36322 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

DEPARTMENT OF HEALTH AND 

HUMAN SERVICES 

Centers for Medicare & Medicaid 

Services 

42 CFR Parts 413 and 512 

[CMS–1749–P] 

RIN 0938–AU39 

Medicare Program; End-Stage Renal 

Disease Prospective Payment System, 

Payment for Renal Dialysis Services 

Furnished to Individuals With Acute 

Kidney Injury, End-Stage Renal 

Disease Quality Incentive Program, 

and End-Stage Renal Disease 

Treatment Choices Model 

AGENCY: Centers for Medicare & 

Medicaid Services (CMS), HHS. 

ACTION: Proposed rule. 

SUMMARY: This proposed rule would 

update the End-Stage Renal Disease 

(ESRD) Prospective Payment System 

(PPS) for calendar year (CY) 2022. This 

rulemaking also proposes to update the 

payment rate for renal dialysis services 

furnished by an ESRD facility to 

individuals with acute kidney injury 

(AKI). In addition, this rulemaking 

proposes to update requirements for the 

ESRD Quality Incentive Program (QIP), 

including a proposed measure 

suppression policy for the duration of 

the coronavirus disease 2019 (COVID– 

19) public health emergency (PHE) and 

as well as proposals to suppress 

individual ESRD QIP measures under 

that proposed measure suppression 

policy. This proposed rule also 

announces an extension of time for 

facilities to report September through 

December 2020 ESRD QIP data under 

our Extraordinary Circumstances 

Exception (ECE) policy due to CMS 

operational issues, and proposes to not 

score facilities or reduce payment to any 

facility in PY 2022. In addition, this 

proposed rule includes requests for 

information on topics that are relevant 

to the ESRD QIP. Further, this rule also 

proposes changes to the ESRD 

Treatment Choices (ETC) Model, which 

is a mandatory payment model that is 

focused on encouraging greater use of 

home dialysis and kidney transplants, to 

reduce Medicare expenditures while 

preserving or enhancing the quality of 

care furnished to Medicare 

beneficiaries. Finally, this proposed rule 

includes several requests for 

information to inform payment reform 

under the ESRD PPS. 

DATES: To be assured consideration, 

comments must be submitted at one of 

the addresses provided below, by 

August 31, 2021. 

ADDRESSES: In commenting, please refer 

to file code CMS–1749–P. Because of 

staff and resource limitations, we cannot 

accept comments by facsimile (FAX) 

transmission. 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–1749–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–1749–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: 

ESRDPayment@cms.hhs.gov, for 

issues related to the ESRD PPS and 

coverage and payment for renal dialysis 

services furnished to individuals with 

AKI. 

ESRDApplications@cms.hhs.gov, for 

issues related to the Transitional AddOn Payment Adjustment for New and 

Innovative Equipment and Supplies 

(TPNIES). 

Delia Houseal, (410) 786–2724, for 

issues related to the ESRD QIP. 

ETC–CMMI@cms.hhs.gov, for issues 

related to the ESRD Treatment Choices 

(ETC) Model. 

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. 

Current Procedural Terminology 

(CPT) Copyright Notice: Throughout this 

proposed rule, we use CPT® codes and 

descriptions to refer to a variety of 

services. We note that CPT® codes and 

descriptions are copyright 2020 

American Medical Association (AMA). 

All Rights Reserved. CPT® is a 

registered trademark of the AMA. 

Applicable Federal Acquisition 

Regulations (FAR) and Defense Federal 

Acquisition Regulations (DFAR) apply. 

Table of Contents 

To assist readers in referencing 

sections contained in this preamble, we 

are providing a Table of Contents. 

I. Executive Summary 

A. Purpose 

B. Summary of the Major Provisions 

C. Summary of Cost and Benefits 

II. Calendar Year (CY) 2022 End-Stage Renal 

Disease (ESRD) Prospective Payment 

System (PPS) 

A. Background 

B. Provisions of the Proposed Rule 

C. Proposed Transitional Add-On Payment 

Adjustment for New and Innovative 

Equipment and Supplies (TPNIES) for 

CY 2022 Payment 

III. Calendar Year (CY) 2022 Payment for 

Renal Dialysis Services Furnished to 

Individuals With Acute Kidney Injury 

(AKI) 

A. Background 

B. Proposed Annual Payment Rate Update 

for CY 2022 

IV. End-Stage Renal Disease Quality 

Incentive Program (ESRD QIP) 

A. Background 

B. Extraordinary Circumstances Exception 

(ECE) Previously Granted for the ESRD 

QIP and Notification of ECE Due to ESRD 

Quality Reporting System Issues 

C. Proposed Flexibilities for the ESRD QIP 

in Response to the COVID–19 PHE 

D. Proposed Special Scoring Methodology 

and Payment Policy for the PY 2022 

ESRD QIP 

E. Proposed Updates to Requirements 

Beginning With the PY 2024 ESRD QIP 

F. Updates for the PY 2025 ESRD QIP 

G. Requests for Information (RFIs) on 

Topics Relevant to ESRD QIP 

V. End-Stage Renal Disease Treatment 

Choices (ETC) Model 

A. Background 

B. Provisions of the Proposed Rule 

C. Requests for Information (RFIs) on 

Topics Relevant to ETC Model 

VI. Requests for Information 

A. Informing Payment Reform Under the 

ESRD PPS 


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B. Technical Expert Panels (TEPs) 

C. Calculation of the Low-Volume Payment 

Adjustment (LVPA) 

D. Calculation of the Case-Mix 

Adjustments 

E. Calculation of the Outlier Payment 

Adjustment 

F. Calculation of the Pediatric Dialysis 

Payment Adjustment 

G. Modifying the ESRD PPS and Hospital 

Cost Reports 

H. Modifying the Pediatric Cost Report 

I. Modifying Site of Services Provided to 

Medicare Beneficiaries with Acute 

Kidney Injury (AKI) 

VII. Collection of Information Requirements 

A. Legislative Requirement for Solicitation 

of Comments 

B. Requirements in Regulation Text 

C. Additional Information Collection 

Requirements 

VIII. Response to Comments 

IX. Economic Analyses 

A. Regulatory Impact Analysis 

B. Detailed Economic Analysis 

C. Accounting Statement 

D. Regulatory Flexibility Act Analysis 

(RFA) 

E. Unfunded Mandates Reform Act 

Analysis (UMRA) 

F. Federalism 

G. Congressional Review Act 

X. Files Available to the Public via the 

Internet 

Regulations Text 

I. Executive Summary 

A. Purpose 

1. End-Stage Renal Disease (ESRD) 

Prospective Payment System (PPS) 

On January 1, 2011, we implemented 

the End-Stage Renal Disease (ESRD) 

Prospective Payment System (PPS), a 

case-mix adjusted, bundled PPS for 

renal dialysis services furnished by 

ESRD facilities as required by section 

1881(b)(14) of the Social Security Act 

(the Act), as added by section 153(b) of 

the Medicare Improvements for Patients 

and Providers Act of 2008 (MIPPA) 

(Pub. L. 110–275). Section 

1881(b)(14)(F) of the Act, as added by 

section 153(b) of MIPPA, and amended 

by section 3401(h) of the Patient 

Protection and Affordable Care Act (the 

Affordable Care Act) (Pub. L. 111–148), 

established that beginning calendar year 

(CY) 2012, and each subsequent year, 

the Secretary of the Department of 

Health and Human Services (the 

Secretary) shall annually increase 

payment amounts by an ESRD market 

basket increase factor, reduced by the 

productivity adjustment described in 

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

This rule proposes updates to the ESRD 

PPS for CY 2022. 

2. Coverage and Payment for Renal 

Dialysis Services Furnished to 

Individuals With Acute Kidney Injury 

(AKI) 

On June 29, 2015, the President 

signed the Trade Preferences Extension 

Act of 2015 (TPEA) (Pub. L. 114–27). 

Section 808(a) of the TPEA amended 

section 1861(s)(2)(F) of the Act to 

provide coverage for renal dialysis 

services furnished on or after January 1, 

2017, by a renal dialysis facility or a 

provider of services paid under section 

1881(b)(14) of the Act to an individual 

with acute kidney injury (AKI). Section 

808(b) of the TPEA amended section 

1834 of the Act by adding a new 

subsection (r) that provides for payment 

for renal dialysis services furnished by 

renal dialysis facilities or providers of 

services paid under section 1881(b)(14) 

of the Act to individuals with AKI at the 

ESRD PPS base rate beginning January 

1, 2017. This rule proposes to update 

the AKI payment rate for CY 2022. 

3. End-Stage Renal Disease Quality 

Incentive Program (ESRD QIP) 

The End-Stage Renal Disease Quality 

Incentive Program (ESRD QIP) is 

authorized by section 1881(h) of the 

Act. The Program fosters improved 

patient outcomes by establishing 

incentives for dialysis facilities to meet 

or exceed performance standards 

established by the Centers for Medicare 

& Medicaid Services (CMS). This rule 

proposes to suppress the use of certain 

ESRD QIP measure data for scoring and 

payment adjustment purposes in the PY 

2022 ESRD QIP because we have 

determined that circumstances caused 

by the Public Health Emergency (PHE) 

for the coronavirus disease 2019 

(COVID–19) pandemic have 

significantly affected the validity and 

reliability of the measures and resulting 

performance scores, as well as special 

scoring and payment policies for PY 

2022. We are also announcing an 

extension of time for facilities to report 

September-December 2020 ESRD QIP 

data under our Extraordinary 

Circumstances Exception (ECE) policy 

due to CMS operational issues. 

Beginning with the PY 2024 ESRD QIP, 

we are proposing to update the 

specifications for the SHR clinical 

measure. We are also proposing for the 

PY 2024 ESRD QIP to adopt CY 2019 as 

the baseline period for purposes of 

calculating the achievement thresholds, 

benchmarks, and performance standard 

values. Although no new requirements 

are proposed for the PY 2025 ESRD QIP, 

this proposed rule includes policies that 

would apply in PY 2025. This proposed 

rule also includes requests for 

information on several important topics, 

including strategies that CMS can use to 

address the gap in existing health 

inequities, the addition of COVID–19 

vaccination measures in future 

rulemaking, and the use of digital 

quality measurement. 

4. End-Stage Renal Disease Treatment 

Choices (ETC) Model 

This rulemaking proposes to 

implement changes to the End-Stage 

Renal Disease (ESRD) Treatment 

Choices Model (ETC) Model, a 

mandatory Medicare payment model 

tested under the authority of section 

1115A of the Act. The ETC Model is 

operated by the Center for Medicare and 

Medicaid Innovation (Innovation 

Center), and tests the use of payment 

adjustments to encourage greater 

utilization of home dialysis and kidney 

transplants, in order to preserve or 

enhance the quality of care furnished to 

Medicare beneficiaries while reducing 

Medicare expenditures. The ETC Model 

includes ESRD facilities and certain 

clinicians caring for beneficiaries with 

ESRD—or Managing Clinicians—located 

in Selected Geographic Areas as 

participants. 

The ETC Model was finalized as part 

of a final rule published in the Federal 

Register on September 29, 2020, titled, 

‘‘Medicare Program; Specialty Care 

Models to Improve Quality of Care and 

Reduce Expenditures’’ (85 FR 61114), 

referred to herein as the ‘‘Specialty Care 

Models final rule.’’ The ETC Model is 

designed to test the effectiveness of 

adjusting certain Medicare payments to 

ETC Participants (ESRD facilities and 

Managing Clinicians—clinicians who 

furnish and bill the Monthly Capitation 

Payment (MCP) for managing ESRD 

Beneficiaries—who have been selected 

to participate in the ETC Model) to 

encourage greater utilization of home 

dialysis and kidney transplantation, 

support beneficiary modality choice, 

reduce Medicare expenditures, and 

preserve or enhance the quality of care. 

In the Specialty Care Models final rule, 

we established that the ETC Model 

adjusts payments for home dialysis and 

home dialysis-related claims with claim 

service dates from January 1, 2021 

through December 31, 2023 through the 

Home Dialysis Payment Adjustment 

(HDPA). We are assessing the rates of 

home dialysis and of kidney transplant 

waitlisting and living donor 

transplantation, among beneficiaries 

attributed to ETC Participants during 

the period beginning January 1, 2021, 

and ending June 30, 2026. Based on 

those rates, we are applying the 

Performance Payment Adjustment (PPA) 

to claims for dialysis and dialysisVerDate Sep<11>2014 19:30 Jul 08, 2021 Jkt 253001 PO 00000 Frm 00003 Fmt 4701 Sfmt 4702 E:\FR\FM\09JYP2.SGM 09JYP2

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related services with claim service dates 

beginning July 1, 2022, and ending June 

30, 2027. We codified these provisions 

in a new subpart of the Code of Federal 

Regulations (CFR) 42 CFR part 512, 

subpart C. 

This rulemaking proposes 

modifications to the ETC Model, 

including changes to the home dialysis 

rate and transplant rate, the PPA 

achievement benchmarking 

methodology, and the PPA 

improvement benchmarking and scoring 

methodology. We are also proposing to 

add processes and requirements for ETC 

Participants to receive certain data from 

CMS and to include certain additional 

waivers and flexibilities as part of the 

ETC Model test. This proposed rule also 

includes requests for information 

regarding the placement of peritoneal 

dialysis catheters and the development 

of a home dialysis beneficiary 

experience measure. 

B. Summary of the Major Provisions 

1. ESRD PPS 

• Update to the ESRD PPS base rate 

for CY 2022: The proposed CY 2022 

ESRD PPS base rate is $255.55. This 

proposed amount reflects the 

application of the wage index budgetneutrality adjustment factor (.999546) 

and a productivity-adjusted market 

basket increase of 1.0 percent as 

required by section 1881(b)(14)(F)(i)(I) 

of the Act, equaling $255.55 (($253.13 × 

.999546) × 1.010 = $255.55). 

• Annual update to the wage index: 

We adjust wage indices on an annual 

basis using the most current hospital 

wage data and the latest core-based 

statistical area (CBSA) delineations to 

account for differing wage levels in 

areas in which ESRD facilities are 

located. For CY 2022, we are proposing 

to update the wage index values based 

on the latest available data and 

continuing the 2-year transition to the 

Office of Management and Budget 

(OMB) delineations as described in the 

September 14, 2018 OMB Bulletin No. 

18–04. 

• Update to the outlier policy: We are 

proposing to update the outlier policy 

using the most current data, as well as 

update the outlier services fixed-dollar 

loss (FDL) amounts for adult and 

pediatric patients and Medicare 

allowable payment (MAP) amounts for 

adult and pediatric patients for CY 2022 

using CY 2020 claims data. Based on the 

use of the latest available data, the 

proposed FDL amount for pediatric 

beneficiaries would decrease from 

$44.78 to $30.38, and the MAP amount 

would decrease from $30.88 to $28.73, 

as compared to CY 2021 values. For 

adult beneficiaries, the proposed FDL 

amount would decrease from $122.49 to 

$111.18, and the MAP amount would 

decrease from $50.92 to $47.87. The 1.0 

percent target for outlier payments was 

not achieved in CY 2020. Outlier 

payments represented approximately 

0.6 percent of total payments rather than 

1.0 percent. 

• Update to the offset amount for the 

transitional add-on payment adjustment 

for new and innovative equipment and 

supplies (TPNIES) for CY 2022: The 

proposed CY 2022 average per treatment 

offset amount for the transitional add-on 

payment adjustment for new and 

innovative equipment and supplies 

(TPNIES) for capital-related assets that 

are home dialysis machines is $9.41. 

This proposed offset amount reflects the 

application of the productivity-adjusted 

market basket increase of 1.0 percent 

($9.32 × 1.010 = $9.41). 

• TPNIES applications received for 

CY 2022: This proposed rule presents a 

summary of the two CY 2022 TPNIES 

applications that we received by the 

February 1, 2021 deadline and our 

analysis of the applicants’ claims related 

to substantial clinical improvement 

(SCI) and other eligibility criteria for the 

TPNIES. 

2. Payment for Renal Dialysis Services 

Furnished to Individuals With AKI 

We are proposing to update the AKI 

payment rate for CY 2022 to $255.55, 

which is the same as the base rate 

proposed under the ESRD PPS for CY 

2022. 

3. ESRD QIP 

We are announcing an extension of 

time for facilities to report September 

through December 2020 ESRD QIP data 

under our Extraordinary Circumstances 

Exception (ECE) policy due to CMS 

operational issues. We are proposing to 

adopt a measure suppression policy for 

the duration of the COVID–19 PHE that 

would enable us to suppress the use of 

one or more measures in the ESRD QIP 

for scoring and payment adjustment 

purposes if we determine that 

circumstances caused by the COVID–19 

PHE have significantly affected the 

measures and resulting performance 

scores. We are also proposing to 

suppress the Standardized 

Hospitalization Ratio (SHR) clinical 

measure, the Standardized Readmission 

Ratio (SRR) clinical measure, the InCenter Hemodialysis Consumer 

Assessment of Healthcare Providers and 

Systems (ICH CAHPS) clinical measure, 

and the Long-Term Catheter Rate 

clinical measure for PY 2022 under the 

proposed measure suppression policy. 

Finally, we are proposing to not score or 

reduce payment to any facility in PY 

2022. Beginning with the PY 2024 ESRD 

QIP, we are proposing to update the 

specifications for the SHR clinical 

measure. We are also proposing for the 

PY 2024 ESRD QIP to adopt CY 2019 as 

the baseline period for purposes of 

calculating the achievement thresholds, 

benchmarks, and performance standard 

values. This proposed rule also 

announces the performance standards 

and payment reductions that would 

apply for PY 2024. This proposed rule 

describes several policies continuing for 

PY 2025, but does not propose any new 

requirements beginning with the PY 

2025 ESRD QIP. 

This proposed rule includes requests 

for public comment on several 

important topics, including closing the 

gap in health equity, adding a COVID– 

19 vaccination measure for health care 

personnel (HCP) and a COVID–19 

vaccination measure for ESRD patients 

to the ESRD QIP measure set in future 

rulemaking, and potential actions and 

priority areas that would enable the 

continued transformation of our quality 

measurement enterprise toward greater 

digital capture of data and use of the 

Fast Healthcare Interoperability 

Resources (FHIR®) standard. 

4. ETC Model 

We are proposing to implement the 

following changes to the ETC Model 

beginning for the third Measurement 

Year (MY3) of the Model, which begins 

January 1, 2022. 

• Beneficiary Attribution for Living 

Kidney Donor Transplants: To better 

reflect the care relationship between 

beneficiaries who receive pre-emptive 

living donor transplants (LDT) and the 

Managing Clinicians who provide their 

care, we propose to modify the 

methodology for attributing Pre-emptive 

LDT Beneficiaries to Managing 

Clinicians, such that a Pre-emptive LDT 

Beneficiary would be attributed to the 

Managing Clinician who submitted the 

most claims for services furnished to the 

beneficiary during the 365 days prior to 

the transplant date. 

• Home Dialysis Rate Calculation: To 

incentivize additional alternative renal 

replacement modalities under the ETC 

Model, we propose adding nocturnal incenter dialysis to the calculation of the 

home dialysis rate for ESRD facilities 

not owned in whole or in part by a large 

dialysis organization (LDO) as well as 

Managing Clinicians. 

• Transplant Rate Beneficiary 

Exclusion: To better align with common 

reasons transplant centers do not place 

patients on the transplant waitlist, we 

propose to exclude beneficiaries with a 

diagnosis of, and who are receiving 


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treatment with chemotherapy or 

radiation for, vital solid organ cancers 

from the calculation of the transplant 

rate. 

• Performance Payment Adjustment 

Achievement Benchmarking 

Methodology: When we originally 

finalized the ETC Model, we stated our 

intent to increase achievement 

benchmarks above rates observed in 

Comparison Geographic Areas for future 

model years. As such, we propose to 

increase achievement benchmarks by 10 

percent over rates observed in 

Comparison Geographic Areas every 

two MYs, beginning in MY3 (2022). We 

also propose to stratify achievement 

benchmarks based on the proportion of 

attributed beneficiaries who are duallyeligible for Medicare and Medicaid or 

receive the Low Income Subsidy (LIS) 

during the MY, in recognition that 

beneficiaries with lower socioeconomic 

status have lower rates of home dialysis 

and transplant than those with higher 

socioeconomic status. 

• Performance Payment Adjustment 

Improvement Benchmarking and 

Scoring: In conjunction with our 

proposal to stratify achievement 

benchmarks based on the proportion of 

beneficiaries who are dual-eligible or 

LIS recipients, we propose to introduce 

the Health Equity Incentive to the 

improvement scoring methodology used 

in calculating the PPA. CMS expects 

that the Health Equity Incentive would 

encourage ETC Participants to decrease 

disparities in renal replacement 

modality choice among beneficiaries 

with lower socioeconomic status by 

rewarding ETC Participants that 

demonstrate significant improvement in 

the home dialysis rate or transplant rate 

among their attributed beneficiaries who 

are dual-eligible or LIS recipients. We 

also propose to adjust the improvement 

scoring calculation to avoid the scenario 

where an ETC Participant cannot 

receive an improvement score because 

its home dialysis rate or transplant rate 

was zero during the Benchmark Year. 

• Performance Payment Adjustment 

Reports and Related Data Sharing: To 

ensure that ETC Participants have 

timely access to ETC Model reports, we 

are proposing a process by which CMS 

would share certain model data with 

ETC Participants. 

• Medicare Waivers: We are 

proposing an additional programmatic 

waiver to provide Managing Clinicians 

who are ETC Participants additional 

flexibility in furnishing the kidney 

disease patient education services 

described in § 410.48: A waiver of 

certain telehealth requirements as 

necessary solely for purposes of 

allowing ETC Participants to furnish 

kidney disease patient education 

services via telehealth under the ETC 

Model. 

• Kidney Disease Patient Education 

Services Coinsurance Waivers: We are 

proposing to permit Managing 

Clinicians who are ETC Participants to 

reduce or waive the beneficiary 

coinsurance for kidney disease patient 

education services, subject to certain 

requirements. We anticipate making the 

determination that the anti-kickback 

statute safe harbor for CMS-sponsored 

model patient incentives (42 CFR 

1001.952(ii)), would be available to 

protect the reduction or elimination of 

coinsurance performed in accordance 

with our proposed policy, if finalized. 

C. Summary of Costs and Benefits 

In section IX.B of this proposed rule, 

we set forth a detailed analysis of the 

impacts that the proposed changes 

would have on affected entities and 

beneficiaries. The impacts include the 

following: 

1. Impacts of the Proposed ESRD PPS 

The impact table in section IX.B.1.a of 

this proposed rule displays the 

estimated change in payments to ESRD 

facilities in CY 2022 compared to 

estimated payments in CY 2021. The 

overall impact of the proposed CY 2022 

changes is projected to be a 1.2 percent 

increase in payments. Hospital-based 

ESRD facilities have an estimated 1.3 

percent increase in payments compared 

with freestanding facilities with an 

estimated 1.2 percent increase. We 

estimate that the aggregate ESRD PPS 

expenditures would increase by 

approximately $140 million in CY 2022 

compared to CY 2021. This reflects a 

$120 million increase from the payment 

rate update and a $20 million increase 

due to the updates to the outlier 

threshold amounts. Because of the 

projected 1.2 percent overall payment 

increase, we estimate there would be an 

increase in beneficiary coinsurance 

payments of 1.2 percent in CY 2022, 

which translates to approximately $30 

million. 

2. Impacts of the Proposed Payment for 

Renal Dialysis Services Furnished to 

Individuals With AKI 

The impact table in section IX.B.2.a of 

this proposed rule displays the 

estimated change in payments to ESRD 

facilities in CY 2022 compared to 

estimated payments in CY 2021. The 

overall impact of the proposed CY 2022 

changes is projected to be a 1.0 percent 

increase in payments for individuals 

with AKI. Hospital-based ESRD 

facilities have an estimated 1.1 percent 

increase in payments compared with 

freestanding ESRD facilities with an 

estimated 1.0 percent increase. The 

overall impact reflects the effects of the 

updated wage index and the proposed 

payment rate update. We estimate that 

the aggregate payments made to ESRD 

facilities for renal dialysis services 

furnished to patients with AKI, at the 

proposed CY 2022 ESRD PPS base rate, 

would increase by $1 million in CY 

2022 compared to CY 2021. 

3. Impacts of the Proposed ESRD QIP 

Our proposals to suppress measures 

for the PY 2022 ESRD QIP and to revise 

the scoring and payment methodology 

such that no facility will receive a 

payment reduction necessitates a 

modification to our previous estimated 

overall economic impact of the PY 2022 

ESRD QIP (84 FR 60651). In the CY 2020 

ESRD PPS final rule, we estimated that 

the overall economic impact of the PY 

2022 ESRD QIP would be approximately 

$229 million as a result of the policies 

we had finalized at that time. The $229 

million figure for PY 2022 included 

costs associated with the collection of 

information requirements, which we 

estimated would be approximately $211 

million, and $18 million in estimated 

payment reductions across all facilities. 

However, as a result of the proposals 

impacting the PY 2022 ESRD QIP we are 

making in this proposed rule, we are 

modifying our previous estimate. We 

now estimate that the overall economic 

impact of the PY 2022 ESRD QIP would 

be approximately $215 million. The 

$215 million figure for PY 2022 

includes costs associated with the 

collection of information requirements. 

If our proposals are finalized as 

proposed, there would be no payment 

reductions in PY 2022. We estimate that 

the overall economic impact of the PY 

2024 ESRD QIP would be approximately 

$232 million as a result of the policies 

we have previously finalized and the 

proposals in this proposed rule. The 

$232 million figure for PY 2024 

includes costs associated with the 

collection of information requirements, 

which we estimate would be 

approximately $215 million, and $17 

million in estimated payment 

reductions across all facilities. We also 

estimate that the overall economic 

impact of the PY 2025 ESRD QIP would 

be approximately $232 million as a 

result of the policies we have previously 

finalized. 

4. Impacts of Proposed Changes to the 

ETC Model 

The impact estimate in section IX.B.4 

of this proposed rule describes the 

estimated change in anticipated 

Medicare program savings arising from 


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the ETC Model over the duration of the 

ETC Model as a result of the changes 

proposed in this proposed rule. We 

estimate that the ETC Model would 

result in $38 million in net savings over 

the 6.5-year duration of the ETC Model. 

We also estimate that $7 million of the 

estimated $38 million in net savings 

would be attributable to changes 

proposed in this proposed rule. 

II. Calendar Year (CY) 2022 End-Stage 

Renal Disease (ESRD) Prospective 

Payment System (PPS) 

A. Background 

1. Statutory Background 

On January 1, 2011, the Centers for 

Medicare & Medicaid Services (CMS) 

implemented the End-Stage Renal 

Disease (ESRD) Prospective Payment 

System (PPS), a case-mix adjusted 

bundled PPS for renal dialysis services 

furnished by ESRD facilities, as required 

by section 1881(b)(14) of the Social 

Security Act (the Act), as added by 

section 153(b) of the Medicare 

Improvements for Patients and 

Providers Act of 2008 (MIPPA). Section 

1881(b)(14)(F) of the Act, as added by 

section 153(b) of MIPPA and amended 

by section 3401(h) of the Patient 

Protection and Affordable Care Act (the 

Affordable Care Act), established that 

beginning with CY 2012, and each 

subsequent year, the Secretary of the 

Department of Health and Human 

Services (the Secretary) shall annually 

increase payment amounts by an ESRD 

market basket increase factor reduced by 

the productivity adjustment described 

in section 1886(b)(3)(B)(xi)(II) of the 

Act. 

Section 632 of the American Taxpayer 

Relief Act of 2012 (ATRA) (Pub. L. 112– 

240) included several provisions that 

apply to the ESRD PPS. Section 632(a) 

of ATRA added section 1881(b)(14)(I) to 

the Act, which required the Secretary, 

by comparing per patient utilization 

data from 2007 with such data from 

2012, to reduce the single payment for 

renal dialysis services furnished on or 

after January 1, 2014 to reflect the 

Secretary’s estimate of the change in the 

utilization of ESRD-related drugs and 

biologicals (excluding oral-only ESRDrelated drugs). Consistent with this 

requirement, in the CY 2014 ESRD PPS 

final rule we finalized $29.93 as the 

total drug utilization reduction and 

finalized a policy to implement the 

amount over a 3- to 4-year transition 

period (78 FR 72161 through 72170). 

Section 632(b) of ATRA prohibited 

the Secretary from paying for oral-only 

ESRD-related drugs and biologicals 

under the ESRD PPS prior to January 1, 

2016. Section 632(c) of ATRA required 

the Secretary, by no later than January 

1, 2016, to analyze the case-mix 

payment adjustments under section 

1881(b)(14)(D)(i) of the Act and make 

appropriate revisions to those 

adjustments. 

On April 1, 2014, the Protecting 

Access to Medicare Act of 2014 (PAMA) 

(Pub. L. 113–93) was enacted. Section 

217 of PAMA included several 

provisions that apply to the ESRD PPS. 

Specifically, sections 217(b)(1) and (2) 

of PAMA amended sections 

1881(b)(14)(F) and (I) of the Act and 

replaced the drug utilization adjustment 

that was finalized in the CY 2014 ESRD 

PPS final rule (78 FR 72161 through 

72170) with specific provisions that 

dictated the market basket update for 

CY 2015 (0.0 percent) and how the 

market basket should be reduced in CY 

2016 through CY 2018. 

Section 217(a)(1) of PAMA amended 

section 632(b)(1) of ATRA to provide 

that the Secretary may not pay for oralonly ESRD-related drugs under the 

ESRD PPS prior to January 1, 2024. 

Section 217(a)(2) of PAMA further 

amended section 632(b)(1) of ATRA by 

requiring that in establishing payment 

for oral-only drugs under the ESRD PPS, 

the Secretary must use data from the 

most recent year available. Section 

217(c) of PAMA provided that as part of 

the CY 2016 ESRD PPS rulemaking, the 

Secretary shall establish a process for (1) 

determining when a product is no 

longer an oral-only drug; and (2) 

including new injectable and 

intravenous products into the ESRD PPS 

bundled payment. 

Finally, on December 19, 2014, the 

President signed the Stephen Beck, Jr., 

Achieving a Better Life Experience Act 

of 2014 (ABLE) (Pub. L. 113–295). 

Section 204 of ABLE amended section 

632(b)(1) of ATRA, as amended by 

section 217(a)(1) of PAMA, to provide 

that payment for oral-only renal dialysis 

services cannot be made under the 

ESRD PPS bundled payment prior to 

January 1, 2025. 

2. System for Payment of Renal Dialysis 

Services 

Under the ESRD PPS, a single pertreatment payment is made to an ESRD 

facility for all of the renal dialysis 

services defined in section 

1881(b)(14)(B) of the Act and furnished 

to individuals for the treatment of ESRD 

in the ESRD facility or in a patient’s 

home. We have codified our definitions 

of renal dialysis services at § 413.171, 

which is in 42 CFR part 413, subpart H, 

along with other ESRD PPS payment 

policies. The ESRD PPS base rate is 

adjusted for characteristics of both adult 

and pediatric patients and accounts for 

patient case-mix variability. The adult 

case-mix adjusters include five 

categories of age, body surface area, low 

body mass index, onset of dialysis, and 

four comorbidity categories (that is, 

pericarditis, gastrointestinal tract 

bleeding, hereditary hemolytic or sickle 

cell anemia, myelodysplastic 

syndrome). A different set of case-mix 

adjusters are applied for the pediatric 

population. Pediatric patient-level 

adjusters include two age categories 

(under age 22, or age 22–26) and two 

dialysis modalities (that is, peritoneal or 

hemodialysis) (§ 413.235(a) and (b)). 

The ESRD PPS provides for three 

facility-level adjustments. The first 

payment adjustment accounts for ESRD 

facilities furnishing a low volume of 

dialysis treatments (§ 413.232). The 

second adjustment reflects differences 

in area wage levels developed from 

core-based statistical areas (CBSAs) 

(§ 413.231). The third payment 

adjustment accounts for ESRD facilities 

furnishing renal dialysis services in a 

rural area (§ 413.233). 

There are four additional payment 

adjustments under the ESRD PPS. The 

ESRD PPS provides adjustments, when 

applicable, for: (1) A training add-on for 

home and self-dialysis modalities 

(§ 413.235(c)); (2) an additional payment 

for high cost outliers due to unusual 

variations in the type or amount of 

medically necessary care (§ 413.237); (3) 

a transitional drug add-on payment 

adjustment (TDAPA) for certain new 

renal dialysis drugs and biological 

products (§ 413.234(c)); and (4) a 

transitional add-on payment adjustment 

for new and innovative equipment and 

supplies (TPNIES) for certain qualifying, 

new and innovative renal dialysis 

equipment and supplies (§ 413.236(d)). 

3. Updates to the ESRD PPS 

Policy changes to the ESRD PPS are 

proposed and finalized annually in the 

Federal Register. The CY 2011 ESRD 

PPS final rule was published on August 

12, 2010 in the Federal Register (75 FR 

49030 through 49214). That rule 

implemented the ESRD PPS beginning 

on January 1, 2011 in accordance with 

section 1881(b)(14) of the Act, as added 

by section 153(b) of MIPPA, over a 4- 

year transition period. Since the 

implementation of the ESRD PPS, we 

have published annual rules to make 

routine updates, policy changes, and 

clarifications. 

On November 9, 2020, we published 

a final rule in the Federal Register 

titled, ‘‘Medicare Program; End-Stage 

Renal Disease Prospective Payment 

System, Payment for Renal Dialysis 

Services Furnished to Individuals With 

Acute Kidney Injury, and End-Stage 


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Renal Disease Quality Incentive 

Program,’’ referred to herein as the ‘‘CY 

2021 ESRD PPS final rule’’. In that rule, 

we updated the ESRD PPS base rate, 

wage index, and outlier policy, for CY 

2021. We also finalized an update to the 

ESRD PPS wage index to adopt the 2018 

OMB delineations with a transition 

period, changes to the eligibility criteria 

and determination process for the 

TPNIES, an expansion of the TPNIES to 

include certain new and innovative 

capital-related assets that are home 

dialysis machines, an addition to the 

ESRD PPS base rate to include 

calcimimetics in the ESRD PPS bundled 

payment, and a change to the lowvolume payment adjustment eligibility 

criteria and attestation requirement to 

account for the coronavirus disease 

2019 (COVID–19) Public Health 

Emergency (PHE). For further detailed 

information regarding these updates, see 

85 FR 71398. 

B. Provisions of the Proposed Rule 

1. Proposed CY 2022 ESRD PPS Update 

a. Proposed CY 2022 ESRD Bundled 

(ESRDB) Market Basket Update, 

Productivity Adjustment, and LaborRelated Share 

In accordance with section 

1881(b)(14)(F)(i) of the Act, as added by 

section 153(b) of MIPPA and amended 

by section 3401(h) of the Affordable 

Care Act, beginning in 2012, the ESRD 

PPS payment amounts are required to be 

annually increased by an ESRD market 

basket increase factor and reduced by 

the productivity adjustment described 

in section 1886(b)(3)(B)(xi)(II) of the 

Act. The application of the productivity 

adjustment may result in the increase 

factor being less than 0.0 percent for a 

year and may result in payment rates for 

a year being less than the payment rates 

for the preceding year. The statute also 

provides that the market basket increase 

factor should reflect the changes over 

time in the prices of an appropriate mix 

of goods and services used to furnish 

renal dialysis services. 

As required under section 

1881(b)(14)(F)(i) of the Act, CMS 

developed an all-inclusive ESRD 

Bundled (ESRDB) input price index (75 

FR 49151 through 49162). In the CY 

2015 ESRD PPS final rule we rebased 

and revised the ESRDB input price 

index to reflect a 2012 base year (79 FR 

66129 through 66136). Subsequently, in 

the CY 2019 ESRD PPS final rule, we 

finalized a rebased ESRDB input price 

index to reflect a 2016 base year (83 FR 

56951 through 56962). 

Although ‘‘market basket’’ technically 

describes the mix of goods and services 

used for ESRD treatment, this term is 

also commonly used to denote the input 

price index (that is, cost categories, their 

respective weights, and price proxies 

combined) derived from a market 

basket. Accordingly, the term ‘‘ESRDB 

market basket,’’ as used in this 

document, refers to the ESRDB input 

price index. 

We propose to use the CY 2016-based 

ESRDB market basket as finalized and 

described in the CY 2019 ESRD PPS 

final rule (83 FR 56951 through 56962) 

to compute the CY 2022 ESRDB market 

basket increase factor based on the best 

available data. Consistent with 

historical practice, we propose to 

estimate the ESRDB market basket 

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

forecast using the most recently 

available data. IGI is a nationally 

recognized economic and financial 

forecasting firm with which we contract 

to forecast the components of the market 

baskets. Using this methodology and the 

IGI first quarter 2021 forecast of the CY 

2016-based ESRDB market basket (with 

historical data through the fourth 

quarter of 2020), the proposed CY 2022 

ESRDB market basket increase factor is 

1.6 percent. 

Under section 1881(b)(14)(F)(i) of the 

Act, for CY 2012 and each subsequent 

year, the ESRD market basket percentage 

increase factor shall be reduced by the 

productivity adjustment described in 

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

The productivity adjustment is 

calculated using a projection of 

multifactor productivity (MFP), which 

is derived by subtracting the 

contribution of labor and capital input 

growth from output growth. We 

finalized the detailed methodology for 

deriving the projection of MFP in the 

CY 2012 ESRD PPS final rule (76 FR 

40503 through 40504). The most up-todate MFP projection methodology is 

available on the CMS website at https:// 

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

Downloads/MFPMethodology.pdf. We 

note that for CY 2022 and beyond, CMS 

is changing the name of this adjustment 

to refer to it as the productivity 

adjustment, which is the term used in 

sections 1881(b)(14)(F)(i) and 

1886(b)(3)(B)(xi)(II) of the Act, rather 

than the multifactor productivity or 

MFP adjustment. This is not a change in 

policy, as we will continue to use the 

same methodology for deriving the 

adjustment and rely on the same 

underlying data. Using this 

methodology and the IGI first quarter 

2021 forecast, the proposed productivity 

adjustment for CY 2022 (the 10-year 

moving average of MFP for the period 

ending CY 2022) is projected to be 0.6 

percent. 

As a result of these provisions, the 

proposed CY 2022 ESRD market basket 

increase factor reduced by the 

productivity adjustment is 1.0 percent. 

The proposed market basket increase 

factor is calculated by starting with the 

proposed CY 2022 ESRDB market basket 

percentage increase factor of 1.6 percent 

and reducing it by the proposed 

productivity adjustment (the 10-year 

moving average of MFP for the period 

ending CY 2022) of 0.6 percent. 

As is our general practice, we are 

proposing that if more recent data 

become available after the publication of 

this proposed rule and before the 

publication of the final rule (for 

example, a more recent estimate of the 

CY 2016-based ESRD market basket 

increase factor or productivity 

adjustment), we would use such data, if 

appropriate, to determine the final CY 

2022 market basket update and 

productivity adjustment. 

For the CY 2022 ESRD payment 

update, we propose to continue using a 

labor-related share of 52.3 percent for 

the ESRD PPS payment, which was 

finalized in the CY 2019 ESRD PPS final 

rule (83 FR 56963). 

b. The Proposed CY 2022 ESRD PPS 

Wage Indices 

(1) Background 

Section 1881(b)(14)(D)(iv)(II) of the 

Act provides that the ESRD PPS may 

include a geographic wage index 

payment adjustment, such as the index 

referred to in section 1881(b)(12)(D) of 

the Act, as the Secretary determines to 

be appropriate. In the CY 2011 ESRD 

PPS final rule (75 FR 49200), we 

finalized an adjustment for wages at 

§ 413.231. Specifically, CMS adjusts the 

labor-related portion of the base rate to 

account for geographic differences in 

the area wage levels using an 

appropriate wage index, which reflects 

the relative level of hospital wages and 

wage-related costs in the geographic 

area in which the ESRD facility is 

located. We use OMB’s CBSA-based 

geographic area designations to define 

urban and rural areas and their 

corresponding wage index values (75 FR 

49117). OMB publishes bulletins 

regarding CBSA changes, including 

changes to CBSA numbers and titles. 

The bulletins are available online at 

https://www.whitehouse.gov/omb/ 

information-for-agencies/bulletins/. 

For CY 2022, we would update the 

wage indices to account for updated 

wage levels in areas in which ESRD 

facilities are located using our existing 

methodology. We use the most recent 


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1We note that for the CY 2020 ESRD PPS final 

rule, we did not apply the statewide urban average 

to Carson City, Nevada because hospital data was 

available to compute the wage index. 

2Transmittal 2033 issued August 20, 2010, was 

rescinded and replaced by Transmittal 2094, dated 

November 17, 2010. Transmittal 2094 identified 

additional drugs and laboratory tests that may also 

be eligible for ESRD outlier payment. Transmittal 

2094 was rescinded and replaced by Transmittal 

2134, dated January 14, 2011, which included one 

technical correction. 

pre-floor, pre-reclassified hospital wage 

data collected annually under the 

inpatient PPS. The ESRD PPS wage 

index values are calculated without 

regard to geographic reclassifications 

authorized under sections 1886(d)(8) 

and (d)(10) of the Act and utilize 

prefloor hospital data that are 

unadjusted for occupational mix. For 

CY 2022, the updated wage data are for 

hospital cost reporting periods 

beginning on or after October 1, 2017, 

and before October 1, 2018 (fiscal year 

[FY] 2018 cost report data). 

We have also adopted methodologies 

for calculating wage index values for 

ESRD facilities that are located in urban 

and rural areas where there is no 

hospital data. For a full discussion, see 

CY 2011 and CY 2012 ESRD PPS final 

rules at 75 FR 49116 through 49117 and 

76 FR 70239 through 70241, 

respectively. For urban areas with no 

hospital data, we compute the average 

wage index value of all urban areas 

within the state to serve as a reasonable 

proxy for the wage index of that urban 

CBSA, that is, we use that value as the 

wage index. For rural areas with no 

hospital data, we compute the wage 

index using the average wage index 

values from all contiguous CBSAs to 

represent a reasonable proxy for that 

rural area. We apply the statewide urban 

average based on the average of all 

urban areas within the state to 

Hinesville-Fort Stewart, Georgia (78 FR 

72173), and we apply the wage index for 

Guam to American Samoa and the 

Northern Mariana Islands (78 FR 

72172).1

A wage index floor value (0.5000) is 

applied under the ESRD PPS as a 

substitute wage index for areas with 

very low wage index values. Currently, 

all areas with wage index values that 

fall below the floor are located in Puerto 

Rico. However, the wage index floor 

value is applicable for any area that may 

fall below the floor. A description of the 

history of the wage index floor under 

the ESRD PPS can be found in the CY 

2019 ESRD PPS final rule (83 FR 56964 

through 56967). 

An ESRD facility’s wage index is 

applied to the labor-related share of the 

ESRD PPS base rate. In the CY 2019 

ESRD PPS final rule (83 FR 56963), we 

finalized a labor-related share of 52.3 

percent, which is based on the 2016- 

based ESRDB market basket. In the CY 

2021 ESRD PPS final rule (85 FR 71436), 

we updated the OMB delineations as 

described in the September 14, 2018 

OMB Bulletin No. 18–04, beginning 

with the CY 2021 ESRD PPS wage 

index. In addition, we finalized the 

application of a 5 percent cap on any 

decrease in an ESRD facility’s wage 

index from the ESRD facility’s wage 

index from the prior CY. We finalized 

that the transition would be phased in 

over 2 years, such that the reduction in 

an ESRD facility’s wage index would be 

capped at 5 percent in CY 2021, and no 

cap would be applied to the reduction 

in the wage index for the second year, 

CY 2022. Thus, for CY 2022, the laborrelated share to which a facility’s wage 

index would be applied is 52.3 percent. 

For CY 2022, we are proposing to 

update the ESRD PPS wage index to use 

the most recent hospital wage data. The 

proposed CY 2022 ESRD PPS wage 

index is set forth in Addendum A and 

is available on the CMS website at 

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

Medicare-Fee-for-Service-Payment/ 

ESRDpayment/End-Stage-RenalDisease-ESRD-Payment-Regulationsand-Notices. Addendum A provides a 

crosswalk between the CY 2021 wage 

index and the proposed CY 2022 wage 

index. Addendum B provides an ESRD 

facility level impact analysis. 

Addendum B is available on the CMS 

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

Medicare/Medicare-Fee-for-ServicePayment/ESRDpayment/End-StageRenal-Disease-ESRD-PaymentRegulations-and-Notices. 

c. Proposed CY 2022 Update to the 

Outlier Policy 

Section 1881(b)(14)(D)(ii) of the Act 

requires that the ESRD PPS include a 

payment adjustment for high cost 

outliers due to unusual variations in the 

type or amount of medically necessary 

care, including variability in the amount 

of erythropoiesis-stimulating agents 

(ESAs) necessary for anemia 

management. Some examples of the 

patient conditions that may be reflective 

of higher facility costs when furnishing 

dialysis care would be frailty, obesity, 

and comorbidities, such as secondary 

hyperparathyroidism. The ESRD PPS 

recognizes high cost patients, and we 

have codified the outlier policy and our 

methodology for calculating outlier 

payments at § 413.237. 

The policy provides that the following 

ESRD outlier items and services are 

included in the ESRD PPS bundle: (1) 

Renal dialysis drugs and biological 

products that were or would have been, 

prior to January 1, 2011, separately 

billable under Medicare Part B; (2) renal 

dialysis laboratory tests that were or 

would have been, prior to January 1, 

2011, separately billable under 

Medicare Part B ; (3) renal dialysis 

medical/surgical supplies, including 

syringes, used to administer renal 

dialysis drugs and biological products 

that were or would have been, prior to 

January 1, 2011, separately billable 

under Medicare Part B; (4) renal dialysis 

drugs and biological products that were 

or would have been, prior to January 1, 

2011, covered under Medicare Part D, 

including renal dialysis oral-only drugs 

effective January 1, 2025; and (5) renal 

dialysis equipment and supplies, except 

for capital-related assets that are home 

dialysis machines(as defined in 

§ 413.236(a)(2)), that receive the 

transitional add-on payment adjustment 

as specified in § 413.236 after the 

payment period has ended. 

In the CY 2011 ESRD PPS final rule 

(75 FR 49142), CMS stated that for 

purposes of determining whether an 

ESRD facility would be eligible for an 

outlier payment, it would be necessary 

for the facility to identify the actual 

ESRD outlier services furnished to the 

patient by line item (that is, date of 

service) on the monthly claim. Renal 

dialysis drugs, laboratory tests, and 

medical/surgical supplies that are 

recognized as outlier services were 

specified in Transmittal 2134, dated 

January 14, 2011.2 https://www.cms.gov/ 

Regulations-and-Guidance/Guidance/ 

Transmittals/downloads/R2134CP.pdf. 

Furthermore, CMS uses administrative 

issuances to update the renal dialysis 

service items available for outlier 

payment via our quarterly update CMS 

Change Requests, when applicable. For 

example, we use these updates to 

identify renal dialysis service drugs that 

were or would have been covered under 

Medicare Part D for outlier eligibility 

purposes and items and services that 

have been incorrectly identified as 

eligible outlier services. 

Under § 413.237, an ESRD facility is 

eligible for an outlier payment if its 

actual or imputed Medicare Allowable 

Payment (MAP) amount per treatment 

for ESRD outlier services exceeds a 

threshold. The MAP amount represents 

the average incurred amount per 

treatment for services that were or 

would have been considered separately 

billable services prior to January 1, 

2011. The threshold is equal to the 

ESRD facility’s predicted ESRD outlier 

services MAP amount per treatment 

(which is case-mix adjusted and 

described in the following paragraphs) 


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plus the fixed-dollar loss (FDL) amount. 

In accordance with § 413.237(c), 

facilities are paid 80 percent of the per 

treatment amount by which the imputed 

MAP amount for outlier services (that is, 

the actual incurred amount) exceeds 

this threshold. ESRD facilities are 

eligible to receive outlier payments for 

treating both adult and pediatric 

dialysis patients. 

In the CY 2011 ESRD PPS final rule 

and codified in § 413.220(b)(4), using 

2007 data, we established the outlier 

percentage, which is used to reduce the 

per treatment base rate to account for 

the proportion of the estimated total 

payments under the ESRD PPS that are 

outlier payments, at 1.0 percent of total 

payments (75 FR 49142 through 49143). 

We also established the FDL amounts 

that are added to the predicted outlier 

services MAP amounts. The outlier 

services MAP amounts and FDL 

amounts are different for adult and 

pediatric patients due to differences in 

the utilization of separately billable 

services among adult and pediatric 

patients (75 FR 49140). As we explained 

in the CY 2011 ESRD PPS final rule (75 

FR 49138 through 49139), the predicted 

outlier services MAP amounts for a 

patient are determined by multiplying 

the adjusted average outlier services 

MAP amount by the product of the 

patient-specific case-mix adjusters 

applicable using the outlier services 

payment multipliers developed from the 

regression analysis used to compute the 

payment adjustments. 

For CY 2022, we propose that the 

outlier services MAP amounts and FDL 

amounts would be derived from claims 

data from CY 2020. Because we believe 

that any adjustments made to the MAP 

amounts under the ESRD PPS should be 

based upon the most recent data year 

available in order to best predict any 

future outlier payments, we propose the 

outlier thresholds for CY 2022 would be 

based on utilization of renal dialysis 

items and services furnished under the 

ESRD PPS in CY 2020. 

We recognize that the utilization of 

ESAs and other outlier services have 

continued to decline under the ESRD 

PPS, and that we have lowered the MAP 

amounts and FDL amounts every year 

under the ESRD PPS. As discussed in 

the CY 2021 ESRD PPS final rule (85 FR 

71438), CY 2019 claims data show 

outlier payments represented 

approximately 0.5 percent of total 

payments. As discussed in section 

II.B.1.c.(1) of this proposed rule, CY 

2020 claims data show outlier payments 

represent approximately 0.6 percent of 

total payments. 

(1) CY 2022 Update to the Outlier 

Services MAP Amounts and FDL 

Amounts 

For CY 2022, we propose to update 

the outlier services MAP amounts and 

FDL amounts to reflect the utilization of 

outlier services reported on 2020 claims. 

For this proposed rule, the outlier 

services MAP amounts and FDL 

amounts were updated using 2020 

claims data. The impact of this update 

is shown in Table 1, which compares 

the outlier services MAP amounts and 

FDL amounts used for the outlier policy 

in CY 2021 with the updated proposed 

estimates for this rule. The estimates for 

the proposed CY 2022 outlier policy, 

which are included in Column II of 

Table 1, were inflation adjusted to 

reflect projected 2022 prices for outlier 

services. 

The estimated FDL amount per 

treatment that determines the CY 2022 

outlier threshold amount for adults 

(Column II; $111.18) is lower than that 

used for the CY 2021 outlier policy 

(Column I; $122.49). The lower 

threshold is accompanied by a decrease 

in the adjusted average MAP for outlier 

services from $50.92 to $47.87. For 


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Average outlier services MAP amount 

er treatment 

Standardization for outlier services 

MIPP A reduction 

Adjusted average outlier services 

MAP amount 

ixed-dollar loss amount that is added 

o the predicted MAP to determine the 

outlier threshold 

atient-month-facilities qualifying for 

outlier payment 

Column I 

Final outlier policy for CY 2021 

(based on 2019 data, price inflated 

to 2021)* 

Age< 18 Age>= 18 

1.0390 0.9789 

0.98 0.98 

$30.88 $50.92 

$44.78 $122.49 

Column II 

Proposed outlier policy for CY 

2022 (based on 2020 data, price 

inflated to 2022) 

Age< 18 Age>= 18 

1.0815 0.9824 

0.98 0.98 

$28.73 $47.87 

$30.38 $111.18 

8.80% 5.15% 11.37% 5.45% 

*Note that Column I was obtained from Column II of Table 5 from the CY 2021 ESRD PPS final rule 

(85 FR 71437). 
36330 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

pediatric patients, there is a decrease in 

the FDL amount from $44.78 to $30.38. 

There is a corresponding decrease in the 

adjusted average MAP for outlier 

services among pediatric patients, from 

$30.08 to $28.73. 

We estimate that the percentage of 

patient months qualifying for outlier 

payments in CY 2022 would be 5.45 

percent for adult patients and 11.37 

percent for pediatric patients, based on 

the 2020 claims data. The outlier MAP 

and FDL amounts continue to be lower 

for pediatric patients than adults due to 

the continued lower use of outlier 

services (primarily reflecting lower use 

of ESAs and other injectable drugs). 

(2) Outlier Percentage 

In the CY 2011 ESRD PPS final rule 

(75 FR 49081) and under 

§ 413.220(b)(4), we reduced the per 

treatment base rate by 1 percent to 

account for the proportion of the 

estimated total payments under the 

ESRD PPS that are outlier payments as 

described in § 413.237. Based on the 

2020 claims, outlier payments 

represented approximately 0.6 percent 

of total payments, which is below the 1 

percent target due to declines in the use 

of outlier services. As noted in past 

rulemaking, recalibration of the 

thresholds using 2020 data is expected 

to result in aggregate outlier payments 

close to the 1 percent target in CY 2022. 

We believe the update to the outlier 

MAP and FDL amounts for CY 2022 

would increase payments for ESRD 

beneficiaries requiring higher resource 

utilization. This would move us closer 

to meeting our 1 percent outlier policy 

goal, because we are using more current 

data for computing the MAP and FDL, 

which is more in line with current 

outlier services utilization rates. We 

note that recalibration of the FDL 

amounts in this proposed rule would 

result in no change in payments to 

ESRD facilities for beneficiaries with 

renal dialysis items and services that are 

not eligible for outlier payments. 

d. Proposed Impacts to the CY 2022 

ESRD PPS Base Rate 

(1) ESRD PPS Base Rate 

In the CY 2011 ESRD PPS final rule 

(75 FR 49071 through 49083), CMS 

established the methodology for 

calculating the ESRD PPS per-treatment 

base rate, that is, ESRD PPS base rate, 

and calculating the per treatment 

payment amount, which are codified at 

§§ 413.220 and 413.230. The CY 2011 

ESRD PPS final rule also provides a 

detailed discussion of the methodology 

used to calculate the ESRD PPS base 

rate and the computation of factors used 

to adjust the ESRD PPS base rate for 

projected outlier payments and budget 

neutrality in accordance with sections 

1881(b)(14)(D)(ii) and 1881(b)(14)(A)(ii) 

of the Act, respectively. Specifically, the 

ESRD PPS base rate was developed from 

CY 2007 claims (that is, the lowest per 

patient utilization year as required by 

section 1881(b)(14)(A)(ii) of the Act), 

updated to CY 2011, and represented 

the average per treatment MAP for 

composite rate and separately billable 

services. In accordance with section 

1881(b)(14)(D) of the Act and our 

regulation at § 413.230, the pertreatment payment amount is the sum of 

the ESRD PPS base rate, adjusted for the 

patient specific case-mix adjustments, 

applicable facility adjustments, 

geographic differences in area wage 

levels using an area wage index, and 

any applicable outlier payment, training 

adjustment add-on, TDAPA, and 

TPNIES. 

(2) Annual Payment Rate Update for CY 

2022 

We are proposing an ESRD PPS base 

rate for CY 2022 of $255.55. This update 

reflects several factors, described in 

more detail as follows: 

Wage Index Budget-Neutrality 

Adjustment Factor: We compute a wage 

index budget-neutrality adjustment 

factor that is applied to the ESRD PPS 

base rate. For CY 2022, we are not 

proposing any changes to the 

methodology used to calculate this 

factor, which is described in detail in 

the CY 2014 ESRD PPS final rule (78 FR 

72174). We computed the proposed CY 

2022 wage index budget-neutrality 

adjustment factor using treatment 

counts from the 2020 claims and 

facility-specific CY 2021 payment rates 

to estimate the total dollar amount that 

each ESRD facility would have received 

in CY 2021. The total of these payments 

became the target amount of 

expenditures for all ESRD facilities for 

CY 2022. Next, we computed the 

estimated dollar amount that would 

have been paid for the same ESRD 

facilities using the ESRD PPS wage 

index for CY 2022. As discussed in 

section II.B.1.b of this proposed rule, the 

proposed ESRD PPS wage index for CY 

2022 includes an update to the most 

recent hospital wage data, use of the 

2018 OMB delineations, and no cap on 

wage index decreases applied for CY 

2022. The total of these payments 

becomes the new CY 2022 amount of 

wage-adjusted expenditures for all 

ESRD facilities. The wage index budgetneutrality factor is calculated as the 

target amount divided by the new CY 

2022 amount. When we multiplied the 

wage index budget neutrality factor by 

the applicable CY 2022 estimated 

payments, aggregate payments to ESRD 

facilities would remain budget neutral 

when compared to the target amount of 

expenditures. That is, the wage index 

budget neutrality adjustment factor 

ensures that wage index adjustments do 

not increase or decrease aggregate 

Medicare payments with respect to 

changes in wage index updates. The CY 

2022 proposed wage index budgetneutrality adjustment factor is .999546. 

This application would yield a CY 2022 

ESRD PPS proposed base rate of $253.02 

prior to the application of the proposed 

market basket increase ($253.13 × 

.999546 = $253.02). 

Market Basket Increase: Section 

1881(b)(14)(F)(i)(I) of the Act provides 

that, beginning in 2012, the ESRD PPS 

payment amounts are required to be 

annually increased by the ESRD market 

basket percentage increase factor. The 

latest CY 2022 projection of the 

proposed ESRDB market basket 

percentage increase factor is 1.6 percent. 

In CY 2022, this amount must be 

reduced by the productivity adjustment 

described in section 1886(b)(3)(B)(xi)(II) 

of the Act, as required by section 

1881(b)(14)(F)(i)(II) of the Act. As 

discussed previously, the proposed 

productivity adjustment for CY 2021 is 

0.6 percent, thus yielding a proposed 

update to the base rate of 1.0 percent for 

CY 2022. Therefore, the CY 2022 ESRD 

PPS proposed base rate is $255.55 

($253.02 × 1.010 = $255.55). 

In summary, we are proposing a CY 

2022 ESRD PPS base rate of $255.55. 

This amount reflects a proposed CY 

2022 wage index budget-neutrality 

adjustment factor of .999546, and the 

CY 2022 ESRD PPS productivityadjusted market basket update of 1.0 

percent. 

e. Update to the Offset Amount for 

TPNIES 

In the CY 2021 ESRD PPS final rule 

(85 FR 71427), we expanded eligibility 

for the TPNIES under § 413.236 to 

include certain capital-related assets 

that are home dialysis machines when 

used in the home for a single patient. 

We finalized the additional steps that 

the Medicare Administrative 

Contractors (MACs) must follow to 

establish the basis of payment of the 

TPNIES for these capital-related assets 

that are home dialysis machines when 

used in the home, including an offset to 

the pre-adjusted per treatment amount 

to account for the cost of the home 

dialysis machine that is already in the 

ESRD PPS base rate. We will pay 65 

percent of the MAC-determined 

preadjusted per treatment amount 

reduced by an offset for 2-calendar 


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years. Section § 413.236(f)(3)(v) states 

that effective January 1, 2022, CMS will 

annually update the amount determined 

in paragraph (f)(3)(iv) of § 413.236 by 

the ESRD bundled market basket 

percentage increase factor minus the 

productivity adjustment factor. 

The CY 2021 offset amount for 

TPNIES for capital-related equipment 

that are home dialysis machines used in 

the home is $9.32. As discussed 

previously in section II.B.1.a of this 

proposed rule, the proposed CY 2022 

ESRD bundled market basket increase 

factor minus the productivity 

adjustment is 1.0 percent (1.6 percent 

minus 0.6 percent). Applying the 

proposed update factor of 1.010 to the 

CY 2021 offset amount results in a 

proposed CY 2022 offset amount of 

$9.41($9.32 × 1.010). We will update 

this calculation to use the most recent 

data available in the CY 2022 ESRD PPS 

final rule. 

C. Proposed Transitional Add-On 

Payment Adjustment for New and 

Innovative Equipment and Supplies 

(TPNIES) for CY 2022 Payment 

1. Background 

In the CY 2020 ESRD PPS final rule 

(84 FR 60681 through 60698), CMS 

established the transitional add-on 

payment adjustment for new and 

innovative equipment and supplies 

(TPNIES) under the ESRD PPS, under 

the authority of section 

1881(b)(14)(D)(iv) of the Act, in order to 

support ESRD facility use and 

beneficiary access to these new 

technologies. We established this addon payment adjustment to help address 

the unique circumstances experienced 

by ESRD facilities when incorporating 

new and innovative equipment and 

supplies into their businesses and to 

support ESRD facilities transitioning or 

testing these products during the period 

when they are new to market. We added 

§ 413.236 to establish the eligibility 

criteria and payment policies for the 

TPNIES. 

In the CY 2020 ESRD PPS final rule 

(84 FR 60650), we established in 

§ 413.236(b) that for dates of service 

occurring on or after January 1, 2020, we 

will provide the TPNIES to an ESRD 

facility for furnishing a covered 

equipment or supply only if the item: 

(1) Has been designated by CMS as a 

renal dialysis service under § 413.171; 

(2) is new, meaning granted marketing 

authorization by the Food and Drug 

Administration (FDA) on or after 

January 1, 2020; (3) is commercially 

available by January 1 of the particular 

calendar year, meaning the year in 

which the payment adjustment would 

take effect; (4) has a Healthcare 

Common Procedure Coding System 

(HCPCS) application submitted in 

accordance with the official Level II 

HCPCS coding procedures by September 

1 of the particular calendar year; (5) is 

innovative, meaning it meets the SCI 

criteria specified in the Inpatient 

Prospective Payment System (IPPS) 

regulations at 42 CFR 412.87(b)(1) and 

related guidance, and (6) is not a capital 

related asset that an ESRD facility has 

an economic interest in through 

ownership (regardless of the manner in 

which it was acquired). 

Regarding the innovation requirement 

in § 413.236(b)(5), in the CY 2020 ESRD 

PPS final rule (84 FR 60690), we stated 

that we will use the following criteria to 

evaluate SCI for purposes of the TPNIES 

under the ESRD PPS based on the IPPS 

SCI criteria in § 412.87(b)(1) and related 

guidance: 

A new technology represents an 

advance that substantially improves, 

relative to renal dialysis services 

previously available, the diagnosis or 

treatment of Medicare beneficiaries. 

First, CMS considers the totality of the 

circumstances when making a 

determination that a new renal dialysis 

equipment or supply represents an 

advance that substantially improves, 

relative to renal dialysis services 

previously available, the diagnosis or 

treatment of Medicare beneficiaries. 

Second, a determination that a new 

renal dialysis equipment or supply 

represents an advance that substantially 

improves, relative to renal dialysis 

services previously available, the 

diagnosis or treatment of Medicare 

beneficiaries means one of the 

following: 

• The new renal dialysis equipment 

or supply offers a treatment option for 

a patient population unresponsive to, or 

ineligible for, currently available 

treatments; or 

• The new renal dialysis equipment 

or supply offers the ability to diagnose 

a medical condition in a patient 

population where that medical 

condition is currently undetectable, or 

offers the ability to diagnose a medical 

condition earlier in a patient population 

than allowed by currently available 

methods, and there must also be 

evidence that use of the new renal 

dialysis service to make a diagnosis 

affects the management of the patient; or 

• The use of the new renal dialysis 

equipment or supply significantly 

improves clinical outcomes relative to 

renal dialysis services previously 

available as demonstrated by one or 

more of the following: A reduction in at 

least one clinically significant adverse 

event, including a reduction in 

mortality or a clinically significant 

complication; a decreased rate of at least 

one subsequent diagnostic or 

therapeutic intervention; a decreased 

number of future hospitalizations or 

physician visits; a more rapid beneficial 

resolution of the disease process 

treatment including, but not limited to, 

a reduced length of stay or recovery 

time; an improvement in one or more 

activities of daily living; an improved 

quality of life; or, a demonstrated greater 

medication adherence or compliance; 

or, 

• The totality of the circumstances 

otherwise demonstrates that the new 

renal dialysis equipment or supply 

substantially improves, relative to renal 

dialysis services previously available, 

the diagnosis or treatment of Medicare 

beneficiaries. 

Third, evidence from the following 

published or unpublished information 

sources from within the U.S. or 

elsewhere may be sufficient to establish 

that a new renal dialysis equipment or 

supply represents an advance that 

substantially improves, relative to renal 

dialysis services previously available, 

the diagnosis or treatment of Medicare 

beneficiaries: Clinical trials, peer 

reviewed journal articles; study results; 

meta-analyses; consensus statements; 

white papers; patient surveys; case 

studies; reports; systematic literature 

reviews; letters from major healthcare 

associations; editorials and letters to the 

editor; and public comments. Other 

appropriate information sources may be 

considered. 

Fourth, the medical condition 

diagnosed or treated by the new renal 

dialysis equipment or supply may have 

a low prevalence among Medicare 

beneficiaries. Fifth, the new renal 

dialysis equipment or supply may 

represent an advance that substantially 

improves, relative to services or 

technologies previously available, the 

diagnosis or treatment of a 

subpopulation of patients with the 

medical condition diagnosed or treated 

by the new renal dialysis equipment or 

supply. 

In the CY 2020 ESRD PPS final rule 

(84 FR 60681 through 60698), we also 

established a process modeled after 

IPPS’s process of determining if a new 

medical service or technology meets the 

SCI criteria specified in § 412.87(b)(1). 

Specifically, similar to the IPPS New 

Technology Add-On Payment, we 

wanted to align our goals with the 

agency’s efforts to transform the 

healthcare delivery system for the ESRD 

beneficiary through competition and 

innovation to provide patients with 

better value and results. As we discuss 

in the CY 2020 ESRD PPS final rule (84 


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36332 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

FR 60682), we believe it is appropriate 

to facilitate access to new and 

innovative equipment and supplies 

through add-on payments similar to the 

IPPS New Technology Add-On Payment 

and to provide stakeholders with 

standard criteria for both inpatient and 

outpatient settings. In § 413.236(c), we 

established a process for our 

announcement of TPNIES 

determinations and a deadline for 

consideration of new renal dialysis 

equipment or supply applications under 

the ESRD PPS. CMS will consider 

whether a new renal dialysis equipment 

or supply meets the eligibility criteria 

specified in § 413.236(b) and summarize 

the applications received in the annual 

ESRD PPS proposed rules. Then, after 

consideration of public comments, we 

will announce the results in the Federal 

Register as part of our annual updates 

and changes to the ESRD PPS in the 

ESRD PPS final rule. In the CY 2020 

ESRD PPS final rule, we also specified 

certain deadlines for the application 

requirements. We noted that we would 

only consider a complete application 

received by February 1 prior to the 

particular calendar year. In addition, we 

required that FDA marketing 

authorization for the equipment or 

supply must occur by September 1 prior 

to the particular calendar year. We also 

stated in the CY 2020 ESRD PPS final 

rule (84 FR 60690 through 60691) that 

we would establish a workgroup of CMS 

medical and other staff to review the 

materials submitted as part of the 

TPNIES application, public comments, 

FDA marketing authorization, and 

HCPCS application information and 

assess the extent to which the product 

provides SCI over current technologies. 

In the CY 2020 ESRD PPS final rule, 

we established § 413.236(d) to provide a 

payment adjustment for a new and 

innovative renal dialysis equipment or 

supply. We stated that the TPNIES is 

paid for 2-calendar years. Following 

payment of the TPNIES, the ESRD PPS 

base rate will not be modified and the 

new and innovative renal dialysis 

equipment or supply will become an 

eligible outlier service as provided in 

§ 413.237. 

Regarding the basis of payment for the 

TPNIES, in the CY 2020 ESRD PPS final 

rule, we finalized at § 413.236(e) that 

the TPNIES is based on 65 percent of 

the price established by the MACs, 

using the information from the invoice 

and other specified sources of 

information. In the CY 2021 ESRD PPS 

final rule (85 FR 71410 through 71464), 

we made several changes to the TPNIES 

eligibility criteria at § 413.236. First, we 

revised the definition of new at 

§ 413.236(b)(2) as within 3 years 

beginning on the date of the FDA 

marketing authorization. Second, we 

changed the deadline for TPNIES 

applicants’ HCPCS Level II code 

application submission from September 

1 of the particular calendar year to the 

HCPCS Level II code application 

deadline for biannual Coding Cycle 2 for 

durable medical equipment, orthotics, 

prosthetics, and supplies (DMEPOS) 

items and services as specified in the 

HCPCS Level II coding guidance on the 

CMS website prior to the calendar year. 

In addition, a copy of the applicable 

FDA marketing authorization must be 

submitted to CMS by the HCPCS Level 

II code application deadline for 

biannual Coding Cycle 2 for DMEPOS 

items and services as specified in the 

HCPCS Level II coding guidance on the 

CMS website in order for the equipment 

or supply to be eligible for the TPNIES 

the following year. Third, we revised 

§ 413.236(b)(5) to remove a reference to 

related guidance on the SCI criterion, as 

the guidance has already been codified. 

Finally, in the CY 2021 ESRD PPS 

final rule, we expanded the TPNIES 

policy to include certain capital-related 

assets that are home dialysis machines 

when used in the home for a single 

patient. We explained that capitalrelated assets are defined in the 

Provider Reimbursement Manual 

(chapter 1, section 104.1) as assets that 

a provider has an economic interest in 

through ownership (regardless of the 

manner in which they were acquired). 

We noted that examples of capitalrelated assets for ESRD facilities are 

dialysis machines and water 

purification systems. We explained that 

while in the CY 2020 ESRD PPS 

proposed rule (84 FR 38354), we stated 

that we did not believe capital-related 

assets should be eligible for additional 

payment through the TPNIES because 

the cost of these items is captured in 

cost reports, they depreciate over time, 

and are generally used for multiple 

patients, there were a number of other 

factors we considered that led us to 

consider expanding eligibility for these 

technologies in the CY 2021 ESRD PPS 

rulemaking. We explained that, 

following publication of the CY 2020 

ESRD PPS final rule, we continued to 

study the issue of payment for capitalrelated assets under the ESRD PPS, 

taking into account information from a 

wide variety of stakeholders and recent 

developments and initiatives regarding 

kidney care. For example, we 

considered various HHS home dialysis 

initiatives, Executive Orders to 

transform kidney care, and how the risk 

of COVID–19 for particularly vulnerable 

ESRD beneficiaries could be mitigated 

by encouraging home dialysis. After 

closely considering these issues, we 

proposed a revision to § 413.236(b)(6) in 

the CY 2021 ESRD PPS proposed rule to 

provide an exception to the general 

exclusion for capital-related assets from 

eligibility for the TPNIES for capitalrelated assets that are home dialysis 

machines when used in the home for a 

single patient and that meet the other 

eligibility criteria in § 413.235(b), and 

finalized the exception as proposed. We 

finalized the same determination 

process for TPNIES applications for 

capital-related assets that are home 

dialysis machines as for all other 

TPNIES applications; that we will 

provide a description of the new home 

dialysis machine and pertinent facts in 

the ESRD PPS proposed rule so the 

public may comment and then publish 

the results in the ESRD PPS final rule. 

We will consider whether the new home 

dialysis machine meets the eligibility 

criteria specified in the proposed 

revisions to § 413.236(b) and announce 

the results in the Federal Register as 

part of our annual updates and changes 

to the ESRD PPS. Per § 413.236(c), we 

will only consider, for additional 

payment using the TPNIES for a 

particular calendar year, an application 

for a capital-related asset that is a home 

dialysis machine received by February 1 

prior to the particular calendar year. If 

the application is not received by 

February 1, the application will be 

denied and the applicant will need to 

reapply within 3 years beginning on the 

date of FDA marketing authorization in 

order to be considered for the TPNIES, 

in accordance with the proposed 

revisions to § 413.236(b)(2). 

In the CY 2021 ESRD PPS final rule, 

at § 413.236(f), we finalized a pricing 

methodology for capital-related assets 

that are home dialysis machines when 

used in the home for a single patient by 

requiring MACs to calculate the annual 

allowance and the preadjusted per 

treatment amount. The pre-adjusted per 

treatment amount is reduced by an 

estimated average per treatment offset 

amount to account for the costs already 

paid through the ESRD PPS base rate. 

The CY 2021 TPNIES offset amount was 

$9.32, and we finalized that this amount 

will be updated on an annual basis so 

that it is consistent with how the ESRD 

PPS base rate is updated. 

We revised § 413.236(d) to reflect that 

we would pay 65 percent of the preadjusted per treatment amount minus 

the offset for capital-related assets that 

are home dialysis machines when used 

in the home for a single patient. 

We revised § 413.236(d)(2) to reflect 

that following payment of the TPNIES, 

the ESRD PPS base rate will not be 


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3Medicare Benefits Policy Manual (Pub. L. 100– 

102), available at: https://www.cms.gov/ 

Regulations-and-Guidance/Guidance/Manuals/ 

Downloads/bp102c11.pdf. 

4United States Renal Data System. 2020 USRDS 

Annual Data Report: Epidemiology of kidney 

disease in the United States, End-Stage Renal 

Disease Chapter 2. National Institutes of Health, 

National Institute of Diabetes and Digestive and 

Kidney Diseases, Bethesda, MD, 2020. Available at: 

https://adr.usrds.org/2020/end-stage-renaldisease/ 

introduction-to-volume-2. Accessed on Jan. 21, 

2021. 

5Seshasai, R.K., et al. (2019). The home 

hemodialysis patient experience: A qualitative 

assessment of modality use and discontinuation. 

Hemodialysis International, 23: 139–150, 2019. 

doi:10.1111/hdi.12713. 

6Weinhandl, Eric D., Collins Allan, Incidence of 

Therapy Cessation among Home Hemodialysis 

Patients in the United States, Abstract presented, 

American Society of Nephrology Kidney Week 

2016. 

7Seshasai, R.K., et al. (2019). The home 

hemodialysis patient experience: A qualitative 

assessment of modality use and discontinuation. 

Hemodialysis International, 23: 139–150, 2019. 

doi:10.1111/hdi.12713. 

8Chan, Christopher T. et al. (2018). Exploring 

Barriers and Potential Solutions in Home Dialysis: 

An NKF–KDOQI Conference Outcomes Report 

Continued 

modified and the new and innovative 

renal dialysis equipment or supply will 

be an eligible outlier service as provided 

in § 413.237, except a capital-related 

asset that is a home dialysis machine 

will not be an eligible outlier service as 

provided in § 413.237. In summary, 

under the current eligibility 

requirements in § 413.236(b), CMS 

provides for a TPNIES to an ESRD 

facility for furnishing a covered 

equipment or supply only if the item: 

(1) Has been designated by CMS as a 

renal dialysis service under § 413.171; 

(2) Is new, meaning within 3 years 

beginning on the date of the FDA 

marketing authorization; (3) Is 

commercially available by January 1 of 

the particular calendar year, meaning 

the year in which the payment 

adjustment would take effect; (4) Has a 

complete HCPCS Level II code 

application submitted in accordance 

with the HCPCS Level II coding 

procedures on the CMS website, by the 

HCPCS Level II code application 

deadline for biannual Coding Cycle 2 for 

DMEPOS items and services as specified 

in the HCPCS Level II coding guidance 

on the CMS website prior to the 

calendar year; (5) Is innovative, meaning 

it meets the criteria specified in 

§ 412.87(b)(1) of this chapter; and (6) Is 

not a capital-related asset, except for 

capital-related assets that are home 

dialysis machines. 

We received two applications for the 

TPNIES for CY 2022. A discussion of 

these applications is presented below. 

The applications received are for 

technologies commonly used for the 

treatment of ESRD: Hemodialysis (HD) 

and peritoneal dialysis (PD). Detailed 

definitions for HD and PD are found in 

Chapter 11, Section 10 of the Medicare 

Benefits Policy Manual (Pub. L. 100– 

02).3 In brief, HD is a process that 

involves blood passing through an 

artificial kidney machine and the waste 

products diffusing across a manmade 

membrane into a bath solution known 

as dialysate after which the cleansed 

blood is returned to the patient’s body. 

HD is accomplished usually in 3 to 5 

hour sessions, 3 times a week. PD is a 

process that involves waste products 

passing from the patient’s body through 

the peritoneal membrane into the 

peritoneal (abdominal) cavity where the 

bath solution (dialysate) is introduced 

and removed periodically. 

a. Tablo® System 

Outset Medical, Inc. submitted an 

application for the TPNIES for the 

Tablo® System (Tablo®) for CY 2022. 

According to the applicant, the 

technology is an HD machine that has 

been designed for patient-driven selfcare and to minimize system training 

time. The applicant also stated that the 

system is intended to substantially 

improve the treatment of people with 

ESRD by removing barriers to home 

dialysis. The applicant explained that 

the Tablo® System is comprised of (1) 

the Tablo® Console with integrated 

water purification, on-demand dialysate 

production, and a simple-to-use 

touchscreen interface; (2) a proprietary, 

disposable, single-use pre-strung 

cartridge that easily clicks into place, 

minimizing steps, touch points, and 

connections; and (3) the Tablo®

Connectivity and Data Ecosystem. Per 

the applicant, the system is built to 

function in a connected setting with 

cloud-based system monitoring, patient 

analytics and clinical recordkeeping. 

The applicant stated that the Tablo®

System’s unique features combine to 

provide a significantly differentiated HD 

solution with many benefits. First, the 

applicant stated that the Tablo®

System’s intuitive touchscreen interface 

makes it easy to learn and use, guiding 

users through treatment from start to 

finish using step-by-step instructions 

with simple words and animation. The 

applicant also stated that instructions 

include non-technical language and 

color-coded parts to enable easier 

training, faster set-up, and simpler 

management including clear alarm 

explanations and resolution 

instructions. 

Second, the applicant stated that the 

Tablo® System can accommodate 

treatments at home allowing for 

flexibility in treatment frequencies, 

durations, and flow rates. Per the 

applicant, the Tablo® System does not 

have a pre-configured dialyzer, which 

allows clinicians to use a broad range of 

dialyzer types and manufactures, 

allowing for greater customization of 

treatment for the patient. The applicant 

stated that this is an improvement over 

the incumbent home device, which 

requires a separate device component 

and complex process to switch to 

another dialyzer. 

Third, the applicant stated that the 

Tablo® System is an all-in-one system 

with integrated water purification and 

on-demand dialysate production, 

eliminating the need for industrial water 

treatment rooms that are required to 

operate traditional HD machines. The 

applicant also stated that electronic data 

capture and automatic wireless 

transmission eliminate the need for 

manual record keeping by the patient, 

care partner, or nurse. Per the applicant, 

a single-use Tablo® Cartridge with userfriendly pre-strung blood, saline, and 

infusion tubing and a series of sensorreceptors mounted to a user-friendly 

organizer snaps easily into the system 

minimizing difficult connections that 

require additional training. The 

applicant stated that automated features, 

including an integrated blood pressure 

monitor, air removal, priming, and 

blood return, minimize user errors, save 

time, and streamline the user 

experience. 

Fourth, the applicant stated that the 

Tablo® System’s two-way wireless 

connectivity and data analytics provide 

the ability to continuously activate new 

capabilities and enhancements through 

wireless software updates, while also 

enabling predictive preventative 

maintenance to maximize machine 

uptime. 

The applicant stated that currently 88 

percent of patients receive HD in a 

clinic 3 times per week, for 3.0 to 4.5 

hours a day and fewer than 2 percent 

perform HD treatment at home.4 The 

applicant stated that 25 to 36 percent of 

home HD patients return to in-center 

care within 1 year of initiating HD at 

home.5 6 Per the applicant, barriers to 

home dialysis adoption and retention 

have been well studied and include 

treatment burden for patients and care 

partner fatigue; technical challenges 

with operating a HD machine; space, 

home modifications, and supplies 

management; patients not wanting 

medical equipment in the home; and 

safety concerns.7 8


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36334 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

American Journal of Kidney Diseases, Volume 73, 

Issue 3, 363–371. 

9Erickson, K.F., Zheng, Y., Ho, V., Winkelmayer, 

W.C., Bhattacharya, J., & Chertow, G.M. (2018). 

Market Competition and Health Outcomes in 

Hemodialysis. Health services research, 53(5), 

3680–3703. https://doi.org/10.1111/1475- 

6773.12835. 

10Blake, P.G., Quinn, R.R., & Oliver, M.J. (2013). 

Peritoneal dialysis and the process of modality 

selection. Peritoneal dialysis international: journal 

of the International Society for Peritoneal Dialysis, 

33(3), 233–241. https://doi.org/10.3747/ 

pdi.2012.00119. 

11 Ibid. 

12Mukhopadhyay, P., Woodside, K.J., Schaubel, 

D.E., Repeck, K., McCullough, K., Shahinian, V.B., 

. . . & Saran, R. (2020). Survival among incident 

peritoneal dialysis versus hemodialysis patients 

who initiate with an arteriovenous fistula. Kidney 

Medicine, 2(6), 732–741. 13United States Renal Data System. 2020 USRDS 

Annual Data Report: Epidemiology of kidney 

disease in the United States, End-Stage Renal 

Disease Chapter 2. National Institutes of Health, 

National Institute of Diabetes and Digestive and 

Kidney Diseases, Bethesda, MD, 2020. Available at: 

https://adr.usrds.org/2020/end-stage-renaldisease/ 

introduction-to-volume-2. Accessed on Jan 21, 2021. 14Canada Institute for Health Information (2020): 

Annual Statistics. Available at: https://

secure.cihi.ca/estore/ 

productSeries.htm?locale=en&pc=PCC24&_

ga=2.265337481.729263172.1612199530- 

510791291.1610562424. Accessed on Jan. 31, 2021. 

The applicant stated that innovation 

in making home dialysis more 

accessible to patients has been lacking 

due to a lack of investment funding, 

limited incremental reimbursement for 

new technology, and a consolidated, 

price-sensitive dialysis provider market 

where the lack of market competition is 

costly and has been associated with 

increased hospitalizations in dialysis 

patients.9 The applicant stated that the 

Tablo® System was designed to address 

many system-related barriers that result 

in patients resigning themselves to incenter care and/or stopping home 

modalities due to the burden of selfmanaged therapy. 

The applicant stated that while PD, 

like HD, removes excess fluid and waste 

from the body, it has a different 

mechanism of action and relies on the 

body’s own membrane, the peritoneum, 

to act as the ‘‘dialyzer’’. Per the 

applicant, PD requires surgical 

placement of a catheter in the abdomen 

and utilizes a cleansing fluid, dialysate, 

that must be infused and dwell in the 

abdomen to remove waste products 

from the blood. The applicant stated 

that PD must be conducted daily to 

achieve adequate dialysis and can be 

conducted manually or via a cycler; 

while in contrast, HD directly cleanses 

the blood with the use of a HD machine, 

dialysate and a dialyzer, which acts as 

an artificial kidney in removing excess 

fluid and toxins. The applicant stated 

that HD also requires surgical placement 

of a dialysis access, which is usually in 

the form of a catheter or a more 

permanent arteriovenous fistula.10

The applicant asserted that PD is the 

dominant home therapy used around 

the world, but should not be solely 

relied upon to increase growth in home 

dialysis, as there are physiological 

contraindications.11 The applicant also 

stated that there is recent evidence that 

post 90-day mortality is higher in PD 

patients than in HD patients. Per the 

applicant, multivariable risk–adjusted 

analyses demonstrate that the mortality 

hazard ratio of HD versus PD is 0.74 (95 

percent confidence interval (CI), 0.68– 

0.80) in the 270 to 360-day period after 

starting dialysis.12 The applicant stated 

that patients and clinicians should 

weigh the risks and benefits of both 

options and select the one that meets 

the individual patient’s preferences, 

goals, values and physiology. Per the 

applicant, because PD relies on the 

patient’s own membrane, physiologic 

changes can occur and result in patients 

who are unable to continue PD due to 

loss of the ability to achieve adequacy. 

The applicant stated that these home 

patients could consider home HD rather 

than a return to in-center and noted that 

the practice of transitioning from one 

home modality to another is 

acknowledged by experts to be 

underutilized and is particularly 

pronounced in the U.S., where the ratio 

of PD use to home HD is 6:1,13 as 

compared to 4:1 in Canada.14

The applicant asserted that that the 

Tablo® System presents a significant 

clinical improvement over NxStage®

System One (NxStage®), the current 

standard of home HD care, with the goal 

of getting patients access to easier to use 

technology and increasing the number 

of patients who can do dialysis at home. 

