Federal Register / Vol. 87, No. 64 / Monday, April 4, 2022 / Proposed Rules 19415 

SUPPLEMENTARY INFORMATION: On March 

14, 2022 (87 FR 14210), EPA proposed 

to find that the Detroit, Michigan area is 

attaining the 2015 primary and 

secondary ozone National Ambient Air 

Quality Standards (NAAQS), and to act 

in accordance with a request from the 

Michigan Department of Environment, 

Great Lakes, and Energy (EGLE) to 

redesignate the area to attainment for 

the 2015 ozone NAAQS because the 

request meets the statutory requirements 

for redesignation under the Clean Air 

Act. The Detroit area includes 

Livingston, Macomb, Monroe, Oakland, 

St. Clair, Washtenaw, and Wayne 

Counties. EGLE submitted this request 

on January 3, 2022. EPA also proposed 

to approve, as a revision to the Michigan 

State Implementation Plan, the State’s 

plan for maintaining the 2015 ozone 

NAAQS through 2035 in the Detroit 

area. EPA also proposed to approve 

Michigan’s 2025 and 2035 volatile 

organic compound and oxides of 

nitrogen motor vehicle emissions 

budgets (budgets) for the Detroit area 

and initiating the adequacy review 

process for these budgets. Finally, EPA 

also proposed to approve portions of 

separate December 18, 2020, submittals 

as meeting the applicable requirements 

for a base year emissions inventory and 

emissions statement program. On March 

21, 2022, Sierra Club requested that EPA 

extend the comment period by 21 days, 

to allow Sierra Club additional time to 

‘‘review the basis for EPA’s proposal 

and confer with local partners.’’ In 

response, EPA is extending the 

comment period for 14 days. This 

extension provides additional time to 

Sierra Club and its local partners, while 

also allowing time for EPA and EGLE to 

plan for additional upcoming 

rulemakings that relate to EPA’s 

findings on this proposed action. 

Dated: March 28, 2022. 

Debra Shore, 

Regional Administrator, Region 5. 

[FR Doc. 2022–07007 Filed 4–1–22; 8:45 am] 

BILLING CODE 6560–50–P 

DEPARTMENT OF HEALTH AND 

HUMAN SERVICES 

Centers for Medicare & Medicaid 

Services 

42 CFR Part 412 

[CMS–1769–P] 

RIN 0938–AU80 

Medicare Program; FY 2023 Inpatient 

Psychiatric Facilities Prospective 

Payment System—Rate Update and 

Quality Reporting—Request for 

Information 

AGENCY: Centers for Medicare & 

Medicaid Services (CMS), HHS. 

ACTION: Proposed rule. 

SUMMARY: This proposed rule would 

update the prospective payment rates, 

the outlier threshold, and the wage 

index for Medicare inpatient hospital 

services provided by Inpatient 

Psychiatric Facilities (IPF), which 

include psychiatric hospitals and 

excluded psychiatric units of an acute 

care hospital or critical access hospital. 

This proposed rule would also establish 

a permanent mitigation policy to 

smooth the impact of year-to-year 

changes in IPF payments related to 

decreases in the IPF wage index. In 

addition, this proposed rule includes a 

request for comment on the results of 

the data analysis of the IPF Prospective 

Payment System adjustments. The 

proposed changes in this rule would be 

effective for IPF discharges occurring 

during the Fiscal Year (FY) beginning 

October 1, 2022 through September 30, 

2023 (FY 2023). Lastly, this proposed 

rule requests information on Measuring 

Equity and Healthcare Quality 

Disparities Across CMS Quality 

Programs. 

DATES: To be assured consideration, 

comments must be received at one of 

the addresses provided below by May 

31, 2022. 

ADDRESSES: In commenting, please refer 

to file code CMS–1769–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–1769–P, P.O. Box 8010, Baltimore, 

MD 21244–8010. 

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–1769–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: The 

IPF Payment Policy mailbox at 

IPFPaymentPolicy@cms.hhs.gov for 

general information. 

Mollie Knight (410) 786–7948 or Eric 

Laib (410) 786–9759, for information 

regarding the market basket update or 

the labor-related share. 

Nick Brock (410) 786–5148 or Theresa 

Bean (410) 786–2287, for information 

regarding the regulatory impact 

analysis. 

Lauren Lowenstein, (410) 786–4507, 

for information regarding the inpatient 

psychiatric facilities 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 Tables 

Exclusively Through the Internet on the 

CMS Website 

Addendum A to this proposed rule 

summarizes the FY 2023 IPF PPS 

payment rates, outlier threshold, cost of 

living adjustment factors (COLA) for 

Alaska and Hawaii, national and upper 

limit cost-to-charge ratios, and 

adjustment factors. In addition, the B 

Addenda to this proposed rule shows 


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19416 Federal Register / Vol. 87, No. 64 / Monday, April 4, 2022 / Proposed Rules 

the complete listing of ICD–10 Clinical 

Modification (CM) and Procedure 

Coding System (PCS) codes, the FY 

2023 IPF PPS comorbidity adjustment, 

and electroconvulsive therapy (ECT) 

procedure codes. The A and B Addenda 

are available online at: https://

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

FacilPPS/tools.html. 

Tables setting forth the FY 2023 Wage 

Index for Urban Areas Based on CoreBased Statistical Area (CBSA) Labor 

Market Areas and the FY 2023 Wage 

Index Based on CBSA Labor Market 

Areas for Rural Areas are available 

exclusively through the internet, on the 

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

Medicare/Medicare-Fee-for-ServicePayment/IPFPPS/WageIndex.html. 

I. Executive Summary 

A. Purpose 

This proposed rule would update the 

prospective payment rates, the outlier 

threshold, and the wage index for 

Medicare inpatient hospital services 

provided by Inpatient Psychiatric 

Facilities (IPFs) for discharges occurring 

during Fiscal Year (FY) 2023 beginning 

October 1, 2022 through September 30, 

2023. This proposed rule would also 

establish a permanent mitigation policy 

to smooth the impact of year-to-year 

changes in IPF payments related to 

changes in the IPF wage index. In 

addition, this proposed rule includes a 

request for comment on the results of 

the data analysis of the IPF Prospective 

Payment System (PPS) adjustments. 

Lastly, this proposed rule requests 

information on Measuring Equity and 

Healthcare Quality Disparities Across 

CMS Quality Programs. 

B. Summary of the Major Provisions 

1. Inpatient Psychiatric Facilities 

Prospective Payment System 

For the IPF PPS, we are proposing 

to— 

• Establish a permanent mitigation 

policy in order to smooth the impact of 

year-to-year changes in IPF payments 

related to decreases to the IPF wage 

index. 

• Solicit comments on the results of 

the data analysis of the IPF PPS 

adjustments, which have been 

summarized in a technical report posted 

to the Centers for Medicare & Medicaid 

Services (CMS) website at https://

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

FacilPPS. 

• Update the IPF PPS base rate by the 

2016-based IPF market basket update 

(3.1 percent) adjusted for economy-wide 

productivity (0.4 percentage point) as 

required by section 1886(s)(2)(A)(i) of 

the Social Security Act (the Act), 

resulting in a proposed IPF payment 

rate update of 2.7 percent for FY 2023. 

• Make technical rate setting updates: 

The IPF PPS payment rates would be 

adjusted annually for inflation, as well 

as statutory and other policy factors. 

This rule proposes to update: 

++ The IPF PPS Federal per diem 

base rate from $832.94 to $856.80. 

++ The IPF PPS Federal per diem 

base rate for providers who failed to 

report quality data to $840.11. 

++ The ECT payment per treatment 

from $358.60 to $368.87. 

++ The ECT payment per treatment 

for providers who failed to report 

quality data to $361.69. 

++ The labor-related share from 77.2 

percent to 77.4 percent. 

++ The wage index budget-neutrality 

factor to 1.0016. 

++ The fixed dollar loss threshold 

amount from $16,040 to $24,270 to 

maintain estimated outlier payments at 

2 percent of total estimated aggregate 

IPF PPS payments. 

2. Inpatient Psychiatric Facilities 

Quality Reporting (IPFQR) Program 

We are not proposing any changes to 

the IPFQR Program. However, we are 

including a request for information (RFI) 

on the Overarching Principles for 

Measuring Healthcare Quality 

Disparities Across CMS Quality 

Programs. Feedback provided will 

inform future efforts in all CMS Quality 

programs and, as applicable, may be 

introduced in the IPFQR as future RFIs 

or proposals. 

C. Summary of Impacts 

Provision description Total transfers & cost reductions 

FY 2023 IPF PPS payment update ............................. The overall economic impact of this proposed rule is an estimated $50 million in increased payments to IPFs during FY 2023. 

II. Background 

A. Overview of the Legislative 

Requirements of the IPF PPS 

Section 124 of the Medicare, 

Medicaid, and State Children’s Health 

Insurance Program Balanced Budget 

Refinement Act of 1999 (BBRA) (Pub. L. 

106–113) required the establishment 

and implementation of an IPF PPS. 

Specifically, section 124 of the BBRA 

mandated that the Secretary of the 

Department of Health and Human 

Services (the Secretary) develop a per 

diem PPS for inpatient hospital services 

furnished in psychiatric hospitals and 

excluded psychiatric units including an 

adequate patient classification system 

that reflects the differences in patient 

resource use and costs among 

psychiatric hospitals and excluded 

psychiatric units. ‘‘Excluded psychiatric 

unit’’ means a psychiatric unit of an 

acute care hospital or of a Critical 

Access Hospital (CAH), which is 

excluded from payment under the 

Inpatient Prospective Payment System 

(IPPS) or CAH payment system, 

respectively. These excluded 

psychiatric units will be paid under the 

IPF PPS. 

Section 405(g)(2) of the Medicare 

Prescription Drug, Improvement, and 

Modernization Act of 2003 (MMA) (Pub. 

L. 108–173) extended the IPF PPS to 

psychiatric distinct part units of CAHs. 

Sections 3401(f) and 10322 of the 

Patient Protection and Affordable Care 

Act (Pub. L. 111–148) as amended by 

section 10319(e) of that Act and by 

section 1105(d) of the Health Care and 

Education Reconciliation Act of 2010 

(Pub. L. 111–152) (hereafter referred to 

jointly as ‘‘the Affordable Care Act’’) 

added subsection (s) to section 1886 of 

the Act. 

Section 1886(s)(1) of the Act titled 

‘‘Reference to Establishment and 

Implementation of System,’’ refers to 

section 124 of the BBRA, which relates 

to the establishment of the IPF PPS. 

Section 1886(s)(2)(A)(i) of the Act 

requires the application of the 

productivity adjustment described in 

section 1886(b)(3)(B)(xi)(II) of the Act to 

the IPF PPS for the rate year (RY) 

beginning in 2012 (that is, a RY that 

coincides with a FY) and each 

subsequent RY. 

Section 1886(s)(2)(A)(ii) of the Act 

required the application of an ‘‘other 

adjustment’’ that reduced any update to 

an IPF PPS base rate by a percentage 

point amount specified in section 

1886(s)(3) of the Act for the RY 

beginning in 2010 through the RY 

beginning in 2019. As noted in the FY 

2020 IPF PPS final rule, for the RY 

beginning in 2019, section 1886(s)(3)(E) 

of the Act required that the other 

adjustment reduction be equal to 0.75 

percentage point; this was the final year 


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the statute required the application of 

this adjustment. Because FY 2021 was a 

RY beginning in 2020, FY 2021 was the 

first-year section 1886(s)(2)(A)(ii) did 

not apply since its enactment. 

Sections 1886(s)(4)(A) through (D) of 

the Act require that for RY 2014 and 

each subsequent RY, IPFs that fail to 

report required quality data with respect 

to such a RY will have their annual 

update to a standard Federal rate for 

discharges reduced by 2.0 percentage 

points. This may result in an annual 

update being less than 0.0 for a RY, and 

may result in payment rates for the 

upcoming RY being less than such 

payment rates for the preceding RY. 

Any reduction for failure to report 

required quality data will apply only to 

the RY involved, and the Secretary will 

not consider such reduction in 

computing the payment amount for a 

subsequent RY. Additional information 

about the specifics of the current 

Inpatient Psychiatric Facilities Quality 

Reporting (IPFQR) Program is available 

in the FY 2020 IPF PPS and Quality 

Reporting Updates for FY Beginning 

October 1, 2019 final rule (84 FR 38459 

through 38468). 

To implement and periodically 

update these provisions, we have 

published various proposed and final 

rules and notices in the Federal 

Register. For more information 

regarding these documents, see the CMS 

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

Medicare/Medicare-Fee-for-ServicePayment/InpatientPsychFacilPPS/ 

index.html?redirect=/InpatientPsych

FacilPPS/. 

B. Overview of the IPF PPS 

On November 15, 2004, we published 

the IPF PPS final rule in the Federal 

Register (69 FR 66922). The November 

2004 IPF PPS final rule established the 

IPF PPS, as required by section 124 of 

the BBRA and codified at 42 CFR part 

412, subpart N. The November 2004 IPF 

PPS final rule set forth the Federal per 

diem base rate for the implementation 

year (the 18-month period from January 

1, 2005 through June 30, 2006), and 

provided payment for the inpatient 

operating and capital costs to IPFs for 

covered psychiatric services they 

furnish (that is, routine, ancillary, and 

capital costs, but not costs of approved 

educational activities, bad debts, and 

other services or items that are outside 

the scope of the IPF PPS). Covered 

psychiatric services include services for 

which benefits are provided under the 

fee-for-service Part A (Hospital 

Insurance Program) of the Medicare 

program. 

The IPF PPS established the Federal 

per diem base rate for each patient day 

in an IPF derived from the national 

average daily routine operating, 

ancillary, and capital costs in IPFs in FY 

2002. The average per diem cost was 

updated to the midpoint of the first year 

under the IPF PPS, standardized to 

account for the overall positive effects of 

the IPF PPS payment adjustments, and 

adjusted for budget-neutrality. 

The Federal per diem payment under 

the IPF PPS is comprised of the Federal 

per diem base rate described previously 

and certain patient- and facility-level 

payment adjustments for characteristics 

that were found in the regression 

analysis to be associated with 

statistically significant per diem cost 

differences; with statistical significance 

defined as p less than 0.05. A complete 

discussion of the regression analysis 

that established the IPF PPS adjustment 

factors can be found in the November 

2004 IPF PPS final rule (69 FR 66933 

through 66936). 

The patient-level adjustments include 

age, Diagnosis-Related Group (DRG) 

assignment, and comorbidities, as well 

as adjustments to reflect higher per 

diem costs at the beginning of a 

patient’s IPF stay and lower costs for 

later days of the stay. Facility-level 

adjustments include adjustments for the 

IPF’s wage index, rural location, 

teaching status, a cost-of-living 

adjustment for IPFs located in Alaska 

and Hawaii, and an adjustment for the 

presence of a qualifying emergency 

department (ED). 

The IPF PPS provides additional 

payment policies for outlier cases, 

interrupted stays, and a per treatment 

payment for patients who undergo 

electroconvulsive therapy (ECT). During 

the IPF PPS mandatory 3-year transition 

period, stop-loss payments were also 

provided; however, since the transition 

ended as of January 1, 2008, these 

payments are no longer available. 

C. Annual Requirements for Updating 

the IPF PPS 

Section 124 of the BBRA did not 

specify an annual rate update strategy 

for the IPF PPS and was broadly written 

to give the Secretary discretion in 

establishing an update methodology. 

