. 

Subsequently, we updated the IRF 

outlier threshold amount in the FYs 

2006 through 2022 IRF PPS final rules 

and the FY 2011 and FY 2013 notices 

(70 FR 47880, 71 FR 48354, 72 FR 

44284, 73 FR 46370, 74 FR 39762, 75 FR 

42836, 76 FR 47836, 76 FR 59256, 77 FR 

44618, 78 FR 47860, 79 FR 45872, 80 FR 

47036, 81 FR 52056, 82 FR 36238, 83 FR 

38514, 84 FR 39054, 85 FR 48444, and 

86 FR 42362, respectively) to maintain 

estimated outlier payments at 3 percent 

of total estimated payments. We also 

stated in the FY 2009 final rule (73 FR 

46370 at 46385) that we would continue 

to analyze the estimated outlier 

payments for subsequent years and 

adjust the outlier threshold amount as 

appropriate to maintain the 3 percent 

target. 

To update the IRF outlier threshold 

amount for FY 2023, we propose to use 

FY 2021 claims data and the same 

methodology that we used to set the 


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. TABLE7 . E xampe 1 o re omputm~t h e FY 2023 IRF P rospect1ve p ayment 

Steps Rural Facility A Urban Facility B 

(Spencer Co .• IN) <Harrison Co .• IN) 

1 Unadjusted Payment $27 722.15 $27,722.15 

2 Labor Share X 0.732 X 0.732 

3 Labor Portion of Payment = $20 292.61 = $20,292.61 

4 CBSA-Based Wage Index\ X 0.8384 X 0.8763 

5 Wage-Adjusted Amount = $17 013.33 = $17,782.42 

6 Non-Labor Amount + $7 429.54 + $7.429.54 

7 Wage-Adjusted Payment = $24 442.86 = $25,211.95 

8 Rural Adiustment X 1.149 X 1.000 

9 Wage- and Rural-Adjusted Payment = $28 084.85 = $25,211.95 

10 LIP Adiustment X 1.0156 X 1.0454 

11 Wage- Rural- and LIP-Adjusted Payment = $28 522.97 = $26,356.58 

12 Wage- and Rural-Adiusted Payment $28 084.85 $25.211.95 

13 Teaching Status Adjustment X 0 X 0.0784 

14 Teaching Status Adiustment Amount = $0.00 = $1.976.62 

15 Wage- Rural- and LIP-Adjusted Payment + $28 522.97 + $26,356.58 

16 Total Adiusted Payment = $28 522.97 = $28.333.19 
20236 Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 

initial outlier threshold amount in the 

FY 2002 IRF PPS final rule (66 FR 41362 

through 41363), which is also the same 

methodology that we used to update the 

outlier threshold amounts for FYs 2006 

through 2022. The outlier threshold is 

calculated by simulating aggregate 

payments and using an iterative process 

to determine a threshold that results in 

outlier payments being equal to 3 

percent of total payments under the 

simulation. To determine the outlier 

threshold for FY 2023, we estimated the 

amount of FY 2023 IRF PPS aggregate 

and outlier payments using the most 

recent claims available (FY 2021) and 

the proposed FY 2023 standard payment 

conversion factor, labor-related share, 

and wage indexes, incorporating any 

applicable budget-neutrality adjustment 

factors. The outlier threshold is adjusted 

either up or down in this simulation 

until the estimated outlier payments 

equal 3 percent of the estimated 

aggregate payments. Based on an 

analysis of the preliminary data used for 

the proposed rule, we estimate that IRF 

outlier payments as a percentage of total 

estimated payments would be 

approximately 3.8 percent in FY 2022. 

Therefore, we propose to update the 

outlier threshold amount from $9,491 

for FY 2022 to $13,038 for FY 2023 to 

maintain estimated outlier payments at 

approximately 3 percent of total 

estimated aggregate IRF payments for 

FY 2023. 

Although we believe that updating the 

outlier threshold for FY 2023 would be 

appropriate to maintain IRF PPS outlier 

payments at 3 percent of total estimated 

payments, we recognize that the 

proposed outlier threshold amount for 

FY 2023 would result in a significant 

increase from the current outlier 

threshold amount for FY 2022. As we 

continue to explore the underlying 

reasons for the large change in the 

proposed outlier threshold amount, we 

welcome comments from stakeholders 

on any observations or information 

related to the increase in the proposed 

update to outlier threshold amount for 

FY 2023. 

B. Proposed Update to the IRF Cost-toCharge Ratio Ceiling and Urban/Rural 

Averages for FY 2023 

CCRs are used to adjust charges from 

Medicare claims to costs and are 

computed annually from facilityspecific data obtained from MCRs. IRF 

specific CCRs are used in the 

development of the CMG relative 

weights and the calculation of outlier 

payments under the IRF PPS. In 

accordance with the methodology stated 

in the FY 2004 IRF PPS final rule (68 

FR45692 through 45694), we propose to 

apply a ceiling to IRFs’ CCRs. Using the 

methodology described in that final 

rule, we propose to update the national 

urban and rural CCRs for IRFs, as well 

as the national CCR ceiling for FY 2023, 

based on analysis of the most recent 

data available. We apply the national 

urban and rural CCRs in the following 

situations: 

• New IRFs that have not yet 

submitted their first MCR. 

• IRFs whose overall CCR is in excess 

of the national CCR ceiling for FY 2023, 

as discussed below in this section. 

• Other IRFs for which accurate data 

to calculate an overall CCR are not 

available. 

Specifically, for FY 2023, we propose 

to estimate a national average CCR of 

0.463 for rural IRFs, which we 

calculated by taking an average of the 

CCRs for all rural IRFs using their most 

recently submitted cost report data. 

Similarly, we propose to estimate a 

national average CCR of 0.393 for urban 

IRFs, which we calculated by taking an 

average of the CCRs for all urban IRFs 

using their most recently submitted cost 

report data. We apply weights to both of 

these averages using the IRFs’ estimated 

costs, meaning that the CCRs of IRFs 

with higher total costs factor more 

heavily into the averages than the CCRs 

of IRFs with lower total costs. For this 

proposed rule, we have used the most 

recent available cost report data (FY 

2020). This includes all IRFs whose cost 

reporting periods begin on or after 

October 1, 2019, and before October 1, 

2020. If, for any IRF, the FY 2020 cost 

report was missing or had an ‘‘as 

submitted’’ status, we used data from a 

previous FY’s (that is, FY 2004 through 

FY 2019) settled cost report for that IRF. 

We do not use cost report data from 

before FY 2004 for any IRF because 

changes in IRF utilization since FY 2004 

resulting from the 60 percent rule and 

IRF medical review activities suggest 

that these older data do not adequately 

reflect the current cost of care. Using 

updated FY 2020 cost report data for 

this proposed rule, we estimate a 

national average CCR of 0.463 for rural 

IRFs, and a national average CCR of 

0.393 for urban IRFs. 

In accordance with past practice, we 

propose to set the national CCR ceiling 

at 3 standard deviations above the mean 

CCR. Using this method, we propose a 

national CCR ceiling of 1.40 for FY 

2023. This means that, if an individual 

IRF’s CCR were to exceed this ceiling of 

1.40 for FY 2023, we will replace the 

IRF’s CCR with the appropriate 

proposed national average CCR (either 

rural or urban, depending on the 

geographic location of the IRF). We 

calculated the proposed national CCR 

ceiling by: 

Step 1. Taking the national average 

CCR (weighted by each IRF’s total costs, 

as previously discussed) of all IRFs for 

which we have sufficient cost report 

data (both rural and urban IRFs 

combined). 

Step 2. Estimating the standard 

deviation of the national average CCR 

computed in step 1. 

Step 3. Multiplying the standard 

deviation of the national average CCR 

computed in step 2 by a factor of 3 to 

compute a statistically significant 

reliable ceiling. 

Step 4. Adding the result from step 3 

to the national average CCR of all IRFs 

for which we have sufficient cost report 

data, from step 1. 

We are also proposing that if more 

recent data become available after the 

publication of this proposed rule and 

before the publication of the final rule, 

we would use such data to determine 

the FY 2023 national average rural and 

urban CCRs and the national CCR 

ceiling in the final rule. We invite 

public comment on the proposed update 

to the IRF CCR ceiling and the urban/ 

rural averages for FY 2023. 

VII. Proposed Codification and 

Clarifications of IRF Teaching Status 

Adjustment Policy 

In the FY 2006 IRF PPS final rule (70 

FR 47928 through 47932), we 

implemented § 412.624(e)(4) to establish 

a facility level adjustment for IRFs that 

are, teaching hospitals or units of 

teaching hospitals. The teaching status 

adjustment accounts for the higher 

indirect operating costs experienced by 

IRFs that participate in training 

residents in graduate medical education 

(GME) programs. The teaching status 

payment adjustment is based on the 

ratio of the number of full-time 

equivalent (FTE) interns and residents 

training in the IRF divided by the IRF’s 

average daily census. Section 

1886(j)(3)(A)(v) of the Act requires the 

Secretary to adjust the prospective 

payment rates for the IRF PPS by such 

factors as the Secretary determines are 

necessary to properly reflect the 

variations in necessary costs of 

treatment among rehabilitation 

facilities. 

We established the IRF teaching status 

adjustment in a manner that limited the 

incentives for IRFs to add FTE interns 

and residents for the purpose of 

increasing their teaching status 

adjustment, as has been done in the 

payment systems for Inpatient 

Psychiatric Facilities (IPF) and acute 

care hospitals. That is, we imposed a 

cap on the number of FTE interns and 


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Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 20237 

residents that the IRF can count for the 

purpose of calculating the teaching 

status adjustment. This cap is similar to 

the cap established by the Balanced 

Budget Act of 1997 (Pub. L. 105–33, 

enacted August 5, 1997) section 4621, 

that added section 1886(d)(5)(B)(v) of 

the Act (indirect medical education 

(IME) FTE cap for IPPS hospitals. The 

cap limits the number of FTE interns 

and residents that teaching IRFs may 

count for the purpose of calculating the 

IRF PPS teaching status adjustment, not 

the number of interns and residents that 

teaching institutions care hire or train. 

The cap is equal to the number of FTE 

interns and residents that trained in the 

IRF during a ‘‘base year,’’ that is based 

on the most recent final settled cost 

report for a cost reporting period ending 

on or before November 15, 2004. A 

complete discussion of how the IRF 

teaching status adjustment was 

calculated appears in the FY 2006 IRF 

PPS final rule (70 FR 47928 through 

47932). 

In the FY 2012 IRF PPS final rule (76 

FR 47846 through 47848) published on 

August 5, 2011, we updated the IRF PPS 

teaching status adjustment policy in 

order to maintain consistency, to the 

extent feasible, with the indirect 

medical education (IME) teaching 

policies that were finalized in the IPPS 

FY 1999 final rule (64 FR 41522), the 

IPPS FY 2001 final rule (66 FR 39900), 

and the IPF PPS teaching adjustment 

policies finalized in the 2012 IPF PPS 

final rule (76 FR 26454 through 26456). 

In that final rule, we adopted a policy 

which permits a temporary increase in 

the FTE intern and resident cap when 

an IRF increases the number of FTE 

residents it trains, in order to accept 

displaced residents because another IRF 

closes or closes a medical residency 

training program. We refer to a 

‘‘displaced’’ resident or intern as one 

that is training in an IRF and is unable 

to complete training in that IRF, either 

because the IRF closes or closes a 

medical residency training program. 

The cap adjustment for IRFs, adopted 

in the FY 2012 IRF PPS final rule, is 

considered temporary because it is 

resident-specific and will only apply to 

the residents until they have completed 

their training in the program in which 

they were training at the time of the IRF 

closure or the closure of the program. 

Similar to the IPPS and IPF policy for 

displaced residents, the IRF PPS 

temporary cap adjustment only applies 

to residents that were still training at the 

IRF at the time the IRF closed or at the 

time the IRF ceased training residents in 

the residency training program(s). 

Residents who leave the IRF, for 

whatever reason, before the closure of 

the IRF or the closure of the medical 

residency training program are not 

considered displaced residents for 

purposes of the IRF temporary cap 

adjustment policy. 

In the FY 2012 IRF PPS final rule, we 

also adopted the IPPS definition of 

‘‘closure of a hospital’’ at 

§ 413.79(h)(1)(i) to mean the IRF 

terminates its Medicare provider 

agreement as specified in § 489.52. In 

this instance, we allow a temporary 

adjustment to an IRF’s FTE cap to reflect 

residents added to their medical 

residency training program because of 

an IRF’s closure. We allow an 

adjustment to an IRF’s FTE cap if the 

IRF meets the criteria outlined in the FY 

2012 IRF PPS final rule (76 FR 47847). 

After the displaced residents leave the 

accepting IRF’s training program or 

complete their medical residency 

training program, the accepting IRF’s 

cap will revert to its original level. As 

such, the temporary adjustment to the 

FTE cap will be available to the IRF 

only for the period of time necessary for 

the displaced residents to complete 

their training. 

Additionally, in the FY 2012 IRF PPS 

final rule, we adopted the IPPS 

definition of ‘‘closure of a hospital 

residency training program,’’ as 

specified in § 413.79(h)(1)(ii), which 

means that the hospital ceases to offer 

training for interns and residents in a 

particular approved medical residency 

training program. In this instance, if an 

IRF ceases training residents in a 

medical residency training program(s) 

and agrees to temporarily reduce its FTE 

cap, another IRF may receive a 

temporary adjustment to its FTE cap to 

reflect the addition of the displaced 

residents. For more discussion regarding 

the methodology for adjusting the caps 

for the ‘‘receiving IRF’’ and the ‘‘IRF 

that closed its program,’’ refer to the FY 

2012 IRF PPS final rule (76 FR 47847). 

A. Proposed Codification of Existing 

Teaching Status Adjustment Policies 

In an effort to streamline the IRF PPS 

teaching status adjustment policies that 

were finalized in the FY 2006 IRF PPS 

final rule (70 FR 47928 through 47932) 

and the FY 2012 IRF PPS final rule (76 

FR 47846 through 47848), we are 

proposing to codify the longstanding 

policy so that these policies can be 

easily located by IRF providers and can 

also align, to the extent feasible, with 

the IPPS IME and IPF teaching 

adjustment policy regulations. 

First, we are proposing to codify 

policy that was finalized in the FY 2006 

IRF PPS final rule with respect to how 

CMS adjusts the Federal prospective 

payment on a facility basis by a factor 

to account for indirect teaching costs. 

When the teaching status adjustment 

policy was finalized in the FY 2006 IRF 

PPS final rule (70 FR 47928 through 

47932), the definition of this ‘‘factor’’ 

and explanations of how it is computed 

were not included in the regulations. 

Rather, the more detailed definition and 

the explanation of the teaching status 

payment adjustment provided in the FY 

2006 IRF PPS final rule, were published 

in the Medicare Claims Processing 

Manual (100–04, chapter 3, 140.2.5.4). 

Currently, § 412.624(e)(4) states, for 

discharges on or after October 1, 2005, 

CMS adjusts the Federal prospective 

payment on a facility basis by a factor 

as specified by CMS for facilities that 

are teaching institutions or units of 

teaching institutions. This adjustment is 

made on a claim basis as an interim 

payment and the final payment in full 

for the claim is made during the final 

settlement of the cost report. 

Second, we are also proposing to 

codify the IRF policy that was adopted 

in the FY 2012 IRF PPS final rule (76 

FR 47846 through 47848) allowing an 

IRF to receive a temporary adjustment to 

its FTE cap to reflect residents added to 

its teaching program because of another 

IRFs closure or an IRFs medical 

residency training program closure. We 

believe that codifying these 

longstanding policies would improve 

clarity and reduce administrative 

burden on IRF providers and others 

trying to locate all relevant information 

pertaining to the teaching hospital 

adjustment. 

Thus, we are proposing to codify 

CMS’ existing IRF PPS’ teaching 

hospital adjustment policies through 

proposed amendments to §§ 412.602 

and 412.624(e)(4) presented in this 

proposed rule; except as specifically 

noted in this proposed rule, our intent 

is to codify the existing IRF PPS 

teaching status adjustment policy. 

We invite public comment on our 

proposal to amend §§ 412.602 and 

412.624(e)(4) to codify our longstanding 

policies regarding the teaching status 

adjustment. 

B. Proposed Update to the IRF Teaching 

Policy on IRF Program Closures and 

Displaced Residents 

For FY 2023, we are also proposing to 

change the IRF policy pertaining to 

displaced residents resulting from IRF 

closures and closures of IRF residency 

teaching programs. Specifically, we are 

proposing to adopt conforming changes 

to the IRF PPS teaching status 

adjustment policy to align with the 

policy changes that the IPPS finalized in 

the FY 2021 IPPS final rule (85 FR 

58865 through 58870) and that the IPF 


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20238 Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 

finalized in the FY 2022 IPF PPS final 

rule (86 FR 42618 through 42621). We 

believe that the IRF teaching status 

adjustment policy relating to hospital 

closure and displaced residents is 

susceptible to the same vulnerabilities 

as IPPS IME policy. Hence, if an IRF 

with residents training in its residency 

program announces it is closing, these 

residents will become displaced and 

will need to find alternative positions at 

other IRFs or risk being unable to 

become board-certified. 