Per the applicant, NxStage® is the only 

other mobile HD machine that is 

approved for home use. 

(1) Renal Dialysis Service Criterion 

(§ 413.236(b)(1)) 

With respect to the first TPNIES 

eligibility criterion under 

§ 413.236(b)(1), whether the item has 

been designated by CMS as a renal 

dialysis service under § 413.171, 

maintenance dialysis treatments and all 

associated services, including 

historically defined dialysis-related 

drugs, laboratory tests, equipment, 

supplies, and staff time, were included 

in the composite rate for renal dialysis 

services as of December 31, 2010 (75 FR 

49036). An in-home HD machine would 

be considered equipment necessary for 

the provision of maintenance dialysis 

and, therefore, we would consider this 

a renal dialysis service under § 413.171. 

(2) Newness Criterion (§ 413.236(b)(2)) 

With respect to the second TPNIES 

eligibility criterion under 

§ 413.236(b)(2), whether the item is 

new, meaning within 3 years beginning 

on the date of the FDA marketing 

authorization, the applicant stated that 

the Tablo® System received FDA 

marketing authorization for home use 

on March 31, 2020. Therefore, the 

Tablo® System is considered new. We 

note that, in reviewing the enclosure to 

which the March 31, 2020 FDA 

authorization letter refers, the 

applicant’s Section 510(k) submission 

indicates that the Tablo® Cartridge was 

reviewed separately from the Tablo®

System and has its own separate 510(k) 

clearance. As discussed in the CY 2021 

ESRD PPS final rule, CMS determined 

that the cartridge did not meet the 

newness criterion for the TPNIES (85 FR 

71464) and as such, the cartridge is not 

new. 

(3) Commercial Availability Criterion 

(§ 413.236(b)(3)) 

With respect to the third TPNIES 

eligibility criterion under 

§ 413.236(b)(3), whether the item is 

commercial available by January 1 of the 

particular calendar year, meaning the 

year in which the payment adjustment 

would take effect, the applicant stated 

that the Tablo® System became 

available for home use on April 1, 2020. 

Therefore, the Tablo® System is 

commercially available. 

(4) HCPCS Level II Application 

Criterion (§ 413.236(b)(4)) 

With respect to the fourth TPNIES 

eligibility criterion under 

§ 413.236(b)(4), whether the applicant 

submitted a HCPCS Level II code 

application by the July 6, 2021 deadline, 

the applicant stated that it intends to 

submit a HCPCS Level II code 

application by the deadline. 

(5) Innovation Criterion 

(§§ 413.236(b)(5) and 412.87(b)(1)) 

With respect to the fifth TPNIES 

eligibility criterion under 

§ 413.236(b)(5), that the item is 

innovative, meaning it meets the SCI 

criteria specified in § 412.87(b)(1), the 

applicant claimed that the Tablo®

System significantly improves clinical 

outcomes relative to the current 

standard of care for home HD services, 

which it identified as the incumbent 

NxStage® home dialysis machine. The 

applicant presented the following SCI 

claims: (1) Decreased treatment 

frequency with adequate dialysis 

clearance; (2) increased adherence to 

dialysis treatment and retention to home 

therapy; and (3) improved patient 


jbell on DSKJLSW7X2PROD with PROPOSALS2
Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 36335 

15Clinicaltrials.gov website. https://

www.clinicaltrials.gov/ct2/show/NCT02460263. 

Last Updated July 1, 2020. https://

www.clinicaltrials.gov/ProvidedDocs/63/ 

NCT02460263/Prot_000.pdf. 

16Chertow, G.M., Alvarez, L., Plumb, T.J., 

Prichard, S.S., & Aragon, M. (2020). Patientreported outcomes from the investigational device 

exemption study of the Tablo hemodialysis system. 

Hemodialysis International, 24(4), 480–486. 

17Leypoldt, J.K., Prichard, S., Chertow, G.M., & 

Alvarez, L. (2019). Differential molecular modeling 

predictions of mid and conventional dialysate 

flows. Blood purification, 47(4), 369–376. 

18Safety and efficacy of the Tablo hemodialysis 

system for in-center and home hemodialysis Plumb, 

T.J., Alvarez, L., Ross, D.L., Lee, J.J., Mulhern, J.G., 

Bell, J.L., Abra, G., Prichard, S.S., Chertow, G.M. 

and Aragon, M.A. (2019), Hemodialysis 

International. 

19Plumb, Troy J., Luis Alvarez, Dennis L. Ross, 

Joseph J. Lee, Jeffrey G. Mulhern, Jeffrey L. Bell, 

Graham E. Abra, Sarah S. Prichard, Glenn M. 

Chertow, and Michael A. Aragon. ‘‘Self-care 

training using the Tablo hemodialysis system.’’ 

Hemodialysis International (2020). 

20Alvarez, Luis et al. Urea Clearance Results in 

Patients Dialyzed Thrice Weekly Using a Dialysate 

Flow of 300 mL/min, clinical abstract, presented 

March 2019, Annual Dialysis Conference, Dallas, 

TX. 

21Chahal, Y., Plumb, T., Aragon M. (2020). 

Patient Device Preference for Home Hemodialysis: 

A Subset Analysis of the Tablo Home IDE Trial. 

Poster Presentation at National Kidney Foundation 

Spring Clinical Conference, March 2020. 

22 Kraus, M., et al., A comparison of center-based 

vs. home-based daily hemodialysis for patients with 

end-stage renal disease. Hemodialysis International, 

11: 468–477, (2007). 

23Finkelstein, F.O., et al. (2012). At-home short 

daily hemodialysis improves the long-term healthrelated quality of life. Kidney international, 82(5), 

561–569. 

24Weinhandl, E.D., Gilbertson, D.T., & Collins, 

A.J. (2016). Mortality, hospitalization, and 

technique failure in daily home hemodialysis and 

matched peritoneal dialysis patients: A matched 

cohort study. American Journal of Kidney Diseases, 

67(1), 98–110. 

25Suri, R.S., Li, L., & Nesrallah, G.E. (2015). The 

risk of hospitalization and modality failure with 

home dialysis. Kidney international, 88(2), 360– 

368. 

26Clinicaltrials.gov website. https://

www.clinicaltrials.gov/ct2/show/NCT02460263. 

Last Updated July 1, 2020. https://

www.clinicaltrials.gov/ProvidedDocs/63/ 

NCT02460263/Prot_000.pdf. 

27Alvarez, Luis et al. Urea Clearance Results in 

Patients Dialyzed Thrice Weekly Using a Dialysate 

Flow of 300 mL/min, clinical abstract, presented 

March 2019, Annual Dialysis Conference, Dallas, 

TX. 

quality of life. The applicant supported 

these claims with the Tablo®

Investigational Device Exemption (IDE) 

Study 15 and secondary support from 

four papers 16 17 18 19 and two posters.20 21

The applicant also provided comparison 

data from three studies directly related 

to the incumbent 22 23 24 and an 

additional study that, based on the 

timeframe of the study, likely involved 

participants undergoing treatment with 

NxStage® although the article does not 

directly reference the incumbent.25

We provide an overview of these ten 

sources below, followed by the 

applicant’s summary of how the data 

support each claim of SCI. We conclude 

with a discussion of the way in which 

we have applied the requirements of 

§ 413.236(b)(5) to our review of the 

application and a summary of our 

concerns. We have not included 

detailed summaries of the remaining 

supplemental content included with the 

application. Specifically, the applicant 

submitted numerous supplemental 

background materials related to the 

dialysis industry, reimbursement 

patterns, modalities, treatment 

frequencies, patient adherence, 

hospitalization rates, and quality of life. 

The applicant also submitted several 

letters of support for the Tablo® System; 

three from dialysis patients, three from 

nephrologists, and one from a dialysis 

clinic nurse. These letters emphasized 

benefits of the Tablo® System, including 

reduced frequency of dialysis treatment, 

improved home dialysis retention, 

reduced patient and caregiver burden, 

reduced patient fatigue, and improved 

patient quality of life. 

(a) Applicant SCI Sources 

As stated previously, the applicant’s 

primary support for its three SCI claims 

comes from a prospective, multicenter, 

open-label, non-randomized crossover 

study that compared in-center and inhome HD performance using the Tablo®

System. Per the applicant, this study is 

referred to as the Tablo® Investigational 

Device Exemption (IDE) Study and the 

original study protocol and amendments 

were approved by FDA and registered 

on http://www.clinicaltrials.gov as ID: 

NCT02460263. The applicant stated that 

of the 30 participants enrolled (17 White 

and 13 Black or African American), 28 

(18 men and 10 women) completed the 

study. Thirteen of the participants had 

previous home HD experience with 

NxStage®, and the remainder had 

previously received conventional incenter HD care. The applicant also 

noted that the Tablo® IDE study sample 

was comprised of a representative 

cohort of dialysis patients and reports 

that it was similar to the population 

studied for the IDE study for the 

incumbent NxStage®. As described in 

the study protocol, the primary and 

secondary efficacy endpoints were a 

standardized weekly Kt/V of greater 

than or equal to 2.1 and ultrafiltration 

(fluid removal) value as reported by the 

device within ten percent of the 

expected fluid removal based on the 

ultrafiltration prescription and the 

Tablo® Console fluid removal algorithm, 

respectively.26 We clarify that Kt/V is a 

value used to quantify dialysis 

treatment adequacy and ‘‘K’’ = dialyzer 

clearance, ‘‘t’’ = time, and ‘‘V’’ = 

Volume of distribution of urea. The 

applicant stated that each participant 

served as his or her own control and 

remained in the trial for approximately 

21 weeks, during which time they were 

prescribed HD with the Tablo® System 

on a 4 times per week schedule. The 

applicant explained that the trial 

consisted of 4 treatment periods: (1) A 

1 week, in-center run-in period; (2) an 

in-center period of 32 treatments 

(approximately 8 weeks) during which 

ESRD facility staff managed the dialysis 

treatments; (3) a transition period of up 

to 4 weeks to train the patient and care 

partner in managing the dialysis; and (4) 

a final in-home period of 32 treatments 

(approximately 8 weeks). 

With respect to the applicant’s 

secondary sources of support, a poster 

presentation from Alvarez, et al., 

presented dialysis adequacy data 

collected from a retrospective review of 

29 patients’ (18 males, 11 females and 

17 percent Black, 10 percent Hispanic) 

dialysis records. The study compared 

Kt/V results of patients aged 34–84 

receiving dialysis using the Tablo®

System to patients receiving dialysis 

from a conventional HD machine. The 

majority of patients used a fistula or 

graft (59 percent fistula, 28 percent graft, 

10 percent catheter). One hundred 

ninety two dialysis treatments were 

conducted on a thrice-weekly schedule 

using the Tablo® System with a 

dialysate flow rate of 300 mL per 

minute. A single pool Kt/V of greater 

than 1.2 was achieved in 94 percent of 

treatments in patients less than 90 kg 

with an average duration of treatment at 

224 +/¥29 minutes and in 79 percent 

of treatments in patients greater than 90 

kg with an average duration of treatment 

at 249 +/¥27 minutes. The average 

achieved Kt/V was 1.4 +/¥0.2 among 

treatments provided with the Tablo®

System. Eighty-eight treatments were 

conducted using a conventional HD 

machine with a dialysate flow rate of 

500 mL per minute. A single pool Kt/ 

V of greater than 1.2 was achieved in 93 

percent of treatments in patients less 

than 90 kg with an average duration of 

treatment at 227 +/¥21 minutes and in 

83 percent of treatments in patients 

greater than 90 kg with an average 

duration of treatment at 249 +/¥14 

minutes. The average achieved Kt/V was 

1.6 +/¥0.4 among the conventional HD 

treatments.27

Next, an article from Chertow, et al., 

described additional data from the 

Tablo® IDE study (discussed 

previously), including health-related 

quality of life, to further assess the 

safety of home HD with the Tablo®


jbell on DSKJLSW7X2PROD with PROPOSALS2
36336 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

28Chertow, G.M., Alvarez, L., Plumb, T.J., 

Prichard, S.S., & Aragon, M. (2020). Patientreported outcomes from the investigational device 

exemption study of the Tablo hemodialysis system. 

Hemodialysis International, 24(4), 480–486. 

29Leypoldt, J.K., Prichard, S., Chertow, G.M., & 

Alvarez, L. (2019). Differential molecular modeling 

predictions of mid and conventional dialysate 

flows. Blood purification, 47(4), 369–376. 

30Safety and efficacy of the Tablo hemodialysis 

system for in-center and home hemodialysis Plumb, 

T.J., Alvarez, L., Ross, D.L., Lee, J.J., Mulhern, J.G., 

Bell, J.L., Abra, G., Prichard, S.S., Chertow, G.M. 

and Aragon, M.A. (2019), Hemodialysis 

International. 

System. Demographic information 

identified the mean age as 49.8 ± 13 

years, 62 percent male, 62 percent 

White, 38 percent Black or African 

American, 23 percent Hispanic or 

Latino, 68 percent Not Hispanic or 

Latino, and 8 percent not reported, 

among patients established on home 

HD. Among the patients new to home 

HD, the mean age was identified as 54.2 

± 10.4 years, 65 percent male, 53 

percent White, 47 percent Black or 

African American, 29 percent Hispanic 

or Latino, 71 percent Not Hispanic or 

Latino, and 0 percent not reported. 

Twenty-eight of 30 patients (93 percent) 

completed all trial periods. Adherence 

to the prescribed 4 treatments per week 

schedule was 96 percent in-center and 

99 percent in-home. The median time to 

recovery was 1.5 hours during the incenter and 2 hours during the at-home 

phase of the trial. Median index values 

on the 5-level EuroQol-5 Dimension 

(EQ–5D–5L) (a self-assessed, health 

related, quality of life questionnaire) 

were similar during the in-center as 

compared to in-home dialysis at 0.832 

and 0.826, respectively. Patients new to 

home HD had lower median values 

(0.751) for both in-center and in-home 

periods. Patients who had used home 

dialysis prior to the trial had higher 

median values during both in-center 

(0.903) and in-home (0.906) periods. 

Patients reported feeling alert or wellrested with little difficulty falling or 

staying asleep or feeling tired and worn 

out when using the Tablo® System in 

either environment. The authors 

concluded that when using the Tablo®

System in-home, patients reported 

similar time to recovery, general health 

status, and sleep quality compared to 

using the Tablo® System in-center.28

Next, an article from Leypoldt, et al., 

described the use of uremic solute 

kinetic models to assess dialysis 

adequacy via theoretical single pool Kt/ 

V levels when varying the dialysis blood 

flow rates and the patient urea volume 

of distribution. A comparison was made 

between dialysate flows of 300 and 500 

mL/min at blood flows of both 300 and 

400 mL/min. The patient urea volume of 

distribution range modeled by the 

authors ranged from 25 to 45 L. Under 

ideal conditions, the authors 

demonstrate that with a blood flow of 

300 mL per minute, a single pool Kt/V 

of greater than 1.2 could be achieved in 

patients with a urea volume of 

distribution of 35 L and 240 minutes of 

dialysis. Patients with a urea volume of 

distribution of 40 L would require 255 

minutes of dialysis. Patients with a urea 

volume of distribution of 45 L would 

require over 270 minutes of dialysis. 

With a blood flow of 400 mL per 

minute, patients with a urea volume of 

distribution of 40 L could achieve the 

target single pool Kt/V of greater than 

1.2 with 240 minutes of dialysis. 

Patients with a volume of distribution of 

45 L could achieve the target with 270 

minutes of dialysis. The authors did not 

model urea kinetics for patients with 

volumes of distribution greater than 45 

L.29

Next, an article by Plumb, et al., 

described the Tablo® IDE study 

(discussed previously). Demographic 

information reflected the mean age as 

52.3 ± 11.6 years, 19 men and the 

following racial and ethnic 

representation: 17 White, 13 Black or 

African American, 8 Hispanic or Latino, 

and 21 Not Hispanic or Latino. 

Comparisons among the 28 patients in 

this study and subsequent secondary 

analyses were either made between the 

8 weeks of using the Tablo® System for 

in-center HD and the 8 weeks of the 

Tablo® System for in-home HD or 

between using the Tablo® System inhome HD and the treatment provided 

prior to study enrollment. In both 

settings, patients dialyzed using the 

Tablo® System 4 times per week. The 

primary efficacy endpoint was 

achievement of a weekly standard Kt/V 

greater than or equal to 2.1 in both the 

8-week in-center phase of the study and 

the 8-week in-home phase of the study. 

This endpoint was achieved in 199 of 

200 weeks in the in-center dialysis 

period and in 168 of 171 weeks in the 

in-home dialysis period. The primary 

safety endpoint of adverse event rates 

were similar at 1.9 percent in the incenter dialysis period and 1.8 percent in 

the in-home dialysis period. The 

secondary efficacy endpoint was 

whether the ultrafiltration volume and 

rate achieved the prescribed levels. In 

both in-center and in-home dialysis, 94 

percent of treatments achieved 

successful delivery of ultrafiltration, 

defined as a rate within ten percent of 

the prescribed value. Of 960 in-center 

dialysis services and 896 in-home 

dialysis services, 922 and 884 were 

completed respectively, yielding 

adherence rates of 96 percent and 99 

percent.30

Next, a separate article by Plumb et 

al., reports additional data from the 

Tablo® IDE study (previously discussed) 

regarding participants’ assessment of the 

Tablo® System’s ease-of-use, the degree 

of dependence on health care workers 

and caregivers after training with the 

system was complete, and the training 

time required for a participant to be 

competent in self-care. Demographic 

information reflected the mean age as 

52.6 years, 18 men, 10 women, 16 

White, 7 Hispanic or Latino, 9 Not 

Hispanic or Latino, and 12 Black or 

African American. Participants were 

stratified according to whether they 

were previously on self-care dialysis at 

home or conventional in-center HD. 

Thirteen participants had previous 

experience performing self-care HD. The 

remaining 15 participants had previous 

experience with in-center HD only. All 

participants rated the Tablo® System’s 

setup, treatment, and takedown on a 

scale from 1 (very difficult) to 5 (very 

simple) and indicated whether they had 

required assistance with treatment over 

the prior 7 days. Set up times were 

similar regardless of whether the 

participants were previously on selfcare HD or conventional in-center HD. 

For the participants previously on incenter HD, the average set up time for 

the concentrates was 0.93 minutes and 

for the cartridge, 9.35 minutes. For 

participants previously on self-care 

home HD, the average set up time for 

the concentrates was 1.22 minutes and 

for the cartridge, 10.28 minutes. The 

average rating of the Tablo® System’s 

ease of use for setup was 4.5, treatment 

4.6, and take down 4.6 among the 

participants previously on self-care 

home HD. In comparison, based on 

recollection (not based on rating during 

time of use) these participants’ average 

rating of their previous device’s ease of 

use for setup was 3.5, treatment 3.3, and 

take down 3.8. The average rating of the 

Tablo® System’s ease of use for setup 

and treatment was 4.6 and 4.7 for take 

down among participants without prior 

self-care experience. 

Among patients surveyed, caregiver 

assistance was required in 62 percent of 

patient-weeks during home self-care. 

Participants previously on self-care 

home HD required some caregiver 

assistance in 42 percent of the in-home 

dialysis treatment weeks. Participants 

previously on conventional in-center 

dialysis required some caregiver 

assistance in 35 percent of the in-home 

dialysis treatment weeks. The 

requirement for some form of assistance 


jbell on DSKJLSW7X2PROD with PROPOSALS2
Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 36337 

31Plumb, Troy J., Luis Alvarez, Dennis L. Ross, 

Joseph J. Lee, Jeffrey G. Mulhern, Jeffrey L. Bell, 

Graham E. Abra, Sarah S. Prichard, Glenn M. 

Chertow, and Michael A. Aragon. ‘‘Self-care 

training using the Tablo hemodialysis system.’’ 

Hemodialysis International (2020). 

32Chahal, Y., Plumb, T., Aragon M. (2020). 

Patient Device Preference for Home Hemodialysis: 

A Subset Analysis of the Tablo Home IDE Trial. 

Poster Presentation at National Kidney Foundation 

Spring Clinical Conference, March 2020. 

33 Kraus, M., et al., A comparison of center-based 

vs. home-based daily hemodialysis for patients with 

end-stage renal disease. Hemodialysis International, 

11: 468–477, (2007). 

34Finkelstein, F.O., et al. (2012). At-home short 

daily hemodialysis improves the long-term healthrelated quality of life. Kidney International, 82(5), 

561–569. 

35Weinhandl, E.D., Gilbertson, D.T., & Collins, 

A.J. (2016). Mortality, hospitalization, and 

Continued 

among participants with or without 

previous self-care experience was not 

meaningfully different. Finally, the 

authors noted that a protocol 

amendment allowed for the recording of 

the number of training sessions 

necessary to deem a patient competent 

to do self-care dialysis. This recording 

was limited to the last 15 participants 

enrolled into the study. Five of these 

participants had previous self-care 

dialysis at home experience. The 

average number of training sessions 

required to be deemed competent was 

3.6 for participants with previous selfcare dialysis at home experience and 3.9 

sessions for participants with only 

conventional in-center HD experience.31

Next, a poster presentation from 

Chahal et al., reported patient device 

preference of prior in-home HD patients 

based on data from the Tablo® IDE 

study (previously discussed). The 

authors noted that 13 of the 30 

participants in the Tablo® IDE trial were 

performing in-home HD at the time of 

enrollment and that prior to the study, 

dialysis prescriptions averaged 4.5 

treatments per week with an average 

time of 3.1 hours per session. Trial 

prescriptions were for 4 days per week 

and an average of 3.4 hours per session. 

Adherence to the study regimen was 97 

percent and 92 percent of surveys were 

completed. The authors concluded that 

participants with prior home HD 

experience preferred the Tablo® System 

compared to their prior device and 85.6 

percent found that the Tablo® System 

was easier to use.32

As stated previously in this section of 

the proposed rule, the applicant 

submitted several sources pertaining to 

the incumbent, NxStage.® First, an 

article from Kraus et al., describes a 

feasibility study to demonstrate the 

safety of center-based versus homebased daily HD with the NxStage®

portable HD device. This retrospective 

analysis examined the extent to which 

clinical effects previously associated 

with short-daily dialysis were also seen 

using the NxStage® device. The authors 

conducted a prospective, two-treatment, 

two-period, open-label, crossover study 

of in-center HD vs. home HD in 32 

patients treated at six U.S. centers. 

Demographic information reflected the 

mean age as 51 years, 63 percent male, 

38 percent female, 24 White, 6 Black or 

African American, 1 American Indian or 

Alaskan native, and 1 Asian. The 8- 

week In-Center Phase (6 days/week) was 

followed by a 2-week transition period 

and then followed by the 8-week Home 

Phase (6 days/week). Data was collected 

retrospectively on HD treatment 

parameters immediately preceding the 

study in a subset of patients. Twenty-six 

out of 32 patients (81 percent) 

successfully completed the study. 

Treatment compliance (defined as 

completing 43 to 48 treatments in a 

given phase) was comparable between 

the 2 treatment environments (88 

percent In-Center vs. 89 percent Home). 

Successful delivery of at least 90 

percent of prescribed fluid volume 

(primary endpoint) was achieved in 98.5 

percent of treatments in-center and 97.3 

percent at home. Total effluent volume 

as a percentage of prescribed volume 

was between 94 percent and 100 percent 

for all study weeks. The composite rate 

of intradialytic and interdialytic adverse 

events per 100 treatments was 

significantly higher for the In-Center 

Phase (5.30) compared with the Home 

Phase (2.10; p=0.007). Compared with 

the period immediately preceding the 

study, there were reductions in blood 

pressure, antihypertensive medications, 

and interdialytic weight gain. The study 

concluded that daily home HD with a 

small, easy-to-use HD device is a viable 

dialysis option for ESRD patients 

capable of self/partner administered 

dialysis.33

Second, an article from Finkelstein et 

al., reports on interim results of the 

Following Rehabilitation, Economics 

and Everyday-Dialysis Outcome 

Measurements (FREEDOM) study, a 

multi-center, prospective, cohort study 

of at-home short daily HD with a 

planned 12-month follow-up 

(ClinicalTrials.gov identifier, 

NCT00288613). Eligible patients were 

adults with ESRD requiring dialysis 

who were being initiated on short daily 

HD (prescribed 6 times per week) at 

home using the NxStage® cycler and 

who had Medicare as their primary 

insurance payer. The authors examined 

the long-term effect of short daily HD on 

health-related quality of life, as 

measured by the Short Form-36 (SF–36) 

health survey. The survey was 

administered at baseline, 4 and 12 

months after initiation of short daily HD 

to 291 (total cohort) participants. 

Demographic information reflected the 

mean age as 53 years, 66 percent male 

and 70 percent White. Of the 291 

participants, 154 completed the 12- 

month follow-up (as-treated cohort). 

In the total cohort analysis, both the 

physical- and mental-component 

summary scores improved over the 12- 

month period, as did all 8 individual 

domains of the SF–36. The as-treated 

cohort analysis showed similar 

improvements with the exception of the 

role-emotional domain. Significantly, in 

the as-treated cohort, the percentage of 

patients achieving a physical 

component summary score at least 

equivalent to the general population 

more than doubled. The authors 

concluded by noting that at-home short 

daily HD is associated with long-term 

improvements in various physical and 

mental health-related quality of life 

measures.34

Third, in Weinhandl et al., authors 

described a cohort study in which 4,201 

new home HD patients in 2007 were 

matched with 4,201 new PD patients in 

2010 from the United States Renal Data 

System (USRDS) database to assess 

relative mortality, hospitalization, and 

technique failure. Demographic 

information reflected the mean age as 

53.8 ± 14.9 years, 67 percent male, 33 

percent female, 24.4 percent Black, and 

75.6 percent Nonblack. Daily home HD 

patients initiated use of NxStage® from 

2007 through 2010. Authors reported 

home HD was associated with 20 

percent lower risk for all-cause 

mortality, 8 percent lower risk for allcause hospitalization, and 37 percent 

lower risk for technique failure, all 

relative to PD. Regarding 

hospitalization, risk comparisons 

favored home HD for cardiovascular 

disease and dialysis access infection 

and PD for bloodstream infection. 

Authors noted that matching was 

unlikely to reduce confounding 

attributable to unmeasured factors, 

including residual kidney function; lack 

of data regarding dialysis frequency, 

duration, and dose in daily home HD 

patients and frequency and solution in 

PD patients; and diagnosis codes used to 

classify admissions. The authors 

concluded that these data suggest that 

relative to peritoneal dialysis, daily 

home HD is associated with decreased 

mortality, hospitalization, and 

technique failure but that risks for 

mortality and hospitalization were 

similar with these modalities in new 

dialysis patients.35


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36338 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

technique failure in daily home hemodialysis and 

matched peritoneal dialysis patients: A matched 

cohort study. American Journal of Kidney Diseases, 

67(1), 98–110. 

36Suri, R.S., Li, L., & Nesrallah, G.E. (2015). The 

risk of hospitalization and modality failure with 

home dialysis. Kidney International, 88(2), 360– 

368. 

37Alvarez, Luis et al. Urea Clearance Results in 

Patients Dialyzed Thrice Weekly Using a Dialysate 

Flow of 300 mL/min, clinical abstract, presented 

March 2019, Annual Dialysis Conference, Dallas, 

TX. 

38Plumb, T.J., Alvarez, L., Ross, D.L., Lee, J.J., 

Mulhern, J.G., Bell, J.L., Abra, G., Prichard, S.S., 

Chertow, G.M. and Aragon, M.A. (2019). Safety and 

efficacy of the Tablo hemodialysis system for incenter and home hemodialysis. Hemodialysis 

International. 

39NxStage Clearance Calculator. Available at: 

https://dosingcalculator.nxstage.com/ 

DosingCalculator/. Accessed on Jan 21, 2021. 

40Tentori F, Zhang J, Li Y, Karaboyas A, Kerr P, 

Saran R, Bommer J, Port F, Akiba T, Pisoni R, 

Robinson B. Longer dialysis session length is 

associated with better intermediate outcomes and 

survival among patients on in-center three times per 

week hemodialysis: results from the Dialysis 

Outcomes and Practice Patterns Study (DOPPS). 

Nephrol Dial Transplant. 2012 Nov;27(11):4180–8. 

doi: 10.1093/ndt/gfs021. Epub 2012 Mar 19. PMID: 

22431708; PMCID: PMC3529546. 

41Health Management Associates (HMA) analysis 

of 2018 100% Medicare Outpatient file. 

42Leypoldt, J.K., Prichard, S., Chertow, G.M., & 

Alvarez, L. (2019). Differential molecular modeling 

predictions of mid and conventional dialysate 

flows. Blood Purification, 47(4), 369–376. 

43 Ibid. 

44 Ibid. 

Fourth, in Suri et al., 1116, daily 

home HD patients were matched by 

propensity scores to 2784, 

contemporaneous USRDS patients 

receiving home peritoneal dialysis. The 

authors compared hospitalization rates 

from cardiovascular, infectious, accessrelated or bleeding causes, and modality 

failure risk. Similar analyses were 

performed for 1187, daily home HD 

patients matched to 3173, USRDS 

patients receiving in-center 

conventional HD. Demographic 

information identified the mean age as 

50.5 years, 67.3 percent male, 70.9 

percent White, 26.6 percent Black, and 

2.5 percent Other, among the daily 

home HD patients. Among the home PD 

patients, the mean age was identified as 

50.9 years, 66.9 percent male, 73.1 

percent White, 25.1 percent Black and 

1.2 percent Other. The composite 

hospitalization rate was significantly 

lower with daily home HD than with PD 

(0.93 vs. 1.35/patient-year). Daily home 

HD patients spent significantly fewer 

days in the hospital than PD patients 

(5.2 vs. 9.2 days/patient-year), and 

significantly more daily home HD 

patients remained admission-free (52 

percent daily home dialysis vs. 32 

percent peritoneal dialysis). In contrast, 

there was no significant difference in 

hospitalizations between daily home HD 

and conventional HD (0.93 vs. 1.10/ 

patient-year). Cardiovascular 

hospitalizations were lower with daily 

home HD than with conventional HD 

(0.68) while infectious and access 

hospitalizations were higher (1.15) and 

1.25 respectively). Significantly more 

PD than daily home HD patients 

switched back to in-center HD (44 

percent vs. 15 percent). In this prevalent 

cohort, daily home HD was associated 

with fewer admissions and hospital 

days than PD, and a substantially lower 

risk of modality failure.36

(b) Applicant SCI Claims 

Regarding the applicant’s first claim 

that the Tablo® System decreases 

treatment frequency with adequate 

dialysis clearance, the applicant stated 

that the Tablo® System is the only 

mobile HD device approved for use in 

the home that can achieve adequate 

dialysis in as little as 3 treatments per 

week, while also providing flexibility 

for more frequent dialysis and thus 

greater personalization of care. The 

applicant stated that adequate dialysis 

for a standard, thrice weekly treatment 

schedule is a single treatment clearance 

of urea, expressed as a single-pool Kt/ 

V (spKt/V) of greater than 1.2 where ‘‘K’’ 

= dialyzer clearance, ‘‘t’’ = time, and 

‘‘V’’ = Volume of distribution of urea. 

The applicant also stated that dialyzer 

clearance, or ‘‘K’’, is dependent on the 

mass transfer coefficient (KoA) 

characteristics of the prescribed dialyzer 

and prescribed blood and dialysate flow 

rates. The applicant further noted that 

limitations in ‘‘K’’ or ‘‘t’’ affect the 

ability of a patient to achieve adequate 

clearance during a dialysis treatment. 

Per the applicant, across a broad range 

of weights, patients using the Tablo®

System can achieve the target of dialysis 

adequacy, a single pool Kt/V of 1.2, with 

3 treatments per week in less than 4 

hours.37 The applicant also stated that 

when used 4 times per week, patients 

using the Tablo® System had a higher 

mean weekly standard Kt/V with 

equivalent or better dialysis-related 

hospitalization rates,38 as compared to 

NxStage® IDE patients prescribed 

therapy at 6 days per week.39

The applicant stated that the Tablo®

System’s on-demand dialysate 

production has no limitation to the 

volume of dialysate that can be 

produced and used during a single 

treatment. The applicant further stated 

that this facilitates the delivery of 

adequate dialysis clearance (Kt/V) in a 

standard duration and target frequency 

of 3 times per week, as well as alternate 

frequencies and durations as preferred 

by a patient or recommended by a 

health care provider. 

The applicant asserted that NxStage,®

when attached to its Pureflow device, 

requires users to batch a set amount of 

dialysate (maximum of 60 liters) in 

advance of a treatment or use sterile 

dialysate bags (maximum of 30 liters). 

The applicant also stated that at its 

maximum dialysate flow rate (Qd) of 

300ml/min, NxStage® greatly limits 

time by restricting treatment to a 

maximum of 200 minutes before 

exhausting its dialysate capacity (200 

min = 60L/300ml/min). 

The applicant stated that Dialysis 

Outcomes and Practice Patterns Study 

(DOPPS) data demonstrate that the 

current U.S. practice for thrice weekly 

dialysis occurs at an average treatment 

time of greater than 220 minutes, and 

has increased in the last 25 years.40 Per 

the applicant, with the limited ‘‘t’’, a 

single-pooled Kt/V of >1.2 cannot be 

expected to be achieved for the majority 

of U.S. patients with ESRD on a thrice 

weekly schedule, requiring increased 

treatment frequency 41 at home for these 

patients to meet the desired clearance 

level. 

In citing Leypoldt et al., the applicant 

stated that data from the Hemodialysis 

(HEMO) trial combined with modeling 

results from Leypoldt et al.,42 allow for 

an estimation of the patients with ESRD, 

based on weight, that cannot be 

expected to achieve target clearance 

with standard thrice weekly dialysis at 

this treatment duration. The applicant 

explained that because urea is evenly 

distributed throughout a body’s water, 

the volume of distribution of urea is 

equal to a patient’s total volume of 

water. The applicant also stated that 

total body water and volume of 

distribution of urea can be expressed as 

a volume or as a percentage of total 

weight and can vary based on numerous 

factors including disease state. The 

applicant stated that it is possible to 

estimate the percent of water for the 

ESRD population from the HEMO trial 

as summarized in Leypoldt et al.43 The 

applicant stated that in the trial, the 

mean patient weight was 69.8kg and the 

mean patient volume of body water (V) 

was 30.9L. The applicant further 

explained that from this, total body 

water (and volume of distribution of 

urea) are calculated as 44.3 percent of 

the mean weight of patients with ESRD 

(44.3 = 30.9L/69.8kg × 100). Per the 

applicant, applying this 44.3 percent of 

total body weight to the volumes of 

distribution in Leypoldt et al.44 allows 

the conversion of the kinetic model 

described into anticipated patient 

weights. The applicant further stated 


jbell on DSKJLSW7X2PROD with PROPOSALS2
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45Leypoldt, J.K., Prichard, S., Chertow, G.M., & 

Alvarez, L. (2019). Differential molecular modeling 

predictions of mid and conventional dialysate 

flows. Blood Purification, 47(4), 369–376. 

46 Daugirdas JT, Greene T, Depner TA, Chumela 

C, Rocco, MJ, Chertow, GM for the Hemodialysis 

(HEMO) Study Group. Anthropometrically 

Estimated Total Body Water Volumes are Larger 

than Modeled Urea Volume in Chronic 

Hemodialysis Patients: Effects of Age, Race and 

Gender. 2003. Kidney Int. 64:1108–1119. 

47United States Renal Data System. 2020 USRDS 

Annual Data Report: Epidemiology of kidney 

disease in the United States, End-Stage Renal 

Disease Chapter 2. National Institutes of Health, 

National Institute of Diabetes and Digestive and 

Kidney Diseases, Bethesda, MD, 2020. Available at: 

https://adr.usrds.org/2020/end-stage-renaldisease/

introduction-to-volume-2. Accessed on Jan 21, 2021. 

48Wilk, A.S., Hirth, R.A., Zhang, W., Wheeler, 

J.R., Turenne, M.N., Nahra, T.A., . . . & Messana, 

J.M. (2018). Persistent variation in Medicare 

payment authorization for home hemodialysis 

treatments. Health services research, 53(2), 649– 

670. 

49Health Management Associates (HMA) analysis 

of 2018 100 percent Medicare Outpatient file. 

50Medicare Coverage Database. Retrieved May 24, 

2021 from: https://www.cms.gov/medicarecoverage-database/details/lcd-details.aspx?

LCDId=35014&ver=39&NCDId=79&ncdver=1&

SearchType=Advanced&CoverageSelection=Both&

NCSelection=NCA%7CCAL%7CNCD%7

CMEDCAC%7CTA%7CMCD&ArticleType=

Ed%7CKey%7CSAD%7CFAQ&PolicyType=Final&

s=-%7C5%7C6%7C66%7C67%7C

9%7C38%7C63%7C41%7C64%7C65%7C44&

KeyWord=transplant&KeyWordLookUp=

Doc&KeyWordSearchType=Exact&kq=true&

bc=IAAAADgAAAAA&. 

51National Kidney Foundation. KDOQI clinical 

practice guideline for hemodialysis adequacy: 2015 

update. Am J Kidney Dis. 2015;66(5):884–930. 

52Shafi T, Wilson RF, Greer R, Zhang A, Sozio 

S, Tan M, Bass EB. End-stage Renal Disease in the 

Medicare Population: Frequency and Duration of 

Hemodialysis and Quality of Life Assessment. 

Technology Assessment Program Project ID No. 

JHE51000. (Prepared by the Johns Hopkins 

University Evidence-based Practice Center under 

contract number HHSA 290–2015–00006I) 

Rockville, MD: Agency for Healthcare Research and 

Quality. July 2020. Available at: http://

www.ahrq.gov/research/findings/ta/index.html. 

53Safety and efficacy of the Tablo hemodialysis 

system for in-center and home hemodialysis Plumb, 

T.J., Alvarez, L., Ross, D.L., Lee, J.J., Mulhern, J.G., 

Bell, J.L., Abra, G., Prichard, S.S., Chertow, G.M. 

and Aragon, M.A. (2019), Hemodialysis 

International. 

54FHN Trial Group. (2010). In-center 

hemodialysis six times per week versus three times 

per week. New England Journal of Medicine, 

363(24), 2287–2300. 

55 Kuo, T.H., Tseng, C.T., Lin, W.H., Chao, J.Y., 

Wang, W.M., Li, C.Y., & Wang, M.C. (2015). 

Association Between Vascular Access Dysfunction 

and Subsequent Major Adverse Cardiovascular 

Events in Patients on Hemodialysis: A PopulationBased Nested Case–Control Study. Medicine, 

94(26). 

56United States Renal Data System. 2020 USRDS 

Annual Data Report: Epidemiology of kidney 

disease in the United States, End-Stage Renal 

Disease Chapter 2. National Institutes of Health, 

National Institute of Diabetes and Digestive and 

Kidney Diseases, Bethesda, MD, 2020. Available at: 

https://adr.usrds.org/2020/end-stage-renaldisease/ 

introduction-to-volume-2. Reference Table G2. 

57Weinhandl, E.D., Gilbertson, D.T., & Collins, 

A.J. (2016). Mortality, hospitalization, and 

technique failure in daily home hemodialysis and 

matched peritoneal dialysis patients: A matched 

cohort study. American Journal of Kidney Diseases, 

67(1), 98–110. 

58Suri, R.S., Li, L., & Nesrallah, G.E. (2015). The 

risk of hospitalization and modality failure with 

home dialysis. Kidney international, 88(2), 360– 

368. 

that in calculating with standard blood 

flow and a higher dialyzer mass transfer 

area coefficient for urea (KoA) diayzer, 

a 200 minute treatment at a dialysate 

flow rate (Qd) of 300ml/min would not 

achieve what the applicant refers to as 

the CMS target spKt/V target 1.2 for 

patients with a volume of distribution of 

urea (V) of 35L or greater. The applicant 

stated that these assumptions were 

drawn from NxStage® technical 

specifications.45 46 The applicant stated 

that at 44.3 percent of total weight, this 

volume of distribution of urea correlates 

to patients with ESRD with a mean 

weight above 79 kg (79 = 35L/.443) or 

approximately 174 pounds. Per the 

applicant, patients at or above this 

weight cannot be expected to achieve a 

spKt/V urea of 1.2 on a thrice weekly 

schedule using the NxStage® system at 

its maximal dialysate flow rate. 

The applicant stated that for the 

majority of the U.S. prevalent ESRD 

population between the ages of 22–74, 

whose mean weight is between 84.3– 

89.1 kg by age group,47 thrice weekly 

therapy at home on NxStage® would not 

achieve the Medicare coverage standard. 

Specifically, per the applicant, 

Medicare’s national coverage policy is 

to reimburse for dialysis care 3 times 

per week, regardless of the modality that 

is used and health care providers are 

expected to ensure that patients receive 

adequate clearance with the 3 times per 

week cadence. The applicant also stated 

that Medicare Administrative 

Contractors (MACs) have discretion in 

reimbursing additional treatments with 

medical justification.48 Per the 

applicant, an analysis of Medicare 

claims data from 2018 finds that despite 

the limitations of the reimbursement 

policy, Medicare is paying for 5 or more 

treatments per week in 50 percent of 

home HD patients nationwide, 

amounting to an estimated annual cost 

to Medicare of $122 to $126 million.49

However, based on CMS review of 

dialysis facility claims data, among all 

beneficiaries who had home dialysis 

treatments in 2018, 39.1 percent had 5 

or more dialysis sessions at least once 

during any week. The overall percentage 

of beneficiary-weeks that had 5 or more 

home HD sessions in 2018 was 20.9 

percent. Medicare payment for these 

additional sessions totaled $17 million. 

We note that, as indicated in Local 

Coverage Determination ID L35014, 

‘‘Frequency of Dialysis’’ (revised 

effective September 26, 2019),50 CMS 

established payment for HD based on 

conventional treatment which is defined 

as 3 times per week. Sessions in excess 

of 3 times per week must be both 

reasonable and necessary in order to 

receive payment. Covered indications 

include metabolic conditions (acidosis, 

hyperkalemia, hyperphosphatemia), 

fluid positive status not controlled with 

routine dialysis, pregnancy, heart 

failure, pericarditis, and incomplete 

dialysis secondary to hypotension or 

access issues. The applicant asserted 

that the use of the Tablo® System would 

decrease the number of necessary 

dialysis treatments, without affecting 

patient outcomes such as clearance or 

hospitalizations. 

The applicant stated that there is 

clinical evidence and expert consensus 

that as treatment frequency increases, 

native residual kidney function drops, 

patient and care partner burden 

increases, and vascular access 

complications increase.51 52 Per the 

applicant, home use of the Tablo®

System can reduce the need for a fifth 

or sixth weekly treatment without 

increasing patients’ symptom burden.53

The applicant stated that by achieving 

adequacy targets with fewer treatments, 

Tablo® System patients can be expected 

to have fewer vascular access 

interventions and health care providers 

will have increased flexibility in 

personalizing the frequency and 

duration of patient treatments.54 55 The 

applicant stated that reducing treatment 

frequency while maintaining adequate 

patient clearance levels may also reduce 

complications that lead to 

hospitalizations. The applicant stated 

that during the Tablo® IDE study, 

patients using the Tablo® System 4 

times per week, for an average duration 

of less than 4 hours per treatment, had 

an all-cause hospital admission rate of 

426 per 1,000 patient-years whereas in 

the general dialysis population, the allcause admission rate is 1,688 per 1,000 

patient-years, and for patients who 

utilize peritoneal dialysis, the 

hospitalization rate is 1,460 per 1,000 

patient years.56

The applicant stated that while 

NxStage® has not specifically reported 

the hospitalization rates per patient-year 

from its IDE study, published data from 

Weinhandl et al.,57 and Suri et al.,58

report hospital admission rates amongst 

patients on daily home HD ranging from 

930 to 1,663 per 1,000 patient-years, 

using a national sample of dialysis 

patients matched for comparison to 

similar peritoneal and in-center dialysis 

patients. We clarify that this would 

represent 930 to 1,663 cases observed 


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36340 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

59Chan, K.E., Thadhani, R.I., & Maddux, F.W. 

(2014). Adherence barriers to chronic dialysis in the 

United States. Journal of the American Society of 

Nephrology, 25(11), 2642–2648. Supporting 

evidence of association between decreased dialysis 

adherence and poor patient health and utilization 

outcomes. 

60Weinhandl, Eric D., Collins Allan, Incidence of 

Therapy Cessation among Home Hemodialysis 

Patients in the United States, Abstract presented, 

American Society of Nephrology Kidney Week 

2016. 

61Safety and efficacy of the Tablo hemodialysis 

system for in-center and home hemodialysis Plumb, 

T.J., Alvarez, L., Ross, D.L., Lee, J.J., Mulhern, J.G., 

Bell, J.L., Abra, G., Prichard, S.S., Chertow, G.M. 

and Aragon, M.A. (2019), Hemodialysis 

International. 

62 Kraus, M., et al., A comparison of center-based 

vs. home-based daily hemodialysis for patients with 

end-stage renal disease. Hemodialysis 

International,11: 468–477, (2007). The authors 

performed a feasibility study to demonstrate the 

safety of center-based vs. home-based daily 

hemodialysis with the NxStage System One 

portable hemodialysis device. 

63Weinhandl, Eric D., Collins Allan, Incidence of 

Therapy Cessation among Home Hemodialysis 

Patients in the United States, Abstract presented, 

American Society of Nephrology Kidney Week 

2016. 

64Safety and efficacy of the Tablo hemodialysis 

system for in-center and home hemodialysis Plumb, 

T.J., Alvarez, L., Ross, D.L., Lee, J.J., Mulhern, J.G., 

Bell, J.L., Abra, G., Prichard, S.S., Chertow, G.M. 

and Aragon, M.A. (2019), Hemodialysis 

International. 

65 Kraus M, Burkart J, Hegeman R, Solomon R, 

Coplon N, Moran J. A comparison of center-based 

vs. home-based daily hemodialysis for patients with 

end-stage renal disease. Hemodial Int. 2007 

Oct;11(4):468–77. doi: 10.1111/j.1542– 

4758.2007.00229.x. PMID: 17922746. 

66Plumb, Troy J., Luis Alvarez, Dennis L. Ross, 

Joseph J. Lee, Jeffrey G. Mulhern, Jeffrey L. Bell, 

Graham E. Abra, Sarah S. Prichard, Glenn M. 

Chertow, and Michael A. Aragon. ‘‘Self-care 

training using the Tablo hemodialysis system.’’ 

Hemodialysis International (2020). 

67 Kraus, M., et al., A comparison of center-based 

vs. home-based daily hemodialysis for patients with 

end-stage renal disease. Hemodialysis 

International,11: 468–477, (2007). 

68Seshasai, R.K., et al. (2019) The home 

hemodialysis patient experience: A qualitative 

assessment of modality use and discontinuation. 

Hemodialysis International, 23: 139–150 (2019). 

69Suri, R.S., Larive, B., Hall, Y., Kimmel, P.L., 

Kliger, A.S., Levin, N., . . . & Frequent 

Hemodialysis Network (FHN) Trial Group. (2014). 

Effects of frequent hemodialysis on perceived 

caregiver burden in the Frequent Hemodialysis 

Network trials. Clinical Journal of the American 

Society of Nephrology, 9(5), 936–942. 

70 Jacquet, S., & Trinh, E. (2019). The potential 

burden of home dialysis on patients and caregivers: 

A narrative review. Canadian journal of kidney 

health and disease, 6, 2054358119893335. 

71Plumb, Troy J., Luis Alvarez, Dennis L. Ross, 

Joseph J. Lee, Jeffrey G. Mulhern, Jeffrey L. Bell, 

among 1,000 persons during 1 year. The 

applicant also noted that all data on 

home patients in Weinhandl et al. came 

from a matched cohort of NxStage®

patients. Per the applicant, in Suri et al., 

data were collected prior to 2015 and 

that during this timeframe, it can be 

reasonably assumed that home HD 

patients were using NxStage® for 

treatment. The applicant stated that the 

results from these studies suggest that 

patients receiving treatment at home 

with NxStage® 5 to 6 times per week do 

not have a lower all-cause 

hospitalization rate, relative to matched 

in-center HD patients. The applicant 

concluded by stating that because of the 

clinical and demographic diversity of 

the Tablo® System’s patient population, 

the applicant’s results show incremental 

improvement over the hospitalization 

rate of the current home HD population. 

Regarding the applicant’s second 

claim that the Tablo® System increases 

adherence to dialysis treatment and 

retention to home therapy, the applicant 

stated that patients using the Tablo®

System have improved adherence to 

prescribed treatments and a higher rate 

of retention to home therapy. The 

applicant further stated that this 

increased adherence and retention is 

likely to improve patient outcomes by 

reducing the rate of dialysis-related 

hospitalizations and other adverse 

events associated with missing 

treatment in this patient population.59

The applicant stated that adherence to 

prescribed dialysis treatments is crucial 

for dialysis patients because missed 

treatments increases the risk of dialysis 

dropout, hospitalization, and death.60

Per the applicant, the Tablo® IDE study 

demonstrated a 99 percent treatment 

adherence rate to all prescribed home 

treatments 61 among both prior in-center 

participants and prior self-care home 

HD participants who used NxStage®. 

The applicant also stated that the 

Tablo® System’s adherence rates were 

similar among both the prior in-center 

and prior self-care participants. The 

applicant stated that these results 

represent a significant improvement 

over the treatment adherence rate 

reported in the NxStage® IDE, where the 

treatment compliance rate was defined 

less stringently as missing 5 or fewer 

treatments of the 48 possible treatments 

and was only 89 percent among patients 

at home and during the study period.62

Per the applicant, using a comparable 

metric of missing 5 or fewer of all 

possible treatments at home, Tablo® IDE 

patients at home had a 100 percent 

treatment compliance rate. 

The applicant stated that technique 

failure in home HD, defined as reduced 

retention at home and a return to incenter care, has been high with 

NxStage®. Per the applicant, real world 

data show that technique failure occurs 

in 36 percent of home HD patients using 

NxStage® within 1 year of initiating 

treatment.63 The applicant stated that 

this is challenging for the patient and 

taxing on the healthcare system that has 

invested in providing patients with 

home dialysis training and in paying for 

more frequent therapy. 

The applicant stated that by directly 

comparing the Tablo® System’s 

retention to that of NxStage®, the 

applicant assessed rates in the 

analogous IDE populations while 

excluding those who exited either study 

for reasons unrelated to the device such 

as receipt of a transplant or death. The 

applicant stated that the Tablo® System 

demonstrated a 97 percent (28 of 29) 

patient retention rate for the entire IDE 

study and a 100 percent retention rate 

in the in-home phase of the trial among 

both prior NxStage® users and prior incenter patients.64 The applicant stated 

that in comparison, 81 percent of 

participants completed the NxStage®

IDE study.65

The applicant stated that the Tablo®

System’s ease of use contributed to the 

improved adherence and retention rates 

and that the Tablo® System is designed 

to enable patients to become proficient 

and independent in using the Tablo®

System after an average of 3.9 days.66

Per the applicant, published NxStage®

IDE data 67 reported an average of 14.5 

days ‘‘to complete device training on 

NxStage®.’’ The applicant stated that, in 

comparison, device-related training time 

is reduced by at least 50 percent on the 

Tablo® System. Per the applicant, the 

reduced training time and ease of use 

will likely improve retention and 

potentially reduce the number of 

reimbursable training sessions. The 

applicant stated that because of the 

significant role that caregivers play in 

supporting home dialysis treatments,68

care partner burnout and a patient’s 

perception of being a burden is 

associated with discontinuation of home 

therapy.69 70 Per the applicant, the 28 

patients who entered the home phase of 

the Tablo® IDE study were asked weekly 

if they needed help with their dialysis 

treatments during the prior 7 days. The 

applicant stated that a 96 percent 

response rate (216 of 224 possible) was 

achieved at the end of the study and 

that for both prior-in-center and 

NxStage® study participants, in 79 

percent of the treatment weeks, patients 

reported needing no assistance from 

their care partner in performing dialysis 

set-up, treatment, or breakdown. The 

applicant explained that among the 13 

prior in-home patients, all of whom 

were formerly NxStage® users, 

participants reported needing help from 

a trained individual with dialysis 

treatment in 69 percent of treatment 

weeks, with 46 percent of instances 

involving a need for device-related help. 

We clarify that per Plumb, et al.,71 this 


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Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 36341 

Graham E. Abra, Sarah S. Prichard, Glenn M. 

Chertow, and Michael A. Aragon. ‘‘Self-care 

training using the Tablo hemodialysis system.’’ 

Hemodialysis International (2020). 

72 Ibid. 

73Chochinov, H.M., Kristjanson, L.J., Hack, T.F., 

Hassard, T., McClement, S., & Harlos, M. (2007). 

Burden to others and the terminally ill. Journal of 

pain and symptom management, 34(5), 463–471. 

74Chertow, G.M., Alvarez, L., Plumb, T.J., 

Prichard, S.S., & Aragon, M. (2020). Patientreported outcomes from the investigational device 

exemption study of the Tablo hemodialysis system. 

Hemodialysis International, 24(4), 480–486. 

75 Gabbay, E., Meyer, K.B., Griffith, J.L., 

Richardson, M.M., & Miskulin, D.C. (2010). 

Temporal trends in healthrelated quality of life 

among hemodialysis patients in the United States. 

Clinical journal of the American Society of 

Nephrology, 5(2), 261–267. 76 Yang, F., Wong, C.K., Luo, N., Piercy, J., Moon, 

R., & Jackson, J. (2019). Mapping the kidney disease 

quality of life 36-item short form survey (KDQOL– 

36) to the EQ–5D–3L and the EQ–5D–5L in patients 

undergoing dialysis. The European Journal of 

Health Economics, 20(8), 1195–1206. 77Finkelstein, F.O., et al. (2012). At-home short 

daily hemodialysis improves the long-term healthrelated quality of life. Kidney international, 82(5), 

561–569. 

78Liem, Y.S., Bosch, J.L., & Hunink, M.M. (2008). 

Preference-based quality of life of patients on renal 

replacement therapy: A systematic review and 

meta-analysis. Value in Health, 11(4), 733–741. 

79Chertow, G.M., Alvarez, L., Plumb, T.J., 

Prichard, S.S., & Aragon, M. (2020). Patientreported outcomes from the investigational device 

exemption study of the Tablo hemodialysis system. 

Hemodialysis International, 24(4), 480–486. 

80 Davison SN, Levin A, Moss AH, Jha V, Brown 

EA, Brennan F, Murtagh FE, Naicker S, Germain MJ, 

O’Donoghue DJ, Morton RL, Obrador GT; Kidney 

Disease: Improving Global Outcomes. Executive 

summary of the KDIGO Controversies Conference 

on Supportive Care in Chronic Kidney Disease: 

Developing a roadmap to improving quality care. 

Kidney Int. 2015 Sep;88(3):447–59. 

81Urquhart-Secord, Rachel et al. (2016). Patient 

and Caregiver Priorities for Outcomes in 

Hemodialysis: An International Nominal Group 

Continued 

is the baseline percentage and reflects 9 

of the 13 patients with previous selfcare experience. The applicant stated 

that patients reported needing help with 

treatment in only 42 percent of 

treatment weeks while using the Tablo®

System, which is a 39 percent reduction 

from baseline NxStage® use; and only 

18 percent of these instances related to 

use of the Tablo® System, which is a 61 

percent reduction in rate from baseline 

NxStage® use.72

The applicant stated that it collected 

weekly data from patients by asking 

them to rate the extent to which they 

believed that they were a burden on a 

scale of 1 to 5, with 1 representing never 

and 5 representing always. The 

applicant stated that this measure was 

adapted from an instrument used in 

assessing terminally ill patients.73 The 

applicant stated that the subpopulation 

of study participants who had 

previously used NxStage® reported an 

average score of 3.1 for self-perceived 

burden on their care partner when using 

their prior device, which subsequently 

reduced to 2.4 when using the Tablo®

System (a 23 percent reduction in score 

from baseline NxStage® use).74 Per the 

applicant, these data underscore that a 

significant increase in patients’ 

confidence, ability to achieve treatment 

independence at home, and subsequent 

reduction in the sense of self burden can 

positively contribute to success in the 

home setting. The applicant further 

noted that the ease of use, reduced 

training time, and substantial reduction 

in care partner assistance required for 

the Tablo® System correlated to the 

improved retention and adherence rates 

in the Tablo® IDE study. The applicant 

stated that on a population level, this 

likely translates to reduced barriers to 

continuing home HD once initiated, and 

ultimately, a reduced risk of adverse 

outcomes due to missed treatments. The 

applicant also stated that the Tablo®

System’s electronic data capture and 

automatic wireless transmission 

eliminates the need for manual record 

keeping, which represents an 

improvement with respect to burden 

and monitoring as compared to 

NxStage®. 

Regarding the applicant’s third claim 

that the Tablo® System improves patient 

quality of life, the applicant stated that 

patients on the Tablo® System 

experience reduced disease burden, 

dialysis related symptoms, and an 

improved quality of life at home as 

compared to in-center and existing 

home care options. Per the applicant, 

patients with ESRD experience 

significant dialysis-related symptoms 

including difficulty sleeping, dizziness, 

and pain associated with recovery time 

that affect mental and physical health 

and lead to decreased overall quality of 

life.75 Per the applicant, the Tablo® IDE 

study assessed several validated PatientReported Outcome Measures (PROMs) 

to better understand overall healthrelated quality of life (HR-QoL). The 

applicant explained that the overall 

measure was the EQ–5D–5L, a 

validated, preference-based PROM in 

which patients self-assess mobility, selfcare, usual activities, pain/discomfort, 

and anxiety/depression.76 The applicant 

stated that from these domains, an index 

value is calculated to report a summary 

score that ranges from 0 (death) to 1 (full 

health). 

Per the applicant, while the NxStage®

IDE study did not report results for a 

quality-of-life instrument, HR-QoL was 

assessed in NxStage® patients in a 

prospective multicenter observational 

study referred to as the FREEDOM trial, 

which examined the effects of at-home 

dialysis 6 times per week with the 

NxStage® System on costs and HR-QoL 

using the SF–36 instrument. The 

applicant further stated that the 

reported results at 4-month follow-up 

among these patients 77 translates to a 

mean EQ–5D score of 0.70. The 

applicant included an appendix 

describing the Methodology to Derive 

EQ–5D Scores from the FREEDOM 

Study Results in its application and 

derived a predicted mean EQ–5D score 

of 0.695–0.70 at follow up for the 

FREEDOM study. The applicant further 

noted that because this estimate is based 

on the average aggregate change for an 

adjusted measure that was then 

translated to the EQ–5D scale, and the 

applicant did not have access to 

standard error estimates for the Mental 

Component Score (MCS) and Physical 

Component Score (PCS), its 

interpretation of this estimate and its 

variance is limited. Per the applicant, 

nonetheless, it provides a sense of the 

comparable HR-QoL of this sample of 

NxStage® patients at follow-up. The 

applicant further noted that mean EQ– 

5D index values for traditional HD and 

PD patients reported from a metaanalysis of existing studies in the 

literature are 0.56 (95 percent CI: 0.49– 

0.62) and 0.58 (95 percent CI: 0.5–0.67), 

respectively.78

Per the applicant, patients in the 

Tablo® IDE study reported mean EQ–5D 

index values of 0.821 (SD: ±0.163) 79 in 

the home phase of the study with final 

measures taken at approximately 5 

months from trial start. The applicant 

stated that this is a significant 

improvement when using traditional HD 

patients as a comparator, and higher 

overall HR-QoL as compared to 

NxStage® patients. The applicant 

emphasized that participants in the 

Tablo® IDE trial underwent a reduced 

treatment frequency as compared to 

participants in the FREEDOM study 

who were prescribed 6 treatments per 

week on NxStage®. The applicant stated 

that among patients in the Tablo® IDE 

study who had previously been using 

NxStage®, the mean EQ–5D score during 

the in-home phase of the study was 

0.906 (SD: ±0.119) and asserted that this 

is significantly greater than index 

population values for HD and peritoneal 

dialysis. 

The applicant stated that sleep 

problems are present in 60 percent of 

patients with chronic kidney disease 

(CKD) and ESRD 80 and that patients 

rank fatigue and lack of energy as the 

most important contributor to their 

decreased quality of life.81 Per the 


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Technique Study American Journal of Kidney 

Diseases, Volume 68, Issue 3, 444–454. 

82Morin, C.M., Belleville, G., Be ́langer, L., & 

Ivers, H. (2011). The Insomnia Severity Index: 

Psychometric indicators to detect insomnia cases 

and evaluate treatment response. Sleep, 34(5), 601– 

608. 

83Natale, V., Fabbri, M., Tonetti, L., & Martoni, 

M. (2014). Psychometric goodness of the mini sleep 

questionnaire. Psychiatry and clinical 

neurosciences, 68(7), 568–573. 

84Chertow, G.M., Alvarez, L., Plumb, T.J., 

Prichard, S.S., & Aragon, M. (2020). Patientreported outcomes from the investigational device 

exemption study of the Tablo hemodialysis system. 

Hemodialysis International, 24(4), 480–486. 

85Urquhart-Secord, Rachel et al. (2016). Patient 

and Caregiver Priorities for Outcomes in 

Hemodialysis: An International Nominal Group 

Technique Study American Journal of Kidney 

Diseases, Volume 68, Issue 3, 444–454. 

86Liem, Y.S., Bosch, J.L., Arends, L.R., 

Heijenbrok-Kal, M.H., & Hunink, M.M. (2007). 

Quality of life assessed with the Medical Outcomes 

Study Short Form 36-Item Health Survey of patients 

on renal replacement therapy: A systematic review 

and meta-analysis. Value in Health, 10(5), 390–397. 

87Lowrie, E.G., Curtin, R.B., LePain, N., & 

Schatell, D. (2003). Medical outcomes study short 

form-36: A consistent and powerful predictor of 

morbidity and mortality in dialysis patients. 

American Journal of Kidney Diseases, 41(6), 1286– 

1292. 

88Food and Drug Administration Center for 

Devices and Radiological Health (2020). ‘‘Patient 

Preference-Sensitive Areas: Using Patient 

Preference Information in Medical Device 

Evaluation’’ Available at: https://www.fda.gov/ 

about-fda/cdrh-patient-engagement/patientpreference-sensitive-areas-using-patientpreferenceinformation-medical-device-evaluation. Accessed 

Jan 21, 2021. 

89Chahal, Y., Plumb, T., Aragon M. (2020). 

Patient Device Preference for Home Hemodialysis: 

A Subset Analysis of the Tablo Home IDE Trial. 

Poster Presentation at National Kidney Foundation 

Spring Clinical Conference, March 2020. 90Alvarez, Luis et al. Urea Clearance Results in 

Patients Dialyzed Thrice Weekly Using a Dialysate 

Flow of 300 mL/min, clinical abstract, presented 

March 2019, Annual Dialysis Conference, Dallas, 

Texas. 

applicant, the frequency of sleep-related 

symptoms among the Tablo® System’s 

patients was assessed by a survey that 

was administered weekly during the 

Tablo® IDE study. The applicant stated 

that, in the absence of a well-validated 

sleep survey specific to the ESRD 

population, study investigators selected 

survey questions from previously 

validated sleep questionnaires in the 

non-ESRD population, based on their 

relevance to the study population.82 83

The applicant explained that questions 

were designed to focus on quality of 

sleep and restfulness and noted that 

these measures are validated for use 

among chronically ill populations and 

measure the frequency of 4 key sleeprelated symptoms. The applicant stated 

that, while at home, patients on the 

Tablo® System reported improved 

quality of sleep, with a measurable 

reduction in rate of patient-reported 

sleep symptoms ranging from a 10–60 

percent reduction, depending on 

symptom.84 The applicant stated that 

this reduction was observed among 

study participants who were previously 

receiving dialysis in-center (average 

magnitude of reduction in rate across 

symptoms: 42 percent) and among study 

participants who were previously 

receiving in-home dialysis on NxStage®

(average magnitude of reduction in rate 

across symptoms: 27 percent). Per the 

applicant, on average, sleep-related 

difficulties reduced from being reported 

in 33 percent of treatment weeks while 

on NxStage® to 23 percent of treatment 

weeks while on the Tablo® System. 

The applicant stated that hypotensive 

symptoms such as feelings of dizziness 

and lightheadedness are associated with 

the drops in blood pressure that can 

occur during dialysis and are also 

among the top ten symptoms dialysis 

patients report that impact their quality 

of life.85 Per the applicant, participants 

in the Tablo® IDE study were asked at 

the time of enrollment regarding 

symptoms previously experienced 

during dialysis. The applicant also 

stated that at the end of each study 

treatment, participants were surveyed 

regarding the presence of any symptoms 

during that treatment on the Tablo®

System. Per the applicant, a total of 8 

(26.7 percent) subjects reported 

hypotensive symptoms during the 

Tablo® System treatments during the inhome treatment period, compared to 27 

(90 percent) subjects reporting 

hypotensive symptoms at baseline (prior 

to initiating care on the Tablo® System). 

The applicant reported a 70 percent 

reduction in the rate of patient-reported 

hypotensive symptoms while on the 

Tablo® System, though we were unable 

to validate the source of this statement. 

The applicant stated that currently, 

ESRD patients on dialysis report 

meaningfully lower quality of life 

compared to those with other chronic 

illnesses.86 The applicant further noted 

that decreased quality of life is 

associated with a meaningful decline in 

continuation of home therapy, dialysis 

frequency, and worse clinical and 

health care utilization outcomes.87

The applicant concluded by asserting 

that the totality of evidence submitted 

in support of the Tablo® System 

demonstrates SCI over the current 

standard of home dialysis care. The 

applicant also stated that patient 

preference for devices is currently used 

by FDA to guide marketing 

authorization decisions and provides 

important information on the benefit 

and risks that some patients are willing 

to trade when choosing a device.88 Per 

the applicant, patients may be more 

likely to choose home dialysis to the 

extent that the device is both accessible 

and easy to use. The applicant also 

stated that 86 percent of prior NxStage®

patients in the Tablo® IDE study found 

the Tablo® System easier to use than 

their incumbent device and preferred to 

remain on the Tablo® System at the end 

of the study.89

In summary, the applicant claimed 

that the Tablo® System improves the 

treatment of Medicare beneficiaries 

relative to the incumbent by focusing on 

outcomes set forth in 

§ 412.87(b)(1)(ii)(C), including a 

decreased number of treatments to 

achieve dialysis adequacy, which the 

applicant stated leads to greater 

adherence to prescribed therapy, and 

improved quality of life. 

(c) CMS Preliminary Assessment of SCI 

Claims and Sources 

After a review of the information 

provided by the applicant, we have 

identified the following concerns 

regarding the SCI eligibility criterion for 

the TPNIES. We note that, consistent 

with § 413.236(c), CMS will announce 

its final determination regarding 

whether Tablo® meets the SCI criterion 

and other eligibility criteria for the 

TPNIES in the CY 2022 ESRD PPS final 

rule. 

With respect to the applicant’s claim 

that patients can achieve dialysis 

adequacy in as little as 3 treatments per 

week, we note that the Tablo® IDE study 

did not test whether patients receive 

adequate dialysis on a thrice-weekly 

schedule. Instead, data published from 

the Tablo® IDE study address a weekly 

measure of dialysis adequacy among 

patients treated on a 4 times per week 

schedule. The applicant relied on 

modeling and unpublished data on 

patients receiving thrice-weekly dialysis 

in making the conclusion that dialysis 

adequacy can be reached on a thriceweekly schedule. Specifically, the 

applicant referred to a theoretical 

modeling study based on historical data 

from the USRDS, Medicare claims, and 

historical outcomes from NxStage®

observational studies. The applicant 

also stated that findings from a 

retrospective review of 29 patients 

receiving treatment with the Tablo®

System on a thrice-weekly schedule 

affirm the results from the modeling 

study. We also note that the authors in 

Alvarez et al.90 stated that conclusions 

about fluid removal could not be made 

from their study. We would be 

interested in whether additional studies 

are available that address issues related 

to effective fluid removal using home 


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91Safety and efficacy of the Tablo hemodialysis 

system for in-center and home hemodialysis Plumb, 

T.J., Alvarez, L., Ross, D.L., Lee, J.J., Mulhern, J.G., 

Bell, J.L., Abra, G., Prichard, S.S., Chertow, G.M. 

and Aragon, M.A. (2019), Hemodialysis 

International. 

92Chertow, G.M., Alvarez, L., Plumb, T.J., 

Prichard, S. S., & Aragon, M. (2020). Patientreported outcomes from the investigational device 

exemption study of the Tablo hemodialysis system. 

Hemodialysis International, 24(4), 480–486. 

93Mayo Clinic Staff, ‘‘Peritonitis,’’ June 18, 2020, 

available at: https://www.mayoclinic.org/diseasesconditions/peritonitis/symptoms-causes/syc20376247. 

94 Ibid. 

95 Ibid. 

96 Kam-Tao Li, Philip, et al., ‘‘ISPD Peritonitis 

recommendations: 2016 Update on Prevention and 

Treatment,’’ Peritoneal Dialysis International 2016; 

36(5):481–508, June 9, 2016, available at: http://

dx.doi.org/10.3747/pdi.2016.00078. 97Briggs, et al., ‘‘Early Detection of Peritonitis in 

Patients Undergoing Peritoneal Dialysis: A Device 

and Cloud-Based Algorithmic Solution,’’ 

unpublished report. 98 Kam-Tao Li, Philip, et al., ‘‘ISPD Peritonitis 

recommendations: 2016 Update on Prevention and 

Treatment,’’ Peritoneal Dialysis International 2016; 

36(5):481–508, June 9, 2016, available at: http://

dx.doi.org/10.3747/pdi.2016.00078. 99 Ibid. 

self-care dialysis thrice-weekly with the 

Tablo® System. We invite comments on 

whether less frequent dialysis sessions 

would represent SCI over shorter, more 

frequent sessions that, according to the 

applicant, are common among users of 

the incumbent technology. 

The applicant’s second claim was that 

the Tablo® System increases adherence 

to dialysis treatment and retention to 

home therapy, which may reduce 

dialysis-related hospitalizations and 

other adverse events associated with 

missing treatment. This claim was 

supported by the Tablo® IDE study (28 

participants completed the study) and 

the use of historical comparisons to 

prior studies involving the NxStage®

System. The applicant noted that 

hospitalization rates from the Tablo®

IDE trial were lower than rates in the 

general dialysis population and rates 

reported in two observational studies of 

patients using the NxStage® device. 

While the applicant cited an all-cause 

hospitalization rate of 426 per 1000 

patient years in the Tablo® IDE study, 

it does not appear that the sources 91 92

published these hospitalization rates. 

We further note that the applicant relied 

on historical comparisons in asserting 

that that patients treated with the 

Tablo® System experience reduced 

disease burden and improved quality of 

life. 

We note that in the Tablo® IDE study, 

the before-after comparisons in patients 

with NxStage® regarding improved 

sleep compared to prior to the Tablo®

System may be prone to recall bias in 

that participants’ experiences with 

NxStage® were not recorded at the time 

they were receiving NxStage®

treatments, but rather, were based on 

recall at the time of the Tablo® IDE 

study. 

We understand that greater flexibility 

for patients in the way that they receive 

their dialysis treatments may represent 

a benefit to Medicare beneficiaries who 

are candidates to receive this treatment 

in the home setting. We invite 

comments on whether this potential 

benefit represents SCI, including 

whether the Tablo® System represents 

an advance that substantially improves, 

relative to renal dialysis services 

previously available, the treatment of 

Medicare beneficiaries. 

(6) Capital Related Assets Criterion 

(§ 413.236(b)(6)) 

With respect to the sixth TPNIES 

eligibility criterion under 

§ 413.236(b)(6), whether the item is a 

capital-related asset and home dialysis 

machine, § 413.236(a)(2) defines these 

terms. First, a capital-related asset is an 

asset that an ESRD facility has an 

economic interest in through ownership 

(regardless of the manner in which it 

was acquired) and is subject to 

depreciation. Equipment obtained by 

the ESRD facility through operating 

leases are not considered capital-related 

assets. Second, home dialysis machines 

are HD machines and PD cyclers in their 

entirety (meaning that one new part of 

a machine does not make the entire 

capital-related asset new) that receive 

FDA marketing authorization for home 

use and when used in the home for a 

single patient. The applicant identified 

the Tablo® System as an asset that an 

ESRD facility has an economic interest 

in through ownership, is subject to 

depreciation, and is an HD machine that 

received FDA marketing authorization 

for home use. Therefore, the Tablo®

System is a capital-related asset that is 

a home dialysis machine. We welcome 

comments on the Tablo® System’s status 

as a capital related asset that is a home 

dialysis machine. 

b. CloudCath Peritoneal Dialysis Drain 

Set Monitoring System (CloudCath 

System) 

CloudCath submitted an application 

for the TPNIES for the CloudCath 

Peritoneal Dialysis Drain Set Monitoring 

System (CloudCath System) for CY 

2022. According to the application, the 

CloudCath System is a tabletop passive 

drainage system that detects and 

monitors solid particles in dialysate 

effluent during PD treatments. Solid 

particles in dialysate effluent, 

manifesting itself as cloudy dialysate, 

may indicate that the patient has 

peritonitis, the inflammation of the 

peritoneum in the abdominal wall 

usually due to a bacterial or fungal 

infection.93 PD therapy is a common 

cause of peritonitis.94 If left untreated, 

the condition can be life threatening.95

PD-related peritonitis is a major 

complication and challenge to the longterm success and adherence of patients 

on PD therapy.96 The applicant stated 

that only about 12 percent of eligible 

patients are on PD therapy.97 The 

applicant claimed that the risk of PDrelated peritonitis, and the challenges to 

detect it, are the main reasons for these 

figures. The guidelines for diagnosis of 

PD-related peritonitis, as outlined by the 

International Society for Peritoneal 

Dialysis (ISPD), recommend that 

peritonitis be diagnosed when at least 2 

of the following criteria are present: (1) 

The patient experiences clinical features 

consistent with peritonitis (abdominal 

pain and/or cloudy dialysate effluent); 

(2) the patient’s dialysate effluent has a 

whole blood count (WBC) >100 cells/mL 

or >0.1 × 10/L with polymorphonuclear 

(PMN) cells >50 percent; and (3) 

positive dialysis effluent culture is 

identified.98 Additionally, the 

guidelines recommend that PD patients 

presenting with cloudy effluent be 

presumed to have peritonitis and treated 

as such until the diagnosis can be 

confirmed or excluded.99 Per the 

guidelines, this means that for patients 

undergoing PD treatments at home, it is 

recommended that they self-monitor for 

symptoms of peritonitis, cloudy 

dialysate and/or abdominal pain, and 

seek medical attention for additional 

testing and treatment upon experiencing 

any or both of these symptoms. 

According to the applicant, despite the 

fact that peritonitis is highly prevalent, 

symptom monitoring is insensitive and 

non-specific, which can contribute to 

late presentation for medical attention 

and treatment. The applicant asserted 

that under the current standard of care, 

PD patients face the following 

challenges in detecting peritonitis. First, 

the applicant stated that patients’ fluid 

observation has low compliance rates as 

it relies on patients’ close examination 

of their own dialysate effluent during 

PD treatments, which often occur while 

patients are asleep. Second, the 

applicant noted that it can be difficult 

for patients to visually detect peritonitis 

in dialysate effluent using a ‘‘newspaper 

test’’ for cloudiness, and can be even 

more difficult to see when the fluid is 

drained into a toilet, where it is diluted 

by water. The applicant stated that, as 

a result of these challenges, patients 

with ESRD suffer unsatisfactorily high 

mortality and morbidity from 


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100Briggs, et al., ‘‘Early Detection of Peritonitis in 

Patients Undergoing Peritoneal Dialysis: A Device 

and Cloud-Based Algorithmic Solution,’’ 

unpublished report. 

101 Kam-Tao Li, Philip, et al., ‘‘ISPD Peritonitis 

recommendations: 2016 Update on Prevention and 

Treatment,’’ Peritoneal Dialysis International 2016; 

36(5):481–508, June 9, 2016, available at: http://

dx.doi.org/10.3747/pdi.2016.00078. 

peritonitis, as well as high rates of PD 

modality loss, meaning they must 

discontinue PD and begin a different 

type of dialysis treatment. Per the 

applicant, the CloudCath System 

addresses these challenges by detecting 

changes in dialysate effluent at much 

lower levels of particle concentrations 

than the amount needed to accumulate 

for visual detection by patients. 

Per the applicant, the CloudCath 

System consists of three components: 

(1) Drain set, (2) sensor, and (3) patient 

monitoring software. As explained in 

the application, the CloudCath System’s 

drain set connects to a compatible PD 

cycler’s drain line to enable draining 

and monitoring of dialysate effluent 

before routing the fluid to the drainage 

receptacle. Per the CloudCath System 

User Guide, included in the application, 

the CloudCath System is compatible 

with the following PD cyclers: Baxter 

Healthcare Home Choice PROTM, Baxter 

Healthcare AMIATM Automated PD 

System, and Fresenius Liberty® Select 

Cycler. Per the applicant, once the 

CloudCath System is attached to a 

compatible cycler, the dialysate effluent 

runs through the drain set, through the 

CloudCath System’s optical sensor. The 

applicant explained that the CloudCath 

System’s optical sensor detects and 

monitors changing concentrations of 

solid particles in the dialysate effluent 

during each dialysis cycle and reports 

the concentrations in a turbidity score. 

Per the applicant, the CloudCath System 

will indicate whether dialysate effluent 

has normal turbidity and will notify the 

patient and/or health care professional 

if the dialysate effluent turbidity has 

exceeded the notification threshold set 

by the patient’s dialysis provider. The 

applicant stated that the optical sensor’s 

hardware and software components 

allow for data trending over time and 

remote monitoring by a healthcare 

professional. 

(1) Renal Dialysis Service Criterion 

(§ 413.236(b)(1)) 

Regarding the first TPNIES eligibility 

criterion in § 413.236(b)(1), that the item 

has been designated by CMS as a renal 

dialysis service under § 413.171, 

monitoring for peritonitis is a service 

that is essential for dialysis, and 

therefore would be considered a renal 

dialysis service under § 413.171. 

(2) Newness Criterion (§ 413.236(b)(2)) 

With respect to the second TPNIES 

eligibility criterion in § 413.236(b)(2), 

that the item is new, meaning within 3 

years beginning on the date of the FDA 

marketing authorization, the applicant 

stated that it is seeking 510(k) marketing 

authorization from the FDA. To be 

eligible for the TPNIES, the applicant 

must apply within three years of the 

FDA marketing authorization date and 

receive FDA marketing authorization by 

the HCPCS Level II deadline of July 6, 

2021. The applicant stated that it 

anticipates the CloudCath System will 

receive FDA marketing authorization by 

the HCPCS Level II deadline. 

(3) Commercial Availability Criterion 

(§ 413.236(b)(3)) 

Regarding the third TPNIES eligibility 

criterion in § 413.236(b)(3), that the item 

is commercially available by January 1 

of the particular calendar year, meaning 

the year in which the payment 

adjustment would take effect, the 

applicant stated that the CloudCath 

System is not currently commercially 

available because it has not received 

FDA marketing authorization. The 

applicant noted that it expects the 

CloudCath System will be commercially 

available immediately after receiving 

FDA marketing authorization. 

(4) HCPCS Level II Application 

Criterion (§ 413.236(b)(4)) 

Regarding the fourth TPNIES 

eligibility criterion in § 413.236(b)(4) 

requiring that the applicant submit a 

complete HCPCS Level II code 

application by the HCPCS Level II 

application deadline of July 6, 2021, the 

applicant stated that it has not 

submitted an application yet, but 

intends to apply by the deadline. 

(5) Innovation Criteria (§§ 413.236(b)(5) 

and 412.87(b)(1)) 

(a) SCI Claims and Sources 

With regard to the fifth TPNIES 

eligibility criterion under 

§ 413.236(b)(5), that the item is 

innovative, meaning it meets the SCI 

criteria specified in § 412.87(b)(1), the 

applicant asserted that the CloudCath 

System offers SCI over technologies 

currently available for the Medicare 

patient population by offering the 

ability to monitor changes in turbidity 

of peritoneal dialysate effluent through 

continuous remote monitoring in 

patients with ESRD receiving PD 

therapy, earlier than the current 

standard of care. By allowing the 

clinical standard of care to be initiated 

earlier, per the applicant, the use of the 

CloudCath System changes the 

management of peritonitis patients by 

enabling clinicians to both diagnose 

peritonitis and initiate antibiotic 

treatment earlier. 

The applicant submitted two studies 

on the technology in support of the SCI 

claims. The applicant included a 

preliminary, unpublished report by 

Briggs, et al. on a clinical study that 

tested the ability of the CloudCath 

System and its dialysate effluent 

monitoring algorithm to detect 

indicators of peritonitis.100 The proof of 

principle observational study consisted 

of 70 PD patients outside of the U.S. 

who had been on PD for a long interval 

of time (>10 days), and thus were at an 

increased risk of developing peritonitis. 

Out of the 64 PD patients whose data 

were included in the study, over 40 PD 

patients were receiving intermittent PD, 

which is not commonly used in the U.S. 

The remainder of the participants were 

receiving Continuous Ambulatory 

Peritoneal Dialysis. The report states 

that in the U.S., PD is generally 

performed in a modality called 

Continuous Cycling Peritoneal Dialysis 

(CCPD), in which a cycler automatically 

administers multiple dialysis exchange 

cycles, typically while patients sleep. 

Samples were collected from patients’ 

PD effluent drainage bags and measured 

in the CloudCath System against a 

proprietary Turbidity Score threshold 

value and also tested for reference 

laboratory measurements according to 

ISPD guidelines for WBC count and 

differential (>100 cells/mL, >50 percent 

PMN).101 Regarding the Turbidity Score 

threshold value, the study set a score to 

determine if the effluent sample in the 

CloudCath System was infected or not; 

samples greater than or equal to the 

Turbidity Score threshold value would 

be classified as infected, and samples 

less than the Turbidity Score threshold 

value would be classified as noninfected. The crude sensitivity and 

specificity of the CloudCath System was 

96.2 percent and 91.2 percent, 

respectively. A majority of false 

positives (44 of 77 samples) occurred 

among patients already receiving 

antibiotic treatment for peritonitis, and 

another 20 false positive reports 

occurred because the patient had 

elevated turbidity due to a cause other 

than peritonitis. The investigators 

subsequently removed samples from 

patients already receiving treatment for 

peritonitis, setting the sensitivity for 

detecting peritonitis using the 

CloudCath System at 99 percent and the 

specificity at 97.6 percent. 

The second study the applicant 

submitted is the Prospective Clinical 

Study to Evaluate the Ability of the 

CloudCath System to Detect Peritonitis 


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102CloudCath, ‘‘A Prospective Clinical Study to 

Evaluate the Ability of the CloudCath System to 

Detect Peritonitis Compared to Standard of Care 

during In-Home Peritoneal Dialysis (CATCH),’’ 

Preliminary Clinical Study Report (NCT04515498), 

Jan 27, 2020. 103CloudCath, ‘‘A Prospective Clinical Study to 

Evaluate the Ability of the CloudCath System to 

Detect Peritonitis Compared to Standard of Care 

during In-Home Peritoneal Dialysis (CATCH),’’ 

Study Protocol (CC–P–001), June 24, 2020. 104 Ibid. 105 Ibid. 106CloudCath, ‘‘A Prospective Clinical Study to 

Evaluate the Ability of the CloudCath System to 

Detect Peritonitis Compared to Standard of Care 

during In-Home Peritoneal Dialysis (CATCH),’’ 

Preliminary Clinical Study Report (NCT04515498), 

Jan 27, 2020. 107 Ibid. 108 Ibid. 109 Ibid. 110 Ibid. 111 Ibid. 112 Ibid. 