Therefore, in the November 2004 IPF 

PPS final rule, we implemented the IPF 

PPS using the following update strategy: 

• Calculate the final Federal per diem 

base rate to be budget-neutral for the 18- 

month period of January 1, 2005 

through June 30, 2006. 

• Use a July 1 through June 30 annual 

update cycle. 

• Allow the IPF PPS first update to be 

effective for discharges on or after July 

1, 2006 through June 30, 2007. 

The November 2004 final rule (69 FR 

66922) implemented the IPF PPS. In 

developing the IPF PPS, and to ensure 

that the IPF PPS can account adequately 

for each IPF’s case-mix, we performed 

an extensive regression analysis of the 

relationship between the per diem costs 

and certain patient and facility 

characteristics to determine those 

characteristics associated with 

statistically significant cost differences 

on a per diem basis. That regression 

analysis is described in detail in our 

November 28, 2003 IPF proposed rule 

(68 FR 66923; 66928 through 66933) and 

our November 15, 2004 IPF final rule 

(69 FR 66933 through 66960). For 

characteristics with statistically 

significant cost differences, we used the 

regression coefficients of those variables 

to determine the size of the 

corresponding payment adjustments. 

In the November 2004 IPF final rule, 

we explained the reasons for delaying 

an update to the adjustment factors, 

derived from the regression analysis, 

including waiting until we have IPF PPS 

data that yields as much information as 

possible regarding the patient-level 

characteristics of the population that 

each IPF serves. We indicated that we 

did not intend to update the regression 

analysis and the patient-level and 

facility-level adjustments until we 

complete that analysis. Until that 

analysis is complete, we stated our 

intention to publish a notice in the 

Federal Register each spring to update 

the IPF PPS (69 FR 66966). 

On May 6, 2011, we published a final 

rule in the Federal Register titled, 

‘‘Inpatient Psychiatric Facilities 

Prospective Payment System—Update 

for Rate Year Beginning July 1, 2011 (RY 

2012)’’ (76 FR 26432), which changed 

the payment rate update period to a RY 

that coincides with a FY update. 

Therefore, final rules are now published 

in the Federal Register in the summer 

to be effective on October 1st. When 

proposing changes in IPF payment 

policy, a proposed rule is issued in the 

spring, and the final rule in the summer 

to be effective on October 1st. For a 

detailed list of updates to the IPF PPS, 

we refer readers to our regulations at 42 

CFR 412.428. 

The most recent IPF PPS annual 

update was published in a final rule on 

August 4, 2021 in the Federal Register 

titled, ‘‘Medicare Program; FY 2022 

Inpatient Psychiatric Facilities 

Prospective Payment System and 

Quality Reporting Updates for Fiscal 

Year Beginning October 1, 2021 (FY 

2022)’’ (86 FR 42608), which updated 

the IPF PPS payment rates for FY 2022. 

That final rule updated the IPF PPS 

Federal per diem base rates that were 


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19418 Federal Register / Vol. 87, No. 64 / Monday, April 4, 2022 / Proposed Rules 

published in the FY 2021 IPF PPS Rate 

Update final rule (85 FR 47042) in 

accordance with our established 

policies. 

III. Provisions of the FY 2023 IPF PPS 

Proposed Rule 

A. Proposed FY 2023 Market Basket 

Update and Productivity Adjustment for 

the IPF PPS 

1. Background 

Originally, the input price index that 

was used to develop the IPF PPS was 

the ‘‘Excluded Hospital with Capital’’ 

market basket. This market basket was 

based on 1997 Medicare cost reports for 

Medicare participating inpatient 

rehabilitation facilities (IRFs), IPFs, 

long-term care hospitals (LTCHs), 

cancer hospitals, and children’s 

hospitals. Although ‘‘market basket’’ 

technically describes the mix of goods 

and services used in providing health 

care at a given point in time, this term 

is also commonly used to denote the 

input price index (that is, cost category 

weights and price proxies) derived from 

that market basket. Accordingly, the 

term market basket as used in this 

document, refers to an input price 

index. 

Since the IPF PPS inception, the 

market basket used to update IPF PPS 

payments has been rebased and revised 

to reflect more recent data on IPF cost 

structures. We last rebased and revised 

the IPF market basket in the FY 2020 

IPF PPS rule, where we adopted a 2016- 

based IPF market basket, using Medicare 

cost report data for both Medicare 

participating freestanding psychiatric 

hospitals and psychiatric units. We refer 

readers to the FY 2020 IPF PPS final 

rule for a detailed discussion of the 

2016-based IPF PPS market basket and 

its development (84 FR 38426 through 

38447). References to the historical 

market baskets used to update IPF PPS 

payments are listed in the FY 2016 IPF 

PPS final rule (80 FR 46656). 

2. Proposed FY 2023 IPF Market Basket 

Update 

For FY 2023 (beginning October 1, 

2022 and ending September 30, 2023), 

we are proposing to update the IPF PPS 

payments by a market basket increase 

factor with a productivity adjustment as 

required by section 1886(s)(2)(A)(i) of 

the Act. Consistent with historical 

practice, we are proposing to estimate 

the market basket update for the IPF 

PPS based on the most recent forecast 

available at the time of rulemaking from 

IHS Global Inc. (IGI). IGI is a nationally 

recognized economic and financial 

forecasting firm with which CMS 

contracts to forecast the components of 

the market baskets and productivity 

adjustment. For the proposed rule, 

based on IGI’s fourth quarter 2021 

forecast with historical data through the 

third quarter of 2021, the 2016-based 

IPF market basket increase factor for FY 

2023 is 3.1 percent. 

Section 1886(s)(2)(A)(i) of the Act 

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 (MFP) (as projected by the 

Secretary for the 10-year period ending 

with the applicable FY, year, cost 

reporting period, or other annual 

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

The United States Department of Labor’s 

Bureau of Labor Statistics (BLS) 

publishes the official measures of 

productivity for the United States 

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 MFP. Beginning with 

the November 18, 2021 release of 

productivity data, BLS replaced the 

term ‘‘multifactor productivity’’ with 

‘‘total factor productivity’’ (TFP). BLS 

noted that this is a change in 

terminology only and will not affect the 

data or methodology. As a result of the 

BLS name change, the productivity 

measure referenced in section 

1886(b)(3)(B)(xi)(II) of the Act is now 

published by BLS as private nonfarm 

business total factor productivity. 

However, as mentioned previously, the 

data and methods are unchanged. We 

refer readers to 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 IPF final rule (86 FR 

42611), 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. 

Section 1886(s)(2)(A)(i) of the Act 

requires the application of the 

productivity adjustment described in 

section 1886(b)(3)(B)(xi)(II) of the Act to 

the IPF PPS for the RY beginning in 

2012 (a RY that coincides with a FY) 

and each subsequent RY. For this FY 

2023 IPF PPS proposed rule, based on 

IGI’s fourth quarter 2021 forecast, the 

proposed productivity adjustment for 

FY 2023 (the 10-year moving average of 

TFP for the period ending FY 2023) is 

projected to be 0.4 percent. Accordingly, 

we are proposing to reduce the 3.1 

percent IPF market basket update by 

this 0.4 percentage point productivity 

adjustment, as mandated by the Act. 

This results in a proposed FY 2023 IPF 

PPS payment rate update of 2.7 percent 

(3.1¥0.4 = 2.7). We are also proposing 

that if more recent data become 

available, we would use such data, if 

appropriate, to determine the FY 2023 

IPF market basket update and 

productivity adjustment for the final 

rule. 

3. Proposed FY 2023 IPF Labor-Related 

Share 

Due to variations in geographic wage 

levels and other labor-related costs, we 

believe that payment rates under the IPF 

PPS should continue to be adjusted by 

a geographic wage index, which would 

apply to the labor-related portion of the 

Federal per diem base rate (hereafter 

referred to as the labor-related share). 

The labor-related 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 IPF market basket, we 

are proposing to continue to include in 

the labor-related share the sum of the 

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 IPF market basket. For more 

details regarding the methodology for 

determining specific cost categories for 

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

final rule (84 FR 38445 through 38447). 

The relative importance reflects the 

different rates of price change for these 

cost categories between the base year 

(FY 2016) and FY 2023. Based on IGI’s 

fourth quarter 2021 forecast of the 2016- 

based IPF market basket, the sum of the 

FY 2023 relative importance moving 

average of Wages and Salaries; 

Employee Benefits; Professional Fees: 


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Federal Register / Vol. 87, No. 64 / Monday, April 4, 2022 / Proposed Rules 19419 

Labor-related; Administrative and 

Facilities Support Services; Installation, 

Maintenance, and Repair Services; All 

Other: Labor-related Services is 74.4 

percent. We also propose, consistent 

with prior rulemaking, that the portion 

of Capital-Related costs that are 

influenced by the local labor market is 

46 percent. Since the relative 

importance for Capital-Related costs are 

6.6 percent of the 2016-based IPF 

market basket for FY 2023, we propose 

to take 46 percent of 6.6 percent to 

determine a labor-related share of 

Capital-Related costs for FY 2023 of 3.0 

percent. Therefore, we propose a total 

labor-related share for FY 2023 of 77.4 

percent (the sum of 74.4 percent for the 

labor-related share of operating costs 

and 3.0 percent for the labor-related 

share of Capital-Related costs). We are 

also proposing that if more recent data 

become available, we would use such 

data, if appropriate, to determine the FY 

2023 labor-related share for the final 

rule. For more information on the laborrelated share and its calculation, we 

refer readers to the FY 2020 IPF PPS 

final rule (84 FR 38445 through 38447). 

Table 1 shows the proposed FY 2023 

labor-related share and the final FY 

2022 labor-related share using the 2016- 

based IPF market basket relative 

importance. 

TABLE 1—FY 2023 PROPOSED IPF LABOR-RELATED SHARE AND FY 2022 IPF LABOR-RELATED SHARE

Relative importance, 

proposed labor-related 

share FY 2023 1

Relative importance, 

labor-related 

share 

FY 2022 2

Wages and Salaries ................................................................................................................ 53.3 52.8 

Employee Benefits ................................................................................................................... 13.4 13.6 

Professional Fees: Labor-related ............................................................................................ 4.3 4.3 

Administrative and Facilities Support Services ....................................................................... 0.6 0.6 

Installation, Maintenance and Repair ...................................................................................... 1.3 1.3 

All Other Labor-related ............................................................................................................

Services ................................................................................................................................... 1.5 1.5 

Subtotal ............................................................................................................................. 74.4 74.1 

Labor-related portion of Capital-Related (.46) ......................................................................... 3.0 3.1 

Total Labor-Related Share ............................................................................................... 77.4 77.2 

1Based on the 4th quarter 2021 IHS Global Inc. forecast of the 2016-based IPF market basket. 

2Based on the 2nd quarter 2021 IHS Global Inc. forecast of the 2016-based IPF market basket. 

We invite public comments on the 

proposed labor-related share for FY 

2023. 

B. Proposed Updates to the IPF PPS 

Rates for FY Beginning October 1, 2022 

The IPF PPS is based on a 

standardized Federal per diem base rate 

calculated from the IPF average per 

diem costs and adjusted for budgetneutrality in the implementation year. 

The Federal per diem base rate is used 

as the standard payment per day under 

the IPF PPS and is adjusted by the 

patient-level and facility-level 

adjustments that are applicable to the 

IPF stay. A detailed explanation of how 

we calculated the average per diem cost 

appears in the November 2004 IPF PPS 

final rule (69 FR 66926). 

1. Determining the Standardized 

Budget-Neutral Federal Per Diem Base 

Rate 

Section 124(a)(1) of the BBRA 

required that we implement the IPF PPS 

in a budget-neutral manner. In other 

words, the amount of total payments 

under the IPF PPS, including any 

payment adjustments, must be projected 

to be equal to the amount of total 

payments that would have been made if 

the IPF PPS were not implemented. 

Therefore, we calculated the budgetneutrality factor by setting the total 

estimated IPF PPS payments to be equal 

to the total estimated payments that 

would have been made under the Tax 

Equity and Fiscal Responsibility Act of 

1982 (TEFRA) (Pub. L. 97–248) 

methodology had the IPF PPS not been 

implemented. A step-by-step 

description of the methodology used to 

estimate payments under the TEFRA 

payment system appears in the 

November 2004 IPF PPS final rule (69 

FR 66926). 

Under the IPF PPS methodology, we 

calculated the final Federal per diem 

base rate to be budget-neutral during the 

IPF PPS implementation period (that is, 

the 18-month period from January 1, 

2005 through June 30, 2006) using a July 

1 update cycle. We updated the average 

cost per day to the midpoint of the IPF 

PPS implementation period (October 1, 

2005), and this amount was used in the 

payment model to establish the budgetneutrality adjustment. 

Next, we standardized the IPF PPS 

Federal per diem base rate to account 

for the overall positive effects of the IPF 

PPS payment adjustment factors by 

dividing total estimated payments under 

the TEFRA payment system by 

estimated payments under the IPF PPS. 

The information concerning this 

standardization can be found in the 

November 2004 IPF PPS final rule (69 

FR 66932) and the RY 2006 IPF PPS 

final rule (71 FR 27045). We then 

reduced the standardized Federal per 

diem base rate to account for the outlier 

policy, the stop loss provision, and 

anticipated behavioral changes. A 

complete discussion of how we 

calculated each component of the 

budget-neutrality adjustment appears in 

the November 2004 IPF PPS final rule 

(69 FR 66932 through 66933) and in the 

RY 2007 IPF PPS final rule (71 FR 27044 

through 27046). The final standardized 

budget-neutral Federal per diem base 

rate established for cost reporting 

periods beginning on or after January 1, 

2005 was calculated to be $575.95. 

The Federal per diem base rate has 

been updated in accordance with 

applicable statutory requirements and 

§ 412.428 through publication of annual 

notices or proposed and final rules. A 

detailed discussion on the standardized 

budget-neutral Federal per diem base 

rate and the electroconvulsive therapy 

(ECT) payment per treatment appears in 

the FY 2014 IPF PPS update notice (78 

FR 46738 through 46740). These 

documents are available on the CMS 

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

Medicare/Medicare-Fee-for-ServicePayment/InpatientPsychFacilPPS/ 

index.html. 

IPFs must include a valid procedure 

code for ECT services provided to IPF 

beneficiaries in order to bill for ECT 


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services, as described in our Medicare 

Claims Processing Manual, Chapter 3, 

Section 190.7.3 (available at https://

www.cms.gov/Regulations-andGuidance/Guidance/Manuals/ 

Downloads/clm104c03.pdf.) There were 

no changes to the ECT procedure codes 

used on IPF claims as a result of the 

final update to the ICD–10–PCS code set 

for FY 2023. Addendum B to this 

proposed rule shows the ECT procedure 

codes for FY 2023 and is available on 

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

Medicare/Medicare-Fee-for-ServicePayment/InpatientPsychFacilPPS/ 

tools.html. 

2. Proposed Update of the Federal per 

Diem Base Rate and Electroconvulsive 

Therapy Payment per Treatment 

The current (FY 2022) Federal per 

diem base rate is $832.94 and the ECT 

payment per treatment is $358.60. For 

the proposed FY 2023 Federal per diem 

base rate, we applied the payment rate 

update of 2.7 percent—that is, the 

proposed 2016-based IPF market basket 

increase for FY 2023 of 3.1 percent less 

the proposed productivity adjustment of 

0.4 percentage point—and the proposed 

wage index budget-neutrality factor of 

1.0016 (as discussed in section III.D.1 of 

this proposed rule) to the FY 2022 

Federal per diem base rate of $832.94, 

yielding a proposed Federal per diem 

base rate of $856.80 for FY 2023. 