We are proposing to implement the 

policy discussed in this section to 

remain consistent with the IPPS policy 

for calculating the temporary IME 

resident cap adjustment in situations 

where the receiving hospital assumes 

the training of displaced residents due 

to another hospital or residency 

program’s closure. We are also 

proposing that, in the future, we would 

deviate from the IPPS IME policy as it 

pertains to counting displaced residents 

for the purposes of the IRF teaching 

status adjustment only when it is 

necessary and appropriate for the IRF 

PPS. 

The policy adopted in the FY 2012 

IRF PPS final rule (76 FR 47846 through 

47848), published August 5, 2011, 

permits an IRF to temporarily adjust its 

FTE cap to reflect displaced residents 

added to their residency program 

because of another IRF closure or IRF 

residency program closure. In that final 

rule, we adopted the IPPS definition of 

‘‘closure of a hospital’’ at 

§ 413.79(h)(1)(i) to also apply to IRF, 

and to mean that the IRF terminates its 

Medicare provider agreement as 

specified in § 489.52. We also adopted 

the IPPS definition of ‘‘closure of a 

hospital residency training program’’ as 

it is currently defined at 

§ 413.79(h)(1)(ii) to also apply to IRF 

residency training program closures, 

and to mean that the IRF ceases to offer 

training for residents in a particular 

approved medical residency training 

program. In this proposed rule, we are 

proposing to codify both of these 

definitions within the IRF PPS 

definitions section provided at 

§ 412.602 so that the IRF teaching 

policies are more centrally located and 

more easily accessible. 

Although not explicitly stated in the 

regulations, our current policy is that a 

displaced resident is one that is 

physically present at the hospital 

training on the day prior to or the day 

of hospital or residency program 

closure. This longstanding policy 

derived from the fact that there are 

requirements that the receiving IRF 

identifies the residents ‘‘who have come 

from the closed IRF’’ or identifies the 

residents ‘‘who have come from another 

IRF’s closed residency program,’’ and 

that the IRF that closed its program 

identifies ‘‘the residents who were in 

training at the time of the residency 

program’s closure.’’ We considered the 

residents who were physically present 

at the IRF to be those residents who 

were ‘‘training at the time of the 

program’s closure,’’ thereby granting 

them the status of ‘‘displaced 

residents.’’ Although we did not want to 

limit the ‘‘displaced residents’’ to only 

those physically present at the time of 

closure, it becomes much more 

administratively challenging for the 

following groups of residents at closing 

IRFs/residency programs to continue 

their training: 

(1) Residents who leave the program 

after the closure is publicly announced 

to continue training at another IRF, but 

before the actual closure; 

(2) Residents assigned to and training 

at planned rotations at other IRFs who 

will be unable to return to their 

rotations at the closing IPF or program; 

and 

(3) Individuals (such as medical 

students or would-be fellows) who 

matched into resident programs at the 

closing IRF or residency program, but 

have not yet started training at the 

closing IRF or residency program. 

Other groups of residents who, under 

current policy, are already considered 

‘‘displaced residents’’ include— 

(1) Residents who are physically 

training in the IRF on the day prior to 

or day of residency program or IRF 

closure; and 

(2) Residents who would have been at 

the closing IRF or IRF residency 

program on the day prior to or day of 

closure, but were on approved leave at 

that time, and are unable to return to 

their training at the closing IRF or IRF 

residency training program. 

We are proposing to amend our IRF 

policy with regard to closing teaching 

IRFs and closing IRF medical residency 

training programs to address the needs 

of interns and residents attempting to 

find alternative IRFs in which to 

complete their training. Additionally, 

this proposal addresses the incentives of 

originating and receiving IRFs with 

regard to ensuring we appropriately 

account for their indirect teaching costs 

by way of an appropriate IRF teaching 

adjustment based on each program’s 

FTE resident count. We are proposing to 

make changes to the current IRF 

teaching status adjustment policy 

related to displaced residents as 

discussed below. 

First, rather than link the status of 

displaced residents for the purpose of 

the receiving IRF’s request to increase 

their FTE cap to the resident’s presence 

at the closing IRF or program on the day 

prior to or the day of the residency 

program or IRF closure, we are 

proposing to link the status of the 

displaced residents to the day that the 

closure was publicly announced (for 

example, via a press release or a formal 

notice to the Accreditation Council on 

Graduate Medical Education). This 

would provide great flexibility for the 

interns and residents to transfer while 

the IRF operations or teaching programs 

are winding down, rather than waiting 

until the last day of IRF or IRF teaching 

program operation. This would address 

the needs of the group of residents who 

would leave the program after the 

closure was publicly announced to 

continue training at another hospital, 

but before the day of actual closure. 

Second, by removing the link between 

the status of displaced residents and 

their presence at the closing IRF or 

residency program on the day prior to 

or the day of the IRF closure or program 

closure, we propose to also allow the 

residents assigned to and training at 

planned rotations at other IRFs who will 

be unable to return to their rotations at 

the closing IRF or program and 

individuals (such as medical students or 

would-be fellows) who matched into 

resident programs at the closing IRF or 

residency program, but have not yet 

started training at the closing IRF or 

residency program, to be considered a 

displaced resident. 

Thus, we are proposing to revise our 

teaching policy with regard to which 

residents can be considered ‘‘displaced’’ 

for the purpose of the receiving IRF’s 

request to increase their IRF cap in the 

situation where an IRF announces 

publicly that it is closing, and/or that it 

is closing an IRF residency program. 

Specifically, we are proposing to adopt 

the FY 2021 IPPS final rule definition of 

‘‘displaced resident’’ as defined at 

§ 413.79(h)(1)(ii), for the purpose of 

calculating the IRF’s teaching status 

adjustment. 

In addition, we are proposing to 

change another detail of the policy 

specific to the requirements for the 

receiving IRF. To apply for the 

temporary increase in the FTE resident 

cap, the receiving IRF would have to 

submit a letter to its Medicare 

Administrative Contractor (MAC) 

within 60 days after beginning to train 

the displaced interns and residents. As 

established in the FY 2012 IRF PPS final 

rule, this letter must identify the 

residents who have come from the 

closed IRF or closed residency program 

and caused the receiving IRF to exceed 

its cap, and must specify the length of 

time that the adjustment is needed. 


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Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 20239 

Furthermore, to maintain consistency 

with the IPPS IME policy, we are 

proposing that the letter must also 

include: 

(1) The name of each displaced 

resident; 

(2) The last four digits of each 

displaced resident’s social security 

number; this will reduce the amount of 

personally identifiable information (PII); 

(3) The name of the IRF and the name 

of the residency program or programs in 

which each resident was training at 

previously; and 

(4) The amount of the cap increase 

needed for each resident (based on how 

much the receiving IRF is in excess of 

its cap and the length of time for which 

the adjustments are needed). 

As we previously discussed in the FY 

2012 IRF PPS final rule (76 FR 47846 

through 47848), we are also clarifying 

that the maximum number of FTE 

resident cap slots that could be 

transferred to all receiving IRFs is the 

number of FTE resident cap slots 

belonging to the IRF that has closed the 

resident training program, or that is 

closing. Therefore, if the originating IRF 

is training residents in excess of its cap, 

then being a displaced resident does not 

guarantee that a cap slot will be 

transferred along with the resident. 

Therefore, we are proposing that if there 

are more IRF displaced residents than 

available cap slots, the slots may be 

apportioned according to the closing 

IRF’s discretion. The decision to transfer 

a cap slot if one is available would be 

voluntary and made at the sole 

discretion of the originating IRF. 

However, if the originating IRF decides 

to do so, then it would be the 

originating IRF’s responsibility to 

determine how much of an available cap 

slot would go with a particular resident 

(if any). We also note that, as we 

previously discussed in the FY 2012 IRF 

PPS final rule (76 FR 47846 through 

47848), only to the extent a receiving 

IRF would exceed its FTE cap by 

training displaced residents would it be 

eligible for a temporary adjustment to its 

resident FTE cap. As such, displaced 

residents are factored into the receiving 

IRF’s ratio of resident FTEs to the 

facility’s average daily census. 

We invite public comment on the 

proposed updates to the IRF teaching 

policy. 

VIII. Solicitation of Comments 

Regarding the Facility-Level 

Adjustment Factor Methodology 

Section 1886(j)(3)(A)(v) of the Act 

confers broad authority upon the 

Secretary to adjust the per unit payment 

rate ‘‘by such . . . factors as the 

Secretary determines are necessary to 

properly reflect variations in necessary 

costs of treatment among rehabilitation 

facilities.’’ Under this authority, we 

currently adjust the prospective 

payment amount associated with a CMG 

to account for facility-level 

characteristics such as a facility’s 

percentage of low-income patients (LIP), 

teaching status, and location in a rural 

area, if applicable, as described in 

§ 412.624(e). 

The facility-level adjustment factors 

are intended to account for differences 

in costs attributable to the different 

types of IRF providers and to better 

align payments with the costs of 

providing IRF care. The LIP and rural 

facility-level adjustment factors have 

been utilized since the inception of the 

IRF PPS, while the teaching status 

adjustment factor was finalized in the 

FY 2006 IRF PPS final rule (70 FR 

47880) when our regression analysis 

indicated that it had become statistically 

significant in predicting IRF costs. Each 

of the facility-level adjustment factors 

were implemented using the same 

statistical approach, that is, utilizing 

coefficients determined from regression 

analysis. 

Historically, we have observed 

relatively large fluctuations in these 

factors from year-to-year which lead us 

to explore a number of options to 

provide greater stability and 

predictability between years and 

increase the accuracy of Medicare 

payments for IRFs. In addition to 

holding these factors constant over 

multiple years to mitigate fluctuations 

in payments, we also implemented a 

number of refinements to the 

methodology used to calculate the 

adjustment factors in efforts to better 

align payments with the costs of care. 

For example, in FY 2010 (74 FR 39762) 

we implemented a 3-year moving 

average approach to updating the 

facility-level adjustment factors to 

promote more consistency in the 

adjustment factors over time. 

Additionally, in FY 2014 (78 FR 47859) 

we added an indicator variable for a 

facility’s freestanding or hospital-based 

status to the payment regression to 

improve the accuracy of the IRF 

payment adjustments. This variable was 

added to control for differences in cost 

structure between hospital-based and 

freestanding IRFs in the regression 

analysis, so that these differences would 

not inappropriately influence the 

adjustment factor estimates. We refer 

readers to the FY 2015 IRF PPS final 

rule (79 FR 45882 through 45883) for a 

full discussion of the refinements that 

have been made to the methodology 

used to determine the facility-level 

adjustment factors and other analysis 

that has been considered over time. Due 

to the revisions to the regression 

analysis and the substantive changes to 

the facility-level adjustment factors that 

were adopted in the FY 2014 IRF PPS 

final rule, we finalized a proposal in the 

FY 2015 IRF PPS final rule (79 FR 

45871) to freeze the facility-level 

adjustment factors for FY 2015 and all 

subsequent years at the FY 2014 levels 

while we continued to monitor changes 

in the adjustment factors over time. 

Table 8 shows how the IRF facility-level 

adjustment factors have changed over 

time since the start of the IRF PPS: 

BILLING CODE 4120–01–P 

We have continued monitoring the 

adjustment factors using the same 

methodology described in the FY 2014 

IRF PPS final rule (78 FR 47869). That 

is, we have continued to calculate the 

facility-level adjustment factors using 

the following the steps: 

(Steps 1 and 2 are performed 

independently for each of three years of 

IRF claims data) 


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TA BLE8: Historic IRF Facility-level Ad_justment Fact ors 

FY FY FY FY 

2002- 2006- 2010- 2014-

2005 2009 2013 Current 

LIP 0.4838 0.6229 0.4613 0.3177 

Teachine: NIA 0.9012 0.6876 1.0163 

Rural 0.191 0.213 0.184 0.149 
20240 Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 

Step 1. Calculate the average cost per 

case for each IRF in the available IRF 

claims data. 

Step 2. Perform a logarithmic 

regression analysis on the average cost 

per case to compute the coefficients for 

the rural, LIP, and teaching status 

adjustments. This regression analysis 

incorporates an indicator variable to 

account for whether a facility is a 

freestanding IRF hospital or a unit of an 

acute care hospital (or a CAH). 

Step 3. Calculate a mean for each of 

the coefficients across the 3 years of 

data (using logarithms for the LIP and 

teaching status adjustment coefficients 

(because they are continuous variables), 

but not for the rural adjustment 

coefficient (because the rural variable is 

either zero (if not rural) or 1 (if rural)). 

To compute the LIP and teaching status 

adjustment factors, we convert these 

factors back out of the logarithmic form. 

Additional information on the 

regression analysis used to calculate the 

facility-level adjustment factors can be 

found on the CMS website at https://

www.cms.gov/Medicare/Medicare-Feefor-Service-Payment/Inpatient

RehabFacPPS/Research. We have 

continued to monitor changes in the 

facility-level adjustment factors for each 

FY since they were frozen in FY 2015 

at the FY 2014 levels. Table 9, contains 

the rural, LIP, and teaching status 

adjustment factors for each FY since 

they were frozen at their 2014 levels. 


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TABLE 9: IRF Facility Level Ad"ustment Factor Chan~es 

FY FY FY FY FY FY FY FY FY FY 

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 

LIP 0.3177 0.3809 0.4363 0.3880 0.4377 0.4572 0.4367 0.4382 0.4165 0.5092 

Teachin2 1.0163 1.9791 3.1820 3.0946 2.2472 2.1450 2.4413 3.0467 3.3506 3.7910 

Rural 0.149 0.141 0.130 0.124 0.107 0.099 0.090 0.096 0.107 0.100 
Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 20241 

Table 10. Shows the potential 

estimated impacts of updating the 

facility-level adjustments for FY 2023. 


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TABLE 10 n· t .b f . 

. IS rI u IOna I Eff ec t f so th e FY 2023 F ac1 Tt I[Y L eve I Ad. 1.1us t men tF ac t ors 

Number Rural LIP Teaching 

Facility Classification Number of IRFs of Cases Ad_justment Ad_justment Ad_justment 

(1) (2) (3) (4) (5) (6) 

Total 1,115 380,165 0.0 0.0 0.0 

Uroan unit 653 143 947 0.2 0.3 1.6 

Rural unit 133 17,660 -3.5 0.0 -2.5 

U roan hospital 317 213,377 0.2 -0.2 -0.9 

Rural hospital 12 5,181 -3.9 -0.7 -2.9 

U roan For-Profit 396 206 158 0.2 -0.3 -1.9 

Rural For-Profit 35 8048 -3.8 -0.4 -2.8 

Utban Non-Profit 489 132,251 0.2 0.3 1.9 

Rural Non-Profit 88 12,252 -3.4 -0.1 -2.4 

Urban Government 85 18 915 0.2 0.7 7.8 

Rural Government 22 2 541 -3.5 0.1 -2.6 

Urban 970 357,324 0.2 0.0 0.2 

Rural 145 22,841 -3.6 -0.2 -2.6 

Urban by re2ion 

Utban New England 29 13,576 0.2 0.1 0.7 

Utban Middle Atlantic 121 41,622 0.2 0.0 5.4 

Utban South Atlantic 158 75,753 0.2 -0.2 -1.2 

Utban East North Central 158 44,520 0.2 0.2 1.8 

Utban East South Central 55 25,224 0.2 -0.4 -1.7 

Utban West North Central 76 21,675 0.2 0.3 1.5 

Utban West South Central 197 83,013 0.2 -0.6 -2.1 

Utban Mountain 79 27,597 0.2 0.6 -0.7 

Utban Pacific 97 24.344 0.2 1.4 -0.4 

Rural by re2ion 

Rural New England 5 1,116 -3.5 -0.3 -2.5 

Rural Middle Atlantic 10 926 -3.4 -0.6 -2.4 

Rural South Atlantic 16 4 000 -3.9 -0.8 -2.9 

Rural East North Central 23 3,379 -3.5 -0.2 -2.5 

Rural East South Central 20 3,626 -3.7 0.6 -2.8 

Rural West North Central 20 2 579 -3.3 -0.4 -2.3 

Rural West South Central 42 6 514 -3.6 -0.1 -2.6 

Rural Mountain 6 379 -3.4 -0.3 -2.4 

Rural Pacific 3 322 -1.7 1.1 -0.8 

Teachin2 status 

Non-teaching 1,012 335,417 0.0 -0.2 -2.7 

Resident to ADC less than 10% 59 32,213 0.2 0.9 9.0 

Resident to ADC 10%-19% 34 11,327 0.2 0.7 23.8 

Resident to ADC greater than 

19% 10 1,208 0.2 1.6 102.1 
20242 Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 

12Office of the Inspector General. December 7, 

2021 Early Discharges From Inpatient 

Rehabilitation Facilities to Home Health Services 

[Report No. A–01–20–00501] https://oig.hhs.gov. 