113 Ibid. 

114Muthucumarana, et al., ‘‘The Relationship 

Between Presentation and the Time of Initial 

Administration of Antibiotics With Outcomes of 

Peritonitis in Peritoneal Dialysis Patients: The 

PROMPT Study.,’’ Kidney Int Rep. 2016 Jun 

11;1(2):65–72. doi: 10.1016/j.ekir.2016.05.003. 

PMID: 29142915; PMCID: PMC5678844. 

115 Ibid. 

116 Ibid. 

117 Ibid. 

118 Ibid. 

119 Gacouin, A. et al., ‘‘Severe pneumonia due to 

Legionella pneumophila: prognostic factors, impact 

of delayed appropriate antimicrobial therapy,’’ 

Intensive Care Medicine 28, 686–691 (2002), 

https://doi.org/10.1007/s00134–002–1304–8. 

120Houck, PM. et al., ‘‘Timing of antibiotic 

administration and outcomes for Medicare patients 

hospitalized with community-acquired 

pneumonia,’’ Arch Intern Med. 2004 Mar 

22;164(6):637–44. doi: 10.1001/archinte.164.6.637. 

PMID: 15037492. 

121Lodise TP, et al., ‘‘Outcomes analysis of 

delayed antibiotic treatment for hospital-acquired 

Staphylococcus aureus bacteremia,’’Clin Infect Dis. 

2003 Jun 1;36(11):1418–23. doi: 10.1086/375057. 

Epub 2003 May 20. PMID: 12766837. 

122Mehrotra, Rajnish et al., ‘‘The Current State of 

Peritoneal Dialysis,’’ Journal of the American 

Society of Nephrology 27: 3238–3252, 2016. doi: 

10.1681/ASN.2016010112, available at: https://

jasn.asnjournals.org/content/jnephrol/27/11/ 

3238.full.pdf?with-ds=yes. 

Compared to Standard of Care during 

In-Home Peritoneal Dialysis 

(CATCH).102 CloudCath initiated this 

ongoing single-arm, open-label, multicenter study to demonstrate that the 

CloudCath System is able to detect 

changes in turbidity associated with 

peritonitis in PD patients prior to 

laboratory diagnosis of peritonitis with 

a high degree of specificity and 

sensitivity. The target enrollment is 186 

participants over 18 years of age using 

CCPD as their PD modality, with at least 

2 exchanges per night.103 Patients with 

active infection and/or cancer are 

excluded from the trial.104 The primary 

endpoint is time of peritonitis detection 

by the CloudCath System (defined as 

two consecutive Turbidity Scores >7.0) 

as compared to laboratory evidence of 

peritonitis (defined as WBC count >100 

cells/mL or >0.1 × 109/L with percentage 

of PMN >50 percent).105 While the 

study is ongoing, the applicant included 

the study protocol and preliminary 

results with its application.106 The 

preliminary results demonstrate that as 

of December 29, 2020, 132 participants 

have been enrolled in the CATCH Study 

at 13 sites.107 Of the 132 enrolled 

participants, 59.1 percent of participants 

were male, 65.9 percent of participants 

were White and 29.6 percent of 

participants were Black or African 

American.108 Enrolled participants 

underwent an average of 4.5 exchanges 

per night.109 The preliminary results 

indicate that, as of December 29, 2020, 

there have been 7 peritonitis events that 

met the ISPD peritoneal fluid cell 

counts and differentials standard.110 All 

7 of the peritonitis events were also 

detected by the CloudCath System.111 In 

5 out of the 7 peritonitis events, the 

CloudCath System detected peritonitis 

44 to 368 hours prior to the time of 

detection from a clinical laboratory.112

The CloudCath System also detected 

peritonitis 27 to 344 hours prior to 

participants presenting to the hospital 

or clinic with signs or symptoms of 

peritonitis.113 The applicant stated that 

these results support the claim that the 

CloudCath System would enable 

diagnosis of peritonitis earlier than the 

current standard of care through 

turbidity monitoring. 

In addition to the studies on the 

technology, the applicant submitted an 

article by Muthucumarana, et al. on the 

impact of time-to-treatment on clinical 

outcomes of PD-related peritonitis.114

The article includes data from the 

Presentation and the Time of Initial 

Administration of Antibiotics With 

Outcomes of Peritonitis (PROMPT) 

Study, a prospective multicenter from 

2012 to 2014 that observed symptom-tocontact time, contact-to-treatment time, 

defined as the time from health care 

presentation to initial antibiotic, and 

symptom-to-treatment time in 

Australian PD patients. 116 patients 

participated in the survey, 83 of which 

were caucasian and 14 were 

aboriginal.115 Out of the sample size of 

116 survey participants, there were 159 

episodes of PD-related peritonitis. Of 

these, 38 patient episodes met the 

primary outcome of PD failure (defined 

as catheter removal or death) at 30 

days.116 The median symptom-totreatment time was 9.0 hours in all 

patients, 13.6 hours in the PD-fail group, 

and 8.0 hours in the PD-cure group.117

The study found that the risk of PDfailure increased by 5.5 percent for each 

hour of delay of administration of 

antibiotics once patients presented to a 

health care provider.118 However, 

neither symptom-to-contact nor 

symptom-to-treatment was associated 

with PD-failure in non-adjusted 

analyses, and the time from presentation 

to a health care provider to treatment 

was only associated with PD-failure 

outcomes in multivariable-adjusted 

analyses in a subset of patients who 

presented to hospital-based facilities. In 

addition to the Muthucumarana et al. 

article, the applicant cited to other 

studies that have found that antibiotic 

treatment should begin as soon as 

possible in order to effectively treat 

infections other than 

peritonitis.119 120 121 Per the applicant, 

these articles on time-to-treatment 

demonstrate that the CloudCath 

System’s ability to detect effluent 

changes substantially earlier improves 

the standard of care, enabling PD-related 

peritonitis diagnosis and antibiotic 

treatment earlier while decreasing the 

likelihood of PD-failure due to PDrelated peritonitis. 

The applicant also submitted letters of 

support from a nephrologist at an 

academic institution and the following 

ESRD patient advocacy groups: The 

American Kidney Fund, the American 

Association of Kidney Patients, and the 

International Society of Nephrology. 

The letter of support from Dr. Thomas 

A. Golper, president-elect of the 

International Society of Nephrology, 

endorsed the CloudCath System’s ability 

to detect peritonitis and enable 

clinicians to begin to treat the infection 

earlier, preventing hospitalizations and 

related complications such as the 

abandonment of home dialysis. The 

letter also stated that the CloudCath 

System helps address the challenge of 

peritonitis as the main reason for 

abandonment of PD for HD, and will 

encourage a greater number of patients 

to select PD as their dialysis modality of 

choice. The letters from the American 

Association of Kidney Patients and the 

International Society of Nephrology 

encouraged CMS to consider the 

CloudCath System’s application, 

explaining that the technology would 

have several benefits to patients, for 

example, by reducing peritonitis-related 

hospitalizations, increasing adherence 

to PD, and encouraging higher 

utilization of PD as a viable alternative 

to in-center HD. The American Kidney 

Fund’s letter emphasized that 

peritonitis is a significant concern for 

PD patients 122 and requested CMS 

support of all efforts that ensure patients 


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123CloudCath, ‘‘A Prospective Clinical Study to 

Evaluate the Ability of the CloudCath System to 

Detect Peritonitis Compared to Standard of Care 

during In-Home Peritoneal Dialysis (CATCH),’’ 

Preliminary Clinical Study Report, NCT04515498, 

Jan 27, 2020. 

124Briggs, et al., ‘‘Early Detection of Peritonitis in 

Patients Undergoing Peritoneal Dialysis: A Device 

and Cloud-Based Algorithmic Solution,’’ 

unpublished report. 

125Muthucumarana, et al., ‘‘The Relationship 

Between Presentation and the Time of Initial 

Administration of Antibiotics With Outcomes of 

Peritonitis in Peritoneal Dialysis Patients: The 

PROMPT Study.,’’ Kidney Int Rep. 2016 Jun 

11;1(2):65–72. doi: 10.1016/j.ekir.2016.05.003. 

PMID: 29142915; PMCID: PMC5678844. 

126 Kam-Tao Li, Philip, et al., ‘‘ISPD Peritonitis 

recommendations: 2016 Update on Prevention and 

Treatment,’’ Peritoneal Dialysis International 2016; 

36(5):481–508, June 9, 2016, available at: http://

dx.doi.org/10.3747/pdi.2016.00078. 

127CloudCath, ‘‘A Prospective Clinical Study to 

Evaluate the Ability of the CloudCath System to 

Detect Peritonitis Compared to Standard of Care 

during In-Home Peritoneal Dialysis (CATCH),’’ 

Preliminary Clinical Study Report (NCT04515498), 

Jan 27, 2020. 

128 Ibid. 

129 Kam-Tao Li, Philip, et al., ‘‘ISPD Peritonitis 

recommendations: 2016 Update on Prevention and 

Treatment,’’ Peritoneal Dialysis International 2016; 

36(5):481–508, June 9, 2016, available at: http://

dx.doi.org/10.3747/pdi.2016.00078. 

130 Ibid. 

131Briggs, et al., ‘‘Early Detection of Peritonitis in 

Patients Undergoing Peritoneal Dialysis: A Device 

and Cloud-Based Algorithmic Solution,’’ 

unpublished report. 

with ESRD undergoing PD treatments 

can quickly detect and treat infections. 

(b) CMS Preliminary Assessment of SCI 

Claims and Sources 

After a review of the information 

provided by the applicant, we note the 

following concerns with regard to the 

SCI criterion under § 413.236(b)(5) and 

§ 412.87(b)(1). We note that, consistent 

with § 413.236(c), CMS will announce 

its final determination regarding 

whether the CloudCath System meets 

the SCI criterion and other eligibility 

criteria for the TPNIES in the CY 2022 

ESRD PPS final rule. 

Because the applicant claims to offer 

the ability to diagnose a medical 

condition, PD-related peritonitis, earlier 

in a patient population than allowed by 

currently available methods, the 

applicant must also include evidence 

that use of the new technology to make 

a diagnosis affects the management of 

the patient, as required under the SCI 

criterion at § 412.87(b)(1)(ii)(B). 

Specifically, § 412.87(b)(1)(ii)(B) states 

that a determination that a technology 

represents SCI over existing technology 

means: The new medical service or 

technology offers the ability to diagnose 

a medical condition in a patient 

population where that medical 

condition is currently undetectable, or 

offers the ability to diagnose a medical 

condition earlier in a patient population 

than allowed by currently available 

methods and there must also be 

evidence that use of the new medical 

service or technology to make a 

diagnosis affects the management of the 

patient. 

It is not clear to us whether the 

studies submitted demonstrate or 

examine the impacts of using the 

technology on patients with ESRD such 

that we can determine whether it 

represents an advance that substantially 

improves the treatment of Medicare 

beneficiaries compared to renal dialysis 

services previously available. We note 

that the studies submitted serve as 

‘‘proof of concept,’’ as they provide 

evidence that the CloudCath System 

detects solid particles in dialysate 

effluent that may indicate PD-related 

peritonitis, and, may do so earlier than 

patient observation and a cell count test. 

However, the studies are limited in that 

they do not observe how the CloudCath 

System, in detecting the solid particles 

in dialysate effluent and doing so earlier 

than a cell count test, affects the 

management of the patient, as required 

under the SCI criterion at 

§ 412.87(b)(1)(ii)(B). For example, as 

part of the CATCH Study, investigators 

deactivated the notification capability of 

the CloudCath System for the duration 

of the study, so that neither the 

participants nor the investigators would 

be aware of the device measurements.123

Therefore, the CATCH study did not 

examine patient and clinician behavior, 

including the medical management of 

the patient, after the CloudCath System 

detected the solid particles in the 

dialysate effluent. The Briggs et al. 

study also did not examine how use of 

the CloudCath System impacted 

management of the patient. The 

investigators in that study stated, ‘‘none 

of the data from our device was used for 

clinical decision making,’’ meaning that 

the study did not test how or if the 

CloudCath System offered the ability to 

diagnose a medical condition and how 

use of the CloudCath System to make a 

diagnosis affected the management of 

the patient.124 Because the studies 

submitted did not observe how patients 

and clinicians use the CloudCath 

System’s monitoring to make decisions 

regarding patient management, we are 

concerned that we will not be able to 

make a determination on whether early 

detection of PD-related peritonitis by 

the CloudCath System meets the SCI 

criterion at § 412.87(b)(1)(ii)(B). 

Similarly, while the applicant submitted 

evidence to show that time-to-treatment 

plays a role in preventing PD failure in 

patients with ESRD with PD-related 

peritonitis,125 CMS has not received any 

information regarding how the 

CloudCath System would affect 

management of the patient by reducing 

time-to-treatment for patients with 

ESRD receiving PD therapy. CMS also 

notes that the applicant referenced 

studies that support beginning 

antibacterial therapy for infections other 

than PD-related peritonitis, like 

pneumonia, and, therefore do not 

directly demonstrate the importance of 

time-to-treatment for PD-related 

peritonitis. 

Additionally, it is not clear to us 

whether the CloudCath System would 

affect medical management of the 

patient because use of the technology 

may potentially detect solid particles in 

dialysate effluent so early, that, in some 

cases, healthcare providers may decide 

to wait for confirmation via patient 

symptoms, cell count, or positive 

culture as stated in the ISPD guidelines 

on diagnosis.126 The preliminary results 

of the CATCH study demonstrate that in 

5 out of 7 PD-related peritonitis events, 

the CloudCath System detected PDrelated peritonitis 33 to 367 hours prior 

to the time of detection from a clinical 

laboratory.127 The CloudCath System 

also detected PD-related peritonitis 27 

to 344 hours prior to participants 

presenting to a healthcare facility with 

symptoms of PD-related peritonitis.128

We note that no evidence was submitted 

to show that clinicians would begin to 

treat suspected peritonitis if the 

CloudCath System alerted the patient 

and clinician of possible PD-related 

peritonitis that was too early to detect 

via any of the ISPD guidelines.129 In 

other words, we have not received 

evidence to demonstrate that the 

CloudCath System would affect medical 

management of the patient by replacing 

one of the ISPD guidelines for 

diagnosis.130

Additionally, CMS notes that the 

applicant has not submitted evidence to 

show that beginning treatment for 

presumed PD-related peritonitis in 

patients with ESRD prior to the 

occurrence of any of the ISPD guidelines 

would not be harmful to patients. In the 

Briggs et al. study, the CloudCath 

System identified 20 false positives that 

occurred because the patient had 

elevated turbidity due to some cause 

other than PD-related peritonitis.131

However, the applicant did not explain 

or provide evidence on whether 

beginning treatment for PD-related 

peritonitis for a group of patients with 

ESRD who tested positive, but were in 

fact negative for the condition, was 

clinically advisable. CMS is concerned 

that the CloudCath System’s potential 

for false positive results may lead to 


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132CMS Provider Reimbursement Manual, 

Chapter 1, Section 104.1. Available at: https://

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

Guidance/Manuals/Paper-Based-Manuals-Items/ 

CMS021929. 133Section 1881(b)(14)(D)(iv)(II) of the Act. 

clinicians beginning treatment for PDrelated peritonitis when not necessary 

in an already vulnerable group of 

Medicare beneficiaries. We welcome 

public comment on these issues. 

(6) Capital Related Assets Criterion 

(§ 413.236(b)(6)) 

Regarding the sixth TPNIES eligibility 

criterion in § 413.236(b)(6), limiting 

capital-related assets from being eligible 

for the TPNIES, except those that are 

home dialysis machines, the applicant 

stated that the CloudCath System is not 

a capital-related asset. The applicant 

explained that the CloudCath System 

does not meet the definition of a capitalrelated asset, as defined in the Provider 

Reimbursement Manual (chapter 1, 

section 104.1), because the device is not 

subject to depreciation, nor is used by 

a provider as part of a regular lease 

agreement.132

III. Calendar Year (CY) 2022 Payment 

for Renal Dialysis Services Furnished 

to Individuals With Acute Kidney 

Injury (AKI) 

A. Background 

The Trade Preferences Extension Act 

of 2015 (TPEA) (Pub. L. 114–27) was 

enacted on June 29, 2015, and amended 

the Act to provide coverage and 

payment for dialysis furnished by an 

ESRD facility to an individual with 

acute kidney injury (AKI). Specifically, 

section 808(a) of the TPEA amended 

section 1861(s)(2)(F) of the Act to 

provide coverage for renal dialysis 

services furnished on or after January 1, 

2017, by a renal dialysis facility or a 

provider of services paid under section 

1881(b)(14) of the Act to an individual 

with AKI. Section 808(b) of the TPEA 

amended section 1834 of the Act by 

adding a subsection (r) to provide 

payment, beginning January 1, 2017, for 

renal dialysis services furnished by 

renal dialysis facilities or providers of 

services paid under section 1881(b)(14) 

of the Act to individuals with AKI at the 

ESRD PPS base rate, as adjusted by any 

applicable geographic adjustment 

applied under section 

1881(b)(14)(D)(iv)(II) of the Act and 

adjusted (on a budget neutral basis for 

payments under section 1834(r) of the 

Act) by any other adjustment factor 

under section 1881(b)(14)(D) of the Act 

that the Secretary elects. 

In the CY 2017 ESRD PPS final rule, 

we finalized several coverage and 

payment policies in order to implement 

subsection (r) of section 1834 of the Act 

and the amendments to section 

1881(s)(2)(F) of the Act, including the 

payment rate for AKI dialysis (81 FR 

77866 through 77872, and 77965). We 

interpret section 1834(r)(1) of the Act as 

requiring the amount of payment for 

AKI dialysis services to be the base rate 

for renal dialysis services determined 

for a year under the ESRD PPS base rate 

as set forth in § 413.220, updated by the 

ESRD bundled market basket percentage 

increase factor minus a productivity 

adjustment as set forth in 

§ 413.196(d)(1), adjusted for wages as set 

forth in § 413.231, and adjusted by any 

other amounts deemed appropriate by 

the Secretary under § 413.373. We 

codified this policy in § 413.372 (81 FR 

77965). 

B. Proposed Annual Payment Rate 

Update for CY 2022 

1. CY 2022 AKI Dialysis Payment Rate 

The payment rate for AKI dialysis is 

the ESRD PPS base rate determined for 

a year under section 1881(b)(14) of the 

Act, which is the finalized ESRD PPS 

base rate, including the applicable 

annual productivity-adjusted market 

basket payment update, geographic 

wage adjustments, and any other 

discretionary adjustments, for such year. 

We note that ESRD facilities have the 

ability to bill Medicare for non-renal 

dialysis items and services and receive 

separate payment in addition to the 

payment rate for AKI dialysis. 

As discussed in section II.B.1.d of this 

proposed rule, the CY 2022 proposed 

ESRD PPS base rate is $255.55, which 

reflects the application of the proposed 

CY 2022 wage index budget-neutrality 

adjustment factor of .999546 and the CY 

2022 proposed ESRDB market basket 

increase of 1.6 percent reduced by the 

productivity adjustment of 0.6 

percentage point, that is, 1.0 percent. 

Accordingly, we are proposing a CY 

2022 per treatment payment rate of 

$255.55 for renal dialysis services 

furnished by ESRD facilities to 

individuals with AKI. This payment rate 

is further adjusted by the wage index, as 

discussed in the next section of this 

proposed rule. 

2. Geographic Adjustment Factor 

Under section 1834(r)(1) of the Act 

and § 413.372, the amount of payment 

for AKI dialysis services is the base rate 

for renal dialysis services determined 

for a year under section 1881(b)(14) of 

the Act (updated by the ESRD bundled 

market basket and reduced by the 

productivity adjustment), as adjusted by 

any applicable geographic adjustment 

factor applied under section 

1881(b)(14)(D)(iv)(II) of the Act.133

Accordingly, we apply the same wage 

index under § 413.231 that is used 

under the ESRD PPS and discussed in 

section II.B.1.b of this proposed rule. 

The AKI dialysis payment rate is 

adjusted by the wage index for a 

particular ESRD facility in the same way 

that the ESRD PPS base rate is adjusted 

by the wage index for that facility (81 

FR 77868). Specifically, we apply the 

wage index to the labor-related share of 

the ESRD PPS base rate that we utilize 

for AKI dialysis to compute the wage 

adjusted per-treatment AKI dialysis 

payment rate. As stated previously, we 

are proposing a CY 2022 AKI dialysis 

payment rate of $255.55, adjusted by the 

ESRD facility’s wage index. 

IV. End-Stage Renal Disease Quality 

Incentive Program (ESRD QIP) 

A. Background 

For a detailed discussion of the EndStage Renal Disease Quality Incentive 

Program’s (ESRD QIP’s) background and 

history, including a description of the 

Program’s authorizing statute and the 

policies that we have adopted in 

previous final rules, we refer readers to 

the following final rules: 

• CY 2011 ESRD PPS final rule (75 FR 

49030), 

• CY 2012 ESRD PPS final rule (76 FR 

628), 

• CY 2012 ESRD PPS final rule (76 FR 

70228), 

• CY 2013 ESRD PPS final rule (77 FR 

67450), 

• CY 2014 ESRD PPS final rule (78 FR 

72156), 

• CY 2015 ESRD PPS final rule (79 FR 

66120), 

• CY 2016 ESRD PPS final rule (80 FR 

68968), 

• CY 2017 ESRD PPS final rule (81 FR 

77834), 

• CY 2018 ESRD PPS final rule (82 FR 

50738), 

• CY 2019 ESRD PPS final rule (83 FR 

56922), 

• CY 2020 ESRD PPS final rule (84 FR 

60648), and 

• CY 2021 ESRD PPS final rule (85 FR 

71398). 

We have also codified many of our 

policies for the ESRD QIP at 42 CFR 

413.177 and 413.178. 


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134CMS, Press Release, CMS Announces Relief 

for Clinicians, Providers, Hospitals and Facilities 

Participating in Quality Reporting Programs in 

Response to COVID–19 (Mar. 22, 2020), https://

www.cms.gov/newsroom/press-releases/cmsannounces-relief-clinicians-providers-hospitalsand-facilities-participating-quality-reporting. 

135CMS, Exceptions and Extensions for Quality 

Reporting Requirements for Acute Care Hospitals, 

PPS-Exempt Cancer Hospitals, Inpatient Psychiatric 

Facilities, Skilled Nursing Facilities, Home Health 

Agencies, Hospices, Inpatient Rehabilitation 

Facilities, Long-Term Care Hospitals, Ambulatory 

Surgical Centers, Renal Dialysis Facilities, and 

MIPS Eligible Clinicians Affected by COVID–19 

(Mar. 27, 2020), https://www.cms.gov/files/ 

document/guidance-memo-exceptions-andextensions-quality-reporting-and-value-basedpurchasing-programs.pdf. 

136https://mycrownweb.org/2020/11/november2020-newsletter/. 137https://mycrownweb.org/2021/02/eqrs-datareporting-update-feb-2021/. 

B. Extraordinary Circumstances 

Exception (ECE) Previously Granted for 

the ESRD QIP and Notification of ECE 

Due to ESRD Quality Reporting System 

Issues 

1. Extraordinary Circumstance 

Exception (ECE) Previously Granted in 

Response to the COVID–19 PHE 

On March 22, 2020, in response to the 

COVID–19 PHE, we announced relief for 

clinicians, providers, hospitals, and 

facilities participating in Medicare 

quality reporting and value-based 

purchasing programs.134 On March 27, 

2020, we published a supplemental 

guidance memorandum that described 

the scope and duration of the ECEs we 

were granting under each Medicare 

quality reporting and VBP program.135

Each of these ECEs relieved these 

providers and facilities of their 

obligation to report data for Q4 CY 2019, 

Q1 and Q2 CY 2020, but we stated that 

we would score such data if optionally 

reported. 

The September 2020 IFC updated the 

ECE we granted in response to the 

COVID–19 PHE for the ESRD QIP and 

several other quality reporting programs 

(85 FR 54827 through 54838). 

In the IFC, we updated the ECE policy 

for the ESRD QIP (85 FR 54828 through 

54830). First, we updated our 

regulations at § 413.178(d)(7) to state 

that a facility has opted out of the ECE 

for COVID–19 with respect to the 

reporting of Q4 CY 2019 NHSN data if 

the facility actually reported the data by 

the March 31, 2020 deadline but did not 

notify CMS that it would do so. 

Additionally, we finalized that facilities 

would not have the option to opt-out of 

the ECE we granted with respect to Q1 

and Q2 2020 ESRD QIP data. We stated 

that measures calculated using excepted 

data could affect the national 

comparability of these data due to the 

geographic differences of COVID–19 

incidence rates and hospitalizations 

along with different impacts resulting 

from different state and local law and 

policy changes implemented in 

response to COVID–19, and therefore 

may not provide a nationally 

comparable assessment of performance 

in keeping with the program goal of 

national comparison. 

In the September 2020 IFC, we 

welcomed public comments on our 

policy to update our regulations at 

§ 413.178(d)(7) to consider a facility as 

having opted out of the ECE with 

respect to NHSN data reported for Q4 

2019 if the facility actually reported the 

data by the submission deadline, 

without notifying CMS, and on the 

exception we finalized to the ECE opt 

out policy for the ESRD QIP to exclude 

any ESRD QIP data that facilities 

optionally reported during Q1 and Q2 

2020 from our calculation of PY 2022 

TPSs and from the baseline for PY 2023. 

We will respond to the public 

comments we received in the CY 2022 

ESRD PPS final rule. 

2. Notification of ECE Due to ESRD 

Quality Reporting System (EQRS) Issues 

On November 9, 2020,136 we 

launched the ESRD Quality Reporting 

System (EQRS). The EQRS contains the 

functionalities of the following three 

legacy ESRD Systems in one global 

application: (1) A quality measure and 

VBP performance score review system 

(ESRD QIP System); (2) an ESRD patient 

registry and quality measure reporting 

system through CROWNWeb; and (3) 

Medicare coverage determination 

support through the Renal Management 

Information System (REMIS). The 

transition to EQRS supports our efforts 

to consolidate the functionalities of the 

CROWNWeb, ESRD QIP System, and 

REMIS applications into a single 

system, and aims to provide ongoing 

support to the ESRD user community to 

foster accurate and timely monthly data 

submission. This migration eliminates 

the need for multiple user accounts, and 

will in the long-term also improve the 

overall user experience and reduce 

burden due to enhanced navigation 

features. 

In order to access EQRS, all 

authorized users must create an account 

with the Health Care Quality 

Information Systems (HCQIS) Access 

Roles and Profile, known as HARP, 

which is a secure identity management 

portal provided by CMS. Previously, 

users created separate accounts for each 

ESRD application through CMS’ 

Enterprise Identity Data Management 

(EIDM) system. Creating an account via 

HARP provides users with a user ID and 

password that can be used to access 

many CMS applications. It also provides 

a single location for users to modify 

their profile, change their password, 

update their challenge question, and 

add or remove two-factor authentication 

devices. Users can register for a HARP 

account by going to the QualityNet 

HARP Registration page, available at 

https://harp.cms.gov/register/profileinfo. 

Since the launch of EQRS, several 

critical data submission issues have 

been identified that impact the overall 

quality and accuracy of data available to 

support the implementation of the ESRD 

QIP, and we suspended all clinical data 

submissions into EQRS to allow time to 

resolve the issue.137 Based on our 

assessment, the data submission issues 

only impact ESRD QIP, Dialysis Star 

Ratings, Dialysis Facility Compare and 

data submitted for ESRD Network 

quality improvement activities. We have 

analyzed the data submission issues and 

believe that the data systems issues will 

be resolved on or about July 12, 2021. 

We recognize that these operational 

systems issues will prevent facilities 

from submitting ESRD QIP clinical data 

until the data systems issues are 

resolved. Therefore, we are announcing 

a blanket extension of remaining CY 

2020 clinical reporting deadlines. Under 

this extension, facilities will have until 

September 1, 2021 to submit September 

through December 2020 ESRD QIP 

clinical data. We believe this reporting 

extension aligns with the time estimated 

for resolution of our operational systems 

issues and will give dialysis facilities 

nearly seven weeks to submit their data 

to EQRS. We will provide further details 

to facilities when the EQRS issues are 

resolved, as well as when facilities can 

begin submitting their data for CY 2020 

and CY 2021, through routine 

communication channels to facilities, 

vendors, Quality Improvement 

Organizations (QIOs) and ESRD 

Networks. The communications could 

include memos, emails, and notices on 

the public QualityNet website (https://

www.qualitynet.org/). We discuss the 

treatment of impacted CY 2020 data in 

this proposed rule. As this situation is 

ongoing, we will announce any relevant 

extension deadlines and data 

submission requirements for impacted 

CY 2021 data through the routine 

communication channels discussed 

above. 

Because the current data submissions 

issue will not be resolved until on or 

about July 12, 2021 and has impacted all 

facilities that participate in ESRD QIP, 

we believe that granting a blanket ECE 


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138https://www.cms.gov/files/document/cy-2021- 

final-technical-specifications-20201130.pdf. 

to all facilities without a request under 

42 CFR 413.178(d)(6)(ii) is the 

appropriate remedy under these 

circumstances. We also believe that 

requiring facilities to report the CY 2020 

data impacted by this ECE by September 

1, 2021 is reasonable. In our data 

suspension announcements, we noted 

that facilities are expected to continue 

to use EQRS to collect clinical data to 

complete tasks such as admit and 

discharge patients, complete CMS forms 

(such as the CMS–2728: End Stage 

Renal Disease Medical Evidence Report 

Medicare Entitlement and/or Patient 

Registration, CMS–2744: End Stage 

Renal Disease Annual Facillity Survey 

Form, and CMS–2746: ESRD Death 

Notification), add or update treatment 

summaries, resolve notifications within 

a timely manner, and should also 

continue to keep facilities’ information 

up-to-date. In other words, although 

facilities were unable to submit clinical 

data through EQRS, facilities were 

advised that they must continue to 

collect the clinical data. 

While we are working to resolve all 

known systems issues by on or about 

July 12, 2021 and reopen submissions so 

that facilities may submit their 

September through December 2020 

ESRD QIP data no later than September 

1, 2021, we will only be able to ensure 

the validity of the impacted data after 

they are submitted. Given that the 

system issues experienced during the 

initial implementation of the EQRS, if 

not fully resolved, could potentially 

impact the accuracy and reliability of 

the data reported, we are concerned that 

facilities may be unfairly penalized 

because the current systems issues may 

impact the quality of the data. The 

EQRS system issues have resulted in 

multiple or incorrect dates of patient 

admissions and/or discharges, as well as 

showing duplicate patient records. 

Facilities have also expressed concerns 

about their experience with EQRS 

issues, noting that there is no way for a 

facility to verify accuracy or 

completeness. They have reported 

issues including missing record status 

in response files, which means that 

facilities do not know if the records 

were accepted or received an error 

response, and issues with determining 

whether clinical data are accepted 

because the information does not show 

in the user interface or the reports that 

facilities are receiving from EQRS. 

We recognize stakeholders’ concerns 

about the potential impact to the quality 

of data for CY 2020. We believe the 

observed system issues, and any 

unresolved issues that may be identified 

only after data submissions are 

resumed, could impact the quality and 

accuracy of the data needed to calculate 

accurate ESRD QIP scores used for PY 

2022 ESRD QIP calculations because 

patient admittance dates, discharge 

dates, record status in response files, 

clinical data, and the number of active 

patient cases are data points that are 

included in measure calculations for all 

of the PY 2022 ESRD QIP measures. If 

these data points are incorrect, then this 

would impact our ability to accurately 

calculate measures and would distort a 

facility’s measure performance. 

Therefore, because of the EQRS 

system issues described above, and 

additionally, due to the impact of the 

COVID–19 PHE on some of the PY 2022 

ESRD QIP measures, as described more 

fully in section IV.C. of this proposed 

rule, we are proposing to not score or 

award a TPS to any facility, or reduce 

payment to any facility, in PY 2022, as 

discussed more fully in section IV.D. 

Although we considered if there may 

be any alternative data sources for the 

measures impacted by these EQRS 

system issues, we concluded that this 

was not feasible for several reasons. 

First, all 14 ESRD QIP measures for PY 

2022 are impacted by these system 

issues. Although certain measures do 

not require that facilities submit clinical 

data into EQRS, we use EQRS data to 

determine whether a facility has treated 

a sufficient number of patients in order 

to meet the measure’s minimum patient 

case threshold necessary to calculate the 

measure for ESRD QIP. For example, the 

National Healthcare Safety Network 

(NHSN) Bloodstream Infection (BSI) 

clinical measure requires that facilities 

report data to NHSN. However, the 

measure also has a requirement to 

exclude facilities that do not treat at 

least 11 eligible in-center hemodialysis 

patients during the 12 month 

performance period. In order to 

determine whether a facility has treated 

at least 11 eligible patients, we use 

EQRS admission data and Medicare 

claims data in order to determine 

whether the facility is eligible to receive 

a score on the measure.138

We ultimately decided to propose the 

special rule for PY 2022, as described 

further, because not only do these 

system issues impact all ESRD QIP 

measures, which could lead to distorted 

performance scores and unfair penalties, 

but we also want to provide facilities 

with the business certainty they need 

regarding their PY 2022 payments. In 

order to determine whether all data 

quality issues have been resolved when 

EQRS reopens for data submissions, we 

will need time to validate the impacted 

data after facilities are able to resume 

data submission. Due to the timing of 

this reporting extension, we believe that 

there are no feasible alternative data 

sources for PY 2022. Therefore, we 

believe that the scoring and payment 

modifications for PY 2022 as proposed 

in section IV.D in this proposed rule are 

appropriate in this situation. 

C. Proposed Flexibilities for the ESRD 

QIP in Response to the COVID–19 PHE 

1. Proposal To Adopt a Measure 

Suppression Policy for the Duration of 

the COVID–19 PHE 

In previous rules, we have identified 

the need for flexibility in our quality 

measurement programs to account for 

changing conditions that are beyond 

participating facilities’ or practitioners’ 

control. We identified this need because 

we would like to ensure that 

participants in our programs are not 

affected negatively when their quality 

performance suffers for reasons not due 

to the care provided, but instead due to 

external factors. 

A significant example of the type of 

external factor that may affect quality 

measurement is the COVID–19 PHE, 

which has had, and continues to have, 

significant and ongoing effects on the 

provision of medical care in the country 

and around the world. The COVID–19 

pandemic and associated PHE have 

impeded effective quality measurement 

in many ways. Changes to clinical 

practices to accommodate safety 

protocols for medical personnel and 

patients, as well as unpredicted changes 

in the number of stays and facility-level 

case mixes, have affected the data used 

in quality measurement and the 

resulting quality scores. Measures used 

in the ESRD QIP need to be evaluated 

to determine whether their 

specifications need to be updated to 

account for new clinical guidelines, 

diagnosis or procedure codes, and 

medication changes that we have 

observed during the PHE. Additionally, 

because COVID–19 prevalence is not 

consistent across the country, dialysis 

facilities located in different areas have 

been affected differently at different 

times throughout the pandemic. Under 

those circumstances, we remain 

significantly concerned that the ESRD 

QIP’s quality measure scores that are 

calculated using data submitted during 

the PHE for COVID–19 will be distorted 

and will result in skewed payment 

incentives and inequitable payments, 

particularly for dialysis facilities that 

have treated more COVID–19 patients 

than others. 

It is not our intention to penalize 

dialysis facilities based on measure 


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scores that we believe are distorted by 

the COVID–19 pandemic and, thus, not 

reflective of the quality of care that the 

measures in the ESRD QIP were 

designed to assess. As discussed above, 

the COVID–19 pandemic has had, and 

continues to have, significant and 

enduring effects on health care systems 

around the world, and affects care 

decisions, including those made on 

clinical topics covered by the ESRD 

QIP’s measures. As a result of the 

COVID–19 PHE, dialysis facilities could 

provide care to their patients that meets 

the underlying clinical standard but 

results in worse measured performance, 

and by extension, payment penalties in 

the ESRD QIP. We are also concerned 

that regional differences in COVID–19 

prevalence during the performance 

period for PY 2022 have directly 

affected dialysis facilities’ measure 

scores on the ESRD QIP for PY 2022. 

Although these regional differences in 

COVID–19 prevalence rates do not 

reflect differences in the quality of care 

furnished by dialysis facilities, they 

could directly affect the payment 

penalties that these facilities could 

receive and could result in an unfair 

and inequitable distribution of those 

penalties. These inequities could be 

especially pronounced for dialysis 

facilities that have treated a large 

number of COVID–19 patients. 

We are therefore proposing to adopt a 

policy for the duration of the COVID–19 

PHE that would enable us to suppress 

the use of ESRD QIP measure data for 

all facilities if we determine that 

circumstances caused by the COVID–19 

PHE have affected those measures and 

the resulting total performance scores 

(TPSs) significantly. We are also 

proposing, as described in more detail 

in section IV.C.2. of this proposed rule, 

to suppress certain measures for the PY 

2022 program year because we have 

determined that circumstances caused 

by the COVID–19 PHE have affected 

those measures significantly. In 

addition, due to both the impacts of the 

COVID–19 PHE on certain measures and 

the EQRS system issues described in 

section IV.B.2. we are proposing to 

adopt a special scoring and payment 

rule for PY 2022, as described more 

fully in section IV.D. 

In developing this proposed policy, 

we considered what circumstances 

caused by the COVID–19 PHE would 

affect a quality measure significantly 

enough to warrant its suppression in a 

value-based purchasing (VBP) program. 

We believe that a significant deviation 

in measured performance that can be 

reasonably attributed to the COVID–19 

PHE is a significant indicator of changes 

in clinical conditions that affect quality 

measurement. Similarly, we believe that 

a measure may be focused on a clinical 

topic or subject that is proximal to the 

disease, pathogen, or other health 

impacts of the PHE. As has been the 

case during the COVID–19 pandemic, 

we believe that rapid or unprecedented 

changes in clinical guidelines and care 

delivery, potentially including 

appropriate treatments, drugs, or other 

protocols may affect quality 

measurement significantly and should 

not be attributed to the participating 

facility positively or negatively. We also 

note that scientific understanding of a 

particular disease or pathogen may 

evolve quickly during an emergency, 

especially in cases of new disease or 

conditions. Finally, we believe that, as 

evidenced during the COVID–19 

pandemic, national or regional shortages 

or changes in health care personnel, 

medical supplies, equipment, diagnostic 

tools, and patient case volumes or case 

mix may result in significant distortions 

to quality measurement. 

Based on these considerations, we 

developed a number of Measure 

Suppression Factors that we believe 

should guide our determination of 

whether to propose to suppress ESRD 

QIP measures for one or more payment 

years that overlap with the COVID–19 

PHE. We are proposing to adopt these 

Measure Suppression Factors for use in 

the ESRD QIP and, for consistency, the 

following other VBP programs: Hospital 

VBP, Hospital Readmissions Reduction 

Program, Hospital-Acquired Condition 

(HAC) Reduction Program, and Skilled 

Nursing Facility VBP Program (see, for 

example, 86 FR 25460 through 25462, 

25470 through 25472, and 25497 

through 25499). We believe that these 

Measure Suppression Factors will help 

us evaluate measures in the ESRD QIP 

and that their adoption in the other VBP 

programs noted above will help ensure 

consistency in our measure evaluations 

across programs. The proposed Measure 

Suppression Factors are: 

• Factor 1: Significant deviation in 

national performance on the measure 

during the COVID–19 PHE, which could 

be significantly better or significantly 

worse compared to historical 

performance during the immediately 

preceding program years. 

• Factor 2: Clinical proximity of the 

measure’s focus to the relevant disease, 

pathogen, or health impacts of the 

COVID–19 PHE. 

• Factor 3: Rapid or unprecedented 

changes in: 

++ Clinical guidelines, care delivery 

or practice, treatments, drugs, or related 

protocols, or equipment or diagnostic 

tools or materials; or 

++ the generally accepted scientific 

understanding of the nature or 

biological pathway of the disease or 

pathogen, particularly for a novel 

disease or pathogen of unknown origin. 

• Factor 4: Significant national 

shortages or rapid or unprecedented 

changes in: 

++ Healthcare personnel; 

++ medical supplies, equipment, or 

diagnostic tools or materials; or 

++ patient case volumes or facilitylevel case mix. 

We also considered alternatives to 

this proposed policy that could fulfill 

our objective to not penalize dialysis 

facilities for measure results that are 

distorted due to the COVID–19 PHE. As 

noted above, the country continues to 

grapple with the effects of the COVID– 

19 pandemic, and in March 2020, CMS 

issued a nationwide, blanket 

Extraordinary Circumstances Exception 

(ECE) for all hospitals and other 

facilities participating in our quality 

reporting and VBP programs in response 

to the COVID–19 PHE. This blanket ECE 

excepted all data reporting requirements 

for Q1 and Q2 2020 data, including 

claims data and data collected through 

the CDC’s web-based surveillance 

system for this data period, and quality 

data collection resumed on July 1, 2020. 

For claims-based measures, we also 

stated that we would exclude all 

qualifying Q1 and Q2 2020 claims from 

our measure calculations. We 

considered extending this blanket ECE 

that we issued for Q1 and Q2 2020 to 

also include Q3 and Q4 2020. This 

alternative would protect providers and 

suppliers from having their quality data 

used for quality scoring purposes while 

those data are likely to have been 

affected significantly by the COVID–19 

PHE. However, this option would make 

quality data collection and reporting to 

CMS no longer mandatory and would 

leave no comprehensive data available 

for us to provide confidential 

performance feedback to providers nor 

for monitoring and to inform decisionmaking for potential future 

programmatic changes, particularly as 

the PHE is extended. 

As an alternative to the proposed 

quality measure suppression policy, we 

also considered not suppressing any 

measures under the ESRD QIP. 

However, this alternative would mean 

assessing dialysis facilities using quality 

measure data that has been significantly 

affected by the COVID–19 pandemic. 

Additionally, given the geographic 

disparities in the COVID–19 pandemic’s 

effects, implementation of the PY 2022 

ESRD QIP as previously finalized would 

place dialysis facilities in regions that 

were more heavily impacted by the 


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pandemic in Q3 and Q4 of 2020 at a 

disadvantage compared to facilities in 

regions that were more heavily 

impacted during the first two quarters 

for CY 2020. 

We view this measure suppression 

proposal as a necessity to ensure that 

the ESRD QIP does not penalize 

facilities based on external factors that 

were beyond the control of facilities. We 

intend for this proposed policy to 

provide short-term relief to dialysis 

facilities when we have determined that 

one or more of the Measure Suppression 

Factors warrants the suppression of an 

ESRD QIP measure. 

We welcome public comments on this 

proposal for the adoption of a measure 

suppression policy for the duration of 

the COVID–19 PHE, and also on the 

proposed Measure Suppression Factors 

that we developed for purposes of this 

proposed policy. 

2. Proposals To Suppress Four ESRD 

QIP Measures for PY 2022 

a. Background 

In response to the PHE for the 

COVID–19 pandemic, we have 

conducted analyses of the fourteen 

current ESRD QIP measures to 

determine whether and how COVID–19 

may have impacted the validity of these 

measures. For the reasons discussed 

below, we have concluded that COVID– 

19 has so severely impacted the validity 

of four measures that we cannot fairly 

and equitably score these measures for 

the PY 2022 program year, and we are 

proposing to suppress these measures 

for the PY 2022 program year for all 

ESRD QIP participants. Specifically, the 

measures we are proposing to suppress 

for the PY 2022 ESRD QIP are as 

follows: 

• SHR clinical measure (under 

proposed Measure Suppression Factor 

1, Significant deviation in national 

performance on the measure during the 

COVID–19 PHE, which could be 

significantly better or significantly 

worse compared to historical 

performance during the immediately 

preceding program years; and proposed 

Measure Suppression Factor 4, 

Significant national shortages or rapid 

or unprecedented changes in: 

++ Healthcare personnel; 

++ medical supplies, equipment, or 

diagnostic tools or materials; or (iii) 

patient case volumes or facility-level 

case mix); 

• Standardized Readmission Ratio 

(SRR) clinical measure (under proposed 

Measure Suppression Factor 1, 

Significant deviation in national 

performance on the measure during the 

COVID–19 PHE, which could be 

significantly better or significantly 

worse compared to historical 

performance during the immediately 

preceding program years; and proposed 

Measure Suppression Factor 4, 

Significant national shortages or rapid 

or unprecedented changes in: 

++ Healthcare personnel; 

++ medical supplies, equipment, or 

diagnostic tools or materials; or 

++ patient case volumes or facilitylevel case mix); 

• In-Center Hemodialysis Consumer 

Assessment of Healthcare Providers and 

Systems (ICH CAHPS) Survey 

Administration clinical measure (under 

proposed Measure Suppression Factor 

1, Significant deviation in national 

performance on the measure during the 

COVID–19 PHE, which could be 

significantly better or significantly 

worse compared to historical 

performance during the immediately 

preceding program years); and 

• Long-Term Catheter Rate clinical 

measure (under proposed Measure 

Suppression Factor 1, Significant 

deviation in national performance on 

the measure during the COVID–19 PHE, 

which could be significantly better or 

significantly worse compared to 

historical performance during the 

immediately preceding program years). 

b. Proposal To Suppress the SHR 

Clinical Measure for PY 2022 

We are proposing to suppress the SHR 

clinical measure for the PY 2022 

program year under proposed Measure 

Suppression Factor 1, Significant 

deviation in national performance on 

the measure during the COVID–19 PHE, 

which could be significantly better or 

significantly worse as compared to 

historical performance during the 

immediately preceding program years. 

The SHR clinical measure is an allcause, risk-standardized rate of 

hospitalizations during a 1-year 

observation window. The standardized 

hospitalization ratio is defined as the 

ratio of the number of hospital 

admissions that occur for Medicare 

ESRD dialysis patients treated at a 

particular facility to the number of 

hospitalizations that would be expected 

given the characteristics of the dialysis 

facility’s patients and the national norm 

for dialysis facilities. This measure is 

calculated as a ratio but can also be 

expressed as a rate. The intent of the 

SHR clinical measure is to improve 

health care delivery and care 

coordination to help reduce unplanned 

hospitalization among ESRD patients. 

Based on our analysis of Medicare 

dialysis patient data from January 2020 

through August 2020, we found that 

hospitalizations involving patients 

diagnosed with COVID–19 resulted in 

higher mortality rates, higher rates of 

discharge to hospice or skilled nursing 

facilities, and lower rates of discharge to 

home than hospitalizations involving 

patients who are not diagnosed with 

COVID–19. Specifically, the 

hospitalization rate for Medicare 

dialysis patients diagnosed with 

COVID–19 was more than 7 times 

greater than the hospitalization rate 

during the same period for Medicare 

dialysis patients who were not 

diagnosed with COVID–19, which is 

much greater than the relative risk of 

hospitalization for any other 

comorbidity. This indicates that 

COVID–19 has had a significant impact 

on the hospitalization rate for dialysis 

patients. Because COVID–19 Medicare 

dialysis patients are at significantly 

greater risk of hospitalization, and the 

SHR clinical measure was not 

developed to account for the impact of 

COVID–19 on this patient population, 

we are concerned about the effects of 

the observed COVID–19 hospitalizations 

on the SHR clinical measure. We also 

note that COVID–19 affected different 

regions of the country at different rates 

depending on factors like time of year, 

geographic density, state and local 

policies, and health care system 

capacity. Because of the increased 

hospitalization risk associated with 

COVID–19 and the Medicare dialysis 

patient population, we are concerned 

that these regional differences in 

COVID–19 rates has led to distorted 

hospitalization rates such that we 

cannot reliably measure national 

performance on the SHR clinical 

measure. 

Our analysis of the available Medicare 

claims data indicates that the COVID–19 

PHE has had significant effects on 

hospital admissions of dialysis patients, 

and will result in significant deviation 

in national performance on the measure 

during the COVID–19 PHE which could 

be significantly worse as compared to 

historical performance during the 

immediately preceding program years. 

Not only are there effects on patients 

diagnosed with COVID–19, but the 

presence of the virus strongly affected 

hospital admission patterns of dialysis 

patients from March 2020 to June 2020, 

and we are concerned that similar 

effects will be seen in the balance of the 

calendar year (CY) as the PHE 

continued. Because the COVID–19 

pandemic swept through geographic 

regions of the country unevenly, we are 

concerned that dialysis facilities in 

different regions of the country would 

have been affected differently 

throughout the 2020 year, thereby 


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skewing measure performance and 

affecting national comparability due to 

significant and unprecedented changes 

in patient case volumes or facility-level 

case mix. Given the limitations of the 

data available to us for CY 2020, we 

believe the resulting performance 

measurement on the SHR clinical 

measure would not be sufficiently 

reliable or valid for use in the ESRD 

QIP. 

We are proposing to suppress this 

measure for the PY 2022 program year, 

rather than remove it, because we 

believe that the SHR clinical measure is 

an important part of the ESRD QIP 

measure set. However, we are concerned 

that the COVID–19 PHE affects measure 

performance on the current SHR clinical 

measure such that we would not be able 

to score facilities fairly or equitably on 

it. Additionally, we would continue to 

collect the measure’s claims data from 

participating facilities so that we can 

monitor the effect of the circumstances 

on quality measurement and determine 

the appropriate policies in the future. 

We would also continue to provide 

confidential feedback reports to 

facilities as part of program activities to 

ensure that they are made aware of the 

changes in performance rates that we 

observe. We also intend to publicly 

report PY 2022 data where feasible and 

appropriately caveated. 

We are currently exploring ways to 

adjust effectively for the systematic 

effects of the COVID–19 PHE on 

hospital admissions for the SHR clinical 

measure. However, we are still working 

to improve these COVID–19 adjustments 

and verify the validity of a potential 

modified version of the SHR clinical 

measure as additional data become 

available. As an alternative, we 

considered whether we could exclude 

patients with a diagnosis of COVID–19 

from the SHR clinical measure cohort, 

but we determined suppression will 

provide us with additional time and 

additional months of data potentially 

impacted by COVID–19 to more 

thoroughly evaluate a broader range of 

alternatives. We want to ensure that the 

measure reflects care provided to 

Medicare dialysis patients and we are 

concerned that excluding otherwise 

eligible patients may not accurately 

reflect the care provided, particularly 

given the unequal distribution of 

COVID–19 patients across facilities and 

hospitals over time. As an alternative 

approach, we also might consider 

updating the specifications for the SHR 

clinical measure to eliminate any 

exposure time and events after infection 

for patients who contract COVID–19, as 

COVID–19 symptoms may continue to 

affect patients after infection. We 

believe this approach might help 

distinguish between ESRD-related 

hospitalizations and COVID–19 related 

hospitalizations that might otherwise 

impact SHR clinical measure 

calculations. 

We welcome public comment on our 

proposal to suppress the SHR clinical 

measure for PY 2022. 

c. Proposal To Suppress the SRR 

Clinical Measure for PY 2022 

We are proposing to suppress the SRR 

clinical measure for the PY 2022 

program year under proposed Measure 

Suppression Factor 1, Significant 

deviation in national performance on 

the measure during the COVID–19 PHE, 

which could be significantly better or 

significantly worse compared to 

historical performance during the 

immediately preceding program years. 

The SRR assesses the number of 

readmission events for the patients at a 

facility, relative to the number of 

readmission events that would be 

expected based on overall national rates 

and the characteristics of the patients at 

that facility as well as the number of 

discharges. The intent of the SRR 

clinical measure has always been to 

improve care coordination between 

dialysis facilities and hospitals to 

improve communication prior to and 

post discharge. 

Based on our analysis, we found that 

index discharge hospitalizations 

involving dialysis patients diagnosed 

with COVID–19 resulted in lower 

readmissions and higher mortality rates 

within the first 7 days. We used index 

hospitalizations occurring from January 

1, 2020 through June 30, 2020 to 

identify eligible index hospitalizations 

and unplanned hospital readmissions. 

In an analysis of unadjusted 

readmission and death rates by COVID– 

19 hospitalization status and days since 

index discharge, during the first 4 to 7 

days after discharge there was a 

readmission rate of 81.3 percent of 

dialysis patients hospitalized with 

COVID–19, as compared to 82.6 percent 

of dialysis patients hospitalized without 

COVID–19. During that same 4 to 7 day 

time period, the unadjusted mortality 

rate for dialysis patients hospitalized 

with COVID–19 was 16.9 percent, 

compared with 10.9 percent of patients 

hospitalized without COVID–19. Based 

on this discrepancy, we are concerned 

about the effects of these observations 

on the calculations for the SRR clinical 

measure. The denominator of SRR 

reflects the expected number of index 

discharges followed by an unplanned 

readmission within 4 to 30 days in each 

facility, which is derived from a model 

that accounts for patient characteristics, 

the dialysis facility to which the patient 

is discharged, and the discharging acute 

care or critical access hospitals 

involved. Our analysis indicates 

potential competing risks of higher 

mortality and lower readmissions due to 

patient death or discharge to hospice, 

both of which would remove them from 

the denominator for the SRR clinical 

measure. If readmissions rates are lower 

because patient mortality is higher due 

to the impact of COVID–19 on dialysis 

patients, then readmission rates are 

distorted by appearing significantly 

better compared to historical 

performance during the immediately 

preceding program years. Based on the 

impact of COVID–19 on SRR results, 

including the deviance in measurement, 

we concluded that the SRR clinical 

measure meets our criteria for Factor 1 

where performance data would 

significantly deviate from historical data 

performance and would be considered 

unreliable. Therefore, we believe the 

resulting performance measurement on 

the SRR clinical measure would not be 

sufficiently reliable or valid for use in 

the ESRD QIP. 

We are proposing to suppress this 

measure for the PY 2022 program year, 

rather than remove it, because we 

believe that the SRR clinical measure is 

an important part of the ESRD QIP 

Program measure set. However, we are 

concerned that the PHE for the COVID– 

19 pandemic affects measure 

performance on the current SRR clinical 

measure such that we will not be able 

to score facilities fairly or equitably on 

it. Additionally, we would continue to 

collect the measure’s claims data from 

participating facilities so that we can 

monitor the effect of the circumstances 

on quality measurement and determine 

the appropriate policies in the future. 

We would also continue to provide 

confidential feedback reports to 

facilities as part of program activities to 

ensure that they are made aware of the 

changes in performance rates that we 

observe. We also intend to publicly 

report PY 2022 data where feasible and 

appropriately caveated. 

We are currently exploring ways to 

adjust effectively for the systematic 

effects of the COVID–19 PHE on 

hospital admissions for the SRR clinical 

measure. However, we are still working 

to improve these COVID–19 adjustments 

and verify the validity of a potential 

modified version of the SRR clinical 

measure as additional data becomes 

available. As an alternative approach, 

we might also consider eliminating from 

the calculation of the SRR clinical 

measure any cases of patients who had 

a COVID–19 event prior to or at the time 

of index hospitalization. We believe this 


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139 Groupings of questions and composite 

measures can be found at https://ichcahps.org/ 

Portals/0/ICH_Composites_English.pdf. 

approach might help distinguish 

between ESRD-related readmissions and 

COVID–19 related readmissions that 

might otherwise impact SRR clinical 

measure calculations. 

We welcome public comment on our 

proposal to suppress the SRR clinical 

measure for PY 2022. 

d. Proposal To Suppress the ICH CAHPS 

Clinical Measure for PY 2022 

We are proposing to suppress the ICH 

CAHPS clinical measure for the PY 2022 

program year under proposed Measure 

Suppression Factor 1, Significant 

deviation in national performance on 

the measure during the COVID–19 PHE, 

which could be significantly better or 

significantly worse compared to 

historical performance during the 

immediately preceding program years. 

Based on our analysis of CY 2020 ICH 

CAHPS data, we have found a 

significant decrease in response scores 

as compared to previous years. 

The ICH CAHPS clinical measure is 

scored based on three composite 

measures and three global ratings.139

Global ratings questions employ a scale 

of 0 to 10, worst to best; each of the 

questions within a composite measure 

use either ‘‘Yes’’ or ‘‘No’’ responses, or 

response categories ranging from 

‘‘Never’’ to ‘‘Always’’ to assess the 

patient’s experience of care at a facility. 

Facility performance on each composite 

measure is determined by the percent of 

patients who choose ‘‘top-box’’ 

responses (that is, most positive or 

‘‘Always’’) to the ICH CAHPS survey 

questions in each domain. The ICH 

CAHPS survey is administered twice 

yearly, once in the spring and once in 

the fall. 

Because of the ECE we granted in 

response to the COVID–19 PHE, 

facilities were not required to submit CY 

2020 spring ICH CAHPS data for 

purposes of the ESRD QIP. On 

September 2, 2020, we published an 

interim final rule with comment (IFC) in 

the Federal Register titled, ‘‘Medicare 

and Medicaid Programs, Clinical 

Laboratory Improvement Amendments 

(CLIA), and Patient Protection and 

Affordable Care Act; Additional Policy 

and Regulatory Revisions in Response to 

the COVID–19 Public Health 

Emergency’’ (85 FR 54820) referred to 

herein as the ‘‘September 2020 IFC’’. In 

the September 2020 IFC, we noted that 

we would not use any first or second 

quarter CY 2020 data to calculate TPSs 

for the applicable performance period 

(85 FR 54829 through 54830). Because 

the PY 2022 performance period for the 

ICH CAHPS measure is January 1, 2020 

through December 31, 2020, and the ICH 

CAHPS survey is administered twice a 

year (once in the spring and once in the 

fall), we only have data available from 

the fall CY 2020 survey to calculate 

facility performance on this measure. 

Therefore, facilities would only be 

scored on data based on one ICH 

CAHPS survey administration for CY 

2020, rather than two. Even if we were 

to score facilities based on the one ICH 

CAHPS survey administered in the fall, 

our preliminary data indicates that 95 

percent of facilities would not be 

eligible for scoring on ICH CAHPS for 

CY 2020. By contrast, 58.9 percent of 

facilities were not eligible for ICH 

CAHPS during CY 2018. If we were to 

score the 5 percent of eligible facilities 

on ICH CAHPS, we believe there would 

be a significant deviation in national 

performance on this measure compared 

to the national performance based on 

41.1 percent of facilities eligible for 

scoring on ICH CAHPS during 2018. 

This is a significant deviation in 

national performance on this measure 

compared to historical performance 

during the immediately preceding 

program years. Given this significant 

deviation in national performance 

during the PHE, we believe the ICH 

CAHPS clinical measure meets the 

criteria for Measure Suppression Factor 

1. 

We also believe that this significant 

change in performance may unfairly 

penalize facilities and that suppressing 

this measure for the PY 2022 program 

year will address concerns about the 

potential unintended consequences of 

penalizing facilities that treat COVID–19 

diagnosed patients in the ESRD QIP. As 

alternative approaches, we considered 

changing the performance period or 

scoring facilities on one survey 

administration, but otherwise meeting 

the 30 completed surveys requirement. 

However, we found that neither of these 

approaches were feasible; extending the 

performance period would not 

accurately reflect ICH CAHPS 

performance during CY 2020, and as 

discussed above, an estimated 95 

percent of facilities would not be 

eligible for ICH CAHPS scoring on one 

survey. Therefore, to avoid unfairly 

penalizing facilities due to their 

performance on the ICH CAHPS survey 

for the PY 2022 ESRD QIP, we believe 

it is appropriate to suppress the ICH 

CAHPS measure for CY 2020, which is 

the performance period for the PY 2022 

ESRD QIP program year (83 FR 57010). 

We are proposing to suppress this 

measure for the PY 2022 program year, 

rather than remove it, because we 

believe that the ICH CAHPS measure is 

an important part of the ESRD QIP 

measure set. However, we are concerned 

that the COVID–19 PHE affects measure 

performance on the current ICH CAHPS 

measure such that we will not be able 

to score facilities fairly or equitably on 

it. Additionally, participating facilities 

would continue to report the measure’s 

data to CMS so that we can monitor the 

effect of the circumstances on quality 

measurement and determine the 

appropriate policies in the future. We 

would also continue to provide 

confidential feedback reports to 

facilities as part of program activities to 

ensure that they are made aware of the 

changes in performance rates that we 

observe. We also intend to publicly 

report PY 2022 data where feasible and 

appropriately caveated. 

We welcome public comment on our 

proposal to suppress the ICH CAHPS 

measure for the PY 2022 program year. 

e. Proposal To Suppress Long-Term 

Catheter Rate Clinical Measure for PY 

2022 

Under the measure suppression 

policy discussion in section IV.C.1 of 

this proposed rule, we are proposing to 

suppress the Long-Term Catheter Rate 

clinical measure for the PY 2022 

program year under proposed Measure 

Suppression Factor 1, Significant 

deviation in national performance on 

the measure during the COVID–19 PHE, 

which could be significantly better or 

significantly worse compared to 

historical performance during the 

immediately preceding program years. 

Based on our analysis of Long-Term 

Catheter Rate clinical measure data 

during CY 2020, we have found a 

significant increase in long-term 

catheter use as compared to previous 

years, which may be the result of 

hesitancy to seek medical treatment 

among dialysis patients concerned 

about being exposed to COVID–19 

during the PHE. 

In the CY 2018 ESRD PPS final rule, 

we finalized the inclusion of the 

Hemodialysis Vascular Access: LongTerm Catheter Rate clinical measure in 

the ESRD QIP measure set beginning 

with the PY 2021 program (82 FR 

50778). The Long-Term Catheter Rate 

clinical measure is defined as the 

percentage of adult hemodialysis 

patient-months using a catheter 

continuously for three months or longer 

for vascular access. The measure is 

based on vascular access data reported 

in the Consolidated Renal Operations in 

a Web-enabled Network (CROWNWeb) 

and excludes patient-months where a 

patient has a catheter in place and has 

a limited life expectancy. 


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140https://www.cms.gov/files/document/esrdmeasures-manual-v61.pdf. 

Our analysis based on the available 

data indicates that long-term catheter 

use rates have increased significantly 

during the COVID–19 PHE. Average 

long-term catheter rates were averaging 

around 12 percent in CY 2017 and CY 

2018. In CY 2019, rates increased to 

average around 12.25 percent. This 

increase continued into CY 2020, with 

rates reaching a peak of 14.7 percent in 

June 2020 and declining slightly to 14.3 

percent in July and August 2020. After 

remaining around 12 percent for 3 

consecutive years, we view a sudden 2 

percent increase in average long-term 

catheter rates as a significant deviation 

compared to historical performance 

during immediately preceding years. We 

are concerned that the COVID–PHE 

impacted the ability of ESRD patients to 

seek treatment from medical providers 

regarding their catheter use, either due 

to difficulty accessing treatment due to 

COVID–19 precautions at healthcare 

facilities, or due to increased patient 

reluctance to seek medical treatment 

because of risk of COVID–19 exposure 

and increased health risks resulting 

therefrom, and that these contributed to 

the significant increase in long-term 

catheter use rates. 

We are proposing to suppress this 

measure for the PY 2022 program year, 

rather than remove it, because we 

believe that the Long-Term Catheter 

Rate clinical measure is an important 

part of the ESRD QIP measure set. 

However, we are concerned that the 

PHE for COVID–19 affects measure 

performance on the current Long-Term 

Catheter Rate clinical measure such that 

we will not be able to score facilities 

fairly or equitably on it. Additionally, 

participating facilities would continue 

to report the measure’s data to CMS so 

that we can monitor the effect of the 

circumstances on quality measurement 

and determine the appropriate policies 

in the future. We would also continue 

to provide confidential feedback reports 

to facilities as part of program activities 

to ensure that they are made aware of 

the changes in performance rates that 

we observe. We also intend to publicly 

report PY 2022 data where feasible and 

appropriately caveated. 

We welcome public comment on our 

proposal to suppress the Long-Term 

Catheter Rate clinical measure for the 

PY 2022 program year. 

D. Proposed Special Scoring 

Methodology and Payment Policy for the 

PY 2022 ESRD QIP 

As described in section IV.B.2 of this 

proposed rule, we have considered the 

impact of operational systems issues 

preventing facilities from submitting 

September through December 2020 

patient and clinical data into the EQRS 

from November 1, 2020 through on or 

about July 12, 2021. Even when 

facilities are able to submit the 

September through December 2020 

patient and clinical data by September 

1, 2021, we will need time to validate 

the quality and reliability of the 

impacted data in order to determine 

whether all data quality issues have 

been resolved. In addition, as described 

in section IV.C. we believe four of the 

ESRD QIP measures have been impacted 

by the COVID–19 PHE that could result 

in distorted measure performance for PY 

2022. 

It is not our intention to penalize 

dialysis facilities based on the 

performance on data that are not 

reliable, thus, not reflective of the 

quality of care that the measures in the 

program are designed to assess. 

Therefore, we are proposing a special 

rule for PY 2022 scoring for the ESRD 

QIP under which we would calculate 

measure rates for all measures, but 

would not calculate achievement and 

improvement points for any of them 

because they have all been impacted by 

the operational systems issues and, as 

proposed above, we believe that four of 

them have additionally been 

significantly impacted by COVID. 

Because we would not calculate 

achievement and improvement scores 

for any measures, we are also proposing 

under this special rule that we would 

not score any of the measures in the four 

domains or calculate or award Total 

Performance Scores for any facility. We 

are also proposing to not apply any 

payment reductions to ESRD facilities 

for PY 2022. 

In order to ensure that a facility is 

aware of any changes to its measure 

rates that we have observed, we are 

proposing to provide confidential 

feedback reports that contain the 

measure rates we calculated for PY 

2022. Performance scores for facilities 

would be released on Dialysis Facility 

Compare and footnoted to indicate 

potential accuracy concerns with the 

scores. Performance score certificates 

would be generated with the TPS 

showing as ‘‘Not Applicable.’’ 

We propose to codify these policies 

for PY 2022 at 42 CFR 413.177(a) and 

§ 413.178(h). 

However if the policies in sections 

IV.C and IV.D of this proposed rule are 

not finalized, the PY 2022 ESRD QIP 

payment would be as implemented in 

accordance with our current policy, as 

well as the payment reduction ranges 

finalized in the CY 2020 ESRD PPS final 

rule (84 FR 60725 through 60727). 

We invite public comment on this 

proposed special scoring and payment 

policy for the PY 2022 ESRD QIP. 

E. Proposed Updates to Requirements 

Beginning With the PY 2024 ESRD QIP 

1. PY 2024 ESRD QIP Measure Set 

Under our current policy, we retain 

all ESRD QIP measures from year to year 

unless we propose through rulemaking 

to remove them or otherwise provide 

notification of immediate removal if a 

measure raises potential safety issues 

(77 FR 67475). Accordingly, the PY 

2024 ESRD QIP measure set will include 

the same 14 measures as the PY 2023 

ESRD QIP measure set (85 FR 71465 

through 71466). These measures are 

described in Table 2. For the most 

recent information on each measure’s 

technical specifications for PY 2024, we 

refer readers to the CMS ESRD Measures 

Manual for the 2021 Performance 

Period.140

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We discuss our proposal to update the 

SHR clinical measure in the following 

section. 

a. Proposal To Update the Standardized 

Hospitalization Ratio (SHR) Clinical 

Measure Beginning With the PY 2024 

ESRD QIP 

In the CY 2017 ESRD PPS final rule, 

we adopted the SHR clinical measure 

under the authority of section 

1881(h)(2)(B)(ii) of the Act (81 FR 77906 

through 77911). The SHR clinical 

measure is a National Quality Forum 

(NQF)-endorsed all-cause, riskstandardized rate of hospitalizations 

during a 1-year observation window. 

The standardized hospitalization ratio is 

defined as the ratio of the number of 

hospital admissions that occur for 

Medicare ESRD dialysis patients treated 

at a particular facility to the number of 

hospitalizations that would be expected 


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TABLE 2: PY 2024 ESRD QIP Measure Set 

National Measure Title and Description 

Quality 

Forum 

(NQF)# 

0258 In-Center Hemodialysis Consumer Assessment of Healthcare Providers and Systems (ICH CARPS) Survey 

Administration, a clinical measure 

Measure assesses patients' self-reported experience of care through percentage of patient responses to 

multiple testing tools. 

2496 Standardized Readmission Ratio (SRR), a clinical measure 

Ratio of the number of observed unplanned 30-day hospital readmissions to the number of expected 

unplanned 30-day readmissions. 

Based on Standardized Transfusion Ratio (STrR), a reporting measure 

NQF Ratio of the number of observed eligible red blood cell transfusion events occurring in patients dialyzing at 

#2979 a facility to the number of eligible transfusions that would be expected. 

NIA (Kt/V) Dialysis Adequacy Comprehensive, a clinical measure 

A measure of dialysis adequacy where K is dialyzer clearance, t is dialysis time, and V is total body water 

volume. Percentage of all patient months for patients whose delivered dose of dialysis ( either hemodialysis 

or peritoneal dialysis) met the specified threshold during the reporting period. 

2977 Hemodialysis Vascular Access: Standardized Fistula Rate clinical measure 

Measures the use of an arteriovenous (AV) fistula as the sole means of vascular access as of the last 

hemodialysis treatment session of the month. 

2978 Hemodialysis Vascular Access: Long-Term Catheter Rate clinical measure 

Measures the use of a catheter continuously for 3 months or longer as of the last hemodialysis treatment 

session of the month. 

1454 Hypercalcemia, a clinical measure 

Proportion of patient-months with 3-month rolling average of total uncorrected serum or plasma calcium 

greater than 10.2 mgldL. 

1463 Standardized Hospitalization Ratio (SHR), a clinical measure 

Risk-adjusted SHR of the number of observed hospitalizations to the number of expected hospitalizations. 

Based on Clinical Depression Screening and Follow-Up, a reporting measure 

NQF Facility reports in End Stage Renal Disease Quality Reporting System (EQRS) one of six conditions for 

#0418 each qualifying patient treated during performance period. 

NIA Ultrafiltration Rate (UFR), a reporting measure 

Number of patient-months for which a facility reports elements required for ultrafiltration rates for each 

qualifying patient. 

Based on National Healthcare Safety Network (NHSN) Bloodstream Infection (BSI) in Hemodialysis Patients, a 

NQF clinical measure 

#1460 The Standardized Infection Ratio (SIR) ofBSis will be calculated among patients receiving hemodialysis at 

outpatient hemodialysis centers. 

NIA NHSN Dialysis Event reporting measure 

Number of months for which facility reports NHSN Dialysis Event data to the Centers for Disease Control 

and Prevention (CDC). 

NIA Percentage of Prevalent Patients Waitlisted (PPPW), a clinical measure 

Percentage of patients at each dialysis facility who were on the kidney or kidney-pancreas transplant 

waitlist averaged across patients prevalent on the last day of each month during the performance period. 

2988 Medication Reconciliation for Patients Receiving Care at Dialysis Facilities (MedRec ), a reporting measure 

Percentage of patient-months for which medication reconciliation was performed and documented by an 

eligible professional. 
36356 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

141United States Renal Data System. 2018 United 

States Renal Data System annual data report: 

Epidemiology of kidney disease in the United 

States. National Institutes of Health, National 

Institute of Diabetes and Digestive and Kidney 

Diseases, Bethesda, MD 2018. 

142 Ibid. 

143Office of the Assistant Secretary for Planning 

and Evaluation, U.S. Department of Health & 

Human Services. Advancing American Kidney 

Health. 2019. Available at: https://aspe.hhs.gov/ 

system/files/pdf/262046/ 

AdvancingAmericanKidneyHealth.pdf. 

144National Quality Forum. List of Measures 

Under Consideration for December 21, 2020. 

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

measures-under-consideration-list-2020-report.pdf 

on January 29 2021. 

145Measure Applications Partnership. Measure 

Applications Partnership Preliminary 

Recommendations 2020–2021. Accessed on January 

24, 2021 at: http://www.qualityforum.org/Project_

Pages/MAP_Hospital_Workgroup.aspx. 

146Measure Applications Partnership. Measure 

Applications Partnership 2020–2021: 

Considerations for Implementing Measures in 

Federal Programs: Clinician, Hospital & PAC/LTC. 

Accessed on April 28, 2021 at: https://

www.qualityforum.org/WorkArea/ 

linkit.aspx?LinkIdentifier=id&ItemID=94893. 

given the characteristics of the dialysis 

facility’s patients and the national norm 

for dialysis facilities. This measure is 

calculated as a ratio but can also be 

expressed as a rate. 

Hospitalizations are an important 

indicator of patient morbidity and 

quality of life. On average, dialysis 

patients are admitted to the hospital 

nearly twice a year and spend an 

average of 11.2 days in the hospital per 

year.141 Hospitalizations account for 

approximately 33 percent of total 

Medicare expenditures for ESRD 

patients.142 Studies have shown that 

improved health care delivery and care 

coordination may help reduce 

unplanned acute care including 

hospitalization.143 Hospitalization rates 

vary across dialysis facilities even after 

adjustment for patient characteristics, 

suggesting that hospitalizations might 

be influenced by dialysis facility 

practices. An adjusted facility-level 

standardized hospitalization ratio, 

accounting for differences in patients’ 

characteristics, plays an important role 

in identifying potential problems, and 

helps facilities provide cost-effective 

quality health care to help limit 

escalating medical costs. 

In the CY 2017 ESRD PPS final rule, 

we finalized our proposal to adopt the 

SHR clinical measure, which was a 

modified version of the NQF-endorsed 

SHR clinical measure (NQF #1463), as 

part of the ESRD QIP measure set (81 FR 

77911). In that final rule, we stated that 

our modified SHR clinical measure 

would incorporate 210 prevalent 

comorbidities into our risk adjustment 

calculation, as our analyses suggested 

that incorporating prevalent 

comorbidities would result in a more 

robust and reliable measure of 

hospitalization (81 FR 77906 through 

77907). In that final rule, we explained 

that data used to calculate the SHR 

clinical measure are derived from an 

extensive national ESRD patient 

database (81 FR 77908). We noted that 

the database is comprehensive for 

Medicare Parts A and B patients, and 

that non-Medicare patients are included 

in all sources except for the Medicare 

payment records. In that final rule, we 

also stated that the Standard 

Information Management System/ 

CROWNWeb provides tracking by 

dialysis provider and treatment 

modality for non-Medicare patients, and 

information on hospitalizations and 

patient comorbidities are obtained from 

Medicare Inpatient Claims Standard 

Analysis Files. In the CY 2019 ESRD 

PPS final rule, we increased the weight 

of the SHR clinical measure from 8.25 

percent to 14 percent of the TPS (83 FR 

56992 through 56997). 

On November 20, 2020, NQF 

completed its most recent review of the 

SHR clinical measure, a measure 

maintenance review, and renewed the 

measure’s endorsement. As part of this 

review, the NQF endorsed updating the 

prevalent comorbidity adjustment, 

which would group 210 individual ICD– 

9–CM prevalent comorbidities into 90 

condition groups, derived from the 

Agency for Healthcare Research and 

Quality (AHRQ) Clinical Classifications 

Software (CCS) groups. The updated 

prevalent comorbidity adjustment 

would also limit the source of prevalent 

comorbidities to inpatient claims. The 

switch to using only Medicare inpatient 

claims to identify prevalent 

comorbidities is due to the lack of 

Medicare outpatient claims data for the 

growing Medicare Advantage (MA) 

patient population. By using the original 

set of Medicare claims datasets 

(inpatient, outpatient, hospice, skilled 

nursing, and home health), the NQF 

stated its concern that MA patient 

prevalent comorbidities would be 

systematically biased. These MA patient 

prevalent comorbidities would only be 

populated by Medicare inpatient claims, 

as compared to non-MA patient 

prevalent comorbidities that would be 

populated by the aforementioned set of 

Medicare claim sources. The updated 

NQF-endorsed SHR clinical measure 

would also include all time at risk for 

MA patients, and added a MA indicator 

for adjustment in the model. The NQFendorsed specifications also included 

updates to parameterization of existing 

adjustment factors and re-evaluation of 

interactions, and also created three 

distinct groups of patients to use in the 

SHR model based on time spent in a 

skilled nursing facility, noting that 

nursing home residence is a marker of 

higher morbidity. 

The updated SHR clinical measure 

was included on the publicly available 

‘‘List of Measures under Consideration 

for December 21, 2020’’ (MUC List), a 

list of measures under consideration for 

use in various Medicare programs.144

When the Measure Applications 

Partnership Hospital Workgroup 

convened on January 11, 2021, it 

reviewed the MUC List, including the 

SHR clinical measure. The Measure 

Applications Partnership Hospital 

Workgroup recognized that 

hospitalization rates vary across dialysis 

facilities, even after adjusting for patient 

characteristics, which suggests that 

hospitalizations might be influenced by 

dialysis facility practices. The Measure 

Applications Partnership Hospital 

Workgroup also noted that the SHR 

clinical measure seeks to improve 

patient outcomes by measuring 

hospitalization ratios among dialysis 

facilities, and that the measure seeks to 

promote communication between the 

dialysis facilities and other care settings 

to improve care transitions.145 In its 

final report, the Measure Applications 

Partnership supported this measure for 

rulemaking.146

In this proposed rule, we are 

proposing to update the SHR clinical 

measure specifications to align with the 

NQF-endorsed updates. These include 

updates to the risk adjustment method 

of the measure, which include a 

prevalent comorbidity adjustment, the 

addition of MA patients and a MA 

indicator in the model, updates to 

parameterization of existing adjustment 

factors and re-evaluation of interactions, 

and an indicator for a patient’s time 

spent in a skilled nursing facility. 

We believe that adopting these 

updates would be consistent with our 

stated goal of evaluating opportunities 

to more closely align ESRD QIP 

measures with NQF measure 

specifications (84 FR 60724). The SHR 

clinical measure seeks to improve 

patient outcomes by measuring 

hospitalization ratios among dialysis 

facilities, and we believe that these 

updates would result in a more reliable 

and robust SHR clinical measure. 

We seek comment on this proposal to 

update the SHR clinical measure 

specifications for use in the ESRD QIP 

beginning with PY 2024. 


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147We note that for most ESRD QIP measures, 

this partial year data would be measure data from 

July and August 2020. 

2. Performance Standards for the PY 

2024 ESRD QIP 

Section 1881(h)(4)(A) of the Act 

requires the Secretary to establish 

performance standards with respect to 

the measures selected for the ESRD QIP 

for a performance period with respect to 

a year. The performance standards must 

include levels of achievement and 

improvement, as required by section 

1881(h)(4)(B) of the Act, and must be 

established prior to the beginning of the 

performance period for the year 

involved, as required by section 

1881(h)(4)(C) of the Act. We refer 

readers to the CY 2013 ESRD PPS final 

rule (76 FR 70277) for a discussion of 

the achievement and improvement 

standards that we have established for 

clinical measures used in the ESRD QIP. 

We define the terms ‘‘achievement 

threshold,’’ ‘‘benchmark,’’ 

‘‘improvement threshold,’’ and 

‘‘performance standard’’ in our 

regulations at § 413.178(a)(1), (3), (7), 

and (12), respectively. 

a. Proposal To Update the Performance 

Standards Applicable to the PY 2024 

Clinical Measures 

Our current policy is to automatically 

adopt a performance and baseline 

period for each year that is 1 year 

advanced from those specified for the 

previous payment year (84 FR 60728). 

Under this policy, CY 2022 is currently 

the performance period and CY 2020 is 

the baseline period for the PY 2024 

ESRD QIP. However, under the 

nationwide ECE that we granted in 

response to the COVID–19 PHE, first 

and second quarter data for CY 2020 are 

excluded from scoring for purposes of 

the ESRD QIP. We are concerned that it 

will be difficult to assess levels of 

achievement and improvement if the 

performance standards are based on 

partial year data.147 Our preliminary 

analysis indicates that the effect of the 

excluded data would create higher 

performance standards for certain 

measures and lower performance 

standards for other measures, which 

may skew achievement and 

improvement thresholds for facilities 

and therefore may result in performance 

standards that do not accurately reflect 

levels of achievement and improvement. 

Our current policy substitutes the 

performance standard, achievement 

threshold, and/or benchmark for a 

measure for a performance year if final 

numerical values for the performance 

standard, achievement threshold, and/or 

benchmark are worse than the 

numerical values for that measure in the 

previous year of the ESRD QIP (82 FR 

50764). We adopted this policy because 

we believe that the ESRD QIP should 

not have lower performance standards 

than in previous years. However, our 

general policy provides flexibility to 

substitute the performance standard, 

achievement threshold and benchmark 

in appropriate cases (82 FR 50764). 

Although the lower performance 

standards would be substituted with 

those from the prior year, the higher 

performance standards would be used to 

set performance standards for certain 

measures, even though they would be 

based on partial year data. We are 

concerned that this may create 

performance standards for certain 

measures that would be difficult for 

facilities to attain with a full 12 months 

of data. 

Therefore, in this proposed rule, we 

are proposing to calculate the 

performance standards for PY 2024 

using CY 2019 data, which is the most 

recently available full calendar year of 

data we can use to calculate those 

standards. Due to the impact of CY 2020 

data that is excluded from the ESRD QIP 

for scoring purposes, we believe that 

using CY 2019 data for performance 

standard setting purposes is 

appropriate. Consistent with our 

established policy, we would continue 

to use the prior year’s numerical values 

for performance standard, achievement 

threshold, and benchmark if the most 

recent full CY’s final numerical values 

are worse. 

We welcome public comments on this 

proposal. 

b. Performance Standards for the PY 

2024 ESRD QIP if Proposal to Use CY 

2019 as the Baseline Period is Finalized 

Table 3 displays the achievement 

thresholds, 50th percentiles of the 

national performance, and benchmarks 

for the PY 2024 clinical measures, and 

we would use these standards if our 

proposal to use CY 2019 as the baseline 

period is finalized. 

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In addition, we have summarized in 

Table 4 existing requirements for 

successful reporting on reporting 

measures in the PY 2024 ESRD QIP. We 

are not making any proposals to change 

these standards as a result of the 

COVID–19 PHE. 


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TABLE 3: Performance Standards for the PY 2024 ESRD QIP Clinical Measures if 

P I t U roposa 0 se CY 2019 as th e B ase I' me P . d . er10 IS F' ma I' 1ze d 

Measure Achievement Median (50th Benchmark (90th 

Threshold (15th Percentile of Percentile of National 

Percentile of National Performance) 

National Performance) 

Performance) 

Vascular Access Type (VAT) - - I 

Standardized Fistula Rate 53.29% 64.36% 

-

76.77% 

Catheter Rate 18.35% 11.04% 4.69% 

Kt/V Comprehensive 94.33% 97.61% 99.42% 

Hypercalcemia 1.54% 0.49% 0.00%* 

Standardized Readmission Ratio 1.268* 0.998* 0.629* 

NHSNBSI 1.193 0.516 0* 

Standardized Hospitalization Ratio 1.230 0.971 0.691 

PPPW 8.12%* 16.73%* 33.90%* 

ICH CARPS: Nephrologists' 58.20% 67.90% 79.15% 

Communication and Caring 

ICH CARPS: Quality of Dialysis Center 54.64% 63.08% 72.66% 

Care and Operations 

ICH CARPS: Providing Information to 74.49% 81.09% 87.80% 

Patients 

ICH CARPS: Overall Rating of 49.33%* 62.22%* 76.57%* 

Nephrologists 

ICH CARPS: Overall Rating of Dialysis 50.02% 63.37% 78.30% 

Center Staff 

ICH CARPS: Overall Rating of the 54.51% 69.04% 83.72% 

Dialysis Facility 

Note: Values marked with an asterisk(*) are also the final performance standards for those measures for PY 

2023. In accordance with our longstanding policy, we are using those numerical values for those measures for 

PY 2024 because they are higher standards than the PY 2024 numerical values for those measures. 

Data sources: VAT measures: 2019 CROWNWeb; SRR, SHR: 2019 Medicare claims; Kt/V: 2019 CROWNWeb; 

Hypercalcemia: 2019 CROWNWeb; NHSN: 2019 CDC; ICH CARPS: CMS 2019; PPPW: 2019 CROWNWeb and 

2019 OPTN. 
Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 36359 

3. Eligibility Requirements for the PY 

2024 ESRD QIP 

Our current minimum eligibility 

requirements for scoring the ESRD QIP 

measures are described in Table 5. 