Similarly, we applied the proposed 2.7 

percent payment rate update and the 

proposed 1.0016 wage index budgetneutrality factor to the FY 2022 ECT 

payment per treatment of $358.60, 

yielding a proposed ECT payment per 

treatment of $368.87 for FY 2023. 

Section 1886(s)(4)(A)(i) of the Act 

requires that for RY 2014 and each 

subsequent RY, in the case of an IPF 

that fails to report required quality data 

with respect to such RY, the Secretary 

will reduce any annual update to a 

standard Federal rate for discharges 

during the RY by 2.0 percentage points. 

Therefore, we are applying a 2.0 

percentage point reduction to the 

Federal per diem base rate and the ECT 

payment per treatment as follows: 

• For IPFs that fail to report required 

data under the IPFQR Program, we 

applied a 0.7 percent payment rate 

update—that is, the proposed IPF 

market basket increase for FY 2023 of 

3.1 percent less the proposed 

productivity adjustment of 0.4 

percentage point for an update of 2.7 

percent, and further reduced by 2.0 

percentage points in accordance with 

section 1886(s)(4)(A)(i) of the Act—and 

the proposed wage index budgetneutrality factor of 1.0016 to the FY 

2022 Federal per diem base rate of 

$832.94, yielding a proposed Federal 

per diem base rate of $840.11 for FY 

2023. 

• For IPFs that fail to report required 

data under the IPFQR Program, we 

applied the proposed 0.7 percent annual 

payment rate update and the proposed 

1.0016 wage index budget-neutrality 

factor to the FY 2022 ECT payment per 

treatment of $358.60, yielding a 

proposed ECT payment per treatment of 

$361.69 for FY 2023. 

Lastly, we are also proposing that if 

more recent data become available, we 

would use such data, if appropriate, to 

determine the FY 2023 Federal per diem 

base rate and ECT payment per 

treatment for the final rule. 

C. Proposed Updates to the IPF PPS 

Patient-Level Adjustment Factors 

1. Overview of the IPF PPS Adjustment 

Factors 

The IPF PPS payment adjustments 

were derived from a regression analysis 

of 100 percent of the FY 2002 Medicare 

Provider and Analysis Review 

(MedPAR) data file, which contained 

483,038 cases. For a more detailed 

description of the data file used for the 

regression analysis, see the November 

2004 IPF PPS final rule (69 FR 66935 

through 66936). We are proposing to 

continue to use the existing regressionderived adjustment factors established 

in 2005 for FY 2023. However, we have 

used more recent claims data to 

simulate payments to finalize the outlier 

fixed dollar loss threshold amount and 

to assess the impact of the IPF PPS 

updates. 

2. IPF PPS Patient-Level Adjustments 

The IPF PPS includes payment 

adjustments for the following patientlevel characteristics: Medicare Severity 

Diagnosis Related Groups (MS–DRGs) 

assignment of the patient’s principal 

diagnosis, selected comorbidities, 

patient age, and the variable per diem 

adjustments. 

a. Proposed Update to MS–DRG 

Assignment 

We believe it is important to maintain 

for IPFs the same diagnostic coding and 

Diagnosis Related Group (DRG) 

classification used under the IPPS for 

providing psychiatric care. For this 

reason, when the IPF PPS was 

implemented for cost reporting periods 

beginning on or after January 1, 2005, 

we adopted the same diagnostic code set 

(ICD–9–CM) and DRG patient 

classification system (MS–DRGs) that 

were utilized at the time under the IPPS. 

In the RY 2009 IPF PPS notice (73 FR 

25709), we discussed CMS’ effort to 

better recognize resource use and the 

severity of illness among patients. CMS 

adopted the new MS–DRGs for the IPPS 

in the FY 2008 IPPS final rule with 

comment period (72 FR 47130). In the 

RY 2009 IPF PPS notice (73 FR 25716), 

we provided a crosswalk to reflect 

changes that were made under the IPF 

PPS to adopt the new MS–DRGs. For a 

detailed description of the mapping 

changes from the original DRG 

adjustment categories to the current 

MS–DRG adjustment categories, we 

refer readers to the RY 2009 IPF PPS 

notice (73 FR 25714). 

The IPF PPS includes payment 

adjustments for designated psychiatric 

DRGs assigned to the claim based on the 

patient’s principal diagnosis. The DRG 

adjustment factors were expressed 

relative to the most frequently reported 

psychiatric DRG in FY 2002, that is, 

DRG 430 (psychoses). The coefficient 

values and adjustment factors were 

derived from the regression analysis 

discussed in detail in the November 28, 

2003 IPF proposed rule (68 FR 66923; 

66928 through 66933) and the 

November 15, 2004 IPF final rule (69 FR 

66933 through 66960). Mapping the 

DRGs to the MS–DRGs resulted in the 

current 17 IPF MS–DRGs, instead of the 

original 15 DRGs, for which the IPF PPS 

provides an adjustment. For FY 2023, 

we are not proposing any changes to the 

IPF MS–DRG adjustment factors. 

Therefore, we are retaining the existing 

IPF MS–DRG adjustment factors. 

In the FY 2015 IPF PPS final rule 

published August 6, 2014 in the Federal 

Register titled, ‘‘Inpatient Psychiatric 

Facilities Prospective Payment 

System—Update for FY Beginning 

October 1, 2014 (FY 2015)’’ (79 FR 

45945 through 45947), we finalized 

conversions of the ICD–9–CM–based 

MS–DRGs to ICD–10–CM/PCS–based 

MS–DRGs, which were implemented on 

October 1, 2015. Further information on 

the ICD–10–CM/PCS MS–DRG 

conversion project can be found on the 

CMS ICD–10–CM website at https://

www.cms.gov/Medicare/Coding/ICD10/ 

ICD-10-MS-DRG-ConversionProject.html. 

For FY 2023, we are proposing to 

continue to make the existing payment 

adjustment for psychiatric diagnoses 

that group to one of the existing 17 IPF 

MS–DRGs listed in Addendum A. 

Addendum A is available on our 

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

Medicare/Medicare-Fee-for-ServicePayment/InpatientPsychFacilPPS/ 

tools.html. Psychiatric principal 

diagnoses that do not group to one of 

the 17 designated MS–DRGs will still 

receive the Federal per diem base rate 

and all other applicable adjustments; 


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however, the payment will not include 

an MS–DRG adjustment. The diagnoses 

for each IPF MS–DRG will be updated 

as of October 1, 2022, using the final 

IPPS FY 2023 ICD–10–CM/PCS code 

sets. The FY 2023 IPPS/LTCH PPS final 

rule includes tables of the changes to 

the ICD–10–CM/PCS code sets, which 

underlie the FY 2023 IPF MS–DRGs. 

Both the FY 2023 IPPS final rule and the 

tables of final changes to the ICD–10– 

CM/PCS code sets, which underlie the 

FY 2023 MS–DRGs, are available on the 

CMS IPPS website at https://

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

PPS/index.html. 

Code First 

As discussed in the ICD–10–CM 

Official Guidelines for Coding and 

Reporting, certain conditions have both 

an underlying etiology and multiple 

body system manifestations due to the 

underlying etiology. For such 

conditions, ICD–10–CM has a coding 

convention that requires the underlying 

condition be sequenced first followed 

by the manifestation. Wherever such a 

combination exists, there is a ‘‘use 

additional code’’ note at the etiology 

code, and a ‘‘code first’’ note at the 

manifestation code. These instructional 

notes indicate the proper sequencing 

order of the codes (etiology followed by 

manifestation). In accordance with the 

ICD–10–CM Official Guidelines for 

Coding and Reporting, when a primary 

(psychiatric) diagnosis code has a ‘‘code 

first’’ note, the provider will follow the 

instructions in the ICD–10–CM Tabular 

List. The submitted claim goes through 

the CMS processing system, which will 

identify the principal diagnosis code as 

non-psychiatric and search the 

secondary codes for a psychiatric code 

to assign a DRG code for adjustment. 

The system will continue to search the 

secondary codes for those that are 

appropriate for comorbidity adjustment. 

For more information on the code first 

policy, we refer readers to the November 

2004 IPF PPS final rule (69 FR 66945) 

and see sections I.A.13 and I.B.7 of the 

FY 2020 ICD–10–CM Coding 

Guidelines, available at https://

www.cdc.gov/nchs/data/icd/ 

10cmguidelines-FY2020_final.pdf. In 

the FY 2015 IPF PPS final rule, we 

provided a code first table for reference 

that highlights the same or similar 

manifestation codes where the code first 

instructions apply in ICD–10–CM that 

were present in ICD–9–CM (79 FR 

46009). In FY 2022 there were 18 codes 

finalized for deletion from the ICD–10– 

CM codes in the IPF Code First table. 

For FY 2023, we are proposing to delete 

2 ICD–10–PCS codes and proposing to 

add 48 ICD–10–PCS codes to the IPF 

Code First table. The proposed FY 2023 

Code First table is shown in Addendum 

B on the CMS website at https://

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

FacilPPS/tools.html. 

b. Proposed Payment for Comorbid 

Conditions 

The intent of the comorbidity 

adjustments is to recognize the 

increased costs associated with 

comorbid conditions by providing 

additional payments for certain existing 

medical or psychiatric conditions that 

are expensive to treat. In our RY 2012 

IPF PPS final rule (76 FR 26451 through 

26452), we explained that the IPF PPS 

includes 17 comorbidity categories and 

identified the new, revised, and deleted 

ICD–9–CM diagnosis codes that generate 

a comorbid condition payment 

adjustment under the IPF PPS for RY 

2012 (76 FR 26451). 

Comorbidities are specific patient 

conditions that are secondary to the 

patient’s principal diagnosis and that 

require treatment during the stay. 

Diagnoses that relate to an earlier 

episode of care and have no bearing on 

the current hospital stay are excluded 

and must not be reported on IPF claims. 

Comorbid conditions must exist at the 

time of admission or develop 

subsequently, and affect the treatment 

received, length of stay (LOS), or both 

treatment and LOS. 

For each claim, an IPF may receive 

only one comorbidity adjustment within 

a comorbidity category, but it may 

receive an adjustment for more than one 

comorbidity category. Current billing 

instructions for discharge claims, on or 

after October 1, 2015, require IPFs to 

enter the complete ICD–10–CM codes 

for up to 24 additional diagnoses if they 

co-exist at the time of admission, or 

develop subsequently and impact the 

treatment provided. 

The comorbidity adjustments were 

determined based on the regression 

analysis using the diagnoses reported by 

IPFs in FY 2002. The principal 

diagnoses were used to establish the 

DRG adjustments and were not 

accounted for in establishing the 

comorbidity category adjustments, 

except where ICD–9–CM code first 

instructions applied. In a code first 

situation, the submitted claim goes 

through the CMS processing system, 

which will identify the principal 

diagnosis code as non-psychiatric and 

search the secondary codes for a 

psychiatric code to assign an MS–DRG 

code for adjustment. The system will 

continue to search the secondary codes 

for those that are appropriate for 

comorbidity adjustment. 

As noted previously, it is our policy 

to maintain the same diagnostic coding 

set for IPFs that is used under the IPPS 

for providing the same psychiatric care. 

The 17 comorbidity categories formerly 

defined using ICD–9–CM codes were 

converted to ICD–10–CM/PCS in our FY 

2015 IPF PPS final rule (79 FR 45947 

through 45955). The goal for converting 

the comorbidity categories is referred to 

as replication, meaning that the 

payment adjustment for a given patient 

encounter is the same after ICD–10–CM 

implementation as it will be if the same 

record had been coded in ICD–9–CM 

and submitted prior to ICD–10–CM/PCS 

implementation on October 1, 2015. All 

conversion efforts were made with the 

intent of achieving this goal. For FY 

2023, we are proposing to continue to 

use the same comorbidity adjustment 

factors in effect in FY 2022. The 

proposed FY 2023 comorbidity 

adjustment factors are found in 

Addendum A, available on the CMS 

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

Medicare/Medicare-Fee-for-ServicePayment/InpatientPsychFacilPPS/ 

tools.html. 

For FY 2023, we are proposing to add 

10 ICD–10–CM/PCS codes and remove 1 

ICD–10–CM/PCS code from the 

Coagulation Factor category; proposing 

to add 3 ICD–10–CM/PCS codes and 

remove 11 ICD–10–CM/PCS codes from 

the Oncology Treatment comorbidity 

category; and proposing to add 4 ICD– 

10–CM/PCS codes to the Poisoning 

comorbidity category. The proposed FY 

2023 comorbidity codes are shown in 

Addenda B, available on the CMS 

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

Medicare/Medicare-Fee-for-ServicePayment/InpatientPsychFacilPPS/ 

tools.html. 

In accordance with the policy 

established in the FY 2015 IPF PPS final 

rule (79 FR 45949 through 45952), we 

reviewed all new FY 2023 ICD–10–CM 

codes to remove codes that were site 

‘‘unspecified’’ in terms of laterality from 

the FY 2023 ICD–10–CM/PCS codes in 

instances where more specific codes are 

available. As we stated in the FY 2015 

IPF PPS final rule, we believe that 

specific diagnosis codes that narrowly 

identify anatomical sites where disease, 

injury, or a condition exists should be 

used when coding patients’ diagnoses 

whenever these codes are available. We 

finalized in the FY 2015 IPF PPS rule, 

that we would remove site 

‘‘unspecified’’ codes from the IPF PPS 

ICD–10–CM/PCS codes in instances 

when laterality codes (site specified 

codes) are available, as the clinician 

should be able to identify a more 


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specific diagnosis based on clinical 

assessment at the medical encounter. 

There were no proposed changes to the 

FY 2023 ICD–10–CM/PCS codes, 

therefore, we are not proposing to 

remove any of the new codes. 

c. Proposed Patient Age Adjustments 

As explained in the November 2004 

IPF PPS final rule (69 FR 66922), we 

analyzed the impact of age on per diem 

cost by examining the age variable 

(range of ages) for payment adjustments. 

In general, we found that the cost per 

day increases with age. The older age 

groups are costlier than the under 45 age 

group, the differences in per diem cost 

increase for each successive age group, 

and the differences are statistically 

significant. For FY 2023, we are 

proposing to continue to use the patient 

age adjustments currently in effect in FY 

2022, as shown in Addendum A of this 

rule (see https://www.cms.gov/ 

Medicare/Medicare-Fee-for-ServicePayment/InpatientPsychFacilPPS/ 

tools.html). 

d. Proposed Variable Per Diem 

Adjustments 

We explained in the November 2004 

IPF PPS final rule (69 FR 66946) that the 

regression analysis indicated that per 

diem cost declines as the length of stay 

(LOS) increases. The variable per diem 

adjustments to the Federal per diem 

base rate account for ancillary and 

administrative costs that occur 

disproportionately in the first days after 

admission to an IPF. As discussed in the 

November 2004 IPF PPS final rule, we 

used a regression analysis to estimate 

the average differences in per diem cost 

among stays of different lengths (69 FR 

66947 through 66950). As a result of this 

analysis, we established variable per 

diem adjustments that begin on day 1 

and decline gradually until day 21 of a 

patient’s stay. For day 22 and thereafter, 

the variable per diem adjustment 

remains the same each day for the 

remainder of the stay. However, the 

adjustment applied to day 1 depends 

upon whether the IPF has a qualifying 

ED. If an IPF has a qualifying ED, it 

receives a 1.31 adjustment factor for day 

1 of each stay. If an IPF does not have 

a qualifying ED, it receives a 1.19 

adjustment factor for day 1 of the stay. 