BILLING CODE 4120–01–C 

Table 10 shows how we estimate that 

the application of the FY 2023 facilitylevel adjustment factors would affect 

particular groups if we were to 

implement updates to these factors for 

FY 2023. Table 10 categorizes IRFs by 

geographic location, including urban or 

rural location, and location for CMS’ 9 

Census divisions of the country. In 

addition, Table 10 divides IRFs into 

those that are separate rehabilitation 

hospitals (otherwise called freestanding 

hospitals in this section), those that are 

rehabilitation units of a hospital 

(otherwise called hospital units in this 

section), rural or urban facilities, 

ownership (otherwise called for-profit, 

non-profit, and government), by 

teaching status, and by disproportionate 

share patient percentage (DSH PP). 

Note that, because the facility-level 

adjustment factors are implemented in a 

budget-neutral manner, total estimated 

aggregate payments to IRFs would not 

be affected. However, these updates 

would affect the distribution of 

payments across providers. 

Typically, the facility-level 

adjustment factors have been updated 

on an intermittent basis to reflect 

changes in the costs of caring for 

patients. However, given the magnitude 

of the increases we are consistently 

seeing in the teaching status adjustment 

we do not believe that they are true 

reflections of the higher costs of 

teaching IRFs. In addition, we are 

concerned with the negative effects that 

the inordinately high teaching status 

adjustments would have on rural IRFs, 

given that the updates would be 

implemented in a budget neutral 

manner. 

Given the changes in the teaching 

status adjustment and the rural 

adjustment from their 2014 levels and 

the potential payment impacts 

associated with these adjustments, we 

are soliciting comments from 

stakeholders on the methodology used 

to determine the facility-level 

adjustment factors and suggestions for 

possible updates and refinements to this 

methodology. Additionally, we 

welcome ideas and suggestions as to 

what could be driving the changes 

observed in these adjustment factors 

from year-to-year. 

IX. Solicitation of Comments Regarding 

the IRF Transfer Payment Policy 

In the Medicare Program; Prospective 

Payment System for Inpatient 

Rehabilitation Facilities final rule that 

appeared in the August 7, 2001 Federal 

Register (66 FR 41353 through 41355), 

we finalized a transfer payment policy 

under § 412.624(f) to provide for 

payments that more accurately reflect 

facility resources used and services 

delivered. This reflected our belief that 

it is important to minimize the inherent 

incentives specifically associated with 

the early transfer of patients in a 

discharge-based payment system. 

Specifically, we were concerned that 

incentives might exist for IRFs to 

discharge patients prematurely, as well 

as to admit patients that may not be able 

to endure intense inpatient therapy 

services. Even if patients were 

transferred before receiving the typical, 

full course of inpatient rehabilitation, 

the IRF could still be paid the full CMG 

payment rate in the absence of a transfer 

payment policy. Length of stay has been 

shown to be a good proxy measure of 

costs. Thus, in general, reducing lengths 

of stay would be profitable under the 

IRF prospective payment system. To 

address these concerns, we therefore 

implemented a transfer payment policy, 

which took effect beginning January 1, 

2002, that, under certain circumstances, 

reduced the full CMG payment rate 

when a Medicare beneficiary is 

transferred. 

The IRF transfer payment policy 

applies to IRF stays that are less than 

the average length of stay for the 

applicable CMG and tier and are 

transferred directly to another 

institutional site, including another IRF, 

an inpatient hospital, a nursing home 

that accepts payment under Medicare 

and Medicaid, or a long-term care 

hospital. However, the IRF transfer 

payment policy currently does not 

apply to IRF stays that are less than the 

average length of stay for the applicable 

CMG and tier and are transferred to 

home health care. 

In the August 7, 2001 final rule (66 FR 

41353 through 41355), we stated that we 

did not propose to include early 

discharges to home health care as part 

of the transfer payment policy because 

there were analytical challenges as a 

result of the recent implementation of 

the new home health prospective 

payment system. However, to date, the 

analytical challenges would not present 

an issue as we feel the home health 

payment system is well established with 

an adequate supply of claims data. 

A recent Office of Inspector General 

(OIG) report, ‘‘Early Discharges From 

Inpatient Rehabilitation Facilities to 

Home Health Services’’ 12 recommends 

that CMS expand the IRF transfer 

payment policy to apply to early 

discharges to home health. The OIG 

recommends that the IRF PPS should 

update its transfer payment policy, 

similar to the IPPS transfer payment 

policy, to include home health. The OIG 

conducted an audit of calendar year 

2017 and 2018 Medicare claims data 

and determined that if CMS had 

expanded its IRF transfer payment 

policy to include early discharges to 

home health it could have realized a 

significant savings of approximately 

$993 million over the 2-year period to 

Medicare. 

Initially, home health was not added 

to the IRF transfer policy due to a lack 

of home health claims data under the 

newly-established prospective payment 

system that we could analyze to 

determine the impact of this policy 


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Number Rural LIP Teaching 

Facility Classification Number of IRFs of Cases Ad_justment Ad_justment Ad_justment 

Disproportionate share 

patient percentaee (DSH PP) 

DSHPP=0% 64 11,557 0.1 -1.8 -2.2 

DSHPP<5% 127 49.049 -0.1 -1.6 -2.7 

DSH PP 5%-10% 260 105 962 0.0 -1.0 -2.6 

DSH PP 10%-20% 388 140,935 0.0 0.1 0.3 

DSH PP irreater than 20% 276 72,662 0.1 2.1 4.2 
Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 20243 

change. However, given the findings 

from the recent OIG report mentioned 

above, we plan to analyze home health 

claims data to determine the 

appropriateness of including home 

health in the IRF transfer policy: 

• Beyond the existing Medicare 

claims data, under what circumstances, 

and for what types of patients (in terms 

of clinical, demographic, and 

geographic characteristics) do IRFs 

currently transfer patients to home 

health? 

• Should we consider a policy similar 

to the IPPS transfer payment policy (see 

§ 412.4(a), (b) and (c))—such as 

including as part of the IRF transfer 

payment policy a discharge from an IRF 

to home health under a written plan for 

the provision of home health services 

from a home health agency and those 

services to begin within 48 hours of 

referral, or within 48 hours of the 

patient’s return home (see 

§ 484.55(a)(1)), or on the provider’s start 

of care date? 

• What impact, if any, do 

stakeholders believe this proposed 

policy change could have on patient 

access to appropriate post-acute care 

services? 

While we are not proposing to include 

home health care as part of the IRF 

transfer payment policy at this time, we 

hope to use this information from 

stakeholders in conjunction with our 

future analysis for potential rulemaking. 

X. Inpatient Rehabilitation Facility 

(IRF) Quality Reporting Program (QRP) 

A. Background and Statutory Authority 

The Inpatient Rehabilitation Facility 

Quality Reporting Program (IRF QRP) is 

authorized by section 1886(j)(7) of the 

Act, and it applies to freestanding IRFs, 

as well as inpatient rehabilitation units 

of hospitals or Critical Access Hospitals 

(CAHs) paid by Medicare under the IRF 

PPS. Under the IRF QRP, the Secretary 

must reduce by 2 percentage points the 

annual increase factor for discharges 

occurring during a fiscal year for any 

IRF that does not submit data in 

accordance with the IRF QRP 

requirements established by the 

Secretary. For more information on the 

background and statutory authority for 

the IRF QRP, we refer readers to the FY 

2012 IRF PPS final rule (76 FR 47873 

through 47874), the CY 2013 Hospital 

Outpatient Prospective Payment 

System/Ambulatory Surgical Center 

(OPPS/ASC) Payment Systems and 

Quality Reporting Programs final rule 

(77 FR 68500 through 68503), the FY 

2014 IRF PPS final rule (78 FR 47902), 

the FY 2015 IRF PPS final rule (79 FR 

45908), the FY 2016 IRF PPS final rule 

(80 FR 47080 through 47083), the FY 

2017 IRF PPS final rule (81 FR 52080 

through 52081), the FY 2018 IRF PPS 

final rule (82 FR 36269 through 36270), 

the FY 2019 IRF PPS final rule (83 FR 

38555 through 38556), the FY 2020 IRF 

PPS final rule (84 FR 39054 through 

39165) and the FY 2022 IRF PPS final 

rule (86 FR 42384 through 42408). 

B. General Considerations Used for the 

Selection of Measures for the IRF QRP 

For a detailed discussion of the 

considerations we use for the selection 

of IRF QRP quality, resource use, or 

other measures, we refer readers to the 

FY 2016 IRF PPS final rule (80 FR 47083 

through 47084). 

1. Quality Measures Currently Adopted 

for the FY 2023 IRF QRP 

The IRF QRP currently has 18 

measures for the FY 2023 program year, 

which are set out in Table 11. 

BILLING CODE 4120–01–P 


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BILLING CODE 4120–01–C 

There are no proposals in this proposed 

rule for new measures for the IRF QRP. 

C. IRF QRP Quality Measure Concepts 

Under Consideration for Future Years: 

Request for Information (RFI) 

We are seeking input on the 

importance, relevance, and applicability 

of each of the concepts under 

consideration listed in Table 12 for 

future years in the IRF 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 

seek input on the value of a COVID–19 

Vaccination Coverage measure that 

would assess whether IRF patients were 

up to date on their COVID–19 vaccine. 


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Application of Functional 

Assessment 

Change in Mobility 

Discharge Mobility Score 

Change in Self-Care 

Discharge Self-Care Score 

DRR 

CAUTI 

CDI 

HCP Influenza Vaccine 

HCP COVID-19 Vaccine 

DTC 

PPR30day 

PPR Within Stay 

Application of Percent of Residents Experiencing One or More Falls with Major 

In· on Sta . 

Application of Percent of Long-Term Care Hospital (L TCH) Patients with an 

Admission and Discharge Functional Assessment and a Care Plan That Addresses 

Function F #2631 . 

IRF Functional Outcome Measure: Change in Mobility Score for Medical 

Rehabilitation Patients F #2634 . 

IRF Functional Outcome Measure: Discharge Mobility Score for Medical 

Rehabilitation Patients F #2636 . 

IRF Functional Outcome Measure: Change in Self-Care Score for Medical 

Rehabilitation Patients F #2633 . 

IRF Functional Outcome Measure: Discharge Self-Care Score for Medical 

Rehabilitation Patients (NQF #2635). 

Drug Regimen Review Conducted With Follow-Up for Identified Issues-Post 

Acute Care (PAC) Inpatient Rehabilitation Facility (IRF) Quality Reporting 

Pro m RP. 

National Healthcare Safety Network (NHSN) Catheter-Associated Urinary Tract 

Infection Outcome Measure F #0138 . 

National Healthcare Safety Network (NHSN) Facility-wide Inpatient Hospitalonset Clostridium di cile Infection CDI Outcome Measure F #1717 . 

Potentially Preventable 30-Day Post-Discharge Readmission Measure for IRF 

QRP. 

Potentially Preventable Within Stay Readmission Measure for IRFs. 

*In response to the public health emergency (PHE) for the Corona virus Disease 2019 (CO VID-19 ), CMS released an interim final 

rule (85 FR 27595 through 27596) which delayed the compliance date for the collection and reporting of the Transfer of Health 

Information measures. The compliance date for the collection and reporting of the Transfer of Health Information measures was 

revised to October 1, 2022 in the CY 2022 Home Health Prospective Payment System Rate Update final rule (86 FR 62381 through 

62386). 
Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 20245 

13Meaningful Measures 2.0: Moving from 

Measure Reduction to Modernization. Available at 

https://www.cms.gov/meaningful-measures-20- 

moving-measure-reduction-modernization. 

14Magil S.M., O’Leary, E., Janelle, S. J. et al. 

Changes in Prevalence of Health Care–Associated 

Infections in U.S. Hospitals. N Engl J Med 2018; 

379:1732–1744. Available at https://www.nejm.org/ 

doi/full/10.1056/NEJMoa1801550. Accessed 

February 3, 2022. 

15U.S. Department of Health and Human 

Services. Centers for Disease Control and 

Prevention. Antibiotic Resistance Threats in the 

United States, 2019. Available at https://

www.cdc.gov/drugresistance/pdf/threats-report/ 

2019-ar-threats-report-508.pdf. Accessed February 

3, 2022. 

16Centers for Medicare and Medicaid Services. 

(2021) Quality Measurement Action Plan. Available 

at https://www.cms.gov/files/document/2021-cmsquality-conference-cms-quality-measurementaction-plan-march-2021.pdf. 

17Clinical Practice Guidelines for Clostridium 

difficile Infection in Adults and Children: 2017 

Update by the Infectious Diseases Society of 

America (IDSA) and Society for Healthcare 

Epidemiology of America (SHEA) (idsociety.org). 

While we will not be responding to 

specific comments in response to this 

Request for Information in the FY 2023 

IRF PPS final rule, we intend to use this 

input to inform our future measure 

development efforts. 

D. Inclusion of the National Healthcare 

Safety Network (NHSN) HealthcareAssociated Clostridioides Difficile 

Infection Outcome Measure in the IRF 

QRP—Request for Information 

1. Background 

The IRF QRP is authorized by section 

1886(j)(7) of the Act and furthers our 

mission to improve the quality of health 

care for beneficiaries through 

measurement, transparency, and public 

reporting of data. The IRF 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 (dQM), reducing 

measurement burden, and promoting 

patient perspectives, while also focusing 

on modernization and innovation. The 

scope of the Meaningful Measures 

Framework has evolved to 

accommodate the changes in the health 

care environment, initially focusing on 

measure and burden reduction to 

include the promotion of innovation 

and modernization of all aspects of 

quality.13 As a result, we have identified 

a need to streamline our approach to 

data collection, calculation, and 

reporting to fully leverage clinical and 

patient-centered information for 

measurement, improvement, and 

learning. 

2. Potential Future Inclusion of an 

Electronic Health Record Driven Digital 

National Healthcare Safety Network 

(NHSN) Measure 

In the FY 2015 IRF PPS final rule (79 

FR 45913 through 45914), we finalized 

the National Healthcare Safety Network 

(NHSN) Facility-Wide Inpatient 

Hospital-onset Clostridium difficile 

Infection (CDI) Outcome Measure (NQF 

#1717) for inclusion in the IRF QRP. 

Clostridioides difficile (C. difficile) is 

responsible for a spectrum of CDIs, 

including uncomplicated diarrhea, 

pseudomembranous colitis, and toxic 

megacolon, which can, in some 

instances, lead to sepsis and even death. 

CDIs are one of the most common 

healthcare-associated infections (HAIs), 

as healthcare-associated CDIs affected 

0.54 percent of all hospitalizations in a 

2015 survey.14 In 2017, the CDC 

estimated there were 223,900 CDIs 

requiring hospitalizations in the United 

States with 12,800 resulting in deaths.15

We have recently identified the NHSN 

Healthcare-Associated Clostridioides 

Difficile Infection (HA–CDI) Outcome 

measure as a potential measure which 

utilizes Electronic Health Record (EHR)- 

derived data to help address hospitalbased adverse events, specifically 

hospital-onset infections. 

CDIs are currently reported to the 

CDC’s NHSN by various mechanisms, 

one of which is based on laboratoryidentified events collected in the NHSN. 

The IRF QRP measure, the NHSN 

Facility-Wide Inpatient Hospital CDI 

Outcome Measure does not utilize EHRderived data. Rather IRFs collect data 

and submit it on a monthly basis to the 

CDC’s NHSN using the CDC’s NHSN 

Multidrug-Resistant Organism & 

Clostridioides difficile Infection (MDRO/ 

CDI) Module. The CDC has now 

developed the NHSN HA–CDI Outcome 

measure that utilizes EHR-derived data. 

The newly-developed version of the 

measure, the NHSN HA–CDI, would 

improve on the original version of the 

measure in two ways. First, the new 

measure would require both 

microbiologic evidence of C. difficile in 

stool and evidence of antimicrobial 

treatment, whereas the original measure 

only requires C. difficile facility-wide 

Laboratory-Identified (Lab-ID) event 

reporting. Second, consistent with the 

Meaningful Measures Framework, we 

specifically believe it would reduce 

reporting and regulatory burden on 

providers and accelerate the move to 

fully digital measures.16 We discuss 

each of these improvements below. 

CDI testing practices have continued 

to evolve, with recent guidelines from 

the Infectious Disease Society of 

America recommending a multi-step 

testing algorithm to better distinguish 

between C. difficile colonization and 

active infection.17 However, the growing 

number of testing algorithms in use, 

each with different performance 

characteristics, poses a challenge for 

CDI surveillance. This new CDI measure 

defines CDI using both a positive 

microbiological test for C. difficile and 

evidence of treatment, increasing the 

specificity and sensitivity of the 

measure. Adding a requirement of CDI 

treatment to a CDI surveillance measure 

would increase the clinical validity of 

the measure, since a record of CDI 

treatment serves as a proxy for C. 

difficile test results that were 

interpreted as true infections by the 

clinician. 