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TABLE 4: Requirements for Successful Reporting on the PY 2024 ESRD QIP Reporting 

Measures 

Measure Reporting Frequency Data Elements 

Ultra.filtration 4 data elements are reported for • In-Center Hemodialysis (ICHD) Kt/V Date 

every HD Kt/V session during • Post-Dialysis Weight 

the week of the monthly Kt/V • Pre-Dialysis Weight 

draw, and the number of • Delivered Minutes of BUN Hemodialysis 

sessions of dialysis is reported • Number of sessions of dialysis delivered by the 

monthly dialysis unit to the patient in the reporting 

Month 

Med.Rec Monthly • Date of the medication reconciliation. 

• Type of eligible professional who completed the 

medication reconciliation: 

o physician, 

o nurse, 

oARNP, 

oPA, 

o pharmacist, or 

o pharmacy technician personnel 

• Name of eligible professional 

Clinical 1 of 6 conditions reported • Screening for clinical depression is documented as 

Depression annually being positive and a follow-up plan is documented. 

Screening • Screening for clinical depression documented as 

and Follow- positive, a follow-up plan 

Up is not documented, and the facility possesses 

documentation that the patient is not 

eligible. 

• Screening for clinical depression documented as 

positive, the facility 

possesses no documentation of a follow-up plan, and no 

reason is given. 

• Screening for clinical depression documented as 

negative and no follow-up plan required. 

• Screening for clinical depression not documented, but 

the facility possesses 

documentation stating the patient is not eligible. 

• Clinical depression screening not documented, and no 

reason is given. 

NHSN Monthly Three types of dialysis events reported: 

Dialysis • IV antimicrobial start; 

Event • positive blood culture; and 

• pus, redness, or increased swelling at the vascular 

access site. 

STrR At least 10 patient-years at risk during the performance 

period. 
36360 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

4. Payment Reduction Scale for the PY 

2024 ESRD QIP 

Under our current policy, a facility 

will not receive a payment reduction for 

a payment year in connection with its 

performance for the ESRD QIP if it 

achieves a TPS that is at or above the 

minimum TPS (mTPS) that we establish 

for the payment year. We have defined 

the mTPS in our regulations at 

§ 413.178(a)(8) as, with respect to a 

payment year, the TPS that an ESRD 

facility would receive if, during the 

baseline period it performed at the 50th 

percentile of national performance on 

all clinical measures and the median of 

national ESRD facility performance on 

all reporting measures. 

Our current policy, which is codified 

at § 413.177 of our regulations, also 

implements the payment reductions on 

a sliding scale using ranges that reflect 

payment reduction differentials of 0.5 

percent for each 10 points that the 

facility’s TPS falls below the mTPS (76 

FR 634 through 635). 

For PY 2024, based on available data, 

a facility must meet or exceed a mTPS 

of 57 in order to avoid a payment 

reduction. We note that the mTPS in 

this proposed rule is based on data from 

CY 2019 instead of the PY 2024 baseline 

period (CY 2020) because we have 

proposed to use CY 2019 as the baseline 

period for that payment year. 

We refer readers to Table 3 for the 

estimated values of the 50th percentile 

of national performance for each clinical 

measure. Under our current policy, a 

facility that achieves a TPS of 56 or 

below would receive a payment 

reduction based on the TPS ranges 

indicated in Table 6. 


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TABLE 5: Eligibility Requirements for Scoring on ESRD QIP Measures 

Measure Minimum data requirements CCN open date Small facility adjuster 

Kt/V Comprehensive 11 qualifying patients NIA 11-25 qualifying patients 

(Clinical) 

VAT: Long-term 11 qualifying patients NIA 11-25 qualifying patients 

Catheter Rate (Clinical) 

VAT: Standardized 11 qualifying patients NIA 11-25 qualifying patients 

Fistula Rate (Clinical) 

Hypercalcemia 11 qualifying patients NIA 11-25 qualifying patients 

(Clinical) 

NHSN BSI (Clinical) 11 qualifying patients Before October 1 prior 11-25 qualifying patients 

to the performance 

period that applies to 

the program year. 

NHSN Dialysis Event 11 qualifying patients NIA NIA 

(Reporting) 

SRR (Clinical) 11 index discharges NIA 11-41 index discharges 

STrR (Reporting) 10 patient-years at risk NIA NIA 

SHR (Clinical) 5 patient-years at risk NIA 5-14 patient-years at risk 

ICH CARPS (Clinical) Facilities with 30 or more survey-eligible Before October 1 prior NIA 

patients during the calendar year to the performance 

preceding the performance period must period that applies to 

submit survey results. Facilities will not the program year. 

receive a score if they do not obtain a 

total of at least 30 completed surveys 

during the performance period 

Depression Screening 11 qualifying patients Before April 1 of the NIA 

and Follow-Up performance 

(Reporting) period that applies to 

the program year. 

Ultrafiltration 11 qualifying patients Before April 1 of the NIA 

(Reporting) performance 

period that applies to 

the program year. 

MedRec (Reporting) 11 qualifying patients Before October 1 prior NIA 

to the performance 

period that applies to 

the program year. 

PPPW (Clinical) 11 qualifying patients NIA 11-25 qualifying patients 
Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 36361 

BILLING CODE 4120–01–C 

If we do not finalize the proposed 

update to our performance standards 

policy as described in section IV.E.2.a of 

this proposed rule, then we would 

update the mTPS for PY 2024, as well 

as the payment reduction ranges for that 

payment year, in the CY 2022 ESRD PPS 

final rule using data from CY 2020. 

F. Updates for the PY 2025 ESRD QIP 

1. Continuing Measures for the PY 2025 

ESRD QIP 

Under our previously adopted policy, 

the PY 2024 ESRD QIP measure set will 

also be used for PY 2025. At this time, 

we are not proposing to adopt any new 

measures beginning with the PY 2025 

ESRD QIP. 

2. Performance Period for the PY 2025 

ESRD QIP 

We continue to believe that 12-month 

performance and baseline periods 

provide us sufficiently reliable quality 

measure data for the ESRD QIP. Under 

this policy, we would adopt CY 2023 as 

the performance period and CY 2021 as 

the baseline period for the PY 2025 

ESRD QIP. 

In this proposed rule, we are not 

proposing any changes to this policy. 

3. Performance Standards for the PY 

2025 ESRD QIP 

Section 1881(h)(4)(A) of the Act 

requires the Secretary to establish 

performance standards with respect to 

the measures selected for the ESRD QIP 

for a performance period with respect to 

a year. The performance standards must 

include levels of achievement and 

improvement, as required by section 

1881(h)(4)(B) of the Act, and must be 

established prior to the beginning of the 

performance period for the year 

involved, as required by section 

1881(h)(4)(C) of the Act. We refer 

readers to the CY 2012 ESRD PPS final 

rule (76 FR 70277) for a discussion of 

the achievement and improvement 

standards that we have established for 

clinical measures used in the ESRD QIP. 

We define the terms ‘‘achievement 

threshold,’’ ‘‘benchmark,’’ 

‘‘improvement threshold,’’ and 

‘‘performance standard’’ in our 

regulations at § 413.178(a)(1), (3), (7), 

and (12), respectively. In section 

IV.E.2.a of this proposed rule, we note 

that we are proposing to use CY 2019 

data for purposes of calculating the 

performance standards for PY 2024 

because, due to the anticipated impact 

of CY 2020 data that is excluded from 

the ESRD QIP for scoring purposes 

during CY 2020, we believe that using 

CY 2019 data for performance standard 

setting purposes would be appropriate. 

a. Performance Standards for Clinical 

Measures in the PY 2025 ESRD QIP 

At this time, we do not have the 

necessary data to assign numerical 

values to the achievement thresholds, 

benchmarks, and 50th percentiles of 

national performance for the clinical 

measures for the PY 2025 ESRD QIP 

because we do not have CY 2021 data. 

We intend to publish these numerical 

values, using CY 2021 data, in the CY 

2023 ESRD PPS final rule. 

b. Performance Standards for the 

Reporting Measures in the PY 2025 

ESRD QIP 

In the CY 2019 ESRD PPS final rule, 

we finalized the continued use of 

existing performance standards for the 

Screening for Clinical Depression and 

Follow-Up reporting measure, the 

Ultrafiltration Rate reporting measure, 

the NHSN Dialysis Event reporting 

measure, and the MedRec reporting 

measure (83 FR 57010 through 57011). 

We will continue use of these 

performance standards in PY 2025. 

4. Scoring the PY 2025 ESRD QIP 

a. Scoring Facility Performance on 

Clinical Measures 

In the CY 2014 ESRD PPS final rule, 

we finalized policies for scoring 

performance on clinical measures based 

on achievement and improvement (78 

FR 72215 through 72216). In the CY 

2019 ESRD PPS final rule, we finalized 

a policy to continue use of this 

methodology for future payment years 

(83 FR 57011) and we codified these 

scoring policies at § 413.178(e). 

In this proposed rule, we are not 

proposing any changes to this policy for 

PY 2025. 

b. Scoring Facility Performance on 

Reporting Measures 

Our policy for scoring performance on 

reporting measures is codified at 

§ 413.178(e), and more information on 

our scoring policy for reporting 

measures can be found in the CY 2020 

ESRD PPS final rule (84 FR 60728). We 

previously finalized policies for scoring 

performance on the NHSN Dialysis 

Event reporting measure in the CY 2018 

ESRD PPS final rule (82 FR 50780 

through 50781), as well as policies for 

scoring the MedRec reporting measure 

and Clinical Depression Screening and 

Follow-up reporting measure in the CY 

2019 ESRD PPS final rule (83 FR 57011). 

We also previously finalized the scoring 

policy for the STrR reporting measure in 

the CY 2020 ESRD PPS final rule (84 FR 

60721 through 60723). In the CY 2021 

ESRD PPS final rule, we finalized our 

updated scoring methodology for the 

Ultrafiltration Rate reporting measure 

(85 FR 71468 through 71470). 

In this proposed rule, we are not 

proposing any changes to this policy for 

PY 2025. 

5. Weighting the Measure Domains and 

the TPS for PY 2025 

Under our current policy, we assign 

the Patient & Family Engagement 


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TABLE 6 E : s t° Ima t d P e avmen t R d e UC t° IOU S ca I e fi or PY 2024 B d ase on CY 2019 Data 

Total uerformance score Reduction (%) 

100-57 0% 

56-47 0.5% 

46-37 1.0% 

36-27 1.5% 

26-0 2.0% 
36362 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

148United States Department of Health and 

Human Services. ‘‘Healthy People 2020: Disparities. 

2014.’’ Available at: https://www.healthy

people.gov/2020/about/foundation-healthmeasures/Disparities. 

149 Joynt KE, Orav E., Jha AK. Thirty-Day 

Readmission Rates for Medicare Beneficiaries by 

Race and Site of Care. JAMA. 2011;305(7):675–681. 

150Lindenauer PK, Lagu T., Rothberg MB, et al. 

Income Inequality and 30 Day Outcomes After 

Acute Myocardial Infarction, Heart Failure, and 

Pneumonia: Retrospective Cohort Study. British 

Medical Journal. 2013;346. 

151Trivedi AN, Nsa W., Hausmann LRM, et al. 

Quality and Equity of Care in U.S. Hospitals. New 

England Journal of Medicine. 2014;371(24):2298– 

2308. 

152Polyakova, M., et al. Racial Disparities In 

Excess All-Cause Mortality During The Early 

COVID–19 Pandemic Varied Substantially Across 

States. Health Affairs. 2021; 40(2): 307–316. 

153Rural Health Research Gateway. Rural 

Communities: Age, Income, and Health Status. 

Rural Health Research Recap. November 2018. 

154Polyakova, M., et al. Racial Disparities In 

Excess All-Cause Mortality During The Early 

COVID–19 Pandemic Varied Substantially Across 

States. Health Affairs. 2021; 40(2): 307–316. 

155www.cdc.gov/mmwr/volumes/70/wr/

mm7005a1.htm. 

156https://www.ncbi.nlm.nih.gov/pmc/articles/

PMC7386532/. 

157Martino, SC, Elliott, MN, Dembosky, JW, 

Hambarsoomian, K., Burkhart, Q., Klein, DJ, 

Gildner, J., and Haviland, AM. Racial, Ethnic, and 

Gender Disparities in Health Care in Medicare 

Advantage. Baltimore, MD: CMS Office of Minority 

Health. 2020. 

158 Guide to Reducing Disparities in 

Readmissions. CMS Office of Minority Health. 

Revised August 2018. Available at: https://

www.cms.gov/About-CMS/Agency-Information/

OMH/Downloads/OMH_Readmissions_Guide.pdf. 

159Singh JA, Lu X., Rosenthal GE, Ibrahim S., 

Cram P. Racial disparities in knee and hip total 

joint arthroplasty: An 18-year analysis of national 

Medicare data. Ann Rheum Dis. 2014 Dec;73(12):

2107–15. 

160Rivera-Hernandez M., Rahman M., Mor V., 

Trivedi AN. Racial Disparities in Readmission Rates 

among Patients Discharged to Skilled Nursing 

Facilities. J Am Geriatr Soc. 2019 Aug;67(8):1672– 

1679. 

161 Joynt KE, Orav E., Jha AK. Thirty-Day 

Readmission Rates for Medicare Beneficiaries by 

Race and Site of Care. JAMA. 2011;305(7):675–681. 

162Tsai TC, Orav EJ, Joynt KE. Disparities in 

surgical 30-day readmission rates for Medicare 

beneficiaries by race and site of care. Ann Surg. Jun 

2014;259(6):1086–1090. 

163Rodriguez F., Joynt KE, Lopez L., Saldana F., 

Jha AK. Readmission rates for Hispanic Medicare 

beneficiaries with heart failure and acute 

myocardial infarction. Am Heart J. Aug 2011;162(2):

254–261 e253. 

164Centers for Medicare and Medicaid Services. 

Medicare Hospital Quality Chartbook: Performance 

Report on Outcome Measures; 2014. 

165 Guide to Reducing Disparities in 

Readmissions. CMS Office of Minority Health. 

Revised August 2018. Available at: https://

www.cms.gov/About-CMS/Agency-Information/

OMH/Downloads/OMH_Readmissions_Guide.pdf. 

166Prieto-Centurion V., Gussin HA, Rolle AJ, 

Krishnan JA. Chronic obstructive pulmonary 

disease readmissions at minority-serving 

institutions. Ann Am Thorac Soc. Dec 

2013;10(6):680–684. 

167 Joynt KE, Orav E., Jha AK. Thirty-Day 

Readmission Rates for Medicare Beneficiaries by 

Race and Site of Care. JAMA. 2011;305(7):675–681. 

168https://www.cms.gov/About-CMS/AgencyInformation/OMH/Downloads/ESRDInfographic.pdf. 

169 Ibid. 

170HHS. Heart disease and African Americans. 

(March 29, 2021). https://

www.minorityhealth.hhs.gov/omh/browse.aspx?lvl=

4&lvlid=19. 

Measure Domain a weight of 15 percent 

of the TPS, the Care Coordination 

Measure Domain a weight of 30 percent 

of the TPS, the Clinical Care Measure 

Domain a weight of 40 percent of the 

TPS, and the Safety Measure domain a 

weight of 15 percent of the TPS. 

In the CY 2019 ESRD PPS final rule, 

we finalized a policy to assign weights 

to individual measures and a policy to 

redistribute the weight of unscored 

measures (83 FR 57011 through 57012). 

In the CY 2020 ESRD PPS final rule, we 

finalized a policy to use the measure 

weights we finalized for PY 2022 for the 

PY 2023 ESRD QIP and subsequent 

payment years, and also to use the PY 

2022 measure weight redistribution 

policy for the PY 2023 ESRD QIP and 

subsequent payment years (84 FR 60728 

through 60729). We are not proposing 

any updates to these policies for PY 

2025. 

G. Requests for Information (RFIs) on 

Topics Relevant to ESRD QIP 

1. Closing the Health Equity Gap in 

CMS Quality Programs Request for 

Information 

Persistent inequities in health care 

outcomes exist in the United States 

(U.S.), including among Medicare 

patients. In recognition of persistent 

health disparities and the importance of 

closing the health equity gap, we 

request information on expanding 

several related CMS programs to make 

reporting of health disparities based on 

social risk factors and race and 

ethnicity, and disability more 

comprehensive and actionable for 

dialysis facilities, providers, and 

patients. The following is part of an 

ongoing effort across CMS to evaluate 

appropriate initiatives to reduce health 

disparities. Feedback will be used to 

inform the creation of a future, 

comprehensive, request for information 

(RFI) focused on closing the health 

equity gap in CMS programs and 

policies. This RFI contains four parts: 

• Background. This section provides 

information on existing statements 

describing our commitment to health 

equity, and existing initiatives with an 

emphasis on reducing disparity. 

• Current CMS Disparity Methods. 

This section describes the methods, 

measures, and indicators of social risk 

currently used with the CMS Disparity 

Methods. 

• Future potential stratification of 

quality measure results. This section 

describes four potential future 

expansions of the CMS Disparity 

Methods, including (a) Future potential 

stratification of quality measure results 

by dual eligibility; (b) Future potential 

stratification of quality measure results 

by race and ethnicity; (c) Improving 

Demographic Data Collection; and (d) 

Potential Creation of an ESRD Facility 

Equity Score to Synthesize Results 

Across Multiple Social Risk Factors. 

• Solicitation of public comment. 

This section specifies 11 requests for 

feedback on the topics specified in this 

RFI. 

a. Background 

Significant and persistent inequities 

in health care outcomes exist in the 

U.S.148 Belonging to a racial or ethnic 

minority group, living with a disability, 

being a member of the lesbian, gay, 

bisexual, transgender, and queer 

(LGBTQ+) community, living in a rural 

area, or being near or below the poverty 

level, is often associated with worse 

health outcomes.149 150 151 152 153 154 155 156

Such disparities in health outcomes are 

the result of number of factors, but 

importantly for CMS programs, although 

not the sole determinant, poor access 

and provision of lower quality health 

care contribute to health disparities. For 

instance, numerous studies have shown 

that among Medicare beneficiaries, 

racial and ethnic minority individuals 

often receive lower quality of care, 

report lower experiences of care, and 

experience more frequent hospital 

readmissions and operative 

complications.157 158 159 160 161 162

Readmission rates for common 

conditions in the Hospital Readmissions 

Reduction Program are higher for Black 

Medicare beneficiaries and higher for 

Hispanic Medicare beneficiaries with 

Congestive Heart Failure and Acute 

Myocardial Infarction.163 164 165 166 167

Although Black Americans represent 7.5 

percent of all older adult Medicare 

beneficiaries, they represent 28 percent 

of those with ESRD.168 Among 

individuals with ESRD the odds of 30- 

day hospital readmission are 19 percent 

higher for Black beneficiaries as 

compared with white beneficiaries.169

Studies have also shown that African 

Americans are significantly more likely 

than white Americans to die 

prematurely from heart disease and 

stroke.170 The COVID–19 pandemic has 

further illustrated many of these 

longstanding health inequities with 

higher rates of infection, hospitalization, 

and mortality among Black, Latino, and 

Indigenous and Native American 


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Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 36363 

171https://www.cms.gov/files/document/

medicare-covid-19-data-snapshot-fact-sheet.pdf. 

172Ochieng N., Cubanski J., Neuman T., Artiga S., 

and Damico A. Racial and Ethnic Health Inequities 

and Medicare. Kaiser Family Foundation. February 

2021. Available at: https://www.kff.org/medicare/

report/racial-and-ethnic-health-inequities-andmedicare/. 

173Weinhandl ED, Wetmore, JB, Peng Y., et al. 

Initial effects of COVID–19 on patients with ESKD. 

J Am Soc Nephrol. Published online April 8, 

2021.doi:10.1681/ASN.2021010009. 

174https://www.cdc.gov/coronavirus/2019-ncov/

community/health-equity/race-ethnicity.html. 

175https://www.cms.gov/Medicare/QualityInitiatives-Patient-Assessment-Instruments/Quality

InitiativesGenInfo/Downloads/CMS-QualityStrategy.pdf. 

176https://www.federalregister.gov/documents/

2021/01/25/2021-01753/advancing-racial-equityand-support-for-underserved-communities-throughthe-federal-government. 

177Centers for Medicare and Medicaid Services 

Office of Minority Health. The CMS Equity Plan for 

Improving Quality in Medicare. 2015. https://

www.cms.gov/About-CMS/Agency-Information/ 

OMH/OMH_Dwnld-CMS_EquityPlanforMedicare_

090615.pdf. 

178Centers for Medicare and Medicaid Services 

Office of Minority Health. Paving The Way To 

Equity: A Progress Report. 2015–2021. https://

www.cms.gov/files/document/paving-way-equitycms-omh-progress-report.pdf. 

179Centers for Medicare Services. CMS Quality 

Strategy. 2016. https://www.cms.gov/Medicare/ 

Quality-Initiatives-Patient-Assessment-Instruments/ 

QualityInitiativesGenInfo/Downloads/CMS-QualityStrategy.pdf. 

180https://www.cms.gov/Medicare/QualityInitiatives-Patient-Assessment-Instruments/ 

QualityInitiativesGenInfo/MMF/General-info-SubPage. 

181https://www.cms.gov/About-CMS/AgencyInformation/OMH/OMH-Mapping-MedicareDisparities. 

182https://www.cms.gov/About-CMS/AgencyInformation/OMH/research-and-data/statistics-anddata/stratified-reporting. 

183Centers for Medicare & Medicaid Services. 

Rural-Urban Disparities in Health Care in Medicare. 

2019. https://www.cms.gov/About-CMS/AgencyInformation/OMH/Downloads/Rural-UrbanDisparities-in-Health-Care-in-Medicare-Report.pdf. 

184 Guide to Reducing Disparities in 

Readmissions. CMS Office of Minority Health. 

Revised August 2018. Available at: https://

www.cms.gov/About-CMS/Agency-Information/ 

OMH/Downloads/OMH_Readmissions_Guide.pdf. 

185CMS. Chronic Kidney Disease Disparities: 

Educational Guide for Primary Care. February 2020. 

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

chronic-kidney-disease-disparities-educationalguide-primary-care.pdf. 

persons relative to white persons.171 172

In the ESRD patient population, one 

study found that the rate of COVID–19 

hospitalizations among dialysis patients 

peaked at 40 times higher than the rate 

in the general population during the 

pandemic, with Black, Latino, and 

Asian persons hospitalized at a higher 

rate than white persons.173 As noted by 

the Centers for Disease Control ‘‘longstanding systemic health and social 

inequities have put many people from 

racial and ethnic minority groups at 

increased risk of getting sick and dying 

from COVID–19.’’ 174 One important 

strategy for addressing these important 

inequities is by improving data 

collection to allow for better 

measurement and reporting on equity 

across our programs and policies. 

We are committed to achieving equity 

in health care outcomes for our 

beneficiaries by supporting providers in 

quality improvement activities to reduce 

health inequities, enabling them to 

make more informed decisions, and 

promoting provider accountability for 

health care disparities.175 For the 

purposes of this rule, we are using a 

definition of equity established in 

Executive Order 13985, as ‘‘the 

consistent and systematic fair, just, and 

impartial treatment of all individuals, 

including individuals who belong to 

underserved communities that have 

been denied such treatment, such as 

Black, Latino, and Indigenous and 

Native American persons, Asian 

Americans and Pacific Islanders and 

other persons of color; members of 

religious minorities; lesbian, gay, 

bisexual, transgender, and queer 

(LGBTQ+) persons; persons with 

disabilities; persons who live in rural 

areas; and persons otherwise adversely 

affected by persistent poverty or 

inequality.’’ 176 We note that this 

definition was recently established by 

the Biden administration, and provides 

a useful, common definition for equity 

across different areas of government, 

although numerous other definitions of 

equity exist. 

Our ongoing commitment to closing 

the equity gap in CMS quality programs 

is demonstrated by a portfolio of 

programs aimed at making information 

on the quality of health care providers 

and services, including disparities, more 

transparent to consumers and providers. 

The CMS Equity Plan for Improving 

Quality in Medicare outlines a path to 

equity which aims to support Quality 

Improvement Networks and Quality 

Improvement Organizations (QIN– 

QIOs); federal, state, local, and tribal 

organizations; providers; researchers; 

policymakers; beneficiaries and their 

families; and other stakeholders in 

activities to achieve health equity.177

The CMS Equity Plan for Improving 

Quality in Medicare focuses on three 

core priority areas which inform our 

policies and programs: (1) Increasing 

understanding and awareness of 

disparities; (2) developing and 

disseminating solutions to achieve 

health equity; and (3) implementing 

sustainable actions to achieve health 

equity.178 The CMS Quality Strategy 179

and Meaningful Measures 

Framework 180 include elimination of 

racial and ethnic disparities as a central 

principle. Our efforts aimed at closing 

the health equity gap to date have 

included both providing transparency of 

health disparities, supporting providers 

with evidence-informed solutions to 

achieve health equity, and reporting to 

providers on gaps in quality in the 

following: 

• The CMS Mapping Medicare 

Disparities Tool which is an interactive 

map that identifies areas of disparities 

and is a starting point to understand and 

investigate geographic, racial and ethnic 

differences in health outcomes for 

Medicare patients.181

• The Racial, Ethnic, and Gender 

Disparities in Health Care in Medicare 

Advantage Stratified Report, which 

highlights racial and ethnic differences 

in health care experiences and clinical 

care, compares quality of care for 

women and men, and looks at racial and 

ethnic differences in quality of care 

among women and men separately for 

Medicare Advantage plans.182

• The Rural-Urban Disparities in 

Health Care in Medicare Report which 

details rural-urban differences in health 

care experiences and clinical care.183

• The Standardized Patient 

Assessment Data Elements for certain 

post-acute care Quality Reporting 

Programs, which now includes data 

reporting for race and ethnicity and 

preferred language, in addition to 

screening questions for social needs (84 

FR 42536 through 42588). 

• The CMS Innovation Center’s 

Accountable Health Communities 

Model which includes standardized 

collection of health-related social needs 

data. 

• The Guide to Reducing Disparities 

which provides an overview of key 

issues related to disparities in 

readmissions and reviews set of 

activities that can help hospital leaders 

reduce readmissions in diverse 

populations.184

• The Chronic Kidney Disease 

Disparities: Educational Guide for 

Primary Care, which is intended to 

foster the development of primary care 

practice teams in order to enhance care 

for vulnerable patients with chronic 

kidney disease (CKD) and are at risk of 

progression of disease or complications. 

The guide provides information about 

disparities in the care of patients with 

CKD, presents potential actions that 

may improve care and suggests other 

available resources that may be used by 

primary care practice teams in caring for 

vulnerable patients.185

• The CMS Disparity Methods which 

provide hospital-level confidential 

results stratified by dual eligibility for 

condition-specific readmission 


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186National Academies of Sciences, Engineering, 

and Medicine. 2016. Accounting for Social Risk 

Factors in Medicare Payment: Identifying Social 

Risk Factors. Washington, DC: The National 

Academies Press. https://doi.org/10.17226/21858. 

187https://aspe.hhs.gov/pdf-report/reportcongress-social-risk-factors-and-performanceunder-medicares-value-based-purchasingprograms. 

188https://aspe.hhs.gov/pdf-report/reportcongress-social-risk-factors-and-performanceunder-medicares-value-based-purchasingprograms. 

189Centers for Medicare Services. CMS Quality 

Strategy. 2016. https://www.cms.gov/Medicare/ 

Quality-Initiatives-Patient-Assessment-Instruments/ 

QualityInitiativesGenInfo/Downloads/CMS-QualityStrategy.pdf. 

190National Academies of Sciences, Engineering, 

and Medicine. 2016. Accounting for Social Risk 

Factors in Medicare Payment: Identifying Social 

Risk Factors. Washington, DC: The National 

Academies Press. https://doi.org/10.17226/21858. 

191https://aspe.hhs.gov/pdf-report/reportcongress-social-risk-factors-and-performanceunder-medicares-value-based-purchasingprograms. 

measures currently included in the 

Hospital Readmissions Reduction 

Program (see 84 FR 42496 through 

42500 for a discussion of using stratified 

data in additional measures). 

These programs are informed by 

reports by the National Academies of 

Science, Engineering and Medicine 

(NASEM) 186 and the Office of the 

Assistant Secretary for Planning and 

Evaluation (ASPE) 187 which have 

examined the influence of social risk 

factors on several of our quality 

programs. In this request for public 

comment, we address only the eighth 

initiative listed above, the CMS 

Disparity Methods, which we have 

implemented for measures in the 

Hospital Readmissions Reduction 

Program and are considering in other 

programs, including the ESRD QIP. We 

discuss the implementation of these 

methods to date and present 

considerations for continuing to 

improve and expand these methods. 

b. Current CMS Disparity Methods 

We first sought public comment on 

potential confidential and public 

reporting of ESRD QIP measure data 

stratified by social risk factors in the CY 

2018 ESRD PPS proposed rule (82 FR 

31202). We initially focused on 

stratification by dual eligibility, which 

is consistent with recommendations 

from ASPE’s First Report to Congress 

which was required by the Improving 

Medicare Post-Acute Care 

Transformation (IMPACT) Act of 2014 

(Pub. L. 113–185).188 This report found 

that in the context of value-based 

purchasing (VBP) programs, dual 

eligibility was among the most powerful 

predictors of poor health outcomes 

among those social risk factors that 

ASPE examined and tested. In the FY 

2018 IPPS/LTCH PPS final rule we also 

solicited feedback on two potential 

methods for illuminating differences in 

outcomes rates among patient groups 

within a provider’s patient population 

that would also allow for a comparison 

of those differences, or disparities, 

across providers for the Hospital IQR 

program (82 FR 38403 through 38409). 

The first method (the Within-Hospital 

disparity method) promotes quality 

improvement by calculating differences 

in outcome rates among patient groups 

within a hospital while accounting for 

their clinical risk factors. This method 

also allows for a comparison of the 

magnitude of disparity across hospitals, 

so hospitals could assess how well they 

are closing disparity gaps compared to 

other hospitals. The second 

methodological approach (the AcrossHospital method) is complementary and 

assesses hospitals’ outcome rates for 

dual-eligible patients only, across 

hospitals, allowing for a comparison 

among hospitals on their performance 

caring for their patients with social risk 

factors. In the CY 2018 ESRD PPS 

proposed rule (82 FR 31202 through 

31203), we also specifically solicited 

feedback on which social risk factors 

provide the most valuable information 

to stakeholders. In addition, feedback 

was solicited on the methodology for 

illuminating differences in outcomes 

rates among patient groups within a 

provider’s patient population that 

would also allow for a comparison of 

those differences, or disparities, across 

providers. Overall, comments supported 

the use of dual eligibility as a proxy for 

social risk, although commenters also 

suggested investigation of additional 

social risk factors, and we continue to 

consider commenter suggestions for 

which risk factors provide the most 

valuable information to stakeholders. 

c. Future Potential Expansion of the 

CMS Disparity Methods to the ESRD 

QIP 

We are committed to advancing 

health equity by improving data 

collection to better measure and analyze 

disparities across programs and 

policies.189 As we previously noted, we 

have been considering, among other 

things, expanding our efforts to provide 

stratified data for additional social risk 

factors and measures, optimizing the 

ease-of-use of the results, enhancing 

public transparency of equity results, 

and building towards provider 

accountability for health equity. We are 

seeking public comment on the 

potential stratification of quality 

measures in the ESRD QIP across two 

social risk factors: Dual eligibility and 

race/ethnicity. 

(1) Stratification of Quality Measure 

Results—Dual Eligibility 

As described above, landmark reports 

by NASEM 190 and ASPE,191 which have 

examined the influence of social risk 

factors on several of our quality 

programs, have shown that in the 

context of VBP programs, dual 

eligibility, as an indicator of social risk, 

is a powerful predictor of poor health 

outcomes. We are considering 

stratification of quality measure results 

in the ESRD QIP and are considering 

which measures would be most 

appropriate for stratification and if dual 

eligibility would be a meaningful social 

risk factor for stratification. 

For the ESRD QIP, we would consider 

disparity reporting using two disparity 

methods the Within-Facility and 

Across-Facility methods. The first 

method (based on the Within-Hospital 

disparity method, described above) 

would aim to promote quality 

improvement by calculating differences 

in outcome rates between dual and nondual eligible patient groups within a 

facility while accounting for their 

clinical risk factors. This method would 

allow for a comparison of those 

differences, or disparities, across 

facilities, so facilities could assess how 

well they are closing disparity gaps 

compared to other facilities. The second 

approach (based on the Across-Hospital 

method) would be complementary and 

assesses facilities’ outcome rates for 

subgroups of patients, such as dual 

eligible patients, across facilities, 

allowing for a comparison among 

facilities on their performance caring for 

their patients with social risk factors. 

(2) Stratification of Quality Measure 

Results—Race and Ethnicity 

The Administration’s Executive Order 

on Advancing Racial Equity and 

Support for Underserved Communities 

Through the Federal Government 

directs agencies to assess potential 

barriers that underserved communities 

and individuals may face to enrollment 

in and access to benefits and services in 

federal programs. As summarized earlier 

in the preamble, studies have shown 

that among Medicare beneficiaries, 

racial and ethnic minority persons often 

experience worse health outcomes, 

including more frequent hospital 

readmissions and procedural 


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192https://www.kff.org/medicare/report/racialand-ethnic-health-inequities-and-medicare/. 

193United States Renal Data System. 2018 Annual 

Data Report: ESRD Incidence, Prevalence, Patient 

Characteristics, and Treatment Modalities. 

Available online at https://www.usrds.org/2018/ 

view/Default.aspx. 

194United States Renal Data System. 2018 Annual 

Data Report, Vol 2, Figure 1.12. Available online at 

https://www.usrds.org/2018/view/Default.aspx. 

195Executive Office of the President Office of 

Management and Budget, Office of Information and 

Regulatory Affairs. Revisions to the standards for 

the classification of federal data on race and 

ethnicity. Vol 62. Federal Register. 1997:58782– 

58790. 

196https://www.census.gov/topics/population/ 

hispanic-origin/about.html. 

197https://phinvads.cdc.gov/vads/ 

ViewValueSet.action?id=67D34BBC-617F-DD11- 

B38D-00188B398520. 

198ONC criteria for certified health IT products: 

https://www.healthit.gov/isa/representing-patientrace-and-ethnicity. 199Eicheldinger, C., & Bonito, A. (2008). More 

accurate racial and ethnic codes for Medicare 

administrative data. Health Care Financing Review, 

29(3), 27–42. 200Filice CE, Joynt KE. Examining Race and 

Ethnicity Information in Medicare Administrative 

Data. Med Care. 2017;55(12):e170-e176. 

doi:10.1097/MLR.0000000000000608. 201Eicheldinger, C., & Bonito, A. (2008). More 

accurate racial and ethnic codes for Medicare 

administrative data. Health Care Financing Review, 

29(3), 27–42. 202Centers for Medicare and Medicaid Services. 

Building an Organizational Response to Health 

Disparities Inventory of Resources for Standardized 

Demographic and Language Data Collection. 2020. 

https://www.cms.gov/About-CMS/AgencyInformation/OMH/Downloads/Data-CollectionResources.pdf. 

203Centers for Medicare and Medicaid Services. 

Building an Organizational Response to Health 

Disparities Inventory of Resources for Standardized 

Demographic and Language Data Collection. 2020. 

https://www.cms.gov/About-CMS/AgencyInformation/OMH/Downloads/Data-CollectionResources.pdf. 

204https://pubmed.ncbi.nlm.nih.gov/18567241/, 

https://pubmed.ncbi.nlm.nih.gov/30506674/, 

Eicheldinger C., Bonito A. More accurate racial and 

ethnic codes for Medicare administrative data. 

Health Care Finance Rev. 2008;29(3):27–42. Haas 

A., Elliott MN, Dembosky JW, et al. Imputation of 

race/ethnicity to enable measurement of HEDIS 

performance by race/ethnicity. Health Serv Res. 

2019;54(1):13–23. doi:10.1111/1475–6773.13099. 

205https://aspe.hhs.gov/pdf-report/secondimpact-report-to-congress. 

complications.192 We also note that the 

prevalence of ESRD is higher among 

racial minorities.193 For example, in 

2016 ESRD prevalence was 

approximately 9.5 times greater in 

Native Hawaiians and Pacific Islanders, 

3.7 times greater in African Americans, 

1.5 times greater in American Indians 

and Alaska Natives, and 1.3 times 

greater in Asians.194 An important part 

of identifying and addressing inequities 

in health care is improving data 

collection to allow us to better measure 

and report on equity across our 

programs and policies. We are 

considering stratification of quality 

measure results in the ESRD QIP by race 

and ethnicity, and are identifying which 

measures would be most appropriate for 

stratification. 

As outlined in the 1997 Office of 

Management and Budget (OMB) 

Revisions to the Standards for the 

Collection of Federal Data on Race and 

Ethnicity, the racial and ethnic 

categories which may be used for 

reporting the disparity methods are 

considered to be social and cultural, not 

biological or genetic.195 The 1997 OMB 

Standard lists five minimum categories 

of race: (1) American Indian or Alaska 

Native; (2) Asian; (3) Black or African 

American; (4) Native Hawaiian or Other 

Pacific Islander; (5) and White. In the 

OMB standards, Hispanic or Latino is 

the only ethnicity category included, 

and since race and ethnicity are two 

separate and distinct concepts, persons 

who report themselves as Hispanic or 

Latino can be of any race.196 Another 

example, the ‘‘Race & Ethnicity—CDC’’ 

code system in Public Health 

Information Network (PHIN) Vocabulary 

Access and Distribution Systems 

(VADS) 197 permits a much more 

granular structured recording of a 

patient’s race and ethnicity with its 

inclusion of over 900 concepts for race 

and ethnicity. The recording and 

exchange of patient race and ethnicity at 

such a granular level can facilitate the 

accurate identification and analysis of 

health disparities based on race and 

ethnicity. Further, the ‘‘Race & 

Ethnicity—CDC’’ code system has a 

hierarchy that rolls up to the OMB 

minimum categories for race and 

ethnicity and, thus, supports 

aggregation and reporting using the 

OMB standard. ONC includes both the 

CDC and OMB standards in its criterion 

for certified health IT products.198 For 

race and ethnicity, a certified health IT 

product must be able to express both 

detailed races and ethnicities using any 

of the 900 plus concepts in the ‘‘Race & 

Ethnicity—CDC’’ code system in PHIN 

VADS, as well as aggregate each one of 

a patient’s races and ethnicities to the 

categories in the OMB standard for race 

and ethnicity. This approach can reduce 

burden on providers recording 

demographics using certified products. 

Self-reported race and ethnicity data 

remain the gold standard for classifying 

an individual according to race or 

ethnicity. However, historical 

inaccuracies in federal data systems and 

limited collection classifications have 

contributed to the limited quality of race 

and ethnicity information in our 

administrative data systems.199 In recent 

decades, to address these data quality 

issues, CMS has undertaken numerous 

initiatives, including updating data 

taxonomies and conducting direct 

mailings to some beneficiaries to enable 

more comprehensive race and ethnic 

identification.200 201 Despite those 

efforts, studies reveal varying data 

accuracy in identification of racial and 

ethnic groups in Medicare 

administrative data, with higher 

sensitivity for correctly identifying 

white and Black individuals, and lower 

sensitivity for correctly identifying 

individuals of Hispanic ethnicity or of 

Asian/Pacific Islander and American 

Indian/Alaskan Native race.202

Incorrectly classified race or ethnicity 

may result in overestimation or 

underestimation in the quality of care 

received by certain groups of 

beneficiaries. 

We continue to work with public and 

private partners to better collect and 

leverage data on social risk to improve 

our understanding of how these factors 

can be better measured in order to close 

the health equity gap. Among other 

things, we have developed an Inventory 

of Resources for Standardized 

Demographic and Language Data 

Collection 203 and supported collection 

of specialized International 

Classification of Disease, 10th Edition, 

Clinical Modification (ICD–10–CM) 

codes for describing the socioeconomic, 

cultural, and environmental 

determinants of health, and sponsored 

several initiatives to statistically 

estimate race and ethnicity information 

when it is absent.204

The Office of the National 

Coordinator for Health Information 

Technology (ONC) included social, 

psychological, and behavioral standards 

in the 2015 Edition health information 

technology certification criteria (2015 

Edition), providing interoperability 

standards LOINC (Logical Observation 

Identifiers Names and Codes) and 

SNOMED CT (Systematized 

Nomenclature of Medicine—Clinical 

Terms) for financial strain, education, 

social connection and isolation, and 

others. Additional stakeholder efforts 

underway to expand capabilities to 

capture additional social determinants 

of health data elements include the 

Gravity Project to identify and 

harmonize social risk factor data for 

interoperable electronic health 

information exchange for EHR fields, as 

well as proposals to expand the ICD–10 

(International Classification of Diseases, 

Tenth Revision) Z-codes, the 

alphanumeric codes used worldwide to 

represent diagnoses.205

While development of sustainable and 

consistent programs to collect data on 

social determinants of health can be 

considerable undertakings, we recognize 


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206 IOM. 2009. Race, Ethnicity, and Language 

Data: Standardization for Health Care Quality 

Improvement. Washington, DC: The National 

Academies Press. 

207 IOM. 2009. Race, Ethnicity, and Language 

Data: Standardization for Health Care Quality 

Improvement. Washington, DC: The National 

Academies Press. 

208Eicheldinger, C., & Bonito, A. (2008). More 

accurate racial and ethnic codes for Medicare 

administrative data. Health Care Financing Review, 

29(3), 27–42. 

209Haas, A., Elliott, M. et al., (2018). Imputation 

of race/ethnicity to enable measurement of HEDIS 

performance by race/ethnicity. Health Services 

Research, 54:13–23. 

210https://www.cms.gov/About-CMS/AgencyInformation/OMH/research-and-data/statistics-anddata/stratified-reporting. 

211Haas, A., Elliott, M. et al., (2018). Imputation 

of race/ethnicity to enable measurement of HEDIS 

performance by race/ethnicity. Health Services 

Research, 54:13–23. 

212https://www.cms.gov/files/document/racialethnic-gender-disparities-health-care-medicareadvantage.pdf. 

213Haas, A., Elliott, M. et al., (2018). Imputation 

of race/ethnicity to enable measurement of HEDIS 

performance by race/ethnicity. Health Services 

Research, 54:13–23 and Bonito AJ, Bann C., 

Eicheldinger C., Carpenter L. Creation of New RaceEthnicity 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. 

214The Centers for Medicare & Medicaid Services. 

CMS Quality Strategy. 2016. https://www.cms.gov/ 

Medicare/Quality-Initiatives-Patient-AssessmentInstruments/QualityInitiativesGenInfo/Downloads/ 

CMS-Quality-Strategy.pdf. 

that another method to identify better 

race and ethnicity data is needed in the 

short term to address the need for 

reporting on health equity. In working 

with our contractors, two algorithms 

have been developed to indirectly 

estimate the race and ethnicity of 

Medicare beneficiaries (as described 

further in the next section). We believe 

that using indirect estimation can help 

to overcome the current limitations of 

demographic information and enable 

timelier reporting of equity results until 

longer term collaborations to improve 

demographic data quality across the 

health care sector materialize. The use 

of indirectly estimated race and 

ethnicity for conducting stratified 

reporting does not place any additional 

collection or reporting burdens on 

facilities as these data are derived using 

existing administrative and Censuslinked data. 

Indirect estimation relies on a 

statistical imputation method for 

inferring a missing variable or 

improving an imperfect administrative 

variable using a related set of 

information that is more readily 

available.206 Indirectly estimated data 

are most commonly used at the 

population level (such as the facility or 

health plan-level), where aggregated 

results form a more accurate description 

of the population than existing, 

imperfect data sets. These methods 

often estimate race and ethnicity using 

a combination of other data sources 

which are predictive of self-identified 

race and ethnicity, such as language 

preference, information about race and 

ethnicity in our administrative records, 

first and last names matched to 

validated lists of names correlated to 

specific national origin groups, and the 

racial and ethnic composition of the 

surrounding neighborhood. Indirect 

estimation has been used in other 

settings to support population-based 

equity measurement when selfidentified data are not available.207

As discussed earlier in the preamble, 

we have previously supported the 

development of two such methods of 

indirect estimation of race and ethnicity 

of Medicare beneficiaries. One indirect 

estimation approach, developed by our 

contractor, uses Medicare 

administrative data, first name and 

surname matching, derived from the 

U.S. Census and other sources, with 

beneficiary language preference, state of 

residence, and the source of the race 

and ethnicity code in Medicare 

administrative data to reclassify some 

beneficiaries as Hispanic or Asian 

Pacific Islander (API).208 In recent years, 

we have also worked with another 

contractor to develop a new approach, 

the Medicare Bayesian Improved 

Surname Geocoding (MBISG), which 

combines Medicare administrative data, 

first and surname matching, geocoded 

residential address linked to the 2010 

U.S. Census, and uses both Bayesian 

updating and multinomial logistic 

regression to estimate the probability of 

belonging to each of six racial/ethnic 

groups.209

The MBISG model is currently used to 

conduct the national, contract-level, 

stratified reporting of Medicare Part C & 

D performance data for Medicare 

Advantage Plans by race and 

ethnicity.210 Validation testing reveals 

concordances of 0.88–0.95 between 

indirectly estimated and self-report 

among individuals who identify as 

White, Black, Hispanic, and Asian 

Pacific Islander for the MBISG version 

2.0 and concordances with self-reported 

race and ethnicity of 0.96–0.99 for these 

same groups for MBISG version 

2.1.211 212 The algorithms under 

consideration are considerably less 

accurate for individuals who selfidentify as American Indian or Alaskan 

Native as well as for those who selfidentify as multiracial.213

Indirect estimation can be a 

statistically reliable approach for 

calculating population-level equity 

results for groups of individuals (such 

as the facility-level) and is not intended, 

nor being considered, as an approach for 

inferring the race and ethnicity of an 

individual. However, despite the high 

degree of statistical accuracy of the 

indirect estimation algorithms under 

consideration there remains the small 

risk of unintentionally introducing bias. 

For example, if the indirect estimation 

is not as accurate in correctly estimating 

race and ethnicity in certain geographies 

or populations it could lead to some 

bias in the method results. Such bias 

might result in slight overestimation or 

underestimation of the quality of care 

received by a given group. We feel this 

amount of bias is considerably less than 

would be expected if stratified reporting 

was conducted using the race and 

ethnicity currently contained in our 

administrative data. Indirect estimation 

of race and ethnicity is envisioned as an 

intermediate step, filling the pressing 

need for more accurate demographic 

information for the purposes of 

exploring inequities in service delivery, 

while allowing newer approaches, as 

described in the next section, for 

enhancing demographic data collection. 

We are interested in learning more 

about, and soliciting comments, about 

the potential benefits and challenges 

associated with measuring facility 

equity using an imputation algorithm to 

enhance existing administrative data 

quality for race and ethnicity until selfreported information is sufficiently 

available. 

(3) Improving Demographic Data 

Collection 

Stratified facility-level reporting using 

indirectly estimated race and ethnicity 

and dual eligibility would represent an 

important advance in our ability to 

provide equity reports to facilities. 

However, self-reported disability status, 

race and ethnicity data remain the gold 

standard for classifying an individual 

according to disability status, race or 

ethnicity. The CMS Quality Strategy 

outlines our commitment to 

strengthening infrastructure and data 

systems by ensuring that standardized 

demographic information is collected to 

identify disparities in health care 

delivery outcomes.214 Collection and 

sharing of a standardized set of social, 

psychological, and behavioral data by 

facilities, including disability status and 

race and ethnicity, using electronic data 

definitions which permit nationwide, 

interoperable health information 


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215The Office of the National Coordinator for 

Health Information Technology. United State Core 

Data for Interoperability Draft Version 2. 2021. 

https://www.healthit.gov/isa/sites/isa/files/2021-01/ 

Draft-USCDI-Version-2-January-2021-Final.pdf. 

216https://minorityhealth.hhs.gov/assets/pdf/ 

checked/1/Fact_Sheet_Section_4302.pdf. 

217https://www.healthit.gov/sites/default/files/ 

2020-08/2015EdCures_Update_CCG_USCDI.pdf. 

218Agniel D., Martino S.C., Burkhart Q., et al. 

Incentivizing Excellent Care to At-Risk Groups with 

a Health Equity Summary Score. J Gen Intern Med. 

Published online November 11, 2019 Nov 11. doi: 

10.1007/s11606–019–05473–x. 

exchange, can significantly enhance the 

accuracy and robustness of our equity 

reporting.215 This could potentially 

include expansion to additional social 

risk factors, such as language preference 

and disability status, where accuracy of 

administrative data is currently limited. 

We are mindful that additional 

resources, including data collection and 

staff training may be necessary to ensure 

that conditions are created whereby all 

patients are comfortable answering all 

demographic questions, and that 

individual preferences for non-response 

are maintained. 

We are also interested in learning 

about and are soliciting comments on 

current data collection practices by 

facilities to capture demographic data 

elements (such as race, ethnicity, sex, 

sexual orientation and gender identity 

(SOGI), language preference, and 

disability status). Further, we are 

interested in potential challenges facing 

facility collection of a minimum set of 

demographic data elements in 

alignment with national data collection 

standards (such as the standards 

finalized by the Affordable Care Act) 216

and standards for interoperable 

exchange (such as the U.S. Core Data for 

Interoperability put forth by the Office 

of the National Coordinator for Health 

Information Technology for 

incorporation in certified health IT 

products as part of the 2015 Edition of 

health IT certification criteria.) 217

Advancing data interoperability through 

collection of a minimum set of 

demographic data collection has the 

potential for improving the robustness 

of the disparity methods results, 

potentially permitting reporting using 

more accurate, self-reported, 

information, such as race and ethnicity, 

and expanding reporting to additional 

dimensions of equity, including 

stratified reporting by disability status. 

(4) Potential Creation of an ESRD 

Facility Equity Score to Synthesize 

Results Across Multiple Social Risk 

Factors 

As we describe above, we are 

considering expanding the disparity 

methods to include two social risk 

factors (dual eligibility and race/ 

ethnicity). This approach would 

improve the comprehensiveness of 

health equity information provided to 

facilities. Aggregated results from 

multiple measures and multiple social 

risk factors, from the CMS Disparity 

Methods, in the format of a summary 

score, can improve the usefulness of the 

equity results. In working with our 

contractors, we recently developed an 

equity summary score for Medicare 

Advantage contract/plans, the Health 

Equity Summary Score (HESS), with 

application to stratified reporting using 

two social risk factors: Dual eligibility 

and race and as described in 

Incentivizing Excellent Care to At-Risk 

Groups with a Health Equity Summary 

Score.218

The HESS calculates standardized 

and combined performance scores 

blended across the two social risk 

factors. The HESS also combines results 

of the within-plan (similar to the 

Within-Facility method) and across-plan 

method (similar to the Across-Facility 

method) across multiple performance 

measures. 

We are considering building an ESRD 

Facility Equity Score, not yet developed, 

which would be modeled off the HESS 

but adapted to the context of riskadjusted facility outcome measures and 

potentially other ESRD QIP quality 

measures. We envision that the ESRD 

Facility Equity Score would synthesize 

results for a range of measures and using 

multiple social risk factors, using 

measures and social risk factors which 

would be reported to facilities as part of 

the CMS Disparity Methods. We believe 

that creation of the ESRD Facility Equity 

Score has the potential to supplement 

the overall measure data already 

reporting on the Care Compare or 

successor website, by providing easy to 

interpret information regarding 

disparities measured within individual 

facilities and across facilities nationally. 

A summary score would decrease 

burden by minimizing the number of 

measure results provided and providing 

an overall indicator of equity. 

The ESRD Facility Equity Score under 

consideration would potentially: 

• Summarize facility performance 

across multiple social risk factors 

(initially dual eligibility and indirectly 

estimated race and ethnicity, as 

described above). 

• Summarize facility performance 

across the two disparity methods (that 

is, the Within-Facility Disparity Method 

and the Across-Facility Disparity 

Method) and potentially multiple 

measures. 

Prior to any future public reporting of 

stratified measure data using indirectly 

estimated race and ethnicity 

information, if we determine that an 

ESRD Facility Equity Score can be 

feasibly and accurately calculated, we 

would provide results of the ESRD 

Facility Equity Score, in confidential 

facility specific reports which facilities 

and their QIN–QIOs would be able to 

download. Any potential future 

proposal to display the ESRD Facility 

Equity Score on the Care Compare or 

successor website would be made 

through future RFI or rulemaking. 

d. Solicitation of Public Comment 

We are seeking comment on the 

possibility of stratifying ESRD QIP 

measures by dual eligibility and race 

and ethnicity. We are soliciting public 

comments on the application of the 

within-facility or across-facility 

disparities methods if we were to 

stratify ESRD QIP measures. We are also 

seeking comment on the possibility of 

facility collection of standardized 

demographic information for the 

purposes of potential future quality 

reporting and measure stratification. In 

addition, we are seeking comment on 

the potential design of a facility equity 

score for calculating results across 

multiple social risk factors and 

measures, including race and disability. 

Any data pertaining to these areas that 

are recommended for collection for 

measure reporting for a CMS program 

and any potential public disclosure on 

Care Compare or successor website 

would be addressed through a separate 

and future notice- and-comment 

rulemaking. We plan to continue 

working with ASPE, facilities, the 

public, and other key stakeholders on 

this important issue to identify policy 

solutions that achieve the goals of 

attaining health equity for all patients 

and minimizing unintended 

consequences. We look forward to 

receiving feedback on these topics and 

note for readers that responses to the 

RFI will not directly impact payment 

decisions. We also note our intention for 

additional RFI or rulemaking on this 

topic in the future. 

Specifically, we are inviting public 

comment on the following: 

Future Potential Stratification of Quality 

Measure Results 

• The possible stratification of 

facility-specific reports for ESRD QIP 

measure data by dual-eligibility status, 

including which measures would be 

most appropriate for stratification; 

• The potential future application of 

indirect estimation of race and ethnicity 

information to permit stratification of 


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36368 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

219U.S. Dept of Health and Human Services, 

Office of the Assistant Secretary for Preparedness 

and Response. (2020). Determination that a Public 

Health Emergency Exists. Available at: https://

www.phe.gov/emergency/news/healthactions/phe/ 

Pages/2019-nCoV.aspx. 

220Centers for Disease Control and Prevention. 

(2020). Your Health: Symptoms of Coronavirus. 

Available at: https://www.cdc.gov/coronavirus/ 

2019-ncov/symptoms-testing/symptoms.html. 

221 Ibid. 

222Centers for Disease Control and Prevention. 

(2020). CDC COVID Data Tracker. Available at: 

https://covid.cdc.gov/covid-data-tracker/#cases_

casesper100klast7days. 

223Associated Press. Tired to the Bone. Hospitals 

Overwhelmed with Virus Cases. November 18, 

2020. Accessed on December 16, 2020, at https:// 

apnews.com/article/hospitals-overwhelmedcoronavirus-cases74a1f0dc3634917a5dc13408455cd895. Also see: 

New York Times. Just how full are U.S. intensive 

care units? New data paints an alarming picture. 

November 18, 2020. Accessed on December 16, 

2020, at: https://www.nytimes.com/2020/12/09/ 

world/just-how-full-are-us-intensive-care-units-newdata-paints-an-alarming-picture.html. 

224U.S. Currently Hospitalized | The COVID 

Tracking Project. Accessed January 31, 2021 at: 

https://covidtracking.com/data/charts/us-currentlyhospitalized. 

225Centers for Disease Control and Prevention. 

(2021). How COVID–19 Spreads. Accessed on April 

3, 2021 at: https://www.cdc.gov/coronavirus/2019- 

ncov/prevent-getting-sick/how-covid-spreads.html. 

226 Ibid. 

227 Ibid. 

228 Ibid. 

229 Ibid. 

230Centers for Disease Control and Prevention. 

(2021). When to Quarantine. Accessed on April 2, 

2021 at: https://www.cdc.gov/coronavirus/2019- 

ncov/if-you-are-sick/quarantine.html. 

231Centers for Disease Control and Prevention. 

(2021). Interim U.S. Guidance for Risk Assessment 

and Work Restrictions for Healthcare Personnel 

with Potential Exposure to COVID–19. Accessed on 

April 2 at: https://www.cdc.gov/coronavirus/2019- 

ncov/hcp/faq.html#Transmission. 

232 Dooling, K, McClung, M, et al. ‘‘The Advisory 

Committee on Immunization Practices’ Interim 

Recommendations for Allocating Initial Supplies of 

COVID–19 Vaccine—United States, 2020.’’ Morb 

Mortal Wkly Rep. 2020; 69(49): 1857–1859. 

233The White House. Remarks by President Biden 

on the COVID–19 Response and the State of 

Vaccinations. Accessed on April 3, 2021 at: https:// 

www.whitehouse.gov/briefing-room/speechesremarks/2021/03/29/remarks-by-president-bidenon-the-covid-19-response-and-the-state-ofvaccinations/. 

234The White House. Remarks by President Biden 

on the COVID–19 Response and the State of 

Vaccinations. Accessed on June 2, 2021 at: https:// 

www.whitehouse.gov/briefing-room/speechesremarks/2021/04/21/remarks-by-president-bidenon-the-covid-19-response-and-the-state-ofvaccinations-2/. 

measure data for reporting ESRD 

facility-level disparity results; 

• Appropriate privacy safeguards 

with respect to data produced from the 

indirect estimation of race and ethnicity 

to ensure that such data is properly 

identified if/when it is shared with 

facilities. 

• Ways to address the challenges of 

defining and collecting, accurate and 

standardized self-identified 

demographic information, including 

information on race and ethnicity, 

disability, and language preference for 

the purposes of reporting, measure 

stratification and other data collection 

efforts relating to quality. 

• Recommendations for other types of 

readily available data elements for 

measuring disadvantage and 

discrimination for the purposes of 

reporting, measure stratification and 

other data collection efforts relating to 

quality, in addition, or in combination 

with race and ethnicity. 

• Recommendations for types of 

quality measures or measurement 

domains to prioritize for stratified 

reporting by dual eligibility, race and 

ethnicity, and disability. 

• Examples of approaches, methods, 

research, and/or considerations for use 

of data-driven technologies that do not 

facilitate exacerbation of health 

inequities, recognizing that biases may 

occur in methodology or be encoded in 

datasets. 

Improving Demographic Data Collection 

• Experiences of users of certified 

health IT regarding local adoption of 

practices for collection of social, 

psychological, and behavioral data 

elements, the perceived value of using 

these data for improving decisionmaking and care delivery, and the 

potential challenges and benefits of 

collecting more granular, structured 

demographic information, such as the 

‘‘Race & Ethnicity—CDC’’ code system. 

• The possible collection of a 

minimum set of social, psychological, 

and behavioral data elements by ESRD 

facilities at the time of admission using 

structured, interoperable electronic data 

standards, for the purposes of reporting, 

measure stratification and other data 

collection efforts relating to quality. 

Potential Creation of an ESRD Facility 

Equity Score To Synthesize Results 

Across Multiple Social Risk Factors 

• The possible creation and 

confidential reporting of an ESRD 

Facility Equity Score to synthesize 

results across multiple social risk factors 

and disparity measures. 

• Interventions ESRD facilities could 

institute to improve a low facility equity 

score and how improved demographic 

data could assist with these efforts. 

2. COVID–19 Vaccination Measures 

Request for Information 

a. Background 

On January 31, 2020, the Secretary 

declared a PHE for the U.S. in response 

to the global outbreak of SARS–CoV–2, 

a novel (new) coronavirus that causes a 

disease named ‘‘coronavirus disease 

2019’’ (COVID–19).219 COVID–19 is a 

contagious respiratory infection 220 that 

can cause serious illness and death. 

Older individuals and those with 

underlying medical conditions are 

considered to be at higher risk for more 

serious complications from COVID– 

19.221

As of April 2, 2021, the U.S. reported 

over 30 million cases of COVID–19 and 

over 550,000 COVID–19 deaths.222

Hospitals and health systems saw 

significant surges of COVID–19 patients 

as community infection levels 

increased.223 From December 2, 2020 

through January 30, 2021, more than 

100,000 Americans were in the hospital 

with COVID–19 at the same time.224

Evidence indicates that COVID–19 

primarily spreads when individuals are 

in close contact with one another.225

The virus is typically transmitted 

through respiratory droplets or small 

particles created when someone who is 

infected with the virus coughs, sneezes, 

sings, talks or breathes.226 Thus, the 

CDC advises that infections mainly 

occur through exposure to respiratory 

droplets when a person is in close 

contact with someone who has COVID– 

19.227 Although less common, COVID– 

19 can also spread when individuals are 

not in close contact if small droplets or 

particles containing the virus linger in 

the air after the person who is infected 

has left the space.228 Another means of 

less common transmission is contact 

with a contaminated surface.229

According to the CDC, those at greatest 

risk of infection are persons who have 

had prolonged, unprotected close 

contact (that is, within 6 feet for 15 

minutes or longer) with an individual 

with confirmed SARS–CoV–2 infection, 

regardless of whether the individual has 

symptoms.230 Although personal 

protective equipment (PPE) and other 

infection-control precautions can reduce 

the likelihood of transmission in health 

care settings, COVID–19 can spread 

between healthcare personnel (HCP) 

and patients, or from patient to patient 

given the close contact that may occur 

during the provision of care.231 The 

CDC has emphasized that health care 

settings can be high-risk places for 

COVID–19 exposure and 

transmission.232

As part of its national strategy to 

address COVID–19, the Biden 

Administration stated that it would 

work with states and the private sector 

to execute an aggressive vaccination 

strategy and outlined a goal of 

administering 200 million shots in 100 

days.233 After achieving this goal,234 the 

Biden Administration announced a new 

goal to administer at least one COVID– 


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235The White House. Remarks by President Biden 

on the COVID–19 Response and the State of 

Vaccinations. Accessed on June 4, 2021, at: https:// 

www.whitehouse.gov/briefing-room/statementsreleases/2021/05/04/fact-sheet-president-biden-toannounce-goal-to-administer-at-least-one-vaccineshot-to-70-of-the-u-s-adult-population-by-july-4th/. 236Health and Human Services, Department of 

Defense. (2020) From the Factory to the Frontlines: 

The Operation Warp Speed Strategy for Distributing 

a COVID–19 Vaccine. Accessed December 18 at: 

https://www.hhs.gov/sites/default/files/strategy-fordistributing-covid-19-vaccine.pdf; Centers for 

Disease Control (2020). COVID–19 Vaccination 

Program Interim Playbook for Jurisdiction 

Operations. Accessed December 18 at: https://

www.cdc.gov/vaccines/imz-managers/downloads/ 

COVID-19-Vaccination-Program-Interim_

Playbook.pdf. 

237 Dooling, K, McClung, M, et al. ‘‘The Advisory 

Committee on Immunization Practices’ Interim 

Recommendations for Allocating Initial Supplies of 

COVID–19 Vaccine—United States, 2020.’’ Morb. 

Mortal Wkly Rep. 2020; 69(49): 1857–1859. ACIP 

also recommended that long-term care residents be 

prioritized to receive the vaccine, given their age, 

high levels of underlying medical conditions, and 

congregate living situations make them high risk for 

severe illness from COVID–19. 

238 Kates, J, Michaud, J, Tolbert, J. ‘‘How Are 

States Prioritizing Who Will Get the COVID–19 

Vaccine First?’’ Kaiser Family Foundation. 

December 14, 2020. Accessed on December 16 at 

https://www.kff.org/policy-watch/how-are-statesprioritizing-who-will-get-the-covid-19-vaccine-first/. 

239Associated Press. ‘Healing is Coming:’ U.S. 

Health Workers Start Getting Vaccine. December 15, 

2020. Accessed on December 16 at: https://

apnews.com/article/us-health-workers-coronavirusvaccine-56df745388a9fc12ae93c6f9a0d0e81f. 

240 Kates, J, Michaud, J, Tolbert, J. ‘‘The COVID– 

19 Vaccine Priority Line Continues to Change as 

States Make Further Updates.’’ Kaiser Family 

Foundation. January 21, 2021. Accessed on January 

29 at https://www.kff.org/policy-watch/the-covid19-vaccine-priority-line-continues-to-change-asstates-make-further-updates/. 

241 Dooling K, Marin M, Wallace M, et al. ‘‘The 

Advisory Committee on Immunization Practices’ 

Updated Interim Recommendation for Allocation of 

COVID–19 Vaccine—United States, December 

2020.’’ MMWR Morb Mortal Wkly Rep 2021; 

69:1657–1660. ACIP recommended that the COVID– 

19 vaccine should be offered to persons aged ≥75 

years and non–health care frontline essential 

workers in Phase 1b, and to persons aged 16–64 

years with high-risk medical conditions in Phase 

1c. 

242Centers for Disease Control and Prevention. 

COVID Data Tracker. COVID–19 Vaccinations in the 

United States. Accessed June 23, 2021 at: https:// 

covid.cdc.gov/covid-data-tracker/#vaccinations. 

243The White House. Remarks by President Biden 

Marking the 150 Millionth COVID–19 Vaccine Shot. 

Accessed April 8, 2021 at: https://

www.whitehouse.gov/briefing-room/speechesremarks/2021/04/06/remarks-by-president-bidenmarking-the-150-millionth-covid-19-vaccine-shot/. 

244The White House. FACT SHEET: Biden 

Administration Announces Historic $10 Billion 

Investment to Expand Access to COVID–19 

Vaccines and Build Vaccine Confidence in HardestHit and Highest-Risk Communities. March 25, 2021. 

Available at: https://www.whitehouse.gov/briefingroom/statements-releases/2021/03/25/fact-sheetbiden-administration-announces-historic-10-billioninvestment-to-expand-access-to-covid-19-vaccinesand-build-vaccine-confidence-in-hardest-hit-andhighest-risk-communities/. 

245https://www.cdc.gov/nhsn/hps/weekly-covidvac/index.html. 

246https://www.cdc.gov/nhsn/dialysis/pt-covidvac/index.html. 

247https://www.cdc.gov/nhsn/pdfs/hps/covidvax/ 

weekly-covid-guidance-508.pdf. 

248https://www.cdc.gov/nhsn/pdfs/dialysis/ 

covidvax/getting-started-508.pdf. 

249Centers for Disease Control and Prevention. 

Overview of Influenza Vaccination among Health 

Continued 

19 vaccine shot to 70 percent of the U.S. 

adult population by July 4th, 2021.235

Although the goal of the U.S. 

government is to ensure that every 

American who wants to receive a 

COVID–19 vaccine can receive one, 

federal agencies recommended that 

early vaccination efforts focus on those 

critical to the PHE response, including 

HCP providing direct care to patients 

with COVID–19, and individuals at 

highest risk for developing severe 

illness from COVID–19.236 For example, 

the CDC’s Advisory Committee on 

Immunization Practices (ACIP) 

recommended that HCP should be 

among those individuals prioritized to 

receive the initial, limited supply of the 

COVID–19 vaccination, given the 

potential for transmission in health care 

settings and the need to preserve health 

care system capacity.237 Research 

suggests most states followed this 

recommendation,238 and HCP began 

receiving the vaccine in mid-December 

of 2020.239 Although the vaccination 

strategy for individuals at highest risk 

for developing severe illness from 

COVID–19, including ESRD patients, 

has varied from state to state,240 ACIP 

recommendations indicated that ESRD 

patients would be offered the COVID–19 

vaccine based on their high-risk status 

as part of phase 1c.241

As of June 22, 2021 the CDC reported 

that over 319 million doses of COVID– 

19 vaccine had been administered, and 

approximately 150.4 million people had 

received a complete vaccination 

course.242 President Biden indicated on 

April 6, 2021 that the U.S. has sufficient 

vaccine supply to make every adult 

eligible to receive a vaccine beginning 

April 19, 2021.243 Furthermore, on 

March 25, 2021, the Biden 

Administration announced a new 

partnership with dialysis facilities to 

provide COVID–19 vaccinations directly 

to people receiving dialysis and HCP in 

dialysis facilities.244

b. COVID–19 Vaccination Coverage 

Among Healthcare Personnel (HCP) 

Measure 

We believe it is important to 

incentivize and track HCP vaccination 

in dialysis facilities through quality 

measurement in order to protect health 

care workers, patients, and caregivers, 

and to help sustain the ability of these 

facilities to continue serving their 

communities throughout the PHE and 

beyond. We recognize the importance of 

COVID–19 vaccination, and have 

proposed to include a COVID–19 HCP 

vaccination measure quality measure in 

various pay for reporting programs, such 

as the Inpatient Psychiatric Facility 

Quality Reporting Program (86 FR 19501 

through 19504), the Hospital Inpatient 

Quality Reporting Program (86 FR 25571 

through 25575), and the Skilled Nursing 

Facility Quality Reporting Program (86 

FR 19994 through 19998). We note that 

there is not a pay for reporting program 

under the ESRD PPS, however, we 

believe that the public reporting of 

vaccination data on Dialysis Facility 

Compare is important and would help 

to inform patients of a facility’s COVID– 

19 vaccination rates of HCP. Currently, 

there is a measure for HCP 245 and 

another for patient COVID–19 

vaccination 246 rates and such measures 

are currently reported to CDC’s National 

Healthcare Safety Network via ESRD 

Networks. The two measures track the 

proportions of a facility’s HCP and 

patient population, respectively, that 

have been fully vaccinated against 

COVID–19. Facilities were able to begin 

weekly COVID–19 vaccination reporting 

for HCP in December 2020,247 and were 

able to begin weekly COVID–19 

vaccination reporting for patients in 

March 2021.248 Currently, 89 percent of 

ESRD facilities are reporting HCP 

vaccination rates and almost 95 percent 

of ESRD facilities are reporting patient 

vaccination rates on these measures. We 

are evaluating options for publicly 

reporting the data on official CMS 

datasets that compare the quality of care 

provided in Medicare-certified dialysis 

facilities nationwide. We are also 

exploring the potential future inclusion 

of a COVID–19 vaccination measure to 

the ESRD QIP. Therefore, we are seeking 

public comment on adding a new 

measure, COVID–19 Vaccination 

Coverage Among HCP, to the ESRD QIP 

measure set in the next rulemaking 

cycle. The measure would assess the 

proportion of a facility’s health care 

workforce that has been vaccinated 

against COVID–19. 

HCP are at risk of carrying COVID–19 

infection to patients, experiencing 

illness or death as a result of COVID– 

19 themselves, and transmitting it to 

their families, friends, and the general 

public. We believe facilities should 

track the level of vaccination among 

their HCP as part of their efforts to 

assess and reduce the risk of 

transmission of COVID–19 within their 

facilities. HCP vaccination can 

potentially reduce illness that leads to 

work absence and limit disruptions to 

care.249 Data from influenza vaccination 


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Care Personnel. October 2020. (2020) Accessed 

March 16, 2021 at: https://www.cdc.gov/flu/toolkit/ 

long-term-care/why.htm. 

250Measure Application Committee Coordinating 

Committee Meeting Presentation. March 15, 2021. 

(2021) Accessed March 16, 2021 at: http://

www.qualityforum.org/Project_Pages/MAP_

Coordinating_Committee.aspx. 

251 Verma, A., Patel, A., Tio, M., Waikar, S., 

‘‘Caring for Dialysis Patients in a Time of COVID– 

19’’. Kidney Medicine, Volume 2, Issue 6, 2020, 

Pages 787–792, ISSN 2590–0595. Available at 

https://doi.org/10.1016/j.xkme.2020.07.006. 

252 Ibid. 

253National Quality Forum. List of Measures 

Under Consideration for December 21, 2020. 

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

measures-under-consideration-list-2020-report.pdf 

on January 29 2021. 

254Measure Applications Partnership. MAP 

Preliminary Recommendations 2020–2021. 

Accessed on January 24, 2021 at: http://

www.qualityforum.org/Project_Pages/MAP_

Hospital_Workgroup.aspx. 

255 Ibid. 

256 Ibid. 

257 Ibid. 

258Measure Applications Partnership. 2020–2021 

MAP Final Recommendations. Accessed on 

February 3, 2021 at: http://www.qualityforum.org/ 

Setting_Priorities/Partnership/Measure_

Applications_Partnership.aspx. 

259Measure Applications Partnership. 2020–2021 

MAP Final Recommendations. Accessed on 

February 23, 2021 at: http://www.qualityforum.org/ 

Project_Pages/MAP_Hospital_Workgroup.aspx. 

260Measure Applications Partnership. 2020–2021 

MAP Final Recommendations. Accessed on 

February 23, 2021 at: http://www.qualityforum.org/ 

Project_Pages/MAP_Hospital_Workgroup.aspx. 

261Specifications for both measures available at: 

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

linkit.aspx?LinkIdentifier=id&ItemID=94650. 

demonstrates that provider uptake of the 

vaccine is associated with that provider 

recommending vaccination to 

patients,250 and we believe HCP 

COVID–19 vaccination in dialysis 

facilities could similarly increase uptake 

among that patient population. We also 

believe that publishing the HCP 

vaccination rates will be helpful to 

many patients, including those who are 

at high-risk for developing serious 

complications from COVID–19, as they 

choose facilities from which to seek 

treatment. Under CMS’ Meaningful 

Measures Framework, the COVID–19 

measure would address the quality 

priority of ‘‘Promoting Effective 

Prevention and Treatment of Chronic 

Disease’’ through the Meaningful 

Measures Area of ‘‘Preventive Care.’’ 

c. COVID–19 Vaccination Coverage for 

Patients in End-Stage Renal Disease 

(ESRD) Facilities Measure 

We believe it is important to 

encourage patient vaccination in 

dialysis facilities in order to protect 

health care workers, patients, and 

caregivers, and to help sustain the 

ability of these facilities to continue 

serving their communities throughout 

the PHE and beyond. COVID–19 can 

cause outbreaks in ESRD facilities, and 

may disproportionately affect ESRD 

patients due to the nature of the 

treatment and sharing of common 

spaces.251 Many patients treated in 

ESRD facilities have other underlying 

chronic conditions, and therefore are 

highly susceptible to illness and 

disease.252 Sufficient vaccination 

coverage among patients in ESRD 

facilities may reduce transmission of 

SARS–CoV–2, thereby protecting them 

from COVID–19 mortality. Therefore, 

we are seeking public comment on 

adding new measure, COVID–19 

Vaccination Coverage Among Patients, 

to the ESRD QIP measure set in future 

rulemaking. The measure would assess 

the proportion of a facility’s patient 

population that has been vaccinated 

against COVID–19. 

We believe facilities should track the 

level of vaccination among their 

patients as part of their efforts to assess 

and reduce the risk of transmission of 

COVID–19 within their facilities. We 

also believe that publishing the 

vaccination rates will be helpful to 

many ESRD patients, including those 

who are at high-risk for developing 

serious complications from COVID–19, 

as they choose facilities from which to 

seek treatment. Under CMS’ Meaningful 

Measures Framework, the COVID–19 

measure addresses the quality priority 

of ‘‘Promoting Effective Prevention and 

Treatment of Chronic Disease’’ through 

the Meaningful Measures Area of 

‘‘Preventive Care.’’ 

d. Review by the Measures Application 

Partnership and NQF 

The COVID–19 HCP vaccination 

measure and the COVID–19 patient 

vaccination measure were included on 

the publicly available ‘‘List of Measures 

under Consideration for December 21, 

2020’’ (MUC List), a list of measures 

under consideration for use in various 

Medicare programs.253 When the 

Measure Applications Partnership 

Hospital Workgroup convened on 

January 11, 2021, it reviewed measures 

on the MUC List including the two 

COVID–19 vaccination measures. The 

Measure Applications Partnership 

Hospital Workgroup recognized that the 

proposed measures represent a 

promising effort to advance 

measurement for an evolving national 

pandemic and that it would bring value 

to the ESRD QIP measure set by 

providing transparency about an 

important COVID–19 intervention to 

help prevent infections in HCP and 

patients.254 The Measure Applications 

Partnership Hospital Workgroup also 

stated that collecting information on 

COVID–19 vaccination coverage among 

HCP and ESRD patients, and providing 

feedback to facilities, will allow 

facilities to benchmark coverage rates 

and improve coverage in their facility. 

The Measure Applications Partnership 

Hospital Workgroup further noted that 

reducing rates of COVID–19 in HCP and 

ESRD patients may reduce transmission 

among a patient population that is 

highly susceptible to illness and 

disease, and also reduce instances of 

staff shortages due to illness.255

In its preliminary recommendations, 

the Measure Applications Partnership 

Hospital Workgroup did not support 

these two measures for rulemaking, 

subject to potential for mitigation.256 To 

mitigate its concerns, the Measure 

Applications Partnership Hospital 

Workgroup believed that both measures 

needed well-documented evidence, 

finalized specifications, testing, and 

NQF endorsement prior to 

implementation.257 Subsequently, the 

Measure Applications Partnership 

Coordinating Committee met on January 

25, 2021, and reviewed the COVID–19 

Vaccination Coverage Among HCP 

measure and the COVID–19 Vaccination 

Coverage for Patients in ESRD Facilities 

Measure. In the 2020–2021 Measure 

Applications Partnership Final 

Recommendations, Measure 

Applications Partnership offered 

conditional support for rulemaking 

contingent on CMS bringing the 

measures back to Measure Applications 

Partnership once the specifications are 

further refined.258 The Measure 

Applications Partnership specifically 

stated, ‘‘the incomplete specifications 

require immediate mitigation and 

further development should 

continue.’’ 259 The Measure 

Applications Partnership further noted 

that the measures would add value to 

the ESRD QIP measure set by providing 

visibility into an important intervention 

to limit COVID–19 infections in HCP 

and the ESRD patients for whom they 

provide care.260 CMS brought both 

measures back to the Measure 

Applications Partnership on March 15, 

2021 to provide additional information 

and continue discussing mitigation. 

e. Request for Public Comment 

In this proposed rule, we would like 

to seek public comment on potentially 

adding the two new COVID–19 

vaccination measures discussed above, 

the COVID–19 vaccination measure for 

HCP and the COVID–19 vaccination 

measure for patients, to the ESRD QIP 

measure set.261

We are also interested in public 

comment on data collection, 


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262What are patient generated health data: 

https://www.healthit.gov/topic/otherhot-topics/ 

what-are-patient-generated-health-data. 

263Application Programming Interfaces (API) 

Resource Guide, Version 1.0. Available at: https:// 

www.healthit.gov/sites/default/files/page/2020-11/ 

API-Resource-Guide_v1_0.pdf. 

264https://www.healthit.gov/isa/united-statescore-data-interoperability-uscdi. 

submission, and reporting for the 

COVID–19 vaccination measure for HCP 

and the COVID–19 vaccination measure 

for patients. For example, we are 

considering requiring reporting for these 

measures on an annual basis for the 

performance period for each calendar 

year corresponding to the associated 

payment year, and the reporting period 

would be January 1 through December 

31 annually. Based on the measures 

currently being developed by the CDC 

that were submitted to the Measure 

Applications Partnership, facilities 

would report the measures through the 

National Healthcare Safety Network 

(NHSN) web-based surveillance system. 

We also seek public comment from 

stakeholders on other ways to collect 

data on COVID–19 vaccination rates at 

dialysis facilities for ESRD QIP purposes 

and their associated costs and burdens. 

Given the immediacy of the PHE for 

COVID–19, as well as the importance of 

continuing to monitor and make 

publicly available COVID–19 

vaccination rates as the PHE ends, we 

anticipate rulemaking on this 

requirement in the CY 2023 rulemaking 

cycle. 

3. Advancing to Digital Quality 

Measurement and the Use of Fast 

Healthcare Interoperability Resources 

(FHIR) 

We aim to move fully to digital 

quality measurement in CMS quality 

reporting and value-based purchasing 

programs by 2025. As part of this 

modernization of our quality 

measurement enterprise, we are issuing 

this request for information (RFI). The 

purpose of this RFI is to gather broad 

public input solely for planning 

purposes for our transition to digital 

quality measurement. Any updates to 

specific program requirements related to 

providing data for quality measurement 

and reporting provisions would be 

addressed through future rulemaking, as 

necessary. This RFI contains four parts: 

• Background. This part provides 

information on our quality measurement 

programs and our goal to move fully to 

digital quality measurement by 2025. 

This part also provides a summary of 

other recent HHS policy developments 

that are advancing interoperability and 

could support our move towards full 

digital quality measurement. 

• Definition of Digital Quality 

Measures (dQMs). This part provides a 

potential definition for dQMs. Specific 

requests for input are included in the 

section. 

• Changes Under Consideration to 

Advance Digital Quality Measurement: 

Actions in Four Areas to Transition to 

Digital Quality Measures by 2025. This 

part introduces four possible steps that 

would enable transformation of CMS’ 

quality measurement enterprise to be 

fully digital by 2025. Specific requests 

for input are included in the section. 

• Solicitation of Comments. This part 

lists all requests for input included in 

the above sections of this RFI. 

a. Background 

As required by law, we implemented 

quality measurement programs and 

value-based purchasing programs across 

a broad range of inpatient, outpatient, 

and post-acute care (PAC) settings, 

consistent with our mission to improve 

the quality of health care for Americans 

through measurement, transparency, 

and increasingly, value-based 

purchasing. These quality programs are 

foundational for incentivizing valuebased care, contributing to 

improvements in health care, enhancing 

patient outcomes, and informing 

consumer choice. We aim to move fully 

to digital quality measurement by 2025. 

We acknowledge providers within the 

various care and practice settings 

covered by our quality programs may be 

at different stages of readiness, and 

therefore, the timeline for achieving full 

digital quality measurement across our 

quality reporting programs may vary. 

We also continue to evolve the 

Medicare Promoting Interoperability 

Program that advances the use of 

certified electronic health record (EHR) 

technology, from an initial focus on 

electronic data capture to enhancing 

information exchange and expanding 

quality measurement (83 FR 41634). 

However, reporting quality data via 

EHRs remains burdensome, and our 

current approach to quality 

measurement does not readily 

incorporate emerging data sources such 

as patient-reported outcomes (PRO) and 

patient-generated health data 

(PGHD).262 There is a need to streamline 

our approach to data collection, 

calculation, and reporting to fully 

leverage clinical and patient-centered 

information for measurement, 

improvement, and learning. 

Additionally, advancements in 

technical standards and regulatory 

initiatives to improve interoperability of 

healthcare data are creating an 

opportunity to significantly improve our 

quality measurement systems. In May 

2020, we finalized interoperability 

requirements in the CMS 

Interoperability and Patient Access final 

rule (85 FR 25510) to support 

beneficiary access to data held by 

certain payers. At the same time, the 

Office of the National Coordinator for 

Health Information Technology (ONC) 

finalized policies in the ONC 21st 

Century Cures Act final rule (85 FR 

25642) to advance the interoperability of 

health IT as defined in section 4003 of 

the Cures Act, including the ‘‘complete 

access, exchange, and use of all 

electronically accessible health 

information.’’ Closely working with 

ONC, we collaboratively identified HL7 

Fast Healthcare Interoperability 

Resources (FHIR®) Release 4.0.1 as the 

standard to support Application 

Programming Interface (API) policies in 

both rules. ONC, on behalf of HHS, 

adopted the HL7 FHIR Release 4.0.1 for 

APIs and related implementation 

specifications at 45 CFR 170.215. We 

believe the FHIR standard has the 

potential to be a more efficient and 

modular standard to enable APIs. We 

also believe this standard enables 

collaboration and information sharing, 

which is essential for delivering highquality care and better outcomes at a 

lower cost. By aligning technology 

requirements for payers, health care 

providers, and health IT developers, 

HHS can advance-an interoperable 

health IT infrastructure that ensures 

providers and patients have access to 

health data when and where it is 

needed. 

In the ONC 21st Century Cures Act 

final rule ONC adopted a ‘‘Standardized 

API for Patient and Population 

Services’’ certification criterion for 

health IT that requires the use of the 

FHIR Release 4 and several 

implementation specifications. Health 

IT certified to this criterion will offer 

single patient and multiple patient 

services that can be accessed by third 

party applications (85 FR 25742).263 The 

ONC 21st Century Cures Act final rule 

also requires health IT developers 

update their certified health IT to 

support the U.S. Core Data for 

Interoperability (USCDI) standard.264

The scope of patient data identified in 

the USCDI and the data standards that 

support this data set are expected to 

evolve over time, starting with data 

specified in Version 1 of the USCDI. In 

November 2020, ONC issued an interim 

final rule with comment period 

extending the date when health IT 

developers must make technology 

meeting updated certification criteria 

available under the ONC Health IT 


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265 Information Blocking and the ONC Health IT 

Certification Program: Extension of Compliance 

Dates and Timeframes in Response to the Covid– 

19 Public Health Emergency. https://

www.govinfo.gov/content/pkg/FR-2020-11-04/pdf/ 

2020-24376.pdf. 