The ED adjustment is explained in more 

detail in section III.D.4 of this propose 

rule. 

For FY 2023, we are proposing to 

continue to use the variable per diem 

adjustment factors currently in effect, as 

shown in Addendum A to this rule, 

which is available on the CMS website 

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

Medicare-Fee-for-Service-Payment/ 

InpatientPsychFacilPPS/tools.html. A 

complete discussion of the variable per 

diem adjustments appears in the 

November 2004 IPF PPS final rule (69 

FR 66946). 

D. Proposed Updates to the IPF PPS 

Facility-Level Adjustments 

The IPF PPS includes facility-level 

adjustments for the wage index, IPFs 

located in rural areas, teaching IPFs, 

cost of living adjustments for IPFs 

located in Alaska and Hawaii, and IPFs 

with a qualifying ED. 

1. Wage Index Adjustment 

a. Background 

As discussed in the RY 2007 IPF PPS 

final rule (71 FR 27061), RY 2009 IPF 

PPS (73 FR 25719) and the RY 2010 IPF 

PPS notices (74 FR 20373), in order to 

provide an adjustment for geographic 

wage levels, the labor-related portion of 

an IPF’s payment is adjusted using an 

appropriate wage index. Currently, an 

IPF’s geographic wage index value is 

determined based on the actual location 

of the IPF in an urban or rural area, as 

defined in § 412.64(b)(1)(ii)(A) and (C). 

Due to the variation in costs and 

because of the differences in geographic 

wage levels, in the November 2004 IPF 

PPS final rule, we required that 

payment rates under the IPF PPS be 

adjusted by a geographic wage index. 

We proposed and finalized a policy to 

use the unadjusted, pre-floor, prereclassified IPPS hospital wage index to 

account for geographic differences in 

IPF labor costs. We implemented use of 

the pre-floor, pre-reclassified IPPS 

hospital wage data to compute the IPF 

wage index since there was not an IPFspecific wage index available. We 

believe that IPFs generally compete in 

the same labor market as IPPS hospitals 

so the pre-floor, pre-reclassified IPPS 

hospital wage data should be reflective 

of labor costs of IPFs. We believe this 

pre-floor, pre-reclassified IPPS hospital 

wage index to be the best available data 

to use as proxy for an IPF specific wage 

index. As discussed in the RY 2007 IPF 

PPS final rule (71 FR 27061 through 

27067), under the IPF PPS, the wage 

index is calculated using the IPPS wage 

index for the labor market area in which 

the IPF is located, without considering 

geographic reclassifications, floors, and 

other adjustments made to the wage 

index under the IPPS. For a complete 

description of these IPPS wage index 

adjustments, we refer readers to the FY 

2019 IPPS/LTCH PPS final rule (83 FR 

41362 through 41390). Our wage index 

policy at § 412.424(a)(2), requires us to 

use the best Medicare data available to 

estimate costs per day, including an 

appropriate wage index to adjust for 

wage differences. 

When the IPF PPS was implemented 

in the November 2004 IPF PPS final 

rule, with an effective date of January 1, 

2005, the pre-floor, pre-reclassified IPPS 

hospital wage index that was available 

at the time was the FY 2005 pre-floor, 

pre-reclassified IPPS hospital wage 

index. Historically, the IPF wage index 

for a given RY has used the pre-floor, 

pre-reclassified IPPS hospital wage 

index from the prior FY as its basis. 

This has been due in part to the prefloor, pre-reclassified IPPS hospital 

wage index data that were available 

during the IPF rulemaking cycle, where 

an annual IPF notice or IPF final rule 

was usually published in early May. 

This publication timeframe was 

relatively early compared to other 

Medicare payment rules because the IPF 

PPS follows a RY, which was defined in 

the implementation of the IPF PPS as 

the 12-month period from July 1 to June 

30 (69 FR 66927). Therefore, the best 

available data at the time the IPF PPS 

was implemented was the pre-floor, prereclassified IPPS hospital wage index 

from the prior FY (for example, the RY 

2006 IPF wage index was based on the 

FY 2005 pre-floor, pre-reclassified IPPS 

hospital wage index). 

In the RY 2012 IPF PPS final rule, we 

changed the reporting year timeframe 

for IPFs from a RY to the FY, which 

begins October 1 and ends September 30 

(76 FR 26434 through 26435). In that FY 

2012 IPF PPS final rule, we continued 

our established policy of using the prefloor, pre-reclassified IPPS hospital 

wage index from the prior year (that is, 

from FY 2011) as the basis for the FY 

2012 IPF wage index. This policy of 

basing a wage index on the prior year’s 

pre-floor, pre-reclassified IPPS hospital 

wage index has been followed by other 

Medicare payment systems, such as 

hospice and inpatient rehabilitation 

facilities. By continuing with our 

established policy, we remained 

consistent with other Medicare payment 

systems. 

In FY 2020, we finalized the IPF wage 

index methodology to align the IPF PPS 

wage index with the same wage data 

timeframe used by the IPPS for FY 2020 

and subsequent years. Specifically, we 

finalized to use the pre-floor, prereclassified IPPS hospital wage index 

from the FY concurrent with the IPF FY 

as the basis for the IPF wage index. For 

example, the FY 2020 IPF wage index 

was based on the FY 2020 pre-floor, prereclassified IPPS hospital wage index 

rather than on the FY 2019 pre-floor, 

pre-reclassified IPPS hospital wage 

index. 


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We explained in the FY 2020 

proposed rule (84 FR 16973), that using 

the concurrent pre-floor, pre-reclassified 

IPPS hospital wage index will result in 

the most up-to-date wage data being the 

basis for the IPF wage index. We noted 

that it would also result in more 

consistency and parity in the wage 

index methodology used by other 

Medicare payment systems. We 

indicated that the Medicare SNF PPS 

already used the concurrent IPPS 

hospital wage index data as the basis for 

the SNF PPS wage index. CMS proposed 

and finalized similar policies to use the 

concurrent pre-floor, pre-reclassified 

IPPS hospital wage index data in other 

Medicare payment systems, such as 

hospice and inpatient rehabilitation 

facilities. Thus, the wage adjusted 

Medicare payments of various provider 

types are based upon wage index data 

from the same timeframe. For FY 2023, 

we propose to continue to use the 

concurrent pre-floor, pre-reclassified 

IPPS hospital wage index as the basis 

for the IPF wage index. 

b. Office of Management and Budget 

(OMB) Bulletins 

1. Background 

The wage index used for the IPF PPS 

is calculated using the unadjusted, prereclassified and pre-floor IPPS wage 

index data and is assigned to the IPF on 

the basis of the labor market area in 

which the IPF is geographically located. 

IPF labor market areas are delineated 

based on the Core-Based Statistical Area 

(CBSAs) established by the OMB. 

Generally, OMB issues major 

revisions to statistical areas every 10 

years, based on the results of the 

decennial census. However, OMB 

occasionally issues minor updates and 

revisions to statistical areas in the years 

between the decennial censuses through 

OMB Bulletins. These bulletins contain 

information regarding CBSA changes, 

including changes to CBSA numbers 

and titles. OMB bulletins may be 

accessed online at https://

www.whitehouse.gov/omb/informationfor-agencies/bulletins/. In accordance 

with our established methodology, the 

IPF PPS has historically adopted any 

CBSA changes that are published in the 

OMB bulletin that corresponds with the 

IPPS hospital wage index used to 

determine the IPF wage index and, 

when necessary and appropriate, has 

proposed and finalized transition 

policies for these changes. 

In the RY 2007 IPF PPS final rule (71 

FR 27061 through 27067), we adopted 

the changes discussed in the OMB 

Bulletin No. 03–04 (June 6, 2003), 

which announced revised definitions 

for MSAs, and the creation of 

Micropolitan Statistical Areas and 

Combined Statistical Areas. In adopting 

the OMB CBSA geographic designations 

in RY 2007, we did not provide a 

separate transition for the CBSA-based 

wage index since the IPF PPS was 

already in a transition period from 

TEFRA payments to PPS payments. 

In the RY 2009 IPF PPS notice, we 

incorporated the CBSA nomenclature 

changes published in the most recent 

OMB bulletin that applied to the IPPS 

hospital wage index used to determine 

the current IPF wage index and stated 

that we expected to continue to do the 

same for all the OMB CBSA 

nomenclature changes in future IPF PPS 

rules and notices, as necessary (73 FR 

25721). 

Subsequently, CMS adopted the 

changes that were published in past 

OMB bulletins in the FY 2016 IPF PPS 

final rule (80 FR 46682 through 46689), 

the FY 2018 IPF PPS rate update (82 FR 

36778 through 36779), the FY 2020 IPF 

PPS final rule (84 FR 38453 through 

38454), and the FY 2021 IPF PPS final 

rule (85 FR 47051 through 47059). We 

direct readers to each of these rules for 

more information about the changes that 

were adopted and any associated 

transition policies. 

In part due to the scope of changes 

involved in adopting the CBSA 

delineations for FY 2021, we finalized a 

2-year transition policy in the FY 2021 

IPF PPS final rule consistent with our 

past practice of using transition policies 

to help mitigate negative impacts on 

hospitals of certain wage index policy 

changes. We applied a 5-percent cap on 

wage index decreases to all IPF 

providers that had any decrease in their 

wage indexes, regardless of the 

circumstance causing the decline, so 

that an IPF’s final wage index for FY 

2021 would not be less than 95 percent 

of its final wage index for FY 2020, 

regardless of whether the IPF was part 

of an updated CBSA. We refer readers 

to the FY 2021 IPF PPS final rule (85 FR 

47058 through 47059) for a more 

detailed discussion about the wage 

index transition policy for FY 2021. 

On March 6, 2020, OMB issued OMB 

Bulletin 20–01 (available on the web at 

https://www.whitehouse.gov/wpcontent/uploads/2020/03/Bulletin-20- 

01.pdf). In considering whether to adopt 

this bulletin, we analyzed whether the 

changes in this bulletin would have a 

material impact on the IPF PPS wage 

index. This bulletin creates only one 

Micropolitan statistical area. As 

discussed in further detail in section 

III.D.1.b.ii of this proposed rule since 

Micropolitan areas are considered rural 

for the IPF PPS wage index, this bulletin 

has no material impact on the IPF PPS 

wage index. That is, the constituent 

county of the new Micropolitan area 

was considered rural effective as of FY 

2021 and would continue to be 

considered rural if we adopted OMB 

Bulletin 20–01. Therefore, we did not 

propose to adopt OMB Bulletin 20–01 in 

the FY 2022 IPF PPS proposed rule. 

2. Micropolitan Statistical Areas 

OMB defines a ‘‘Micropolitan 

Statistical Area’’ as a CBSA associated 

with at least one urban cluster that has 

a population of at least 10,000, but less 

than 50,000 (75 FR 37252). We refer to 

these as Micropolitan Areas. After 

extensive impact analysis, consistent 

with the treatment of these areas under 

the IPPS as discussed in the FY 2005 

IPPS final rule (69 FR 49029 through 

49032), we determined the best course 

of action would be to treat Micropolitan 

Areas as ‘‘rural’’ and include them in 

the calculation of each state’s IPF PPS 

rural wage index. We refer readers to the 

FY 2007 IPF PPS final rule (71 FR 27064 

through 27065) for a complete 

discussion regarding treating 

Micropolitan Areas as rural. 

c. Proposed Permanent Cap on Wage 

Index Decreases 

As discussed in section III.D.1.b.(1) of 

this proposed rule, we have proposed 

and finalized temporary transition 

policies in the past to mitigate 

significant changes to payments due to 

changes to the IPF PPS wage index. 

Specifically, for FY 2016 (80 FR 46652), 

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 

47059), we implemented a 2-year 

transition to mitigate any negative 

effects of wage index changes by 

applying a 5-percent cap on any 

decrease in an IPF’s wage index from 

the IPF’s 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 the 

second year, that is, 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 


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market area delineations and wage 

index values. 

In FY 2022 (86 FR 42616 through 

42617), a couple of commenters 

recommended CMS extend the 

transition period adopted in the FY 

2021 IPF PPS final rule. Because we did 

not propose to modify the transition 

policy that was finalized in the FY 2021 

IPF PPS final rule, we did not extend 

the transition period for FY 2022. In the 

FY 2022 IPF PPS final rule, we stated 

that we continued to believe that 

applying the 5-percent cap transition 

policy in year one provided an adequate 

safeguard against any significant 

payment reductions associated with the 

adoption of the revised CBSA 

delineations in FY 2021, allowed for 

sufficient time to make operational 

changes for future FYs, and provided a 

reasonable balance between mitigating 

some short-term instability in IPF 

payments and improving the accuracy 

of the payment adjustment for 

differences in area wage levels. 

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, we considered for 

this FY 2023 proposed rule how best to 

address the potential scenarios about 

which commenters raised concerns; 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 IPF PPS wage 

index regulations at § 412.424(a)(2), we 

use an appropriate wage index based on 

the best available data, including the 

best available labor market area 

delineations, to adjust IPF 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, and for an individual provider, 

these fluctuations can be difficult to 

predict. 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 IPF 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 

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 

IPF 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 IPF 

PPS payments due to any significant 

wage index decreases that may affect 

providers in a year. Therefore, we 

believe this approach would address 

concerns about instability that 

commenters raised in the FY 2022 IPF 

PPS rule. In addition, we believe that 

applying a 5-percent cap on all wage 

index decreases would support 

increased predictability about IPF 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 

III.D.1.e 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. Therefore, 

we anticipate that the impact to the 

wage index budget neutrality factor in 

future years would continue to be 

minimal. 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 IPFs 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 IPFs 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. 

The Secretary has broad authority to 

establish appropriate payment 

adjustments under the IPF PPS, 

including the wage index adjustment. 

As discussed earlier in this section, the 

IPF 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 IPF 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 IPF’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 

IPF 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 IPF’s 


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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 IPF’s 

capped wage index in the prior FY. For 

example, if an IPF’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 IPF 

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 IPF 

would not have a wage index in the 

prior FY. We would reflect the proposed 

permanent cap on wage index decreases 

at § 412.424(d)(1)(i). 

As previously discussed, we believe 

this proposed methodology would 

maintain the IPF PPS wage index as a 

relative measure of the value of labor in 

prescribed labor market areas, increase 

predictability of IPF PPS payments for 

providers, and mitigate instability and 

significant negative impacts to providers 

resulting from significant changes to the 

wage index. In section VIII.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. 

d. Proposed Adjustment for Rural 

Location 

In the November 2004 IPF PPS final 

rule, (69 FR 66954) we provided a 17 

percent payment adjustment for IPFs 

located in a rural area. This adjustment 

was based on the regression analysis, 

which indicated that the per diem cost 

of rural facilities was 17 percent higher 

than that of urban facilities after 

accounting for the influence of the other 

variables included in the regression. 