We believe there are important 

reasons for IRFs to adopt and utilize 

EHRs, although we understand that for 

IRFs who do not yet use EHRs, there 

will be initial implementation and 

training costs. EHRs facilitate moving to 

fully digital measures which we believe 

reduces reporting and regulatory burden 

on providers. Additionally, both 


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TABLE 12: ts Under Consideration for the IRF QRP 

PAC- COVID-19 Vaccination Coverage among 

Patients 
20246 Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 

18 King J., Patel, V., Jamoom, E., & Furukawa, M. 

(2012, August). Clinical Benefits of Electronic 

Health Record Use: National Findings. Health Serv 

Res. 2014 Feb; 49(1 pt 2): 392–404. Available at 

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

PMC3925409/. 

19Hoover, R. Benefits of using an electronic 

health record. Nursing Critical Care: January 2017— 

Volume 12—Issue 1—p 9–10. Available at https:// 

journals.lww.com/nursingcriticalcare/fulltext/2017/ 

01000/benefits_of_using_an_electronic_health_

record.3.aspx. 

20Escobar, G., Turk B., Ragins A., Ha J., et al. 

Piloting electronic medical record-based early 

detection of inpatient deterioration in community 

hospitals. J Hosp Med. 2016 Nov; 11 Suppl 1(Suppl 

1):S18–S24. Available at https://www.ncbi.

nlm.nih.gov/pmc/articles/PMC5510649/. 

21Uslu A., Stausberg J. Value of the Electronic 

Medical Record for Hospital Care: Update from the 

literature. JMed internet Res 2021;23(12):e26323. 

Available at https://www.jmir.org/2021/12/e26323. 

22U.S. bureau of Labor Statistics. Occupational 

Employment and Wage Statistics. May 2020 

National Occupational Employment and Wage 

Estimates. United States. Available at https://

www.bls.gov/oes/current/oes_nat.htm#43-0000. 

Accessed February 3, 2022. 

23Estimated using 10 minutes of clinical nursing 

time (Occupation Code 29–1141) and 15 minutes of 

clerical time (Occupation Code 43–6013) necessary 

to enter the data into the NHSN. 

24More information on how ARM and SIR 

compare can be found at https://www.cdc.gov/nhsn/ 

ps-analysis-resources/arm/index.html. 

25Centers 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 February 7, 2022. 

26 2021–2022 MAP Final Recommendations. 

Available at https://www.qualityforum.org/map/. 

Accessed February 3, 2021. 

surveys 18 19 and studies 20 21 have 

demonstrated that when healthcare 

providers have access to complete and 

accurate information, patients receive 

better medical care, including timely 

identification and treatment of 

infections. We believe the utilization of 

EHRs can improve the ability to 

diagnose diseases and reduce (even 

prevent) medical errors, both of which 

improve patient outcomes. 

Additionally, the use of a fully digital 

measure using a Measure Calculation 

Tool (MCT) that pulls data directly from 

the EHR via a standardized FHIR 

interface would eliminate multiple steps 

for the provider, including creating or 

updating monthly reporting plans, and 

completing the data fields required for 

both numerator and denominator every 

month, even when no events were 

identified. Finally, the locally installed 

MCT would be responsible for 

extracting data, calculating the measure 

and submitting the data and would 

eliminate the need for the IRF to 

manually enter the data into the NHSN 

web-based application or via file 

imports. For example, if each IRF 

executed approximately one C. difficile 

event per month (12 events per IRF 

annually), then using 2020 Bureau of 

Labor Statistics (BLS) data,22 we 

estimate a potential cost savings of 

approximately 3 hours per IRF per year 

and a total of $191.38 per IRF per year 

if a digital version of the measure 

replaced the NHSN-based measure.23

3. Overview of the NHSN HealthcareAssociated Clostridioides difficile 

Infection Outcome Measure 

The EHR driven digital version of the 

NHSN HA–CDI Outcome measure 

would track the development of new 

CDI among patients already admitted to 

IRFs, using algorithmic determinations 

from data sources widely available in 

EHRs. 

The numerator would include those 

patient records with a qualifying C. 

difficile-positive assay on an inpatient 

encounter on day 4 or later of an IRF 

admission and with no previously 

positive event in ≤14 days before the 

IRF encounter, and new qualifying 

antimicrobial therapy for C. difficile 

started within the appropriate window 

period of stool specimen collection. The 

denominator would be the number of 

patients admitted to IRFs. 

The NHSN HA–CDI Outcome measure 

would use the Standardized Infection 

Ratio (SIR) of hospital-onset CDIs among 

patients to compare within facility 

types. SIR is a primary summary 

statistic used by the NHSN to track 

HAIs. The Adjusted Ranking Metric 

(ARM) is a new statistic currently 

available for acute care hospitals that 

accounts for differences in the volume 

of exposure (specifically, in the 

denominator) between facilities. ARM 

provides complementary information to 

SIR and was developed for use in acutecare hospitals, but is also intended for 

use in post-acute care facilities.24

4. Measure Application Partnership 

(MAP) Review 

The NHSN HA–CDI Outcome measure 

(MUC2021–098) was included in the 

publicly available ‘‘List of Measures 

Under Consideration for December 1, 

2021’’ (MUC List),25 a list of measures 

under consideration for use in various 

Medicare programs, including the IRF 

QRP. This allows multi-stakeholder 

groups to provide recommendations to 

the Secretary on the measures included 

on the list. 

The NHSN HA–CDI Outcome measure 

was included under the IRF QRP 

Program on the MUC List. The National 

Quality Forum (NQF)-convened MAP 

Post-Acute Care—Long Term Care 

(PAC–LTC) Workgroup met on January 

19, 2022 and provided input on the 

proposed measure. The MAP offered 

conditional support of the NHSN HA– 

CDI Outcome measure for rulemaking 

contingent upon NQF endorsement, 

noting that the measure has the 

potential to mitigate unintended 

consequences from the current 

measure’s design, which counts a case 

based on a positive test only, which may 

have led to a historical under-counting 

of observed HA–CDIs. The MAP 

recognized that the measure is 

consistent with the program’s priority to 

measure HAIs and the Patient Safety 

Meaningful Measures 2.0 area.26 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. 

5. Data Sources 

The data source for the NHSN HA– 

CDI Outcome measure would be the 

IRFs’ EHR. The primary sources of data 

for determining numerator events would 

include microbiology data (C. difficile 

infection test), medication 

administration data (C. difficile 

infection antimicrobial treatment), and 

patient encounter, demographic, and 

location information. 

To facilitate rapid, automated, and 

secure data exchange, the CDC’s NHSN 

is planning to enable and promote 

reporting of this measure using Health 

Level 7 (HL7) Fast Healthcare 

Interoperability Resources (FHIR). 

However, as HL7 FHIR capabilities are 

evolving and not uniform across 

healthcare systems, CDC is also 

planning on enabling reporting using 

the existing HL7 Clinical Document 

Architecture (CDA), and potentially 

other formats as well in order to provide 

all facilities with an option for 

reporting. Furthermore, this measure 

would not immediately replace the 

current NHSN CDI measure. NHSN 

would continue to host and support the 

current CDI measure until sufficient 

experience is achieved with the new 

measure to phase out the current CDI 

measure in each applicable setting. 

6. Solicitation of Public Comment 

In this proposed rule, we are 

requesting stakeholder input on the 

potential electronic submission of 

quality data from IRFs via their EHRs 

under the IRF QRP. We specifically seek 

comment on the future inclusion of the 

NHSN Healthcare-Associated 

Clostridioides difficile Infection 

Outcome measure (HA–CDI) 


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27 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. 

28Lindenauer 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. 

29Trivedi 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. 

30Polyakova, 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. 

31Rural 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. 

32U.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. 

33Centers 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. 

34Poteat 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. 

35Milkie 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. 

36Centers for Medicare and Medicaid Services. 

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

37CMS Quality Strategy. 2016. Available at 

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

geninfo/downloads/cms-quality-strategy.pdf. 

Accessed February 3, 2022. 

(MUC2021–098) as a digital quality 

measure in the IRF QRP. 

Specifically, we seek comment on the 

following: 

• Would you support utilizing IRF 

EHRs as the mechanism of data 

collection and submission for IRF QRP 

measures? 

• Would your EHR support exposing 

data via HL7 FHIR to a locally installed 

MCT? For IRFs using certified health IT 

systems, how can existing certification 

criteria under the Office of the National 

Coordinator (ONC) Health Information 

Technology (IT) Certification Program 

support reporting of this data? What 

updates, if any, to the Certification 

Program would be needed to better 

support capture and submission of this 

data? 

• Is a transition period between the 

current method of data submission and 

an electronic submission method 

necessary? If so, how long of a transition 

would be necessary and what specific 

factors are relevant in determining the 

length of any transition? 

• Would vendors, including those 

that service IRFs, be interested in or 

willing to participate in pilots or 

voluntary electronic submission of 

quality data? 

• Do IRFs anticipate challenges, other 

than the adoption of EHR to adopting 

the HA–CDI, and if so, what are 

potential solutions for those challenges? 

While we will not be responding to 

specific comments submitted in 

response to this RFI in the FY 2023 IRF 

PPS final rule, we will actively consider 

all input as we develop future 

regulatory proposals. 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. 

E. Overarching Principles for Measuring 

Equity and Healthcare Quality 

Disparities Across CMS Quality 

Programs—Request for Information 

Significant and persistent disparities 

in healthcare outcomes exist in the 

United States. Belonging to an 

underserved community is often 

associated with worse health 

outcomes.27 28 29 30 31 32 33 34 35 With this 

in mind, CMS aims 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. CMS is 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.36

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 

disparities.37 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 X.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 approaches that 

could be used in the IRF QRP to assess 

drivers of healthcare quality disparities 

in the IRF QRP. Additionally, this 

section discusses measures of health 

equity that could be adapted for use in 

the IRF 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 IRF 

QRP. 

1. Cross-Setting Framework To Assess 

Healthcare Quality Disparities 

CMS has identified five key 

considerations that we could apply 

consistently across CMS 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 Measures 

Stratification Across CMS Quality 

Programs 

By quantifying healthcare disparities 

through 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 

providers 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 


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38Centers for Medicare & Medicaid Services 

(CMS), HHS. Disparity Methods Confidential 

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

inpatient/measures/disparity-methods. Accessed 

February 3, 2022. 

39 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. 

40Lindenauer 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. 

41Trivedi 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. 

42Polyakova, 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. 

43Rural 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. 

44HHS 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. 

45Poteat 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. medRxiv [Preprint]. 

2020.07.21.20159327. doi: 10.1101/ 

2020.07.21.20159327. PMID: 32743608; PMCID: 

PMC7386532. 

46Milkie 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. 

disparities in the acute care setting, 

including two complementary 

approaches that 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. These approaches can be used by 

a healthcare provider to compare their 

own measure performance on a 

particular subgroup of patients against 

subgroup-specific state and national 

benchmarks. Alone, each approach may 

provide an incomplete picture of 

disparities in care for a particular 

measure, but when reported together 

with overall quality performance, these 

approaches may provide detailed 

information about where differences in 

care may exist or where additional 

scrutiny may be appropriate. For 

example, the between-provider disparity 

method may indicate that an IRF 

underperformed (when compared to 

other facilities on average) for patients 

with a given social risk factor, which 

would signal the need to improve care 

for this population. However, if the IRF 

also underperformed for patients 

without that social risk factor, the 

measured difference, or disparity in 

care, (the ‘‘within-hospital’’ disparity, as 

described above) 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.38

CMS is interested in whether similar 

approaches to the two discussed in the 

previous paragraph could be used to 

produce confidential stratified measure 

results for selected IRF QRP measures, 

as appropriate and feasible. However, 

final decisions regarding disparity 

reporting will be made at the programlevel, as CMS intends 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 the next generation 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 social risk factors 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 social risk 

factor 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. 

(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 and 

Demographic Data Selection and Use 

Social risk factors are the wide array 

of non-clinical drivers of health known 

to negatively impact patient outcomes. 

These include factors such as 

socioeconomic status, housing 

availability, and nutrition (among 

others), often inequitably affecting 

historically marginalized communities 

on the basis of race and ethnicity, 

rurality, sexual orientation and gender 

identity, religion, and 

disability.39 40 41 42 43 44 45 46

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 social 

risk factors 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 


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

Lin H. (2020). Validity of race and ethnicity codes 

in Medicare administrative data compared with 

gold-standard self-reported race collected during 

routine home health care visits. Med Care, 58(1):e1– 

e8. doi: 10.1097/MLR.0000000000001216. PMID: 

31688554; PMCID: PMC6904433. 48Office 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. 

49Bonito A., Bann C., Eicheldinger C., Carpenter 

L. Creation of New Race-Ethnicity Codes 

andSocioeconomic Status (SES) Indicators for 

Medicare Beneficiaries. Final Report, Sub-Task 2. 

(Prepared by RTI International for the Centers for 

Medicare and 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/finalreports/medicareindicators/medicareindicators1.

html. Accessed February 7, 2022. 

50Flanagan, B.E., Gregory, E.W., Hallisey, E.J., 

Heitgerd, J.L., Lewis, B. (2011). A social 

vulnerability index for disaster management. 

Journal of Homeland Security and Emergency 

Management, 8(1). Available at https://

www.atsdr.cdc.gov/placeandhealth/svi/img/pdf/ 

Flanagan_2011_SVIforDisasterManagement508.pdf. Accessed February 3, 2022. 51Center 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. 

52Haas A., Elliott M.N., Dembosky J.W., Adams 

J.L., Wilson-Frederick S.M., Mallett J.S., Gaillot S, 

Haffer S.C., Haviland A.M. (2019). Imputation of 

race/ethnicity to enable measurement of HEDIS 

performance by race/ethnicity. Health Serv Res, 

54(1):13–23. doi: 10.1111/1475–6773.13099. Epub 

2018 Dec 3. PMID: 30506674; PMCID: PMC6338295. 

Imputation of race/ethnicity to enable measurement 

of HEDIS performance by race/ethnicity. Available 

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

PMC6338295/pdf/HESR-54-13.pdf. Accessed 

February 3, 2022. 

patient-level data is 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 social risk factors.47 While data 

sources for many social risk factors and 

demographic variables are still 

developing among several CMS settings, 

demographic data elements collected 

through assessments already exist in 

IRFs. Beginning October 1, 2022, IRFs 

(86 FR 62386) will also begin collecting 

additional standardized patient data 

elements about race, ethnicity, preferred 

language, transportation, health literacy, 

and social isolation. 

• 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.48 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 the neighborhood or context 

that a patient resides in. Several 

indexes, such as Agency for Healthcare 

Research and Quality (AHRQ) 

Socioeconomic Status (SES) Index,49

Centers for Disease Control and 

Prevention/Agency for Toxic Substances 

and Disease Registry (CDC/ATSDR) 

Social Vulnerability Index (SVI),50 and 

Health Resources and Services 

Administration (HRSA) Area 

Deprivation Index (ADI),51 provide 

multifaceted contextual information 

about an area and may be considered as 

an efficient way to stratify measures that 

include many social risk factors. 

• 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 patient 

race and ethnicity.52

d. Identifying Meaningful Performance 

Differences 

While we aim to use standardized 

approaches where possible, identifying 

differences in performance on stratified 

results will be made 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 below: 

• 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 healthcare 

providers. 

• Healthcare providers could be 

categorized into groups based on their 

performance using defined thresholds, 

such as fixed intervals of results of 

disparity 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 may not 

be appropriate for all programs and care 

settings, and some programs may only 

report disparity results. 

e. Guiding Principles for Reporting 

Disparity Measures 

Reporting of the results discussed 

above 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 CMS. 

One important consideration is to assess 

differential impact on IRFs, 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. 


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53Rahimi 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. Retrieved 2/24/2022. 

2. Approaches To Assessing Drivers of 

Healthcare Quality Disparities and 

Developing Measures of Healthcare 

Equity in the IRF QRP 

This section presents information on 

two approaches for the IRF QRP. The 

first section presents information about 

a method that could be used to assist 

IRFs in identifying potential drivers of 

healthcare quality disparities. The 

second section describes measures of 

healthcare equity that might be 

appropriate for inclusion in the IRF 

QRP. 

a. Performance Disparity Decomposition 

In response to the FY 2022 IRF PPS 

proposed rule’s RFI (86 FR 19110 

through 19112), ‘‘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 IRFs 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 IRF has limited 

information or resources to determine 

the extent to which a patient’s social 

determinants of health (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 IRF which of 

these factors are the primary drivers. 

Consequently, CMS 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.53 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, CMS 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 

healthcare outcomes. Additionally, 

provider-specific decomposition results 

could be shared through confidential 

results so that IRFs 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 IRFs 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 IRF. 

Figures 1 through 3 depict an example 

(using hypothetical data) of how a 

disparity in a measure of Medicare 

Spending Per Beneficiary (MSPB) 

between dual eligible beneficiaries (that 

is, those enrolled in Medicare and 

Medicaid) and non-dual 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 IRF, the healthcare provider could 

mitigate their effects. Additionally, high 

volume ED use is used as a potential 

mediating factor that could be difficult 

for IRFs to determine on their own, as 

it would require having longitudinal 

data for patients across multiple 

facilities. 