266The Office of the National Coordinator for 

Health Information Technology, Strategy on 

Reducing Regulatory and Administrative Burden 

Relating to the Use of Health IT and EHRs, Final 

Report (Feb. 2020). Available at: https://

www.healthit.gov/sites/default/files/page/2020-02/ 

BurdenReport_0.pdf. 

267 eCQI Resource Center, https://

ecqi.healthit.gov/. 

Certification Program until December 

31, 2022 (85 FR 70064).265

The CMS Interoperability and Patient 

Access final rule (85 FR 25510) and 

program policies build on the ONC 21st 

Century Cures Act final rule (85 FR 

25642). The CMS Interoperability and 

Patient Access final rule and policies 

require certain payers (for example, 

Medicare Advantage organizations, 

Medicaid, and CHIP fee for service 

programs, Medicaid managed care 

plans, CHIP managed care entities, and 

Qualified Health Plan [QHP] issuers on 

the Federally-facilitated Exchanges 

[FFEs]) to implement and maintain a 

standards-based Patient Access API 

using HL7 FHIR Release 4.0.1 to make 

available certain data to their enrollees 

and beneficiaries (called ‘‘patients’’ in 

the CMS interoperability rule). These 

certain data include data concerning 

claims and encounters, with the intent 

to ensure access to their own health care 

information through third-party 

software applications. The rule also 

established new Conditions of 

Participation for Medicare and Medicaid 

participating hospitals, psychiatric 

hospitals, and critical access hospitals 

(CAHs), requiring them to send 

electronic notifications to another 

healthcare facility or community 

provider or practitioner when a patient 

is admitted, discharged, or transferred 

(85 FR 25603). In the CY 2021 Physician 

Fee Schedule (PFS) final rule (85 FR 

84472), we finalized a policy to align 

the certified EHR technology required 

for use in the Promoting Interoperability 

programs and the MIPS Promoting 

Interoperability performance category 

with the updates to health IT 

certification criteria finalized in the 

ONC 21st Century Cures Act. Under this 

policy, eligible clinicians, MIPS eligible 

clinicians, and eligible hospitals and 

CAHs participating in the Promoting 

Interoperability Programs, must use 

technology meeting the updated 

certification criteria for performance 

and reporting periods beginning in 2023 

(85 FR 84825). 

The use of APIs can also reduce longstanding barriers to quality 

measurement. Currently, health IT 

developers are required to implement 

individual measure specifications 

within their health IT product. The 

health IT developer must also 

accommodate how that product 

connects with the unique variety of 

systems within a specific care setting.266

This may be further complicated by 

systems which integrate a wide range of 

data schemas. This process is 

burdensome and costly, and it is 

difficult to reliably obtain high quality 

data across systems. As health IT 

developers map their health IT data to 

the FHIR standard and related 

implementation specifications, APIs can 

enable these data to be easily accessible 

for measurement or other use cases, 

such as care coordination, clinical 

decision support, and supporting 

patient access. 

We believe the emerging data 

standardization and interoperability 

enabled by APIs will support the 

transition to full digital quality 

measurement by 2025, and are 

committed to exploring and seeking 

input on potential solutions for the 

transition to digital quality 

measurement as described in this RFI. 

b. Definition of Digital Quality Measures 

In this section we seek to refine the 

definition of digital quality measures 

(dQMs) to further operationalize our 

objective of fully transitioning to dQMs 

by 2025. We previously noted dQMs use 

‘‘sources of health information that are 

captured and can be transmitted 

electronically and via interoperable 

systems.’’ (85 FR 84845). In this RFI, we 

seek input on future elaboration that 

would define a dQM as a software that 

processes digital data to produce a 

measure score or measure scores. Data 

sources for dQMs may include 

administrative systems, electronically 

submitted clinical assessment data, case 

management systems, EHRs, 

instruments (for example, medical 

devices and wearable devices), patient 

portals or applications (for example, for 

collection of patient-generated health 

data), health information exchanges 

(HIEs) or registries, and other sources. 

We also note that dQMs are intended to 

improve the patient experience 

including quality of care, improve the 

health of populations, and/or reduce 

costs. 

We discuss one potential approach to 

developing dQM software in section 

IV.G.3.c of this proposed rule. In this 

section, we are seeking comment on the 

potential definition of dQMs in this RFI. 

We also seek feedback on how 

leveraging advances in technology (for 

example, FHIR APIs) to access and 

electronically transmit interoperable 

data for dQMs could reinforce other 

activities to support quality 

measurement and improvement (for 

example, the aggregation of data across 

multiple data sources, rapid-cycle 

feedback, and alignment of 

programmatic requirements). 

The transition to dQMs relies on 

advances in data standardization and 

interoperability. As providers and 

payers work to implement the required 

advances in interoperability over the 

next several years, we will continue to 

support reporting of eCQMs through 

CMS quality reporting programs and 

through the Promoting Interoperability 

programs.267 These fully digital 

measures continue to be important 

drivers of interoperability advancement 

and learning. CMS is currently respecifying and testing these measures to 

use FHIR rather than the currently 

adopted Quality Data Model (QDM) in 

anticipation of the wider use of FHIR 

standards. CMS intends to apply 

significant components of the output of 

this work, such as the re-specified 

measure logic and the learning done 

through measure testing with FHIR 

APIs, to define and build future dQMs 

that take advantage of the expansion of 

standardized, interoperable data. 

c. Changes Under Consideration To 

Advance Digital Quality Measurement: 

Potential Actions in Four Areas To 

Transition to Digital Quality Measures 

by 2025 

Building on the advances in 

interoperability and learning from 

testing of FHIR-converted eCQMs, we 

aim to move fully to dQMs, originating 

from sources of health information that 

are captured and can be transmitted 

electronically via interoperable systems, 

by 2025. 

To enable this transformation, we are 

considering further modernizing the 

quality measurement enterprise in four 

major ways: (1) Leverage and advance 

standards for digital data and obtain all 

EHR data required for quality measures 

via provider FHIR-based APIs; (2) 

redesign our quality measures to be selfcontained tools; (3) better support data 

aggregation; and (4) work to align 

measure requirements across our 

reporting programs, other federal 

programs and agencies, and the private 

sector where appropriate. 

These changes would enable us to 

collect and utilize more timely, 

actionable, and standardized data from 

diverse sources and care settings to 

improve the scope and quality of data 


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used in quality reporting and payment 

programs, reduce quality reporting 

burden, and make results available to 

stakeholders in a rapid-cycle fashion. 

Data collection and reporting efforts 

would become more efficient, supported 

by advances in interoperability and data 

standardization. Aggregation of data 

from multiple sources would allow 

assessments of costs and outcomes to be 

measured across multiple care settings 

for an individual patient or clinical 

conditions. We believe that aggregating 

data for measurement can incorporate a 

more holistic assessment of an 

individual’s health and healthcare and 

produce the rich set of data needed to 

enable patients and caregivers to make 

informed decisions by combining data 

from multiple sources (for example, 

patient reported data, EHR data, and 

claims data) for measurement. 

Perhaps most importantly, these steps 

would help us deliver on the full 

promise of quality measurement and 

drive us toward a learning health system 

that transforms healthcare quality, 

safety, and coordination and effectively 

measures and achieves value-based care. 

The shift from a static to a learning 

health system hinges on the 

interoperability of healthcare data, and 

the use of standardized data. dQMs 

would leverage this interoperability to 

deliver on the promise of a learning 

health system wherein standards-based 

data sharing and analysis, rapid-cycle 

feedback, and quality measurement and 

incentives are aligned for continuous 

improvement in patient-centered care. 

Similarly, standardized, interoperable 

data used for measurement can also be 

used for other use cases, such as clinical 

decision support and care coordination 

and care decision support, which 

impacts health care and care quality. 

We are requesting comments on four 

potential future actions that would 

enable transformation to a fully digital 

quality measurement enterprise by 

2025. 

(1) Leveraging and Advancing Standards 

for Digital Data and Obtaining All EHR 

Data Required for Quality Measures via 

Provider FHIR-Based APIs 

We are considering targeting the data 

required for our quality measures that 

utilize EHR data to be data retrieved via 

FHIR-based APIs based on standardized, 

interoperable data. Utilizing 

standardized data for EHR-based 

measurement (based on FHIR and 

associated implementation guides) and 

aligning where possible with 

interoperability requirements can 

eliminate the data collection burden 

providers currently experience with 

required chart-abstracted quality 

measures and reduce the burden of 

reporting digital quality measure results. 

We can fully leverage this advance to 

adapt eCQMs and expand to other 

dQMs through the adoption of 

interoperable standards across other 

digital data sources. We are considering 

methods and approaches to leverage the 

interoperability data requirements for 

APIs set by the ONC 21st Century Cures 

Act final rule for certified health 

technology to support modernization of 

CMS quality measure reporting. As 

discussed previously, these 

requirements will be included in 

certified technology in future years (85 

FR 84825), including availability of data 

included in the USCDI via standardsbased APIs, and CMS will require 

clinicians and hospitals participating in 

MIPS and the Promoting 

Interoperability Programs, respectively, 

to transition to use of certified 

technology updated consistent with the 

2015 Cures Edition Update (85 FR 

84825). 

Digital data used for measurement 

could expand beyond data captured in 

traditional clinical settings, 

administrative claims data, and EHRs. 

Many important data sources are not 

currently captured digitally, such as 

survey and PGHD. We intend to work to 

innovate and broaden the digital data 

used across the quality measurement 

enterprise beyond the clinical EHR and 

administrative claims. Agreed upon 

standards for these data, and associated 

implementation guides will be 

important for interoperability and 

quality measurement. We will consider 

developing clear guidelines and 

requirements for these digital data that 

align with interoperability 

requirements, for example, expressing 

in standards, exposing via APIs, and 

incentivizing technologies that innovate 

data capture and interoperability. 

High quality data are also essential for 

reliable and valid measurement. Hence, 

in implementing the shift to capture all 

clinical EHR data via FHIR-based APIs, 

we would support efforts to strengthen 

and test the quality of the data obtained 

through FHIR-based APIs for quality 

measurement. We currently conduct 

audits of electronic data with functions 

including checks for data completeness 

and data accuracy, confirmation of 

proper data formatting, alignment with 

standards, and appropriate data 

cleaning. These functions would 

continue and be applied to dQMs and 

further expanded to automate the 

manual validation of the data compared 

to the original data source (for example, 

the medical record) where possible. 

Analytic advancements such as natural 

language processing, big data analytics, 

and artificial intelligence, can support 

this evolution. These techniques can be 

applied to validating observed patterns 

in data and inferences or conclusions 

drawn from associations, as data are 

received, to ensure high quality data are 

used for measurement. 

We are seeking feedback on the goal 

of aligning data needed for quality 

measurement with interoperability 

requirements and the strengths and 

limitations of this approach. We are also 

seeking feedback on the importance of 

and approaches to supporting inclusion 

of PGHD and other currently nonstandardized data. We also welcome 

comment on approaches for testing data 

quality and validity. 

(2) Redesigning Quality Measures To Be 

Self-Contained Tools 

We are considering approaches for 

deploying quality measures to take 

advantage of standardized data and 

interoperability requirements that have 

expanded flexibility and functionality 

compared to CMS’ current eCQMs. We 

are considering defining and developing 

dQM software as end-to-end measure 

calculation solutions that retrieve data 

from primarily FHIR resources 

maintained by providers, payers, CMS, 

and others; calculate measure score(s); 

and produce reports. In general, we 

believe to optimize the use of 

standardized and interoperable data, the 

software solution for dQMs should do 

the following: 

• Have the flexibility to support 

calculation of single or multiple quality 

measure(s). 

• Perform three functions: (i) Obtain 

data via automated queries from a broad 

set of digital data sources (initially from 

EHRs, and in the future from claims, 

PRO, and PGHD); (ii) calculate the 

measure score according to measure 

logic; and (iii) generate measure score 

report(s). 

• Be compatible with any data source 

systems that implement standard 

interoperability requirements. 

• Exist separately from digital data 

source(s) and respect the limitations of 

the functionality of those data sources. 

• Be tested and updated 

independently of the data source 

systems. 

• Operate in accordance with health 

information protection requirements 

under applicable laws and comply with 

governance functions for health 

information exchange. 

• Have the flexibility to be deployed 

by individual health systems, health IT 

vendors, data aggregators, and health 

plans; and/or run by CMS depending on 

the program and measure needs and 

specifications. 


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268Calendar Year (CY) 2021 Physician Fee 

Schedule Final Rule: Finalized (New and Updated) 

Qualified Clinical Data Registry (QCDR) and 

Qualified Registry Policies, https://qpp-cm-prodcontent.s3.amazonaws.com/uploads/1362/ 

QCDR%20and%20QR%20Updates

%202021%20Final%20Rule%20Fact%20Sheet.pdf. 

269 Department of Health and Human Services, 

National Health Quality Roadmap (May 2020). 

Available at: https://www.hhs.gov/sites/default/ 

files/national-health-quality-roadmap.pdf. 

• Be designed to enable easy 

installation for supplemental uses by 

medical professionals and other nontechnical end-users, such as local 

calculation of quality measure scores or 

quality improvement. 

• Have the flexibility to employ 

current and evolving advanced analytic 

approaches such as natural language 

processing. 

• Be designed to support procompetitive practices for development, 

maintenance, and implementation and 

diffusion of quality measurement and 

related quality improvement and 

clinical tools through for example the 

use of open-source core architecture. 

We seek comment on these suggested 

functionalities and other additional 

functionalities that quality measure 

tools should ideally have particularly in 

the context of the pending availability of 

standardized and interoperable data (for 

example, standardized EHR data 

available via FHIR-based APIs). 

We are also interested whether and 

how this more open, agile strategy may 

facilitate broader engagement in quality 

measure development, the use of tools 

developed for measurement for local 

quality improvement, and/or the 

application of quality tools for related 

purposes such as public health or 

research. 

(3) Building a Pathway to Data 

Aggregation in Support of Quality 

Measurement 

Using multiple sources of collected 

data to inform measurement would 

reduce data fragmentation (or, different 

pieces of data regarding a single patient 

stored in many different places). 

Additionally, we are also considering 

expanding and establishing policies and 

processes for data aggregation and 

measure calculation by third-party 

aggregators that include, but are not 

limited to, HIEs and clinical registries. 

Qualified Clinical Data Registries and 

Qualified Registries that report quality 

measures for eligible clinicians in the 

Merit-based Incentive Payment System 

(MIPS) program are potential 

examples 268 at 42 CFR 

414.1440(b)(2)(iv) and (v) and 

§ 414.1440(c)(2)(iii) and (iv) and can 

also support measure reporting. We are 

considering establishing similar policies 

for third-party aggregators to maintain 

the integrity of our measure reporting 

process and to encourage market 

innovation. 

We seek feedback on aggregation of 

data from multiple sources being used 

to inform measurement. We also seek 

feedback on the role data aggregators 

can and should play in CMS quality 

measure reporting in collaboration with 

providers, and how we can best 

facilitate and enable aggregation. 

(4) Potential Future Alignment of 

Measures Across Reporting Programs, 

Federal and State Agencies, and the 

Private Sector 

We are committed to using policy 

levers and working with stakeholders to 

solve the issue of interoperable data 

exchange and to transition to full digital 

quality measurement. We are 

considering the future potential 

development and multi-staged 

implementation of a common portfolio 

of dQMs across our regulated programs, 

agencies, and private payers. This 

common portfolio would require 

alignment of: (1) Measure concepts and 

specifications including narrative 

statements, measure logic, and value 

sets, and (2) the individual data 

elements used to build these measure 

specifications and calculate the measure 

logic. Further, the required data 

elements would be limited to 

standardized, interoperable data 

elements to the fullest extent possible; 

hence, part of the alignment strategy 

will be the consideration and 

advancement of data standards and 

implementation guides for key data 

elements. We would coordinate closely 

with quality measure developers, 

federal and state agencies, and private 

payers to develop and to maintain a 

cohesive dQM portfolio that meets our 

programmatic requirements and that 

fully aligns across federal and state 

agencies and payers to the extent 

possible. 

We intend for this coordination to be 

ongoing and allow for continuous 

refinement to ensure quality measures 

remain aligned with evolving healthcare 

practices and priorities (for example, 

PROs, disparities, care coordination), 

and track with the transformation of 

data collection, alignment with health 

IT module updates including 

capabilities and standards adopted by 

ONC (for example, standards to enable 

APIs). This coordination would build on 

the principles outlined in HHS’ 

National Health Quality Roadmap.269 It 

would focus on the quality domains of 

safety, timeliness, efficiency, 

effectiveness, equitability, and patientcenteredness. It would leverage several 

existing federal and public-private 

efforts including our Meaningful 

Measures 2.0 Framework; the Federal 

Electronic Health Record Modernization 

(DoD/VA); the Agency for Healthcare 

Research and Quality’s Clinical 

Decision Support Initiative; the Centers 

for Disease Control and Prevention’s 

Adapting Clinical Guidelines for the 

Digital Age initiative; the Core Quality 

Measure Collaborative, which convenes 

stakeholders from America’s Health 

Insurance Plans (AHIP), CMS, NQF, 

provider organizations, private payers, 

and consumers and develops consensus 

on quality measures for provider 

specialties; and the NQF-convened 

Measure Applications Partnership, 

which recommends measures for use in 

public payment and reporting programs. 

We would coordinate with HL7’s 

ongoing work to advance FHIR 

resources in critical areas to support 

patient care and measurement such as 

social determinants of health. Through 

this coordination, we would identify 

which existing measures could be used 

or evolved to be used as dQMs, in 

recognition of current healthcare 

practice and priorities. 

This multi-stakeholder, joint federal 

and industry, made possible and 

enabled by the pending advances 

towards true interoperability, would 

yield a significantly improved quality 

measurement enterprise. The success of 

the dQM portfolio would be enhanced 

by the degree to which the measures 

achieve our programmatic requirements 

for measures as well as the requirements 

of other agencies and payers. 

We seek feedback on initial priority 

areas for the dQM portfolio given 

evolving interoperability requirements 

(for example, measurement areas, 

measure requirements, tools, and data 

standards). We also seek to identify 

opportunities to collaborate with other 

federal agencies, states, and the private 

sector to adopt standards and 

technology-driven solutions to address 

our quality measurement priorities 

across sectors. 

d. Solicitation of Comments 

As noted previously, we seek input on 

the future development of the following: 

• Definition of Digital Quality 

Measures: We are seeking feedback on 

the following as described in section 

IV.G.3.c.(2): 

++ Do you have feedback on the dQM 

definition? 

++ Does this approach to defining 

and deploying dQMs to interface with 

FHIR-based APIs seem promising? We 


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270ZIP code TM is a trademark of the United 

States Postal Service. 

also welcome more specific comments 

on the attributes or functions to support 

such an approach of deploying dQMs. 

• Changes Under Consideration to 

Advance Digital Quality Measurement: 

Actions in Four Areas to Transition to 

Digital Quality Measures by 2025 

++ We are seeking feedback on the 

following as described in section 

IV.G.3.c.(1) of this proposed rule: 

—Do you agree with the goal of 

aligning data needed for quality 

measurement with that required for 

interoperability? What are the strengths 

and limitations of this approach? 

—How important is a data 

standardization approach that also 

supports inclusion of PGHD and other 

currently non-standardized data? 

—What are possible approaches for 

testing data quality and validity? 

++ We are seeking feedback on the 

following as described in section 

IV.G.3.c.(2) of this proposed rule: 

—What functionalities, described in 

section IV.G.3.c.(2) of this proposed rule 

or others, should quality measure tools 

ideally have in the context of the 

pending availability of standardized and 

interoperable data (for example, 

standardized EHR data available via 

FHIR-based APIs)? 

—How would this more open, agile 

strategy for end-to-end measure 

calculation facilitate broader 

engagement in quality measure 

development, the use of tools developed 

for measurement for local quality 

improvement, and/or the application of 

quality tools for related purposes such 

as public health or research? 

++ We seek feedback on the 

following as described in section 

IV.G.3.c.(3) of this proposed rule: 

—Do you have feedback on policy 

considerations for aggregation of data 

from multiple sources being used to 

inform measurement? 

—Do you have feedback on the role 

data aggregators can and should play in 

CMS quality measure reporting in 

collaboration with providers? How can 

CMS best facilitate and enable 

aggregation? 

++ We seek feedback on the 

following as described in section 

IV.G.3.c.(4) of this proposed rule: 

—What are initial priority areas for 

the dQM portfolio (for example, 

measurement areas, measure 

requirements, tools)? 

—We also seek to identify 

opportunities to collaborate with other 

federal agencies, states, and the private 

sector to adopt standards and 

technology-driven solutions to address 

our quality measurement priorities and 

across sectors. 

We plan to continue working with 

other agencies and stakeholders to 

coordinate and to inform any potential 

transition to dQMs by 2025. While we 

will not be responding to specific 

comments submitted in response to this 

RFI in the CY 2022 ESRD 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. 

V. End-Stage Renal Disease Treatment 

Choices (ETC) Model 

A. Background 

1. Overview of the ETC Model 

As described in the Specialty Care 

Models final rule (85 FR 61114), 

beneficiaries with ESRD are among the 

most medically fragile and high-cost 

populations served by the Medicare 

program. ESRD Beneficiaries require 

dialysis or kidney transplantation to 

survive, and the majority of ESRD 

Beneficiaries receiving dialysis receive 

hemodialysis in an ESRD facility. 

However, as described in the Specialty 

Care Models final rule, alternative renal 

replacement modalities to in-center 

hemodialysis, including home dialysis 

and kidney transplantation, are 

associated with improved clinical 

outcomes, better quality of life, and 

lower costs than in-center hemodialysis 

(85 FR 61264). 

Section 1115A of the Act authorizes 

the Innovation Center to test innovative 

payment and service delivery models 

expected to reduce Medicare, Medicaid, 

and CHIP expenditures while preserving 

or enhancing the quality of care 

furnished to such programs’ 

beneficiaries. The purpose of the ETC 

Model is to test the effectiveness of 

adjusting certain Medicare payments to 

ESRD facilities and Managing Clinicians 

to encourage greater utilization of home 

dialysis and kidney transplantation, 

support beneficiary modality choice, 

reduce Medicare expenditures, and 

preserve or enhance the quality of care. 

The ETC Model is a mandatory 

payment model, as we seek to test the 

effect of payment incentives on 

availability and choice of treatment 

modality among a diverse group of 

providers and suppliers. ESRD facilities 

and Managing Clinicians are selected as 

ETC Participants based on their location 

in Selected Geographic Areas—a set of 

30 percent of Hospital Referral Regions 

(HRRs) that have been randomly 

selected to be included in the ETC 

Model, as well as HRRs with at least 20 

percent of component ZIP codes 270

located in Maryland. CMS excludes all 

U.S. Territories from the Selected 

Geographic Areas. 

Under the ETC Model, ETC 

Participants are subject to two payment 

adjustments. The first is the Home 

Dialysis Payment Adjustment (HDPA), 

which is an upward adjustment on 

certain payments made to participating 

ESRD facilities under the ESRD PPS on 

home dialysis claims, and an upward 

adjustment to the MCP paid to 

participating Managing Clinicians on 

home dialysis-related claims. The HDPA 

applies to claims with claim service 

dates beginning in January 1, 2021, and 

ending on December 31, 2023. 

The second payment adjustment 

under the ETC Model is the 

Performance Payment Adjustment 

(PPA). For the PPA, we assess ETC 

Participants’ home dialysis rate and 

transplant rate during a Measurement 

Year (MY), which includes 12 months of 

performance data. Each MY overlaps 

with the previous MY, if any, and the 

subsequent MY, if any, for a period of 

6 months. Each MY has a corresponding 

PPA Period—a 6-month period which 

begins 6 months after the conclusion of 

the MY. We adjust certain payments for 

ETC Participants during the PPA Period 

based on the ETC Participant’s home 

dialysis rate and transplant rate, 

calculated as the sum of the transplant 

waitlist rate and the living donor 

transplant rate, during the 

corresponding MY. Based on an ETC 

Participant’s achievement in relation to 

benchmarks based on the home dialysis 

rate and transplant rate observed in 

Comparison Geographic Areas during 

the Benchmark Year, and the ETC 

Participant’s improvement in relation to 

its own home dialysis rate and 

transplant rate during the Benchmark 

Year, we make an upward or downward 

adjustment to certain payments to the 

ETC Participant. The magnitude of the 

positive and negative PPAs for ETC 

Participants increases over the course of 

the ETC Model. These PPAs apply to 

claims with claim service dates 

beginning July 1, 2022, and ending June 

30, 2027. 

2. Summary of Proposed Changes to the 

ETC Model 

In this proposed rule, we are 

proposing a number of policy changes to 

the ETC Model beginning for the third 

Measurement Year (MY3) of the Model, 

which begins January 1, 2022. We are 

proposing changes to the methodology 


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for attributing Pre-emptive LDT 

Beneficiaries to Managing Clinicians to 

better reflect the care relationship 

between beneficiaries who receive preemptive LDT transplants and the 

Managing Clinicians who provide their 

care. We are also proposing to include 

nocturnal in-center dialysis in the 

numerator of the home dialysis rate 

calculation for ESRD facilities not 

owned in whole or in part by an LDO 

as well as Managing Clinicians, to 

incentivize additional alternative renal 

replacement modalities. In addition, we 

are proposing to exclude beneficiaries 

who are diagnosed with and receiving 

treatment with chemotherapy or 

radiation for vital solid organ cancers 

from the transplant rate to align with 

common transplant center requirements. 

We are proposing to modify the PPA 

achievement benchmarking 

methodology to increase achievement 

benchmarks by 10 percent above rates 

observed in Comparison Geographic 

Areas every two MYs, beginning for 

MY3 (2022). We are proposing to stratify 

PPA achievement benchmarks based on 

the proportion of attributed 

beneficiaries who are dually-eligible for 

Medicare and Medicaid or receive the 

Low-Income Subsidy during the MY, 

and to introduce the Health Equity 

Incentive to the PPA improvement 

scoring methodology, both in an effort 

to encourage ETC Participants to 

address disparities in renal replacement 

modality choice among beneficiaries 

with lower socioeconomic status. We 

are proposing to modify the PPA 

improvement benchmarking and scoring 

methodology to ensure an ETC 

Participant can receive an improvement 

score even if its home dialysis rate or 

transplant rate was zero during the 

relevant Benchmark Year. 

We are proposing to add processes 

and requirements for CMS to share 

certain model data with ETC 

Participants. We are also proposing 

additional programmatic waivers as 

necessary solely for purposes of 

allowing Managing Clinicians who are 

ETC participants to furnish kidney 

disease patient education services via 

telehealth under the ETC Model. In 

addition, we propose to permit 

Managing Clinicians who are ETC 

Participants to reduce or waive 

beneficiary coinsurance for kidney 

disease patient education services, 

subject to certain requirements. CMS 

expects that the proposed changes 

would continue to promote the larger 

goals of increased renal replacement 

modality choice and are based on many 

of the issues we laid out in the Specialty 

Care Models final rule as issues for 

which CMS was considering further 

rulemaking, including updating 

benchmarks for ETC Participants and 

adjusting model parameters based on 

our implementation experience. 

3. Impact of Proposed Changes on the 

ETC Model Evaluation 

As we described in the Specialty Care 

Models final rule, an evaluation of the 

ETC Model will be conducted in 

accordance with section 1115A(b)(4) of 

the Act, which requires the Secretary to 

evaluate each model tested by the 

Innovation Center. We noted that we 

believe an independent evaluation of 

the Model is necessary to understand 

the impacts of the Model on quality of 

care and Medicare program 

expenditures (85 FR 61345). 

We propose to update the evaluation 

plan presented in the Specialty Care 

Models final rule to account for all the 

policies proposed in this rule, if 

finalized. However, changes in the 

construction of the PPA, if finalized, 

would have no impact on the evaluation 

approach to analyzing the final PPA 

values. This is because the evaluation 

plan already includes a consideration of 

the final PPA values, rather than an 

evaluation of each step in the PPA 

calculation. However, we expect to 

conduct subgroup analyses in the 

evaluation to determine the effect of the 

proposed Health Equity Incentive, if 

finalized, in reducing health disparities 

among beneficiaries with lower 

socioeconomic status. 

As part of the detailed economic 

analysis included in section IX.B.4 of 

this proposed rule, the transplant 

waitlist benchmarks were annually 

inflated by approximately 3-percentage 

points growth. This was a change from 

the Specialty Care Models final rule (85 

FR 61352), where the waitlist 

benchmarks were annually inflated by 

approximately 2-percentage points 

growth observed during years 2017 

through 2019 to project rates of growth. 

By increasing the expected effect to a 

3-percentage point change, we improve 

our ability to detect such an effect at the 

ETC Model’s current size. In the 

Specialty Care Models final rule, we 

stated that to detect a 2-percentage point 

increase in the transplant waitlist rate, 

we would need 30 percent of the 306 

HRRs in order to detect an effect of this 

size with 80 percent power and an alpha 

of 0.05. Further, we stated that a model 

of this size would be large enough to 

detect a one and one-half percentage 

point change in the home dialysis rate 

(85 FR 61280). We clarify that our 

unadjusted power calculations show 

that the model requires 30 percent of the 

306 HRRs to detect the one and one-half 

percentage point change in the home 

dialysis rate with 80 percent power and 

an alpha of 0.05. Given the updated 

expectation that the transplant waitlist 

rate is likely to increase by 3-percentage 

points as a result of the ETC Model, the 

power analysis shows the evaluation 

would also have sufficient sample size 

to detect, as statistically significant, a 3- 

percentage point change in the 

transplant waitlist rate with 80 percent 

power and an alpha of 0.05. 

B. Provisions of the Proposed Rule 

1. Technical Clarifications 

For ESRD facilities that are ETC 

Participants, the ETC Model makes 

certain upward and downward 

adjustments to the Adjusted ESRD PPS 

per Treatment Base Rate for certain 

dialysis claims via the Home Dialysis 

Payment Adjustment (HDPA) and the 

Performance Payment Adjustment 

(PPA). The term ‘‘Adjusted ESRD PPS 

per Treatment Base Rate’’ is defined at 

42 CFR 512.310 as the per-treatment 

payment amount as defined in § 413.230 

of this chapter, including patient-level 

adjustments and facility-level 

adjustments, and excluding any 

applicable training adjustment, add-on 

payment amount, outlier payment 

amount, TDAPA amount, and TPNIES 

amount. In this proposed rule, we are 

clarifying the claims subject to 

adjustment under the ETC Model. 

Specifically, as § 413.230 is specific to 

the calculation of payment amounts 

under the ESRD PPS, we clarify that the 

HDPA and PPA do not apply to claims 

from ESRD facilities that are not paid 

under ESRD PPS and are instead paid 

through other Medicare payment 

systems. 

We are also updating the name of one 

of the sources of data used throughout 

the ETC Model. In the Specialty Care 

Models final rule, we specify that one 

source of data for the ETC Model is 

CROWNWeb, a data management 

system that CMS uses to collect data 

from ESRD facilities (85 FR 61317). 

Since publication, CMS has replaced 

CROWNWeb with the End Stage Renal 

Disease Quality Reporting System 

(EQRS). As such, we will refer to 

CROWNWeb for data that was generated 

before the change to EQRS, which CMS 

began using in 2020, and EQRS for data 

that was generated after the change to 

EQRS. 

2. Performance Payment Adjustment 

(PPA) Beneficiary Attribution for Living 

Kidney Donor Transplants 

In the Specialty Care Models final 

rule, we established that beneficiaries 

are attributed to Managing Clinicians for 

the purposes of calculating the home 


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271Wilk, Adam S., Lea, Janice P. (2019). How 

Extended Hemodialysis Treatment Time Can Affect 

Continued 

dialysis rate and transplant rate (85 FR 

61297). For the home dialysis rate and 

the transplant waitlist and living donor 

kidney transplant portions of the 

transplant rate, as described in 42 CFR 

512.360(c)(2)(i), an ESRD Beneficiary is 

generally attributed to the Managing 

Clinician with the earliest monthly 

capitation payment (MCP) claim billed 

during the month. If more than one 

Managing Clinician submits a claim for 

the MCP furnished to a single ESRD 

Beneficiary with the same earliest claim 

service date at the claim line through 

date for the month, the ESRD 

Beneficiary is randomly attributed to 

one of these Managing Clinicians. 

However, a beneficiary who receives 

a pre-emptive living donor transplant 

(Pre-emptive LDT Beneficiary) is not on 

dialysis and therefore cannot be 

attributed to a Managing Clinician using 

an MCP claim. As a result, under 

§ 512.360(c)(2)(ii), a Pre-emptive LDT 

Beneficiary is generally attributed to the 

Managing Clinician with whom the Preemptive LDT Beneficiary had the most 

claims between the start of the MY and 

the month of the transplant. If no 

Managing Clinician has had the 

plurality of claims for a given Preemptive LDT Beneficiary such that 

multiple Managing Clinicians each had 

the same number of claims for that 

beneficiary during the MY, the Preemptive LDT Beneficiary is attributed to 

the Managing Clinician associated with 

the latest claim service date during the 

MY up to and including the month of 

the transplant, as described in 

§ 512.360(c)(2)(ii)(A). If no Managing 

Clinician had the plurality of claims for 

a given Pre-emptive LDT Beneficiary 

such that multiple Managing Clinicians 

each had the same number of services 

for that beneficiary during the MY, and 

more than one of those Managing 

Clinicians had the latest claim service 

date during the MY up to and including 

the month of the transplant, the Preemptive LDT Beneficiary is randomly 

attributed to one of these Managing 

Clinicians, as described in 

§ 512.360(c)(2)(ii)(B). 

Upon further review of the beneficiary 

attribution methodology for living donor 

kidney transplants, we realized that an 

unintended consequence of the current 

attribution methodology is that Preemptive LDT Beneficiaries may be 

attributed to the nephrologist who 

manages their transplant, not the 

Managing Clinician who has seen them 

through the living donor transplant 

process. To avoid this effect, CMS 

believes it is necessary to update the 

attribution methodology for Pre-emptive 

LDT Beneficiaries. Living donor 

transplants are relatively rare events 

that require nephrologist support over 

time in order to inform beneficiaries of 

their transplant options and to assist 

them in finding a living donor. 

However, the current Pre-emptive LDT 

Beneficiary attribution methodology is 

based on visits from the beginning of a 

MY. As a result, if a Pre-emptive LDT 

Beneficiary has a transplant early in a 

MY, the beneficiary may be attributed to 

a transplant nephrologist who may have 

had only a single visit with the 

beneficiary, rather than the Managing 

Clinician who oversaw the largest share 

of the care that led to the beneficiary 

receiving the living donor transplant. 

As a result, we propose to update the 

attribution methodology for Pre-emptive 

LDT Beneficiaries to Managing 

Clinicians, beginning for MY3, in new 

provisions at § 512.360(c)(2)(iii). Rather 

than attributing a Pre-emptive LDT 

Beneficiary to the Managing Clinician 

with the plurality of claims from the 

start of the MY and the month of the 

transplant, beginning for MY3, we 

propose to attribute Pre-emptive LDT 

Beneficiaries to the Managing Clinician 

with whom the beneficiary has had the 

most claims during the 365 days prior 

to the transplant date. Further, we 

propose that if no Managing Clinician 

has had the most claims for the Preemptive LDT Beneficiary such that 

multiple Managing Clinicians each had 

the same number of claims for that 

beneficiary in the 365 days preceding 

the date of the transplant, the Preemptive LDT Beneficiary would be 

attributed to the Managing Clinician 

associated with the latest claim service 

date at the claim line through date 

during the 365 days preceding the date 

of the transplant. We propose that if 

more than one of those Managing 

Clinicians had the latest claim service 

date at the claim line through date 

during the 365 days preceding the date 

of the transplant, the Pre-emptive LDT 

Beneficiary would be randomly 

attributed to one of these Managing 

Clinicians. We propose that the Preemptive LDT Beneficiary would be 

considered eligible for attribution to a 

Managing Clinician under this proposed 

new § 512.360(c)(2)(iii) if the Preemptive LDT Beneficiary has at least 1 

eligible–month during the 12-month 

period that includes the month of the 

transplant and the 11 months prior to 

the transplant month. We propose that 

an eligible month would refer to a 

month during which the Pre-emptive 

LDT Beneficiary not does not meet 

exclusion criteria in § 512.360(b). CMS 

is proposing changes for Pre-emptive 

LDT Beneficiary attribution to Managing 

Clinicians in order to identify and 

attribute Pre-emptive LDT Beneficiaries 

to the Managing Clinician who assisted 

the Beneficiary through the living donor 

transplant process. We seek comment 

on these proposed changes for Preemptive LDT Beneficiary attribution to 

Managing Clinicians beginning for MY3 

in proposed new § 512.360(c)(2)(iii). 

3. PPA Home Dialysis Rate 

a. Background on Home Dialysis Rate 

Calculation 

A primary goal of the ETC Model is 

to support beneficiary modality choice 

by encouraging ETC Participants to 

support beneficiaries in selecting 

alternatives to in-center dialysis. Under 

42 CFR 512.365(b), CMS includes incenter self-dialysis treatment beneficiary 

years in the numerator of the home 

dialysis rate. Specifically, the home 

dialysis rate for both Managing 

Clinicians and ESRD facilities is 

calculated as the number of dialysis 

treatment beneficiary years during the 

MY in which attributed beneficiaries 

received dialysis at home, plus one half 

of the total number of dialysis treatment 

beneficiary years during the MY in 

which the attributed beneficiaries 

received self-dialysis in center. As 

described in the Specialty Care Models 

final rule, we included self-dialysis in 

the home dialysis rate calculation 

because we believe in-center selfdialysis may provide a gradual 

transition from in-center to home 

dialysis, and provide beneficiaries with 

the time needed to get comfortable 

conducting dialysis by themselves, 

under medical supervision (85 FR 

61306). 

The denominator for the home 

dialysis rate is the total dialysis 

treatment beneficiary years for 

attributed ESRD beneficiaries during the 

MY, as described in §§ 512.365(b)(1)(i) 

and 512.365(b)(2)(i). This includes the 

months during which attributed 

beneficiaries received maintenance 

dialysis at home or in an ESRD facility. 

b. Nocturnal Dialysis 

Nocturnal in-center dialysis is a form 

of in-center dialysis conducted 

overnight for extended hours while the 

beneficiary is asleep. This dialysis is 

longer and slower than traditional incenter dialysis, can take more than 5 

hours per treatment, and can be 

performed 3 to 7 days a week. As this 

type of in-center dialysis is conducted 

overnight, it allows the beneficiary more 

time and flexibility to have a continuous 

job, as well as a social and family life.271


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Patient Quality of Life. Clinical Journal of the 

American Society of Nephrology, 23, 479–485. 

doi:10.1111/hdi.12782. 

272Burton, J. and Graham-Brown, M., 2018. 

Nocturnal hemodialysis. Current Opinion in 

Nephrology and Hypertension, 27(6), pp.472–477. 

273 Kalim, S., Wald, R., Yan, A. T., Goldstein, M. 

B., Kiaii, M., Xu, D., . . . Perl, J. (2018). Extended 

duration nocturnal hemodialysis and changes in 

plasma metabolite profiles. Clinical Journal of the 

American Society of Nephrology, 13(3), 436–444. 

doi:10.2215/cjn.08790817. 

274Nesrallah, G. E., Lindsay, R. M., Cuerden, M. 

S., Garg, A. X., Port, F., Austin, P. C., . . . Suri, R. 

S. (2012). Intensive hemodialysis associates with 

improved survival compared with conventional 

hemodialysis. Journal of the American Society of 

Nephrology, 23(4), 696–705. doi:10.1681/ 

asn.2011070676. 

275Wong, B., Collister, D., Muneer, M., Storie, D., 

Courtney, M., Lloyd, A., . . . Pauly, R. P. (2017). Incenter nocturnal hemodialysis versus conventional 

hemodialysis: A systematic review of the evidence. 

American Journal of Kidney Diseases, 70(2), 218– 

234. doi:10.1053/j.ajkd.2017.01.047. 

276 Wilk, Adam S., Lea, Janice P. (2019). How 

Extended Hemodialysis Treatment Time Can Affect 

Patient Quality of Life. Clinical Journal of the 

American Society of Nephrology, 23, 479–485. 

doi:10.1111/hdi.12782. 

276Lacson E, Diaz-Buxo J. In-center nocturnal 

hemodialysis performed thrice-weekly—a 

provider’s perspective. Semin Dial. 2011 Nov– 

Dec;24(6):668–73. doi: 10.1111/j.1525– 

139X.2011.00998.x. Epub 2011 Nov 22. PMID: 

22106828. 

277Bugeja A, Dacouris N, Thomas A, Marticorena 

R, McFarlane P, Donnelly S, Goldstein M. In-center 

nocturnal hemodialysis: another option in the 

management of chronic kidney disease. Clin J Am 

Soc Nephrol. 2009 Apr;4(4):778–83. doi: 10.2215/ 

CJN.05221008. Epub 2009 Apr 1. PMID: 19339410; 

PMCID: PMC2666425. 

278Lacson E, Diaz-Buxo J. In-center nocturnal 

hemodialysis performed thrice-weekly—a 

provider’s perspective. Semin Dial. 2011 Nov– 

Dec;24(6):668–73. doi: 10.1111/j.1525– 

139X.2011.00998.x. Epub 2011 Nov 22. PMID: 

22106828. 

279 Ibid. 

280United States Renal Data System. 2015. ‘‘2015 

Researcher’s Guide to the USRDS Database.’’ 

Dialysis conducted at a slower rate 

over a longer period of time is also 

associated with positive health impacts 

in comparison to traditional dialysis, 

including improved blood pressure 

control, better phosphate control, better 

management of anemia and bone and 

mineral metabolism, improved 

cardiovascular disease, increases in urea 

reduction ratio, and better beneficiary 

quality of life measures.272 273 274 275 276

In addition to the clinical benefits, 

nocturnal in-center dialysis also 

provides an alternative to traditional incenter dialysis for those beneficiaries for 

whom home dialysis is not an option 

due to limited financial resources, 

housing insecurity, lack of social 

support, or personal preference. For 

example, a beneficiary experiencing 

housing insecurity may be unable to 

dialyze at home due to inability to 

receive and store home dialysis 

materials. However, that beneficiary 

could receive nocturnal in-center 

dialysis, thereby receiving the clinical 

benefits of a longer, slower dialysis 

process and the flexibility associated 

with not having to receive traditional incenter dialysis during the day.277 278

While nocturnal in-center dialysis 

offers some of the same clinical and 

quality of life benefits as home dialysis 

in comparison to traditional in-center 

dialysis, use of nocturnal in-center 

dialysis is rare. Based on analyses 

described in section IX.B.4.a.(4) of this 

proposed rule, less than 1 percent of 

beneficiaries eligible for attribution to 

ETC Participants were receiving selfdialysis or nocturnal in-center dialysis 

in 2019. Potential limitations to 

nocturnal in-center dialysis utilization 

include supply factors. At present, few 

ESRD facilities offer nocturnal dialysis; 

in 2019, approximately 1 percent of 

ESRD facilities furnished nocturnal incenter dialysis based on our analysis of 

claims data. ESRD facilities may face 

staffing challenges to initiating a 

nocturnal dialysis program. Potential 

limitations to nocturnal in-center 

dialysis also include demand factors: 

beneficiaries may be unaware of 

nocturnal in-center dialysis, or may be 

averse to sleeping at an ESRD facility or 

experience difficulty sleeping while 

receiving dialysis.279

c. Proposed Inclusion of Nocturnal InCenter Dialysis in Home Dialysis Rate 

We propose to modify the home 

dialysis rate calculation, for ETC 

Participants that are either ESRD 

facilities not owned in whole or in part 

by an LDO or Managing Clinicians, to 

include nocturnal in-center dialysis in 

the numerator beginning for MY3. As 

described previously in this section of 

the proposed rule, we believe this 

modality allows beneficiaries to 

continue to receive maintenance 

dialysis in an ESRD facility under 

medical supervision, but at a time of 

day that is more convenient for them, 

and in a manner that is associated with 

improved health outcomes. In 

particular, we believe that including 

nocturnal in-center dialysis in the home 

dialysis rate may improve access to 

alternative renal replacement modalities 

for beneficiaries who are unable to 

dialyze at home. 

In addition to promoting access to the 

benefits of additional alternative renal 

replacement modalities for ESRD 

Beneficiaries who may not be able to 

dialyze at home, we believe that 

including nocturnal in-center dialysis in 

the calculation of the home dialysis rate 

offers an additional pathway to success 

for ETC Participants with more limited 

resources. As described in the Specialty 

Care Models final rule, we received 

comments that some ESRD facilities, 

particularly independent ESRD facilities 

or ESRD facilities owned by small 

dialysis organizations, may be unable to 

develop and maintain a home dialysis 

program (85 FR 61322 through 61324). 

Operating a home dialysis program 

requires specialized staff, as well as 

upfront investment in additional 

equipment and certification. 

Establishing a nocturnal in-center 

dialysis program does not require 

additional equipment or certification, 

and may be more feasible for 

independent ESRD facilities or ESRD 

facilities owned by small dialysis 

organizations, and by extension, the 

Managing Clinicians who serve their 

patients. 

We considered including nocturnal 

in-center dialysis in the numerator of 

the home dialysis rate for ESRD 

facilities owned in whole or in part by 

LDOs as well. However, we do not 

believe that ESRD facilities owned in 

whole or in part by LDOs face the same 

resource constraints in establishing a 

home dialysis program as independent 

ESRD facilities or ESRD facilities owned 

by small dialysis organizations. ESRD 

facilities owned in whole or in part by 

LDOs may be more likely to have access 

to a home dialysis program, either in the 

ESRD facility itself or within the 

network of facilities owned by the same 

parent company in that facility’s 

aggregation group. ESRD facilities 

owned in whole or in part by LDOs may 

also have greater access to the upfront 

capital necessary to establish a home 

dialysis program if they do not already 

have, or have access to, a home dialysis 

program. 

At present, there is not a single 

definition of what qualifies a legal entity 

that owns ESRD facilities as an LDO. In 

general, definitions of LDO focus on the 

number of ESRD facilities owned by the 

legal entity. Other Innovation Center 

models have used such definitions: The 

Comprehensive ESRD Care (CEC) Model 

defined an LDO as a legal entity owning 

200 or more ESRD facilities; the Kidney 

Care Choices (KCC) Model defines an 

LDO as a legal entity owning 35 or more 

ESRD facilities. Outside of Innovation 

Center models, definitions used by 

academic researchers vary significantly. 

For example, in 2015 the United States 

Renal Data System (USRDS), a national 

data registry funded by the National 

Institutes of Health (NIH), defined an 

LDO as a dialysis organization one that 

owns and operates 200 or more ESRD 

facilities.280 Other academic research 


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https://usrds.org/media/2219/2015_usrds_

researchers_guide_15.pdf. 

281Mehrotra R, Khawar O, Duong U, Fried L, 

Norris K, Nissenson A, Kalantar-Zadeh K. 

Ownership patterns of dialysis units and peritoneal 

dialysis in the United States: utilization and 

outcomes. Am J Kidney Dis. 2009 Aug;54(2):289– 

98. doi: 10.1053/j.ajkd.2009.01.262. Epub 2009 Apr 

8. PMID: 19359081. 

282 Gander JC, Zhang X, Ross K, et al. Association 

Between Dialysis Facility Ownership and Access to 

Kidney Transplantation. JAMA. 2019;322(10):957– 

973. doi:10.1001/jama.2019.12803. 

283Medicare Payment Advisory Commission. 

2021. Report to the Congress: Medicare and the 

health care delivery system. Washington, DC: 

MedPAC. http://www.medpac.gov/docs/defaultsource/reports/mar21_medpac_report_to_the_

congress_sec.pdf. 

has employed thresholds as low as 

owning 20 or more ESRD facilities and 

as high as owning 1,000 or more ESRD 

facilities to consider a legal entity an 

LDO.281 282 Other definitions do not 

focus on the number of ESRD facilities 

owned, but on the relative size of 

dialysis organizations in the market, or 

rather, the individual dialysis 

organizations themselves. For example, 

in its March 2021 report to Congress, the 

Medicare Payment Advisory 

Commission (MedPAC) refers to the two 

largest dialysis organizations in the 

country as LDOs based on their relative 

share of ESRD facilities and Medicare 

treatments.283 Based on our review of 

definitions commonly used, for the 

purposes of the ETC Model we propose 

to define the term ‘‘ETC Large Dialysis 

Organization,’’ abbreviated ‘‘ETC LDO,’’ 

as a legal entity that owns, in whole or 

in part, 500 or more ESRD facilities. 

Based on the current distribution of 

numbers of ESRD facilities owned by 

dialysis organizations operating in the 

market, we believe this threshold is 

appropriate, as it differentiates the 

largest dialysis organizations, which at 

present own over 2,500 ESRD facilities, 

from smaller dialysis organizations, the 

next largest of which owns 

approximately 350 ESRD facilities. We 

believe the difference in size represents 

a meaningful difference in access to 

resources necessary to establish a home 

dialysis program, as well as the 

likelihood that an ESRD facility’s 

aggregation group would have at least 

one ESRD facility with a home dialysis 

program in the aggregation group. We 

seek comment on our proposal to 

include nocturnal in-center dialysis 

beneficiary years in the numerator of the 

home dialysis rate calculation only for 

ESRD facilities not owned in whole or 

in part by an ETC LDO, as well as our 

proposal to define an ETC LDO as a 

legal entity owning 500 or more ESRD 

facilities. 

While nocturnal in-center dialysis can 

potentially result in better patient health 

outcomes and savings to Medicare 

compared to traditional in-center 

dialysis, we acknowledge that its 

inclusion in the home dialysis rate may 

reduce the incentive for ESRD facilities 

not owned in whole or in part by an 

LDO to invest in a home dialysis 

infrastructure. We therefore propose to 

include nocturnal in-center dialysis as 

one half of the total number of dialysis 

treatment beneficiary years during the 

MY in which the attributed beneficiaries 

received nocturnal in-center dialysis in 

the numerator of the home dialysis rate 

calculation for ESRD facilities not 

owned in whole or in part by an ETC 

LDO as well as Managing Clinicians. We 

believe this policy would effectively 

balance the benefits of nocturnal incenter dialysis and its ability to help 

beneficiaries transition to home dialysis 

with the recognition that in-center 

nocturnal dialysis is not home dialysis 

and does not have all of the same 

benefits. As described in the Specialty 

Care Models final rule, we included one 

half of the total number of dialysis 

treatment beneficiary years during the 

MY in which the attributed beneficiaries 

received self-dialysis in center in the 

home dialysis rate calculation for a 

similar reason (85 FR 61306). 

As such, we propose to amend 

§ 512.365(b) such that, beginning for 

MY3, the numerator for the home 

dialysis rate for ESRD facilities not 

owned in whole or in part by an ETC 

LDO and Managing Clinicians would be 

the total number of dialysis treatment 

beneficiary years during the MY in 

which attributed ESRD Beneficiaries 

received maintenance dialysis at home, 

plus one half of the total number of 

dialysis treatment beneficiary years 

during the MY in which attributed 

ESRD Beneficiaries received 

maintenance dialysis via self-dialysis, 

plus one half of the total number of 

dialysis treatment beneficiary years 

during the MY in which attributed 

ESRD Beneficiaries received 

maintenance dialysis via in-center 

nocturnal dialysis. We further propose 

to add paragraph (C) to both 

§§ 512.365(b)(1)(ii) and 512.365(b)(2)(ii) 

to specify that nocturnal in-center 

dialysis beneficiary years included in 

the numerator of the home dialysis rate 

calculation would be composed of those 

months during which attributed ESRD 

Beneficiaries received nocturnal incenter dialysis, such that 1-beneficiary 

year is comprised of 12-beneficiary 

months. The months in which an 

attributed ESRD Beneficiary received 

nocturnal in-center dialysis would be 

identified by claims with Type of Bill 

072X, where the type of facility code is 

7 and the type of care code is 2, and 

with the modifier UJ, which specifies 

that a claim with Type of Bill 072X is 

for nocturnal in-center dialysis. We seek 

comment on these proposed changes to 

§ 512.365(b). 

4. Performance Payment Adjustment 

Transplant Rate 

a. Status of Organ Availability 

The ETC Model is designed to 

encourage greater rates of 

transplantation. In the proposed rule 

published on July 18, 2019 in the 

Federal Register titled, ‘‘Medicare 

Program; Specialty Care Models to 

Improve Quality of Care and Reduce 

Expenditures’’ (84 FR 34478), referred to 

herein as the ‘‘Specialty Care Models 

proposed rule,’’ CMS proposed to 

include the rate of transplants, both 

living and deceased donor transplants, 

in the numerator for the ETC Model’s 

transplant rate. However, in the 

Specialty Care Models final rule, we 

recognized the limitations of supply of 

deceased donor organs and updated the 

transplant rate to be calculated as the 

sum of the transplant waitlist rate and 

the living donor transplant rate (85 FR 

61310). We stated that though a 

transplant is often the best treatment for 

a beneficiary with ESRD, in light of the 

current shortage of deceased donor 

organs for transplant, the transplant 

waitlist rate and living donor transplant 

rate are currently more within the 

control of an ETC Participant (85 FR 

61309). 

However, in the Specialty Care 

Models final rule, we indicated our 

intent to observe the supply of deceased 

donor organs available for 

transplantation, with the goal of 

potentially modifying the transplant rate 

calculation for the future (85 FR 61309). 

Since the Specialty Care Models final 

rule was published on September 29, 

2020, there have been several initiatives 

pursued by the federal government that 

could potentially have the effect of 

increasing the supply of both living 

donor organs and deceased donor 

organs. 

On September 22, 2020, the Health 

Resources and Services Administration 

(HRSA) published a final rule in the 

Federal Register titled ‘‘Removing 

Financial Disincentives to Living Organ 

Donation’’ (85 FR 59438). This rule 

removes financial barriers to organ 

donation by expanding the scope of 

reimbursable expenses incurred by 

living organ donors to include lost 

wages, and child-care and elder-care 

expenses incurred by a caregiver. The 

rule went into effect on October 22, 

2020. 


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Additionally, on December 2, 2020, 

CMS published in the Federal Register 

a final rule titled, ‘‘Medicare and 

Medicaid Programs; Organ Procurement 

Organizations Conditions for Coverage: 

Revisions to the Outcome Measure 

Requirements for Organ Procurement 

Organizations’’ (85 FR 77898), revising 

Conditions for Coverage (CfCs) for 

Organ Procurement Organizations 

(OPOs). The final rule revised the CfCs 

for OPOs in order to increase donation 

rates and organ transplantation rates 

and replaced the old outcome measures 

with new transparent, reliable, and 

objective measures. The final rule went 

into effect on March 30, 2021. The new 

outcome measures will be implemented 

for the recertification cycle beginning in 

2022 and ending in 2026. The goals of 

this rule are complementary to the goals 

of the ETC Model, as the revised CfCs 

are intended to increase the supply of 

organs, and the ETC Model is designed 

to incentivize higher rates of 

transplantation. 

Finally, as described in the Specialty 

Care Models final rule, CMS is in the 

process of implementing the ETC 

Learning Collaborative (85 FR 61346). 

The ETC Learning Collaborative is a 

voluntary learning system focused on 

increasing the availability of deceased 

donor kidneys for transplantation. The 

ETC Learning Collaborative works with 

and supports ETC Participants and other 

stakeholders required for successful 

kidney transplantation, such as 

transplant centers, OPOs, and large 

donor hospitals. CMS is currently in the 

process of jointly implementing the ETC 

Learning Collaborative with HRSA. 

We are pleased that these efforts have 

progressed since the publication of the 

Specialty Care Models final rule. 

However, given that these efforts are 

still in the implementation process, we 

do not believe that it would be 

appropriate to update the transplant rate 

to include accountability for deceased 

donor transplants, rather than transplant 

waitlisting, at this time. We still intend 

to update the transplant rate through 

future rulemaking to include 

accountability for deceased donor 

transplants, but we are not proposing to 

do so at this time. 

Beneficiary Exclusions From the 

Transplant Rate 

As we discussed in the Specialty Care 

Models final rule (85 FR 61300), CMS 

received comments about excluding 

ESRD Beneficiaries with cancer from 

attribution to ETC Participants, as there 

was concern about treatment 

appropriateness. However, at that time, 

CMS did not have any evidence to 

suggest that this is a concern. 

Accordingly, we did not exclude 

beneficiaries with cancer from 

attribution to ETC Participants for 

purposes of calculating the home 

dialysis rate or the transplant rate in the 

Specialty Care Models final rule. 

Nevertheless, after we published the 

Specialty Care Models final rule, we 

conducted further analysis, to determine 

if a difference existed in either the home 

dialysis rate or transplant rate in 

beneficiaries with cancer and 

beneficiaries without cancer. Using the 

Medicare claims data and input from 

clinical specialists in the field of 

nephrology, we found that the majority 

of ESRD Beneficiaries with cancer, 

specifically ESRD Beneficiaries with 

cancer in vital solid organs (heart, lung, 

liver, and kidney), are not considered to 

be eligible candidates for transplant. 

Many transplant centers do not consider 

these beneficiaries for transplant and 

require them to be cancer-free for a 

specific period of time prior to assessing 

their eligibility for transplant. This is 

true for getting on a transplant waitlist 

and for receiving living donor 

transplants, as a beneficiary either needs 

to be cancer-free or be in an initial stage 

of cancer diagnosis to be considered for 

transplant. 

In addition, we found that ESRD 

Beneficiaries who have a diagnosis of 

solid organ cancer for which they were 

receiving treatment, specifically 

radiation or chemotherapy, are less 

likely to be in the numerator of the 

transplant rate—so, being placed on the 

transplant waitlist or receive a living 

donor transplant—than ESRD 

Beneficiaries without a diagnosis of 

vital solid organ cancer. By contrast, we 

did not find any evidence to suggest that 

ESRD Beneficiaries with cancer had a 

significant difference in the home 

dialysis rate compared to the ESRD 

Beneficiaries without cancer. 

As noted previously, under 

§§ 512.310 and 512.365(c), the 

transplant rate has two components: 

The transplant waitlist rate and the 

living donor transplant rate. Upon 

further review and analysis, beginning 

for MY3, we propose to exclude ESRD 

Beneficiaries and, if applicable, Preemptive LDT Beneficiaries who have 

been diagnosed with vital solid organ 

cancers (heart, lung, liver and kidney) 

and who are receiving treatment, in the 

form of radiation or chemotherapy, for 

such cancers from both components of 

the denominator of the transplant rate 

for both ESRD facilities and Managing 

Clinicians for the duration of the MY. 

Furthermore, we propose to include a 

lookback period, a period of time prior 

to the MY, to appropriately identify the 

ESRD Beneficiaries and, if applicable, 

Pre-emptive LDT Beneficiaries with a 

diagnosis of vital solid organ cancer for 

which they are receiving chemotherapy 

or radiation therapy. Both a diagnosis 

code and a treatment code are necessary 

to appropriately identify an ESRD 

Beneficiary or Pre-emptive LDT 

Beneficiary with a vital solid organ 

cancer who is receiving treatment with 

either radiation or chemotherapy. 

However, through our analysis we have 

identified beneficiaries who have only a 

treatment code available during the MY 

and do not have a diagnosis code during 

that period. Hence, we are proposing to 

include a lookback period of 6-months 

prior to the MY, so that the appropriate 

diagnosis code can be identified for 

ESRD Beneficiaries and Pre-emptive 

LDT Beneficiaries who have only 

treatment codes available in the current 

MY. In the alternative, we considered a 

12-month lookback period, but did not 

find any significant difference in the 

number of ESRD Beneficiaries and Preemptive LDT Beneficiaries that had a 

diagnosis code for a vital organ solid 

cancer during a 12-month lookback 

period as compared to a 6-month 

lookback period. 

We propose to identify ESRD 

Beneficiaries and, if applicable, Preemptive LDT Beneficiaries with a 

diagnosis of vital solid organ cancer and 

receiving treatment with radiation or 

chemotherapy by using Medicare 

claims. For purposes of the transplant 

rate calculations, an ESRD Beneficiary 

or Pre-emptive LDT Beneficiary would 

be considered to have a diagnosis of 

vital solid cancer during the MY, if the 

ESRD Beneficiary has a claim with one 

of the following ICD–10 diagnosis 

codes: 

• C22.0–C22.9 (malignant neoplasm 

of liver and intrahepatic bile ducts), 

• C34.10–C34.12 (malignant 

neoplasm of upper lobe, bronchus or 

lung), 

• C34.2 (malignant neoplasm of 

middle lobe, bronchus or lung), 

• C34.30–C34.32 (malignant 

neoplasm of lower lobe, bronchus or 

lung), 

• C34.80–C34.82 (malignant 

neoplasm of overlapping sites of 

bronchus and lung), 

• C34.90–C34.92 (malignant 

neoplasm of unspecified part of 

bronchus or lung), 

• C38.0 (malignant neoplasm of 

heart), 

• C38.8 (malignant neoplasm of 

overlapping sites of heart, mediastinum 

and pleura), 

• C46.50–C46.52 (Kaposi’s sarcoma of 

lung), 


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• C64.1, C64.2, C64.9 (malignant 

neoplasm of kidney, except renal 

pelvis), 

• C78.00–C78.02 (secondary 

malignant neoplasm of lung), 

• C78.7 (secondary malignant 

neoplasm of liver and intrahepatic bile 

duct), 

• C79.00–C79.02 (secondary 

malignant neoplasm of kidney and renal 

pelvis), 

• C7A.090 (malignant carcinoid 

tumor of the bronchus and lung), 

• C7A.093 (malignant carcinoid 

tumor of the kidney), or 

• C7B.02 (secondary carcinoid tumors 

of liver). 

We propose that for the purposes of 

the transplant rate calculations, an 

ESRD Beneficiary or Pre-emptive LDT 

Beneficiary would be considered to be 

receiving treatment for vital solid organ 

cancer with either chemotherapy or 

radiation in the MY if the ESRD 

Beneficiary or Pre-emptive LDT 

Beneficiary has a claim with one of the 

following codes: 

• CPT® 96401–96402, 96405–96406, 

96409, 96411, 96413, 96415–96417, 

96420, 96422–26423, 96425, 96440, 

96446 (chemotherapy administration); 

• CPT® 96549 (unlisted 

chemotherapy procedure); 

• CPT® 77373 (stereotactic body 

radiation therapy); 

• CPT® 77401–77402, 77407, 77412 

(radiation treatment delivery); 

• CPT® 77423 (high energy neutron 

radiation treatment delivery); 

• CPT® 77424–77425 (intraoperative 

radiation treatment delivery); 

• CPT® 77520, 77522–77523, 77525 

(proton treatment delivery); 

• CPT® 77761–77763 (intracavitary 

radiation source application); 

• CPT® 77770–77772, 77778, 77789, 

77799 (clinical brachytherapy radiation 

treatment); 

• CPT® 79005, 79101, 79200, 79300, 

79403, 79440, 79445, 79999 

(radiopharmaceutical therapy); 

• ICD–10–PCS DB020ZZ, DB021ZZ, 

DB022ZZ, DB023Z0, DB023ZZ, 

DB024ZZ, DB025ZZ, DB026ZZ, 

DB1297Z, DB1298Z, DB1299Z, 

DB129BZ, DB129CZ, DB129YZ, 

DB12B6Z, DB12B7Z, DB12B8Z, 

DB12B9Z, DB12BB1, DB12BBZ, 

DB12BCZ, DB12BYZ, DB22DZZ, 

DB22HZZ, DB22JZZ, DBY27ZZ, 

DBY28ZZ, DBY2FZZ, DBY2KZZ 

(radiation of lung); 

• ICD–10–PCS DB070ZZ, DB071ZZ, 

DB072ZZ, DB073Z0, DB073ZZ, 

DB074ZZ, DB075ZZ, DB076ZZ, 

DB1797Z, DB1798Z, DB1799Z, 

DB179BZ, DB179CZ, DB179YZ, 

DB17B6Z, DB17B7Z, DB17B8Z, 

DB17B9Z, DB17BB1, DB17BBZ, 

DB17BCZ, DB17BYZ, DB27DZZ, 

DB27HZZ, DB27JZZ, DBY77ZZ, 

DBY78ZZ, DBY7FZZ, DBY7KZZ 

(radiation of chest wall); 

• ICD–10–PCS DF000ZZ, DF001ZZ, 

DF002ZZ, DF003Z0, DF003ZZ, 

DF004ZZ, DF005ZZ, DF006ZZ, 

DF1097Z, DF1098Z, DF1099Z, 

DF109BZ, DF109CZ, DF109YZ, 

DF10B6Z, DF10B7Z, DF10B8Z, 

DF10B9Z, DF10BB1, DF10BBZ, 

DF10BCZ, DF10BYZ, DF0DZZ, 

DF20HZZ, DF20JZZ, DFY07ZZ, 

DFY08ZZ, DFY0CZZ, DFY0FZZ, 

DFY0KZZ (radiation of liver); 

• ICD–10–PCS DT000ZZ, DT001ZZ, 

DT002ZZ, DT003Z0, DT003ZZ, 

DT004ZZ, DT005ZZ, DT006ZZ, 

DT1097Z, DT1098Z, DT1099Z, 

DT109BZ, DT109CZ, DT109YZ, 

DT10B6Z, DT10B7Z, DT10B8Z, 

DT10B9Z, DT10BB1, DT10BBZ, 

DT10BCZ, DT10BYZ, DT20DZZ, 

DT20HZZ, DT20JZZ, DTY07ZZ, 

DTY08ZZ, DTY0CZZ, DTY0FZZ 

(radiation of kidney); 

• ICD–10–PCS DW020ZZ, DW021ZZ, 

DW022ZZ, DW023Z0, DW023ZZ, 

DW024ZZ, DW025ZZ, DW026ZZ, 

DW1297Z, DW1298Z, DW1299Z, 

DW129BZ, DW129CZ, DW129YZ, 

DW12B6Z, DW12B7Z, DW12B8Z, 

DW12B9Z, DW12BB1, DW12BBZ, 

DW12BCZ, DW12BYZ, DW22DZZ, 

DW22HZZ, DW22JZZ, DWY27ZZ, 

DWY28ZZ, DWY2FZZ (radiation of 

chest); or 

• ICD–10–PCS DW030ZZ, DW031ZZ, 

DW032ZZ, DW033Z0, DW033ZZ, 

DW034ZZ, DW035ZZ, DW036ZZ, 

DW1397Z, DW1398Z, DW1399Z, 

DW139BZ, DW139CZ, DW139YZ, 

DW13B6Z, DW13B7Z, DW13B8Z, 

DW13B9Z, DW13BB1, DW13BBZ, 

DW13BCZ, DB13BYZ, DW23DZZ, 

DW23HZZ, DW23JZZ, DWY37ZZ, 

DWY38ZZ, DWY3FZZ (radiation of 

abdomen); 

We seek comment on the proposal to 

amend § 512.365(c) to exclude ESRD 

Beneficiaries and, if applicable, Preemptive LDT Beneficiaries with a 

diagnosis of vital solid organ cancer and 

receiving treatment with chemotherapy 

or radiation from the denominator of the 

transplant rate as a whole, including 

both the transplant waitlist rate 

component and the living donor 

transplant rate component, for the 

duration of the MY for both ESRD 

facilities and Managing Clinicians. 

5. PPA Achievement Benchmarking 

a. Background on Achievement 

Benchmarking 

Under the ETC Model, the PPA is a 

positive or negative adjustment on 

dialysis and dialysis-related Medicare 

payments, for both home dialysis and 

in-center dialysis. To calculate an ETC 

Participant’s PPA, we assess ETC 

Participant achievement on the home 

dialysis rate and transplant rate in 

relation to achievement and 

improvement benchmarks, as described 

in 42 CFR 512.370(b) and § 512.370(c), 

respectively. The Model more heavily 

weights achievement of results, 

allowing participating Managing 

Clinicians or ESRD facilities to earn up 

to 2 points in the scoring methodology, 

as opposed to only 1.5 points for 

maximum level of improvement, as 

described in §§ 512.370(b) and 

512.370(c). 

The achievement benchmarks are 

constructed based on the home dialysis 

rate and transplant rate observed in 

Comparison Geographic Areas during 

corresponding Benchmark Years. 

Achievement benchmarks are percentile 

based, and an ETC Participant receives 

the achievement points that correspond 

with its performance, at the aggregation 

group level, on the home dialysis rate 

and transplant rate in relation to the 

achievement benchmarks, as described 

in § 512.370(b). Table 7 details the 

achievement score scale described in 

§ 512.370(b). 


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In the Specialty Care Models 

proposed rule, we proposed to apply 

this achievement benchmark policy 

only for MY1 and MY2, and stated our 

intent to increase achievement 

benchmarks for ETC Participants above 

the rates observed in Comparison 

Geographic Areas. We stated our belief 

that increasing the achievement 

benchmarks for future MYs, which we 

would do through subsequent 

rulemaking, was necessary in order to 

provide sufficient incentive for ETC 

Participants to increase rates of home 

dialysis and transplantation at a rate 

faster than would occur absent the ETC 

Model (84 FR 34556 through 34557). In 

the Specialty Care Models final rule, in 

response to comments, we finalized the 

applicability of the achievement 

benchmarks for MY1–MY2 and for 

subsequent MYs (85 FR 61323), but 

reiterated our intent to establish a 

different method for establishing 

achievement benchmarks for future 

years of the Model through subsequent 

rulemaking (85 FR 61320). We stated 

our belief that future modifications to 

the achievement benchmark 

methodology finalized in the Specialty 

Care Models final rule would be 

necessary to provide sufficient incentive 

for ETC Participants to raise home 

dialysis and transplant rates at a rate 

faster than would occur absent the ETC 

Model (85 FR 61321). However, we 

clarified that while we had stated a goal 

of 80 percent of an ETC Participant’s 

receiving home dialysis or a transplant 

in order to receive the maximum 

upward payment adjustment by the 

final MYs, we were not finalizing that 

goal in the Specialty Care Models final 

rule (85 FR 61321). 

b. Addressing Socioeconomic Factors 

That Impact ETC Participant 

Achievement 

In the Specialty Care Models final 

rule, we acknowledged commenters’ 

concerns that non-clinical factors, such 

as socioeconomic status, may impact a 

beneficiary’s likelihood to receive home 

dialysis or transplant. We discussed 

commenters’ suggestions to incorporate 

consideration of socioeconomic status 

in two elements of the ETC Model: (1) 

Beneficiary attribution; and (2) risk 

adjustment. However, we declined to 

exclude beneficiaries from attribution 

based on socioeconomic status. Noting 

the importance of not excluding these 

beneficiaries, CMS stated its intent to 

assess the use of various codes for 

purposes of adding any additional 

beneficiary exclusions from attribution 

to ETC Participants based on 

socioeconomic status, homelessness, or 

other social determinants of health 

through future rulemaking (85 FR 

61299). We also noted that commenters’ 

suggestions for ways to risk adjust the 

home dialysis rate based on 

socioeconomic status were a significant 

departure from the policy originally 

proposed (85 FR 61315). 

We continue to acknowledge the 

impact that non-clinical factors, such as 

socioeconomic status, have on a 

beneficiary’s likelihood to receive home 

dialysis or a transplant. Based on our 

additional analysis of Medicare claims 

data show that beneficiaries who are 

dual-eligible for Medicare and Medicaid 

or receive the Medicare Low-Income 

Subsidy (LIS) are less likely than 

beneficiaries who are not dual-eligible 

and are not LIS recipients to dialyze at 

home or to receive a kidney transplant. 

As such, ETC Participants who have a 

higher proportion of attributed 

beneficiaries who are dual-eligible or 

LIS recipients may be less likely to 

achieve high home dialysis and 

transplant rates than ETC Participants 

who have a lower proportion of 

attributed beneficiaries who are dualeligible or LIS recipients. 

c. Proposed Achievement Benchmarking 

and Scoring 

(1) Achievement Benchmarking and 

Scoring for MY3 Through MY10 

We propose to modify the percentilebased achievement benchmarking 

methodology based on the home 

dialysis rate and transplant rate 

observed in Comparison Geographic 

Areas during the Benchmark Year as the 

basis for achievement benchmarks in 

MY3 through MY10. Rather than using 

rates observed in Comparison 

Geographic Areas, we propose to modify 

§ 512.370(b)(1) to use rates observed in 

Comparison Geographic Areas as the 

base for the achievement benchmarks, 

and to increase the achievement 

benchmarks above the Comparison 

Geographic Area rates during the 

Benchmark Year by 10 percent every 

two MYs, beginning for MY3. As such, 

we propose that achievement 

benchmarks would be calculated by 

multiplying the percentile rate observed 

in Comparison Geographic Areas during 

the Benchmark Year by 1.1 for MY3 and 

MY4, by 1.2 for MY5 and MY6, by 1.3 

for MY7 and MY8, and by 1.4 for MY9 

and MY10. 

Based on CMS analyses detailed in 

section IX.B.4 of this proposed rule, this 

proposed methodology for increasing 

benchmarks by 10 percent every two 

MYs would produce results in keeping 

with the initial impact estimates for the 

ETC Model, as described in the 

Specialty Care Models final rule (85 FR 

61353 through 61354). In the Specialty 

Care Models final rule, we estimated 

impacts based on projected growth rates 

for the home dialysis and transplant 

rates based on historical observation, 

projected a 1.5 percentage point growth 

rate (85 FR 61354). In section IX.B.4 of 

this proposed rule, updated projections 

assume the same projected growth rate, 

but note that observed rates of increase 

have accelerated in more recent data. As 

such, we believe that this proposed rate 


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TABLE 7: Current Scoring Methodology for Achievement Scores 

Achievement Score Scale for MYl and MY2 Points 

901:h+ Percentile of benchmark rates for Comparison Geographic Areas during the 2 

Benchmark Year 

751:h+ Percentile of benchmark rates for Comparison Geographic Areas during the 1.5 

Benchmark Year 

501:h+ Percentile of benchmark rates for Comparison Geographic Areas during the 1 

Benchmark Year 

30th+ Percentile of benchmark rates for Comparison Geographic Areas during the 0.5 

Benchmark Year 

<30th Percentile of benchmark rates for Comparison Geographic Areas during the 0 

Benchmark Year 
Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 36383 

of increase would be attainable for ETC 

Participants, as initial impact estimates 

were based on rates of increase observed 

on the home dialysis rate and transplant 

rate before the ETC Model began (85 FR 

61353). We also note that, unlike in the 

Specialty Care Models proposed rule (84 

FR 34556), we are not proposing to 

increase achievement benchmarks such 

that of 80 percent of an ETC 

Participant’s attributed beneficiaries 

would need to be receiving home 

dialysis or a transplant in order for the 

ETC Participant to receive the maximum 

upward payment adjustment by the 

final MYs. 

Table 8 details the proposed scoring 

methodology for assessment of MY3 

through MY10 achievement scores. 

We considered increasing 

achievement benchmarks by a 

percentage point amount, rather than by 

a percent amount, every two MYs (for 

example, increasing achievement 

benchmarks by 10-percentage points for 

MY3 and MY4, by 20-percentage points 

for MY5 and MY6, etc.). However, we 

believe that this percentage point-based 

approach would be less flexible to and 

accommodating of variation in the 

underlying distributions of home 

dialysis and transplant rates than the 

percent-based approach we are 

proposing. We also believe this 

percentage point-based approach would 

add additional complexity, as we would 

likely need to develop separate 

percentage point amounts by which to 

increase benchmarks as the home 

dialysis rate and transplant rate 

observed in Comparison Geographic 

Areas are not sufficiently similar to 

expect the same percentage point 

growth rate for the two rates. 

We also considered proposing to 

modify the Benchmark Year, such that 

the Benchmark Year would be a fixed 

duration (for example, July 1, 2018 

through June 30, 2019), rather than a 

period of time defined in relation to the 

relevant MY. However, we determined 

that this approach would not account 

for aggregate changes in the home 

dialysis rate and transplant rate over 

time. 

We believe that the proposed 

approach for increasing achievement 

benchmarks over the course of the ETC 

Model balances the intent of the model 

design to increase rates of home dialysis 

and transplantation above what would 

have occurred in the absence of the 

Model with what is achievable for ETC 

Participants, based on rates of home 

dialysis and transplantation observed at 

the high ends of the distributions (for 

additional discussion, see section 

IX.B.4.a.(3) of this proposed rule). We 

also believe the proposed approach 

would provide clarity to ETC 

Participants about the benchmarking 

methodology for the duration of the ETC 

Model while maintaining flexibility in 


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TABLE 8: Proposed Scoring Methodology for Assessment of Measurement Years 3 

th roue h 10 A c h. 1evemen t S cores on th e H ome D. 1a1vs1s I . R t d T I t R t a e an ransp an ae 

Achievement Score Scale Points 

MY3 andMY4 MY5andMY6 MY7 andMY8 MY9 andMYlO 

1.1 * (90lh+ Percentile 1.2 * (90lh+ Percentile 1.3 * (90th+ Percentile 1.4 * (90lh+ Percentile 2 

of benchmark rates for of benchmark rates for of benchmark rates for of benchmark rates for 

Comparison Comparison Comparison Comparison 

Geographic Areas Geographic Areas Geographic Areas Geographic Areas 

during the Benchmark during the Benchmark during the Benchmark during the Benchmark 

Year) Year) Year) Year) 

1.1 * (7 5th+ Percentile 1.2 * (75lh+ Percentile 1.3 * (7 5th+ Percentile 1.4 * (75lh+ Percentile 1.5 

of benchmark rates for of benchmark rates for of benchmark rates for of benchmark rates for 

Comparison Comparison Comparison Comparison 

Geographic Areas Geographic Areas Geographic Areas Geographic Areas 

during the Benchmark during the Benchmark during the Benchmark during the Benchmark 

Year) Year) Year) Year) 

1.1 * (50lh+ Percentile 1.2 * (50lh+ Percentile 1.3 * (50th+ Percentile 1.4 * (50lh+ Percentile 1 

of benchmark rates for of benchmark rates for of benchmark rates for of benchmark rates for 

Comparison Comparison Comparison Comparison 

Geographic Areas Geographic Areas Geographic Areas Geographic Areas 

during the Benchmark during the Benchmark during the Benchmark during the Benchmark 

Year) Year) Year) Year) 

1.1 * (30th+ Percentile 1.2 * (30th+ Percentile 1.3 * (30th+ Percentile 1.4 * (30th+ Percentile 0.5 

of benchmark rates for of benchmark rates for of benchmark rates for of benchmark rates for 

Comparison Comparison Comparison Comparison 

Geographic Areas Geographic Areas Geographic Areas Geographic Areas 

during the Benchmark during the Benchmark during the Benchmark during the Benchmark 

Year) Year) Year) Year) 

1.1 * (<30th Percentile 1.2 * (<30th Percentile 1.3 * ( <30th Percentile 1.4 * (<30th Percentile 0 

of benchmark rates for of benchmark rates for of benchmark rates for of benchmark rates for 

Comparison Comparison Comparison Comparison 

Geographic Areas Geographic Areas Geographic Areas Geographic Areas 

during the Benchmark during the Benchmark during the Benchmark during the Benchmark 

Year) Year) Year) Year) 
36384 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

that methodology to address long term 

trends in the home dialysis rate and 

transplant rate. 

We seek public comment on our 

proposal to modify the achievement 

benchmarking methodology under 

§ 512.370(b) beginning for MY3 to 

increase achievement benchmarks, and 

the proposal to increase achievement 

benchmarks by 10 percent every two 

MYs above percentile-based rates of 

observed in Comparison Geographic 

Areas. 

(2) Achievement Benchmark 

Stratification by Dual-Eligible and Low 

Income Subsidy (LIS) Status 

We also propose to modify 

§ 512.370(b) to stratify achievement 

benchmarks based on the proportion of 

beneficiary years attributed to the ETC 

Participant’s aggregation group for 

which attributed beneficiaries were 

dually-eligible for Medicare and 

Medicaid or received the LIS, based on 

rates in Comparison Geographic Areas. 

Under our proposal, we would create 

two strata with the cutpoint set at 50 

percent of attributed beneficiary years 

being for attributed beneficiaries who 

were dual-eligible or received the LIS. 

As such, there would be one stratum for 

ETC Participants whose aggregation 

groups had 50 percent or more of their 

attributed beneficiary years during the 

MY for beneficiaries who were dualeligible or received the LIS, based on 

rates in Comparison Geographic Areas 

for aggregation groups with 50 percent 

or more attributed beneficiary years 

during the Benchmark Year being for 

dual-eligible or LIS beneficiaries. There 

would be a second stratum for ETC 

Participants whose aggregation groups 

had less than 50 percent of their 

attributed beneficiary years during the 

MY for beneficiaries who were dualeligible or received the LIS, based on 

rates in Comparison Geographic Areas 

for aggregation groups with less than 50 

percent attributed beneficiary years 

during the Benchmark Year being for 

dual-eligible or LIS beneficiaries. We 

propose to determine whether an 

attributed beneficiary was dual-eligible 

or received the LIS for a given month 

using Medicare administrative data. We 

believe this proposal would address 

concerns that socioeconomic factors 

may impact a beneficiary’s likelihood to 

receive alternative renal replacement 

modalities, lowering the transplant rate 

and home dialysis rates for ETC 

Participants who provide services to 

low income beneficiaries. We expect 

that stratifying the achievement 

benchmarks as proposed would increase 

home dialysis rate and transplant rates 

for such ETC Participants. 

We considered using more than two 

strata, in order to increase the precision 

of the achievement benchmarks and the 

degree of similarity between ETC 

Participants within a given stratum. 

However, increasing the number of 

strata would decrease the number of 

observations within each stratum, in 

turn decreasing statistical reliability. 

Additionally, analysis of the 

distribution of the home dialysis rate 

and transplant rate demonstrates that 

the underlying distribution does not 

lend itself to more than two strata, as 

the distribution is not multi-modal. For 

this reason, we are proposing only two 

strata. 

We seek public comment on our 

proposal to amend § 512.370(b) to 

stratify achievement benchmarks based 

on the proportion of attributed 

beneficiary years for which attributed 

beneficiaries were dual-eligible or 

received the LIS, and on our proposal to 

create two strata for this purpose. 

6. PPA Improvement Benchmarking and 

Scoring 

a. Background on Improvement 

Benchmarking and Scoring 

Another part of the scoring 

methodology for the PPA is 

improvement scoring. We calculate an 

ETC Participant’s improvement score 

under § 512.370(c) by comparing MY 

performance on the home dialysis rate 

and transplant rate against past ETC 

Participant performance. As described 

in the Specialty Care Models final rule, 

the purpose of the improvement score is 

to acknowledge efforts made in practice 

transformation to improve rates of home 

dialysis and transplants (85 FR 61318). 

The percentage improvement in the ETC 

Participant’s MY performance on the 

home dialysis rate and the transplant 

rate relative to the Benchmark Year rate 

is scored as follows: 

• Greater than 10 percent improvement 

relative to the Benchmark Year rate: 

1.5 points 

• Greater than 5 percent improvement 

relative to the Benchmark Year rate: 1 

point 

• Greater than 0 percent improvement 

relative to the Benchmark Year rate: 

0.5 points 

• Less than or equal to the Benchmark 

Year rate: 0 points 

However, when the Benchmark Year 

rate is zero, an improvement score for 

the MY cannot be calculated. This is 

because, when calculating percent 

change, as used in improvement 

scoring, the Benchmark Year rate is the 

denominator. As such, we cannot 

calculate percent improvement for an 

aggregation group with a rate of zero 

during the Benchmark Year because the 

denominator of the improvement score 

calculation is zero, and division by zero 

is undefined. Thus, an aggregation 

group in this situation will not receive 

an improvement score if the Benchmark 

Year rate is zero, even if the aggregation 

group has made improvements in the 

home dialysis rate and/or the transplant 

rate between the Benchmark Year and 

MY. 

b. Incentivizing Improvement for 

Socioeconomically Disadvantaged 

Beneficiaries 

As described in section V.B.5.b of this 

proposed rule, beneficiaries who are 

dual-eligible or receive the LIS are less 

likely than beneficiaries who are not 

dual-eligible and do not receive the LIS 

to dialyze at home or receive a kidney 

transplant. As described previously in 

this section of the proposed rule, we are 

proposing to stratify achievement 

benchmarks by the proportion of 

attributed beneficiary years for 

beneficiaries who are dual-eligible or 

LIS recipients to avoid disadvantaging 

ETC Participants who provide care for a 

high proportion of these beneficiaries. 