This 17 percent adjustment has been 

part of the IPF PPS each year since the 

inception of the IPF PPS. For FY 2023, 

we propose to continue to apply a 17 

percent payment adjustment for IPFs 

located in a rural area as defined at 

§ 412.64(b)(1)(ii)(C) (see 69 FR 66954 for 

a complete discussion of the adjustment 

for rural locations). 

e. Proposed Budget Neutrality 

Adjustment 

Changes to the wage index are made 

in a budget-neutral manner so that 

updates do not increase expenditures. 

Therefore, for FY 2023, we are 

proposing to continue to apply a budgetneutrality adjustment in accordance 

with our existing budget-neutrality 

policy. This policy requires us to update 

the wage index in such a way that total 

estimated payments to IPFs 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 IPF PPS rates. We use the 

following steps to ensure that the rates 

reflect the FY 2023 update to the wage 

indexes (based on the FY 2019 hospital 

cost report data) and the labor-related 

share in a budget-neutral manner: 

Step 1: Simulate estimated IPF PPS 

payments, using the FY 2022 IPF wage 

index values (available on the CMS 

website) and labor-related share (as 

published in the FY 2022 IPF PPS final 

rule (86 FR 42608). 

Step 2: Simulate estimated IPF PPS 

payments using the proposed FY 2023 

IPF wage index values (available on the 

CMS website), the proposed 5-percent 

cap on any decrease to a provider’s 

wage index from its wage index in the 

prior year, and the proposed FY 2023 

labor-related share (based on the latest 

available data as discussed previously). 

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 1.0016. 

Step 4: Apply the FY 2023 budgetneutral wage adjustment factor from 

step 3 to the FY 2022 IPF PPS Federal 

per diem base rate after the application 

of the market basket update described in 

section III.A of this proposed rule, to 

determine the FY 2023 IPF PPS Federal 

per diem base rate. 

For this proposed rule, we also 

followed these steps to separately 

calculate the budget neutrality factor 

associated with the proposed 5-percent 

cap on any decrease to a provider’s 

wage index from its wage index in the 

prior year. First, we calculated the 

budget neutrality factor associated with 

the proposed FY 2023 IPF wage index 

and proposed FY 2023 labor-related 

share. We divided the amount of 

simulated payments using the FY 2022 

IPF wage index and labor-related share 

by the amount of simulated payments 

using the proposed FY 2023 wage index 

and proposed FY 2023 labor-related 

share. The resulting quotient is 1.0017. 

Next, we calculated the budget 

neutrality factor associated with the 

proposed 5-percent cap on any decrease 

to a provider’s wage index from its wage 

index in the prior year. We divided the 

amount of simulated payments using 

the proposed FY 2023 wage index and 

proposed FY 2023 labor-related share by 

the amount of simulated payments 

using the proposed FY 2023 wage index, 

the proposed 5-percent cap on any 

decrease to a provider’s wage index 

from its wage index in the prior year, 

and the proposed FY 2023 labor-related 

share. The resulting quotient is 0.9999. 

The combined budget neutrality factor, 

which is the proposed FY 2023 budgetneutral wage adjustment factor as 

discussed earlier in this section, is 

1.0016. 

2. Proposed Teaching Adjustment 

In the November 2004 IPF PPS final 

rule, we implemented regulations at 

§ 412.424(d)(1)(iii) to establish a facilitylevel adjustment for IPFs that are, or are 

part of, teaching hospitals. The teaching 

adjustment accounts for the higher 

indirect operating costs experienced by 

hospitals that participate in graduate 

medical education (GME) programs. The 

payment adjustments are made based on 

the ratio of the number of full-time 

equivalent (FTE) interns and residents 

training in the IPF and the IPF’s average 

daily census (ADC). 

Under the Inpatient Prospective 

Payment System (IPPS), Medicare 

makes direct GME payments (for direct 

costs such as resident and teaching 

physician salaries, and other direct 

teaching costs) to all teaching hospitals 

including those paid under a PPS, and 

those paid under the TEFRA rate-ofincrease limits. These direct GME 

payments are made separately from 

payments for hospital operating costs 

and are not part of the IPF PPS. In 

addition, direct GME payments do not 

address the estimated higher indirect 

operating costs teaching hospitals may 

face. 

The results of the regression analysis 

of FY 2002 IPF data established the 

basis for the payment adjustments 

included in the November 2004 IPF PPS 

final rule. The results showed that the 

indirect teaching cost variable is 

significant in explaining the higher 

costs of IPFs that have teaching 

programs. We calculated the teaching 

adjustment based on the IPF’s ‘‘teaching 

variable,’’ which is (1 + (the number of 

FTE residents training in the IPF/the 

IPF’s ADC)). The teaching variable is 

then raised to the 0.5150 power to result 

in the teaching adjustment. This 

formula is subject to the limitations on 

the number of FTE residents, which are 

described in this section of the proposed 

rule. 

We established the teaching 

adjustment in a manner that limited the 

incentives for IPFs to add FTE residents 

for the purpose of increasing their 

teaching adjustment. We imposed a cap 

on the number of FTE residents that 

may be counted for purposes of 

calculating the teaching adjustment. The 

cap limits the number of FTE residents 

that teaching IPFs may count for the 

purpose of calculating the IPF PPS 


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teaching adjustment, not the number of 

residents teaching institutions can hire 

or train. We calculated the number of 

FTE residents that trained in the IPF 

during a ‘‘base year’’ and used that FTE 

resident number as the cap. An IPF’s 

FTE resident cap is ultimately 

determined based on the final 

settlement of the IPF’s most recent cost 

report filed before November 15, 2004 

(publication date of the IPF PPS final 

rule). A complete discussion of the 

temporary adjustment to the FTE cap to 

reflect residents due to hospital closure 

or residency program closure appears in 

the RY 2012 IPF PPS proposed rule (76 

FR 5018 through 5020) and the RY 2012 

IPF PPS final rule (76 FR 26453 through 

26456). 

In the regression analysis, the 

logarithm of the teaching variable had a 

coefficient value of 0.5150. We 

converted this cost effect to a teaching 

payment adjustment by treating the 

regression coefficient as an exponent 

and raising the teaching variable to a 

power equal to the coefficient value. We 

note that the coefficient value of 0.5150 

was based on the regression analysis 

holding all other components of the 

payment system constant. A complete 

discussion of how the teaching 

adjustment was calculated appears in 

the November 2004 IPF PPS final rule 

(69 FR 66954 through 66957) and the 

RY 2009 IPF PPS notice (73 FR 25721). 

As with other adjustment factors 

derived through the regression analysis, 

we do not plan to rerun the teaching 

adjustment factors in the regression 

analysis until we more fully analyze IPF 

PPS data. Therefore, in this FY 2023 

proposed rule, we are proposing to 

continue to retain the coefficient value 

of 0.5150 for the teaching adjustment to 

the Federal per diem base rate. 

3. Proposed Cost of Living Adjustment 

for IPFs Located in Alaska and Hawaii 

The IPF PPS includes a payment 

adjustment for IPFs located in Alaska 

and Hawaii based upon the area in 

which the IPF is located. As we 

explained in the November 2004 IPF 

PPS final rule, the FY 2002 data 

demonstrated that IPFs in Alaska and 

Hawaii had per diem costs that were 

disproportionately higher than other 

IPFs. Other Medicare prospective 

payment systems (for example, the IPPS 

and LTCH PPS) adopted a COLA to 

account for the cost differential of care 

furnished in Alaska and Hawaii. 

We analyzed the effect of applying a 

COLA to payments for IPFs located in 

Alaska and Hawaii. The results of our 

analysis demonstrated that a COLA for 

IPFs located in Alaska and Hawaii will 

improve payment equity for these 

facilities. As a result of this analysis, we 

provided a COLA in the November 2004 

IPF PPS final rule. 

A COLA for IPFs located in Alaska 

and Hawaii is made by multiplying the 

non-labor-related portion of the Federal 

per diem base rate by the applicable 

COLA factor based on the COLA area in 

which the IPF is located. 

The COLA factors through 2009 were 

published by the Office of Personnel 

Management (OPM), and the OPM 

memo showing the 2009 COLA factors 

is available at https://www.chcoc.gov/ 

content/nonforeign-area-retirementequity-assurance-act. 

We note that the COLA areas for 

Alaska are not defined by county as are 

the COLA areas for Hawaii. In 5 CFR 

591.207, the OPM established the 

following COLA areas: 

• City of Anchorage, and 80-kilometer 

(50-mile) radius by road, as measured 

from the Federal courthouse. 

• City of Fairbanks, and 80-kilometer 

(50-mile) radius by road, as measured 

from the Federal courthouse. 

• City of Juneau, and 80-kilometer 

(50-mile) radius by road, as measured 

from the Federal courthouse. 

• Rest of the state of Alaska. 

As stated in the November 2004 IPF 

PPS final rule, we update the COLA 

factors according to updates established 

by the OPM. However, sections 1911 

through 1919 of the Non-foreign Area 

Retirement Equity Assurance Act, as 

contained in subtitle B of title XIX of the 

National Defense Authorization Act 

(NDAA) for FY 2010 (Pub. L. 111–84, 

October 28, 2009), transitions the Alaska 

and Hawaii COLAs to locality pay. 

Under section 1914 of NDAA, locality 

pay was phased in over a 3-year period 

beginning in January 2010, with COLA 

rates frozen as of the date of enactment, 

October 28, 2009, and then 

proportionately reduced to reflect the 

phase-in of locality pay. 

When we published the proposed 

COLA factors in the RY 2012 IPF PPS 

proposed rule (76 FR 4998), we 

inadvertently selected the FY 2010 

COLA rates, which had been reduced to 

account for the phase-in of locality pay. 

We did not intend to propose the 

reduced COLA rates because that would 

have understated the adjustment. Since 

the 2009 COLA rates did not reflect the 

phase-in of locality pay, we finalized 

the FY 2009 COLA rates for RY 2010 

through RY 2014. 

In the FY 2013 IPPS/LTCH final rule 

(77 FR 53700 through 53701), we 

established a new methodology to 

update the COLA factors for Alaska and 

Hawaii, and adopted this methodology 

for the IPF PPS in the FY 2015 IPF final 

rule (79 FR 45958 through 45960). We 

adopted this new COLA methodology 

for the IPF PPS because IPFs are 

hospitals with a similar mix of 

commodities and services. We believe it 

is appropriate to have a consistent 

policy approach with that of other 

hospitals in Alaska and Hawaii. 

Therefore, the IPF COLAs for FY 2015 

through FY 2017 were the same as those 

applied under the IPPS in those years. 

As finalized in the FY 2013 IPPS/LTCH 

PPS final rule (77 FR 53700 and 53701), 

the COLA updates are determined every 

4 years, when the IPPS market basket 

labor-related share is updated. Because 

the labor-related share of the IPPS 

market basket was most recently 

updated for FY 2022, the COLA factors 

were updated in FY 2022 IPPS/LTCH 

rulemaking (86 FR 45547). As such, we 

also updated the IPF PPS COLA factors 

for FY 2022 (86 FR 42621 through 

42622) to reflect the updated COLA 

factors finalized in the FY 2022 IPPS/ 

LTCH rulemaking. Table 2 shows the 

proposed IPF PPS COLA factors 

effective for FY 2022 through FY 2025. 

TABLE 2—IPF PPS COST-OF-LIVING ADJUSTMENT FACTORS: IPFS LOCATED IN ALASKA AND HAWAII

Area 

FY 2022 

through 

FY 2025 

Alaska: 

City of Anchorage and 80-kilometer (50-mile) radius by road ............................................................................................... 1.22 

City of Fairbanks and 80-kilometer (50-mile) radius by road ................................................................................................ 1.22 

City of Juneau and 80-kilometer (50-mile) radius by road .................................................................................................... 1.22 

Rest of Alaska ........................................................................................................................................................................ 1.24 

Hawaii: 

City and County of Honolulu .................................................................................................................................................. 1.25 


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TABLE 2—IPF PPS COST-OF-LIVING ADJUSTMENT FACTORS: IPFS LOCATED IN ALASKA AND HAWAII—Continued 

Area 

FY 2022 

through 

FY 2025 

County of Hawaii .................................................................................................................................................................... 1.22 

County of Kauai ...................................................................................................................................................................... 1.25 

County of Maui and County of Kalawao ................................................................................................................................ 1.25 

The proposed IPF PPS COLA factors 

for FY 2023 are also shown in 

Addendum A to this proposed rule, and 

is available on the CMS website at 

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

Medicare-Fee-for-Service-Payment/ 

InpatientPsychFacilPPS/tools.html. 

4. Proposed Adjustment for IPFs With a 

Qualifying Emergency Department (ED) 

The IPF PPS includes a facility-level 

adjustment for IPFs with qualifying EDs. 

We provide an adjustment to the 

Federal per diem base rate to account 

for the costs associated with 

maintaining a full-service ED. The 

adjustment is intended to account for 

ED costs incurred by a psychiatric 

hospital with a qualifying ED or an 

excluded psychiatric unit of an IPPS 

hospital or a CAH, for preadmission 

services otherwise payable under the 

Medicare Hospital Outpatient 

Prospective Payment System (OPPS), 

furnished to a beneficiary on the date of 

the beneficiary’s admission to the 

hospital and during the day 

immediately preceding the date of 

admission to the IPF (see § 413.40(c)(2)), 

and the overhead cost of maintaining 

the ED. This payment is a facility-level 

adjustment that applies to all IPF 

admissions (with one exception which 

we described), regardless of whether a 

particular patient receives preadmission 

services in the hospital’s ED. 

The ED adjustment is incorporated 

into the variable per diem adjustment 

for the first day of each stay for IPFs 

with a qualifying ED. Those IPFs with 

a qualifying ED receive an adjustment 

factor of 1.31 as the variable per diem 

adjustment for day 1 of each patient 

stay. If an IPF does not have a qualifying 

ED, it receives an adjustment factor of 

1.19 as the variable per diem adjustment 

for day 1 of each patient stay. 

The ED adjustment is made on every 

qualifying claim except as described in 

this section of the proposed rule. As 

specified in § 412.424(d)(1)(v)(B), the ED 

adjustment is not made when a patient 

is discharged from an IPPS hospital or 

CAH and admitted to the same IPPS 

hospital’s or CAH’s excluded 

psychiatric unit. We clarified in the 

November 2004 IPF PPS final rule (69 

FR 66960) that an ED adjustment is not 

made in this case because the costs 

associated with ED services are reflected 

in the DRG payment to the IPPS hospital 

or through the reasonable cost payment 

made to the CAH. 

Therefore, when patients are 

discharged from an IPPS hospital or 

CAH and admitted to the same 

hospital’s or CAH’s excluded 

psychiatric unit, the IPF receives the 

1.19 adjustment factor as the variable 

per diem adjustment for the first day of 

the patient’s stay in the IPF. For FY 

2023, we are proposing to continue to 

retain the 1.31 adjustment factor for 

IPFs with qualifying EDs. A complete 

discussion of the steps involved in the 

calculation of the ED adjustment factors 

are in the November 2004 IPF PPS final 

rule (69 FR 66959 through 66960) and 

the RY 2007 IPF PPS final rule (71 FR 

27070 through 27072). 