In Figure 1, the overall Medicare 

spending disparity is $1,000: Spending, 

on average, is $5,000 per non-dual 

beneficiary and $6,000 per dual 

beneficiary. We can also see from Figure 

2 that in this IRF, 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 

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-duals 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, an IRF 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 IRF 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 


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outcomes (that is, facilities may choose to focus efforts on the largest drivers of 

a disparity). 

BILLING CODE 4120–01–P 


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Figure 1 

MSPB 

$6,000 

$5,500 

$5,000 

$4,500 

$4,000 

$3,500 

$3,000 

$2,500 

$2,000 

$1,500 

$1,000 

$500 

$0 

Non-Dual Dual 
20252 Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 

BILLING CODE 4120–01–C 

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 

IRF QRP, CMS 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; 

and 

• Measures should adhere to high 

scientific acceptability standards. 

CMS has developed measures 

assessing health equity, or designed to 

promote health equity, in other settings 

outside of the IRF. As a result, there 

may be measures that could be adapted 

for use in the IRF 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. 


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Figure 2 

Non-Dual Dual 

Figure3 

for 

$5,000 
Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 20253 

54Agniel D., Martino S.C., Burkhart Q., 

Hambarsoomian K., Orr N., Beckett M.K., James C., 

Scholle S.H., WilsonFrederick S., Ng J., Elliott M.N. 

(2021). Incentivizing excellent care to at-risk groups 

with a health equity summary score. J Gen Intern 

Med, 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. 

55 2021 Quality Conference. Health Equity as a 

‘‘New Normal’’: CMS Efforts to Address the Causes 

of Health Disparities. Available at https://

s3.amazonaws.com/bizzabo.file.upload/ 

83kO1DYXTs6mKHjVtuk8_1%20- 

%20Session%2023%20Health

%20Equity%20New%20Normal%20FINAL_

508.pdf. Accessed March 2, 2022. 

56Centers for Medicare & Medicaid Services, FY 

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

May 10, 2021. 

57Centers 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 2–3, 

2021. 

58Centers 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 3/1/2022. 

59Quality 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 (e.g., 

standard operating procedures) or human capital 

(e.g., education and training). Available at https:// 

www.cms.gov/Medicare/Quality-Initiatives-PatientAssessment-Instruments/MMS/Quality-Measureand-Quality-Improvement-. Accessed 3/1/2022. 

60Agency 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. 

(1) Health Equity Summary Score 

The HESS measure was developed by 

the CMS OMH 54 55 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 

social risk factors (SRFs), as well as to 

discourage the non-treatment of patients 

who are potentially high-risk, in the 

context of value-based purchasing. 

Additionally, a version of the HESS is 

under consideration for the Hospital 

Inpatient Quality Reporting (HIQR) 

program.56 The HESS 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.57

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 

HEDIS data and seven MA Consumer 

Assessment of Healthcare Providers and 

Systems (CAHPS) patient experience 

measures. A provider’s overall HESS 

score is calculated once using only 

CAHPS-based measures and once using 

only HEDIS-based measures, due to 

incompatibility between the two data 

sources. The HESS uses a composite of 

these measures to form a cross-sectional 

score, a nationally benchmarked 

improvement score, and a within-plan 

improvement score, one for each SRF. 

These scores are combined to produce 

an SRF-specific blended score, which is 

then combined with the blended score 

for another SRF to produce the overall 

HESS. 

(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 (MUC2021–106) was included 

on the 2021 CMS List of Measures 

Under Consideration (MUC List) 58 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 

they attest to the corresponding 

statement (for a total of 5 points). At a 

high level, the five domains cover the 

following areas: (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; 59 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 List 

of MUC for December 2021 at https://

www.cms.gov/files/document/measuresunder-consideration-list-2021- 

report.pdf. An IRF could receive a point 

for each domain where data are 

submitted through a CMS portal to 

reflect actions taken by the IRF for each 

corresponding domain (for a point 

total). 

CMS believes this type of 

organizational commitment structural 

measure may complement the health 

disparities approach described in 

previous sections, and support IRFs 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.’’ 60 We acknowledge that 

collection of this structural measure 

may impose administrative and/or 

reporting requirements for IRFs. 

We are interested in obtaining 

feedback from stakeholders on 

conceptual and measurement priorities 

for the IRF QRP to better illuminate 

organizational commitment to health 

equity. 


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61National Quality Forum. MAP Coordination 

Strategy for Post-Acute Care and Long-Term Care 

Performance Measurement. February 2012. 

Available at https://www.qualityforum.org/ 

Publications/2012/02/MAP_Coordination_Strategy_

for_Post-Acute_Care_and_Long-Term_Care_

Performance_Measurement.aspx. Accessed January 

31, 2022. 62https://www.cms.gov/Medicare/QualityInitiatives-Patient-Assessment-Instruments/MMS/ 

MMS-Blueprint. Accessed January 31, 2022. 

3. Solicitation of Public Comment 

The goal of this request for 

information is to describe key principles 

and approaches that we will consider 

when advancing the use of quality 

measure development and stratification 

to address healthcare 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 or stratification 

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 betweenprovider disparity methods in IRFs 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 Social Risk Factor and 

Demographic Data Selection and Use 

++ Principles to be considered for the 

selection of social risk factors 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 social risk 

factors, 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 

IRFs, 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 an IRF’s 

commitment to health equity, the 

specific domains that should be 

captured, and options for reporting 

this 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 IRF 

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. 

F. Proposals Relating to the Form, 

Manner, and Timing of Data 

Submission Under the IRF QRP 

1. Background 

We refer readers to the regulatory text 

at § 412.634(b) for information regarding 

the current policies for reporting IRF 

QRP data. 

2. Proposal To Require Quality Data 

Reporting on All IRF Patients Beginning 

With the FY 2025 IRF QRP 

a. Background 

We have received public input for the 

past 10 years on the need to standardize 

measurement data collection across all 

payers in the PAC settings. For example, 

as part of their recommendations on 

Coordination Strategy for Post-Acute 

Care and Long-term Care Performance 

Measurement,61 the National Quality 

Forum (NQF)-convened Measures 

Application Partnership (MAP) defined 

priorities and core measure concepts for 

PAC, including IRFs, in order to 

improve care coordination for patients. 

The MAP concluded that standardized 

measurement data collection is needed 

to support the flow of information and 

data among PAC providers and 

recommended CMS collect data across 

all payers. Since the implementation of 

the Improving Medicare Post-Acute Care 

Transformation Act of 2014 (IMPACT 

Act) and the development of the 

statutorily required quality measures, 

we have also received public input 

suggesting that the quality measures 

used in the IRF QRP should be 

calculated using data collected from all 

IRF patients, regardless of the patients’ 

payer. This input has been provided to 

us through different mechanisms, 

including comments requested about 

quality measure development. 

Specifically, in response to the call for 

public comment on quality measures to 

satisfy the IMPACT Act domain of 

Transfer of Health Information and Care 

Preferences When an Individual 

Transitions,62 the majority of comments 

expressed concern over the nonstandardized populations across the 

PAC setting and urged CMS to 

standardize the patient populations. 

One commenter stated having an all 

payer policy in place in some, but not 

all PAC settings, limits the ability of 

providers and consumers to interpret 

the information. In the FY 2018 IRF PPS 

proposed rule, (82 FR 20740), we sought 

input on expanding the quality 

measures to include all patients 

regardless of payer status. In response to 

the Request for Information (RFI), 

several commenters supported 

expanding the IRF QRP to include all 

patients regardless of payer. The 

Medicare Payment Advisory Committee 

(MedPAC) was supportive of the effort 

to ensure quality care for all patients, 

but sensitive to the issue of additional 

burden, while another commenter 

questioned whether the use of 

additional data would outweigh the 

burden of additional reporting. Other 

commenters were also supportive, 

noting that it would not be overly 

burdensome since most of their 


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63 In the FY 2010 IRF PPS final rule (74 FR 39798 

through 39800), CMS revised the regulation text in 

§§ 412.604, 412.606, 412.610, 412.614, and 412.618 

to require that all IRFs submit IRF–PAI data on all 

of their Medicare Part C patients. 

64https://www.cms.gov/Medicare/QualityInitiatives-Patient-Assessment-Instruments/ 

QualityInitiativesGenInfo/Downloads/CMS-QualityStrategy.pdf. 

65Report 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. 

organizations’ members already 

complete the IRF–PAI on all patients, 

regardless of payer status. One 

commenter supported the idea since 

collecting information on only a subset 

of patients could be interpreted as 

having provided different levels of care 

based on the payer. 

In the FY 2020 IRF PPS Proposed 

Rule (84 FR 17326 to 17327), CMS 

proposed to expand IRF quality data 

reporting on all patients regardless of 

payer for purposes of the IRF QRP. In 

the FY 2020 IRF PPS final rule (84 FR 

39161 through 39163), we decided not 

to finalize the proposal at the time, but 

rather use the comments to help inform 

a future all payer proposal. 

b. Support for Expanding Quality 

Reporting Data on All IRF Patients 

Currently, IRF–PAI assessment data 

are collected on patients admitted under 

the Medicare Part A fee-for-service 

(FFS) and Medicare Part C benefits.63

The concept of requiring quality data 

reporting on all patients regardless of 

payer is not new; as part of the LongTerm Care Hospital (LTCH) quality 

reporting program, CMS currently 

collects quality data on all patients 

regardless of payer. CMS also collects 

quality data on all Hospice patients for 

the Hospice Quality Reporting Program 

(HQRP) regardless of payer. Eligible 

clinicians participating in the Meritbased Incentive Payment System (MIPS) 

who submit quality measure data on 

Qualified Clinical Qualified Data 

Registry (QCDR) measures, MIPS 

clinical quality measures (CQMs) or 

electronic clinical quality measures 

(eCQMs) must submit such data on a 

specified percentage of patients 

regardless of payer. Collecting such 

quality data on all patients in the IRF 

setting would provide the most robust 

and accurate representation of quality in 

the IRFs since CMS does not have 

access to other payer claims. 

Additionally, the data would promote 

higher quality and more efficient health 

care for Medicare beneficiaries and all 

patients through the exchange of 

information and longitudinal analysis of 

that data. 

We believe that data reporting on 

standardized patient assessment data 

elements using the IRF–PAI should 

include all IRF patients for the same 

reasons we believe that collecting data 

on Medicare beneficiaries for the IRF 

QRP’s quality measures is important: To 

achieve equity in healthcare outcomes 

for our beneficiaries by supporting 

providers in quality improvement 

activities, enabling them to make more 

informed decisions, and promoting 

provider accountability for healthcare 

disparities.64 65 We believe that we have 

authority to collect all payer data for the 

IRF QRP under section 1886(j)(7) of the 

Act. We believe it is necessary to obtain 

admission and discharge assessment 

information on all patients admitted to 

IRFs in order to obtain full and 

complete data regarding the quality of 

care provided by the IRF to the 

Medicare patients receiving care in that 

facility. We note, however that this data 

would not be used by CMS for purposes 

of updating the IRF PPS payment rates 

annually. In addition, we note that 

section 1886(j)(7) of the Act does not 

limit the Secretary to collecting data 

only on individuals with Medicare, and 

therefore this proposal is not 

inconsistent with CMS’ statutory 

obligations. 

We take the appropriate access to care 

in IRFs very seriously, and routinely 

monitor the QRP measures’ 

performance, including performance 

gaps across IRFs. We intend to monitor 

closely whether any proposed change to 

the IRF QRP has unintended 

consequences on access to care for high 

risk patients. Should we find any 

unintended consequences, we will take 

appropriate steps to address these issues 

in future rulemaking. Expanding the 

reporting of quality measures to include 

all patients, regardless of payer, would 

ensure that the IRF QRP makes publicly 

available information regarding the 

quality of services furnished to the IRF 

population as a whole, rather than 

limiting it to only those patients with 

Medicare fee-for service or Medicare 

Advantage benefits. 

We also take the privacy and security 

of protected health information (PHI) 

very seriously. Our systems conform to 

all applicable Federal laws and 

regulations as well as Federal 

government, HHS, and CMS policies 

and standards as they relate to 

information security and data privacy. 

The system limits data access to 

authorized users and monitors such 

users to ensure against unauthorized 

data access or disclosures. 

While we appreciate that collecting 

quality data on all patients regardless of 

payer may create additional burden, we 

also note that this burden may be 

partially offset by eliminating the effort 

to separate out Medicare beneficiaries 

from other patients, which is also 

burdensome. We also acknowledge the 

concerns raised by some stakeholders in 

the past with respect to the 

administrative challenges of 

implementing all payer data collection 

and the need to account for the burden 

related to this proposal. In section XI.B. 

of this proposed rule, we have provided 

an estimate of additional burden related 

to this proposal. 

c. Proposal To Require Quality Data 

Reporting on All IRF Patients 

In order to facilitate and ensure that 

high quality care is delivered to all 

patients, including Medicare 

beneficiaries, in the IRF setting, we are 

proposing to require that the IRF–PAI 

assessment be collected on each patient 

receiving care in an IRF, regardless of 

payer, beginning with the FY 2025 IRF 

QRP. If finalized as proposed, IRFs 

would be required to report these data 

with respect to admission and discharge 

for all patients, regardless of payer, 

discharged between October 1, 2023 and 

December 31, 2023. This data would be 

used (in addition to the data collected 

January 1, 2023 through September 30, 

2023) to calculate an IRF’s data 

completion threshold for the FY 2025 

IRF QRP. 

If finalized as proposed, we would 

revise the IRF–PAI in order for IRFs to 

submit data pursuant to the finalized 

policy. A new item would replace the 

current item identifying payment source 

on the IRF–PAI admission assessment to 

collect additional payer(s) information. 

The collection of this item would align 

with the LTCH setting. A draft IRF PAI 

containing this new item will be 

available at https://www.cms.gov/ 

Medicare/Quality-Initiatives-PatientAssessment-Instruments/IRF-QualityReporting. We will notify stakeholders 

when the draft IRF PAI is available. 

We invite public comments on this 

proposal. 

3. Proposed Revisions to the Regulation 

Text To Require IRFs To Submit Patient 

Assessments on All Patients Beginning 

With the FY 2025 IRF QRP 

As discussed in section X.F.2 of this 

proposed rule, we are proposing to 

require that the IRF–PAI assessment be 

collected on each patient receiving care 

in an IRF, regardless of payer. Therefore, 

we also propose, subject to the 

aforementioned proposal becoming 

final, to revise the regulation text in 


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66https://www.bls.gov/oes/current/oes_nat.htm. 

§§ 412.604, 412.606, 412.610, 412.614, 

and 412.618 so that the requirements 

that IRFs must currently satisfy with 

respect to collection and submission of 

IRF–PAI data for Medicare Part A and 

Medicare Part C patients would also 

apply to data on all other IRF patients, 

regardless of payer. 

In addition, we note that CMS’ 

regulations at § 412.610(f) currently 

require IRFs to maintain all PAIs 

completed on Medicare Part A fee-forservice patients within the previous 5 

years and Medicare Part C (Medicare 

Advantage) patients within the previous 

10 years either in a paper format in the 

patient’s clinical record or in an 

electronic computer file format that the 

IRF can easily obtain and produce upon 

request to CMS or its contractors. 

Subject to the aforementioned all-payer 

proposal becoming final, we are 

therefore also proposing to revise the 

regulation text at § 410.610(f) to require 

that IRFs maintain PAIs completed on 

patients receiving care under all other 

payer sources (that is, other than 

Medicare Part A and Medicare Part C) 

for 5 years. We are proposing a 5-year 

period for the same reasons we 

proposed a 5-year requirement for 

Medicare Part A patients in the original 

Medicare Program; Prospective Payment 

System for Inpatient Rehabilitation 

Facilities final rule that appeared in the 

August 7, 2001 Federal Register (66 FR 

41329). Specifically, the assessments 

may be needed as part of a retrospective 

review conduced at the IRF for various 

purposes, including the fact that the 

completed patient assessments could be 

beneficial to other entities that 

appropriately have access to these 

records (for example, a State or Federal 

agency conducting an investigation due 

to a complaint of patient abuse). 

The proposed revisions are outlined 

in §§ 412.604, 412.606, 412.610, 

412.614, and 412.618 in the regulation 

text of this proposed rule. We invite 

public comments on this proposal. 

4. Proposed Revisions to § 412.614(d)(2) 

To Correct an Error to the Regulatory 

Text 

In accordance with the 

Administrative Procedure Act, 5 U.S.C. 

553, it is the Secretary’s practice to offer 

interested parties the opportunity to 

comment on proposed regulations. 

However, the regulatory changes in this 

proposal are necessary to correct an 

error and do not establish any new 

substantive rules. 

We are proposing to revise the 

regulatory text at § 412.614(d)(2) to 

correct a reference to another part of the 

regulations. Specifically, we are 

replacing a reference to § 412.23(b)(2) 

with the correct reference to 

§ 412.29(b)(1). The proposed revisions 

are outlined in the regulation text of this 

proposed rule. 