However, this proposed stratification 

would not provide a direct financial 

incentive for ETC Participants to focus 

on reducing disparities by improving 

the home dialysis rate and transplant 

rate for beneficiaries who are dualeligible or receive the LIS. We are 

interested in creating that incentive as 

part of the ETC Model, as these 

beneficiaries may require additional 

support from ETC Participants to pursue 

home dialysis and transplant as 

alternative renal replacement 

modalities. 

c. Proposed Changes to Improvement 

Benchmarking and Scoring 

(1) Revised Improvement Calculation 

As described above, when the 

Benchmark Year rate for an aggregation 

group is zero, the aggregation group 

cannot receive an improvement score, 

even if the aggregation group has made 

improvements in the home dialysis rate 

and transplant rate between the 

Benchmark Year and MY. To address 

this issue, we propose to amend 

§ 512.370(c)(1) to change the 

improvement calculation such that the 

aggregation group’s Benchmark Year 

rate cannot be zero. Specifically, for 

MY3 through MY10, we propose to add 

one beneficiary month to the numerator 

of the home dialysis rate and the 

transplant rate for the Benchmark Year 

rate for an ETC Participant’s aggregation 

group Benchmark Year when that rate is 

zero. CMS does not propose to change 


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the denominator of the Benchmark Year 

rate calculations because doing so 

would negate the purpose of 

mathematically correcting ETC 

Participants’ improvement scoring. CMS 

does not expect that adding a 

beneficiary month to the numerator of 

the Benchmark Year rate calculations, as 

proposed, would affect the 

improvement scoring enough to change 

the number of points awarded to the 

ETC Participant, and has the advantage 

that it would enable an improvement 

score to be calculated, even when the 

Benchmark Year rate is zero. 

(2) Health Equity Incentive 

To incentivize ETC Participants to 

decrease disparities in the home dialysis 

rate and transplant rate between 

beneficiaries who are dual-eligible or 

LIS recipients and those who are not, 

we propose to add a Health Equity 

Incentive to the improvement scoring 

methodology. We propose to define the 

Health Equity Incentive at § 512.310 as 

the amount added to the ETC 

Participant’s improvement score 

calculated as described in 

§ 512.370(c)(1) if the ETC Participant’s 

aggregation group demonstrated 

sufficient improvement on the home 

dialysis rate or transplant rate for 

attributed beneficiaries who are dualeligible or LIS recipients between the 

Benchmark Year and the MY. We 

propose that this improvement on the 

home dialysis rate or transplant rate 

would be based on the performance of 

the ETC Participant’s aggregation group. 

As noted previously in this section of 

the proposed rule, socioeconomic 

factors impact a beneficiary’s receipt of 

alternative renal replacement 

modalities. Beneficiaries with limited 

resources may require more assistance 

from ESRD facilities and Managing 

Clinicians to use alternative renal 

replacement modalities. We believe our 

proposal to add a Health Equity 

Incentive would benefit these 

beneficiaries and improve scoring for 

home dialysis rate and transplant rate 

for ETC Participants that serve 

disproportionately high numbers of 

beneficiaries with lower socioeconomic 

status. To earn the Health Equity 

Incentive, ETC Participants would have 

to demonstrate sufficiently significant 

improvement on the home dialysis rate 

or transplant rate among their attributed 

beneficiaries who are dual eligible or 

receive the LIS between the Benchmark 

Year and the MY. ETC Participants who 

earn the Health Equity Incentive would 

receive a 0.5 point increase on their 

improvement score, thus increasing the 

maximum improvement score to 2 

points. We believe the proposed Health 

Equity Incentive would benefit 

attributed beneficiaries who are dual 

eligible or receive the LIS, by 

encouraging ETC Participants to address 

disparities in access to alternative renal 

replacement modalities among these 

beneficiaries. We believe that providing 

this incentive for ETC Participants to 

increase their home dialysis and 

transplant rate among their dual eligible 

or LIS beneficiary population would 

ultimately reduce this disparity in 

access for the beneficiaries in question. 

Therefore, we believe this incentive to 

reduce socioeconomic disparities in 

access to alternative renal replacement 

modalities would be an improvement to 

the PPA scoring methodology. 

We propose to amend § 512.370(c) to 

add the Health Equity Incentive to the 

improvement scoring methodology, 

beginning for MY3. We propose that the 

Health Equity Incentive would be equal 

to 0.5 points, which would be added to 

the ETC Participant’s improvement 

score for the home dialysis rate or for 

the transplant rate, calculated as 

described in § 512.370(c)(1), such that 

the maximum improvement score 

would increase from 1.5 points to 2 

points for ETC Participants that earn the 

Health Equity Incentive. Therefore, for 

those ETC Participants that earn the 

Home Equity Incentive, we propose that 

the ETC Participant’s improvement 

score for the home dialysis rate and for 

the transplant rate would be the sum of 

the improvement score calculated as 

described in § 512.370(c)(1) and the 

Health Equity Incentive. The Health 

Equity Incentive would allow ETC 

Participants to increase their 

improvement score, and thereby 

increase their payment adjustment. 

We propose to award the Health 

Equity Incentive to an ETC Participant 

if the ETC Participant’s aggregation 

group’s home dialysis rate and/or 

transplant rate among attributed 

beneficiaries who are dual-eligible or 

LIS recipients increases by 5 or more 

percentage points from the Benchmark 

Year to the MY. We believe that 5- 

percentage points is the correct 

threshold for awarding the Health 

Equity Incentive based on our analysis 

of Medicare claims. Five percentage 

points is one standard deviation above 

the average difference between the 

home dialysis rate and the transplant 

rate for attributed beneficiaries who are 

dual-eligible or LIS recipients and those 

beneficiaries who are not dual-eligible 

or LIS recipients, rounded to the nearest 

integer. We anticipate improvement in 

home dialysis and transplant rates 

among dual-eligible or LIS recipients 

between the MY and the Benchmark 

Year, but we expect that attaining the 

proposed threshold for earning the 

Health Equity Incentive would generally 

require significant effort on the part of 

the ETC Participant. 

We propose that an ESRD Beneficiary 

or Pre-emptive LDT Beneficiary would 

be considered to be dual eligible or an 

LIS recipient for a given month if at any 

point during the month the beneficiary 

was dually eligible for Medicare and 

Medicaid or an LIS recipient. We 

propose to determine whether an 

attributed beneficiary was dual-eligible 

or received the LIS using Medicare 

administrative data. 

We propose to modify § 512.370(c) 

such that the improvement 

benchmarking and scoring methodology 

for MY1 and MY2 would be specified at 

§ 512.370(c)(1), and the improvement 

benchmarking and scoring methodology 

for MY3 through MY10, described 

above, would be specified at 

§ 512.370(c)(2). We seek comment on 

the proposal to modify § 512.370(c) 

accordingly. 

We considered using a rolling 

approach to setting the threshold for 

earning the Health Equity Incentive, 

such that the threshold would be 

recalculated every other MY, to reflect 

changes in underlying disparities. 

Under this approach, we would 

calculate the threshold as one standard 

deviation above the average difference 

between the home dialysis rate and the 

transplant rate for attributed 

beneficiaries who are dual-eligible or 

LIS recipients and those beneficiaries 

who are not dual-eligible or LIS 

recipients, rounded to the nearest 

integer. We would calculate this 

threshold either using data from the 

Benchmark Year, such that ETC 

Participants would know the threshold 

for earning the Health Equity Incentive 

in advance of the MY, or using data 

from the MY, such that the threshold for 

earning the Health Equity Incentive 

would accurately reflect the magnitude 

of the disparity observed during the MY. 

However, we believe that setting a 

threshold for earning the Health Equity 

Incentive applicable for all MYs, 

beginning for MY3, is more appropriate. 

This approach would be in keeping with 

the intent of the proposed Health Equity 

Incentive, which is to provide ETC 

Participants a financial incentive to 

focus on decreasing the disparity in the 

home dialysis and transplant rates 

between beneficiaries who are dualeligible or LIS recipients, and those who 

are not. We believe providing ETC 

Participants clear information about 

what they need to achieve to earn the 

Health Equity Incentive in advance 

would best enable them to work towards 

the goal. 


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We propose that ETC Participants in 

aggregation groups that fall below a lowvolume threshold would be ineligible to 

earn the Health Equity Incentive. 

Specifically, we propose that an ETC 

Participant in an aggregation group with 

fewer than 11 attributed beneficiary 

years comprised of months in which 

ESRD Beneficiaries and, if applicable, 

Pre-emptive LDT Beneficiaries are dual 

eligible or LIS recipients during either 

the Benchmark Year or the MY would 

be ineligible to earn the Health Equity 

Incentive. We selected this particular 

low-volume threshold for consistency 

with the low-volume threshold for the 

applicability of the PPA generally, as 

specified at § 512.385. We believe it is 

necessary to apply a low volume 

threshold in determining whether an 

ETC Participant has earned the Home 

Equity Incentive to ensure statistical 

reliability of the home dialysis rate and 

transplant rate calculations. This 

statistical reliability provides 

consistency in the home dialysis rate 

and transplant rate calculations. 

Therefore, similar results are produced 

under consistent conditions when 

applying a low volume threshold to ETC 

Participants. We are proposing a lowvolume threshold specific to attributed 

beneficiaries who are dual-eligible or 

receive the LIS because whether an ETC 

Participant has earned the Health Equity 

Incentive is being assessed on this 

subset of attributed beneficiaries. 

We propose to amend the Modality 

Performance Score (MPS) methodology 

to incorporate the Health Equity 

Incentive. To that end, we propose to 

modify § 512.370(d) such that the 

calculation of the MPS for MY1 and 

MY2 is specified at § 512.370(d)(1), and 

the calculation of the MPS for MY3 

through MY10 is specified at 

§ 512.370(d)(2). We propose that the 

formula for the MPS for MY3 through 

MY10 would be the following: 

Modality Performance Score 

= 2 

× (Higher of the home dialysis 

achievement or (home dialysis 

improvement score + Health Equity 

Bonus †)) 

+ (Higher of the transplant achievement 

or (transplant improvement score 

+ Health Equity Bonus †)) 

† The Health Equity Incentive is 

applied to the home dialysis 

improvement score or transplant 

improvement score only if earned by the 

ETC Participant and provided that the 

ETC Participant is not ineligible to 

receive the Home Equity Incentive as 

described in proposed 

§ 512.370(c)(2)(iii). 

We seek comment on our proposed 

definition for the Health Equity 

Incentive at § 512.310 and our proposal 

to amend § 512.370(c) to add the Health 

Equity Incentive to the improvement 

scoring methodology for the home 

dialysis rate and the transplant rate. We 

also seek comment on our proposal to 

set the threshold for earning the Health 

Equity Incentive at 5-percentage points 

improvement from the Benchmark Year 

to the MY. 

7. PPA Reports and Data Sharing 

a. Background on Beneficiary 

Attribution and Performance Reporting 

Under the ETC Model, as described in 

42 CFR 512.360, CMS attributes ESRD 

Beneficiaries and, if applicable, Preemptive LDT Beneficiaries to an ETC 

Participant for each month during a MY 

based on the beneficiary’s receipt of 

services during that month. CMS 

performs this attribution for a MY 

retrospectively, after the end of the MY. 

As described in § 512.365, each ETC 

Participant’s performance is assessed 

based on the transplant rate and home 

dialysis rate among the population of 

beneficiaries attributed to the ETC 

Participant. As described in 42 CFR 

512.370 and 42 CFR 512.380, these rates 

are used to calculate the ETC 

Participant’s MPS and, in turn, the ETC 

Participant’s PPA. The PPA is then used 

to adjust certain Medicare payments of 

the ETC Participant during 6-month 

PPA periods, with the first PPA Period 

taking place from July 1, 2022, through 

December 31, 2022. As described in 42 

CFR 512.390(a), CMS will notify each 

ETC Participant, in a form and manner 

determined by CMS, of the ETC 

Participant’s attributed beneficiaries, 

MPS, and PPA for a PPA Period no later 

than one month before the start of the 

applicable PPA Period. 

In order to ensure ETC Participant 

have timely access to these ETC Model 

reports, we are proposing to add a new 

paragraph (b) to § 512.390 to establish a 

process for CMS to share certain 

beneficiary-identifiable and aggregate 

data with ETC Participants pertaining to 

their participation in the ETC Model. 

CMS believes that ETC Participants 

need this data to successfully coordinate 

the care of their ESRD Beneficiaries and, 

if applicable, Pre-emptive LDT 

Beneficiaries; to succeed under the ETC 

Model; and to assess CMS’s calculations 

of the individual ETC Participant’s PPA 

for a given PPA Period. Specifically, 

CMS believes that ETC Participants 

must have a clear understanding of the 

beneficiaries CMS has attributed to 

them under the ETC Model and how 

each attributed beneficiary has factored 

into the ETC Participant’s home dialysis 

rate, transplant waitlist rate, and living 

donor transplant rate, to better identify 

care coordination and care management 

opportunities, and to have the 

opportunity to seek targeted review of 

CMS’s calculation of the MPS. The 

purpose of the targeted review process, 

established under current § 512.390(b), 

which we would redesignate as 

paragraph (c), is to determine whether 

an incorrect PPA has been applied 

during the PPA Period. CMS 

additionally believes that timely access 

to this data is important and proposes 

to require CMS to make this data 

available twice a year, prior to each PPA 

Period in an MY. 

In the following sections of this 

proposed rule, we describe our 

proposed process for CMS to share and 

for ETC Participants to retrieve certain 

beneficiary-identifiable attribution data 

and performance data, as well as the 

protections that would apply to this 

data under a data sharing agreement 

with CMS. We also describe our 

proposed process for sharing certain 

aggregate, de-identified performance 

data with ETC Participants. 

b. CMS Sharing of BeneficiaryIdentifiable Data 

We propose to establish a process in 

new § 512.390(b)(1) under which CMS 

would share certain beneficiaryidentifiable data with ETC Participants 

regarding their attributed beneficiaries 

and performance under the ETC Model. 

We are proposing that, in accordance 

with the timing of the notification 

requirement described in § 512.390(a), 

CMS would be required to make the 

beneficiary-identifiable data pertaining 

to a given PPA Period available for 

retrieval by ETC Participants no later 

than 1 month before the start of that 

PPA Period. The ETC Participant would 

be able to retrieve this data at any point 

during the relevant PPA Period, but, in 

accordance with current § 512.390(b)(1), 

which would be redesignated as 

paragraph (c)(1), the ETC Participant 

would have 90 days from the date that 

CMS shares the MPS, including the data 

CMS used in calculating the MPS, to 

request a targeted review. We propose 

that CMS would notify ETC Participants 

of the availability of the beneficiaryidentifiable data for a relevant PPA 

Period and the process for retrieving 

that data, through the ETC listserv and 

through the ETC Model website, 

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

innovation-models/esrd-treatmentchoices-model. 

Regarding the specific beneficiaryidentifiable data that CMS would be 

required to share with ETC Participants, 


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284Under 45 CFR 164.103, ‘‘Required by law’’ 

means ‘‘a mandate contained in law that compels 

an entity to make a use or disclosure of protected 

health information and that is enforceable in a court 

of law.’’ It includes, among other things, ‘‘statutes 

or regulations that require the production of 

information, including statutes or regulations that 

require such information if payment is sought 

under a government program providing public 

benefits.’’ 

we are proposing in 

§ 512.390(b)(1)(ii)(A) to include, when 

available, the following data for each 

PPA Period: The ETC Participant’s 

attributed beneficiaries’ names, 

Medicare Beneficiary Identifiers (MBIs), 

dates of birth, dual-eligible status, and 

LIS recipient status. We believe that the 

patient’s name, MBI, and date of birth 

constitute the minimum elements to 

enable an ETC Participant to properly 

identify an attributed beneficiary, and to 

confirm the identity of an attributed 

during any communications with a 

beneficiary or a beneficiary’s caregiver, 

as appropriate and allowable. In 

addition, the ETC Participant needs to 

be aware of each attributed beneficiary’s 

dual-eligible status and LIS recipient 

status to understand how each 

attributed beneficiary contributed to 

how CMS calculated the ETC 

Participant’s Health Equity Incentive, if 

finalized. We propose in 

§ 512.390(b)(1)(ii)(B) that this 

beneficiary-identifiable data also would 

include, when available, data regarding 

the ETC Participant’s performance 

under the ETC Model, including, for 

each attributed beneficiary, as 

applicable, the number of months the 

beneficiary was attributed to the ETC 

Participant, received home dialysis, selfdialysis, or nocturnal in-center dialysis, 

or was on a transplant waitlist; and the 

number of months that have passed 

since the beneficiary has received a 

living donor transplant, as applicable. 

We believe that sharing these data 

elements would help the ETC 

Participant understand and, as 

appropriate, seek targeted review of 

CMS’s calculation of the ETC 

Participant’s MPS, and otherwise 

understand how CMS adjusted the ETC 

Participant’s Medicare payments by the 

PPA. 

We recognize there are sensitivities 

surrounding the disclosure of 

individually-identifiable (beneficiaryspecific) health information, and we 

note that a number of laws place 

constraints on the sharing of 

individually identifiable health 

information. For example, section 1106 

of the Act generally bars the disclosure 

of information collected under the Act 

without consent unless a law (statute or 

regulation) permits for the disclosure. In 

this instance, the HIPAA Privacy Rule 

provides that legal authority and 

authorizes this proposed disclosure of 

individually identifiable health 

information by us to ETC Participants. 

Under the HIPAA Privacy Rule, covered 

entities (defined as health care plans, 

health care providers that submit certain 

transactions electronically, and health 

care clearinghouses) are barred from 

using or disclosing individually 

identifiable health information (called 

‘‘protected health information’’ or PHI) 

in a manner that is not explicitly 

permitted or required under the HIPAA 

Privacy Rule, without the individual’s 

authorization. The Medicare FFS 

program, a ‘‘health plan’’ function of the 

Department, is subject to the HIPAA 

Privacy Rule limitations on the 

disclosure of PHI, without an 

individual’s authorization. ETC 

Participants are also covered entities, 

provided they are health care providers 

as defined by 45 CFR 160.103 and they 

or their agents electronically engage in 

one or more HIPAA standard 

transactions, such as for claims, 

eligibility or enrollment transactions. 

The proposed disclosure of ETC 

Model beneficiary-identifiable data 

would be permitted by the HIPAA 

Privacy Rule under the provisions that 

permit disclosures of PHI as ‘‘required 

by law.’’ Under 45 CFR 164.512(a)(1), a 

covered entity may use or disclose PHI 

to the extent that such use or disclosure 

is required by law and the use or 

disclosure complies with and is limited 

to the relevant requirements of such 

law.284 We are proposing to establish a 

requirement under § 512.390(b)(1) for 

CMS to share this data with ETC 

Participants. 

The Privacy Act of 1974 also places 

limits on agency data disclosures. The 

Privacy Act applies when federal 

agencies maintain systems of records by 

which information about an individual 

is retrieved by use of one of the 

individual’s personal identifiers (names, 

Social Security numbers, or any other 

codes or identifiers that are assigned to 

the individual). The Privacy Act 

generally prohibits disclosure of 

information from a system of records to 

any third party without the prior written 

consent of the individual to whom the 

records apply, 5 U.S.C. 552a(b). 

‘‘Routine uses’’ are an exception to this 

general principle. A routine use is a 

disclosure outside of the agency that is 

compatible with the purpose for which 

the data was collected. Routine uses are 

established by means of a publication in 

the Federal Register about the 

applicable system of records describing 

to whom the disclosure will be made 

and the purpose for the disclosure. We 

believe that the proposed data 

disclosures are consistent with the 

purposes for which the data discussed 

in this rule was collected, and thus, 

should not run afoul of the Privacy Act, 

provided we ensure that an appropriate 

Privacy Act system of records ‘‘routine 

use’’ is in place prior to making any 

disclosures. The systems of records from 

which CMS would share data are the 

Medicare Integrated Data Repository 

(‘‘IDR’’), system of records number 09– 

70–0571, and the Health Resources and 

Services Administration (‘‘HRSA’’) 

Organ Procurement and Transplantation 

Network (‘‘OPTN’’)/Scientific Registry 

of Transplant Recipients (‘‘SRTR’’) Data 

System, system of records number 09– 

15–0055. 

We believe that establishing a 

regulatory requirement for CMS to share 

the beneficiary-identifiable data 

described above would be appropriate 

for the ETC Model for several reasons. 

First, we believe that all ETC 

Participants not only desire but need 

this data to know which beneficiaries 

CMS has attributed to them (and thus is 

holding them financially accountable 

for such beneficiaries’ individual 

contributions to the ETC Participant’s 

performance measures described in 42 

CFR part 512, subpart C, with the 

proposed modifications described in 

this proposed rule, if finalized), and for 

each ETC Participant to understand the 

basis by which CMS computed their 

MPS. Second, CMS believes that all ETC 

Participants, regardless of size, would 

have the capability of managing and 

meaningfully using the shared data. We 

would provide the data in a form and 

manner that CMS believes is userfriendly. In addition, the ETC 

Participant would be able to review the 

beneficiary-identifiable data along with 

the aggregated data, which should help 

the ETC Participant understand the data 

CMS would share with the ETC 

Participant. Finally, CMS believes that 

any other approach to making 

beneficiary-identifiable data available, 

including the alternative proposal 

considered by CMS and described 

below, would impose additional 

operational burdens on CMS and 

administrative burdens on both CMS 

and the ETC Participants without 

producing any meaningful privacy or 

security benefit. 

We considered an alternative proposal 

for making beneficiary-identifiable data 

available to ETC Participants based on 

the data sharing policies currently used 

in many models tested under section 

1115A of the Act, which would involve 

ETC Participants formally requesting the 

data from CMS before CMS could share 

the data. In particular, ETC Participants 


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would have the opportunity to request 

the data for their own ‘‘health care 

operations’’ and CMS would be 

permitted to disclose the requested data 

based on the HIPAA Privacy Rule 

provisions that permit disclosures of 

PHI for the recipient’s health care 

operations purposes as described in 45 

CFR 164.506(c)(4) and § 164.501. Under 

this alternative approach, ETC 

Participants that request this 

information would have to attest to 

compliance with specific HIPAA 

requirements in addition to, or as part 

of, the data sharing agreement described 

in the next section of this proposed rule. 

After considering this option, we 

believe that having the ETC Participant 

request the data from CMS would add 

steps in the process that would cause 

administrative burden for both CMS and 

ETC Participants, and operational cost 

and burden for CMS. We further believe 

that adding these steps would not 

produce a meaningful privacy or 

security benefit based on the specific 

circumstances of this ETC Model. Both 

this option and the approach proposed 

above would require that the ETC 

Participant complete and sign a data 

sharing agreement, and both would 

allow an ETC Participant to decline 

receiving beneficiary-identifiable data 

by declining to complete or sign a data 

sharing agreement. As such, there are no 

meaningful privacy or security benefits 

that this option would create that are 

not already realized by the proposed 

approach to data sharing in the ETC 

Model. We also anticipate that all ETC 

Participants would want and need, and 

overwhelmingly would request, the data 

described previously in this section, 

would be capable of handling such data, 

and would take the steps necessary to 

obtain the data. In addition, under an 

alternative approach based on the 

HIPAA provisions for the ETC 

Participant’s ‘‘health care operations,’’ 

CMS would only be able to disclose the 

beneficiary-identifiable data for a 

purpose listed in paragraph (1) or (2) of 

the definition of ‘‘health care 

operations’’ in 45 CFR 164.501. 

However, we also believe it is crucial 

that an ETC Participant has the 

opportunity to understand how CMS 

calculated the ETC Participant’s PPA for 

a PPA Period, and have the information 

needed to request a targeted review of 

CMS’s MPS calculation if the ETC 

Participant believes CMS made an error. 

Given the policies proposed in this 

section and the following sections 

related to data sharing, we propose to 

modify the title of § 512.390 from 

‘‘Notification and targeted review’’ to 

‘‘Notification, data sharing, and targeted 

review.’’ We propose this change so that 

the section title will more accurately 

reflect the contents of the section. 

We solicit public comment on our 

proposal to require, under proposed 

§ 512.390(b)(1), that CMS make 

available certain beneficiary-identifiable 

attribution and performance data for 

retrieval by ETC Participants no later 

than one month prior to the start of each 

PPA Period, and on our considered 

alternative to this proposal. 

(1) Conditions for Retrieving 

Beneficiary-Identifiable Data 

Given the sensitive nature of the 

beneficiary-identifiable data that CMS 

would be required to share under our 

proposal, we are proposing certain 

conditions for ETC Participants to be 

able to retrieve this data and certain 

protections that would govern use of the 

data following retrieval. First, we 

propose that CMS would only share the 

beneficiary-identifiable data on the 

condition that the ETC Participant 

observes all relevant statutory and 

regulatory provisions regarding the 

appropriate use of data and the 

confidentiality and privacy of 

individually identifiable health 

information as would apply to a covered 

entity under the Health Insurance 

Portability and Accountability Act of 

1996 (HIPAA) regulations and agrees to 

comply with the terms of a separate data 

sharing agreement. Although we expect 

ETC Participants are covered entities 

and must comply with the HIPAA 

regulations directly, we are including 

this provision to ensure an ETC 

Participant would abide by those rules 

with respect to the data, even if, for 

example, the ETC Participant is a hybrid 

entity under HIPAA and the component 

requesting the data has not been 

designated as a health care component 

under 45 CFR 164.105. The HIPAA 

provisions that the ETC Participant 

would have to observe would include, 

but would not be necessarily limited to, 

standards regarding the use and 

disclosure of PHI; administrative, 

physical, and technical safeguards and 

other security provisions; and breach 

notification. 

We propose that, if an ETC Participant 

wishes to retrieve the beneficiaryidentifiable data, the ETC Participant 

would be required to first complete, 

sign, and submit—and thereby agree to 

the terms of—a data sharing agreement 

with CMS, which we would call the 

ETC Data Sharing Agreement. This 

agreement would include certain 

protections and limitations on the ETC 

Participant’s use and further disclosure 

of the beneficiary-identifiable data, and 

would be provided in a form and 

manner specified by CMS, which we 

discuss in more detail in later sections 

of this proposed rule. This agreement 

also potentially would require the ETC 

Participant to make certain attestations, 

for example, if required under the 

applicable Privacy Act system of records 

notice. An ETC Participant that wishes 

to retrieve the beneficiary-identifiable 

data would be required to complete and 

submit a signed ETC Data Sharing 

Agreement at least annually. CMS 

believes that it is important for the ETC 

Participant to complete and submit a 

signed ETC Data Sharing Agreement at 

least annually so that CMS has up-todate information that the ETC 

Participant wishes to retrieve the 

beneficiary-identifiable data attestations 

(if required), and information on the 

designated data custodian(s). As 

described in greater detail below, we 

propose that a designated data 

custodian would be the individual(s) 

that an ETC Participant would identify 

as responsible for ensuring compliance 

with all privacy and security 

requirements and for notifying CMS of 

any incidents relating to unauthorized 

disclosures of beneficiary-identifiable 

data. 

CMS believes it is important for the 

ETC Participant to first complete and 

submit a signed ETC Data Sharing 

Agreement before it retrieves any 

beneficiary-identifiable data to help 

protect the privacy and security of any 

beneficiary-identifiable data shared by 

CMS with the ETC Participant. As 

described previously in this section of 

the proposed rule, there are important 

sensitivities surrounding the sharing of 

this type of individually identifiable 

health information, and CMS must 

ensure to the best of its ability that any 

beneficiary-identifiable data that it 

shares with ETC Participants would be 

further protected in an appropriate 

fashion. 

We considered an alternative proposal 

under which ETC Participants would 

not need to complete and submit a 

signed ETC Data Sharing Agreement, 

but we concluded that, if we proceeded 

with this option, we would not have 

adequate assurances that the ETC 

Participants would appropriately 

protect the privacy and security of the 

beneficiary-identifiable data that we are 

proposing to share with them. We also 

considered an alternative proposal 

under which the ETC Participant would 

need to complete and submit a signed 

ETC Data Sharing Agreement only once 

for the duration of the ETC Model. 

However, we concluded that this 

similarly would not give CMS adequate 

assurances that the ETC Participant 

would protect the privacy and security 

of the beneficiary-identifiable data from 


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CMS. We concluded that it is critical 

that we have up-to-date information and 

designated data custodians, and that 

requiring the ETC Participant to submit 

an ETC Data Sharing Agreement at least 

annually would represent the best 

means of achieving this goal. 

We solicit public comment on our 

proposal to require, in 

§ 512.390(b)(1)(iii), that the ETC 

Participant agree to comply with all 

applicable laws and the terms of the 

ETC Data Sharing Agreement as a 

condition of retrieving the beneficiaryidentifiable data, and on our proposal in 

§ 512.390(b)(1)(iv) that the ETC 

Participant would need to submit the 

signed ETC Data Sharing Agreement at 

least annually if the ETC Participant 

wishes to retrieve the beneficiaryidentifiable data. 

(2) Content of ETC Data Sharing 

Agreement Provisions for BeneficiaryIdentifiable Data 

We are proposing in new 

§ 512.390(b)(iv) that, under the ETC 

Data Sharing Agreement, ETC 

Participants would agree to certain 

terms, namely: (1) To comply with the 

requirements for use and disclosure of 

this beneficiary-identifiable data that are 

imposed on covered entities by the 

HIPAA regulations and the 

requirements of the ETC Model set forth 

in 42 CFR part 512; (2) to comply with 

additional privacy, security, and breach 

notification requirements to be specified 

by CMS in the ETC Data Sharing 

Agreement; (3) to contractually bind 

each downstream recipient of the 

beneficiary-identifiable data that is a 

business associate of the ETC 

Participant or performs a similar 

function for the ETC Participant, to the 

same terms and conditions to which the 

ETC Participant is itself bound in its 

data sharing agreement with CMS as a 

condition of the downstream recipient’s 

receipt of the beneficiary-identifiable 

data retrieved by the ETC Participant 

under the ETC Model; and (4) that if the 

ETC Participant misuses or discloses the 

beneficiary-identifiable data in a 

manner that violates any applicable 

statutory or regulatory requirements or 

that is otherwise non-compliant with 

the provisions of the ETC Data Sharing 

Agreement, the ETC Participant would 

no longer be eligible to retrieve the 

beneficiary-identifiable data and may be 

subject to additional sanctions and 

penalties available under the law. CMS 

believes that these terms for sharing 

beneficiary-identifiable data with ETC 

Participants are appropriate and 

important, as CMS must ensure to the 

best of its ability that any beneficiaryidentifiable data that it shares with ETC 

Participants would be further protected 

by the ETC Participant, and any 

business associates of the ETC 

Participant, in an appropriate fashion. 

CMS believes that these proposals 

would allow CMS to accomplish that. 

CMS seeks public comment on the 

additional privacy, security, breach 

notification, and other requirements that 

we would include in the ETC Data 

Sharing Agreement. CMS has these 

types of agreements in place as part of 

the governing documents of other 

models tested under section 1115A of 

the Act and in the Medicare Shared 

Savings Program. In these agreements, 

CMS typically requires the 

identification of data custodian(s) and 

imposes certain requirements related to 

administrative, physical, and technical 

safeguards relating to data storage and 

transmission; limitations on further use 

and disclosure of the data; procedures 

for responding to data incidents and 

breaches; and data destruction and 

retention. These provisions would be 

imposed in addition to any restrictions 

required by law, such as those provided 

in the HIPAA privacy, security and 

breach notification regulations. These 

provisions would not prohibit the ETC 

Participant from making any disclosure 

of the data otherwise required by law. 

CMS is considering limiting the use of 

beneficiary-identifiable data for specific 

purposes, either alone or in 

combination. For example, in the ETC 

Data Sharing Agreement, CMS is 

considering imposing limits on how the 

ETC Participant may use the 

beneficiary-identifiable data without 

prior written authorization from CMS to 

specific purposes, such as assessing 

CMS’s calculation of the MPS for a 

given PPA Period, the ETC Participant’s 

clinical care or ‘‘treatment’’ (as that term 

is defined at 45 CFR 164.501) of an 

attributed beneficiary, and certain 

‘‘health care operations’’ (as that term is 

defined at 45 CFR 164.501) of the ETC 

Participant. As noted previously in this 

section of the proposed rule, CMS 

believes that ETC Participants would 

require this data to be able to request a 

targeted review of CMS’s calculation of 

the MPS as it relates to a given PPA 

Period, as understanding and being able 

to seek review of CMS’s calculation of 

the MPS, and thus the reason CMS 

adjusted the ETC Participant’s Medicare 

payments in the manner it did, is 

critical for the ETC Model. Importantly, 

there is no other source of this 

information outside of CMS. In addition 

to limiting use to reviewing how CMS 

calculated the ETC Participant’s MPS, 

CMS is also considering limiting, in the 

ETC Data Sharing Agreement, use of the 

beneficiary-identifiable data without 

prior written authorization from CMS to 

use for clinical treatment purposes. 

CMS believes that this beneficiaryidentifiable data would be important in 

helping the ETC Participant determine 

which of its ESRD Beneficiaries are not 

on the transplant waitlist or have not 

received a living donor transplant, to 

inform how the ETC Participant engages 

in clinical care of the subject ESRD 

Beneficiary. 

In addition to the previous two uses, 

CMS is also considering limiting, in the 

ETC Data Sharing Agreement, the ETC 

Participant’s use of the beneficiaryidentifiable data without prior written 

authorization from CMS to care 

management and coordination, quality 

improvement activities, and provider 

incentive design and implementation, to 

the extent these activities would 

constitute ‘‘health care operations’’ that 

fall within the first and second 

paragraphs of the definition of that 

phrase under the HIPAA Privacy Rule 

(45 CFR 164.501). As it relates to case 

management and coordination and 

quality improvement activates, CMS 

believes that this beneficiaryidentifiable data would help the ETC 

Participant to conduct the important 

task of identifying which ESRD 

Beneficiaries are not currently on the 

transplant waitlist and thus better 

enable the ETC Participant to engage 

those beneficiaries, as clinically 

appropriate, about the process of signing 

up for the transplant waitlist, thereby 

improving the ETC Participant’s 

performance on the transplant waitlist 

rate, and increasing the likelihood that 

the subject ESRD Beneficiaries would 

receive a transplant. In addition, CMS 

believes that sharing this data with the 

ETC Participant would help the ETC 

Participant to conduct the important 

task of identifying which ESRD 

Beneficiaries are receiving dialysis incenter, and to consider whether 

furnishing kidney disease patient 

education services or otherwise making 

such beneficiaries aware of the 

possibility of receiving home dialysis, 

self-dialysis, or nocturnal in-center 

dialysis, as clinically appropriate in the 

ESRD Beneficiary’s individual situation. 

We seek public comment on how an 

ETC Participant might need to, and 

want to, use the beneficiary-identifiable 

data retrieved from CMS under the ETC 

Model to accomplish the goals of the 

ETC Model in accordance with 

applicable law. 

CMS also seeks public comment on 

what further disclosures of the 

beneficiary-identifiable data might be 

appropriate to permit or prohibit under 

the ETC Data Sharing Agreement. For 

example, CMS is considering 


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prohibiting, in the ETC Data Sharing 

Agreement, any further disclosure, not 

otherwise required by law, of the 

beneficiary-identifiable data described 

previously in this section of the 

proposed rule to anyone who is not a 

HIPAA covered entity or business 

associate, as defined in 45 CFR 160.103, 

or to an individual practitioner in a 

treatment relationship with the subject 

ESRD Beneficiary or Pre-emptive LDT 

Beneficiary, or that practitioner’s 

business associates. Such a prohibition 

would be similar to that imposed by 

CMS in other models tested under 

section 1115A of the Act in which CMS 

shares beneficiary-identifiable data with 

model participants. In the alternative, 

CMS is also considering including more 

restrictive prohibitions in the ETC Data 

Sharing Agreement, which would limit 

further discloses to only some, one, or 

none of the categories of individuals or 

entities described above. 

CMS is considering all of these 

possibilities because there exist 

important legal and policy limitations 

on the sharing of the beneficiaryidentifiable data discussed previously in 

this section of the proposed rule, and 

CMS must consider carefully the ways 

in which and reasons for which we 

would provide access to this data for 

purposes of the ETC Model. CMS 

believes that some ETC Participants may 

require the assistance of business 

associates, such as contractors, to 

perform data analytics or other 

functions using this beneficiaryidentifiable data to support the ETC 

Participant’s review of CMS’s MPS 

calculations, care management and 

coordination, quality improvement 

activities, or clinical treatment of 

attributed beneficiaries. CMS also 

believes that this beneficiaryidentifiable data may be helpful for any 

HIPAA covered entities who are in a 

treatment relationship with the subject 

ESRD Beneficiary or Pre-emptive LDT 

Beneficiary. 

We seek public comment on how an 

ETC Participant might need to, and 

want to, disclose the beneficiaryidentifiable data to other individuals 

and entities to accomplish the goals of 

the ETC Model, in accordance with 

applicable law. 

Under our proposal, the ETC Data 

Sharing Agreement would include other 

provisions, including requirements 

regarding data security, retention, 

destruction, and breach notification. For 

example, we are considering including, 

in the ETC Data Sharing Agreement, a 

requirement that the ETC Participant 

designate one or more data custodians 

who would be responsible for ensuring 

compliance with the privacy, security 

and breach notification requirements for 

the data set forth in the ETC Data 

Sharing Agreement; various security 

requirements like those found in other 

models tested under section 1115A of 

the Act, but no less restrictive than 

those provided in the relevant Privacy 

Act system of records notices; how and 

when beneficiary-identifiable data could 

be retained by the ETC Participant or its 

downstream recipients of the 

beneficiary-identifiable data; procedures 

for notifying CMS of any breach or other 

incident relating to the unauthorized 

disclosure of beneficiary-identifiable 

data; and provisions relating to 

destruction of the data. These are only 

examples, and are not the only terms 

CMS would potentially include in the 

ETC Data Sharing Agreement. 

We solicit public comment on this 

proposal that CMS, by adding 

§ 512.390(b)(1)(iv)(B), would impose 

certain requirements in the ETC Data 

Sharing Agreement related to privacy, 

security, data retention, breach 

notification, and data destruction. 

Finally, as described above, CMS 

proposes, at § 512.390(b)(1)(iv)(D), that 

the ETC Data Sharing Agreement would 

include a term providing that if the ETC 

Participant misuses or discloses the 

beneficiary-identifiable data in a 

manner that violates any applicable 

statutory or regulatory requirements or 

that is otherwise non-compliant with 

the provisions of the ETC Data Sharing 

Agreement, the ETC Participant would 

no longer be eligible to retrieve 

beneficiary-identifiable data under 

proposed § 512.390(b)(1)(i) and may be 

subject to additional sanctions and 

penalties available under law. We also 

propose to make conforming 

amendments to 42 CFR 512.160. Section 

512.160(b) outlines the remedial actions 

available under the RO Model and ETC 

Model, and paragraph (b)(8), in 

particular provides that, if CMS 

determines that one or more grounds for 

remedial action specified in § 512.160(a) 

has taken place, CMS may discontinue 

the provision of data sharing and reports 

to the model participant. We propose to 

add a new § 512.160(a)(9) to specify 

that, for the ETC Model only, CMS may 

take remedial action if the model 

participant misuses or discloses the 

beneficiary-identifiable data in a 

manner that violates any applicable 

statutory or regulatory requirements or 

that is otherwise non-compliant with 

the provisions of the applicable data 

sharing agreement. This proposed 

change, if finalized, would align the 

regulatory provision on remedial action 

with the proposed remedial action we 

propose to include in the ETC Data 

Sharing Agreement. 

We solicit public comment on this 

proposal, to prohibit the ETC 

Participant from obtaining beneficiaryidentifiable data pertaining to the ETC 

Model if the ETC Participant fails to 

comply with applicable laws and 

regulations, the terms of the ETC Model, 

or the ETC Data Sharing Agreement. 

(3) Process for Retrieving the ETC Data 

Sharing Agreement and BeneficiaryIdentifiable Data 

We propose that we would make the 

ETC Data Sharing Agreement and 

beneficiary-identifiable data available in 

a form and manner specified by CMS. 

We expect to provide a web-based 

platform for ETC Participants to use to 

retrieve the beneficiary-identifiable 

data. CMS would provide ETC 

Participants further information about 

this web-based platform through the 

ETC listserv and the ETC Model website 

at a date to be determined by CMS, but 

at least 1 month before the first PPA 

Period begins on June 1, 2022. We 

expect that CMS would notify ETC 

Participants of each opportunity to 

retrieve a new set of beneficiaryidentifiable data and the process for 

accessing the web-based platform to 

receive the data through the ETC listserv 

and on the ETC Model website. Under 

this proposal, the ETC Participant 

would be required to use the form and 

manner specified by CMS (which we 

expect will be a web-based platform) to 

retrieve the data. If the ETC Participant 

did not use the form and manner 

specified by CMS or did not agree to the 

ETC Data Sharing Agreement, the ETC 

Participant would be unable to retrieve 

the beneficiary-identifiable data 

described previously in this section of 

the proposed rule. We propose that ETC 

Participants would be permitted to 

retrieve this data at any point during the 

relevant PPA Period. We considered 

establishing certain periods of time 

within a PPA Period during which the 

ETC Participant would be able to 

retrieve the data, but we concluded that 

permitting the ETC Participant to obtain 

the data at any point during the relevant 

PPA Period would be relatively 

operationally low-burden for CMS while 

providing additional flexibility to the 

ETC Participant. 

CMS believes that it is important that 

the ETC Participant complete and 

submit its signed ETC Data Sharing 

Agreement, and retrieve the beneficiaryidentifiable data, in the same form and 

manner (which we expect to be a webbased platform). 

In the alternative, we considered 

providing the beneficiary-identifiable 

data to ETC Participants via paper mail 

rather than through a web-based 


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platform, but we concluded that making 

the data available through a web-based 

platform would reduce administrative 

burden on both CMS and the ETC 

Participants. We also concluded that 

making this beneficiary-identifiable data 

available through a web-based platform 

would allow CMS to provide the data in 

a manner that is more secure than if 

CMS were to make the data available 

through paper mail. By using a webbased platform, to be further described 

by CMS through the ETC listserv and 

the ETC Model website, CMS would 

help ensure that only authorized users 

would be able to obtain the data, and 

would be able to implement a two-factor 

authentication to help ensure that no 

one other than an ETC Participant 

would have access to the data. In 

addition, we concluded that it would be 

more efficient to provide the ETC Data 

Sharing Agreement and the beneficiaryidentifiable data itself through the same 

form and manner (which we expect to 

be a web-based platform), rather than 

using two different processes and that 

using a web-based platform would be 

more efficient than paper mail. For 

these reasons, we believe the best option 

would be for us to use only the webbased platform both for providing the 

ETC Data Sharing Agreement and for 

sharing data pertaining to the ETC 

Model. 

We solicit public comment on our 

proposal to require the ETC Participant 

to complete and submit a signed ETC 

Data Sharing Agreement before the ETC 

Participant could retrieve the 

beneficiary-identifiable data, and on our 

proposal that the ETC Participant would 

be required to retrieve the beneficiaryidentifiable data in the same form and 

manner as the ETC Participant receives 

and submits the ETC Data Sharing 

Agreement. We also solicit comment 

regarding our expectation that we will 

use a web based platform, rather than 

paper mail, for these purposes. 

e. CMS Sharing of Aggregate Data 

In addition to the proposed process 

for sharing beneficiary-identifiable data 

described previously in this section, we 

are proposing in § 512.390(b)(2) that 

CMS would make available certain 

aggregate data for retrieval by the ETC 

Participant, in a form and manner to be 

specified by CMS, no later than one 

month before each PPA Period. This 

aggregate performance data, would 

include, when available, the following 

information for each PPA Period, deidentified in accordance with 45 CFR 

164.514(b): The ETC Participant’s 

performance scores on the home 

dialysis rate, transplant waitlist rate, 

living donor transplant rate, and, if 

finalized, Health Equity Incentive; the 

ETC Participant’s aggregation group’s 

scores on the home dialysis rate, 

transplant waitlist rate, living donor 

transplant rate, and, if finalized, Health 

Equity Incentive; information on how 

the ETC Participant’s and ETC 

Participant’s aggregation group’s scores 

relate to the achievement benchmark 

and improvement benchmark (that is, 

whether the ETC Participant met or 

exceeded the threshold for each such 

benchmark); and the ETC Participant’s 

MPS and PPA for the corresponding 

PPA Period. CMS believes that sharing 

this aggregate, de-identified data with 

the ETC Participant would be important 

to help the ETC Participant better 

understand its performance in the ETC 

Model relative to its aggregation group 

and to the achievement and 

improvement benchmarks against which 

CMS is measuring the ETC Participant’s 

performance. Whereas the beneficiaryidentifiable data described previously in 

this section of the proposed rule would 

indicate which ESRD Beneficiaries and, 

if applicable, Pre-emptive LDT 

Beneficiaries the ETC Participant could 

devote greater resources to, CMS 

believes this aggregate, de-identified 

data would better enable the ETC 

Participant to see which performance 

rates the ETC Participant might need to 

improve to more generally improve its 

performance under the ETC Model. 

We are proposing that CMS would 

make this data available to the ETC 

Participant for retrieval in a form and 

manner to be specified by CMS no less 

than one month prior to each PPA 

Period. We expect that CMS would 

make this data available to the ETC 

Participant on the same web-based 

platform on which CMS would be 

providing the beneficiary-identifiable 

data described previously in this 

section. The ETC Participant would be 

required to use the form and manner 

specified by CMS to retrieve this 

aggregate data, but would not have to 

agree to the ETC Data Sharing 

Agreement to retrieve this aggregated 

data, as it is not beneficiary-identifiable. 

We believe that using a web-based 

platform for sharing this aggregate data 

would be appropriate for the same 

reasons it would be appropriate for 

sharing the beneficiary-identifiable data. 

By using a web-based platform, CMS 

would help ensure that only authorized 

users would be able to obtain the data, 

and would be able to implement a twofactor authentication to help ensure that 

no one other than an ETC Participant 

would have access to the data. In 

addition, because CMS would be 

providing the ETC Data Sharing 

Agreement and beneficiary-identifiable 

data on the same web-based platform, 

we believe it would be convenient for 

the ETC Participant if CMS shared the 

aggregate data on the same web-based 

platform. 

In the alternative, we considered 

sending this aggregate data to the ETC 

Participant via paper mail. However, 

CMS concluded that it would be more 

convenient to the ETC Participant to 

retrieve this data from a web-based 

platform rather than via paper mail, and 

that sending this data via paper mail 

would represent significant 

administrative and operational burdens 

for CMS. 

We solicit public comment on our 

proposal to share aggregate data 

generally, to share aggregated data in the 

same form and manner we are 

proposing to use for sharing beneficiaryidentifiable data. We also solicit public 

comment on our expectation to use a 

web-based platform for this purpose, as 

well as our considered alternative to 

share the aggregate data via paper mail. 

8. Medicare Waivers and Additional 

Flexibilities 

a. Background on Kidney Disease 

Patient Education Services Waiver 

Pursuant to section 1861(ggg)(1) of the 

Act and § 410.48 of our regulations, 

Medicare Part B covers outpatient, faceto-face kidney disease patient education 

services provided by certain qualified 

persons to beneficiaries with Stage IV 

chronic kidney disease. As noted in the 

Specialty Care Models final rule, kidney 

disease patient education services play 

an important role in educating patients 

about their kidney disease and to help 

them make informed decisions on the 

appropriate type of care and/or dialysis 

needed for them (85 FR 61337). In 

addition, we noted in the Specialty Care 

Models final rule that kidney disease 

patient education services are designed 

to educate and inform beneficiaries 

about the effects of kidney disease, their 

options for transplantation, dialysis 

modalities, and vascular access (85 FR 

61337). Because kidney disease patient 

education services have been 

infrequently billed, we found it 

necessary for purposes of testing the 

ETC Model to waive select requirements 

of kidney disease patient education 

services authorized in section 

1861(ggg)(1) of the Act and in the 

implementing regulation at 42 CFR 

410.48. Specifically, to broaden the 

availability of kidney disease patient 

education services under the ETC 

Model, we have used our authority 

under section 1115A(d) of the Act to 

waive certain requirements for 


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individuals and entities that furnish and 

bill for kidney disease patient education 

services. We codified these waivers at 

§ 512.397(b). These include waivers to 

allow more types of beneficiaries to 

have access to kidney disease patient 

education services, as well as greater 

flexibility in how the kidney disease 

patient education services are 

performed. For instance, CMS waived 

the requirement that kidney disease 

patient education services are covered 

only for Stage IV chronic kidney disease 

(CKD) patients to permit beneficiaries to 

receive kidney disease patient education 

services if they are diagnosed with CKD 

Stage V or are in the first 6 months of 

starting dialysis to receive the benefit. 

CMS also waived the requirements in 

section 1861(ggg)(2)(A)(i) of the Act and 

§ 410.48(a) and (c)(2)(i) of the applicable 

regulations pertaining to the definition 

of ‘‘qualified person’’ such that 

registered dieticians/nutrition 

professionals, licensed clinical social 

workers, or a clinic/group practice may 

furnish kidney disease patient 

education services under the direction 

of, and incident to the services of a 

Managing Clinician who is an ETC 

Participant. 

Finally, CMS waived two 

requirements relating to the content of 

kidney disease patient education 

services furnished to a beneficiary. CMS 

waived the requirement under 

§ 410.48(d)(1) of our regulations that the 

content of kidney disease patient 

education services include the 

management of co-morbidities, 

including delaying the need for dialysis, 

when such services are furnished to 

beneficiaries with CKD Stage V or 

ESRD, unless such content is relevant 

for the beneficiary. In addition, CMS 

waived the requirement under 

§ 410.48(d)(5)(iii) of our regulations that 

an outcomes assessment designed to 

measure beneficiary knowledge about 

chronic kidney disease and its treatment 

be performed during one of the kidney 

disease patient education services, 

requiring instead that such outcomes 

assessment is performed within 1 month 

of the final kidney disease patient 

education services session furnished by 

qualified staff. 

b. Proposed Kidney Disease Patient 

Education Services Telehealth Waiver 

and Additional Flexibilities 

Many changes took place in 2020 and 

early 2021 due to the COVID–19 PHE. 

Legislation enacted to address the PHE 

for COVID–19 provided the Secretary 

with new authorities under section 

1135(b)(8) of the Act to waive or modify 

Medicare telehealth payment 

requirements during the PHE for 

COVID–19. We established several 

flexibilities to accommodate these 

changes in the delivery of care. Through 

waiver authority under section 

1135(b)(8) of the Act, in response to the 

PHE for COVID–19, we temporarily 

waived the geographic and site of 

service originating site restrictions in 

section 1834(m)(4)(C) of the Act. For 

example, CMS waived the rural area 

requirement at section 1834(m) of the 

Act to allow for telehealth services, 

including kidney disease patient 

education services that can be furnished 

via telehealth, to be furnished to 

beneficiaries in any geographic area, 

regardless of location and in their 

homes, for the duration of the PHE. 

These waivers are set to terminate at the 

end of the COVID–19 PHE. 

We believe that, once the PHE ends, 

these waivers removing the geographic 

and site of service originating site 

restrictions for kidney disease patient 

education services furnished via 

telehealth would be necessary solely for 

purposes of testing the ETC Model. 

Except under very limited 

circumstances, under section 1834(m) of 

the Act and its implementing 

regulations, the originating site where 

the beneficiary is located at the time a 

telehealth service is furnished is limited 

to certain, mostly rural, geographic 

locations and a site of service that is one 

of certain types of health care facilities. 

We believe that allowing qualified staff 

to furnish kidney disease patient 

education services via telehealth, 

regardless of the beneficiary’s 

geographic area or the site of the 

beneficiary, and regardless of the site of 

service of the practitioner, would 

increase access to kidney disease patient 

education services for a few reasons. 

First, some beneficiaries may not have 

access to reliable transportation, 

especially those beneficiaries who 

suffered economically during the 

ongoing PHE, but may have access to 

the technology necessary for 

practitioners to furnish kidney disease 

patient education services. Moreover, 

some beneficiaries, even those with 

reliable transportation, may be more 

comfortable receiving kidney disease 

patient education services via telehealth 

rather than appearing in person after 

over a year of social distancing, even 

when it becomes safe according to 

Federal guidance for such beneficiaries 

to enter physical spaces with other 

individuals. This is especially likely to 

be the case for instances in which a 

practitioner would furnish kidney 

disease patient education services in a 

group session rather than an individual 

session. Increasing access to kidney 

disease patient education services is 

consistent with one of the main goals of 

the ETC Model, insofar as we believe 

that education, as delivered through 

kidney disease patient education 

services, helps improve beneficiary 

choice of dialysis modality. 

In addition, we believe that removing 

beneficiary cost barriers for kidney 

disease patient education services 

would be helpful. As we demonstrate 

below, there is a significant relationship 

between household income or poverty 

status and kidney disease, and removing 

or mitigating cost barriers to access to 

kidney disease patient education 

services would likely increase the 

number of beneficiaries who would be 

willing to receive kidney disease patient 

education services. 

We therefore propose that, starting in 

MY3, kidney disease patient education 

services may be furnished to certain 

beneficiaries via telehealth in a manner 

that is more flexible than that required 

under existing telehealth requirements. 

In addition, we propose to permit the 

reduction or waiver of coinsurance for 

the kidney disease patient education 

services, starting in MY3. 

(1) Kidney Disease Patient Education 

Services Telehealth Waiver 

CMS proposes to amend § 512.397 to 

add a waiver of certain telehealth 

requirements to provide qualified staff, 

as we are proposing to define for 

purposes of the ETC Model at § 512.310, 

the flexibility to furnish kidney disease 

patient education services via telehealth 

for the reasons described above. 

Specifically, we propose to waive the 

geographic and site of service 

originating site requirements in sections 

1834(m)(4)(B) and 1834(m)(4)(C) of the 

Act, and in our regulations at 42 CFR 

410.78(b)(3) and (4), for kidney disease 

patient education services furnished via 

telehealth. We believe the kidney 

disease patient education services 

telehealth waiver would allow more 

Medicare beneficiaries to receive kidney 

disease patient education services via 

telehealth by removing the originating 

site restrictions, thus allowing for the 

beneficiary to be located anywhere, and 

including at a site not specified in 

§ 410.78(b)(3) of our regulations; and by 

allowing for the beneficiary to be 

located outside of a rural area. CMS also 

proposes to waive the requirement in 

section 1834(m)(2)(B) of the Act and 42 

CFR 414.65(b) such that CMS would not 

pay an originating site facility fee for 

kidney disease patient education 

services furnished via telehealth to a 

beneficiary at a site not specified in 

§ 410.78(b)(3) of our regulations under 

this proposed waiver, if finalized. 


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285Table 1.2 in United States Renal Data System, 

2020 Annual Report, Chronic Kidney Disease: 

Chapter 1, CKD in the General Population, available 

at https://adr.usrds.org/2020/chronic-kidneydisease/1-ckd-in-the-general-population (indicating 

that the prevalence of CKD in those above the 

poverty line is 14.4 percent while the prevalence of 

CKD in those below the poverty line is 17.4 percent. 

See also McClellan, W.M., et al., Poverty and Racial 

Disparities in Kidney Disease: The REGARDS Study, 

Am. J Nephrol, 2010, Volume 32, Issue 1, pages 38– 

46, available at https://www.ncbi.nlm.nih.gov/pmc/ 

articles/PMC2914392/ (providing data suggesting 

that lower household income is associated with 

higher prevalence of CKD). 

286Morton, R.L, et al., Impact of CKD on 

Household Income, Kidney International Reports, 

Volume 3, Issue 3, 2018, pages 610–618, available 

at https://www.sciencedirect.com/science/article/ 

pii/S2468024917304795?via%3Dihub. 

However, we do not propose to waive 

the requirement under section 

1834(m)(1) of the Act and 42 CFR 

410.78(b) that telehealth services be 

furnished via an ‘‘interactive 

telecommunications system,’’ as that 

term is defined in § 410.78(a)(3) to mean 

multimedia communications equipment 

that includes, at a minimum, audio and 

video equipment permitting two-way, 

real-time interactive communication 

between the patient and distant site 

physician or practitioner. Accordingly, 

we would continue to require that the 

kidney disease patient education 

services furnished via telehealth be 

provided through an interactive 

telecommunications system; audio-only 

telehealth services would not be 

permitted. 

We propose that kidney disease 

patient education services could be 

furnished via telehealth health only by 

qualified staff. We used the term 

‘‘clinical staff’’ and ‘‘qualified staff’’ in 

the Specialty Care Models final rule, but 

did not provide definitions of these 

terms. For clarity, we now propose to 

define ‘‘clinical staff’’ and ‘‘qualified 

staff’’ in 42 CFR 512.310. We propose to 

define ‘‘clinical staff’’ to mean a 

licensed social worker or registered 

dietician/nutrition professional who 

furnishes services for which payment 

may be made under the physician fee 

schedule under the direction of and 

incident to the services of the Managing 

Clinician who is an ETC Participant. We 

are proposing to define the term clinical 

staff in this manner to describe those 

clinicians who are authorized to furnish 

kidney disease patient education 

services only pursuant to the waiver 

specified at § 512.390(b)(1)—namely 

licensed social workers and registered 

dieticians/nutrition professionals. The 

remaining clinicians currently specified 

in § 512.390(b)(1)—doctors, physician 

assistants, nurse practitioners, and 

clinical nurse specialists—fall within 

the existing definition of qualified 

person at 42 CFR 410.48(a). We 

therefore propose to define ‘‘qualified 

staff’’ to mean both clinical staff and any 

qualified person (as defined at 

§ 410.48(a) of our regulations) who is an 

ETC Participant. 

We seek comment on our proposal to 

waive the originating site requirements 

for telehealth services to allow qualified 

staff to furnish kidney disease patient 

education services via telehealth to a 

beneficiary regardless of where the 

beneficiary is geographically located 

such that kidney disease patient 

education services could be furnished 

via telehealth regardless of the 

beneficiary’s location, including at a site 

not specified in § 410.78(b)(3) of our 

regulations. We further seek comment 

on our proposal to waive the originating 

site facility fee requirements such that 

CMS would not pay an originating site 

facility fee for kidney disease patient 

education services furnished via 

telehealth to a beneficiary at a site not 

specified in § 410.78(b)(3) of our 

regulations. 

(2) Kidney Disease Patient Education 

Services Beneficiary Coinsurance 

Waiver 

Available data and scholarly research 

suggest that there is a significant 

relationship between socioeconomic 

status and prevalence of CKD. For 

example, evidence suggests that CKD is 

more prevalent among individuals with 

lower income.285 In addition, at least 

one study suggests that as an 

individual’s CKD severity increases (for 

example, from CKD III to CKD IV), the 

likelihood of the CKD patient falling 

into poverty increases.286 In light of this 

research, CMS believes that cost 

represents a meaningful barrier for 

beneficiaries in accessing kidney 

disease patient education services. 

While there does not appear to be any 

research that explicitly investigates to 

what extent cost barriers preclude 

access to kidney disease patient 

education services, the identified 

relationship between household income 

or poverty status and prevalence of CKD 

suggests that cost is an important factor 

when considering a beneficiary’s access 

to kidney disease patient education 

services. 

Under section 1833 of the Act, the 

amounts paid by Medicare for kidney 

disease patient education services are 

equal to 80 percent of the applicable 

payment amount; beneficiaries are thus 

subject to a 20 percent coinsurance for 

kidney disease patient education 

services. Kidney disease patient 

education services can be billed under 

G0420 for an individual session, or 

under G0421 for a group session. The 

current national unadjusted payment for 

G0420 under the CY2021 Physician Fee 

Schedule is $114.10; for G0421, it is 

$27.22. As such, a beneficiary would be 

required to pay $22.82 for an individual 

session of kidney disease patient 

education services or $5.44 for kidney 

disease patient education services 

furnished to a group, which may be 

higher or lower depending on certain 

factors, such as the geographic location 

of the beneficiary. Medicare covers up 

to six kidney disease patient education 

services for an individual beneficiary 

during that beneficiary’s lifetime, 

meaning that a beneficiary may be 

required to pay $136.92 if six individual 

kidney disease patient education 

services are clinically appropriate for 

that beneficiary, or $32.64 if six group 

kidney disease patient education 

services are clinically appropriate for 

that beneficiary. 

CMS believes that it is necessary, for 

purposes of testing the ETC Model, to 

permit ETC participants the flexibility 

to reduce or waive the 20 percent 

coinsurance requirement for kidney 

disease patient education services. We 

believe this patient incentive, if 

finalized, would increase the provision 

of kidney disease patient education 

services to beneficiaries, given the 

relationship between income or poverty 

and prevalence of CKD, and the 

relationship between kidney disease 

patient education services and 

progression of CKD. CMS has 

determined that, if this proposal were 

finalized, this CMS-sponsored patient 

incentive would advance the ETC 

Model’s goal of increasing access to 

kidney disease patient education 

services, and to making beneficiaries 

more aware of their choices in preparing 

for kidney treatment, including the 

choice of receiving home dialysis, selfdialysis, or nocturnal in-center dialysis, 

rather than traditional in-center dialysis. 

Accordingly, beginning January 1, 

2022, we propose at § 512.397(c) to 

permit ETC Participants to reduce or 

waive the beneficiary coinsurance 

obligations for kidney disease patient 

education services furnished to an 

eligible beneficiary who does not have 

secondary insurance on the date the 

kidney disease patient education 

services are furnished if certain 

conditions are satisfied. We refer to this 

patient incentive herein as the ‘‘kidney 

disease patient education services 

coinsurance patient incentive.’’ As more 

fully explained below, we expect to 

make a determination that the antikickback statute safe harbor for CMSsponsored model patient incentives (42 

CFR 1001.952(ii)(2)) would be available 

to protect cost-sharing support that is 

furnished in compliance with ETC 


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Model requirements with respect to 

kidney disease patient education 

services. If CMS makes such a 

determination, the safe harbor for CMSsponsored model patient incentives 

would protect an ETC Participant, as 

that term is defined at § 512.310, who 

offers a reduction or waiver of 

coinsurance for kidney disease patient 

education services to beneficiaries who 

are eligible to receive kidney disease 

patient education services, including 

those eligible pursuant to the waiver 

described in § 512.397(b)(2), and who 

do not have secondary insurance on the 

date that the kidney disease patient 

education services were furnished. 

We are proposing that the kidney 

disease patient education services 

coinsurance patient incentive would be 

available to the ETC Participant for 

kidney disease patient education 

services furnished by an individual or 

entity who is qualified staff. This 

proposal would align with the 

individuals who may furnish kidney 

disease patient education services under 

§ 512.397(b) of this subpart, which are 

we replacing in its entirety to 

standardize certain terms and add 

clarity, as described in greater detail 

below. 

We are proposing to limit the kidney 

disease patient education services 

coinsurance patient incentive to 

beneficiaries who do not have 

secondary insurance, as secondary 

insurance typically provides costsharing support of the type CMS is 

proposing in this proposed rule. We also 

believe that limiting the kidney disease 

patient education services coinsurance 

patient incentive to beneficiaries 

without secondary insurance would 

better ensure that only beneficiaries 

who need cost-sharing support would 

receive it, rather than permitting costsharing support for all beneficiaries for 

whom kidney disease patient education 

services are clinically appropriate. 

We are also proposing that the kidney 

disease patient education services 

coinsurance patient incentive would be 

available only for kidney disease patient 

education services that were furnished 

in compliance with the applicable 

provisions of § 410.48 of our 

regulations, which includes a 

requirement that a beneficiary obtain a 

referral from the physician (as defined 

in section 1861(r)(1) of the Act) 

managing the beneficiary’s kidney 

condition in order for the beneficiary to 

be eligible to receive kidney disease 

patient education services. We are 

proposing to include this requirement 

because we waived some but not all 

provisions of § 410.48, and we believe 

that the requirement that the beneficiary 

receive a referral from their physician is 

important for ensuring that kidney 

disease patient education services are 

furnished only to beneficiaries for 

whom it is clinically appropriate. 

CMS proposes that such coinsurance 

support would be permitted for the 

kidney disease patient education 

services offered either in-person or via 

telehealth, and that it would be 

permitted for both individual sessions 

and group sessions. However, we are 

considering limiting the kidney disease 

patient education services coinsurance 

patient incentive to kidney disease 

patient education services furnished to 

an individual beneficiary, rather than 

allowing the kidney disease patient 

education services coinsurance patient 

incentive for kidney disease patient 

education services furnished either 

individually or to a group. The cost 

burden on beneficiaries who receive 

kidney disease patient education 

services in a group setting is much 

lower than it is on beneficiaries who 

receive kidney disease patient education 

services individually. However, we are 

concerned that any cost barrier to 

kidney disease patient education 

services, even if low, represents a 

meaningful barrier to some beneficiaries 

who would otherwise elect to receive 

such services. We solicit comments on 

this issue. 

An ETC Participant that offers 

coinsurance support for kidney disease 

patient education services would be 

required to maintain records of certain 

information. Specifically, an ETC 

Participant that offers the kidney 

disease patient education services 

coinsurance patient incentive would be 

required to maintain records of the 

following: The identity of the qualified 

staff who furnished the kidney disease 

patient education services for which the 

coinsurance was reduced or waived; the 

date the kidney disease patient 

education services coinsurance patient 

incentive was provided; the identity of 

the beneficiary to whom the kidney 

disease patient education services 

coinsurance patient incentive was 

provided; evidence that the beneficiary 

who received the kidney disease patient 

education services coinsurance patient 

incentive was eligible to receive the 

kidney disease patient education 

services and did not have secondary 

insurance; and the amount of the kidney 

disease patient education services 

coinsurance patient incentive reduced 

or waived by the ETC Participant. We 

propose to require an ETC Participant 

that offers this kidney disease patient 

education services coinsurance patient 

incentive to maintain and provide the 

government with access to these records 

in accordance with 42 CFR 512.135(b) 

and (c) of this part. 

We further propose in proposed 42 

CFR 512.160(b)(6)(ii) that, for the ETC 

Model only, CMS could suspend or 

terminate the ability of an ETC 

Participant to offer the kidney disease 

patient education services coinsurance 

patient incentive if CMS determined 

that any grounds for remedial action 

exist pursuant to § 512.160(a). 

In lieu of a waiver of certain fraud and 

abuse provisions in sections 1128A and 

1128B of the Act, CMS may determine 

that the anti-kickback statute safe harbor 

CMS-sponsored model patient 

incentives (42 CFR 1001.952(ii)(2)) is 

available to protect the reduction or 

waiver of kidney disease patient 

education services coinsurance 

permitted under the ETC Model final 

rule, if issued. Specifically, we expect to 

determine that the CMS-sponsored 

model safe harbor will be available to 

protect the reduction or waiver of 

coinsurance that satisfies the 

requirements of such safe harbor and 

the provisions of proposed 

§ 512.397(c)(1). We propose that, if we 

make this determination, we would 

specify in regulation text at 

§ 512.397(c)(4) that the safe harbor is 

available. 

We are also considering prohibiting 

on an ESRD facility or other entity from 

providing qualified staff or the ETC 

Participant with financial support to 

enable such qualified staff or ETC 

Participant to provide the kidney 

disease patient education services 

coinsurance patient incentive. CMS is 

concerned that permitting such 

financial support may encourage 

unlawful or abusive arrangements 

designed to induce or reward referrals 

for Federal health care program 

business. We solicit comments on 

whether this prohibition is a necessary 

to safeguard against fraud and abuse or 

if other laws effectively provide 

sufficient protection. 

We also considered waiving Medicare 

payment requirements such that CMS 

would pay the full amount of the kidney 

disease patient education services 

furnished to a beneficiary who does not 

have secondary insurance, rather than 

just 80 percent of the amount. Under 

section 1115A(d)(1) of the Act, the 

Secretary may waive such requirements 

of titles XI and XVIII and of sections 

1902(a)(1), 1902(a)(13), 

1903(m)(2)(A)(iii) of the Act, and certain 

provisions of section 1934 of the Act as 

may be necessary solely for purposes of 

carrying out section 1115A of the Act 

respect to testing models described in 

section 1115A(b) of the Act. This is the 

authority under which we would waive 


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287https://www.ncbi.nlm.nih.gov/pmc/articles/ 

PMC2924406/#B38. 

such Medicare payment requirements. 

Under such a policy, Medicare would 

pay 100 percent of the payment amount 

for kidney disease patient education 

services furnished by Managing 

Clinicians who are ETC Participants to 

beneficiaries who do not have 

secondary insurance, and such 

beneficiaries would have no costsharing obligation for that benefit. 

However, we determined that this 

policy would likely represent too large 

an impact to the ETC Model’s savings 

estimates, and thus would potentially 

jeopardize our ability to continue to test 

the ETC Model, if such a policy were 

finalized. 

Given the policies proposed in this 

section related to programmatic waivers 

and additional flexibilities available 

under the ETC Model, we propose to 

modify the title of § 512.397 from ‘‘ETC 

Model Medicare program waivers’’ to 

‘‘ETC Model Medicare program waivers 

and additional flexibilities.’’ We 

propose this change so that the section 

title would more accurately reflect the 

contents of the section if our proposed 

kidney disease patient education 

services coinsurance patient incentive is 

finalized. 

We solicit public comments on our 

proposal to allow qualified staff, as we 

propose to define the term under 

§ 512.310, to offer coinsurance support 

for kidney disease patient education 

services to beneficiaries who are eligible 

for such services, including those 

eligible under § 512.397(b)(2), and who 

do not have secondary insurance on the 

date the kidney disease patient 

education services are furnished. We 

also solicit comment on our proposal to 

require the ETC Participant to maintain 

and provide the government with access 

to records regarding the use of the 

kidney disease patient education 

services coinsurance patient incentive. 

(3) Revising Language Providing Other 

ETC Model Medicare Program Waivers 

We propose to revise § 512.397(b)(1) 

through (4) in their entirety to 

accomplish a few goals. First, we 

propose to make conforming changes 

throughout § 512.397(b) to the manner 

in which CMS discusses kidney disease 

patient education services. Currently, 

§ 512.397(b) includes references to 

‘‘KDE services,’’ ‘‘the KDE benefit,’’ 

‘‘KDE sessions,’’ and, simply, ‘‘KDE.’’ 

CMS would change all of these 

references to ‘‘kidney disease patient 

education services’’ for clarity and to 

conform with the term used elsewhere 

in our regulations. In addition, we 

propose to make conforming changes 

through § 512.397(b) to the manner in 

which CMS discusses the individuals 

who are permitted to furnish kidney 

disease patient education services under 

the ETC model programmatic waivers. 

Specifically, as discussed previously, 

CMS is proposing to add definitions for 

‘‘clinical staff’’ and ‘‘qualified staff’’ in 

this proposed rule, and CMS believes 

clarifying how CMS discusses these 

individuals in § 512.397(b) will enhance 

clarity. Finally, CMS is proposing to 

remove the ‘‘clinic/group practice’’ from 

the list of individuals or entities that are 

permitted to furnished kidney disease 

patient education services under the 

ETC Model programmatic waivers, and 

to remove the waiver of 42 CFR 

410.48(c)(2)(i) from § 512.397(b)(1) of 

this part. CMS believes that its inclusion 

of clinic/group practices previously was 

in error; a clinic/group practice is not 

able to furnish or bill for kidney disease 

patient education services under 

existing law and CMS did not intend for 

the waiver described in § 512.397(b) to 

permit anyone other than a clinican to 

furnish kidney disease patient 

education services. Because the waiver 

of the requirements under 42 CFR 

410.48(c)(2)(i) was implemented only to 

broaden the ‘‘qualified person’’ that 

could furnish kidney disease patient 

education services pursuant to 

§ 512.397(b)(1) to include a clinic/group 

practice, we are proposing to remove 

references to 42 CFR 410.48(c)(2)(i) in 

§ 512.397(b)(1) of this part. 

We solicit public comments on these 

proposed changes to § 512.397(b) to 

make conforming and clarifying changes 

to the manner in which CMS discusses 

kidney disease patient education 

services and the individuals who are 

permitted to furnish kidney disease 

patient education services under the 

ETC Model waivers described in 

§ 512.397(b), and to our proposed 

removal of ‘‘clinic/group practice’’ from 

the list of individuals or entities who 

may, under the ETC Model waivers 

described in § 512.397(b), furnish 

kidney disease patient education 

services. 

C. Requests for Information (RFIs) on 

Topics Relevant to the ETC Model 

This section includes several requests 

for information (RFIs). In responding to 

the RFIs, the public is encouraged to 

provide complete, but concise 

responses. These RFIs are issued solely 

for information and planning purposes; 

RFIs do not constitute a Request for 

Proposal (RFP), application, proposal 

abstract, or quotation. The RFIs do not 

commit the U.S. Government to contract 

for any supplies or services or make a 

grant award. Further, CMS is not 

seeking proposals through these RFIs 

and will not accept unsolicited 

proposals. Respondents are advised that 

the U.S. Government will not pay for 

any information or administrative costs 

incurred in response to this RFI; all 

costs associated with responding to 

these RFIs will be solely at the 

respondent’s expense. Failing to 

respond to either RFI will not preclude 

participation in any future procurement, 

if conducted. 

Please note that CMS will not respond 

to questions about the policy issues 

raised in these RFIs. CMS may or may 

not choose to contact individual 

respondents. Such communications 

would only serve to further clarify 

written responses. Contractor support 

personnel may be used to review RFI 

responses. Responses to these RFIs are 

not offers and cannot be accepted by the 

U.S. Government to form a binding 

contract or issue a grant. Information 

obtained because of this RFI may be 

used by the U.S. Government for 

program planning on a non-attribution 

basis. Respondents should not include 

any information that might be 

considered proprietary or confidential. 

All submissions become U.S. 

Government property and will not be 

returned. CMS may publicly post the 

comments received, or a summary 

thereof. 

1. Peritoneal Dialysis Catheter 

Placement 

The most common modality of home 

dialysis is peritoneal dialysis (PD). In 

order to perform PD, a beneficiary needs 

placement of a PD catheter. A PD 

catheter is a flexible plastic tube that 

enables dialysate to enter the abdomen 

for blood filtration purposes. The 

catheter is generally installed via 

outpatient surgery, as it is an invasive 

procedure. 

However, CMS has heard concerns 

from numerous stakeholders about their 

ability to effectively get PD catheters 

installed in beneficiaries who may be 

otherwise interested in home dialysis. 

These stakeholders reported a variety of 

issues related to PD catheter placement, 

including the lack of availability of 

vascular surgeons to perform PD 

catheter placements, lack of appropriate 

operating room time, and a lack of 

training on PD catheter placement for 

vascular surgeons.287 As many 

stakeholders have pointed out, the lack 

of timely PD catheter placement is a key 

barrier preventing many beneficiaries 

from being able to use PD as a dialysis 

modality. 

Based on these issues, we seek 

feedback about how CMS can test and 


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use Medicare payment policy, under the 

ETC model, to promote placement of PD 

catheters. Specifically, we are seeking 

feedback on the following questions: 

• What are the key barriers to 

increased placement of PD catheters? 

• How can CMS promote placement 

of PD catheters in a more timely 

manner? 

• Should the Innovation Center use 

its authority to test alternative payment 

structures to address the barriers to PD 

catheter placement as a part of the ETC 

Model? If so, why and how? 

2. Beneficiary Experience Measure 

The ETC Model uses two ESRD 

facility quality measures; Standardized 

Mortality Ratio (SMR) (NQF #0369) and 

Standardized Hospitalization Ratio 

(NQF #1463). Both measures are 

currently calculated and displayed on 

Dialysis Facility Compare, a public 

reporting tool maintained by CMS. 

Because data collection and measure 

reporting are ongoing through claims, 

there is no additional burden to ETC 

Participants. 

In the Specialty Care Models 

proposed rule, we considered including 

the In-Center Hemodialysis (ICH) 

Consumer Assessment of Healthcare 

Providers and Systems Survey 

(CAHPS)® survey to monitor beneficiary 

perceptions of changes in quality of care 

as a result of the ETC Model (84 FR 

34565). However, the ICH CAHPS 

survey includes only beneficiaries who 

receive in-center dialysis, and 

specifically excludes the two 

beneficiary populations that the ETC 

Model focuses on: Beneficiaries who 

dialyze at home and beneficiaries who 

receive transplants. 

We are considering the inclusion of a 

measure to capture the beneficiary 

experience of home dialysis care. The 

measure could be either an existing 

measure or one that CMS would 

develop. The measure could assess any 

aspect of the patient experience. The 

domains could include, but are not 

limited to, patient satisfaction, patient 

activation, and quality of life. If a new 

measure is developed, CMS would like 

to make it useful to other CMS kidney 

disease programs. 

We seek comments on any aspect of 

a patient experience measure. Questions 

to consider include: 

• What domains of a patient 

experience of care with home dialysis 

would be the most useful to assess and 

why? 

• Would you prefer the measure to be 

newly developed or an update to an 

existing measure? If an update, which 

existing measure should be updated? 

• How would a patient experience 

measure be best used to further the 

purpose of the ETC Model? 

• How should CMS use a patient 

experience measure to assess the quality 

of care of beneficiaries? 

• How should CMS use a patient 

experience measure to incentivize 

improved quality of care in the ETC 

Model and/or for other CMS programs? 

While we will not be responding to 

specific comments submitted in 

response to this Request for Information, 

CMS intends to use this input to inform 

our future quality measure efforts. 

CMS is considering publishing the 

quality outcomes for the ETC Model. 

While we seek comments on any aspect 

of reporting quality data, we specifically 

want input on the following: 

• What is the frequency with which 

CMS should disseminate the results? 

• What should be the unit of analysis 

for the reported data? 

VI. Requests for Information 

This section addresses several 

requests for information (RFIs). Upon 

reviewing the RFIs, respondents are 

encouraged to provide complete, but 

concise responses. These RFIs are 

issued solely for information and 

planning purposes; RFIs do not 

constitute a Request for Proposal (RFP), 

application, proposal abstract, or 

quotation. The RFIs do not commit the 

United States (U.S.) Government to 

contract for any supplies or services or 

make a grant award. Further, CMS is not 

seeking proposals through these RFIs 

and will not accept unsolicited 

proposals. Responders are advised that 

the U.S. Government will not pay for 

any information or administrative costs 

incurred in response to this RFI; all 

costs associated with responding to 

these RFIs will be solely at the 

interested party’s expense. Failing to 

respond to either RFI will not preclude 

participation in any future procurement, 

if conducted. 

Please note that CMS will not respond 

to questions about the policy issues 

raised in these RFIs. CMS may or may 

not choose to contact individual 

responders. Such communications 

would only serve to further clarify 

written responses. Contractor support 

personnel may be used to review RFI 

responses. Responses to these RFIs are 

not offers and cannot be accepted by the 

U.S. Government to form a binding 

contract or issue a grant. Information 

obtained because of this RFI may be 

used by the U.S. Government for 

program planning on a non-attribution 

basis. Respondents should not include 

any information that might be 

considered proprietary or confidential. 

All submissions become U.S. 

Government property and will not be 

returned. CMS may publically post the 

comments received, or a summary 

thereof. 

A. Informing Payment Reform Under the 

ESRD PPS 

Over the last several years, CMS, in 

conjunction with its contractor, has 

been conducting research, including 

holding three technical expert panels 

(TEPs), to explore possible 

improvements to the ESRD payment 

model. Additionally, in the CY 2020 

ESRD PPS proposed rule (84 FR 38398 

through 38400), CMS invited further 

comment on a number of topics, 

including expanding the outlier policy 

to include composite rate drugs, 

laboratory tests and supplies; reporting 

the length of each dialysis session 

directly on the ESRD claim; patient 

characteristics which contribute 

significantly to the cost of dialysis care; 

and improving the quality of facilitylevel data as reflected in the Medicare 

cost report. Stakeholders have asked 

CMS to explore a refined case-mix 

adjustment model for the ESRD PPS, 

stating that the existing case mix 

adjustors may not correlate well with 

the current cost of dialysis treatment. 

Under section 632(b) of ATRA, as 

amended by section 217(a) of PAMA 

and section 204 of the ABLE Act, oralonly drugs cannot be incorporated into 

the ESRD PPS bundled payment until 

January 1, 2025. In order to provide 

payment for oral-only renal dialysis 

service drugs and biologicals under the 

ESRD PPS beginning January 1, 2025, as 

provided in 42 CFR 413.174(f)(6), we 

will need to propose refinements to the 

payment system through notice-and 

comment rulemaking. A refinement 

involves revising the patient and 

facility-level adjustments by changing 

the adjustment payment amounts based 

on updated regression analysis using 

more recent ESRD claims and cost 

report data. When refinements occur, 

due to the nature of regression analysis, 

all patient-level and facility-level 

adjustments are affected which can 

impact budget neutrality requirements 

and impact ESRD facilities differently 

than if adopted incrementally. Payment 

system changes can also require 

extensive efforts by CMS and health 

care providers to implement. 

Consequently, we believe CMS and 

ESRD facilities would best be served if 

these major payment methodology 

changes occur as a unified approach for 

CY 2025. 

In order to obtain additional feedback 

from as wide of an audience as possible, 

we are soliciting comments from the 


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public through this proposed rule. We 

are seeking comments from all 

perspectives, including differing 

beneficiary populations of ESRD 

facilities and ESRD facilities located in 

remote locations and their infrastructure 

issues. Obtaining a variety of 

perspectives enables CMS to ultimately 

work toward an improved payment 

methodology for the ESRD PPS that is 

both patient-data focused and accounts 

for the changing landscape in providing 

renal dialysis services to Medicare 

beneficiaries. 

We encourage the public, and all 

stakeholders to provide comments and 

recommend approaches that will assist 

CMS in making refinements to the ESRD 

PPS through rulemaking in the future. 

We are soliciting comments this year so 

that we have time to consider them for 

potential proposals in the CY 2023 

ESRD PPS proposed rule for a CY 2025 

implementation. 

B. Technical Expert Panels (TEPs) 

CMS’ contractor held three TEPs to 

discuss refinements to the ESRD PPS. 

The TEPs included panelists 

representing dialysis providers, 

independent researchers, patient 

advocates, and representatives from 

professional associations and industry 

groups. The first TEP held in 2018 

explored the components of the existing 

ESRD PPS, and identified limitations of 

the current model. The TEP discussed 

topics such as current measures of ESRD 

PPS costs, costs associated with length 

of dialysis treatment, variations in cost 

associated with complex patients, 

facility level drivers of cost, and 

additional patient attributes necessary 

for developing a revised ESRD payment 

model. One of the main goals of the TEP 

was to identify items and services 

potentially appropriate for either 

itemized data collection on claims or 

improved reporting on the cost reports. 

The second TEP held in 2019 elaborated 

on the previous TEP’s themes and 

focused on alternative approaches to 

measuring the cost of a dialysis session 

to better reflect treatment-level variation 

in cost. Topics included measurement 

of costs for determining case-mix 

adjustments, wage index, low volume 

payment adjustments and rural 

adjustments, TDAPA, outlier 

determinations, TPNIES, and home 

dialysis. The third TEP held in 2020 

focused on aspects of the ESRD PPS for 

which refinements or enhancements 

were being considered. The topics 

discussed included adult and pediatric 

case-mix adjustments, low volume 

payment adjustments, the acute kidney 

injury payment system, and cost report 

revisions. 