E. Other Final Payment Adjustments 

and Policies 

1. Outlier Payment Overview 

The IPF PPS includes an outlier 

adjustment to promote access to IPF 

care for those patients who require 

expensive care and to limit the financial 

risk of IPFs treating unusually costly 

patients. In the November 2004 IPF PPS 

final rule, we implemented regulations 

at § 412.424(d)(3)(i) to provide a percase payment for IPF stays that are 

extraordinarily costly. Providing 

additional payments to IPFs for 

extremely costly cases strongly 

improves the accuracy of the IPF PPS in 

determining resource costs at the patient 

and facility level. These additional 

payments reduce the financial losses 

that would otherwise be incurred in 

treating patients who require costlier 

care, and therefore, reduce the 

incentives for IPFs to under-serve these 

patients. We make outlier payments for 

discharges in which an IPF’s estimated 

total cost for a case exceeds a fixed 

dollar loss threshold amount 

(multiplied by the IPF’s facility-level 

adjustments) plus the Federal per diem 

payment amount for the case. 

In instances when the case qualifies 

for an outlier payment, we pay 80 

percent of the difference between the 

estimated cost for the case and the 

adjusted threshold amount for days 1 

through 9 of the stay (consistent with 

the median LOS for IPFs in FY 2002), 

and 60 percent of the difference for day 

10 and thereafter. The adjusted 

threshold amount is equal to the outlier 

threshold amount adjusted for wage 

area, teaching status, rural area, and the 

COLA adjustment (if applicable), plus 

the amount of the Medicare IPF 

payment for the case. We established 

the 80 percent and 60 percent loss 

sharing ratios because we were 

concerned that a single ratio established 

at 80 percent (like other Medicare PPSs) 

might provide an incentive under the 

IPF per diem payment system to 

increase LOS in order to receive 

additional payments. 

After establishing the loss sharing 

ratios, we determined the current fixed 

dollar loss threshold amount through 

payment simulations designed to 

compute a dollar loss beyond which 

payments are estimated to meet the 2 

percent outlier spending target. Each 

year when we update the IPF PPS, we 

simulate payments using the latest 

available data to compute the fixed 

dollar loss threshold so that outlier 

payments represent 2 percent of total 

estimated IPF PPS payments. 

2. Proposed Update to the Outlier Fixed 

Dollar Loss Threshold Amount 

In accordance with the update 

methodology described in § 412.428(d), 

we are proposing to update the fixed 

dollar loss threshold amount used under 

the IPF PPS outlier policy. Based on the 

regression analysis and payment 

simulations used to develop the IPF 

PPS, we established a 2 percent outlier 

policy, which strikes an appropriate 

balance between protecting IPFs from 

extraordinarily costly cases while 

ensuring the adequacy of the Federal 

per diem base rate for all other cases 

that are not outlier cases. 

Our longstanding methodology for 

updating the outlier fixed dollar loss 

threshold involves using the best 

available data, which is typically the 

most recent available data. Last year for 

the FY 2022 IPF PPS final rule, we 

finalized the use of FY 2019 claims 

rather than the more recent FY 2020 

claims for updating the outlier fixed 

dollar loss threshold (86 FR 42623). We 


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noted that our use of the FY 2019 claims 

to set the final outlier fixed dollar loss 

threshold for FY 2022 deviated from our 

longstanding practice of using the most 

recent available year of claims, but 

remained otherwise consistent with the 

established outlier update methodology. 

We explained that we finalized our 

proposal to deviate from our 

longstanding practice of using the most 

recent available year of claims only 

because, and to the extent that, the 

‘‘coronavirus disease 2019’’ (abbreviated 

‘‘COVID–19’’) Public Health Emergency 

(PHE) appeared to have significantly 

impacted the FY 2020 IPF claims. We 

further stated that we intended to 

continue to analyze further data in order 

to better understand both the short-term 

and long-term effects of the COVID–19 

PHE on IPFs (86 FR 42624). 

For this FY 2023 IPF PPS proposed 

rulemaking, consistent with our 

longstanding practice, we analyzed the 

most recent available data for simulating 

IPF PPS payments in FY 2023. We 

observed a continuation of two main 

trends that we noted in our analysis of 

FY 2020 claims for FY 2022—that is, an 

overall increase in average cost per day 

and an overall decrease in the number 

of covered days. However, we also 

identified that some providers had 

significant increases in their charges, 

resulting in higher than normal 

estimated cost per day that would skew 

our estimate of outlier payments for FY 

2022 and FY 2023. 

Historically, we have applied 

statistical trims under the IPF PPS in 

order to improve the statistical validity 

of the data used for ratesetting. In the 

November 2004 final rule, we explained 

that we applied a 3 standard deviation 

trim on cost per day prior to calculating 

the average per diem cost used to 

calculate the IPF PPS Federal per diem 

base rate (69 FR 66927). Furthermore, as 

discussed in section III.E.3 of this 

proposed rule, our longstanding policy 

applies a ceiling on a provider’s cost-tocharge ratio when it exceeds 3 standard 

deviations from the mean cost-to-charge 

ratio for urban or rural providers. We 

are proposing a similar approach in 

order to address the skew in estimated 

cost per day that we observed in the FY 

2021 claims. Specifically, we are 

proposing for FY 2023 to exclude 

providers from our simulation of IPF 

PPS payments for FY 2022 and FY 2023 

if their change in estimated average cost 

per day is outside 3 standard deviations 

from the mean. 

Based on an analysis of the December 

2021 update of FY 2021 IPF claims and 

the FY 2022 rate increases, we believe 

it is necessary to update the fixed dollar 

loss threshold amount to maintain an 

outlier percentage that equals 2 percent 

of total estimated IPF PPS payments. We 

are proposing to update the IPF outlier 

threshold amount for FY 2023 using FY 

2021 claims data and the same 

methodology that we used to set the 

initial outlier threshold amount in the 

RY 2007 IPF PPS final rule (71 FR 27072 

and 27073), which is also the same 

methodology that we used to update the 

outlier threshold amounts for years 2008 

through 2022. However, as discussed 

earlier in this section, we also propose 

for FY 2023 to exclude providers from 

our impact simulations whose change in 

simulated cost per day is outside 3 

standard deviations from the mean. 

Based on an analysis of these updated 

data, we estimate that IPF outlier 

payments as a percentage of total 

estimated payments are approximately 

3.2 percent in FY 2022. Therefore, we 

are proposing to update the outlier 

threshold amount to $24,270 to 

maintain estimated outlier payments at 

2 percent of total estimated aggregate 

IPF payments for FY 2023. This 

proposed update is an increase from the 

FY 2022 threshold of $16,040. 

3. Proposed Update to IPF Cost-toCharge Ratio Ceilings 

Under the IPF PPS, an outlier 

payment is made if an IPF’s cost for a 

stay exceeds a fixed dollar loss 

threshold amount plus the IPF PPS 

amount. In order to establish an IPF’s 

cost for a particular case, we multiply 

the IPF’s reported charges on the 

discharge bill by its overall cost-tocharge ratio (CCR). This approach to 

determining an IPF’s cost is consistent 

with the approach used under the IPPS 

and other PPSs. In the FY 2004 IPPS 

final rule (68 FR 34494), we 

implemented changes to the IPPS policy 

used to determine CCRs for IPPS 

hospitals, because we became aware 

that payment vulnerabilities resulted in 

inappropriate outlier payments. Under 

the IPPS, we established a statistical 

measure of accuracy for CCRs to ensure 

that aberrant CCR data did not result in 

inappropriate outlier payments. 

As we indicated in the November 

2004 IPF PPS final rule (69 FR 66961), 

we believe that the IPF outlier policy is 

susceptible to the same payment 

vulnerabilities as the IPPS; therefore, we 

adopted a method to ensure the 

statistical accuracy of CCRs under the 

IPF PPS. Specifically, we adopted the 

following procedure in the November 

2004 IPF PPS final rule: 

• Calculated two national ceilings, 

one for IPFs located in rural areas and 

one for IPFs located in urban areas. 

• Computed the ceilings by first 

calculating the national average and the 

standard deviation of the CCR for both 

urban and rural IPFs using the most 

recent CCRs entered in the most recent 

Provider Specific File (PSF) available. 

For FY 2023, we propose to continue 

to follow this methodology. 

To determine the rural and urban 

ceilings, we multiplied each of the 

standard deviations by 3 and added the 

result to the appropriate national CCR 

average (either rural or urban). The 

upper threshold CCR for IPFs in FY 

2023 is 2.0472 for rural IPFs, and 1.7279 

for urban IPFs, based on CBSA-based 

geographic designations. If an IPF’s CCR 

is above the applicable ceiling, the ratio 

is considered statistically inaccurate, 

and we assign the appropriate national 

(either rural or urban) median CCR to 

the IPF. 

We apply the national median CCRs 

to the following situations: 

• New IPFs that have not yet 

submitted their first Medicare cost 

report. We continue to use these 

national median CCRs until the facility’s 

actual CCR can be computed using the 

first tentatively or final settled cost 

report. 

• IPFs whose overall CCR is in excess 

of three standard deviations above the 

corresponding national geometric mean 

(that is, above the ceiling). 

• Other IPFs for which the MAC 

obtains inaccurate or incomplete data 

with which to calculate a CCR. 

We are proposing to continue to 

update the FY 2023 national median 

and ceiling CCRs for urban and rural 

IPFs based on the CCRs entered in the 

latest available IPF PPS PSF. 

Specifically, for FY 2023, to be used in 

each of the three situations listed 

previously, using the most recent CCRs 

entered in the CY 2022 PSF, we provide 

an estimated national median CCR of 

0.5720 for rural IPFs and a national 

median CCR of 0.4200 for urban IPFs. 

These calculations are based on the 

IPF’s location (either urban or rural) 

using the CBSA-based geographic 

designations. A complete discussion 

regarding the national median CCRs 

appears in the November 2004 IPF PPS 

final rule (69 FR 66961 through 66964). 

IV. Comment Solicitation on Analysis 

of IPF PPS Adjustments 

A. Background 

As discussed in section III.C.1 of this 

proposed rule, we are proposing to 

continue to use the existing regressionderived adjustment factors for FY 2023. 

In the November 15, 2004 final rule, we 

indicated that we did not intend to 

update the regression analysis and the 

patient-level and facility-level 

adjustments until we complete further 


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analysis of IPF costs using IPF PPS data 

that yields as much information as 

possible regarding the patient-level 

characteristics of the population that 

each IPF serves. 

Since that time, we undertook 

analysis to better understand IPF 

industry practices so that we may refine 

the IPF PPS in the future, as 

appropriate. For RY 2012, we identified 

several areas of concern for future 

refinement, and we invited comments 

on these issues in the RY 2012 IPF PPS 

proposed and final rules. For further 

discussion of these issues and to review 

the public comments, we refer readers 

to the RY 2012 IPF PPS proposed rule 

(76 FR 4998) and final rule (76 FR 

26432). 

Our preliminary analysis, which we 

previously discussed in the FY 2016 IPF 

PPS final rule (80 FR 46693 through 

46694), also revealed variation in cost 

and claim data, particularly related to 

labor costs, drugs costs, and laboratory 

services. We found that some providers 

have very low labor costs, or very low 

or missing drug or laboratory costs or 

charges, relative to other providers. As 

we noted in the FY 2016 IPF PPS final 

rule, our preliminary analysis of 2012 to 

2013 IPF data found that over 20 

percent of IPF stays reported no 

ancillary costs, such as laboratory and 

drug costs, in their cost reports, or 

laboratory or drug charges on their 

claims. In the past, we stated that we 

expect that most patients requiring 

hospitalization for active psychiatric 

treatment would need drugs and 

laboratory services, and we reminded 

providers that the IPF PPS Federal per 

diem base rate includes the cost of all 

ancillary services, including drugs and 

laboratory services. 

On November 17, 2017, we issued 

Transmittal 12, which made changes to 

the hospital cost report form CMS– 

2552–10 (OMB No. 0938–0050), and 

included the requirement that cost 

reports from psychiatric hospitals 

include certain ancillary costs, or the 

cost report will be rejected. On January 

30, 2018, we issued Transmittal 13, 

which changed the implementation date 

for Transmittal 12 to be for cost 

reporting periods ending on or after 

September 30, 2017. For details, we 

refer readers to see these Transmittals, 

which are available on the CMS website 

at https://www.cms.gov/RegulationsandGuidance/Guidance/Transmittals/ 

index.html. CMS suspended the 

requirement that cost reports from 

psychiatric hospitals include certain 

ancillary costs effective April 27, 2018, 

in order to consider excluding allinclusive rate providers from this 

requirement. CMS issued Transmittal 15 

on October 19, 2018, reinstating the 

requirement that cost reports from 

psychiatric hospitals, except allinclusive rate providers, include certain 

ancillary costs. 

B. Update and Comment Solicitation on 

Analysis of IPF PPS Adjustments 

Working in collaboration with a 

contractor, we have undertaken further 

analysis of more recent IPF cost and 

claim information. We have posted a 

report on the CMS website, which 

summarizes the results of the latest 

analysis. For public awareness, this 

report is available online at https://

www.cms.gov/medicare/medicare-feefor-service-payment/inpatientpsych

facilpps. This updated analysis finds 

that the existing IPF PPS model 

continues to be generally appropriate in 

terms of effectively aligning IPF PPS 

payments with the cost of providing IPF 

services, but suggests that certain 

updates to the codes, categories, 

adjustment factors, and ECT payment 

amount per treatment could improve 

payment accuracy. We are requesting 

comments on the results of our latest 

analysis as summarized in the report. In 

particular, we are interested in 

comments about the following topics, 

which are discussed in detail in the 

report: 

• The report summarizes results of 

the analysis regarding patient-level 

characteristics, about which we are 

requesting comments: 

++ The updated regression analysis 

suggests that certain technical changes 

to the DRG and comorbidity adjustment 

factors, consolidation of the age 

categories for the patient age 

adjustment, and changes to the 

adjustment factors for age and length of 

stay could be appropriate. 

++ The analysis of ancillary costs for 

IPF stays with ECT suggests that a 

higher ECT payment amount per 

treatment could better align IPF PPS 

payments with the costs of furnishing 

ECT. 

++ The analysis of the outlier 

percentage suggests that fewer IPF cases 

qualify for outliers under the current 2 

percent outlier target than were 

estimated when the IPF PPS was 

established. We estimate that increasing 

the outlier percentage would increase 

the number of IPF cases that qualify for 

outliers, but would have distributional 

effects due to budget neutrality. 

• The report summarizes the results 

of analysis regarding facility-level 

characteristics, about which we are 

requesting comments: 

++ The updated regression analysis 

suggests that updating the adjustment 

factors for teaching facilities, rural 

facilities, and facilities with an ED 

could improve payment accuracy; 

however, we estimate such changes 

could have positive and negative effects 

on payments for different types of IPFs. 

++ The analysis of occupancy-related 

control variables included in the 

regression model indicates that these 

control variables are correlated with the 

rural adjustment factor, and that 

removal of these control variables from 

the model could result in an increase to 

the rural adjustment factor in the 

regression model. 

• The report summarizes certain areas 

where we believe additional research is 

needed. We are requesting comments 

about the results summarized in the 

report. We are also requesting comments 

about additional analyses that we 

should undertake to better understand 

how these issues affect the cost of 

providing IPF services, and how the IPF 

PPS could better account for these costs: 

++ We analyzed the costs associated 

with social determinants of health, but 

found that our analysis was confounded 

by a low frequency of IPF claims 

reporting the applicable ICD–10 

diagnosis codes. We are soliciting 

public comments about the results of 

this analysis, and whether there are 

additional patient characteristics that 

affect the cost of providing IPF services 

that may not be consistently reported on 

claims. Additionally, we are soliciting 

public comments about how we could 

better identify such patient 

characteristics and their effects on costs. 