We invite public comments on this 

proposal. 

G. Policies Regarding Public Display of 

Measure Data for the IRF QRP 

We are not proposing any new 

policies regarding the public display of 

measure data at this time. 

XI. Collection of Information 

Requirements 

A. Statutory Requirement for 

Solicitation of Comments 

Under the Paperwork Reduction Act 

of 1995, we are required to provide 60- 

day notice in the Federal Register and 

solicit public comment before a 

collection of information requirement is 

submitted to the Office of Management 

and Budget (OMB) for review and 

approval. In order to fairly evaluate 

whether an information collection 

should be approved by OMB, section 

3506(c)(2)(A) of the Paperwork 

Reduction Act of 1995 requires that we 

solicit comment on the following issues: 

• The need for the information 

collection and its usefulness in carrying 

out the proper functions of our agency. 

• The accuracy of our estimate of the 

information collection burden. 

• The quality, utility, and clarity of 

the information to be collected. 

• Recommendations to minimize the 

information collection burden on the 

affected public, including automated 

collection techniques. 

This proposed rule makes reference to 

associated information collections that 

are not discussed in the regulation text 

contained in this document. 

B. Collection of Information 

Requirements for Updates Related to the 

IRF QRP Beginning With the FY 2025 

IRF QRP 

An IRF that does not meet the 

requirements of the IRF QRP for a fiscal 

year will receive a 2-percentage point 

reduction to its otherwise applicable 

annual increase factor for that fiscal 

year. 

We believe that the burden associated 

with the IRF QRP is the time and effort 

associated with complying with the 

requirements of the IRF QRP. In section 

X.F.2. of this proposed rule, we are 

proposing to update the data reporting 

requirements for the IRF QRP beginning 

with the FY 2025 IRF QRP. We are 

proposing to require IRFs to collect IRF– 

PAI assessment information on each 

patient receiving care in an IRF, 

regardless of payer. We believe the IRF– 

PAI items are completed by Registered 

Nurses (RN), Licensed Practical and 

Licensed Vocational Nurses (LVN), 

Respiratory Therapists (RT), SpeechLanguage Pathologists (SLP), 

Occupational Therapists (OT), Physical 

Therapists (PT), and/or Psychologists 

(Psy), depending on the item. We 

identified the staff type per item based 

on past IRF burden calculations in 

conjunction with expert opinion. Our 

assumptions for staff type were based on 

the categories generally necessary to 

perform an assessment. Individual 

providers determine the staffing 

resources necessary; therefore, we 

averaged the national average for these 

labor types and established a composite 

cost estimate. This composite estimate 

was calculated by weighting each salary 

based on the following breakdown 

regarding provider types most likely to 

collect this data: RN 50 percent; LVN 

31.7 percent; RT 7 percent; SLP 6 

percent; PT 2.5 percent; OT 2.5 percent; 

Psy 2 percent. For the purposes of 

calculating the costs associated with the 

collection of information requirements, 

we obtained mean hourly wages for 

these staff from the U.S. Bureau of Labor 

Statistics’ May 2020 National 

Occupational Employment and Wage 

Estimates.66 To account for overhead 

and fringe benefits, we have doubled the 

hourly wage. These amounts are 

detailed in Table 13. 


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As a result of this proposal, the 

estimated burden and cost for IRFs for 

complying with requirements of the FY 

2025 IRF QRP will increase. 

Specifically, we believe that there will 

be a 1.8 hours addition in clinical staff 

time to report data for each additional 

IRF–PAI completed. We estimate the 

collection of an additional 263,988 IRF– 

PAIs from 1,115 IRFs annually. This 

equates to an increase of 475,178 hours 

in burden for all IRFs (1.8 hours × 

263,988 discharges). Given the clinician 

times estimated in the previous 

paragraph and the wages in Table 13, 

we calculated a blended hourly rate of 

$66.82. We estimate that each IRF will 

complete an average of 237 additional 

IRF–PAIs per year, the total cost related 

to the additional reporting requirements 

is estimated at $28,505.41 per IRF 

annually [(237 assessment × 1.8 hours) 

x $66.82], or $31,783,532.15 for all IRFs 

annually ($28.505.41 × 1,115). The 

increase in burden will be accounted for 

in a revised information collection 

request under OMB control number 

(0938–0842). The required 60-day and 

30-day notices will publish in the 

Federal Register and the comment 

periods will be separate from those 

associated with this rulemaking. A 60- 

day Federal Register notice was 

published on February 3, 2022 (87 FR 

6175) to extend the information 

collection request. The 60-day comment 

period for the extension ends April 4, 

2022. The revision will be submitted at 

the conclusion of the extension process. 

As described in section X.F.2.c. of this 

proposed rule, a new item would 

replace Item 20 on the IRF–PAI V4.0. 

However, since this item is replacing 

another item already accounted for in 

the PRA, we do not believe this would 

add any additional burden to the 

estimate described above. 

C. Submission of PRA-Related 

Comments 

We have submitted a copy of this 

rule’s information collection and 

recordkeeping requirements to OMB for 

review and approval. These 

requirements are not effective until they 

have been approved by the OMB. 

To obtain copies of the supporting 

statement and any related forms for the 

proposed collections discussed above, 

please visit CMS’ website at 

www.cms.hhs.gov/ 

PaperworkReductionActof1995, or call 

the Reports Clearance Office at 410– 

786–1326. 

We invite public comments on these 

potential information collection 

requirements. If you wish to comment, 

please refer to the DATES and ADDRESSES

sections of this rulemaking for 

instructions. We will consider all ICRrelated comments received by the date 

and time specified in the DATES section, 

and when we proceed with a 

subsequent document, we will respond 

to the comments in the preamble to that 

document. 

XII. 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. 

XIII. Regulatory Impact Analysis 

A. Statement of Need 

This proposed rule would update the 

IRF prospective payment rates for FY 

2023 as required under section 

1886(j)(3)(C) of the Act and in 

accordance with section 1886(j)(5) of the 

Act, which requires the Secretary to 

publish in the Federal Register on or 

before August 1 before each FY, the 

classification and weighting factors for 

CMGs used under the IRF PPS for such 

FY and a description of the 

methodology and data used in 

computing the prospective payment 

rates under the IRF PPS for that FY. 

This proposed rule would also 

implement section 1886(j)(3)(C) of the 

Act, which requires the Secretary to 

apply a productivity adjustment to the 

market basket increase factor for FY 

2012 and subsequent years. 

Furthermore, this proposed rule 

would adopt policy changes under the 

statutory discretion afforded to the 

Secretary under section 1886(j) of the 

Act. We are proposing to update the 

data reporting requirements for the IRF 

QRP and to amend the regulations 

consistent with the proposed 

requirements. We are also proposing to 

correct an error in the regulations text 

at § 412.614(d)(2). Finally, we are 

seeking comment on three issues: (1) 

Future measure concepts under 

consideration for the IRF QRP; (2) the 

potential future inclusion of the 

National Healthcare Safety Network 

(NHSN) Healthcare associated 

Clostridioides difficile Infection 

Outcome measure in the IRF QRP; and 

(3) overarching principles for measuring 

equity and health disparities across 


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TABLE 13: U.S. Bureau of Labor and Statistics' May 2020 National Occupational 

E I mp oyment an dW age E st1mates . 

Occupation title Occupation Mean Hourly Wage Overhead and Fringe Adjusted Hourly 

code ($/hr) Benefit ($/hr) Wage ($/hr) 

Registered Nurse 29-1141 $38.47 $38.47 $76.94 (RN) 

Licensed Vocational 29-2061 $24.08 $24.08 $48.16 Nurse (LVN) 

Respiratory 29-1126 $31.56 $31.56 $63.12 Therapist (RT) 

Speech Language 29-1127 $40.02 $40.02 $80.04 Pathologist (SLP) 

Physical Therapist 29-1123 $44.08 $44.08 $88.16 (PT) 

Occupational 29-1122 $42.06 $42.06 $84.12 Therapist (OT) 

Psychologist (Psy) 19-3030 $43.61 $43.61 $87.22 
20258 Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 

CMS Quality Programs, including the 

IRF QRP. 

B. Overall Impact 

We have examined the impacts of this 

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 Social 

Security Act, section 202 of the 

Unfunded Mandates Reform Act of 1995 

(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). Section 3(f) of Executive Order 

12866 defines a ‘‘significant regulatory 

action’’ as an action that is likely to 

result in a rule: (1) Having an annual 

effect on the economy of $100 million 

or more in any 1 year, or adversely and 

materially affecting a sector of the 

economy, productivity, competition, 

jobs, the environment, public health or 

safety, or State, local or tribal 

governments or communities (also 

referred to as ‘‘economically 

significant’’); (2) creating a serious 

inconsistency or otherwise interfering 

with an action taken or planned by 

another agency; (3) materially altering 

the budgetary impacts of entitlement 

grants, user fees, or loan programs or the 

rights and obligations of recipients 

thereof; or (4) raising novel legal or 

policy issues arising out of legal 

mandates, the President’s priorities, or 

the principles set forth in Executive 

Order 12866. 

Section (6)(a) of Executive Order 

12866 provides that a regulatory impact 

analysis (RIA) must be prepared for 

major rules with economically 

significant effects ($100 million or more 

in any 1 year). We estimate the total 

impact of the policy updates described 

in this proposed rule by comparing the 

estimated payments in FY 2023 with 

those in FY 2022. This analysis results 

in an estimated $170 million increase 

for FY 2023 IRF PPS payments. 

Additionally, we estimate that costs 

associated with the proposal to update 

the reporting requirements under the 

IRF QRP result in an estimated 

$31,783,532.15 additional cost in FY 

2025 for IRFs. Based on our estimates 

OMB’s Office of Information and 

Regulatory Affairs has determined that 

this rulemaking is ‘‘economically 

significant’’ as measured by the $100 

million threshold. Also, the rule has 

been reviewed by OMB. Accordingly, 

we have prepared an RIA that, to the 

best of our ability, presents the costs 

and benefits of the rulemaking. 

C. Anticipated Effects 

1. Effects on IRFs 

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 IRFs 

and most other providers and suppliers 

are small entities, either by having 

revenues of $8.0 million to $41.5 

million or less in any 1 year depending 

on industry classification, or by being 

nonprofit organizations that are not 

dominant in their markets. (For details, 

see the Small Business Administration’s 

final rule that set forth size standards for 

health care industries, at 65 FR 69432 at 

https://www.sba.gov/sites/default/files/ 

2019-08/SBA%20Table%20of%20

Size%20Standards_

Effective%20Aug%2019%2C%202019_

Rev.pdf, effective January 1, 2017 and 

updated on August 19, 2019.) Because 

we lack data on individual hospital 

receipts, we cannot determine the 

number of small proprietary IRFs or the 

proportion of IRFs’ revenue that is 

derived from Medicare payments. 

Therefore, we assume that all IRFs (an 

approximate total of 1,115 IRFs, of 

which approximately 52 percent are 

nonprofit facilities) are considered small 

entities and that Medicare payment 

constitutes the majority of their 

revenues. HHS generally uses a revenue 

impact of 3 to 5 percent as a significance 

threshold under the RFA. As shown in 

Table 14, we estimate that the net 

revenue impact of this proposed rule on 

all IRFs is to increase estimated 

payments by approximately 2.0 percent. 

The rates and policies set forth in this 

proposed rule would not have a 

significant impact (not greater than 3 

percent) on a substantial number of 

small entities. The estimated impact on 

small entities is shown in Table 14. 

MACs are not considered to be small 

entities. Individuals and States are not 

included in the definition of a small 

entity. 

In addition, section 1102(b) of the Act 

requires us to prepare an RIA 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 a Metropolitan 

Statistical Area and has fewer than 100 

beds. As shown in Table 14, we estimate 

that the net revenue impact of this 

proposed rule on rural IRFs is to 

increase estimated payments by 

approximately 1.8 percent based on the 

data of the 133 rural units and 12 rural 

hospitals in our database of 1,115 IRFs 

for which data were available. We 

estimate an overall impact for rural IRFs 

in all areas between –1.8 percent and 

2.9 percent. As a result, we anticipate 

that this proposed rule would not have 

a significant impact on a substantial 

number of small entities. 

Section 202 of the Unfunded 

Mandates Reform Act of 1995 (Pub. L. 

104–04, enacted on March 22, 1995) 

(UMRA) 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 does not 

mandate any requirements for State, 

local, or tribal governments, or for the 

private sector. 

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. As stated, this 

proposed rule would not have a 

substantial effect on State and local 

governments, preempt State law, or 

otherwise have a federalism 

implication. 

2. Detailed Economic Analysis 

This proposed rule would update the 

IRF PPS rates contained in the FY 2022 

IRF PPS final rule (86 FR 42362). 

Specifically, this proposed rule would 

update the CMG relative weights and 

ALOS values, the wage index, and the 

outlier threshold for high-cost cases. 

This proposed rule would apply a 

productivity adjustment to the FY 2023 

IRF market basket increase factor in 

accordance with section 

1886(j)(3)(C)(ii)(I) of the Act. Further, 

this proposed rule would codify CMS’ 

existing teaching status adjustment 

policy through proposed amendments to 

the regulation text and would update 

and clarify the IRF teaching policy with 

respect to IRF hospital closures and 


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Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 20259 

displaced residents. Additionally, this 

proposed rule would establish a 

permanent cap policy to smooth the 

impact of year-to-year changes in IRF 

payments related to changes in the IRF 

wage index. 

We estimate that the impact of the 

changes and updates described in this 

proposed rule would be a net estimated 

increase of $170 million in payments to 

IRF providers. The impact analysis in 

Table 14 of this proposed rule 

represents the projected effects of the 

updates to IRF PPS payments for FY 

2023 compared with the estimated IRF 

PPS payments in FY 2022. We 

determine the effects by estimating 

payments while holding all other 

payment variables constant. We use the 

best data available, but we do not 

attempt to predict behavioral responses 

to these changes, and we do not make 

adjustments for future changes in such 

variables as number of discharges or 

case-mix. 

We note that certain events may 

combine to limit the scope or accuracy 

of our impact analysis, because such an 

analysis is future-oriented and, thus, 

susceptible to forecasting errors because 

of other changes in the forecasted 

impact time period. Some examples 

could be legislative changes made by 

the Congress to the Medicare program 

that would impact program funding, or 

changes specifically related to IRFs. 

Although some of these changes may 

not necessarily be specific to the IRF 

PPS, the nature of the Medicare program 

is such that the changes may interact, 

and the complexity of the interaction of 

these changes could make it difficult to 

predict accurately the full scope of the 

impact upon IRFs. 

In updating the rates for FY 2023, we 

are proposing standard annual revisions 

described in this proposed rule (for 

example, the update to the wage index 

and market basket increase factor used 

to adjust the Federal rates). We are also 

reducing the FY 2023 IRF market basket 

increase factor by a productivity 

adjustment in accordance with section 

1886(j)(3)(C)(ii)(I) of the Act. We 

estimate the total increase in payments 

to IRFs in FY 2023, relative to FY 2022, 

would be approximately $170 million. 

This estimate is derived from the 

application of the FY 2023 IRF market 

basket increase factor, as reduced by a 

productivity adjustment in accordance 

with section 1886(j)(3)(C)(ii)(I) of the 

Act, which yields an estimated increase 

in aggregate payments to IRFs of $240 

million. However, there is an estimated 

$70 million decrease in aggregate 

payments to IRFs due to the proposed 

update to the outlier threshold amount. 

Therefore, we estimate that these 

updates would result in a net increase 

in estimated payments of $170 million 

from FY 2022 to FY 2023. 

The effects of the proposed updates 

that impact IRF PPS payment rates are 

shown in Table 14. The following 

proposed updates that affect the IRF 

PPS payment rates are discussed 

separately below: 

• The effects of the proposed update 

to the outlier threshold amount, from 

approximately 3.8 percent to 3.0 percent 

of total estimated payments for FY 2023, 

consistent with section 1886(j)(4) of the 

Act. 

• The effects of the proposed annual 

market basket update (using the IRF 

market basket) to IRF PPS payment 

rates, as required by sections 

1886(j)(3)(A)(i) and (j)(3)(C) of the Act, 

including a productivity adjustment in 

accordance with section 

1886(j)(3)(C)(i)(I) of the Act. 

• The effects of applying the 

proposed budget-neutral labor-related 

share and wage index adjustment, as 

required under section 1886(j)(6) of the 

Act. 

• The effects of applying the 

proposed budget-neutral permanent cap 

on wage index decreases policy. 

• The effects of the proposed budgetneutral changes to the CMG relative 

weights and ALOS values under the 

authority of section 1886(j)(2)(C)(i) of 

the Act. 

• The total change in estimated 

payments based on the FY 2023 

payment changes relative to the 

estimated FY 2022 payments. 

3. Description of Table 14 

Table 14 shows the overall impact on 

the 1,115 IRFs included in the analysis. 