During each TEP, the data contractor 

presented to the panelists, and the 

panelists presented to all the TEP 

participants, innovative methodological 

approaches that addressed stakeholder 

concerns about the current payment 

model and presented alternative 

approaches with the goal of soliciting 

specific input for developing a more 

refined case-mix adjusted payment 

system. Panelists discussed potential 

approaches while weighing the ESRD 

facility burden those approaches may 

require. Alternative approaches were 

presented to solicit feedback from 

panelists about feasibility and 

acceptability of the options. The TEPs 

did not provide formal 

recommendations, but discussion items 

and suggestions were captured in three 

subsequent reports. The materials from 

the TEPs and summary reports can be 

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

Medicare/Medicare-Fee-for-ServicePayment/ESRDpayment/Educational_

Resources. 

The following sections of this RFI 

provide information and solicit 

feedback specifically on the following 

topics: Low-volume payment 

adjustment (LVPA), calculations for 

case-mix adjustment, the calculation for 

the outlier payment adjustment, the 

current pediatric dialysis payment 

model, recommendations for ESRD PPS 

and hospital cost report modifications, 

recommendations for modifying the 

pediatric cost report, and home dialysis 

for Medicare beneficiaries with acute 

kidney injury. While TEP discussions 

are noted in each section, CMS 

encourages the public to reference the 

TEP reports on CMS’ website, noted 

above, for more details. 

C. Calculation of the Low-Volume 

Payment Adjustment (LVPA) 

1. Background on the LVPA 

Section 1881(b)(14)(D)(iii) of the Act 

provides that the ESRD PPS ‘‘shall 

include a payment adjustment that 

reflects the extent to which costs 

incurred by low-volume facilities (as 

defined by the Secretary) in furnishing 

renal dialysis services exceed the costs 

incurred by other facilities in furnishing 

such services, and for payment for renal 

dialysis services furnished on or after 

January 1, 2011, and before January 1, 

2014, such payment adjustment shall 

not be less than 10 percent.’’ 

In the CY 2011 ESRD PPS final rule 

(75 FR 49118 through 49125), we 

finalized the methodology used to target 

the appropriate population of ESRD 

facilities that were low-volume and to 

determine the treatment threshold for 

those facilities identified. After 

consideration of public comments, we 

established an 18.9 percent adjustment 

for facilities that furnish less than 4,000 

treatments annually with the intention 

of encouraging small facilities to 

continue providing access to care. 

In the CY 2016 ESRD PPS proposed 

rule (80 FR 37819), we analyzed ESRD 

facilities that met the definition of lowvolume under § 413.232(b) as part of the 

updated regression analysis and found 

that the facilities still had higher costs 

compared to other facilities. A 

regression analysis of CYs 2012 and 

2013 low-volume facility claims and 

cost report data indicated a multiplier of 

1.239 percent; therefore, we proposed 

an updated LVPA adjustment factor of 

23.9 percent in the CY 2016 ESRD PPS 

proposed rule (80 FR 37819) and 

finalized this policy in the CY 2016 

ESRD PPS final rule (80 FR 69001). In 

CY 2019, 332 facilities received the 

LVPA and using the most recent 

available data for CY 2020, the number 

of facilities receiving the LVPA was 344 

as of April 2021. 

2. Current LVPA Methodology 

Under § 413.232(b), a low-volume 

facility is an ESRD facility that, based 

on the submitted documentation: (1) 

Furnished less than 4,000 treatments in 

each of the 3 cost reporting years (based 

on as-filed or final settled 12- 

consecutive month costs reports, 

whichever is most recent) preceding the 

payment year; and (2) has not opened, 

closed, or received a new provider 

number due to a change in ownership 

in the three cost reporting years (based 

on as-filed or final settled 12- 

consecutive month cost reports, 

whichever is most recent) preceding the 

payment year. 

In addition, under § 413.232(c), for 

purposes of determining the number of 

treatments furnished by the ESRD 

facility, the number of treatments 

considered furnished by the ESRD 

facility equals the aggregate number of 

treatments furnished by the ESRD 

facility and the number of treatments 

furnished by other ESRD facilities that 

are both under common ownership 

with, and 5 road miles or less from, the 

ESRD facility in question. In order to 

receive the LVPA, an ESRD facility must 

submit a written attestation statement to 

its Medicare Administrative Contractor 

(MAC) confirming that it meets all of the 

requirements specified in § 413.232 and 

qualifies as a low-volume ESRD facility. 

For purposes of determining eligibility 

for the LVPA, ‘‘treatments’’ mean total 

hemodialysis equivalent treatments 

(Medicare and non-Medicare). For 

peritoneal dialysis patients, one week of 

peritoneal dialysis is considered 


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sec.pdf?sfvrsn=0. 289https://www.cms.gov/files/document/endstage-renal-disease-prospective-payment-systemtechnical-expert-panel-summary-report-april2021.pdf. 

290https://www.cms.gov/files/document/endstage-renal-disease-prospective-payment-systemtechnical-expert-panel-summary-report-april2021.pdf. 

291https://www.cms.gov/files/document/endstage-renal-disease-prospective-payment-systemtechnical-expert-panel-summary-report-april2021.pdf. 

292https://www.cms.gov/files/document/endstage-renal-disease-prospective-payment-systemtechnical-expert-panel-summary-report-april2021.pdf. 

293http://www.medpac.gov/docs/default-source/ 

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sec.pdf?sfvrsn=0. 

equivalent to two hemodialysis 

treatments (80 FR 68994). Section 

413.232(e) imposes a yearly November 1 

deadline for attestation submissions, 

with a few exceptions where the 

deadline is December 31. The November 

1 timeframe provides 60 days for a MAC 

to verify that an ESRD facility meets the 

LVPA eligibility criteria (76 FR 70236). 

The ESRD facility would then receive 

the LVPA payment for all the Medicareeligible treatments in the payment year. 

Once a facility is determined to be 

eligible for the LVPA, a 23.9 percent 

increase is applied to the ESRD PPS 

base rate for all treatments furnished by 

the facility (80 FR 69001). 

In the CY 2021 ESRD PPS final rule 

(85 FR 71443), we finalized a policy to 

allow ESRD facilities flexibility for 

LVPA eligibility due to the COVID–19 

PHE. Under § 413.232(g)(4), for 

purposes of determining ESRD facilities’ 

eligibility for payment years 2021, 2022, 

and 2023, we will only consider total 

dialysis treatments for any 6 months of 

their cost-reporting period ending in 

2020. ESRD facilities will attest that 

their total dialysis treatments for those 

6 months of their cost reporting period 

ending in 2020 are less than 2,000. The 

attestation must further include that 

although the total number of treatments 

furnished in the entire year otherwise 

exceeded the LVPA threshold, the 

excess treatments furnished were due to 

temporary patient shifting resulting 

from the COVID–19 PHE. MACs will 

annualize the total dialysis treatments 

for the total treatments reported in those 

6 months by multiplying by 2. 

3. Current Issues and Stakeholder 

Concerns 

ESRD facilities, the Medicare Payment 

Advisory Commission (MedPAC), and 

the Government Accountability 

Office 288 have recommended that we 

make refinements to the LVPA to better 

target ESRD facilities that are critical to 

beneficiary access to dialysis care in 

remote or isolated areas.289 These 

groups have also have expressed 

concern that the strict treatment count 

introduces a ‘‘cliff-effect’’ that may 

incentivize facilities to restrict their 

patient caseload to remain below the 

4,000 treatments per year for the LVPA 

threshold.290

In addition, we have heard from 

stakeholders that the eligibility criteria 

for the LVPA are very explicit and leave 

little room for flexibility in certain 

circumstances (85 FR 71442). Finally, 

some view the attestation process as 

burdensome to facilities and believe it 

may discourage participation by small 

facilities with limited resources that 

would otherwise qualify for the 

LVPA.291 Given these concerns, we have 

been asked to consider alternative 

approaches to the LVPA that would 

reduce burden, remove negative 

incentives that may cause gaming, and 

better target facilities that are critical for 

beneficiary access. 

4. Suggestions for Calculating the LVPA 

a. Census Tract 

During the 2020 ESRD PPS TEP, 

panelists discussed alternatives to the 

current LVPA. One methodology 

discussed utilized census tracts to 

identify geographic areas with low 

demand, which suggested increased 

beneficiary access by incentivizing 

dialysis organizations to continue 

operating facilities in otherwise nonviable locations.292 As discussed during 

the TEP, an advantage to this approach 

would be a shift in the focus from 

identifying low volume facilities to 

identifying geographical areas, 

specifically census tracts, with low 

demand for dialysis. 

This census tract methodology often 

results in a single facility being the only 

dialysis provider for a number of miles. 

The process would involve dividing the 

U.S. into geographic areas based on a 

reasonable assessment of ESRD 

beneficiaries’ ability or willingness to 

travel. Latent demand is then calculated 

by counting the number of ESRD 

beneficiaries near each facility. ‘‘Near’’ 

is defined by driving time to facilities. 

Latent demand is calculated by 

multiplying the number of beneficiaries 

near an ESRD facility by average 

number of treatments for ESRD 

beneficiaries. The LVPA threshold is 

then applied by determining the 

threshold of adjusted latent demand. 

That is, those facilities, which fall below 

the threshold are LVPA eligible. The 

panelists noted that this methodology 

appears administratively simple and 

could eliminate the burden associated 

with the LVPA attestation process for 

facilities and MACs. 

b. Low-Volume and Isolated (LVI) 

Adjustment 

In its June 2020 report to Congress, 

MedPAC recommended that the 

Secretary replace the LVPA and rural 

adjustment under the ESRD PPS with a 

single payment adjustment, a lowvolume and isolated (LVI) adjustment, 

in an effort to better protect isolated, 

low-volume ESRD facilities that are 

critical to ensure beneficiary access.293

A determination that a facility is low 

volume and isolated would be based on 

that facility’s distance from the nearest 

facility and its total treatment volume. 

MedPAC stated that the facilities that 

would receive the adjustment would be 

more appropriately targeted. This 

methodology would be accomplished 

via a single facility-level regression 

approach instead of the current tworegression approach utilized by CMS. 

As an example of how the LVI 

adjustment would more directly target 

isolated, low-volume dialysis facilities, 

the TEP compared the current LVPA 

and suggested LVI methodologies using 

2017 data. In this example, 575 facilities 

would have been eligible for the LVI 

verses 1,734 facilities under the current 

LVPA and rural adjustment 

methodology. 

5. Request for Information on 

Calculating the LVPA 

CMS is considering alternative 

approaches to the LVPA that directly 

address stakeholder concerns, and is 

issuing a request for information to seek 

feedback on the approaches suggested 

above, other alternate approaches, and 

support of the current LVPA 

methodology. We are soliciting 

information that will better inform 

potential future modifications to the 

methodology. In addition to any other 

input the public wants to provide 

regarding the LVPA under the ESRD 

PPS, we are requesting responses to the 

following questions. 

• Should a distinction other than 

census tract information be considered? 

• What criteria should be used to 

determine the threshold(s) of adjusted 

latent demand (in treatment counts) 

which determine LVPA eligibility (for 

example, a threshold of high average 

cost per-treatment)? 

• What are the concerns for facilities 

that would lose the LVPA under the LVI 

methodology? 

• What are the concerns about the 

potential for gaming within the LVI 

methodology? 


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• To the extent that the LVI 

methodology captures more isolated 

(and most often rural) facilities, should 

a separate rural adjustment be 

maintained? 

D. Calculation of the Case-Mix 

Adjustments 

1. Background on the Case-Mix 

Adjustments 

Section 1881(b)(14)(D)(i) of the Act 

mandates that the single payment 

system under the ESRD PPS 

implemented by the Secretary ‘‘shall 

include a payment adjustment based on 

case mix that may take into account 

patient weight, body mass index, 

comorbidities, length of time on 

dialysis, age, race, ethnicity, and other 

appropriate factors.’’ The ESRD PPS 

includes facility-level and patient-level 

adjustments to the base rate associated 

with resource utilization and the cost of 

providing dialysis treatment. The goal of 

case-mix adjustment is to ensure that 

payment for a dialysis treatment reflects 

expected resource use. Payment 

adjustments protect access to care for 

the most costly beneficiaries by 

mitigating financial disincentives to 

providing that care. The ESRD PPS is a 

case-mix adjusted, bundled payment 

model intended to reflect total treatment 

costs, which consist of formerly 

separately billable costs and composite 

rate costs (75 FR 49032). As required by 

section 1881(b)(14) of the Act, formerly 

separately billable services were 

included in the ESRD PPS bundled 

payment, effective January 1, 2011. 

Refinements to the current case-mix 

adjusters were implemented in the CY 

2016 ESRD PPS final rule, effective 

January 1, 2016, and are currently in 

use. 

2. Current Case-Mix Methodology 

The current model uses two 

equations, including a patient-level 

equation for formerly separately billable 

costs and a facility-level equation for 

composite rate costs (75 FR 49083 

through 49127). Formerly separately 

billable services are itemized on the 

ESRD Facility claim, (Type of Bill: 72x) 

and include injectable drugs and their 

oral equivalents plus certain laboratory 

tests and supplies. Composite rate 

services, which are captured on the cost 

report, constitute approximately 90 

percent of a treatment’s cost and include 

capital, labor, and administrative costs 

plus certain drugs, laboratory tests, and 

supplies (75 FR 49036; 84 FR 38396). 

Final case-mix adjusters for adults are 

the weighted average of estimated 

coefficients from these two equations 

(that is, patient level and facility level 

equations). Weights are the fraction of 

costs that are composite rate versus 

formerly separately billable. The 

regression equations and weighted 

averages are calculated using 2012 

through 2013 claims and cost report 

data. Case-mix factors in the current 

model include age categories, body 

surface area (BSA), low body mass 

index (BMI) indicator, onset status, and 

comorbidities (that is, pericarditis, 

gastrointestinal tract bleeding, 

hereditary hemolytic or sickle cell 

anemia, and myelodysplastic syndrome) 

(80 FR 68989 through 68992). Facility 

adjusters include wage index, low 

volume status, and rural status (80 FR 

68972 and 69001). 

3. Current Issues and Stakeholder 

Concerns 

Over the last several years, 

stakeholders have asked CMS to explore 

a refined case-mix adjustment model for 

the ESRD PPS, arguing that the existing 

case-mix adjustors may not correlate 

well with the current cost of dialysis 

treatment. They stated that: 

• The current adult case-mix 

adjustors were calculated using old data 

(that is, 2012–2013 claims and cost 

report data); 

• current adjustors may not align 

with resource-intensive patient-level 

services such as isolation rooms, 

behavioral issues, or neurocognitive 

issues; 

• apportioned composite rate costs 

(such as labor and capital related costs), 

from the cost reports, used in the casemix adjustment are currently only 

observable at the facility level and do 

not include patient or treatment level 

variations; and 

• composite rate items are not 

individually collected on the claim, 

resulting in the payment not 

differentiating between the cost of 

hemodialysis verses peritoneal dialysis, 

which are affected by different labor and 

equipment costs. 

Other stakeholders raised similar 

concerns during the TEP meetings. 

Additionally, panel members 

questioned the magnitude/significance 

of age, BMI, and BSA coefficients; the 

validity of taking weighted average of 

estimates across the two equations when 

the joint distribution of composite rate 

and formerly separately billable costs is 

not accounted for in the case-mix; and 

logistical challenges in obtaining the 

accurate diagnosis and comorbidity data 

that it is not routinely reported in the 

72x claims. 

In a comment letter to the Acting CMS 

Administrator on July 29, 2016,294

MedPAC noted the current ESRD PPS 

does not have patient-level variation of 

composite rate (resource) costs and 

suggested CMS move to a ‘‘one-equation 

model’’ (that is, a patient-data focused 

model). MedPAC specifically stated that 

CMS should develop payment 

adjustment factors using a singleequation methodology that accounts for 

variation in the cost of providing the 

full PPS payment bundle. CMS is not 

currently able to implement this 

recommendation for the ESRD PPS 

because we do not have data on the 

charges associated with the components 

of dialysis treatment costs that vary 

across patients in the use of the formerly 

composite rate services. 

4. Suggestions for Allocating Composite 

Rate Costs 

CMS has been carefully studying 

MedPAC’s suggestion to base the ESRD 

PPS on a ‘‘one-equation model’’ (that is, 

a patient-data focused model). CMS has 

over the years publicly discussed 

potential changes with our stakeholders 

who support a patient-data focused 

model. For instance, during the 2018 

and 2019 TEP meetings discussions 

included using time on machine to 

address allocation of composite rate 

costs, case mix, and patient level 

adjustments. Time on machine would 

not be used to directly adjust payment; 

rather, it would be used to apportion 

composite rate costs (such as labor and 

capital-related costs) that are currently 

only observable at the facility level to 

the patient or treatment level for use in 

the case-mix adjustment. Data on the 

time on machine receiving dialysis 

would allow for a proportionately 

higher amount of composite rate costs to 

be allocated to patients with longer 

dialysis treatment times. During the 

December 2019 TEP, a panelist 

indicated that this option would reduce 

burden since dialysis treatment time 

(that is, time on machine) is 

automatically generated by the dialysis 

machine and easily entered into the 

patient’s medical record. Under this 

option, a single aggregate number would 

be reported on each claim. That number 

corresponds to the total number of 

minutes the beneficiary spent on 

dialysis during that claim period. A 

panelist noted that reporting a single 

number would minimize provider 

burden. Panelists reached consensus 

that the reporting of actual time on 


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297https://www.cms.gov/files/document/endstage-renal-disease-prospective-payment-systemtechnical-expert-panel-summary-report-april2021.pdf. 

machine offered the best solution for 

capturing patient-level differences in 

the cost of dialysis sessions and would 

be superior to the current case-mix 

adjusters. 

We included discussions about 

expanding the data elements, moving to 

a patient-data focused model, and the 

use of time on machine to determine 

patient level variation in dialysis 

treatment costs in the CY 2019 ESRD 

PPS final rule (83 FR 56963 through 

56970) as well as the CY 2020 ESRD 

PPS proposed rule (84 FR 38396 

through 38400) A comment letter from 

a large dialysis organization in response 

to the CY 2019 ESRD PPS proposed rule 

stated that costs in the remaining 

category—wages, salaries, and 

benefits—account for nearly 40 percent 

of the market basket weight. 

Additionally, the large dialysis 

organization noted that these costs 

represent the majority of expenses 

associated with dialysis treatment and 

will vary by patient because they are 

dependent on dialysis treatment times. 

The large dialysis organization stated 

that time on machine was a good proxy 

for costs in dialysis. 

Based on information gathered from 

our stakeholders and panelists from the 

first two TEP meetings and comments 

received based on RFIs in the CY 2020 

ESRD PPS proposed rule, CMS took 

steps towards developing a patient-data 

focused model. Based on stakeholder 

input, CMS chose to utilize time on 

machine to determine patient level 

variation in dialysis treatment costs. In 

order to collect this information from 

ESRD facilities, CMS petitioned the 

National Uniform Billing Committee 

(NUBC) for a new value code for time 

on machine. This value code allows 

CMS to add time on machine to the 

ESRD claim. In April 2020, NUBC 

approved the request. CMS included a 

requirement to collect time on machine 

data effective January 1, 2021 in two 

technical direction letters and two 

Medicare Learning Network articles. 

CMS later rescinded the time on 

machine requirement,295 but we are 

discussing this potential requirement in 

this RFI as a possible future refinement 

of the ESRD PPS to address allocation 

of composite rate costs, case mix, and 

patient level adjustments. 

During the 2020 TEP, the data 

contractor for CMS presented and the 

panelists discussed potential refinement 

to concerns regarding the current casemix adjustment. One of the refinements 

discussed was collecting time on 

machine data on the 72x claim using a 

value code. Specifically, the suggested 

method includes the costs per 

beneficiary-facility-month which are the 

sum of formerly separately billable 

costs, directly calculated from claims 

(quantities) and from Part B prices, and 

composite rate costs for each 

beneficiary-facility-month, calculated by 

allocating annual facility costs (less 

formerly separately billable costs) to the 

beneficiary-facility-month level using 

time on machine (duration of all 

treatments). For some modalities and 

settings, time on machine is not 

available and must be imputed. Finally, 

a regression is run of beneficiaryfacility-month costs on case-mix 

adjusters and facility characteristics. 

Following a presentation by the data 

contractor, the panelists agreed that this 

method would identify a magnitude of 

factors that best reflect variation in this 

measure of total cost per treatment. This 

method would select a set of case-mix 

adjusters that account for a significant 

portion of the variance of total costs, 

subject to intuitive clinical relationship 

to dialysis treatment costs, reasonable 

number of risk adjusters, easy to 

diagnose, identify, or report, and not 

gameable. 

Panelists at the TEPs and stakeholder 

comments received in response to the 

CY 2019 ESRD PPS proposed rule 

believe this one-equation model is more 

intuitive than current ESRD PPS casemix adjusters.296 The suggested casemix adjusters discussed during the 

December 2019 and 2020 TEPS are 

derived relative to variation in total cost 

of case and that the change in reporting 

burden is small and would change 

claims in two ways, including reporting 

total machine reported treatment 

minutes and reporting codes for new 

comorbidities. Finally, stakeholders 

believe that a magnitude of case-mix 

adjusters appears to be significantly 

attenuated relative to the existing ESRD 

PPS adjusters. As discussed in the TEP 

Report for the December 2020 TEP,297 a 

budget neutral implementation of such 

a system would result in a 5–10 percent 

increase in the base rate. Options 

discussed by the panelists included the 

one-equation model and keeping the 

current ESRD PPS case-mix 

adjustments. CMS is seeking feedback 

from the public on these options and 

any additional approaches not yet 

considered. 

5. Request for Information on 

Calculation of the Case-Mix 

Adjustments 

CMS welcomes the opportunity to 

inform the public and solicit 

stakeholder feedback on potential 

changes to the modeling used to 

develop the case-mix payment 

adjustments under the ESRD PPS, in 

order to inform future model 

refinements. CMS is considering 

alternative approaches to calculating the 

case-mix adjustment that directly 

address stakeholder concerns, and more 

appropriately reflects resource use and 

costs, and is issuing this RFI both to 

seek feedback on the suggested 

approach discussed previously, and to 

solicit information that will better 

inform future modifications to this 

methodology. In particular, we are 

soliciting comments on the 

methodology to collect data to reflect 

patient-level differences in composite 

rate costs, including the use of a value 

code to collect time on machine on the 

claim. In addition to any other input the 

public wants to provide regarding the 

calculation of the case-mix adjustment, 

we are requesting responses to the 

following questions. 

• Which of the five composite rate 

cost components (that is, age, BSA, BMI, 

onset of dialysis, comorbidities) are 

most likely to vary with treatment 

duration? 

• Should new information for these 

cost components be collected on cost 

reports, for use in better inferring the 

composite rate costs associated with 

treatment duration? 

• What are the advantages and 

disadvantages of obtaining treatment 

duration information from blood urea 

nitrogen time on dialysis through the 

End Stage Renal Disease Quality 

Reporting System (EQRS) (our new 

system that has replaced the 

Consolidated Renal Operations in a 

Web-enabled Network (CROWNWeb)), 

versus collecting treatment duration 

through new fields on claims? 

• What challenges would be 

encountered in reporting treatment 

duration on claims, using one of the 

options discussed? 

• Are there alternative proxies for 

resource utilization that can be reported 

at the patient/treatment level? 

E. Calculation of the Outlier Payment 

Adjustment 

1. Background on the Outlier Payment 

Adjustment 

Section 1881(b)(14)(D)(ii) of the Act 

requires that the ESRD PPS include a 


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298The FDL amount is the amount by which an 

ESRD facility’s per-treatment Medicare allowable 

payment amount for furnishing ESRD outlier 

services to an adult/pediatric beneficiary must 

exceed the adult/pediatric predicted ESRD outlier 

services Medicare allowable payment amount to be 

eligible for an outlier payment. 

299Outlier percentages for the pediatric 

population have high variability from year to year, 

but have consistently met or exceeded the 1.0 

percent target. The methodological modifications in 

this RFI do not apply to the pediatric population. 

payment adjustment for high-cost 

outliers due to unusual variations in the 

type or amount of medically necessary 

care, including variations in the amount 

of ESAs necessary for anemia 

management. As discussed in section 

II.B.1.c of this proposed rule, we 

recognize that the utilization of ESAs 

and other outlier services have 

continued to decline under the ESRD 

PPS, and that we have lowered the MAP 

amount and FDL amounts every year 

under the ESRD PPS. As discussed in 

the CY 2021 ESRD PPS final rule (85 FR 

71439), we acknowledge that, even with 

annually adjusting the MAP and FDL to 

reflect the most recent utilization and 

costs of ESRD PPS eligible outlier 

services, total outlier payments have not 

yet reached the 1 percent target. 

2. Current Outlier Payment Adjustment 

Methodology 

The current outlier policy was 

implemented in the CY 2011 ESRD PPS 

final rule (75 FR 49134 through 49145) 

and codified at § 413.237. Under 

§ 413.237, an ESRD facility will receive 

an outlier payment if its actual or 

imputed Medicare Allowable Payment 

(MAP) amount per treatment for ESRD 

outlier services exceeds a threshold. The 

MAP amount represents the average 

incurred amount per treatment for 

services that were or would have been 

considered separately billable services 

prior to January 1, 2011. The threshold 

is equal to the ESRD facility’s predicted 

ESRD outlier services MAP amount per 

treatment (which is case-mix adjusted) 

plus the FDL amount, set each year by 

CMS.298 The predicted outlier service 

MAP amount is the outlier MAP amount 

published by CMS adjusted for the case 

mix in the payment year; that is, it is 

calculated by multiplying the separately 

billable case mix multipliers by the 

outlier MAP amount. The outlier MAP 

and FDL amounts are estimated using 

the most recent, complete data set 

available, which are data from 2 years 

prior to the payment year in question. 

The predicted outlier MAPamounts 

and FDLs create thresholds where, if the 

outlier MAP amount per treatment on 

the claim is above the threshold, there 

will be a per-treatment outlier payment 

equal to 80 percent of the amount 

exceeding the threshold. The losssharing percentage was set at 80 percent 

in the CY 2011 ESRD PPS final rule (75 

FR 49144) to make it consistent with the 

loss-sharing percentages in other 

Medicare payment systems. 

In the CY 2011 ESRD PPS final rule 

and codified in § 413.220(b)(4), using 

2007 data, we established the outlier 

percentage, which is used to reduce the 

per treatment base rate to account for 

the proportion of the estimated total 

payments under the ESRD PPS that are 

outlier payments, at 1.0 percent of total 

payments (75 FR 49142 through 49143). 

The policy provides that the following 

ESRD outlier items and services are 

included in the ESRD PPS bundled 

payment: (1) Renal dialysis drugs and 

biological products that were or would 

have been, prior to January 1, 2011, 

separately billable under Medicare Part 

B; (2) Renal dialysis laboratory tests that 

were or would have been, prior to 

January 1, 2011, separately billable 

under Medicare Part B; (3) Renal 

dialysis medical/surgical supplies, 

including syringes, used to administer 

renal dialysis drugs and biological 

products that were or would have been, 

prior to January 1, 2011, separately 

billable under Medicare Part B; (4) 

Renal dialysis drugs and biological 

products that were or would have been, 

prior to January 1, 2011, covered under 

Medicare Part D, including renal 

dialysis oral-only drugs effective 

January 1, 2025; and (5) Renal dialysis 

equipment and supplies that receive the 

transitional add-on payment adjustment 

as specified in § 413.236 after the 

payment period has ended. Beginning 

January 1, 2021, calcimimetics became 

outlier services (85 FR 71405). 

In the CY 2011 ESRD PPS final rule 

(75 FR 49064 through 49065), CMS 

explained that it estimates an ESRD 

facility’s costs based on most recent 

available data. Since the rulemaking is 

done in the year prior to the effective 

date, the most complete available data 

would be from the year before. This 

means that for CY 2022 (as discussed in 

section II.B.1.c of this proposed rule), 

CMS is proposing to recalibrate the 

outlier MAP and FDL amounts for each 

calendar year using data from 2 years 

prior, which is the most recent and 

complete claims data. This methodology 

assumes consistent utilization over 

time, that is, it assumes that 2020 

utilization rates for ESRD PPS outlier 

items and services are the same as those 

for 2018. However, the use of ESRD PPS 

outlier items and services has in fact 

declined each year since the 

implementation of the ESRD PPS. 

For example, the CY 2020 FDL 

amount ($48.33 for adult patients) was 

calculated and added to the predicted 

MAP to determine the outlier thresholds 

using 2018 data. However, ESRD PPS 

outlier spending continued to fall from 

2018 to 2020. Consequently, outlier 

payments for CY 2020 claims comprised 

only 0.6 percent of total ESRD PPS 

payments, demonstrating that the use of 

2018 data results in thresholds too high 

to achieve the targeted 1.0 percent 

outlier payment. Outlier payments for 

the adult population have constituted 

less than 1.0 percent of total ESRD PPS 

payments since such payments began in 

2011.299

3. Current Issue and Stakeholder 

Concerns 

As the outlier payments have 

consistently landed below the targeted 

1.0 percent of total ESRD PPS payment 

threshold, stakeholders have noted that 

the methodology currently used to 

calculate the outlier results in 

underpayment to the providers, as 

money was removed from the base rate 

to balance the outlier payment (85 FR 

71409, 71438 through 71439; 84 FR 

60705 through 60706; 83 FR 56969). 

Therefore, they have urged us to adopt 

an alternative modeling approach, one 

that accounts for declining trends in 

outlier-eligible items and services 

spending over time. MedPAC echoed 

these concerns in a comment letter in 

response to the CY 2021 ESRD PPS 

proposed rule, where it also suggested 

that the introduction of calcimimetics as 

outlier-eligible items could perpetuate 

the pattern of underpayment. MedPAC 

stated that if calcimimetic use decreases 

between 2019 (when the products were 

paid under the ESRD PPS using the 

TDAPA) and 2021 (when the products 

will be paid as part of the ESRD PPS 

base rate), the outlier threshold will be 

set too high, and outlier payments will 

be lower than the 1 percent of total 2021 

payments. 

4. Suggestions for Outlier Payment 

Adjustment 

During the second and third annual 

TEP meetings convened by the CMS 

contractor in 2019 and 2020, panelists 

discussed concerns regarding the 

current outlier adjustment policy and 

alternative methodologies to achieve the 

1 percent outlier target. Some TEP 

panelists and stakeholders have strongly 

advocated that we establish a new 

outlier threshold using alternative 

modeling approaches that account for 

trends in separately billable spending 

over time. Overall, panelists expressed 

support for any change to outlier 

calculations that result in total outlier 

payments closer to the target. 


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300The example uses CY 2020 to judge the 

performance of the alternative methodology. The 

most recent year with complete data when the 2020 

FDL was determined was 2018. 

Panelists noted that the underlying 

basis of an alternative methodology 

could be to re-examine the assumption 

of constant utilization over time. Unlike 

the current outlier methodology that 

predicts FDL amounts using a single 

year of claims data, this approach 

allows for the modeling of the MAP 

amounts as they change over a longer 

period of time. CMS has received a 

number of suggested techniques that 

could be employed to reach the 1.0 

percent target more predictably. 

One of these suggestions is a 

calculation of ‘‘after the fact’’ FDLs that 

would achieve the 1.0 percent outlier 

target for each year included in the FDL 

calculation. This has been referred to as 

the retrospective FDL, which would be 

lower than the FDLs published in the 

final rule for each corresponding year. 

This calculation would be used for 

future outlier calculations. For more 

information, please refer to the TEP 

reports here: https://www.cms.gov/ 

Medicare/Medicare-Fee-for-ServicePayment/ESRDpayment/Educational_

Resources. 

Data presented during the TEP 

meeting showed that using the three 

most recent years to simulate FDLs and 

outlier payments for 2020 resulted in an 

FDL amount for adults of $33.83 and a 

MAP amount of $37.41, respectively. By 

contrast, the 2020 FDLs and MAPs 

published in the CY 2020 ESRD PPS 

final rule (84 FR 60649) were $48.33 

and $35.78, respectively. The simulated 

outlier percentage for 2020 using the 

alternative methodology was 0.8 

percent. The actual outlier payment 

percentage made for 2020 claims was 

0.6 percent. Therefore, the alternative 

methodology results in an outlier 

percentage that is closer to the 1.0 

percent target in the adult population. 

6. Request for Information 

CMS is considering potential 

revisions to the calculation of the outlier 

threshold to address stakeholder 

concerns, and is issuing a request for 

information both to seek feedback on 

the approach suggested above, and to 

solicit information that will better 

inform future modifications to the 

methodology. In addition to any other 

input the public wants to provide for 

calculating the outlier payment 

adjustment, we are requesting responses 

to the following questions. 

• An alternative approach could be to 

estimate the retrospective FDL trend by 

using historical utilization data. The 

example above was constructed by 

using 2016–2018 data. There is 

flexibility in the time used to estimate 

this trend. The data must contain at 

least 2 years’ worth of claims data and 

may begin as early 2011. Additionally, 

it must end with the most recent year 

with complete data (typically 2 years 

before the year in which the FDL will 

take effect).300

++ How many years of data should be 

included in calculation of this trend to 

best capture changes in treatment 

patterns? 

• The simulation of the FDL can be 

improved by better anticipating changes 

in utilization of ESRD outlier services. 

What are the factors that affect the use 

of ESRD outlier services over time, and 

to what extent should CMS try to 

forecast the effect of these factors? 

• ESRD beneficiaries can now choose 

to enroll in Medicare Advantage. 

++ Please describe any anticipated 

effects of this enrollment change on the 

use of ESRD outlier services in the 

ESRD PPS. 

• Adoption of the suggested 

methodology may account for 

systematic changes in the use of highcost outlier items. However, inherently 

unpredictable changes may still push 

the outlier payment off the 1.0 percent 

target. 

++ Please comment on the 

acceptability of the below payment 

adjustment methods. 

++ Payment reconciliation—in the 

form of an add-on payment adjustment 

or a payment reduction—might be 

necessary to bring payments in line with 

the 1 percent target. 

++ An add-on payment adjustment 

would be distributed after sufficient 

data reveal the magnitude of the 

deviation (1 year after the end of the 

payment year). The distribution of these 

monies could be done via a lump sum 

or via a per-treatment payment add-on 

effective for 1 year. This add-on 

payment adjustment would be paid 

irrespective of the outlier claim status in 

that year. 

++ A payment reduction could take 

the form of a reduction in the base rate, 

also to be applied 1 year after the end 

of the payment year. 

F. Calculation of the Pediatric Dialysis 

Payment Adjustment 

1. Background on the Pediatric Dialysis 

Payment Adjustment 

Section 1881(b)(14)(D)(iv)(I) of the Act 

provides that the ESRD PPS may 

include such other payment 

adjustments as the Secretary determines 

appropriate, such as a payment 

adjustment for pediatric providers of 

services and renal dialysis facilities. 

Below we discuss the current ESRD PPS 

with regard to ESRD facilities that 

furnish renal dialysis services to 

pediatric patients, and request 

information on specific approaches as 

well as other topics related to 

developing a pediatric payment 

adjustment under the ESRD PPS. 

Prior to implementation of the ESRD 

PPS, payment for dialysis treatments 

was made through a composite rate per 

treatment that was based on cost report 

data and did not account for differences 

among patients with ESRD (48 FR 

21254). The initial payment rates were 

established at $127 per treatment for 

independent facilities and $131 for 

hospital-based facilities, which reflect 

the costs incurred by dialysis facilities 

furnishing outpatient maintenance 

dialysis, including some routinely 

provided drugs, laboratory tests, and 

supplies, whether furnished by hospitalbased and independent facilities in a 

facility or at home. 

In addition, we provided a process 

under which facilities with costs per 

treatment in excess of their composite 

rates could seek exceptions to those 

rates under specified circumstances in 

§§ 413.182 and 413.184. For example, 

when a substantial proportion of the 

facility’s outpatient maintenance 

dialysis treatments involve atypically 

intense dialysis services, special 

dialysis procedures, or supplies 

necessary to meet special medical needs 

of the facility’s patients could qualify 

for an exception rate. Under § 413.182, 

CMS could approve exceptions if the 

facility demonstrates, by convincing 

objective evidence, that its total per 

treatment costs are reasonable and 

allowable under the relevant cost 

reimbursement principles of part 413 

and that its per treatment costs in excess 

of its payment rate are directly 

attributable to its patient mix. As a 

result of these provisions, many 

pediatric facilities secured an exception 

rate and were paid the exception rate 

until the transition to the ESRD PPS 

ended in CY 2014. 

Section 1881(b)(12) of the Act, added 

by section 623(d) of the Medicare 

Prescription Drug, Improvement, and 

Modernization Act of 2003 (MMA) 

required the Secretary to implement a 

basic case-mix adjustment to an ESRD 

facility’s composite payment rate 

reflecting a limited number of patient 

characteristics. On August 5, 2004 and 

November 15, 2004, we published a 

proposed rule and final rule with 

comment period (69 FR 47487 through 

47730 and 69 FR 66235 through 66915), 

respectively, implementing the 

provisions affecting the composite 

payment system. The development and 


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application of the basic case-mix 

adjustments, using regression-based 

adjustment factors for the patient 

variables of age, BSA and BMI are 

explained in each of those rules (69 FR 

47529 through 47531 and 69 FR 66323 

through 66324, respectively). The 

product of the specific adjusters for each 

patient, multiplied by the otherwise 

applicable composite payment rate, 

yielded the basic case-mix adjustment 

as required by statute. The basic casemix adjusted composite payment system 

was effective April 1, 2005 and 

continued until the ESRD PPS was 

implemented on January 1, 2011. 

As we explained in the CY 2005 ESRD 

PPS final rule with comment period (69 

FR 66326 through 66327), we attempted 

to develop case-mix adjusters for 

outpatient patients with ESRD under 

age 18. However, we found that for the 

approximately 600 Medicare pediatric 

patients for whom claims were available 

from 2000 through 2002, the results 

were highly variable and statistically 

unstable, and therefore, inappropriate 

for the development of case-mix 

adjusters in accordance with the same 

methodology otherwise applicable to 

adult Medicare patients with ESRD. 

For this reason, we described an 

alternative methodology we used to 

develop a 62 percent pediatric increase 

(that is, an adjustment factor of 1.62) 

applied to the composite payment rate 

per treatment for any facility furnishing 

outpatient dialysis services to pediatric 

patients. That factor was based on the 

average amount of the atypical services 

exceptions granted for 20 ESRD 

facilities, each of which sought and 

received an exception for the atypical 

costs incurred for the treatment of 

outpatient pediatric patients, compared 

to the average unadjusted composite 

payment rate (that is, the payment 

without regard to exception amounts) 

for these same 20 facilities. We 

explained that application of the 

pediatric adjustment factor of 1.62 in 

lieu of an explicit pediatric case-mix 

adjustment was temporary, and would 

be eliminated once an appropriate 

methodology, preferably one applicable 

to both pediatric and adult Medicare 

patients, could be developed. 

In the CY 2011 ESRD PPS proposed 

rule (74 FR 49986 through 49987), we 

proposed a pediatric payment 

methodology with comorbidity 

adjusters. However, in the CY 2011 

ESRD PPS final rule (75 FR 49130 

through 49134), in response to public 

comments, we explained that instead of 

using the regression-based composite 

rate multiplier of 1.199, we established 

the pediatric payment adjusters using 

the overall difference in average 

payments per treatment between 

pediatric and adult dialysis patients for 

composite rate services in CY 2007 

based on the 872 pediatric dialysis 

patients reflected in the data. That is, 

the average CY 2007 MAP for composite 

rate services for pediatric dialysis 

patients was $216.46, compared to 

$156.12 for adult patients. We used CY 

2007 data consistent with our 

determination that 2007 represented the 

year with the lowest per patient 

utilization of dialysis services in 

accordance with section 

1881(b)(14)(A)(ii) of the Act. We 

developed payment adjusters using the 

variables of age (that is, <13 and 13–17) 

and modality (peritoneal dialysis or 

hemodialysis). 

In the CY 2016 ESRD PPS final rule 

(80 FR 68968), we refined the ESRD PPS 

in accordance with section 632(c) of 

ATRA, which required CMS to conduct 

an analysis and make appropriate 

revisions to the case mix payment 

adjustments. We analyzed the case-mix 

payment adjustments under the ESRD 

PPS and revised the payment adjusters 

using CYs 2012 and 2013 ESRD claims 

and cost report data. For pediatric 

dialysis, we used the same methodology 

that was used for the CY 2011 ESRD 

PPS final rule, except for the use of 

more recent data years (2012 through 

2013) and in the method of obtaining 

payment data. Specifically, we used the 

projected MAP based on 2013 claims to 

calculate the ratio of pediatric total 

MAP per session to adult total MAP per 

session. The resulting adjustment 

factors reflected an 8.21 percent 

increase to account for the overall 

difference in average payments per 

treatment for pediatric patients. The 

pediatric adjusters that were finalized 

for CY 2016 and are currently in effect 

are: 

<13 peritoneal dialysis = 1.063 

<13 hemodialysis = 1.306 

13–17 peritoneal dialysis = 1.102 

13–17 hemodialysis = 1.327 

2. Current Issues and Stakeholder 

Concerns 

Since 2015, we have continued to 

hear from organizations associated with 

pediatric dialysis about the 

undervaluation of pediatric ESRD care, 

which requires significantly different 

staffing and supply needs from those 

required to deliver ESRD care to adults. 

These organizations support CMS efforts 

to explore ways to improve collecting 

pediatric-specific data to better 

characterize the necessary resources and 

associated costs of delivering pediatric 

ESRD care. Commenters have also 

suggested that we reinstate the 

exceptions process that would provide 

individual hospitals and ESRD facilities 

with their own payment rate based on 

their costs. We note that this approach 

would require a statutory change 

because section 1881(b)(14)(A)(i) of the 

Act requires the Secretary to implement 

a payment system under which a single 

payment is made to all ESRD facilities. 

Stakeholders have informed us that 

costs unique to pediatric dialysis are not 

adequately captured in current cost 

reports or claims, and therefore are not 

accounted for in the pediatric 

adjustments. In addition, they have 

explained that pediatric dialysis often 

requires developmental and behavioral 

specialists, pediatric dieticians, and 

social workers, and that pediatric 

comorbidities require unique 

specialized care. Further, pediatric 

nephrologists have told CMS that 

pediatric patients disproportionately 

receive treatment in hospital-based 

facilities, but the hospital cost report 

(CMS Form 2552–10) does not 

distinguish pediatric and adult dialysis 

cost. 

One organization suggested that we 

expand the pediatric age groups and 

create either pediatric modifiers or a 

pediatric add-on payment by age group. 

Alternatively, the organization 

suggested that we create a pediatricspecific ESRD bundle that would allow 

for full accounting of costs for pediatric 

staffing and specialized equipment, and 

the economic implications of pediatric 

medical comorbidities that are not 

addressed in the current PPS. In order 

to engage dialysis stakeholders in 

advance of rulemaking, CMS’ data 

contractor conducted TEP discussions 

for the past three years on various 

dialysis payment approaches and issues. 

For the 2020 TEP, one of the discussion 

topics was pediatric dialysis. Based on 

discussions and meetings with 

stakeholders and TEP panelists, the 

contractor performed several analyses 

on pediatric dialysis to inform the TEP 

discussion. The analyses confirmed 

many of the challenges reflected in 

stakeholder comments regarding 

pediatric dialysis. 

For example, a small number of 

facilities provide 95 percent of pediatric 

dialysis treatments (approximately 100) 

and those pediatric facilities are 

hospitals, mostly children’s hospitals. 

Pediatric treatments are split between 

home peritoneal dialysis (mostly for 

children younger than 13) and in-center 

hemodialysis (for older children 13–17). 

One analysis, using cost report data, 

found that the median registered nurse/ 

licensed practical nurse hours pertreatment is higher in pediatric facilities 

and pediatric comorbidities require 

more (specialized) staffing. Dialysis for 


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301https://www.cms.gov/files/document/endstage-renal-disease-prospective-payment-systemtechnical-expert-panel-summary-report-april2021.pdf. 

pediatric patients is furnished in 

hospitals, primarily children’s hospitals 

or in large dialysis organization 

facilities. For more information, please 

refer to the TEP reports. 

The contractor performed analyses 

using the expanded age groupings 

suggested by the commenters and found 

that finer stratification of the age groups 

reveals differences in cost per treatment. 

The contractor found that the median 

cost per treatment for the pediatric 

population using the same methodology 

used in the 2016 refinement but using 

more recent data (2018 and 2019) 

resulted in significant differences in 

cost among the pediatric age categories. 

The contractor also found that the 

median cost per treatment for the 

pediatric population using the national 

average treatment duration, the 

relationship between total cost pertreatment and age is consistent with 

stakeholder comments. 

3. Suggestions for the Pediatric Dialysis 

Payment Adjustment 

During the December 2020 TEP, three 

approaches were discussed among the 

panelists that could potentially lead to 

a more accurate estimate of pediatric 

dialysis costs under a revised payment 

model: (1) The addition of pediatricspecific case-mix adjustment 

multipliers; (2) the creation of a separate 

payment bundle for pediatric ESRD 

treatment costs; and (3) revisions to 

current data collection practices. 

To illustrate how the refined model 

would incorporate the pediatric 

population, the contractor applied the 

model using each of the two current age 

groupings, resulting in an increased 

effect of age on costs, with multipliers 

of 1.61 and 1.74 for age <13 years and 

age 13 to 17 years, respectively, 

compared to the reference adult 

population. Please refer to the TEP 

report 301 for more specific information 

on the analyses and discussion. 

Stakeholders suggest that the 

variables affecting pediatric dialysis 

costs are sufficiently different from 

those associated with adult dialysis 

costs, and that a separate payment 

system may be warranted. Although the 

creation of a pediatric bundle or 

separate pediatric ESRD PPS may 

improve cost estimates for the pediatric 

population, if there were a statutory 

change to authorize this separate 

payment system, the time required for 

implementation would be substantial 

due to the subsequent need for new, 

pre-implementation data collection, 

which providers may find burdensome. 

The TEP panelists also discussed 

several modifications to the cost reports 

that they believe would better capture 

resources utilized in the pediatric 

dialysis setting. These include adding 

lines itemizing pediatric specific labor 

categories and pediatric specific 

supplies, clarifying cost report 

instructions as they pertain to pediatric 

dialysis, and better aligning the 

freestanding facility cost report with the 

hospital cost report. Although these 

changes have the advantage of being 

highly feasible to implement, 

stakeholders have noted that uptake 

may take additional time, as pediatric 

facility accounting and billing staff are 

not generally familiar with Medicare 

cost reports. Furthermore, stakeholders 

have noted that changes to the 

freestanding facility cost report would 

be of limited value, since pediatric 

dialysis primarily takes place in 

hospital-based facilities. 

Panelists generally favored the 

addition of pediatric case-mix 

adjustment multipliers. One panelist 

noted that prior to the current case-mix 

adjustment; the multiplier applied to 

pediatric facilities was based on actual 

costs incurred during treatment that 

were more accurate than the costs being 

reported currently. The case-mix 

adjustment multipliers presented during 

the TEP were similar to the multipliers 

from the prior payment method, which 

the panelist found encouraging. 

However, there was shared concern 

among TEP panelists that there will 

continue to be underpayment for 

pediatric dialysis patients. One panelist 

noted that time on dialysis may not 

accurately reflect all costs, and may be 

especially misleading for those under 2 

years of age. For this patient population, 

expenditures on some fixed costs (for 

example, dialysate) will decrease, but 

staffing costs would be considerably 

higher, as they require one-on-one 

nursing and child life specialists and are 

more difficult to initiate on dialysis. 

Therefore, panelists expressed the 

concern that the multipliers based on 

duration of treatment would not 

accurately reflect costs. Another 

panelist noted that certain state laws 

with personnel requirements for 

pediatric dialysis could also increase 

costs. 

Panelists supported moving forward 

with the cost report and case-mix 

multiplier modifications due to the 

burden of implementing a new bundle. 

One panelist noted that a time and 

motion study attempted by their dialysis 

organization failed, as there was a high 

degree of variation among facilities. 

However, another panelist described 

their facility’s success in securing 

additional funding for their pediatric 

dialysis unit as a result of a time and 

motion study. 

Panelists affirmed that accounting and 

billing departments at children’s 

hospitals are not well equipped to 

accurately complete Medicare cost 

reports and suggest that this may be due 

both to their general lack of familiarity 

with Medicare (one panelist noted that 

only 30 percent of pediatric patients are 

Medicare beneficiaries) and the cost 

report’s current structure. 

One panelist cautioned that because 

most pediatric dialysis is delivered in 

the hospital setting, if the revised 

hospital cost report does not include the 

modifications recommended for the 

dialysis facility’s cost report, pediatric 

expertise for dieticians, social workers, 

child life specialists, and behavioral 

specialists may remain overlooked. 

Despite this, panelists expressed the 

desire to move forward with the 

suggested cost report modifications to 

improve pediatric payment, which is 

presented later on in the preamble in 

section VI.H of this proposed rule. 

4. Request for Information for Pediatric 

Dialysis Payment 

CMS is soliciting feedback from the 

public on pediatric dialysis payment. In 

addition to any other input the public 

wants to provide for the pediatric 

dialysis payment adjustment, we are 

requesting responses to the following 

questions. 

• Does the magnitude of total costs 

and pediatric multipliers reflect ESRD 

facilities’ actual incurred costs? If not, 

what specific costs are not being 

reported on claims and/or cost reports? 

• Is there sufficient variation in 

composite rate costs among pediatric 

patients to justify use of a proxy to 

distribute facility-level composite rate 

costs to individual treatments? 

• If duration of treatment is not a 

valid proxy for composite rate costs per 

treatment, what are alternative proxies 

to consider? 

• What, if any, are the specific 

concerns about incorporating pediatric 

patients into the estimation of 

multipliers for both the adult and 

pediatric populations? 

• What are the issues facing pediatric 

billing and accounting staff with regard 

to completion of claims and cost 

reports? How can these problems be 

remedied? 

• Are there additional costs factors 

for pediatric patients that are not 

adequately captured on the 72X claim? 


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G. Modifying the ESRD PPS and 

Hospital Cost Reports 

1. Special Audit Adjustment Summary 

a. Background 

Throughout the years, we have 

received comments about updating the 

Medicare Renal Cost Reports (CMSForm-265–11). Data from the Medicare 

Renal Cost Reports is received by the 

Hospital Cost Report Information 

System (HCRIS). Stakeholders have 

asserted that the cost reports need more 

granularity to align resource use with 

payment. In addition, section 217(e) of 

PAMA mandated an audit of Medicare 

cost reports beginning during 2012 for a 

representative sample of providers of 

services and renal dialysis facilities 

furnishing renal dialysis services. The 

following discusses CMS’s audit process 

and findings. 

Organizations that consist of multiple 

ESRD facilities or business entities may 

have Home Offices that furnish central 

management and administrative 

services (for example, centralized 

accounting, purchasing, personnel 

services, and management) to other 

organizations within the chain. To the 

extent that the Home Office furnishes 

services related to patient care to a 

provider, the reasonable costs of such 

services are included in the ESRD 

facility’s cost report and are 

reimbursable as part of the ESRD 

facility’s costs. The CMS’ Office of the 

Actuary (OACT) selected a sample of 

1,479 freestanding ESRD facilities from 

five Home Offices of large dialysis 

organizations for the cost audit. A 

contractor performed cost audits of 

these ESRD facilities in September of 

2015. All audits were completed by 

September of 2018. 

Upon completion of the audits, 

adjustments for unallowable costs were 

made by CMS’s Office of Financial 

Management to the ESRD cost reports 

and reflected in the HCRIS data. As of 

March 2020, 1,395 of the 1,479 ESRD 

facilities had complete HCRIS data (that 

is, containing both pre-and post-audit 

information). A summary of the audit 

adjustments include Home Office costs, 

drugs, and treatments, which are 

discussed in this section. 

b. Home Office Cost 

Of the ESRD facilities sampled, 1,278 

of 1,479 received an allocation of Home 

Office costs from the five Home Offices 

selected for review. Any adjustments of 

unallowable Home Office costs would 

flow down and be reflected in the ESRD 

facilities’ cost reports. 

c. Adjustments 

Using the HCRIS data, of the 1,395 

ESRD freestanding facilities analyzed, a 

total of $147.5 million of unallowable 

costs were removed from the total costs 

reported on Worksheet A. Noteworthy 

adjustment areas included $136.5 

million of the unallowable costs 

initially reported in the administrative 

and general cost center on Worksheet A, 

with $75 million of this $136.5 million 

pertaining to related-party adjustments 

recorded on Worksheet A–3. Of the $75 

million, $72 million were for Home 

Office costs, including disallowed 

related party costs associated with 

Home Office and management fee 

adjustments. Some of the major 

adjustments noted at the Home Office 

level reviews included the following: 

Unsupported documentation; relatedparty management fees; lobbying 

expense; taxes for items not related to 

patient care; executive compensation in 

excess of reasonable guidelines, and 

related party laboratory costs, which 

were reduced to cost. Other certain nonallowable items included: Advertising, 

legal fees interest expense and financing 

fees, corporate travel/lodging/relocation, 

various consulting fees, business 

development expenses; insurance 

settlement payments; insurance 

expenses (malpractice, etc.). 

d. Drugs 

In general, there were minimal 

adjustments to drugs cost and these 

were made to both drug expense and 

drug rebates (<1.0 percent in aggregate). 

The top five ESRD dialysis 

organizations were examined based on 

total reimbursable cost and average cost 

per treatment for adult hemodialysis 

(the most common treatment type). No 

material adjustment was made to total 

number of treatments. However, there 

was a significant decrease in the average 

cost per treatment because of material 

adjustments made to the total allowable 

costs. The number of Epoetin Units 

furnished during the Cost Reporting 

Period (reported on Worksheet S–1, 

Line 14) was reduced by approximately 

13 percent in aggregate. However, the 

majority of these adjustments related to 

two specific facilities, with one of the 

facilities having the total amount 

reported reduced to zero. The number of 

Aranesp Units furnished during the cost 

reporting period (reported on Worksheet 

S–1, Line 15) was reduced by 

approximately 18 percent in aggregate. 

However, the majority of these 

adjustments related to two specific 

facilities, both of which were reduced to 

zero. 

e. Treatments 

The total number of treatments not 

billed to Medicare and furnished 

directly (Worksheet S–1, Line 1) 

decreased by an average of 2.6 percent. 

However, the total number of treatments 

not billed to Medicare and Furnished 

under Arrangement (Worksheet S–1, 

Line 2) had no change. The average cost 

per treatment among the various types 

of treatments and categories appears to 

have decreased by an average of 1.75 

percent. However, some of the adult 

average costs per treatment related to 

home program continuous ambulatory 

peritoneal dialysis increased after the 

audit by an average of 1.5 percent. 

Based on this audit, our cost report 

data was corrected. 

2. Suggestions for Modifying the ESRD 

PPS and Hospital Cost Reports 

a. Independent Dialysis Facility Cost 

Report 

During the 2020 ESRD PPS TEP, the 

data contractor engaged the panelists in 

a discussion regarding potential 

revisions to the Independent Dialysis 

Facility Cost Report (CMS Form 265– 

11). (See https://www.cms.gov/ 

Research-Statistics-Data-and-Systems/ 

Downloadable-Public-Use-Files/CostReports/Renal-Facility-265-2011-form.) 

These potential revisions, which would 

support the efforts to develop a refined 

case-mix model for the ESRD PPS, are 

described in this section. CMS seeks 

input from the public on the feasibility 

of implementing these suggestions in 

freestanding ESRD facilities. These 

potential reporting changes would 

require facilities to allocate composite 

rate costs across settings and modalities. 

Taken together, the resulting cost report 

data would enable the determination of 

variation in costs across patient types 

(by risk groups and dialysis modalities). 

In the CY 2020 ESRD PPS proposed 

rule (84 FR 38396 through 38400) CMS 

sought input on identifying components 

of composite rate costs, including 

specific facility-level costs that, in 

combination with treatment-level data, 

could be used to understand variation in 

dialysis treatment costs across patients. 

While composite rate costs constitute 

nearly 90 percent of total treatment 

costs, they are not itemized on claims, 

leaving facility cost reports as the only 

source of information on these costs. 

Commenters’ suggestions included 

adding detail and stratifying the 

reporting detail of selected composite 

rate costs by setting and modality and 

providing additional data to determine 

variation in treatment costs across 

patient risk groups and treatment 

modalities. 


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The facility-level cost components of 

interest include capital costs related to 

dialysis machines and other equipment 

used in dialysis treatment, labor costs, 

and supply costs. Based on the input 

received and further analysis conducted 

by the data contractor, several specific 

changes to the cost reports were 

suggested. These include changes in the 

reporting of composite rate components: 

(1) Capital costs for dialysis machines 

and related equipment, (2) direct patient 

labor costs, (3) administrative and 

managerial costs, and (4) differentiation 

of separately billable from composite 

rate laboratory and supply costs. The 

suggested changes would also require 

reporting of these costs by modality. 

While the ‘‘step down’’ worksheet 

(Worksheet B–1) in the current cost 

report separates capital and labor costs 

by modality, this separation is based on 

proportionally allocating costs 

according to a specified statistical basis 

(for example, treatment counts), rather 

than the reporting of actual capital and 

labor resources associated with each 

modality. The data contractor and 

panelists agreed that changing the 

specifications in the instructions to the 

cost report to indicate that the 

allocations be made on the basis of 

actual resource use, would allow for a 

better estimation of component costs per 

treatment and analysis of how these 

costs vary among patient groups and 

across modalities. 

b. Costs for Capital-Related Assets That 

Are Dialysis Machines 

Based on stakeholder feedback, CMS 

would like to understand difficulties 

ESRD facilities have in reporting capital 

costs, particularly as they relate to 

dialysis machines. Both TEP panelists 

and dialysis associations have suggested 

that modifications to reporting of the 

capital costs of dialysis machines focus 

on two goals. The first goal is to 

improve the fidelity and comparability 

of dialysis machine capital cost 

reporting across individual facilities. 

They suggested that this would be 

achieved with more specific 

instructions for completing the cost 

report. The second goal is to ensure 

CMS’s ability to distinguish between 

dialysis machine capital costs among 

various modalities and dialysis settings 

in a way that preserves fidelity and 

comparability among facilities. This 

could be achieved with revisions to the 

cost report itself. As suggested by 

panelists and some stakeholders, to 

achieve these ends, revisions to the cost 

report related to dialysis machine 

capital costs might include: 

• Improve the instructions related to 

the reporting of dialysis machine capital 

costs. 

++ For purchased equipment: Specify 

purchase price, depreciation, 

maintenance, repair, insurance, 

replacement. 

++ For rented equipment: Specify 

rental rates, maintenance, repair, 

insurance, rent escalators. 

• List and stratify the costs of capital 

equipment used in dialysis treatment by 

setting and modality. 

++ Differentiate between rented and 

purchased equipment. 

++ Differentiate among machines used 

in-facility and in the home setting. 

++ Differentiate machine costs in the 

home setting by modality for home 

hemodialysis and home peritoneal 

dialysis. 

++ Include water treatment machines 

and indicate location of use: Home 

versus in-facility. 

• Location in Form 265–11 

++ Expand Worksheet A, Line 6. 

++ Revise instructions for Worksheet 

A–1, adding specificity corresponding 

to item definitions discussed earlier in 

the preamble. 

c. Direct Patient Labor Allocation 

Currently, the cost report does not 

stratify full-time equivalent (FTE) hours 

for direct patient care staff by dialysis 

modality. It also does not include 

several job classifications that are 

commonly found in present-day ESRD 

facilities. 

At present, the statistical basis for 

allocating direct patient care costs is 

hours of service (as seen in Worksheet 

B–1, Column 5). Using this metric and 

allocating resource (or labor) use 

proportionally by labor hour 

(independent of labor type) can result in 

miscalculation of labor costs by 

modality. For example, if labor for the 

provision of home dialysis is on average 

more expensive than labor for in-facility 

hemodialysis, then a strict by-hour cost 

allocation will result in a calculation of 

home dialysis labor costs that is less 

costly per-hour than in practice. 

Suggestions have included that by 

substituting FTE for hours for each 

appropriate direct patient care labor 

category, and using labor categories that 

more accurately reflect current staffing 

patterns in ESRD facilities, any potential 

misrepresentations of relative labor 

costs across modalities can be remedied. 

To this end, CMS has received a 

suggestion to consider the use of Bureau 

of Labor Statistics (BLS) occupational 

categories for outpatient care centers to 

remedy this situation, as it would 

provide up-to-date job classifications 

that the comment believes would better 

correspond to staffing patterns in ESRD 

facilities than the currently used 

Inpatient Prospective Payment System 

job categories. Selecting BLS 

occupational categories for outpatient 

care centers could be added or 

substituted in Lines 23–31 on 

Worksheet S–1 of CMS Form 265–11 to 

reflect current staffing patterns, and 

columns could be added to separately 

report home dialysis FTE and in-facility 

dialysis FTE for each relevant 

occupational category. Additional labor 

categories might include registered 

nurses with varying credentials, 

dieticians, pharmacists, and nurse 

practitioners and other intermediatelevel providers, as appropriate. 

d. Managerial and Administrative Labor 

Allocation 

The data contractor and TEP panelists 

discussed Medicare cost report’s nondirect patient care positions, specifically 

the current managerial and labor 

allocation. They made 

recommendations for differentiating 

high-cost management from lower-cost 

administrative and clerical functions, 

which included a set of potential 

revisions to bring management and 

administrative labor categories up to 

date using occupational categories that 

reflect current usage in dialysis 

facilities. 

As with the direct patient labor 

allocation above, suggestions include 

the use of BLS occupational categories 

for outpatient care centers that 

correspond to the roles employed in 

contemporary dialysis facilities. 

Suggested additions to these job 

categories might include business and 

financial operations personnel, office 

and administrative workers, facility 

support workers, and programmers and 

analysts. With more accurate data, it 

may be possible to determine how 

management and administrative costs 

are differentially allocated across 

facilities (by region and treatment-type 

specialization). These suggested changes 

to managerial and administrative job 

categories would be made to Worksheet 

S–1, Lines 31–34. 

e. Supplies and Laboratory Services 

While composite rate and separately 

billable drug costs are differentiated on 

the cost report, supplies and laboratory 

tests are not differentiated. Supplies 

comprise approximately 10 percent of 

composite rate costs. To bring 

uniformity to the reporting of drugs, 

laboratory tests, and supplies, we have 

received suggestions that supplies and 

laboratory tests be similarly stratified. 

These costs are currently reported on 

Worksheet B/B–1. Specifically, 


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302The fraction would be 158/100, that is $1.58 

is spent overall on pediatric dialysis treatments for 

every $1.00 spent for adult patients. 

303 $7.30 is spent, overall, on supplies for a 

pediatric dialysis treatment for every $1.00 spent on 

supplies for an adult treatment. 

stakeholders have suggested the 

following changes: (1) Add separate 

columns differentiating composite rate 

from separately billable supplies 

(Worksheet B/B–1, Column 7–8); (2) add 

separate columns differentiating 

composite rate from separately billable 

laboratory services (Worksheet B/B–1, 

Column 9–10). 

3. Request for Information on 

Independent Facility Cost Report 

CMS invites comments on the 

suggested changes to the Independent 

Facility Cost Report (CMS Form 265– 

11), as described earlier in this section 

of the proposed rule. In addition to any 

other input the public wants to provide 

on modifying the Independent Facility 

Cost Report, we are requesting 

responses to the following questions. 

• What challenges, including 

operational difficulties, do ESRD 

facilities currently face in reporting 

capital costs: 

++ In general. 

++ Due to inadequate instructions: 

—Which instructions should be 

revised for clarity? 

—Of those above, which are most 

problematic? 

++ In responding, please indicate 

whether you are representing the views 

of a 

—Large dialysis organization. 

—Regional organization. 

—Independent and/or rural facility or 

another entity. 

++ What level of expertise do 

personnel typically filling out cost 

reports have: 

—With cost accounting principles and 

practices? 

—With health care cost accounting 

principles and practices? 

—With operational details of how 

capital equipment is used in their ESRD 

facility? 

++ Are accounting record-keeping 

systems currently used by ESRD 

facilities adequate to the task of 

responding to current and contemplated 

(in this RFI) cost reporting 

requirements? 

• What challenges, including 

operational difficulties, would ESRD 

facilities face: 

++ In reporting dialysis-related 

machine costs by modality and 

location? 

++ In determining the facility level 

distribution of direct patient labor FTE 

across modalities for each type of direct 

patient labor? 

++ In reporting separate costs for 

composite rate supplies and separately 

billable supplies? 

++ In reporting separate costs for 

composite rate laboratory services and 

separately billable laboratory services? 

• What categories of direct patient 

care labor, such as registered nurses 

(North American Classification System 

(NAICS) 29–1141) and dieticians 

(NAICS 29–1031), are routinely 

employed by your dialysis facility and 

which can be documented in cost 

reports? Please provide the specific 

Bureau of Labor Statistics NAICS code 

associated with each labor category for 

outpatient care centers found at this 

website: https://www.bls.gov/oes/ 

current/naics4_621400.htm. 

• Please detail the specific categories 

of administrative and management 

personnel currently employed by your 

ESRD facility and which can be reported 

on CMS Form 265–11. Please provide 

the specific Bureau of Labor Statistics 

NAICS code associated with each labor 

category for management (https://

www.bls.gov/oes/current/naics4_

541600.htm#11-0000) and 

administrative (https://www.bls.gov/oes/ 

2018/may/naics3_561000.htm). Please 

indicate if relevant labor categories are 

not represented here and how these 

categories can be documented and 

reported on CMS Form 265–11. 

• Stakeholders have commented on 

other categorical costs that are not 

reported on the cost report. These 

include missed treatments and use of 

isolation rooms. 

++ Specifically, please comment on 

adding reporting of (1) missed 

treatments, and (2) maintenance of 

isolation rooms. 

++ Where on CMS Form 265–11 

should these items be inserted (if at all)? 

• What challenges would hospitalbased facilities face were the hospitalbased cost report to be revised to 

harmonize with the changes suggested 

for the independent facility cost report? 

How can the two cost reporting forms be 

brought into congruence as related to: 

Dialysis related equipment, direct 

patient care, administrative labor, drugs, 

laboratory services, and supplies? 

• Costing accuracy is difficult to 

achieve for home dialysis. The 

suggested revisions described above 

strive to differentiate costs among the 

different modalities. Are there other 

means for facilities to report more 

accurate cost data for home dialysis 

modalities? Specifically, how can staff 

time dedicated to home dialysis 

treatment be better reported? 

• What other changes might be made 

to the cost report to better differentiate 

costs across modalities and patient risk 

groups? 

H. Modifying the Pediatric Cost Report 

1. Background 

Pediatric composite rate costs are not 

differentiated from adult costs on 

hospital cost reports, while some 

pediatric-specific costs are itemized on 

the existing free-standing cost report. 

Using CY 2019 cost report data, CMS’ 

data contractor computed total and 

component specific cost per treatment 

for hemodialysis-equivalent treatments, 

stratified by modality, and obtained the 

ratio of pediatric to adult cost per 

treatment for each dialysis facility that 

reported both adult and pediatric 

treatments. The results indicate that 

there is variation in costs across 

composite rate cost components for 

pediatric and adult treatments. Overall 

the cost ratio of pediatric to adult 

treatment costs is 1.58,302 indicating 

that pediatric treatments are more 

expensive to administer than adult 

treatments. For one cost component in 

particular, supplies, the ratio is 7.30,303

indicating much higher costs for 

pediatric dialysis supplies than for adult 

supplies. Further analysis, however, 

revealed that a substantial portion of 

facilities does not differentiate between 

adult and pediatric costs in their cost 

report accounting. Overall, we found 

that 13 percent of facilities that treat 

both pediatric and adult dialysis 

patients do not differentiate costs 

between the two age groups. 

2. Suggestions for the Pediatric Cost 

Report 

In response, CMS is considering that 

two types of changes be made to the 

hospital and free-standing ESRD facility 

cost report that would facilitate the 

separate reporting of adult and pediatric 

treatment costs: (1) Changes that 

differentiate pediatric from adult 

composite rate component costs, and (2) 

changes that allow for further 

differentiation of component costs by 

modality and age group within the 

pediatric population. The potential 

revisions for which stakeholder input is 

being sought include the addition of 

select direct patient care labor 

categories, which correspond to the type 

of labor typically employed by pediatric 

dialysis facilities, and the differentiation 

of pediatric supplies and equipment. 

Specifically, CMS is considering 

adding the following staff categories to 

CMS Form 265–11, Worksheet S–1, 

Lines 21–31 (Renal Dialysis Facility— 

Number of Employees (Full Time 

Equivalents)): Pediatric dialysis nurses 

and nurse practitioners, pediatric social 

workers, pediatric dieticians, child life 

specialists, teachers, and pediatric 


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dialysis unit coordinator. We have also 

received recommendations that 

additional columns be added to this 

section of the cost report to differentiate 

pediatric home dialysis and in-facility 

dialysis. 

With regard to pediatric supplies and 

equipment, stakeholders have suggested 

that there be clear differentiation of 

supplies used in dialysis treatment of 

pediatric patients, which vary in type 

and size, from those used with adult 

dialysis patients. Stakeholders have 

further indicated that there is added 

cost involved with the stocking of the 

range of sizes and types of supplies 

needed for this population. Categories of 

supplies for which there is a 

significantly increased cost for the 

pediatric population include: Dialyzers, 

catheter kits, fistula needles, saline 

flushes, monitors for vitals, blood 

pressure cuffs and items used to occupy 

children during their treatment. 

Pediatric nephrologists have noted 

that these suggested revisions would 

have the greatest impact on the hospital 

cost report, which currently does not 

differentiate pediatric from adult 

dialysis patients. Approximately twothirds of pediatric dialysis treatments 

take place in the hospital or medical 

center setting. 

3. Request for Information on the 

Pediatric Cost Report 

CMS invites comments on the 

potential changes to cost reports, 

described previously in this section of 

the proposed rule, as these changes (if 

proposed and finalized in the future) 

would apply to ESRD facilities treating 

pediatric dialysis patients. In addition 

to any other input the public wants to 

provide regarding the cost reports, we 

are requesting responses to the 

following questions. 

• What degree of specificity is needed 

in the reporting of pediatric dialysis 

costs? 

• Are there dialysis supply costs 

associated with the treatment of 

pediatric patients that cannot be 

reported currently on the cost reports? 

If so, please specify. 

• For ESRD facilities that administer 

dialysis to both adult and pediatric 

patients: 

++ To what extent can ESRD facilities 

differentiate dialysis supply costs for 

adult versus pediatric patients? 

—Are there specific high-cost 

supplies unique to the treatment of 

pediatric patients that could be used to 

isolate additional costs related to 

pediatric dialysis? 

—When differentiating pediatric 

dialysis supply costs on the cost reports, 

would providers prefer that the cost 

reports include additional specific items 

for pediatric supplies or a separate 

section for supply costs associated with 

pediatric dialysis? 

++ To what extent can providers 

differentiate dialysis labor costs for 

adult versus pediatric patients? 

• Are there potential revisions that 

could be made to the cost report, other 

than those described above, that would 

help identify costs unique to the 

pediatric population (for example, 

revisions to items and services being 

reported; format revisions to help 

facilitate reporting on pediatric costs)? 

• What obstacles do providers face in 

reporting pediatric specific costs of 

dialysis treatment? How can these 

obstacles be overcome? 

• Pediatric dialysis patients comprise 

a small number of patients in ESRD 

facilities other than children’s hospitals 

or medical centers. How can pediatric 

dialysis costs be reported in nonspecialized ESRD facilities that 

predominantly serve adult patients 

without undue burden on the provider? 

I. Modifying Site of Services Provided to 

Medicare Beneficiaries With Acute 

Kidney Injury (AKI) 

1. Background on Medicare Payment for 

AKI 

On June 29, 2015, the TPEA was 

enacted. In the TPEA, Congress 

amended the Act to include coverage 

and provide for payment for dialysis 

furnished by an ESRD facility to an 

individual with AKI. Specifically, 

section 808(a) of the TPEA amended 

section 1861(s)(2)(F) of the Act to 

provide coverage for renal dialysis 

services furnished on or after January 1, 

2017, by a renal dialysis facility or a 

provider of services paid under section 

1881(b)(14) of the Act to individuals 

with AKI at the ESRD PPS base rate, as 

adjusted by any applicable geographic 

adjustment applied under section 

1881(b)(14)(D)(iv)(II) of the Act and may 

be adjusted by the Secretary on a budget 

neutral basis for payments under section 

1834(r) of the Act by any other 

adjustment factor under section 

1881(b)(14)(D) of the Act. In CY 2017 

ESRD PPS final rule (81 FR 77870 

through 77872), we finalized the AKI 

dialysis payment rate. 

2. Current Issues and Stakeholder 

Concerns 

Over the years, we have received 

several comments, including concerns 

from ESRD facilities; national renal 

groups, nephrologists and patient 

organizations; patients and care 

partners; manufacturers; health care 

systems; and nurses regarding the site of 

renal dialysis services for Medicare 

beneficiaries with AKI. A patient 

advocacy organization supported the 

proposal in the CY 2017 ESRD PPS 

proposed rule to adjust the AKI 

payment rate by only the geographic 

and wage indices, and stated that some 

patients with AKI can safely dialyze at 

home and have their urine and blood 

tests performed for the assessment of 

kidney function in a location closer to 

home. The organization recommended 

that home training be paid separately, 

without dollars removed from the base 

rate. In the CY 2017 ESRD PPS final 

rule, we finalized several coverage and 

payment policies in order to implement 

subsection (r) of section 1834 of the Act 

and the amendments to section 

1881(s)(2)(F) of the Act, including the 

payment rate for AKI dialysis (81 FR 

77866 through 77872). We interpreted 

section 1834(r)(1) of the Act to mean the 

amount of payment for AKI dialysis 

services is the base rate for renal 

dialysis services determined for such 

year under the ESRD base rate as set 

forth in § 413.220, updated by the ESRD 

bundled market basket percentage 

increase factor minus a productivity 

adjustment as set forth in 

§ 413.196(d)(1), adjusted for wages as set 

forth in § 413.231, and adjusted by any 

other amounts deemed appropriate by 

the Secretary under § 413.373. We 

codified this policy in § 413.372 and 

finalized a CY 2021 payment rate for 

renal dialysis services furnished by 

ESRD facilities to individuals with AKI 

as $253.13 (85 FR 71399). 

In the CY 2017 ESRD PPS final rule, 

we stated that we do not expect that AKI 

beneficiaries will dialyze at home (81 

FR 77871). We affirmed in the CY 2017 

ESRD PPS final rule that payment will 

only be made for in-center peritoneal 

dialysis or hemodialysis treatments for 

AKI beneficiaries. CMS also stated in 

the CY 2017 ESRD PPS final rule that 

we would monitor this policy to 

determine if changes are necessary in 

the future, understanding that there may 

be a subset of patients for whom AKI 

dialysis at home is an appropriate 

treatment. Currently, CMS continues to 

believe that this population requires 

close medical supervision by qualified 

staff during their dialysis treatment. 

Due to the COVID–19 PHE and an 

increase in the number of hospitalized 

patients with AKI receiving peritoneal 

dialysis, stakeholders have raised 

concerns about patients with AKI 

having to both travel to, and be present 

in, an ESRD facility post hospitalization. 

CMS received comments that patients 

with AKI require more vigilant 

monitoring, particularly in infection 

prevention, blood pressure 


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management, more frequent laboratory 

testing, additional medication 

administration and increased 

educational needs. Commenters stated 

that patients with AKI are distinct from 

regular patients with ESRD in that they 

need specific critical treatment. CMS 

continued to receive comments in 

response to the CY 2021 ESRD PPS 

proposed rule regarding this concern, 

including the recommendation that 

CMS allow patients with AKI to be 

dialyzed at home. Specifically, the 

commenters requested that CMS allow 

patients with AKI to pursue peritoneal 

dialysis in the home if the patient and 

nephrologist agree it is safe to do so and 

the home setting is the patient’s choice. 

We also received comments from 

organizations requesting that CMS 

remove barriers that make it difficult for 

patients who want to select home 

dialysis. They specifically requested 

that, for the duration of the COVID–19 

PHE, CMS waive the requirement that 

health care providers are paid for 

providing care to patients with AKI only 

when they receive in-center 

hemodialysis. 

The 2020 TEP included a session on 

AKI and the current Medicare payment 

system. The panelists discussed cost 

and utilization of AKI related dialysis 

services since the policy change in 

2017, including the incorporation of 

payment for dialysis treatment for 

patients with AKI into the ESRD PPS, 

assessment of the accuracy of the 

reported data and the effectiveness of 

the current AKI payment parameters for 

accurately capturing the costs of this 

population. 

Panelists agreed that some patients 

with AKI could benefit from different 

treatment regimens. In particular, they 

noted that more frequent, gentler 

dialysis would be a viable option for 

some patients, possibly preventing 

hypotension. During the COVID–19 

PHE, many patients received acute 

peritoneal dialysis treatments in the 

hospital upon developing AKI, and 

panelists expressed support for allowing 

patients with AKI to continue receiving 

acute peritoneal dialysis once they are 

discharged from the hospital. One 

panelist noted that their hospital tries to 

get patients with AKI accustomed to a 

more standard treatment regimen such 

as three treatments per week before 

discharging them to an ESRD facility. 

Another panelist expressed support for 

the implementation of transitional care 

units, noting they would help patients 

new to dialysis adjust to dialysis and 

the lifestyle changes that accompany it. 

Panelists also advocated for allowing 

patients with AKI to be treated at home, 

especially in light of the COVID–19 

PHE. 

Members of the TEP commented on 

the similar treatment frequencies 

observed for patients with AKI and 

ESRD, stating that the payment system 

is currently constructed to facilitate the 

observed treatment patterns for patients 

with AKI. Panelists stressed that the 

payment system should continue to be 

flexible in terms of number of 

treatments for patients with AKI so that 

those who need more frequent 

treatments are not impeded from 

receiving them. 

Panelists expressed support for the 

CMS guidance temporarily allowing 

dialysis facilities to send dialysis 

facility staff to furnish 72x dialysis to 

their patients in nursing homes, from 

both a cost and patient health 

perspective. (See https://www.cms.gov/ 

files/document/covid-19-emergencydeclaration-waivers.pdf.) Panelists 

noted that it was more efficient to send 

ESRD facility staff to the skilled nursing 

facilities rather than the costly routine 

and ambulance-required transportation 

and physical isolation expenses 

incurred during the public health 

emergency. Panelists stated that the full 

spectrum of care provided in the SNF 

setting is invaluable, particularly for the 

patients with multiple comorbidities. 

Panelists commented on the costs per 

treatment observed for patients with 

AKI, expressing that the higher observed 

costs compared to ESRD treatments 

aligns with their expectations. Members 

of the panel noted that patients with 

AKI receive more laboratory tests to 

monitor for recovery, but typically are 

not prescribed calcimimetics or ESAs. 

Some panelists also noted that due to 

the very small population size of 

Medicare beneficiaries with AKI, 

reporting AKI costs and statistics on 

cost reports at a granular level 

introduces an outsized reporting burden 

on the part of the providers. 

Overall, panelists expressed that the 

current AKI payment structure is 

effective and benefits both patients and 

facilities. One panelist pointed out that 

the AKI policy change, which we 

implemented in the CY 2017 ESRD PPS 

final rule (81 FR 77866 through 77872), 

helps hospitals, as they can send 

patients with AKI requiring dialysis to 

ESRD facilities and consequently free 

up capacity at the hospital. 

4. Request for Information on Modifying 

the Site of Services Provided to 

Medicare Beneficiaries With AKI 

CMS is soliciting feedback from the 

public on the differences in care for 

patients with AKI versus patients with 

ESRD and whether it has bearing on the 

ability of patients with AKI to perform 

home dialysis safely. We request any 

additional comments regarding 

potentially modifying site of renal 

dialysis services and payment for AKI in 

the home setting. 

VII. Collection of Information 

Requirements 

A. Legislative Requirement for 

Solicitation of Comments 

Under the Paperwork Reduction Act 

of 1995, we are required to provide 60- 

day notice in the Federal Register and 

solicit public comment before a 

collection of information requirement is 

submitted to the Office of Management 

and Budget (OMB) for review and 

approval. In order to fairly evaluate 

whether an information collection 

requirement should be approved by 

OMB, the Paperwork Reduction Act of 

1995 (44 U.S.C. 3506(c)(2)(A)) requires 

that we solicit comment on the 

following issues: 

• The need for the information 

collection and its usefulness in carrying 

out the proper functions of our agency. 

• The accuracy of our estimate of the 

information collection burden. 

• The quality, utility, and clarity of 

the information to be collected. 

• Recommendations to minimize the 

information collection burden on the 

affected public, including automated 

collection techniques. 

We are soliciting public comment on 

each of these issues for the following 

sections of this document that contain 

information collection requirements 

(ICRs): 

B. Requirements in Regulation Text 

In sections V through V.B of this 

proposed rule, we are proposing 

changes to the regulatory text for the 

ETC Model. However, the changes that 

are being proposed do not impose any 

new information collection 

requirements. 

C. Additional Information Collection 

Requirements 

This proposed rule does not impose 

any new information collection 

requirements in the regulation text, as 

specified above. However, there are 

changes in some currently approved 

information collections. The following 

is a discussion of these information 

collections. 

1. ESRD QIP—Wage Estimates (OMB 

control numbers 0938–1289 and 0938– 

1340) 

To derive wages estimates, we used 

data from the U.S. Bureau of Labor 

Statistics’ May 2020 National 

Occupational Employment and Wage 


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304https://www.bls.gov/oes/current/ 

oes292098.htm. Accessed on June 7, 2021. 

Estimates. In the CY 2016 ESRD PPS 

final rule (80 FR 69069), we stated that 

it was reasonable to assume that 

Medical Records and Health 

Information Technicians, who are 

responsible for organizing and managing 

health information data, are the 

individuals tasked with submitting 

measure data to CROWNWeb (now 

EQRS) and NHSN, as well as compiling 

and submitting patient records for 

purpose of the data validation studies, 

rather than a Registered Nurse, whose 

duties are centered on providing and 

coordinating care for patients. We stated 

that the median hourly wage of a 

Medical Records and Health 

Information Technician is $21.20 per 

hour.304 We also stated that fringe 

benefit and overhead are calculated at 

100 percent. Therefore, using these 

assumptions, we estimated an hourly 

labor cost of $42.40 as the basis of the 

wage estimates for all collections of 

information calculations in the ESRD 

QIP. We adjusted these employee hourly 

wage estimates by a factor of 100 

percent to reflect current HHS 

department-wide guidance on 

estimating the cost of fringe benefits and 

overhead. We stated that these are 

necessarily rough adjustments, both 

because fringe benefits and overhead 

costs vary significantly from employer 

to employer and because methods of 

estimating these costs vary widely from 

study to study. Nonetheless, we stated 

that there is no practical alternative and 

we believe that these are reasonable 

estimation methods. 

We used this updated wage estimate, 

along with updated facility and patient 

counts to re-estimate the total 

information collection burden in the 

ESRD QIP for PY 2024 that we 

discussed in the CY 2021 ESRD QIP 

final rule (85 FR 71473 through 71474) 

and to estimate the total information 

collection burden in the ESRD QIP for 

PY 2025. We provide the re-estimated 

information collection burden 

associated with the PY 2024 ESRD QIP 

and the newly estimated information 

collection burden associated with the 

PY 2025 ESRD QIP in section VII.C.3 of 

this proposed rule. 