++ We analyzed the costs associated 

with the percentage of low-income 

patients that IPFs treat, based on a 

construction of the Disproportionate 

Share Hospitals (DSH) percentage that is 

used in other payment systems using 

the data currently available for IPFs. We 

are soliciting public comments about 

the results of this analysis, which 

suggest that the addition of an 

adjustment factor for disproportionate 

share intensity could improve the 

accuracy of IPF PPS payments. 

V. Inpatient Psychiatric Facility Quality 

Reporting (IPFQR) Program 

A. 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 


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19430 Federal Register / Vol. 87, No. 64 / Monday, April 4, 2022 / Proposed Rules 

1 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. 

2Lindenauer 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. 

3Trivedi 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. 

4Polyakova, 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. 

5Rural 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. 

6U.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. 

7Centers 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. 

8Poteat 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. 

9Milkie 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. 

10Centers for Medicare and Medicaid Services. 

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

11CMS 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. 

12Centers for Medicare & Medicaid Services 

(CMS), HHS. Disparity Methods Confidential 

outcomes.123456789 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.10

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.11 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 2018. 

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 IPFQR Program to 

assess drivers of healthcare quality 

disparities in the IPFQR Program. 

Additionally, this section discusses 

measures of health equity that could be 

adapted for use in the IPFQR Program. 

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 IPFQR Program. 

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 

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 IPF 

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 IPF 

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.12


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

inpatient/measures/disparity-methods. Accessed 

February 3, 2022. 

13 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. 

14Lindenauer 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. 

15Trivedi 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. 

16Polyakova, 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. 

17Rural 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. 

18HHS 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. 

19Poteat 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. 

20Milkie 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. 

21 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. 

22Office 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. 

23Bonito A., Bann C., Eicheldinger C., Carpenter 

L. Creation of New Race-Ethnicity Codes and 

Socioeconomic Status (SES) Indicators for Medicare 

Beneficiaries. Final Report, Sub-Task 2. (Prepared 

by RTI International for the Centers for Medicare 

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/final-reports/ 

Continued 

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 IPF 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.13 14 15 16 17 18 19 20

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 

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.21 While data 

sources for many social risk factors and 

demographic variables are still 

developing among several CMS settings, 

the IPFQR Program will begin collecting 

mandatory patient-level data for certain 

chart-abstracted measures the FY 2024 

payment determination and subsequent 

years (86 FR 42608). 

• 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.22 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,23


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medicareindicators/medicareindicators1.html. 

Accessed February 7, 2022. 

24Flanagan, 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. 

25Center 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. 

26Haas 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. 

27Rahimi 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. 

Centers for Disease Control and 

Prevention/Agency for Toxic Substances 

and Disease Registry (CDC/ATSDR) 

Social Vulnerability Index (SVI),24 and 

Health Resources and Services 

Administration (HRSA) Area 

Deprivation Index (ADI),25 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.26

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 IPFs, such as 

those located in rural, or critical access 

areas, to ensure that reporting does not 

disadvantage already resource-limited 

settings. The type of reporting chosen by 

programs will depend on the program 

context. 

Regardless of the methods used to 

report results, it is important to report 

stratified measure data alongside overall 

measure results. Review of both 

measures results along with stratified 

results can illuminate greater levels of 

detail about quality of care for 

subgroups of patients, providing 

important information to drive quality 

improvement. Unstratified quality 

measure results address general 

differences in quality of care between 

healthcare providers and promote 

improvement for all patients, but unless 

stratified results are available, it is 

unclear if there are subgroups of 

patients that benefit most from 

initiatives. Notably, even if overall 

quality measure scores improve, 

without identifying and measuring 

differences in outcomes between groups 

of patients, it is impossible to track 

progress in reducing disparity for 

patients with heightened risk of poor 

outcomes. 

2. Approaches to Assessing Drivers of 

Healthcare Quality Disparities and 

Developing Measures of Healthcare 

Equity in the IPFQR Program 

This section presents information on 

two approaches for the IPFQR Program. 

The first section presents information 

about a method that could be used to 

assist IPFs in identifying potential 

drivers of healthcare quality disparities. 

The second section describes measures 

of healthcare equity that might be 

appropriate for inclusion in the IPFQR 

Program. 

a. Performance Disparity Decomposition 

In response to the FY 2022 IPF PPS 

proposed rule’s RFI (86 FR 19494 

through 19500), ‘‘Closing the Health 

Equity Gap in CMS Quality Programs’’, 

some stakeholders noted that 

identifying which factors are 

contributing to the performance gaps 

may not always be straightforward, 

especially if the IPF 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 IPF 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.27 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 


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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 IPFs 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 IPFs 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 IPF. 

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 they are 

factors the healthcare provider could 

mitigate the effects of, if they were 

shown to be drivers of disparity in their 

IPF. Additionally, high volume ED use 

is used as a potential mediating factor 

that could be difficult for IPFs 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 IPF, 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 IPF 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 IPF can see that 

simply having more patients with low 

health literacy and high ED use 

accounts for a disparity of $400. In 

addition, there is still a $200 disparity 

stemming from differences in costs 

between non-dual and dual patients for 

a given risk factor, and another $400 

that is not explained by either low 

health literacy or high ED use. These 

differences may instead be explained by 

other SDOH that have not yet been 

included in this breakdown, or by the 

distinctive pattern of care decisions 

made by providers for dual and nondual beneficiaries. These cost estimates 

would provide additional information 

that facilities could use when 

determining where to devote resources 

aimed at achieving equitable health 

outcomes (for example, facilities may 

choose to focus efforts on the largest 

drivers of a disparity). 

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 
Federal Register / Vol. 87, No. 64 / Monday, April 4, 2022 / Proposed Rules 19435 

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 

the IPFQRP Program, 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 IPF. As a result, there may 

be measures that could be adapted for 

use in the IPFQR Program. 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 

I 

Figure3 

to 

Duals 

for 
19436 Federal Register / Vol. 87, No. 64 / Monday, April 4, 2022 / Proposed Rules 

28Agniel 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. 

29 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. 

30Centers for Medicare & Medicaid Services, FY 

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

May 10, 2021. 

31Centers 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. 

32Centers 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. 

33Quality 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. 

34Agency 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 28 29 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.30 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.31

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 

CMS has 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) 32 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; 33 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 IPF could receive a point 

for each domain where data are 

submitted through a CMS portal to 

reflect actions taken by the IPF 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 IPFs 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.’’ 34 We acknowledge that 

collection of this structural measure 

may impose administrative and/or 

reporting requirements for IPFs. 

We are interested in obtaining 

feedback from stakeholders on 

conceptual and measurement priorities 

for the IPFQR Program to better 

illuminate organizational commitment 

to health equity. 


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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 

between-provider disparity methods in 

IPFs 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 patientreported 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 IPFs, 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 IPF’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 IPF 

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. 

VI. Collection of Information 

Requirements 

This rule proposes updates to the 

prospective payment rates, outlier 

threshold, and wage index for Medicare 

inpatient hospital services provided by 

IPFs. It also proposes to establish a 

default mitigation policy for providers 

negatively affected by changes to the IPF 

PPS wage index. While discussed in 

section IV (Comment Solicitation on 

Analysis of IPF PPS Adjustments) of 

this preamble, the active requirements 

and burden associated with our hospital 

cost report form CMS–2552–10 (OMB 

control number 0938–0050) are 

unaffected by this rule. 

Overall, this rule’s proposed changes 

would not impose any new or revised 

‘‘collection of information’’ 

requirements or burden as defined 

under 5 CFR 1320.3(c).). Consequently, 

this rule is not subject to the 

requirements of the Paperwork 

Reduction Act of 1995 (44 U.S.C. 3501 

et seq.). 

VII. 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. 

VIII. Regulatory Impact Analysis 

A. Statement of Need 

This rule proposes updates to the 

prospective payment rates for Medicare 

inpatient hospital services provided by 

IPFs for discharges occurring during FY 

2023 (October 1, 2022 through 

September 30, 2023). We are proposing 

to apply the 2016-based IPF market 

basket increase of 3.1 percent, less the 

productivity adjustment of 0.4 

percentage point as required by 

1886(s)(2)(A)(i) of the Act for a proposed 

total FY 2023 payment rate update of 

2.7 percent. In this proposed rule, we 

are proposing to update the outlier fixed 

dollar loss threshold amount, update the 

IPF labor-related share, and update the 

IPF wage index to reflect the FY 2023 

hospital inpatient wage index. Lastly, 

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. 

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 


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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 the Executive 

Order. In accordance with the 

provisions of Executive Order 12866, 

this regulation was reviewed by the 

Office of Management and Budget. 

A regulatory impact analysis (RIA) 

must be prepared for major rules with 

significant regulatory action/s and/or 

with economically significant effects 

($100 million or more in any 1 year). We 

estimate that the total impact of these 

changes for FY 2023 payments 

compared to FY 2022 payments will be 

a net increase of approximately $50 

million. This reflects a $90 million 

increase from the update to the payment 

rates (+$105 million from the 4th 

quarter 2021 IGI forecast of the 2016- 

based IPF market basket of 3.1 percent, 

and -$15 million for the productivity 

adjustment of 0.4 percentage point), as 

well as a $40 million decrease as a 

result of the update to the outlier 

threshold amount. Outlier payments are 

estimated to change from 3.2 percent in 

FY 2022 to 2.0 percent of total estimated 

IPF payments in FY 2023. 

Based on our estimates, OMB’s Office 

of Information and Regulatory Affairs 

has determined this rulemaking is 

‘‘economically significant’’ as measured 

by the $100 million threshold. 

Accordingly, we have prepared a 

Regulatory Impact Analysis that to the 

best of our ability presents the costs and 

benefits of the rulemaking. Based on our 

estimates, OMB’s Office of Information 

and Regulatory Affairs has determined 

that this rulemaking is ‘‘significant’’. 

Therefore, OMB has reviewed these 

proposed regulations, and the 

Departments have provided the 

following assessment of their impact. 

C. Detailed Economic Analysis 

In this section, we discuss the 

historical background of the IPF PPS 

and the impact of this proposed rule on 

the Federal Medicare budget and on 

IPFs. 

1. Budgetary Impact 

As discussed in the November 2004 

and RY 2007 IPF PPS final rules, we 

applied a budget neutrality factor to the 

Federal per diem base rate and ECT 

payment per treatment to ensure that 

total estimated payments under the IPF 

PPS in the implementation period 

would equal the amount that would 

have been paid if the IPF PPS had not 

been implemented. This Budget 

neutrality factor included the following 

components: Outlier adjustment, stoploss adjustment, and the behavioral 

offset. As discussed in the RY 2009 IPF 

PPS notice (73 FR 25711), the stop-loss 

adjustment is no longer applicable 

under the IPF PPS. 

As discussed in section III.D.1 of this 

proposed rule, we are proposing to 

update the wage index and labor-related 

share, as well as apply the proposed 5- 

percent cap on any decrease to a 

provider’s wage index from its wage 

index in the prior year, in a budget 

neutral manner by applying a wage 

index budget neutrality factor to the 

Federal per diem base rate and ECT 

payment per treatment. Therefore, the 

budgetary impact to the Medicare 

program of this proposed rule will be 

due to the market basket update for FY 

2023 of 3.1 percent (see section III.A.2 

of this proposed rule) less the 

productivity adjustment of 0.4 

percentage point required by section 

1886(s)(2)(A)(i) of the Act and the 

update to the outlier fixed dollar loss 

threshold amount. 

We estimate that the FY 2023 impact 

will be a net increase of $50 million in 

payments to IPF providers. This reflects 

an estimated $90 million increase from 

the update to the payment rates and a 

$40 million decrease due to the update 

to the outlier threshold amount to set 

total estimated outlier payments at 2.0 

percent of total estimated payments in 

FY 2023. This estimate does not include 

the implementation of the required 2.0 

percentage point reduction of the 

productivity-adjusted market basket 

update factor for any IPF that fails to 

meet the IPF quality reporting 

requirements (as discussed in section 

III.B.2. of this proposed rule). 

2. Impact on Providers 

To show the impact on providers of 

the changes to the IPF PPS discussed in 

this proposed rule, we compare 

estimated payments under the proposed 

IPF PPS rates and factors for FY 2023 

versus those under FY 2022. We 

determined the percent change in the 

estimated FY 2023 IPF PPS payments 

compared to the estimated FY 2022 IPF 

PPS payments for each category of IPFs. 

In addition, for each category of IPFs, 

we have included the estimated percent 

change in payments resulting from the 

proposed update to the outlier fixed 

dollar loss threshold amount; the 

updated wage index data including the 

proposed labor-related share and the 

proposed 5-percent cap on any decrease 

to a provider’s wage index from its wage 

index in the prior year; and the 

proposed market basket update for FY 

2023, as reduced by the proposed 

productivity adjustment according to 

section 1886(s)(2)(A)(i) of the Act. 

To illustrate the impacts of the 

proposed FY 2023 changes in this 

proposed rule, our analysis begins with 

FY 2021 IPF PPS claims (based on the 

2021 MedPAR claims, December 2021 

update). As discussed in section III.E.2 

of this proposed rule, we also proposed 

to exclude providers from our impact 

simulations whose change in estimated 

cost per day is outside 3 standard 

deviations from the mean. We estimate 

FY 2022 IPF PPS payments using these 

2021 claims, the finalized FY 2022 IPF 

PPS Federal per diem base rates, and the 

finalized FY 2022 IPF PPS patient and 

facility level adjustment factors (as 

published in the FY 2022 IPF PPS final 

rule (86 FR 42608)). We then estimate 

the FY 2022 outlier payments based on 

these simulated FY 2022 IPF PPS 

payments using the same methodology 

as finalized in the FY 2022 IPF PPS final 

rule (86 FR 42623 through 42624) where 

total outlier payments are maintained at 

2 percent of total estimated FY 2022 IPF 

PPS payments. 

Each of the following changes is 

added incrementally to this baseline 

model in order for us to isolate the 

effects of each change: 

• The proposed update to the outlier 

fixed dollar loss threshold amount. 

• The proposed FY 2023 IPF wage 

index, the proposed 5-percent cap on 

any decrease to a provider’s wage index 

from its wage index in the prior year, 

and the proposed FY 2023 labor-related 

share. 

• The proposed market basket update 

for FY 2023 of 3.1 percent less the 

proposed productivity adjustment of 0.4 

percentage point in accordance with 

section 1886(s)(2)(A)(i) of the Act for a 

payment rate update of 2.7 percent. 

Our proposed column comparison in 

Table 3 illustrates the percent change in 

payments from FY 2022 (that is, October 

1, 2022, to September 30, 2022) to FY 

2023 (that is, October 1, 2022, to 

September 30, 2023) including all the 

proposed payment policy changes. 