The next 12 rows of Table 14 contain 

IRFs categorized according to their 

geographic location, designation as 

either a freestanding hospital or a unit 

of a hospital, and by type of ownership; 

all urban, which is further divided into 

urban units of a hospital, urban 

freestanding hospitals, and by type of 

ownership; and all rural, which is 

further divided into rural units of a 

hospital, rural freestanding hospitals, 

and by type of ownership. There are 970 

IRFs located in urban areas included in 

our analysis. Among these, there are 653 

IRF units of hospitals located in urban 

areas and 317 freestanding IRF hospitals 

located in urban areas. There are 145 

IRFs located in rural areas included in 

our analysis. Among these, there are 133 

IRF units of hospitals located in rural 

areas and 12 freestanding IRF hospitals 

located in rural areas. There are 431 forprofit IRFs. Among these, there are 396 

IRFs in urban areas and 35 IRFs in rural 

areas. There are 577 non-profit IRFs. 

Among these, there are 489 urban IRFs 

and 88 rural IRFs. There are 107 

government-owned IRFs. Among these, 

there are 85 urban IRFs and 22 rural 

IRFs. 

The remaining four parts of Table 14 

show IRFs grouped by their geographic 

location within a region, by teaching 

status, and by DSH patient percentage 

(PP). First, IRFs located in urban areas 

are categorized for their location within 

a particular one of the nine Census 

geographic regions. Second, IRFs 

located in rural areas are categorized for 

their location within a particular one of 

the nine Census geographic regions. In 

some cases, especially for rural IRFs 

located in the New England, Mountain, 

and Pacific regions, the number of IRFs 

represented is small. IRFs are then 

grouped by teaching status, including 

non-teaching IRFs, IRFs with an intern 

and resident to average daily census 

(ADC) ratio less than 10 percent, IRFs 

with an intern and resident to ADC ratio 

greater than or equal to 10 percent and 

less than or equal to 19 percent, and 

IRFs with an intern and resident to ADC 

ratio greater than 19 percent. Finally, 

IRFs are grouped by DSH PP, including 

IRFs with zero DSH PP, IRFs with a 

DSH PP less than 5 percent, IRFs with 

a DSH PP between 5 and less than 10 

percent, IRFs with a DSH PP between 10 

and 20 percent, and IRFs with a DSH PP 

greater than 20 percent. 

The estimated impacts of each policy 

described in this rule to the facility 

categories listed are shown in the 

columns of Table 14. The description of 

each column is as follows: 

• Column (1) shows the facility 

classification categories. 

• Column (2) shows the number of 

IRFs in each category in our FY 2023 

analysis file. 

• Column (3) shows the number of 

cases in each category in our FY 2023 

analysis file. 

• Column (4) shows the estimated 

effect of the proposed adjustment to the 

outlier threshold amount. 

• Column (5) shows the estimated 

effect of the proposed update to the IRF 

labor-related share and wage index, in a 

budget-neutral manner. 

• Column (6) shows the estimated 

effect of the proposed permanent cap on 

wage index decreases policy, in a 

budget-neutral manner. 

• Column (7) shows the estimated 

effect of the proposed update to the 

CMG relative weights and ALOS values, 

in a budget-neutral manner. 

• Column (8) compares our estimates 

of the payments per discharge, 

incorporating all of the policies 

reflected in this proposed rule for FY 


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20260 Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 

2023 to our estimates of payments per 

discharge in FY 2022. 

The average estimated increase for all 

IRFs is approximately 2.0 percent. This 

estimated net increase includes the 

effects of the proposed IRF market 

basket increase factor for FY 2023 of 2.8 

percent, which is based on a proposed 

IRF market basket update of 3.2 percent, 

less a 0.4 percentage point productivity 

adjustment, as required by section 

1886(j)(3)(C)(ii)(I) of the Act. It also 

includes the approximate 0.8 percent 

overall decrease in estimated IRF outlier 

payments from the proposed update to 

the outlier threshold amount. Since we 

are making the updates to the IRF wage 

index, labor-related share and the CMG 

relative weights in a budget-neutral 

manner, they will not be expected to 

affect total estimated IRF payments in 

the aggregate. However, as described in 

more detail in each section, they will be 

expected to affect the estimated 

distribution of payments among 

providers. 

BILLING CODE 4120–01–P 


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TABLE 14: IRF Im oact Table for FY 2023 (Columns 4 throu2h 8 in percenta~ e) 

Proposed 

Wage Permanent Total 

Facility Classification Number Number Outlier Index Wage CMG Percent ofIRFs of Cases FY23 Index Weights Change 1 

Decreases 

Cap 

(1) (2) (3) (4) (5) (6) (7) (8) 

Total 1,115 380 165 -0.8 0.0 0.0 0.0 2.0 

Urban unit 653 143,947 -1.4 0.0 0.0 -0.l 1.2 

Rural unit 133 17,660 -1.0 -CU 0.0 -0.1 1.5 

Urban hosoital 317 213,377 -0.3 0.0 0.0 0.1 2.6 

Rural hospital 12 5,181 -0.3 -0.2 0.0 0.2 2.5 

Urban For-Profit 396 206,158 -0.3 0.0 0.0 0.1 2.6 

Rural For-Profit 35 8,048 -0.4 -0.1 0.0 0.1 2.4 

Urban Non-Profit 489 132 251 -1.3 0.0 0.0 -0.1 1.4 

Rural Non-Profit 88 12 252 -1.1 -0.2 0.0 -0.1 1.4 

Urban Government 85 18 915 -1.6 -0.1 0.0 -0.2 0.9 

Rural Government 22 2.541 -0.9 -0.2 0.0 -0.1 1.6 

Urban 970 357 324 -0.8 0.0 0.0 0.0 2.0 

Rural 145 22,841 -0.8 -0.2 0.0 0.0 1.8 

Urban bv rcl!ion 

Urban New England 29 13 576 -0.5 -1.1 0.0 -0.1 1.1 

Urban Middle Atlantic 121 41622 -1.2 0.2 0.0 0.0 1.8 

Urban South Atlantic 158 75,753 -0.6 -0.2 0.0 0.0 1.9 

Urban East North Central 158 44,520 -0.8 -0.1 0.0 -0.1 1.8 

Urban East South Central 55 25,224 -0.2 -0.3 0.0 0.0 2.3 

Urban West North Central 76 21,675 -0.7 -0.5 0.0 -0.1 1.4 

Urban West South Central 197 83,013 -0.5 0.2 0.0 0.2 2.7 

Urban Mountain 79 27,597 -0.6 0.3 0.0 0.0 2.5 

Urban Pacific 97 24,344 -1.7 0.5 0.0 -0.2 1.3 

Rural bv rcl!ion 

Rural New England 5 1,116 -0.9 1.2 0.0 -0.2 2.9 

Rural Middle Atlantic 10 926 -1.l -0.3 0.0 0.0 1.3 
20262 Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 

BILLING CODE 4120–01–C 

4. Impact of the Proposed Update to the 

Outlier Threshold Amount 

The estimated effects of the proposed 

update to the outlier threshold 

adjustment are presented in column 4 of 

Table 14. 

For this proposed rule, we are using 

preliminary FY 2021 IRF claims data, 

and, based on that preliminary analysis, 

we estimated that IRF outlier payments 

as a percentage of total estimated IRF 

payments would be 3.8 percent in FY 

2023. Thus, we propose to adjust the 

outlier threshold amount in this 

proposed rule to maintain total 

estimated outlier payments equal to 3 

percent of total estimated payments in 

FY 2023. The estimated change in total 

IRF payments for FY 2023, therefore, 

includes an approximate 0.8 percentage 

point decrease in payments because the 

estimated outlier portion of total 

payments is estimated to decrease from 

approximately 3.8 percent to 3 percent. 

The impact of this proposed outlier 

adjustment update (as shown in column 

4 of Table 14) is to decrease estimated 

overall payments to IRFs by 0.8 

percentage point. 

5. Impact of the Proposed Wage Index 

and Labor-Related Share 

In column 5 of Table 14, we present 

the effects of the proposed budgetneutral update of the wage index and 

labor-related share. The proposed 

changes to the wage index and the 


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Proposed 

Wage Permanent Total Number Number Wage CMG Facility Classification ofIRFs of Cases Outlier Index Index Weights Percent 

FY23 Decreases Change 1 

Cao 

Rural South Atlantic 16 4,000 -0.2 -0.7 0.0 0.1 

Rural East North Central 23 3,379 -0.8 -0.8 0.0 -0.1 

Rural East South Central 20 3,626 -0.5 -0.4 0.0 -0.1 

Rural West North Central 20 2,579 -1.4 0.1 0.0 0.0 

Rural West South Central 42 6,514 -0.8 0.4 0.0 0.1 

Rural Mountain 6 379 -1.2 -0.5 0.1 0.1 

Rural Pacific 3 322 -3.9 -0.3 0.0 -0.3 

Teaching status 

Non-teaching 1,012 335,417 -0.7 0.0 0.0 0.0 

Resident to ADC less than 10% 59 32,213 -1.0 0.0 0.0 -0.1 

Resident to ADC 10%-19% 34 11,327 -1.6 0.1 0.0 -0.2 

Resident to ADC greater than 19% 10 1,208 -1.1 0.5 0.0 -0.1 

Disproportionate share patient 

oercenta2e (DSH PP) 

DSHPP=03⁄4 64 11,557 -1.5 0.2 0.0 0.0 

DSHPP<5% 127 49,049 -0.6 -0.2 0.0 0.1 

DSH PP 5%-10% 260 105,962 -0.6 0.1 0.0 0.1 

DSH PP 10%-20% 388 140,935 -0.7 0.0 0.0 0.0 

DSH PP greater than 20% 276 72,662 -1.2 0.1 0.0 -0.1 

1 This column includes the impact of the updates in columns ( 4 ), ( 5), ( 6) and (7) above, and of the IRF market basket 

update for FY 2023 (3.2 percent), reduced by 0.4 percentage point for the productivity adjustment as required by section 

1886(j)(3)(C)(ii)(I) of the Act. Note, the products of these impacts may be different from the percentage changes shown 

here due to rounding effects. 

1.9 

1.0 

1.7 

1.5 

2.4 

1.2 

-1.8 

2.1 

1.7 

1.0 

2.1 

1.5 

2.0 

2.4 

2.0 

1.6 
Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 20263 

labor-related share are discussed 

together because the wage index is 

applied to the labor-related share 

portion of payments, so the proposed 

changes in the two have a combined 

effect on payments to providers. As 

discussed in section V.C. of this 

proposed rule, we are proposing to 

update the labor-related share from 72.9 

percent in FY 2022 to 73.2 percent in 

FY 2023. In aggregate, we do not 

estimate that these proposed updates 

will affect overall estimated payments to 

IRFs. However, we do expect these 

updates to have small distributional 

effects. 

6. Impact of the Proposed Wage Index 

Policy 

In column 6 of Table 14, we present 

the effects of the budget-neutral 

proposed permanent cap on wage index 

decreases policy. As discussed in 

section V.D.3 of this proposed rule, 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 to smooth the 

impact of year-to-year changes in IRF 

payments related to changes in the IRF 

wage index. We are required by section 

1886(j)(6) of the Act, to implement 

changes to the wage index in a budgetneutral manner. Thus, there will not be 

an impact on aggregate Medicare 

payments to IRFs. 

7. Impact of the Proposed Update to the 

CMG Relative Weights and ALOS 

Values 

In column 7 of Table 14, we present 

the effects of the proposed budgetneutral update of the CMG relative 

weights and ALOS values. In the 

aggregate, we do not estimate that these 

proposed updates will affect overall 

estimated payments of IRFs. However, 

we do expect these updates to have 

small distributional effects. 

8. Effects of Proposed Codification and 

Clarifications of IRF Teaching Status 

Adjustment Policy 

As discussed in section VII. of this 

proposed rule, we are proposing to 

codify the longstanding teaching status 

adjustment policy through the proposed 

amendments to the regulation text at 

§ 412.602 and § 412.624(e)(4) provided 

in this proposed rule. 

We do not anticipate a financial 

impact associated with the proposed 

codification of the IRF teaching status 

adjustment policies. However, the 

clarification of certain teaching status 

adjustment policies and proposed 

codification of these policies will enable 

us to align the IRF policies with recent 

updates to the IPPS and IPF teaching 

status adjustment policies. Aligning the 

policy guidance with other post-acute 

care setting regulations will also assist 

stakeholders in providing care for 

Medicare beneficiaries. 

9. Effects of Requirements for the IRF 

QRP for FY 2025 

In accordance with 

section1886(j)(7)(A) of the Act, the 

Secretary must reduce by 2 percentage 

points the annual market basket 

increase factor otherwise applicable to 

an IRF for a fiscal year if the IRF does 

not comply with the requirements of the 

IRF QRP for that fiscal year. In section 

X.A. of this proposed rule, we discuss 

the method for applying the 2 

percentage point reduction to IRFs that 

fail to meet the IRF QRP requirements. 

As discussed in section X.F.2. of this 

proposed rule, we are proposing to 

require the reporting of quality data on 

all patients discharged from the IRF 

beginning with the FY 2025 IRF QRP. 

We describe the estimated burden for 

this proposal in section XI.B. of this 

proposed rule. In summary, the 

proposed changes to the IRF QRP will 

result in a burden addition of 

$28,505.41 per IRF annually, or 

$31,783,532.15 for all IRFs annually 

beginning with the FY 2025 IRF QRP. 

We note, however, that this estimate 

may be partially offset by eliminating 

the effort to separate out Medicare 

beneficiaries from other patients. 

D. Alternatives Considered 

The following is a discussion of the 

alternatives considered for the IRF PPS 

updates contained in this proposed rule. 

Section 1886(j)(3)(C) of the Act 

requires the Secretary to update the IRF 

PPS payment rates by an increase factor 

that reflects changes over time in the 

prices of an appropriate mix of goods 

and services included in the covered 

IRF services. 

As noted previously in this proposed 

rule, section 1886(j)(3)(C)(ii)(I) of the 

Act requires the Secretary to apply a 

productivity adjustment to the market 

basket increase factor for FY 2023. Thus, 

in accordance with section 1886(j)(3)(C) 

of the Act, we propose to update the IRF 

prospective payments in this proposed 

rule by 2.8 percent (which equals the 

3.2 percent estimated IRF market basket 

increase factor for FY 2023 reduced by 

a 0.4 percentage point productivity 

adjustment as determined under section 

1886(b)(3)(B)(xi)(II) of the Act (as 

required by section 1886(j)(3)(C)(ii)(I) of 

the Act)). 

We considered maintaining the 

existing CMG relative weights and 

average length of stay values for FY 

2023. However, in light of recently 

available data and our desire to ensure 

that the CMG relative weights and 

average length of stay values are as 

reflective as possible of recent changes 

in IRF utilization and case mix, we 

believe that it is appropriate to propose 

to update the CMG relative weights and 

average length of stay values at this time 

to ensure that IRF PPS payments 

continue to reflect as accurately as 

possible the current costs of care in 

IRFs. 

We considered maintaining the 

existing outlier threshold amount for FY 

2022. However, analysis of updated FY 

2021 data indicates that estimated 

outlier payments would be more than 3 

percent of total estimated payments for 

FY 2023, by approximately 0.8 percent, 

unless we updated the outlier threshold 

amount. Consequently, we propose 

adjusting the outlier threshold amount 

in this proposed rule to reflect a 0.8 

percent decrease thereby setting the 

total outlier payments equal to 3 

percent, instead of 3.8 percent, of 

aggregate estimated payments in FY 

2023. 

We considered not amending 

§ 412.602 and § 412.624(e)(4) to codify 

our longstanding guidance on the 

teaching status adjustment policies and 

update the IRF teaching policy on IRF 

program closures and displaced 

residents. However, we believe that 

codifying these longstanding policies 

into regulation text would improve 

clarity and reduce administrative 

burden on IRF providers trying to locate 

all relevant information regarding the 

teaching status adjustment. 

Additionally, we believe that we should 

streamline all teaching status 

adjustment policy information in the 

same place for ease of reference. 

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 the FY 2023 IRF PPS 

proposed rule will be the number of 

reviewers of last year’s proposed rule. 

We acknowledge that this assumption 

may understate or overstate the costs of 

reviewing this proposed. It is possible 

that not all commenters reviewed the 

FY 2022 IRF PPS proposed rule in 

detail, and it is also possible that some 

reviewers chose not to comment on the 

FY 2022 proposed rule. For these 

reasons, we thought that the number of 

commenters would be a fair estimate of 


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20264 Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 

the number of reviewers of this 

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. We seek comments 

on this assumption. 

Using the national mean hourly wage 

data from the May 2020 BLS for 

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 3 hours for 

the staff to review half of this proposed 

rule. For each reviewer of the rule, the 

estimated cost is $342.72 (3 hours × 

$114.24). Therefore, we estimate that 

the total cost of reviewing this 

regulation is $17,478.72 ($342.72 × (50 

reviewers). 

F. Accounting Statement and Table 

As required by OMB Circular A–4 

(available at https://

www.whitehouse.gov/wp-content/ 

uploads/legacy_drupal_files/omb/ 

circulars/A4/a-4.pdf), in Table 15 we 

have prepared an accounting statement 

showing the classification of the 

expenditures associated with the 

provisions of this proposed rule. Table 

15 provides our best estimate of the 

increase in Medicare payments under 

the IRF PPS as a result of the proposed 

updates presented in this proposed rule 

based on the data for 1,115 IRFs in our 

database. 

G. Conclusion 

Overall, the estimated payments per 

discharge for IRFs in FY 2023 are 

projected to increase by 2.0 percent, 

compared with the estimated payments 

in FY 2022, as reflected in column 8 of 

Table 14. 

IRF payments per discharge are 

estimated to increase by 2.0 percent in 

urban areas and 1.8 percent in rural 

areas, compared with estimated FY 2022 

payments. Payments per discharge to 

rehabilitation units are estimated to 

increase 1.2 percent in urban areas and 

1.5 percent in rural areas. Payments per 

discharge to freestanding rehabilitation 

hospitals are estimated to increase 2.6 

percent in urban areas and increase 2.5 

percent in rural areas. 

Overall, IRFs are estimated to 

experience a net increase in payments 

as a result of the proposed policies in 

this proposed rule. The largest payment 

increase is estimated to be a 2.9 percent 

increase for rural IRFs located in the 

rural New England region. The analysis 

above, together with the remainder of 

this preamble, provides an RIA. 

In accordance with the provisions of 

Executive Order 12866, this regulation 

was reviewed by OMB. 

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 412 

Administrative practice and 

procedure, 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 412—PROSPECTIVE PAYMENT 

SYSTEMS FOR INPATIENT HOSPITAL 

SERVICES 

■ 1. The authority citation for part 412 

continues to read as follows: 

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

■ 2. Amend § 412.602 by adding the 

definitions of ‘‘Closure of an IRF’’, 

‘‘Closure of an IRF’s residency training 

program’’, and ‘‘Displaced resident’’ in 

alphabetical order to read as follows: 

§ 412.602 Definitions. 

* * * * * 

Closure of an IRF has the same 

meaning as ‘‘closure of a hospital’’ as 

defined in § 413.79(h)(1)(i) as applied to 

an IRF meeting the requirements of 

§ 412.604(b) for the purposes of 

accounting for indirect teaching costs. 

Closure of an IRF’s residency training 

program has the same meaning as 

‘‘closure of a hospital residency training 

program’’ as defined in § 413.79(h)(1)(ii) 

as applied to an IRF meeting the 

requirements of § 412.604(b) for the 

purposes of accounting for indirect 

teaching costs. 

* * * * * 

Displaced resident has the same 

meaning as a ‘‘displaced resident’’ as 

defined in § 413.79(h)(1)(iii) as applied 

to an IRF, for purposes of accounting for 

indirect teaching costs. 

* * * * * 

■ 3. Amend § 412.604 by revising 

paragraph (c) to read as follows: 

§ 412.604 Conditions for payment under 

the prospective payment system for 

inpatient rehabilitation facilities. 

* * * * * 

(c) Completion of patient assessment 

instrument. For each Medicare part A 

fee-for-service patient admitted to or 

discharged from an IRF on or after 

January 1, 2002, the inpatient 

rehabilitation facility must complete a 

patient assessment instrument in 

accordance with § 412.606. IRFs must 

also complete a patient assessment 

instrument in accordance with 

§ 412.606 for each Medicare Part C 

(Medicare Advantage) patient admitted 

to or discharged from an IRF on or after 

October 1, 2009. In addition, IRFs must 

complete a patient assessment 

instrument in accordance with 

§ 412.606 for all other patients, 

regardless of payer, admitted to or 

discharged from an IRF on or after 

October 1, 2023. 

* * * * * 


lotter on DSK11XQN23PROD with PROPOSALS3EP06AP22.031</GPH>

Statement: Classification of Estimated Ex enditure 

Annualized Monetized Transfers $170 million Change in Estimated Transfers from FY 1----------------+---F-ed-e-ral----'-G-o_v_e_mm_e_n_t_to-lRF----1 

2022 IRF PPS to FY 2023 IRF PPS From Whom to Whom? Medicare Providers 

Estimated Costs for the FY 2025 IRF Annualized monetized cost in FY 2025 $31,783,532.15 

QRP for IRFs due to new quality reporting 

Estimated Costs Associated with 

Review Cost for FY 2023 IRF PPS 

ro ram re uirements 

Cost associated with regulatory review 

cost 

$17,478.72 
Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 20265 

■ 4. Amend § 412.606 by— 

■ a. Revising paragraphs (a) and (b)(1) to 

read as follows: 

§ 412.606 Patient assessments. 

(a) Patient assessment instrument. An 

inpatient rehabilitation facility must use 

the CMS inpatient rehabilitation facility 

patient assessment instrument to assess 

Medicare Part A fee-for-service and 

Medicare Part C (Medicare Advantage) 

inpatients who are admitted on or after 

January 1, 2002, or were admitted before 

January 1, 2002, and are still inpatients 

as of January 1, 2002. 

(1) Starting on October 1, 2023, 

inpatient rehabilitation facilities must 

use the CMS inpatient rehabilitation 

facility patient assessment instrument to 

assess all inpatients, regardless of payer, 

who are admitted on or after October 1, 

2023, or who were admitted before 

October 1, 2023 and are still inpatients 

as of October 1, 2023. 

(2) [Reserved] 

(b) * * * (1) A clinician of the 

inpatient rehabilitation facility must 

perform a comprehensive, accurate, 

standardized, and reproducible 

assessment of each Medicare Part A feefor-service inpatient using the inpatient 

rehabilitation facility patient assessment 

instrument specified in paragraph (b) of 

this section as part of his or her patient 

assessment in accordance with the 

schedule described in § 412.610. IRFs 

must also complete a patient assessment 

instrument in accordance with 

§ 412.606 for each Medicare Part C 

(Medicare Advantage) patient admitted 

to or discharged from an IRF on or after 

October 1, 2009. In addition, IRFs must 

complete a patient assessment 

instrument in accordance with 

§ 412.606 for all other patients, 

regardless of payer, admitted to or 

discharged from an IRF on or after 

October 1, 2023. 

* * * * * 

■ 5. Amend § 412.610 by revising 

paragraphs (a), (b), (c) introductory text, 

(c)(1)(i)(A), (c)(2)(ii)(B) and (f) to read as 

follows: 

§ 412.610 Assessment schedule. 

(a) General. For each inpatient, an 

inpatient rehabilitation facility must 

complete a patient assessment 

instrument as specified in § 412.606 that 

covers a time period that is in 

accordance with the assessment 

schedule specified in paragraph (c) of 

this section. 

(b) Starting the assessment schedule 

day count. The first day that the 

inpatient is furnished services during 

his or her current inpatient 

rehabilitation facility hospital stay is 

counted as day one of the patient 

assessment schedule. 

(c) Assessment schedules and 

references dates. The inpatient 

rehabilitation facility must complete a 

patient assessment instrument upon the 

patient’s admission and discharge as 

specified in paragraphs (c)(1) and (2) of 

this section. 

(1) * * * 

(i) * * * 

(A) General. Time period is a span of 

time that covers calendar days 1 through 

3 of the patient’s current 

hospitalization. 

* * * * * 

(2) * * * 

(ii) * * * 

(B) The patient stops being furnished 

inpatient rehabilitation services. 

* * * * * 

(f) Patient assessment instrument 

record retention. An inpatient 

rehabilitation facility must maintain all 

patient assessment data sets completed 

on all Medicare Part A fee-for-service 

patients within the previous 5 years, on 

Medicare Part C (Medicare Advantage) 

patients within the previous 10 years, 

and all other patients within the 

previous 5 years either in a paper format 

in the patient’s clinical record or in an 

electronic computer file format that the 

inpatient rehabilitation facility can 

easily obtain and produce upon request 

to CMS or its contractors. 

■ 6. Amend § 412.614 by— 

■ a. Revising paragraphs (a) 

introductory text and (b)(1); 

■ b. Revising paragraph (d)(2) 

■ c. Adding paragraph (d)(3). 

■ d. Revising paragraph (e). 

The revisions and additions read as 

follows: 

§ 412.614 Transmission of patient 

assessment data. 

(a) Data format. General Rule. The 

inpatient rehabilitation facility must 

encode and transmit data for each 

inpatient— 

* * * * * 

(b) * * * 

(1) Electronically transmit complete, 

accurate, and encoded data from the 

patient assessment instrument for each 

inpatient to our patient data system in 

accordance with the data format 

specified in paragraph (a) of this 

section; and 

* * * * * 

(d) * * * 

(2) Medicare Part C (Medicare 

Advantage) data. Failure of the 

inpatient rehabilitation facility to 

transmit all of the required patient 

assessment instrument data for its 

Medicare Part C (Medicare Advantage) 

patients to our patient data system in 

accordance with the transmission 

timeline in paragraph (c) of this section 

will result in a forfeiture of the facility’s 

ability to have any of its Medicare Part 

C (Medicare Advantage) data used in the 

calculations for determining the 

facility’s compliance with the 

regulations in § 412.29(b)(1). 

(3) All other payer data. Failure of the 

inpatient rehabilitation facility to 

transmit all of the required patient 

assessment instrument data for all other 

patients, regardless of payer, to our 

patient data system in accordance with 

the transmission timeline in paragraph 

(c) of this section will result in a 

forfeiture of the facility’s ability to have 

any of its other payer data used in the 

calculations for determining the 

facility’s compliance with the 

regulations in § 412.29(b)(1). 

(e) Exemption to the consequences for 

transmitting the IRF–PAI data late for 

Medicare Part C (Medicare Advantage) 

patients and all other patients, 

regardless of payer. CMS may waive the 

consequences of failure to submit 

complete and timely IRF–PAI data 

specified in paragraph (d) of this section 

when, due to an extraordinary situation 

that is beyond the control of an 

inpatient rehabilitation facility, the 

inpatient rehabilitation facility is unable 

to transmit the patient assessment data 

in accordance with paragraph (c) of this 

section. Only CMS can determine if a 

situation encountered by an inpatient 

rehabilitation facility is extraordinary 

and qualifies as a situation for waiver of 

the forfeiture specified in paragraphs 

(d)(2) or (3) of this section. An 

extraordinary situation may be due to, 

but is not limited to, fires, floods, 

earthquakes, or similar unusual events 

that inflect extensive damage to an 

inpatient facility. An extraordinary 

situation may be one that produces a 

data transmission problem that is 

beyond the control of the inpatient 

rehabilitation facility, as well as other 

situations determined by CMS to be 

beyond the control of the inpatient 

rehabilitation facility. An extraordinary 

situation must be fully documented by 

the inpatient rehabilitation facility. 

■ 7. Amend § 412.618 by amending the 

introductory text to read as follows: 

§ 412.618 Assessment process for 

interrupted stays. 

For purposes of the patient 

assessment process, if any patient has 

an interrupted stay, as defined under 

§ 412.602, the following applies: 

* * * * * 

■ 8. Amend § 412.624 by revising 

paragraphs (e)(1) and (4) to read as 

follows: 


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20266 Federal Register / Vol. 87, No. 66 / Wednesday, April 6, 2022 / Proposed Rules 

§ 412.624 Methodology for calculating the 

Federal prospective payment rates. 

* * * * * 

(e) * * * 

(1) Adjustment for area wage levels. 

The labor portion of a facility’s Federal 

prospective payment is adjusted to 

account for geographical differences in 

the area wage levels using an 

appropriate wage index. 

(i) 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 § 412.602. 

(ii) Starting on October 1, 2022, CMS 

applies a cap on decreases to the wage 

index such that the wage index applied 

to an IRF is not less than 95 percent of 

the wage index applied to that IRF in 

the prior FY. 

(iii) Adjustments or updates to the 

wage data used to adjust a facility’s 

Federal prospective payment rate under 

paragraph (e)(1) of this section will be 

made in a budget neutral manner. CMS 

determines a budget neutral wage 

adjustment factor, based on any 

adjustment or update to the wage data, 

to apply to the standard payment 

conversion factor. 

* * * * * 

(4) Adjustments for teaching 

hospitals.(i) General. For discharges on 

or after October 1, 2005, CMS adjusts 

the Federal prospective payment on a 

facility basis by a factor as specified by 

CMS for facilities that are teaching 

institutions or units of teaching 

institutions. 

(A) An IRF’s teaching adjustment is 

based on the ratio of the number of fulltime equivalent residents training in the 

IRF divided by the facility’s average 

daily census. 

(B) As described in 

§ 412.105(f)(1)(iii)(A), residents with 

less than full-time status are counted as 

partial full time equivalent based on the 

proportion of time assigned to the 

inpatient rehabilitation facility 

compared to the total time necessary to 

fill a residency slot. Residents rotating 

to more than one hospital or nonhospital setting will be counted in 

proportion to the time they are assigned 

to inpatient rehabilitation facility 

compared to the total time worked in all 

locations. An inpatient rehabilitation 

facility cannot claim time spent by the 

resident at another inpatient 

rehabilitation facility or hospital. 

(C) Except as described in paragraph 

(e)(4)(i)(D) of this section, the actual 

number of current year full-time 

equivalent residents used in calculating 

the teaching adjustment is limited to the 

number of full-time equivalent residents 

in the IRF’s final settled cost report for 

the most recent cost reporting period 

ending on or before November 15, 2004 

(base year). 

(D) If the inpatient rehabilitation 

facility first begins training residents in 

a new approved graduate medical 

education program after November 15, 

2004, the number of full-time equivalent 

residents determined under paragraph 

(e)(4)(i)(C) of this section may be 

adjusted using the method described in 

§ 413.79(e)(1)(i). 

(E) The teaching adjustment is made 

on a claim basis as an interim payment, 

and the final payment in full for the 

claim is made during the final 

settlement of the cost report. 

(ii) Closure of an IRF or IRF residency 

training program. (A) Closure of an IRF. 

For cost reporting periods beginning on 

or after October 1, 2011, an IRF may 

receive a temporary adjustment to its 

FTE cap to reflect displaced residents 

added because of another IRFs closure 

if the IRF meets the following criteria: 

(1) The IRF is training additional 

displaced residents from an IRF that 

closed on or after October 1, 2011. 

(2) No later than 60 days after the IRF 

begins to train the displaced residents, 

the IRF submits a request to its 

Medicare contractor for a temporary 

adjustment by identifying the displaced 

residents who have come from the 

closed IRF and have caused the IRF to 

exceed its cap, and specifies the length 

of time the adjustment is needed. 

(B) Closure of an IRF’s residency 

training program. If an IRF that closes 

its residency training program on or 

after October 1, 2011, agrees to 

temporarily reduce its FTE cap 

according to the criteria specified in 

paragraph (e)(4)(ii)(A)(2) of this section, 

another IRF(s) may receive a temporary 

adjustment to its FTE cap to reflect 

displaced residents added because of 

the closure of the residency training 

program if the criteria specified in 

paragraph (e)(4)(ii)(A)(1) of this section 

are met. 

(1) Receiving IRF(s). For cost reporting 

periods beginning on or after October 1, 

2011, an IRF may receive a temporary 

adjustment to its FTE cap to reflect 

displaced residents added because of 

the closure of another IRF’s residency 

training program if the IRF is training 

additional displaced residents from the 

residency training program of an IRF 

that closed a program; and if no later 

than 60 days after the IRF begins to train 

the displaced residents the IRF submits 

to its Medicare Contractor a request for 

a temporary adjustment to its FTE cap, 

documents that it is eligible for this 

temporary adjustment by identifying the 

displaced residents who have come 

from another IRF’s closed program and 

have caused the IRF to exceed its cap, 

specifies the length of time the 

adjustment is needed, and submits to its 

Medicare Contractor a copy of the FTE 

reduction statement by the hospital that 

closed its program, as specified in 

paragraph (ii)(A)(2) of this section. 

(2) IRF that closed its program. An 

IRF that agrees to train displaced 

residents who have been displaced by 

the closure of another IRF’s program 

may receive a temporary FTE cap 

adjustment only if the hospital with the 

closed program temporarily reduces its 

FTE cap based on the FTE of displaced 

residents in each program year training 

in the program at the time of the 

programs closure. This yearly reduction 

in the FTE cap will be determined based 

on the number of those displaced 

residents who would have been training 

in the program during that year had the 

program not closed. No later than 60 

days after the displaced residents who 

were in the hospital that closed its 

program(s) begin training at another 

hospital must submit to its Medicare 

Contractor a statement signed and dated 

by its representative that specifies that 

it agrees to the temporary reduction in 

its FTE cap to allow the IRF training the 

displaced residents to obtain a 

temporary adjustment to its cap; 

identifies the displaced residents who 

were in the training at the time of the 

program’s closure; identifies the IRFs to 

which the displaced residents are 

transferring once the program closes; 

and specifies the reduction for the 

applicable program years. 

* * * * * 

Dated: March 30, 2022. 

Xavier Becerra, 

Secretary, Department of Health and Human 

Services. 

[FR Doc. 2022–07019 Filed 3–31–22; 4:15 pm] 

BILLING CODE 4120–01–P 


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