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TABLE 3—FY 2023 IPF PPS PROPOSED PAYMENT IMPACTS

Facility by type Number of 

facilities Outlier 

FY 2023 

wage index 

(with cap) 

and LRS 

Total percent 

change 1

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

All Facilities ...................................................................................................... 1,418 ¥1.2 0.0 1.5 

Total Urban ............................................................................................... 1,148 ¥1.3 0.0 1.4 

Urban unit .......................................................................................... 677 ¥1.9 0.0 0.7 

Urban hospital ................................................................................... 471 ¥0.4 0.1 2.4 

Total Rural ................................................................................................ 270 ¥0.8 ¥0.2 1.7 

Rural unit ........................................................................................... 213 ¥0.9 ¥0.2 1.6 

Rural hospital .................................................................................... 57 ¥0.4 ¥0.3 2.0 

By Type of Ownership: 

Freestanding IPFs: 

Urban Psychiatric Hospitals: 

Government ....................................................................................... 119 ¥1.8 0.1 0.9 

Non-Profit .......................................................................................... 88 ¥0.7 0.3 2.3 

For-Profit ............................................................................................ 264 ¥0.1 0.0 2.7 

Rural Psychiatric Hospitals: 

Government ....................................................................................... 30 ¥0.7 ¥0.3 1.7 

Non-Profit .......................................................................................... 12 ¥1.5 ¥0.1 1.1 

For-Profit ............................................................................................ 15 ¥0.1 ¥0.3 2.3 

IPF Units: 

Urban: 

Government ....................................................................................... 92 ¥2.4 0.0 0.3 

Non-Profit .......................................................................................... 450 ¥2.2 ¥0.1 0.4 

For-Profit ............................................................................................ 135 ¥1.0 0.1 1.8 

Rural: 

Government ....................................................................................... 48 ¥0.8 0.0 1.9 

Non-Profit .......................................................................................... 123 ¥0.9 ¥0.2 1.5 

For-Profit ............................................................................................ 42 ¥1.0 ¥0.2 1.4 

By Teaching Status: 

Non-teaching ............................................................................................ 1,234 ¥0.9 0.1 1.8 

Less than 10% interns and residents to beds .......................................... 99 ¥1.6 ¥0.2 0.8 

10% to 30% interns and residents to beds .............................................. 61 ¥2.9 ¥0.4 ¥0.7 

More than 30% interns and residents to beds ......................................... 24 ¥3.7 0.2 ¥0.9 

By Region: 

New England ............................................................................................ 102 ¥1.8 ¥0.5 0.4 

Mid-Atlantic ............................................................................................... 181 ¥1.6 ¥0.1 1.0 

South Atlantic ........................................................................................... 219 ¥0.7 ¥0.1 1.9 

East North Central .................................................................................... 233 ¥1.0 ¥0.2 1.4 

East South Central ................................................................................... 143 ¥1.0 ¥0.3 1.4 

West North Central ................................................................................... 102 ¥1.7 ¥0.3 0.7 

West South Central .................................................................................. 211 ¥0.5 0.3 2.5 

Mountain ................................................................................................... 99 ¥0.7 0.1 2.0 

Pacific ....................................................................................................... 128 ¥1.7 0.9 1.8 

By Bed Size: 

Psychiatric Hospitals: 

Beds: 0–24 ........................................................................................ 82 ¥0.5 0.2 2.4 

Beds: 25–49 ...................................................................................... 73 ¥0.1 0.1 2.7 

Beds: 50–75 ...................................................................................... 78 ¥0.1 ¥0.1 2.5 

Beds: 76 + ......................................................................................... 295 ¥0.5 0.1 2.2 

Psychiatric Units: 

Beds: 0–24 ........................................................................................ 486 ¥1.5 0.0 1.2 

Beds: 25–49 ...................................................................................... 240 ¥1.7 ¥0.1 0.9 

Beds: 50–75 ...................................................................................... 100 ¥2.2 ¥0.1 0.3 

Beds: 76 + ......................................................................................... 64 ¥2.1 ¥0.1 0.5 

1This column includes the impact of the updates in columns (3) through (5) above, and of the proposed IPF market basket update factor for 

FY 2023 (3.1 percent), reduced by 0.4 percentage point for the proposed productivity adjustment as required by section 1886(s)(2)(A)(i) of the 

Act. 

3. Impact Results 

Table 3 displays the results of our 

analysis. The table groups IPFs into the 

categories listed here based on 

characteristics provided in the Provider 

of Services file, the IPF PSF, and cost 

report data from the Healthcare Cost 

Report Information System: 

• Facility Type. 

• Location. 

• Teaching Status Adjustment. 

• Census Region. 

• Size. 

The top row of the table shows the 

overall impact on the 1,418 IPFs 

included in the analysis. In column 2, 

we present the number of facilities of 

each type that had information available 

in the PSF, had claims in the MedPAR 

dataset for FY 2021, and were not 

excluded due to the proposed trim on 

providers whose change in estimated 

cost per day is outside 3 standard 

deviations from the mean. 


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In column 3, we present the effects of 

the update to the outlier fixed dollar 

loss threshold amount. We estimate that 

IPF outlier payments as a percentage of 

total IPF payments are 3.2 percent in FY 

2022. Therefore, we propose to adjust 

the outlier threshold amount to set total 

estimated outlier payments equal to 2.0 

percent of total payments in FY 2023. 

The estimated change in total IPF 

payments for FY 2023, therefore, 

includes an approximate 1.2 percent 

decrease in payments because we would 

expect the outlier portion of total 

payments to decrease from 

approximately 3.2 percent to 2.0 

percent. 

The overall impact of the estimated 

decrease to payments due to updating 

the outlier fixed dollar loss threshold (as 

shown in column 3 of Table 3), across 

all hospital groups, is a 1.2 percent 

decrease. The largest decrease in 

payments due to this change is 

estimated to be 3.7 percent for teaching 

IPFs with more than 30 percent interns 

and residents to beds. 

In column 4, we present the effects of 

the proposed budget-neutral update to 

the IPF wage index, the proposed LaborRelated Share (LRS), and the 5-percent 

cap on any decrease to a provider’s 

wage index from its wage index in the 

prior year discussed in section III.D.2 of 

this proposed rule. This represents the 

effect of using the concurrent hospital 

wage data as discussed in section 

III.D.1.a of this proposed rule. That is, 

the impact represented in this column 

reflects the proposed update from the 

FY 2022 IPF wage index to the proposed 

FY 2023 IPF wage index, which 

includes basing the FY 2023 IPF wage 

index on the FY 2023 pre-floor, prereclassified IPPS hospital wage index 

data, applying a 5-percent cap on any 

decrease to a provider’s wage index 

from its wage index in the prior year, 

and updating the LRS from 77.2 percent 

in FY 2022 to 77.4 percent in FY 2023. 

We note that there is no projected 

change in aggregate payments to IPFs, as 

indicated in the first row of column 4; 

however, there would be distributional 

effects among different categories of 

IPFs. For example, we estimate the 

largest increase in payments to be 0.9 

percent for Pacific IPFs, and the largest 

decrease in payments to be 0.5 percent 

for New England IPFs. 

IPF payments are therefore estimated 

to increase by 1.4 percent in urban areas 

and 1.7 percent in rural areas. Overall, 

IPFs are estimated to experience a net 

increase in payments as a result of the 

updates in this proposed rule. The 

largest payment increases are estimated 

at 2.7 percent for freestanding urban forprofit IPFs and IPF hospitals with 25– 

49 beds. 

4. Effect on Beneficiaries 

Under the FY 2023 IPF PPS, IPFs will 

continue to receive payment based on 

the average resources consumed by 

patients for each day. Our longstanding 

payment methodology reflects the 

differences in patient resource use and 

costs among IPFs, as required under 

section 124 of the BBRA. We expect that 

updating IPF PPS rates in this proposed 

rule will improve or maintain 

beneficiary access to high quality care 

by ensuring that payment rates reflect 

the best available data on the resources 

involved in inpatient psychiatric care 

and the costs of these resources. We 

continue to expect that paying 

prospectively for IPF services under the 

FY 2023 IPF PPS will enhance the 

efficiency of the Medicare program. 

5. 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 be directly impacted 

and will review this proposed rule, we 

assume that the total number of unique 

commenters on the most recent IPF 

proposed rule will be the number of 

reviewers of this proposed rule. For this 

FY 2023 IPF PPS proposed rule, the 

most recent IPF proposed rule was the 

FY 2022 IPF PPS proposed rule, and we 

received 898 unique comments on this 

proposed rule. We acknowledge that 

this assumption may understate or 

overstate the costs of reviewing this 

proposed rule. It is possible that not all 

commenters reviewed the FY 2022 IPF 

proposed rule in detail, and it is also 

possible that some reviewers chose not 

to comment on that proposed rule. For 

these reasons, we thought that the 

number of commenters would be a fair 

estimate of the number of reviewers 

who are directly impacted by this 

proposed rule. We are soliciting 

comments on this assumption. 

We also recognize that different types 

of entities are in many cases affected by 

mutually exclusive sections of this 

proposed rule; therefore, for the 

purposes of our estimate, we assume 

that each reviewer reads approximately 

50 percent of this proposed rule. 

Using the May, 2020 mean (average) 

wage information from the BLS for 

medical and health service managers 

(Code 11–9111), we estimate that the 

cost of reviewing this proposed rule is 

$114.24 per hour, including overhead 

and fringe benefits https://www.bls.gov/ 

oes/current/oes119111.htm. Assuming 

an average reading speed of 250 words 

per minute, we estimate that it would 

take approximately 50 minutes (0.833 

hours) for the staff to review half of this 

proposed rule, which contains a total of 

approximately 25,000 words. For each 

IPF that reviews the proposed rule, the 

estimated cost is (0.833 × $114.24) or 

$95.16. Therefore, we estimate that the 

total cost of reviewing this proposed 

rule is $85,453.68 ($95.16 × 898 

reviewers). 

D. Alternatives Considered 

The statute does not specify an update 

strategy for the IPF PPS and is broadly 

written to give the Secretary discretion 

in establishing an update methodology. 

We continue to believe it is appropriate 

to routinely update the IPF PPS so that 

it reflects the best available data about 

differences in patient resource use and 

costs among IPFs as required by the 

statute. Therefore, we are proposing to: 

Update the IPF PPS using the 

methodology published in the 

November 2004 IPF PPS final rule; 

apply the proposed 2016-based IPF PPS 

market basket update for FY 2023 of 3.1 

percent, reduced by the statutorily 

required proposed productivity 

adjustment of 0.4 percentage point along 

with the proposed wage index budget 

neutrality adjustment to update the 

payment rates; and use a FY 2023 IPF 

wage index which uses the FY 2023 prefloor, pre-reclassified IPPS hospital 

wage index as its basis. Additionally, 

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. Lastly, we are proposing for 

FY 2023 to exclude providers from our 

simulation of IPF PPS payments for FY 

2022 and FY 2023 if their change in 

estimated cost per day is outside 3 

standard deviations from the mean. 

E. Accounting Statement 

As required by OMB Circular A–4 

(available at www.whitehouse.gov/sites/ 

whitehouse.gov/files/omb/circulars/A4/ 

a-4.pdf), in Table 4, we have prepared 

an accounting statement showing the 

classification of the expenditures 

associated with the updates to the IPF 

wage index and payment rates in this 

proposed rule. Table 4 provides our best 

estimate of the increase in Medicare 

payments under the IPF PPS as a result 

of the changes presented in this 

proposed rule and based on the data for 

1,418 IPFs with data available in the 

PSF, with claims in our FY 2021 

MedPAR claims dataset, and which 

were not excluded due to the proposed 

trim on providers whose change in 


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estimated cost per day is outside 3 

standard deviations from the mean. 

Lastly, Table 4 also includes our best 

estimate of the costs of reviewing and 

understanding this proposed rule. 

TABLE 4—ACCOUNTING STATEMENT: CLASSIFICATION OF ESTIMATED COSTS, SAVINGS, AND TRANSFERS

Category 

Primary 

estimate 

($million/year) 

Low 

estimate 

High 

estimate 

Units 

Year 

dollars 

Discount 

rate 

Period 

covered 

Regulatory Review Costs ........................ 0.07 ........................ ........................ 2020 ........................ FY 2023 

Annualized Monetized Transfers from 

Federal Government to IPF Medicare 

Providers .............................................. 50 ........................ ........................ FY 2023 ........................ FY 2023 

F. Regulatory Flexibility Act 

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 IPFs 

and most other providers and suppliers 

are small entities, either by nonprofit 

status or having revenues of $8 million 

to $41.5 million or less in any 1 year. 

Individuals and states are not included 

in the definition of a small entity. 

Because we lack data on individual 

hospital receipts, we cannot determine 

the number of small proprietary IPFs or 

the proportion of IPFs’ revenue derived 

from Medicare payments. Therefore, we 

assume that all IPFs are considered 

small entities. 

The Department of Health and Human 

Services generally uses a revenue 

impact of 3 to 5 percent as a significance 

threshold under the RFA. As shown in 

Table 3, we estimate that the overall 

revenue impact of this proposed rule on 

all IPFs is to increase estimated 

Medicare payments by approximately 

1.5 percent. As a result, since the 

estimated impact of this proposed rule 

is a net increase in revenue across 

almost all categories of IPFs, the 

Secretary has determined that this 

proposed rule will have a positive 

revenue impact on a substantial number 

of small entities. 

In addition, section 1102(b) of the Act 

requires us to prepare a regulatory 

impact analysis if a rule may have a 

significant impact on the operations of 

a substantial number of small rural 

hospitals. This analysis must conform to 

the provisions of section 603 of the 

RFA. For purposes of section 1102(b) of 

the Act, we define a small rural hospital 

as a hospital that is located outside of 

a metropolitan statistical area and has 

fewer than 100 beds. As discussed in 

section VIII.C.2 of this proposed rule, 

the rates and policies set forth in this 

proposed rule will not have an adverse 

impact on the rural hospitals based on 

the data of the 213 rural excluded 

psychiatric units and 57 rural 

psychiatric hospitals in our database of 

1,418 IPFs for which data were 

available. Therefore, the Secretary has 

determined that this proposed rule will 

not have a significant impact on the 

operations of a substantial number of 

small rural hospitals. 

G. Unfunded Mandate Reform Act 

(UMRA) 

Section 202 of the Unfunded 

Mandates Reform Act of 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. This proposed rule 

would not impose a mandate that will 

result in the expenditure by state, local, 

and tribal governments, in the aggregate, 

or by the private sector, of more than 

$165 million in any 1 year. 

H. Federalism 

Executive Order 13132 establishes 

certain requirements that an agency 

must meet when it promulgates a 

proposed rule that imposes substantial 

direct requirement costs on state and 

local governments, preempts state law, 

or otherwise has Federalism 

implications. This proposed rule does 

not impose substantial direct costs on 

state or local governments or preempt 

state law. 

Chiquita Brooks-LaSure, 

Administrator of the Centers for 

Medicare & Medicaid Services, 

approved this document on March 24, 

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 part 412 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. Section 412.424 is amended by 

revising paragraph (d)(1)(i) to read as 

follows: 

§ 412.424 Methodology for calculating the 

Federal per diem payment amount. 

* * * * * 

(d) * * * 

(1) * * * 

(i) Adjustment for wages. CMS adjusts 

the labor portion of the Federal per 

diem base rate to account for geographic 

differences in the area wage levels using 

an appropriate wage index. 

(A) The application of the wage index 

is made on the basis of the location of 

the inpatient psychiatric facility in an 

urban or rural area as defined in 

§ 412.402. 

(B) Beginning October 1, 2022, CMS 

applies a cap on decreases to the wage 

index, such that the wage index applied 

to an inpatient psychiatric facility is not 

less than 95 percent of the wage index 

applied to that inpatient psychiatric 

facility in the prior fiscal year. 

* * * * * 

Dated: March 29, 2022. 

Xavier Becerra, 

Secretary, Department of Health and Human 

Services. 

[FR Doc. 2022–06906 Filed 3–31–22; 4:15 pm] 

BILLING CODE 4120–01–P 


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