2. Estimated Burden Associated With 

the Data Validation Requirements for PY 

2024 and PY 2025 (OMB Control 

Numbers 0938–1289 and 0938–1340) 

In the CY 2020 ESRD PPS final rule, 

we finalized a policy to adopt the 

CROWNWeb data validation 

methodology that we previously 

adopted for the PY 2016 ESRD QIP as 

the methodology we would use to 

validate CROWNWeb data for all 

payment years, beginning with PY 2021 

(83 FR 57001 through 57002). Although, 

as noted in section IV.B.2 of this 

proposed rule, we are now using EQRS 

to report data that was previously 

reported in CROWNWeb, the data 

validation methodology remains the 

same. Under this methodology, 300 

facilities are selected each year to 

submit 10 records to CMS, and we 

reimburse these facilities for the costs 

associated with copying and mailing the 

requested records. The burden 

associated with these validation 

requirements is the time and effort 

necessary to submit the requested 

records to a CMS contractor. In this 

proposed rule, we are updating these 

estimates using a newly available wage 

estimate of a Medical Records and 

Health Information Technician. In the 

CY 2020 ESRD PPS final rule, we 

estimated that it would take each 

facility approximately 2.5 hours to 

comply with this requirement. If 300 

facilities are asked to submit records, we 

estimated that the total combined 

annual burden for these facilities would 

be 750 hours (300 facilities x 2.5 hours). 

Since we anticipate that Medical 

Records and Health Information 

Technicians or similar administrative 

staff would submit these data, we 

estimate that the aggregate cost of the 

EQRS data validation each year would 

be approximately $31,800 (750 hours × 

$42.40), or an annual total of 

approximately $106.00 ($31,800/300 

facilities) per facility in the sample. The 

burden cost increase associated with 

these requirements will be revised in 

information collection request (OMB 

control number 0938–1289). 

In the CY 2021 ESRD PPS final rule, 

we finalized our policy to reduce the 

number of records that a facility 

selected to participate in the NHSN data 

validation must submit to a CMS 

contractor, beginning with PY 2023 (85 

FR 71471 through 71472). Under this 

finalized policy, a facility is required to 

submit records for 20 patients across 

any two quarters of the year, instead of 

20 records for each of the first two 

quarters of the year. The burden 

associated with this policy is the time 

and effort necessary to submit the 

requested records to a CMS contractor. 

Applying our policy to reduce the 

number of records required from each 

facility participating in the NHSN 

validation, we estimated that it would 

take each facility approximately 5 hours 

to comply with this requirement. If 300 

facilities are asked to submit records 

each year, we estimated that the total 

combined annual burden hours for these 

facilities per year would be 1,500 hours 

(300 facilities × 5 hours). Since we 

anticipate that Medical Records and 

Health Information Technicians or 

similar staff would submit these data, 

using the newly available wage estimate 

of a Medical Records and Health 

Information Technician, we estimate 

that the aggregate cost of the NHSN data 

validation each year would be 

approximately $63,600 (1,500 hours × 

$42.40), or a total of approximately $212 

($63,600/300 facilities) per facility in 

the sample. While the burden hours 

estimate will not change, the burden 

cost updates associated with these 

requirements will be revised in the 

information collection request (OMB 

control number 0938–1340). 

3. EQRS Reporting Requirements for PY 

2024 and PY 2025 (OMB Control 

Number 0938–1289) 

To determine the burden associated 

with the EQRS reporting requirements 

(previously known as the CROWNWeb 

reporting requirements), we look at the 

total number of patients nationally, the 

number of data elements per patientyear that the facility would be required 

to submit to EQRS for each measure, the 

amount of time required for data entry, 

the estimated wage plus benefits 

applicable to the individuals within 

facilities who are most likely to be 

entering data into EQRS, and the 

number of facilities submitting data to 

EQRS. In the CY 2021 ESRD PPS final 

rule, we estimated that the burden 

associated with CROWNWeb (now 

EQRS) reporting requirements for the 

PY 2024 ESRD QIP was approximately 

$208 million (85 FR 71400). 

As discussed in section IV.C and 

section IV.D of this proposed rule, we 

are proposing measure suppressions 

that would apply for PY 2022 and 

updates to the scoring methodology and 

payment reductions for the PY 2022 

ESRD QIP. We also announce an 

extension of EQRS reporting 

requirements for facilities due to 

systems issues. However, we believe 

that none of the policies proposed in 

this proposed rule would affect our 

estimates of the annual burden 

associated with the Program’s 

information collection requirements, as 

facilities are still expected to continue 

to collect measure data during this time 

period. We are not proposing any 

changes that would affect the burden 

associated with EQRS reporting 

requirements for PY 2024 or PY 2025. 

However, we have re-calculated the 

burden estimate for PY 2024 using 

updated estimates of the total number of 

dialysis facilities, the total number of 


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patients nationally, and wages for 

Medical Records and Health 

Information Technicians or similar staff 

as well as a refined estimate of the 

number of hours needed to complete 

data entry for EQRS reporting. 

Consistent with our approach in the CY 

2021 ESRD PPS final rule (85 FR 71474), 

in this proposed rule we estimated that 

the amount of time required to submit 

measure data to EQRS was 2.5 minutes 

per element and did not use a rounded 

estimate of the time needed to complete 

data entry for EQRS reporting. There are 

229 data elements for 532,931 patients 

across 7,610 facilities. At 2.5 minutes 

per element, this yields approximately 

668.21 hours per facility. Therefore, the 

PY 2024 burden is 5,085,050 hours 

(668.21 hours × 7,610 facilities). Using 

the wage estimate of a Medical Records 

and Health Information Technician, we 

estimate that the PY 2024 total burden 

cost is approximately $215 million 

(5,085,050 hours × $42.40). There is no 

net incremental burden change from PY 

2024 to PY 2025 because we are not 

changing the reporting requirements for 

PY 2025. 

VIII. 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. 

IX. Economic Analyses 

A. Regulatory Impact Analysis 

1. Introduction 

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. 801(2)). 

Executive Orders 12866 and 13563 

direct agencies to assess all costs and 

benefits of available regulatory 

alternatives and, if regulation is 

necessary, to select regulatory 

approaches that maximize net benefits 

(including potential economic, 

environmental, public health and safety 

effects, distributive impacts, and 

equity). Section 3(f) of Executive Order 

12866 defines a ‘‘significant regulatory 

action’’ as an action that is likely to 

result in a rule: (1) Having an annual 

effect on the economy of $100 million 

or more in any 1 year, or adversely and 

materially affecting a sector of the 

economy, productivity, competition, 

jobs, the environment, public health or 

safety, or state, local or tribal 

governments or communities (also 

referred to as ‘‘economically 

significant’’); (2) creating a serious 

inconsistency or otherwise interfering 

with an action taken or planned by 

another agency; (3) materially altering 

the budgetary impacts of entitlement 

grants, user fees, or loan programs or the 

rights and obligations of recipients 

thereof; or (4) raising novel legal or 

policy issues arising out of legal 

mandates, the President’s priorities, or 

the principles set forth in the Executive 

Order. 

A regulatory impact analysis (RIA) 

must be prepared for major rules with 

economically significant effects ($100 

million or more in any 1 year). We 

estimate that this rulemaking is 

‘‘economically significant’’ as measured 

by the $100 million threshold, and 

hence also a major rule under the 

Congressional Review Act. Accordingly, 

we have prepared a Regulatory Impact 

Analysis that to the best of our ability 

presents the costs and benefits of the 

rulemaking. We solicit comments on the 

regulatory impact analysis provided. 

2. Statement of Need 

a. ESRD PPS 

This rule proposes a number of 

routine updates to the ESRD PPS for CY 

2022. The proposed routine updates 

include the CY 2022 wage index values, 

the wage index budget-neutrality 

adjustment factor, and outlier payment 

threshold amounts. Failure to publish 

this proposed rule would result in ESRD 

facilities not receiving appropriate 

payments in CY 2022 for renal dialysis 

services furnished to ESRD 

beneficiaries. 

b. AKI 

This rule also proposes routine 

updates to the payment for renal 

dialysis services furnished by ESRD 

facilities to individuals with AKI. 

Failure to publish this proposed rule 

would result in ESRD facilities not 

receiving appropriate payments in CY 

2022 for renal dialysis services 

furnished to patients with AKI in 

accordance with section 1834(r) of the 

Act. 

c. ESRD QIP 

This proposed rule proposes to 

implement requirements for the ESRD 

QIP, including a proposal to adopt a 

measure suppression policy and to 

suppress several ESRD QIP measures 

under that proposed measure 

suppression policy, proposals regarding 

the scoring methodology and payment 

reductions for the PY 2022 ESRD QIP, 

a proposed update to the SHR measure, 

and a proposed update to the PY 2024 

performance standards. This proposed 

rule also includes a request for public 

comment on closing the gap in health 

equity, as well as a request for public 

comment on potential actions and 

priority areas that would enable the 

continued transformation of our quality 

measurement enterprise toward greater 

digital capture of data and use of the 

FHIR standard. 

d. ETC Model 

Beneficiaries with ESRD are among 

the most medically fragile and high-cost 

populations served by the Medicare 

program. One of CMS’ goals in 

designing the ETC Model is to test ways 

to incentivize home dialysis and kidney 

transplants, to enhance beneficiary 

choice of modality for renal replacement 

therapy, and improve quality of care 

and quality of life while reducing 

Medicare program expenditures. The 

substantially higher expenditures, 

mortality, and hospitalization rates for 

dialysis patients in the U.S. compared to 

those for individuals with ESRD in 

other countries indicate a population 

with poor clinical outcomes and 

potentially avoidable expenditures. This 

proposed rule would refine the 

methodology for setting and updating 

achievement and improvement 

benchmarks for participating ESRD 

facilities and Managing Clinicians 

serving the ESRD population over the 

remaining years of the ETC Model, 

among other proposed changes. 

Notwithstanding the proposed changes, 

we continue to anticipate improvement 

in quality of care for beneficiaries and 

reduced expenditures under the ETC 

Model inasmuch as the Model is 

designed to create incentives for 

beneficiaries, along with their families 

and caregivers, to choose the optimal 

kidney replacement modality. 

As noted in section IV.B of the 

Specialty Care Models final rule (85 FR 

61264), Medicare payment rules and a 

deficit in beneficiary education result in 

a bias toward in-center hemodialysis, 

which is often not preferred by patients 

or physicians relative to home dialysis 

or kidney transplantation. We provided 

evidence from the published literature 


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to support the projection that higher 

rates of home dialysis and kidney 

transplants would likely reduce 

Medicare expenditures, and, not only 

enhance beneficiary choice, 

independence, and quality of life, but 

also preserve or enhance the quality of 

care for ESRD beneficiaries. 

As described in detail in section V of 

this proposed rule, we believe it is 

necessary to propose certain changes to 

the ETC Model. Under the proposed 

changes to the ETC Model, ETC 

Participants would continue to receive 

adjusted payments but beginning for 

MY3, certain aspects of the ETC Model 

that determine those payment 

adjustments would change. The 

proposed change to the achievement 

benchmarking methodology is necessary 

to the ETC Model as this change 

maintains the ETC Model’s expectation 

of savings. The proposed changes to the 

transplant rate, the achievement 

benchmarking methodology, and the 

improvement benchmarking and scoring 

methodology are necessary to increase 

accuracy and fairness of performance 

assessment. The proposed changes to 

the home dialysis rate, data sharing, and 

kidney disease patient education 

services waivers are necessary to 

support ETC Participants operating in 

the ETC Model. 

3. Overall Impact 

a. ESRD PPS 

We estimate that the proposed 

revisions to the ESRD PPS would result 

in an increase of approximately $140 

million in payments to ESRD facilities 

in CY 2022, which includes the amount 

associated with updates to the outlier 

thresholds, and updates to the wage 

index. 

b. AKI 

We estimate that the proposed 

updates to the AKI payment rate would 

result in an increase of approximately 

$1 million in payments to ESRD 

facilities in CY 2022. 

c. ESRD QIP 

For PY 2024 and PY 2025, we have reestimated the costs associated with the 

information collection requirements 

under the ESRD QIP with updated 

estimates of the total number of dialysis 

facilities, the total number of patients 

nationally, wages for Medical Records 

and Health Information Technicians or 

similar staff, and a refined estimate of 

the number of hours needed to complete 

data entry for EQRS reporting. We have 

made no changes to our methodology 

for calculating the annual burden 

associated with the information 

collection requirements for the EQRS 

validation study (previously known as 

the CROWNWeb validation study), the 

NHSN validation study, and EQRS 

reporting. As discussed in section IV.C 

and section IV.D of this proposed rule, 

we are proposing measure suppressions 

that would apply for PY 2022 and 

updates to the scoring methodology and 

payment reductions for the PY 2022 

ESRD QIP. We also announce an 

extension of EQRS reporting 

requirements for facilities due to 

systems issues. However, we believe 

that none of the policies proposed in 

this proposed rule would affect our 

estimates of the annual burden 

associated with the Program’s 

information collection requirements, as 

facilities are still expected to continue 

to collect measure data during this time 

period. 

We also updated the payment 

reduction scale using more recent data 

for the measures in the ESRD QIP 

measure set. We estimate approximately 

$215 million in information collection 

burden, which includes the cost of 

complying with this rule, and an 

additional $17 million in estimated 

payment reductions across all facilities 

for PY 2024. 

For PY 2025, we estimate that the 

proposed revisions to the ESRD QIP 

would result in $215 million in 

information collection burden, and $17 

million in estimated payment 

reductions across all facilities, for an 

impact of $232 million as a result of the 

policies we have previously finalized 

and the policies we have proposed in 

this proposed rule. 

d. ETC Model 

We estimate that the proposed 

changes to the ETC Model would 

increase the Model’s projected direct 

savings from payment adjustments 

alone by $7 million over the duration of 

the Model. We estimate that the Model 

would generate $38 million in direct 

savings related to payment adjustments 

over 6.5 years with the proposed 

changes, and would generate $31 

million in savings in the absence of the 

proposed changes. 

4. Regulatory Review Cost Estimation 

If regulations impose administrative 

costs on private entities, such as the 

time needed to read and interpret this 

proposed rule or final rule, we should 

estimate the cost associated with 

regulatory review. Due to the 

uncertainty involved with accurately 

quantifying the number of entities that 

will review the rule, we assume that the 

total number of unique commenters on 

last year’s proposed rule will be the 

number of reviewers of this proposed 

rule. We acknowledge that this 

assumption may understate or overstate 

the costs of reviewing this rule. It is 

possible that not all commenters 

reviewed last year’s rule in detail, and 

it is possible that some reviewers chose 

not to comment on the proposed rule. 

For these reasons, we thought that the 

number of past commenters would be a 

fair estimate of the number of reviewers 

of this rule. We welcome any comments 

on the approach in estimating the 

number of entities, which will review 

this proposed rule. We also recognize 

that different types of entities are in 

many cases affected by mutually 

exclusive sections of this proposed rule, 

and therefore for the purposes of our 

estimate we assume that each reviewer 

reads approximately 50 percent of the 

rule. We seek comments on this 

assumption. 

Using the wage information from the 

BLS for medical and health service 

managers (Code 11–9111), we estimate 

that the cost of reviewing this rule is 

$110.74 per hour, including overhead 

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

oes/current/oes_nat.htm. Assuming an 

average reading speed, we estimate that 

it would take approximately 6.25 hours 

for the staff to review half of this 

proposed rule. For each entity that 

reviews the rule, the estimated cost is 

$692.13 (6.25 hours × $110.74). 

Therefore, we estimate that the total cost 

of reviewing this regulation is $ 78,903 

($692.13 × 114). 

B. Detailed Economic Analysis 

1. CY 2021 End-Stage Renal Disease 

Prospective Payment System 

a. Effects on ESRD Facilities 

To understand the impact of the 

changes affecting payments to different 

categories of ESRD facilities, it is 

necessary to compare estimated 

payments in CY 2021 to estimated 

payments in CY 2022. To estimate the 

impact among various types of ESRD 

facilities, it is imperative that the 

estimates of payments in CY 2021 and 

CY 2022 contain similar inputs. 

Therefore, we simulated payments only 

for those ESRD facilities for which we 

are able to calculate both current 

payments and new payments. 

For this proposed rule, we used CY 

2020 data from the Part A and Part B 

Common Working Files as of February 

12, 2021, as a basis for Medicare dialysis 

treatments and payments under the 

ESRD PPS. We updated the 2020 claims 

to 2021 and 2022 using various updates. 

The updates to the ESRD PPS base rate 

are described in section II.B.1.d of this 

proposed rule. Table 9 shows the impact 

of the estimated CY 2022 ESRD PPS 


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payments compared to estimated 

payments to ESRD facilities in CY 2021. 


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TABLE 9: Impact of Proposed Changes in Payments to ESRD 

Facilities for CY 2022 ESRD PPS Pro osed Rule 

Number Effect of Effect of 

Numbe of 2022 2022 

rof Treatmen Changes in Changes Facility Type Facilitie ts (in Outlier in Wage 

s (A) millions) Policy Index 

(B) (C) (D) 

Large dialysis organization 5,886 33.6 0.2% 0.0% 

Regional chain 887 5.3 0.3% 0.1% 

Independent 515 2.8 0.3% -0.1% 

Hospital based1 378 1.7 0.4% -0.1% 

East North Central 1,213 5.6 0.2% -0.1% 

East South Central 609 3.2 0.3% -0.6% 

Middle Atlantic 859 5.1 0.3% -0.2% 

Mountain 428 2.3 0.1% -0.1% 

New England 201 1.3 0.2% -0.5% 

Pacific2 955 6.3 0.2% 0.5% 

Puerto Rico and Virgin 

Islands 52 <U 0.2% -0.7% 

South Atlantic 1,794 10.4 0.2% 0.3% 

West North Central 503 2.3 0.2% 0.1% 

Less than 4,000 treatments 1.248 2.4 0.2% 0.0% 

4,000 to 9,999 treatments 2,905 11.9 0.2% 0.0% 

10,000 or more treatments 3,384 28.9 0.2% 0.0% 

Unknown 180 0.2 0.2% -0.2% 

Effect of Effect of 2022 

Payment Total 2022 

Rate Proposed 

Update Changes 

(E) (F) 

1.0% 1.2% 

1.0% 1.4% 

1.0% 1.1% 

1.0% 1.3% 

1.0% 1.1% 

1.0% 0.7% 

1.0% 1.1% 

1.0% 1.1% 

1.0% 0.6% 

1.0% 1.6% 

1.0% 0.4% 

1.0% 1.6% 

1.0% 1.3% 

1.0% 1.1% 

1.0% 1.2% 

1.0% 1.2% 

1.0% 1.0% 
36414 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

BILLING CODE 4120–01–C 

Column A of the impact table 

indicates the number of ESRD facilities 

for each impact category and column B 

indicates the number of dialysis 

treatments (in millions). The overall 

effect of the proposed changes to the 

outlier payment policy described in 

section II.B.1.c of this proposed rule is 

shown in column C. For CY 2022, the 

impact on all ESRD facilities as a result 

of the proposed changes to the outlier 

payment policy would be a 0.2 percent 

increase in estimated payments. All 

ESRD facilities are anticipated to 

experience a positive effect in their 

estimated CY 2022 payments as a result 

of the proposed outlier policy changes. 

Column D shows the effect of the 

annual update to the wage index, as 

described in section II.B.1.b of this 

proposed rule. That is, this column 

reflects the update from the CY 2021 

ESRD PPS wage index using 2018 OMB 

delineations as finalized in the CY 2021 

ESRD PPS final rule, with a basis of the 

FY 2022 pre-floor, pre-reclassified IPPS 

hospital wage index data in a budget 

neutral manner. The total impact of this 

change is 0.0 percent; however, there 

are distributional effects of the change 

among different categories of ESRD 

facilities. The categories of types of 

facilities in the impact table show 

changes in estimated payments ranging 

from a 0.7 percent decrease to a 0.5 

percent increase due to the annual 

update to the ESRD PPS wage index. 

Column E shows the effect of the 

proposed CY 2022 ESRD PPS payment 

rate update as described in section 

II.B.1.a of this proposed rule. The 

proposed ESRD PPS payment rate 

update is 1.0 percent, which reflects the 

proposed ESRDB market basket 

percentage increase factor for CY 2022 

of 1.6 percent and the proposed 

productivity adjustment of 0.6 percent. 

Column F reflects the overall impact, 

that is, the effects of the proposed 

outlier policy changes, the proposed 

updated wage index, and the payment 

rate update. We expect that overall 

ESRD facilities would experience a 1.2 

percent increase in estimated payments 

in CY 2022. The categories of types of 

facilities in the impact table show 

impacts ranging from a 0.4 percent 

increase to a 1.6 percent increase in 

their CY 2022 estimated payments. 

b. Effects on Other Providers 

Under the ESRD PPS, Medicare pays 

ESRD facilities a single bundled 

payment for renal dialysis services, 

which may have been separately paid to 

other providers (for example, 

laboratories, durable medical equipment 

suppliers, and pharmacies) by Medicare 

prior to the implementation of the ESRD 

PPS. Therefore, in CY 2022, we estimate 

that the proposed ESRD PPS would 

have zero impact on these other 

providers. 

c. Effects on the Medicare Program 

We estimate that Medicare spending 

(total Medicare program payments) for 

ESRD facilities in CY 2022 would be 

approximately $8.9 billion. This 

estimate takes into account a projected 

decrease in fee-for-service Medicare 

dialysis beneficiary enrollment of 5.9 

percent in CY 2022. 

d. Effects on Medicare Beneficiaries 

Under the ESRD PPS, beneficiaries are 

responsible for paying 20 percent of the 

ESRD PPS payment amount. As a result 

of the projected 1.2 percent overall 

increase in the proposed CY 2022 ESRD 

PPS payment amounts, we estimate that 

there would be an increase in 

beneficiary co-insurance payments of 

1.2 percent in CY 2022, which translates 

to approximately $30 million. 

e. Alternatives Considered 

CY 2022 Impacts: 2019 Versus 2020 

Claims Data 

Each year CMS uses the latest 

available ESRD claims to update the 

outlier threshold, budget neutrality 

factor, and payment rates. Due to the 

COVID–19 PHE, we compared the 

impact of using CY 2019 claims against 

CY 2020 claims to determine if there 

was any substantial difference in the 

results that would justify potentially 

deviating from our longstanding policy 

to use the latest available data. Analysis 

suggested that ESRD utilization did not 

change substantially during the 

pandemic, likely due to the patients’ 

vulnerability and need for these 

services. Consequently, we proposed to 

use the CY 2020 data because it does not 

negatively impact ESRD facilities and 

keeps with our longstanding policy to 

make updates using the latest available 

ESRD claims data. 

2. Proposed Payment for Renal Dialysis 

Services Furnished to Individuals With 

AKI 

a. Effects on ESRD Facilities 

To understand the impact of the 

changes affecting payments to different 

categories of ESRD facilities for renal 

dialysis services furnished to 

individuals with AKI, it is necessary to 

compare estimated payments in CY 

2021 to estimated payments in CY 2022. 

To estimate the impact among various 

types of ESRD facilities for renal 

dialysis services furnished to 

individuals with AKI, it is imperative 

that the estimates of payments in CY 

2021 and CY 2022 contain similar 

inputs. Therefore, we simulated 

payments only for those ESRD facilities 

for which we are able to calculate both 

current payments and new payments. 

For this proposed rule, we used CY 

2020 data from the Part A and Part B 

Common Working Files as of February 

12, 2021, as a basis for Medicare for 

renal dialysis services furnished to 

individuals with AKI. We updated the 

2020 claims to 2021 and 2022 using 

various updates. The proposed updates 

to the AKI payment amount are 

described in section III.B of this 

proposed rule. Table 10 shows the 

impact of the estimated CY 2022 

payments for renal dialysis services 

furnished to individuals with AKI 

compared to estimated payments for 

renal dialysis services furnished to 

individuals with AKI in CY 2021. 

BILLING CODE 4120–01–P 


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Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 36415 


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TABLE 10: Impact of Proposed Changes in Payment for Renal Dialysis Services Furnished to 

Individuals with AKI for CY 2022 ESRD PPS Pro osed Rule 

Number Number of Effect of Effect of 2022 

of Treatments 2022 Payment Effect of Total 2022 Facility Type (in Changes in Facilities thousands) Wage Index Rate Update Proposed Changes (E) 

(A) (B) (C) (D) 

Large dialysis 

organization 4,'.B2 256.0 0.0% 1.0% 1.0% 

Regional 

chain 576 31.6 0.1% 1.0% 1.1% 

Independent 206 13.3 0.0% 1.0% 1.0% 

Hospital 

based1 122 5.2 0.1% 1.0% 1.1% 

Unknown 11 0.2 0.3% 1.0% 1.3% 

East North 

Central 881 55.1 -0.2% 1.0% 0.8% 

East South 

Central 425 22.5 -0.5% 1.0% 0.5% 

Middle 

Atlantic 587 33.1 -0.3% 1.0% 0.7% 

Mountain 303 18.8 -0.2% 1.0% 0.8% 

New England 141 6.4 -0.7% 1.0% 0.3% 

Pacific2 646 47.5 0.6% 1.0% 1.6% 

Puerto Rico 

and Virgin 

Islands 3 0.0 0.0% 1.0% 1.0% 

South 

Atlantic 1,236 74.5 0.2% 1.0% 1.2% 

West North 

Central 340 16.0 0.1% 1.0% 1.1% 

West South 

Central 685 32.4 -0.3% 1.0% 0.7% 
36416 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

BILLING CODE 4120–01–C 

Column A of the impact table 

indicates the number of ESRD facilities 

for each impact category and column B 

indicates the number of AKI dialysis 

treatments (in thousands). Column C 

shows the effect of the proposed CY 

2022 wage indices. Column D shows the 

effect of the proposed CY 2022 ESRD 

PPS payment rate update. The proposed 

ESRD PPS payment rate update is 1.0 

percent, which reflects the proposed 

ESRDB market basket percentage 

increase factor for CY 2022 of 1.6 

percent and the proposed productivity 

adjustment of 0.6 percent. 

Column E reflects the overall impact, 

that is, the effects of the updated wage 

index and the payment rate update. We 

expect that overall ESRD facilities 

would experience a 1.0 percent increase 

in estimated payments in CY 2022. The 

categories of types of facilities in the 

impact table show impacts ranging from 

an increase of 0.0 percent to 1.6 percent 

in their CY 2022 estimated payments. 

b. Effects on Other Providers 

Under section 1834(r) of the Act, as 

added by section 808(b) of TPEA, we 

propose to update the payment rate for 

renal dialysis services furnished by 

ESRD facilities to beneficiaries with 

AKI. The only two Medicare providers 

and suppliers authorized to provide 

these outpatient renal dialysis services 

are hospital outpatient departments and 

ESRD facilities. The patient and his or 

her physician make the decision about 

where the renal dialysis services are 

furnished. Therefore, this proposal will 

have zero impact on other Medicare 

providers. 

c. Effects on the Medicare Program 

We estimate approximately $52 

million would be paid to ESRD facilities 

in CY 2022 as a result of patients with 

AKI receiving renal dialysis services in 

the ESRD facility at the lower ESRD PPS 

base rate versus receiving those services 

only in the hospital outpatient setting 

and paid under the outpatient 

prospective payment system, where 

services were required to be 

administered prior to the TPEA. 

d. Effects on Medicare Beneficiaries 

Currently, beneficiaries have a 20 

percent co-insurance obligation when 

they receive AKI dialysis in the hospital 

outpatient setting. When these services 

are furnished in an ESRD facility, the 

patients would continue to be 

responsible for a 20 percent 

coinsurance. Because the AKI dialysis 

payment rate paid to ESRD facilities is 

lower than the outpatient hospital PPS’s 

payment amount, we would expect 

beneficiaries to pay less co-insurance 

when AKI dialysis is furnished by ESRD 

facilities. 

e. Alternatives Considered 

As we discussed in the CY 2017 ESRD 

PPS proposed rule (81 FR 42870), we 

considered adjusting the AKI payment 

rate by including the ESRD PPS casemix adjustments, and other adjustments 

at section 1881(b)(14)(D) of the Act, as 

well as not paying separately for AKI 

specific drugs and laboratory tests. We 

ultimately determined that treatment for 

AKI is substantially different from 

treatment for ESRD and the case-mix 

adjustments applied to ESRD patients 

may not be applicable to AKI patients 

and as such, including those policies 

and adjustment would be inappropriate. 

We continue to monitor utilization and 

trends of items and services furnished to 

individuals with AKI for purposes of 

refining the payment rate in the future. 

This monitoring would assist us in 

developing knowledgeable, data-driven 

proposals. 

3. ESRD QIP 

a. Effects of the PY 2022 ESRD QIP on 

ESRD Facilities 

The ESRD QIP is intended to prevent 

reductions in the quality of ESRD 

dialysis facility services provided to 

beneficiaries. Although the general 

methodology that we use to determine 

a facility’s TPS is described in our 

regulations at 42 CFR 413.178(e), we are 

proposing to codify special scoring 

policies for PY 2022 at 42 CFR 

413.178(h). Under these proposed 

regulations, we would calculate 

measure rates for all measures but 

would not calculate achievement and 

improvement points for any measures. 

We would also not calculate or award a 

TPS for any facility. Finally, we would 

not reduce payment to any facility for 

PY 2022. 

If these policies are finalized as 

proposed, we believe there will be no 

effects of the PY 2022 ESRD QIP on 

ESRD Facilities, as no facilities will 

receive a TPS or payment reductions for 

PY 2022. 


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Less than 

4,000 treatments 643 28.8 -0.1% 1.0% 0.9% 

4,000 to 

9,999 treatments 2,0ll 108.4 0.0% 1.0% 1.0% 

10,000 or 

more treatments 2,525 167.2 0.0% 1.0% 1.0% 

Unknown 68 1.9 0.0% 1.0% 1.0% 

Less than2% 5,247 306.3 0.0% 1.0% 1.0% 

Between2% 

and 19% 0 0.0 0.0% 0.0% 0.0% 

Between20% 

and49% 0 0.0 0.0% 0.0% 0.0% 

More than 

50% 0 0.0 0.0% 0.0% 0.0% 

1Includes hospital-based ESRD facilities not reported to have large dialysis organization or regional chain ownership. 2Includes ESRD facilities located in Guam, American Samoa, and the Northern Mariana Islands 

This information should not be deleted. 
Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 36417 

b. Effects of the PY 2024 ESRD QIP on 

ESRD Facilities 

Any reductions in the ESRD PPS 

payments as a result of a facility’s 

performance under the PY 2024 ESRD 

QIP will apply to the ESRD PPS 

payments made to the facility for 

services furnished in CY 2024, as 

codified in our regulations at 42 CFR 

413.177. 

For the PY 2024 ESRD QIP, we 

estimate that, of the 7,610 dialysis 

facilities (including those not receiving 

a TPS) enrolled in Medicare, 

approximately 24.4 percent or 1,799 of 

the facilities that have sufficient data to 

calculate a TPS would receive a 

payment reduction for PY 2024. We are 

presenting an estimate for the PY 2024 

ESRD QIP to update the estimated 

impact that was provided in the CY 

2021 ESRD PPS final rule (85 FR 71481 

through 71483). If our proposals are 

finalized as proposed, the total 

estimated payment reductions for all the 

1,799 facilities expected to receive a 

payment reduction in PY 2024 would 

decrease from $18,247,083.76 to 

approximately $17,154,657.12. Facilities 

that do not receive a TPS do not receive 

a payment reduction. 

Table 11 shows the overall estimated 

distribution of payment reductions 

resulting from the PY 2024 ESRD QIP. 

To estimate whether a facility would 

receive a payment reduction for PY 

2024, we scored each facility on 

achievement and improvement on 

several clinical measures we have 

previously finalized and for which there 

were available data from EQRS and 

Medicare claims. Payment reduction 

estimates are calculated using the most 

recent data available (specified in Table 

12) in accordance with the policies 

proposed in this proposed rule. 

Measures used for the simulation are 

shown in Table 12. 

For all measures except the SHR 

clinical measure, the Standardized 

Readmission Ratio (SRR) clinical 

measure, and the STrR reporting 

measure, measures with less than 11 

patients for a facility were not included 

in that facility’s TPS. For the SHR 

clinical measure and the SRR clinical 

measure, facilities were required to have 

at least 5 patient-years at risk and 11 

index discharges, respectively, in order 

to be included in the facility’s TPS. For 

the STrR reporting measure, facilities 

were required to have at least 10 

patient-years at risk in order to be 

included in the facility’s TPS. Each 

facility’s TPS was compared to an 

estimated mTPS and an estimated 

payment reduction table that were 

consistent with the proposals outlined 

in sections IV.E and IV.F of this 

proposed rule. Facility reporting 

measure scores were estimated using 

available data from CY 2019. Facilities 


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TABLE 11: Estimated Distribution of PY 2024 ESRD QIP Payment Reductions 

Percent of 

Payment Reduction Number of Facilities Facilities* 

0.0% 5,570 75.59o/c 

0.5% 1,343 18.22o/c 

1.0% 363 4.93o/c 

1.5% 71 0.96o/c 

2.0% 22 0.30o/c 

*241 facilities not scored due to insufficient data 

TABLE 12: Data Used to Estimate PY 2024 ESRD QIP Payment Reductions 

Period of time used to calculate 

achievement thresholds, 50th 

Measure percentiles of the national performance, Performance period 

benchmarks, and improvement 

thresholds 

ICH CARPS Survey Jan 2018-Dec 2018 Jan 2019-Dec 2019 

SRR Jan 2018-Dec 2018 Jan 2019-Dec 2019 

SHR Jan 2018-Dec 2018 Jan 2019-Dec 2019 

PPPW Jan 2018-Dec 2018 Jan 2019-Dec 2019 

Kt/V Dialysis Adequacy Jan 2018-Dec 2018 Jan 2019-Dec 2019 

Comprehensive 

VAT 

Standardized Fistula Ratio Jan 2018-Dec 2018 Jan 2019-Dec 2019 

% Catheter Jan 2018-Dec 2018 Jan 2019-Dec 2019 

Hypercalcemia Jan 2018-Dec 2018 Jan 2019-Dec 2019 
36418 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

were required to have at least one 

measure in at least two domains to 

receive a TPS. 

To estimate the total payment 

reductions in PY 2024 for each facility 

resulting from this final rule, we 

multiplied the total Medicare payments 

to the facility during the 1-year period 

between January 2019 and December 

2019 by the facility’s estimated payment 

reduction percentage expected under 

the ESRD QIP, yielding a total payment 

reduction amount for each facility. 

Table 13 shows the estimated impact 

of the finalized ESRD QIP payment 

reductions to all ESRD facilities for PY 

2024. The table also details the 

distribution of ESRD facilities by size 

(both among facilities considered to be 

small entities and by number of 

treatments per facility), geography (both 

rural and urban and by region), and 

facility type (hospital based and 

freestanding facilities). Given that the 

performance period used for these 

calculations differs from the 

performance period we are using for the 

PY 2024 ESRD QIP, the actual impact of 

the PY 2024 ESRD QIP may vary 

significantly from the values provided 

here. 

c. Effects of the PY 2025 ESRD QIP on 

ESRD Facilities 

For the PY 2025 ESRD QIP, we 

estimate that, of the 7,610 dialysis 

facilities (including those not receiving 

a TPS) enrolled in Medicare, 

approximately 24.4 percent or 1,799 of 

the facilities that have sufficient data to 

calculate a TPS would receive a 

payment reduction for PY 2025. The 

total payment reductions for all the 

1,799 facilities expected to receive a 


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TABLE 13: Estimated Impact of QIP Payment Reductions to ESRD Facilities for PY 2024 

Number of Payment 

Facilities Reduction 

Number of Number of Expected to (percent 

Treatments Facilities Receive a change in 

Number of 2019 (in with QIP Payment total ESRD 

Facilities millions) Score Reduction payments) 

All Facilities 7,610 44.8 7,369 1,799 -0.16% 

Facility Type: 7,224 43.1 7,024 1,691 -0.15% 

Freestanding 

Hospital-based 386 1.8 345 108 -0.26% 

Ownership Type: 5,809 34.8 5,686 1,200 -0.12% 

Large Dialysis 

Regional Chain 944 5.7 921 268 -0.20% 

Independent 534 2.9 491 240 -0.38% 

Hospital-based (non-chain) 299 1.3 264 89 -0.29% 

Unknown 24 0.0 7 2 -0.21% 

Facility Size: 6,753 40.6 6,607 1,468 -0.13% 

Large Entities 

Small Entities 1 833 4.3 755 329 -0.35% 

Unknown 24 0.0 7 2 -0.21% 

Rural Status: 1,292 6.5 1,237 203 -0.09% 

I) Yes 

2)No 6,318 38.4 6,132 1,596 -0.17% 

Census Region: 1,046 6.7 1,000 261 -0.16% 

Northeast 

Midwest 1,734 8.3 1,663 431 -0.18% 

South 3,452 20.6 3,364 909 -0.17% 

West 1,318 8.7 1,283 165 -0.08% 

US Territories2 60 0.4 59 33 -0.36% 

Census Division: 8 0.1 8 4 -0.37% 

Unknown 

East North Central 1,220 6.0 1,171 355 -0.21% 

East South Central 604 3.3 592 135 -0.13% 

Middle Atlantic 845 5.4 806 227 -0.17% 

Mountain 419 2.4 405 52 -0.08% 

New England 201 1.4 194 34 -0.10% 

Pacific 899 6.3 878 113 -0.08% 

South Atlantic 1,746 10.7 1,700 494 -0.19% 

West North Central 514 2.3 492 76 -0.10% 

West South Central 1,102 6.7 1,072 280 -0.17% 

US Territories2 52 0.3 51 29 -0.36% 

Facility Size(# oftotal treatments) 1,315 2.6 1,195 265 -0.17% 

Less than 4,000 treatments 

4, 000-9 ,999 treatments 2,803 12.2 2,771 555 -0.13% 

Over 10,000 treatments 3,246 29.7 3,240 947 -0.17% 

Unknown 246 0.3 163 32 -0.18% 

1Small Entities include hospital-based and satellite facilities, and non-chain facilities based on DFC self-reported status. 

2Includes American Samoa, Guam, Northern Mariana Islands, Puerto Rico, and Virgin Islands. 
Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 36419 

payment reduction is approximately 

$17,154,657.121. Facilities that do not 

receive a TPS do not receive a payment 

reduction. 

Table 14 shows the overall estimated 

distribution of payment reductions 

resulting from the PY 2025 ESRD QIP. 

To estimate whether a facility would 

receive a payment reduction in PY 2025, 

we scored each facility on achievement 

and improvement on several clinical 

measures we have previously finalized 

and for which there were available data 

from EQRS and Medicare claims. 

Payment reduction estimates were 

calculated using the most recent data 

available (specified in Table 14) in 

accordance with the policies finalized 

in this proposed rule. Measures used for 

the simulation are shown in Table 15. 

For all measures except the SHR 

clinical measure, the SRR clinical 

measure, and the STrR reporting 

measure, measures with less than 11 

patients for a facility were not included 

in that facility’s TPS. For SHR and SRR, 

facilities were required to have at least 

5 patient-years at risk and 11 index 

discharges, respectively, in order to be 

included in the facility’s TPS. For the 

STrR reporting measure, facilities were 

required to have at least 10 patient-years 

at risk in order to be included in the 

facility’s TPS. Each facility’s TPS was 

compared to an estimated mTPS and an 

estimated payment reduction table that 

incorporates the policies outlined in 

section IV.E and IV.F of this proposed 

rule. Facility reporting measure scores 

were estimated using available data 

from CY 2019. Facilities were required 

to have at least one measure in at least 

two domains to receive a TPS. 

To estimate the total payment 

reductions in PY 2025 for each facility 

resulting from this proposed rule, we 

multiplied the total Medicare payments 

to the facility during the 1-year period 

between January 2019 and December 

2019 by the facility’s estimated payment 

reduction percentage expected under 

the ESRD QIP, yielding a total payment 

reduction amount for each facility. 

Table 16 shows the estimated impact 

of the finalized ESRD QIP payment 

reductions to all ESRD facilities for PY 

2025. The table details the distribution 

of ESRD facilities by size (both among 

facilities considered to be small entities 

and by number of treatments per 

facility), geography (both rural and 

urban and by region), and facility type 

(hospital based and freestanding 

facilities). Given that the performance 

period used for these calculations 

differs from the performance period we 

are proposing to use for the PY 2025 


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TABLE 14: Estimated Distribution of PY 2025 ESRD QIP Payment Reductions 

Percent of 

Payment Reduction Number of Facilities Facilities* 

0.0% 5,570 75.59o/c 

0.5% 1,343 18.22o/c 

1.0% 363 4.93o/c 

1.5% 71 0.96o/c 

2.0% 22 0.30o/c 

*Note: 241 facilities not scored due to insufficient data 

TABLE 15: Data Used to Estimate PY 2025 ESRD QIP Payment Reductions 

Period of time used to calculate 

achievement thresholds, 50th 

Measure percentiles of the national Performance period 

performance, benchmarks, and 

improvement thresholds 

ICH CARPS Survey Jan 2018-Dec 2018 Jan 2019-Dec 2019 

SRR Jan 2018-Dec 2018 Jan 2019-Dec 2019 

SHR Jan 2018-Dec 2018 Jan 2019-Dec 2019 

PPPW Jan 2018-Dec 2018 Jan 2019-Dec 2019 

Kt/V Dialysis Adequacy Jan 2018-Dec 2018 Jan 2019-Dec 2019 

Comprehensive 

VAT 

Standardized Fistula Ratio Jan 2018-Dec 2018 Jan 2019-Dec 2019 

% Catheter Jan 2018-Dec 2018 Jan 2019-Dec 2019 

Hypercalcemia Jan 2018-Dec 2018 Jan 2019-Dec 2019 
36420 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

ESRD QIP, the actual impact of the PY 2025 ESRD QIP may vary significantly 

from the values provided here. 

BILLING CODE 4120–01–P 

d. Effects on Other Providers 

The ESRD QIP is applicable to 

dialysis facilities. We are aware that 

several of our measures impact other 

providers. For example, with the 

introduction of the SRR clinical 

measure in PY 2017 and the SHR 

clinical measure in PY 2020, we 

anticipate that hospitals may experience 

financial savings as dialysis facilities 

work to reduce the number of 

unplanned readmissions and 

hospitalizations. We are exploring 

various methods to assess the impact 

these measures have on hospitals and 

other facilities, such as through the 

impacts of the Hospital Readmissions 

Reduction Program and the HospitalAcquired Condition Reduction Program, 

and we intend to continue examining 

the interactions between our quality 

programs to the greatest extent feasible. 

e. Effects on the Medicare Program 

For PY 2025, we estimate that the 

ESRD QIP would contribute 

approximately $17,154,657.12 in 

Medicare savings. For comparison, 

Table 17 shows the payment reductions 

that we estimate will be applied by the 

ESRD QIP from PY 2018 through PY 

2025. This includes our PY 2022 scoring 

and payment proposals as described in 

section IV.D of this proposed rule. 


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TABLE 16: Estimated Impact of QIP Payment Reductions to ESRD Facilities for PY 2025 

Number of Payment 

Facilities Reduction 

Number of Number of Expected to (percent 

Treatments Facilities Receive a change in 

Number of 2019 (in with QIP Payment total ESRD 

Facilities millions) Score Reduction payments) 

All Facilities 7,610 44.8 7,369 1,799 -0.16% 

Facility Type: 7,224 43.1 7,024 1,691 -0.15% 

Freestanding 

Hospital-based 386 1.8 345 108 -0.26% 

Ownership Type: 5,809 34.8 5,686 1,200 -0.12% 

Large Dialysis 

Regional Chain 944 5.7 921 268 -0.20% 

Independent 534 2.9 491 240 -0.38% 

Hospital-based (non-chain) 299 1.3 264 89 -0.29% 

Unknown 24 0.0 7 2 -0.21% 

Facility Size: 6,753 40.6 6,607 1,468 -0.13% 

Large Entities 

Small Entities 1 833 4.3 755 329 -0.35% 

Unknown 24 0.0 7 2 -0.21% 

Rural Status: 1,292 6.5 1,237 203 -0.09% 

I) Yes 

2)No 6,318 38.4 6,132 1,596 -0.17% 

Census Region: 1,046 6.7 1,000 261 -0.16% 

Northeast 

Midwest 1,734 8.3 1,663 431 -0.18% 

South 3,452 20.6 3,364 909 -0.17% 

West 1,318 8.7 1,283 165 -0.08% 

US Territories2 60 0.4 59 33 -0.36% 

Census Division: 8 0.1 8 4 -0.37% 

Unknown 

East North Central 1,220 6.0 1,171 355 -0.21% 

East South Central 604 3.3 592 135 -0.13% 

Middle Atlantic 845 5.4 806 227 -0.17% 

Mountain 419 2.4 405 52 -0.08% 

New England 201 1.4 194 34 -0.10% 

Pacific 899 6.3 878 113 -0.08% 

South Atlantic 1,746 10.7 1,700 494 -0.19% 

West North Central 514 2.3 492 76 -0.10% 

West South Central 1,102 6.7 1,072 280 -0.17% 

US Territories2 52 0.3 51 29 -0.36% 

Facility Size(# oftotal treatments) 1,315 2.6 1,195 265 -0.17% 

Less than 4,000 treatments 

4,000-9,999 treatments 2,803 12.2 2,771 555 -0.13% 

Over I 0,000 treatments 3,246 29.7 3,240 947 -0.17% 

Unknown 246 0.3 163 32 -0.18% 

1Small Entities include hospital-based and satellite facilities, and non-chain facilities based on DFC self-reported status. 

2Includes American Samoa, Guam, Northern Mariana Islands, Puerto Rico, and Virgin Islands. 
Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 36421 

BILLING CODE 4120–01–C 

f. Effects on Medicare Beneficiaries 

The ESRD QIP is applicable to 

dialysis facilities. Since the Program’s 

inception, there is evidence on 

improved performance on ESRD QIP 

measures. As we stated in the CY 2018 

ESRD PPS final rule, one objective 

measure we can examine to demonstrate 

the improved quality of care over time 

is the improvement of performance 

standards (82 FR 50795). As the ESRD 

QIP has refined its measure set and as 

facilities have gained experience with 

the measures included in the Program, 

performance standards have generally 

continued to rise. We view this as 

evidence that facility performance (and 

therefore the quality of care provided to 

Medicare beneficiaries) is objectively 

improving. We are in the process of 

monitoring and evaluating trends in the 

quality and cost of care for patients 

under the ESRD QIP, incorporating both 

existing measures and new measures as 

they are implemented in the Program. 

We will provide additional information 

about the impact of the ESRD QIP on 

beneficiaries as we learn more. 

However, in future years we are 

interested in examining these impacts 

through the analysis of available data 

from our existing measures. 

g. Alternatives Considered 

In section IV.D. of this proposed rule, 

we are proposing a special rule to 

modify the scoring methodology such 

that no facility would receive a payment 

reduction for PY 2022. Under this 

special rule for PY 2022, we would 

calculate measure rates for all measures 

for that payment year, but would not 

use those measure rates to generate an 

achievement or improvement score, 

domain scores, or a TPS. We considered 

retaining our current scoring policy for 

PY 2022. However, we concluded that 

this was not feasible because of the 

EQRS system issues described in section 

IV.B.2, and additionally, due to the 

impact of the COVID–19 PHE on some 

of the PY 2022 ESRD QIP measures, as 

described more fully in section IV.C. of 

this proposed rule. This approach 

would help to ensure that a facility 

would not be penalized due to 

extraordinary circumstances beyond the 

facility’s control. 

4. ETC Model 

(1) Overview 

Under the ESRD PPS under Medicare 

Part B, a single per-treatment payment 

is made to an ESRD facility for all of the 

renal dialysis services defined in section 

1881(b)(14)(B) of the Act and furnished 

to individuals for the treatment of ESRD 

in the ESRD facility or in a patient’s 

home. Under the Physician Fee 

Schedule, medical management of an 

ESRD beneficiary receiving dialysis by a 

physician or other practitioner is paid 

through the MCP. The ETC Model is a 

mandatory payment model designed to 

test payment adjustments to certain 

dialysis and dialysis-related payments, 

as discussed in the Specialty Care 

Models final rule (85 FR 6114), for 

ESRD facilities and for Managing 

Clinicians for claims with dates of 

service from January 1, 2021 to June 30, 

2027. The requirements for the ETC 

Model are set forth in 42 CFR part 512, 

subpart C. The changes proposed in this 

proposed rule (discussed in detail in 

section V.B of this proposed rule) would 

impact model payment adjustments for 

PPA Period 3, starting in July 1, 2023. 

Under the current ETC Model, there 

are two payment adjustments designed 

to increase rates of home dialysis and 

kidney transplantation through financial 

incentives. The HDPA is an upward 

payment adjustment on certain home 

dialysis claims for ESRD facilities, as 

described in §§ 512.340 and 512.350, 

and to certain home dialysis-related 

claims for Managing Clinicians, as 

described in §§ 512.345 and 512.350, 

during the initial 3 years of the ETC 

Model. 

The PPA is an upward or downward 

payment adjustment on certain dialysis 

and dialysis-related claims submitted by 

ETC Participants, as described in 

§§ 512.375(a) and 512.380 for ESRD 

facilities and §§ 512.375(b) and 512.380 

for Managing Clinicians, which will 

apply to claims with claim service dates 

beginning on July 1, 2022 and increase 

in magnitude over the duration of the 

ETC Model. We will assess each ETC 

Participant’s home dialysis rate, as 

described in § 512.365(b), and 

transplant rate, as described in 

§ 512.365(c), for each MY. The ETC 

Participant’s transplant rate will be 

aggregated, as described in § 512.365(e), 

and the ETC Participant’s home dialysis 

rate will be aggregated, as described in 

§ 512.365(e). The ETC Participant will 

receive a Modality Performance Score 

(MPS) based on the weighted sum of the 

higher of the ETC Participant’s 

achievement score or improvement 

score for the home dialysis rate and the 

higher of the ETC Participant’s 

achievement score or improvement 

score for the transplant rate, as 

described in § 512.370(d). 

For MY1 and MY2 (January 1, 2021 

through July 6, 2022), the achievement 

scores will be calculated in relation to 

a set of benchmarks based on the 

historical rates of home dialysis and 

inclusion on the transplant waitlist 

among ESRD facilities and Managing 

Clinicians located in Comparison 

Geographic Areas. The improvement 

scores will be calculated in relation to 

a set of benchmarks based on the ETC 

Participant’s own historical 

performance. The ETC Participant’s 

MPS for a MY will determine the 

magnitude of its PPA during the 

corresponding 6-month PPA Period, 

which will begin 6 months after the end 

of the MY. An ETC Participant’s MPS 

will be updated on a rolling basis every 

6 months. 

As mentioned in section IV.C.2.b(1) of 

the Specialty Care Models final rule (85 

FR 61351), the intention was to increase 

these benchmarks over time through 

subsequent notice and comment 


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TABLE 17: Estimated Payment Reductions Payment Years 2018 through 2025 

Payment year Estimated payment reductions 

PY 2025 $17,154,657 

PY 2024 $17,154,657 

PY 2023 $15,770,179 (85 FR 71483) 

PY 2022 NIA 

PY 2021 $32,196,724 (83 FR 57062) 

PY 2020 $31,581,441 (81 FR 77960) 

PY 2019 $15,470,309 (80 FR 69074) 

PY 2018 $11,576,214 (79 FR 66257) 
36422 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

rulemaking. In this proposed rule, the 

changes listed with bullets are being 

proposed for MY3 (beginning January 1, 

2022) through the final MY of ETC 

Model (MY10). More detail on these 

changes is provided in section V.B of 

this proposed rule. The proposed 

changes that are most likely to affect the 

impact estimate for the ETC Model are: 

• Include nocturnal in-center dialysis 

in the home dialysis rate calculation for 

Managing Clinicians and ESRD facilities 

not owned in whole or in part by an 

ETC LDO. 

• Exclude beneficiaries with a 

diagnosis of and who are receiving 

chemotherapy or radiation for vital solid 

organ cancer from the transplant rate 

calculation. 

• Modify the PPA achievement 

benchmarking methodology: 

++ Stratify the home dialysis and 

transplant rate benchmark by the 

proportion of beneficiaries who are 

dual-eligible for Medicare and 

Medicaid, or, receive the Low-Income 

Subsidy (LIS), resulting in two strata. 

++ Increase the home dialysis and 

transplant rate benchmarks by 10 

percent for each MY couplet (that is, 

1.10 for MY3 and MY4, 1.20 for MY5 

and MY6, 1.30 for MY7 and MY8, and 

1.40 for MY9 and MY10). 

• Modify the PPA improvement 

benchmarking methodology: 

++ Health Equity Incentive: 

Participants can earn 0.5 improvement 

points in addition to their improvement 

score for a significant increase in the 

rate of dual eligible or LIS recipient 

beneficiaries. 

++ Modify improvement calculation 

to ensure that the Benchmark Year rate 

cannot be zero, such that improvement 

is calculable for all participants. 

The ETC Model is not a total cost of 

care model. ETC Participants will still 

bill FFS Medicare, and items and 

services not subject to the ETC Model’s 

payment adjustments will continue to 

be paid as they would in the absence of 

the Model. 

(2) Data and Methods 

A stochastic simulation was created to 

estimate the financial impacts of the 

proposed changes to the ETC Model 

relative to baseline expenditures, where 

baseline expenditures were defined as 

data from CYs 2018 and 2019 without 

the proposed changes applied. The 

simulation relied upon statistical 

assumptions derived from 

retrospectively constructed ESRD 

facilities’ and Managing Clinicians’ 

Medicare dialysis claims, transplant 

claims, and transplant waitlist data 

reported during 2018 and 2019, the 

most recent years with complete data 

available. Both datasets and the riskadjustment methodologies for the ETC 

Model were developed by the CMS 

Office of the Actuary (OACT). 

The ESRD facilities and Managing 

Clinicians datasets were restricted to the 

following eligibility criteria. 

Beneficiaries must be residing in the 

United States, 18 years of age or older, 

and enrolled in Medicare Part B. 

Beneficiaries enrolled in Medicare 

Advantage or other cost or Medicare 

managed care plans, who have elected 

hospice, are receiving dialysis for acute 

kidney injury (AKI) only, with a 

diagnosis of dementia, who are 

receiving dialysis in a nursing facility, 

or reside in a skilled nursing facility 

were excluded. In addition, 

beneficiaries who have a diagnosis of 

and are receiving treatment with 

chemotherapy or radiation for a vital 

solid organ cancer were excluded from 

the transplant rate calculations. 

Diagnosis of a vital solid organ cancer 

was defined as a beneficiary that had a 

claim with any of 39 ICD–10–CM codes 

ranging from C22.0 through C79.02. 

Treatment of a vital solid organ cancer 

was defined as a beneficiary with a 

claim with any of 2,087 radiation 

administration ICD–10–PCS codes, 19 

chemotherapy administration CPT 

codes, or 41 radiation administration 

CPT codes. Last, the HRR was matched 

to the claim service facility zip code or 

the rendering physician zip code for 

ESRD facility and Managing Clinician, 

respectively. 

For the modeling exercise used to 

estimate changes in payment to 

providers and suppliers and the 

resulting savings to Medicare, OACT 

maintained the previous method to 

identify ESRD facilities with common 

ownership, the low-volume exclusion 

threshold, and the aggregation 

assumptions as CMS has not proposed 

changes to these model policies. To 

clarify OACT’s methodology, the ESRD 

facilities’ data were aggregated to the 

CMS Certification Number (CCN) level 

for beneficiaries on dialysis identified 

by outpatient claims with Type of Bill 

072X to capture all dialysis services 

furnished at or through ESRD facilities. 

Beneficiaries receiving home dialysis 

services were defined as condition 

codes 74 and 76 (§ 512.340). Condition 

code 75 was removed from the home 

dialysis definition because that billing 

code is no longer in use. Condition code 

80 was removed because we want to 

exclude beneficiaries who received 

home dialysis furnished in a SNF or 

nursing facility. Beneficiaries receiving 

in-center dialysis services were defined 

using condition code 71. Two new 

variables were created: In-center selfdialysis, condition code 72 (§ 512.365) 

and in-center nocturnal dialysis, based 

on any of the claims’ lines 1–5 HCPCS 

codes equal to the ‘‘UJ’’ modifier. Selfcare in training and ESRD self-care 

retraining, condition codes 73 and 87, 

respectively, were only included in the 

denominator for the home dialysis rate 

calculation. For consistency with the 

exclusion in § 512.385(a), after grouping 

within each HRR, aggregated ESRD 

facilities with less than 132 total 

attributed beneficiary months during a 

given MY were excluded. When 

constructing benchmarks, for 

consistency with the methodology for 

aggregating performance for purposes of 

the PPA calculation, we aggregated all 

ESRD facilities owned in whole or in 

part by the same dialysis organization 

located in the same HRR. 

The Managing Clinicians’ 

performance data were aggregated to the 

Tax Identification Number (TIN) level 

(for group practices) and the individual 

National Provider Identifier (NPI) level 

(for solo practitioners). For purposes of 

calculating the home dialysis rate, 

beneficiaries on home dialysis were 

identified using outpatient claims with 

CPT® codes 90965 and 90966 

(§ 512.345). Beneficiaries receiving incenter dialysis were identified by 

outpatient claims with CPT® codes 

90957, 90958, 90959, 90960, 90961, and 

90962 (§ 512.360). Last, following the 

low-volume threshold described in 

§ 512.385(b), after grouping within each 

HRR, Managing Clinicians with less 

than 132 total attributed beneficiary 

months during a given MY were 

excluded. 

The Scientific Registry of Transplant 

Recipients (SRTR) transplant waitlist 

data were obtained from the Center for 

Clinical Standards and Quality (CCSQ). 

To construct the transplant waitlist rate, 

the numerator was based on per-patient 

counts and included every addition to 

the waitlist for a patient in any past 

year. The waitlist counts for the 

numerator included waitlists for kidney 

transplants, alone or with another organ, 

active and inactive records, multi-organ 

listings, and patients that have 

subsequently been removed from the 

waitlist. The denominator was a unique 

count of prevalent dialysis patients as of 

the end of the year. Only patients on 

dialysis as of December 31st for the 

selected year were included. Facility 

attribution was based on the facility the 

patient was admitted to on the last day 

of the year. 

For MY1 and MY2, the home dialysis 

score and transplant score for the PPA 

were calculated using the following 

methodology for the ESRD facilities and 

Managing Clinicians. ETC Participant 


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behavior for each year was simulated by 

adjusting the ETC Participant’s baseline 

home dialysis (or transplant) rate for a 

simulated statistical fluctuation and 

then summing with the assumed 

increase in home dialysis (or transplant) 

rate multiplied by a randomly generated 

improvement scalar. The achievement 

and improvement scores were assigned 

by comparing the ETC Participant’s 

simulated home dialysis (or transplant) 

rate for the MY to the percentile 

distribution of home dialysis (or 

transplant) rates in the prior year. Last, 

the MPS was calculated using the 

weighted sum of the higher of the 

achievement or improvement score for 

the home dialysis rate and the 

transplant waitlist rate. The home 

dialysis rate constituted two-thirds of 

the MPS, and the transplant rate onethird of the MPS. 

For MY3 through MY10, the home 

dialysis rate calculation accounts for 

modifications proposed in this proposed 

rule. For Managing Clinicians, the 

proposed revisions include changing the 

numerator for the home dialysis rate 

from the home dialysis beneficiary 

months to the home dialysis beneficiary 

months + 0.5(in-center self-dialysis 

beneficiary months) + 0.5*(nocturnal incenter dialysis beneficiary months), 

such that 1-beneficiary year is 

comprised of 12-beneficiary months. 

The proposed revision for the numerator 

of the home dialysis rate for ESRD 

facilities varied if the facility was 

owned in whole or in part by an ETC 

LDO, as identified by ownership 

information for the associated CCN. If 

the CCN had facilities owned by an ETC 

LDO, then the proposed numerator for 

the home dialysis rate was the home 

dialysis beneficiary months + 0.5*(incenter self-dialysis beneficiary months); 

therefore, not including nocturnal incenter dialysis months from the 

numerator. Otherwise, if the CCN did 

not have facilities owned by an ETC 

LDO, then the numerator was the same 

as described above for Managing 

Clinicians, such that the numerator for 

the home dialysis rate was home 

dialysis beneficiary months + 0.5*(incenter self-dialysis beneficiary months) 

+ 0.5*(nocturnal in-center dialysis 

beneficiary months). 

The number of beneficiaries on incenter self-dialysis who met the 

eligibility criteria for the ETC Model 

was very small, ranging from 102 to 277 

over the period 2012–2019 and 

decreasing 89.9 percent to 22 

beneficiaries in 2020 (based on 

preliminary 2020 data at CMS). With 

such a small sample size, the growth 

rate vacillated significantly. In addition, 

the in-center nocturnal dialysis UJ 

modifier code did not become effective 

until January 1, 2017; therefore, there 

were insufficient data to generate 

growth rate assumptions. The in-center 

nocturnal dialysis beneficiary growth 

rate decreased by 91.3 percent in 2020. 

As a solution to these data limitations, 

to simulate the impact of incorporating 

in-center self-dialysis and in-center 

nocturnal dialysis for the purpose of the 

savings to Medicare estimate, the 

simulation assumed any given ESRD 

facility or Managing Clinician would 

have a one percent chance of receiving 

an increased achievement score due to 

this policy proposal. 

The overall process for generating 

achievement and improvement scoring 

followed modeling from section VI.C.2 

of the Specialty Care Models final rule 

(85 FR 61352), with the exception of the 

following changes. 

Beginning for MY3 and beyond, the 

achievement benchmarking 

methodology had two proposed 

modifications. First, the home dialysis 

rate and transplant waitlist rate 

benchmarks were increased by a total of 

10 percent relative to ESRD facilities 

and Managing Clinicians not selected 

for participation, every two MYs. To 

clarify, no changes to the achievement 

benchmarking methodology were made 

to MYs 1 and 2. The latter MY couplets’ 

achievement benchmarking included 

the following preset benchmark 

updates: 

• MYs 3 and 4: Comparison 

Geographic Area percentiles*1.10, 

• MYs 5 and 6: Comparison 

Geographic Area percentiles*1.20, 

• MYs 7 and 8: Comparison 

Geographic Area percentiles*1.30, and 

• MYs 9 and 10: Comparison 

Geographic Area percentiles*1.40. 

The percentiles represented the 30th, 

50th, 75th, and 90th percentile of the 

home dialysis rate and transplant rate 

for ESRD facilities and Managing 

Clinicians not selected for participation. 

The preset benchmark updates method 

provides greater certainty to ETC 

Participants than the rolling updates in 

section IV.C.2.b(3) of the Specialty Care 

Models final rule (85 FR 61353), which 

would have involved updating 

benchmarks based on emerging trends 

over the most recent experience periods 

for which data were available. 

Second, in this proposed rule, we 

proposed to incorporate two proxies for 

socioeconomic status, dual eligibility 

status or receipt of the Low Income 

Subsidy (LIS), as part of the 

achievement benchmarking starting for 

MY3 and beyond. Dual eligibility status 

was defined as a Medicare beneficiary 

with any of the following full-time dual 

type codes: 02=Eligible is entitled to 

Medicare Qualified Medicare 

Beneficiary (QMB) and Medicaid 

coverage including prescription drugs, 

04=Eligible is entitled to Medicare 

Specified Low-Income Medicare 

Beneficiary (SLMB) and Medicaid 

coverage including prescription drugs, 

or 08=Eligible is entitled to Medicare 

Other dual eligible with Medicaid 

coverage including prescription drugs. 

Separately, a yes/no indicator was 

created for any beneficiary that was 

either deemed or determined by the 

Social Security Administration (SSA) to 

be receiving the LIS. The home dialysis 

rate and transplant waitlist rate 

achievement benchmarks were then 

stratified by the proportion of attributed 

beneficiaries who are dual-eligible or 

receive the LIS. Two strata were created 

with a cutpoint of approximately 50 

percent for participants with any dualeligible or LIS recipient beneficiaries 

and those who do not have beneficiaries 

meeting the socioeconomic status 

proxies. 

Third, a Health Equity Incentive was 

proposed for improvement scoring 

starting in MY3. For the purpose of the 

estimates in this Regulatory Impact 

Analysis, we incorporated a random 

variable to simulate each ETC 

Participant’s baseline variation and 

behavioral improvement for each MY. If 

the participant’s simulated 

improvement behavior in MY3 through 

MY10 was greater than 5 percent, then 

the participant received a 0.5 point 

increase on their improvement score, 

allowing for a maximum of 2.0 total 

points. 

For all MYs, the transplant waitlist 

benchmarks were annually inflated by 

approximately 3-percentage points 

growth. This was a modification from 

section VI.C.2 of the Specialty Care 

Models final rule (85 FR 61352), where 

the waitlist benchmarks were annually 

inflated by approximately 2-percentage 

points growth observed during years 

2017 through 2019 in the CCSQ data, to 

project rates of growth. The additional 

1 percentage point growth in this 

proposed rule was included to account 

for uncertainty from the COVID–19 PHE 

disruption and section 17006 of the 21st 

Century Cures Act (Cures Act) (Pub. L. 

114–255), which amended the Act to 

increase enrollment options for 

individuals with ESRD into Medicare 

Advantage. To clarify, applying the 3- 

percentage point annual growth from 

the median transplant waitlist rate 

across HRR condensed facilities grew 

from 8 percent in 2017 to 11 percent in 

2018 to 14 percent in 2019 (that is, not 

a growth rate of 1.03 percent per year). 

To assess the impact of the COVID– 

19 PHE on the kidney transplant 


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36424 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

305UNOS. 2021. COVID–19 and Solid Organ 

Transplants. Transplant and Waitlist Data 

Visualizations. https://unos.org/covid/. 

waitlist, we analyzed data from the 

United Network for Organ Sharing 

(UNOS).305. The UNOS data suggest that 

the number of new patients added to the 

kidney transplant waitlist steadily 

decreased between the weeks of March 

15, 2020 through May 10, 2020, when 

between 16 to 81 percent of patients 

listed on the weekly kidney transplant 

waitlist became inactive due to COVID– 

19 precautions. During July through 

December 2020, the number of new 

patients added to the kidney transplant 

waitlist increased to near pre-pandemic 

levels with an average of less than 3 

percent of patients listed as inactive due 

to COVID–19. Anomalous dips in the 

number of new patients added to the 

kidney transplant waitlist were 

observed during the weeks of November 

22, 2020 and December 27, 2020, which 

correspond with federal holidays in 

addition to a period that Americans 

were asked to social distance to slow the 

spread of COVID–19. Continuing into 

the first quarter of 2021, new additions 

to the kidney transplant waitlist 

remained at approximately prepandemic rates. Therefore, we assume 

that the number of new patients added 

to the waitlist will not decrease as a 

result of the pandemic and the linear 

2-percentage point growth rate for the 

transplant waitlist calculated using 

years 2017 through 2019 CCSQ data 

remains a reasonable assumption for 

baseline growth going forward. In the 

proposed rule, we also included a 1 

percent increase to the standard error to 

account for a new variation assumption 

to address how year-over-year changes 

could fluctuate at the ESRD facility or 

Managing Clinician level, which was 

potentially exacerbated by the exclusion 

criteria (that is, residents of a nursing 

facility, receiving dialysis in a skilled 

nursing facility, dialysis for AKI only) 

applied to the updated model data 

source used for estimates in this 

proposed rule. 

No changes were proposed to the 

payment structure for the HDPA 

calculation described in the final rule 

(§ 512.350). As such, the HDPA was 

calculated using the home dialysis and 

home dialysis-related payments 

adjusted by decreasing amounts (3, 2, 

and 1 percent) during each of the first 

3 years of the Model. 

The kidney disease patient education 

services utilization and cost data were 

identified by codes G0420 and G0421, to 

capture face-to-face individual and 

group training sessions for chronic 

kidney disease beneficiaries on 

treatment modalities. The home dialysis 

training costs for incident beneficiaries 

on home dialysis for Continuous 

Ambulatory Peritoneal Dialysis (CAPD) 

or Continuous Cycler-Assisted 

Peritoneal Dialysis (CCPD) were defined 

using CPT® codes 90989 and 90993 for 

complete and incomplete training 

sessions, respectively. 

Data from CY 2019 were used to 

project baseline expenditures (that is, 

expenditures before the proposed 

changes were applied) and the 

traditional FFS payment system billing 

patterns were assumed to continue 

under current law. 

(3) Medicare Estimate—Primary 

Specification, Assume Proposed 

Benchmark Updates 

BILLING CODE 4120–01–P 


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TABLE 18. Estimates of Medicare Program Savings (Rounded $M) for ETC MODEL 

Year of Model 

2021 2022 2023 2024 2025 2026 2027 6.5 Year 

Total* 

Net Impact to 14 8 -3 -12 -14 -21 -11 -38 

Medicare Soendin2 

Overall PPA Net & 13 6 -5 -14 -17 -24 -14 -53 

HDPA 

Clinician PPA -1 -2 -2 -3 -4 -2 -14 

Downward 

Adiustment 

Clinician PPA 0 1 1 1 1 1 5 

Uoward Adiustment 

Clinician PPA Net 0 -1 -1 -2 -2 -1 -9 

Clinician HDPA 0 0 0 0 

Facility Downward -9 -21 -26 -32 -40 -22 -149 

Adiustment 

Facility Upward 5 11 14 17 18 9 75 

Ad.iustment 

Facility PPA Net -3 -10 -12 -15 -22 -12 -74 

Facility HDPA 13 10 6 29 

Total PPA Downward -9 -23 -29 -35 -44 -24 -163 

Adiustment 

Total PPA Upward 6 12 15 18 19 10 80 

Adjustment 

Total PPA Net -4 -11 -14 -17 -24 -14 -83 

TotalHDPA 13 10 6 29 

Kidney Disease 0 1 1 1 1 1 1 5 

Patient Education 

Services Costs 

HD Trainin!! Costs 1 1 1 1 2 2 2 10 

*Totals may not sum due to rounding and from beneficiaries that have dialysis treatment spanning multiple years. 

Negative spending reflects a reduction in Medicare spending. The Kidney Disease Patient Education Services Costs are 

less than $IM each year, but are rounded up to $IM to show what years they apply to. Similarly, the HD Training Costs 

are less than $ IM for years 2021-2024, but are rounded up to $ IM to indicate that costs were applied those years. 
36426 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

BILLING CODE 4120–01–C 

Table 18 summarizes the estimated 

impact of the ETC Model when 

assuming preset benchmark updates 

where the achievement benchmarks for 

each year are set using the average of the 

home dialysis rates for year t-1 and year 

t-2 for the HRRs randomly selected for 

participation in the ETC Model. We 

estimate the Medicare program will save 

a net total of $53 million from the PPA 

and HDPA between January 1, 2021 and 

June 30, 2027 less $15 million in 

increased training and education 

expenditures. Therefore, the net impact 

to Medicare spending is estimated to be 

$38 million in savings. In Table 18 and 

Table 19, negative spending reflects a 

reduction in Medicare spending, while 

positive spending reflects an increase. 

The results for both tables were 

generated from an average of 400 

simulations under the assumption that 

benchmarks are rolled forward with a 

1.5-year lag. 

Table 19 is provided to isolate the 

total impact of the changes proposed in 

this proposed rule for years 2023 going 

forward by calculating the difference 

from our final estimates in Table 18 less 

totals from our current baseline 

estimates that used the same years of 

data, but without the model changes 

applied. To clarify, the baseline 

estimates are not the estimates reported 

in Table 19 of the Specialty Care Models 

final rule (85 FR 61354); the final rule 

used data from CYs 2016 and 2017 and 

this proposed rule used the most recent 

data available, from CYs 2018 and 2019. 

There was no impact reported in years 

2021 and 2022 since the proposed 

payment adjustments were not effective 

until MY3. In addition, the proposed 

changes did not apply to the HDPA or 

the Kidney Disease Patient Education 

Services Costs and HD Training Costs. 

As expected, Table 19 shows that the 

proposed changes had a very small 

effect on Medicare savings; only $7 

million in savings for the net impact to 

Medicare spending over the 4.5-year 

period can be attributed to the changes 

proposed in this rule. 

As was the case in the Specialty Care 

Models final rule (85 FR 61353), the 

projections do not include the Part B 

premium revenue offset because the 

payment adjustments under the ETC 

Model will not affect beneficiary costsharing. Any potential effects on 

Medicare Advantage capitation 

payments were also excluded from the 

projections. This approach is consistent 

with how CMS has previously conveyed 

the primary FFS effects anticipated for 

an uncertain model without also 

assessing the potential impact on 

Medicare Advantage rates. 

Returning to Table 18, as anticipated, 

the expected Medicare program savings 

were driven by the net effect of the 

Facility PPA; a reduction in Medicare 

spending of $74 million over the period 

from July 1, 2022 through June 30, 2027. 

In comparison, the net effect of the 

Clinician PPA was only $9 million in 

Medicare savings. This estimate was 

based on an empirical study of 

historical home dialysis utilization and 

transplant waitlist rates for Medicare 

FFS beneficiaries that CMS virtually 

attributed to ESRD facilities and to 

Managing Clinicians based on the 

plurality of associated spending at the 

beneficiary level. We analyzed the base 

variation in those facility/practice level 

measures and simulated the effect of the 

payment policy assuming providers and 

suppliers respond by marginally 

increasing their share of patients 

utilizing home dialysis. Random 

variables were used to vary the 

effectiveness that individual providers 

and suppliers might show in such 

progression over time and to simulate 

the level of year-to-year variation 

already noted in the base multi-year 

data that was analyzed. The uncertainty 

in the projection was illustrated in 

sections VII.C.2.b.(3)(a) and 

VII.C.2.b.(3)(b) of the Specialty Care 

Models final rule (85 FR 61354), 

respectively, through alternate scenarios 

assuming that the benchmarks against 

which ETC Participants are measured 

were to not be updated. In those 

sensitivity analyses, we analyzed a 

modified version of the model that 

included a fixed benchmark for the 

home dialysis and transplant waitlist 

rates as well as a separate sensitivity 

analysis that assumed a rolling 

benchmark for the home dialysis rate 

and a fixed benchmark for the 

transplant waitlist rate. 

For this proposed rule, we are 

modeling a preset benchmark growth 

rate as proposed in this rule but 

continue to incorporate sensitivity to a 

range of potential behavioral changes for 

the home dialysis rate and transplant 

waitlist rate for ETC facilities and 

Managing Clinicians assumed to 

participate in the model. Kidney disease 

patient education services on treatment 

modalities and home dialysis (HD) 

training for incident dialysis 

beneficiaries are relatively small outlays 


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TABLE 19: Difference from Baseline (Rounded $M) 

Year of Model 

2021 2022 2023 2024 2025 2026 2027 4.5 Year 

Total* 

Net Impact to -2 -2 0 -1 -1 -7 

Medicare Spendin2 

Overall PPA Net & -2 -2 0 -1 -1 -7 

HDPA 

Total PPA Downward -1 0 3 4 2 8 

Ad.iustment 

TotalPPA Upward -1 -2 -3 -5 -3 -15 

Adjustment 

Total PPA Net -2 -2 0 -1 -1 -7 

TotalHDPA 0 0 

* Model changes proposed effective for MY 3. Payments adjusted beginning in PPA Period 3, effective July 1, 2023 

going forward. No changes to the HDP A. No changes to the Kidney Disease Patient Education Services Costs or the HD 

Training Costs. See Table El for additional footnotes. 
Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 36427 

and were projected to represent only 

relatively modest increases in Medicare 

spending each year. 

The key assumptions underlying the 

impact estimate are that each 

consolidated ESRD facility or Managing 

Clinician’s share of total maintenance 

dialysis provided in the home setting 

was assumed to grow by up to an 

assumed maximum growth averaging 

3-percentage points per year. Factors 

underlying this assumption about the 

home dialysis growth rate include: 

Known limitations that may prevent 

patients from being able to dialyze at 

home, such as certain common disease 

types that make peritoneal dialysis 

impractical (for example, obesity); 

current equipment and staffing 

constraints; and the likelihood that a 

patient new to maintenance dialysis 

starts dialysis at home compared to the 

likelihood that a current dialysis patient 

who dialyzes in center switches to 

dialysis at home. In any given trial of 

the simulation, the maximum growth 

rate was chosen from a uniform 

distribution of 0 to 5-percentage points 

per year. Preliminary data from CMS 

show that the growth rate for home 

dialysis was 3.9 percent in CY 2020 for 

beneficiaries meeting the eligibility 

criteria for the ETC Model. This growth 

rate is within range to what was 

observed prior to the establishment of 

the Advancing American Kidney Health 

initiative in 2019 and it also shows that 

the COVID–19 PHE did not cause the 

home dialysis growth assumption to 

become invalid. The 3-percentage point 

per year average max growth rate will, 

in effect, move the average market 

peritoneal dialysis rate (about 10 

percent) to the highest market baseline 

peritoneal dialysis rate (for example, 

Bend, Oregon HRR at about 25 percent), 

which we believe is a reasonable upper 

bound on growth over the duration of 

the ETC Model for the purposes of this 

actuarial model. 

Consolidated ESRD facilities at the 

HRR level or Managing Clinicians were 

assumed to achieve anywhere from zero 

to 100 percent of such maximum growth 

in any given year. Thus, the average 

projected growth for the share of 

maintenance dialysis provided in the 

home was 1.5-percentage points per 

year (expressed as the percentage of 

total dialysis). In contrast, we do not 

include an official assumption that the 

overall number of kidney transplants 

will increase and provide justification 

for this assumption in sections 

VI.C.2.b.(4) and VI.C.2.b.(5) of the 

Specialty Care Models final rule (85 FR 

61355). However, as part of the 

sensitivity analysis for the savings 

calculations for the model, we laid out 

a different savings scenario if the ETC 

Learning Collaborative described in 

VI.C.2.b.(6) of the Specialty Care Models 

final rule (85 FR 61355) were to be 

successful in decreasing the discard rate 

of deceased donor kidneys and 

increasing the utilization rate of 

deceased donor kidneys that have been 

retrieved. 

(a) Sensitivity Analysis: Medicare 

Savings Estimate—Results for the 10th 

and 90th Percentiles 

Using the primary specification for 

the Medicare estimate with preset 

benchmark updates for home dialysis 

and transplant waitlist rates, we 

compare the results for the top 10th and 

90th percentiles of the 400 individual 

simulations to the average of all 

simulation results reported in Table 18. 

Since the impact on Medicare spending 

for the ETC Model using the present 

benchmark updates is estimated to be in 

savings rather than losses, the top 10th 

and 90th percentiles represent the most 

optimistic and conservative projections, 

respectively. The overall net PPA and 

HDPA for the top 10th and 90th 

percentiles using the present benchmark 

updates method are $117 million in 

savings and $3 million in losses 

(encompassing the mean estimate of $53 

million in savings in Table 18). The 

overall uncertainty of the impact of the 

model is further illustrated in Table 18, 

the change from baseline, where the 

mean $7 million dollars in savings 

reported for the Overall PPA Net & 

HDPA has $83 million in savings and 

$75 million in losses, for the top 10th 

and 90th percentiles, respectively. 

(4) Effects on the Home Dialysis Rate 

This proposed rule proposes to 

modify the home dialysis rate equation 

by adding 0.5 multiplied by the sum of 

the self-dialysis beneficiary months and 

the in-center nocturnal dialysis 

beneficiary months to the numerator 

such that 1-beneficiary year is 

comprised of 12-beneficiary months. 

The proposed modification was 

different for ESRD facilities with an 

aggregation group that had facilities 

owned by an ETC LDO, for which the 

nocturnal dialysis months were not 

included in the numerator. 

Less than 1 percent of beneficiaries 

eligible for attribution into the ETC 

Model were receiving either self-dialysis 

or nocturnal in-center dialysis in CY 

2019. In addition, in CY 2020, the 

annual growth rate decreased by 89.9 

and 91.3 percent for beneficiaries 

receiving self-dialysis or nocturnal 

dialysis, respectively. The sharp decline 

in these dialysis modalities is 

potentially in response to the COVID–19 

pandemic. The low historical take-up 

for self-dialysis and shortage of 

historical years for nocturnal dialysis 

(that is, a nocturnal dialysis claims line 

instruction became effective in 2017) 

result in these proposed modifications 

having an insignificant impact on the 

savings to Medicare. 

Two of the changes proposed in this 

proposed rule have the potential to 

generate higher PPA scores for a limited 

subset of providers and therefore a small 

negative impact on estimated savings for 

the model. First, we proposed two strata 

for the achievement and improvement 

benchmarking based on a 50 percent 

cutpoint for the proportion of attributed 

beneficiaries with dual eligibility status 

or receipt of the LIS. This proposed 

modification would allow participants 

to be compared to participants who 

serve ESRD patients with a similar 

socioeconomic status, essentially 

making the comparison groups fairer 

and potentially increasing the cost to 

Medicare. Second, the proposed Health 

Equity Incentive rewarded participants 

with 0.5 points to their improvement 

score who demonstrated a sufficiently 

significant improvement on the home 

dialysis rate among their attributed 

beneficiaries who are dual eligible or 

receive the LIS. 

Furthermore, we modeled the home 

dialysis rate achievement and 

improvement benchmarks by 

incrementally increasing every two 

measurement periods the benchmarks 

by 10 percent relative to ESRD facilities 

and Managing Clinicians not selected 

for participation. Applying the preset 

benchmarks update method balanced 

out the negative impact to Medicare 

savings generated from stratification and 

the Health Equity Incentive, essentially 

preserving the overall savings level 

reported in the Specialty Care Models 

final rule. 

(5) Effects on Kidney Transplantation 

Kidney transplantation is considered 

the optimal treatment for most ESRD 

beneficiaries. The PPA includes a onethird weight on the ESRD facilities’ or 

Managing Clinician’s transplant waitlist 

rate, with the ultimate goal of increasing 

the rate of kidney transplantation. 

However, the changes proposed in this 

proposed rule do not impact our 

decision in the previous final rule to not 

include an assumption that the overall 

number of kidney transplants will 

increase. The number of ESRD patients 

on the kidney transplant waitlist has for 

many years far exceeded the annual 

number of transplants performed. 

Transplantation rates have not increased 

to meet such demand because of the 

limited supply of deceased donor 


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36428 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

306United States Renal Data System. 2020. ‘‘ADR 

Reference Table E6 Renal Transplants by Donor 

Type.’’ https://adr.usrds.org/2020/reference-tables. 

307Organ Procurement and Transplantation 

Network. 2021. ‘‘Current US Waiting List, Overall 

by Organ.’’ https://optn.transplant.hrsa.gov/data/ 

view-data-reports/national-data/#. 

kidneys. The U.S. Renal Data System 306

reported 22,393 kidney transplants in 

2018 compared to a kidney transplant 

waiting list 307 of over 98,000. Refer to 

section VI.C.2.b(4) in the Specialty Care 

Models final rule (85 FR 61355) for a 

detailed justification for our assumption 

that the overall number of kidney 

transplants will not increase in response 

to ESRD facilities and Managing 

Clinicians participating in the ETC 

Model. 

(6) Effects of the Transplant Waitlist 

Rate 

This proposed rule includes the 

transplant waitlist rate described in the 

Specialty Care Models final rule 

(§ 512.365) with the following proposed 

modifications. First, we are proposing to 

exclude Medicare beneficiaries with a 

diagnosis of and treatment with 

chemotherapy or radiation for vital solid 

organ cancers. In our analysis of 

beneficiaries’ eligible for the ETC 

Model, we found that less than 1 

percent of the beneficiaries had claims 

for any vital solid organ cancers. 

Therefore, the effect of this proposed 

exclusion criterion is to make the 

beneficiaries included in the calculation 

of the transplant rate an improved 

representation of beneficiaries who are 

able to join the transplant waitlist and/ 

or receive pre-emptive living donor 

kidney transplantation. But, due to the 

very low number of ETC Model 

potential beneficiaries with these types 

of cancer, the exclusion criterion is 

unlikely to have any significant impact 

on the transplant waitlist rate. 

Two of the changes proposed in this 

proposed rule have the potential to 

generate higher scores for a limited 

subset of health care providers and 

therefore a small negative impact on 

estimated savings for the model. First, 

we proposed two strata for the 

achievement and improvement 

benchmarking based on a 50 percent 

cutpoint for the proportion of attributed 

beneficiaries with dual eligibility status 

or receipt of the LIS. This proposed 

modification allowed participants to be 

compared to participants who serve 

ESRD patients with a similar 

socioeconomic status, essentially 

making the comparison groups fairer 

and potentially increasing the cost to 

Medicare. Second, the proposed Health 

Equity Incentive rewarded participants 

with 0.5 points to their improvement 

score who demonstrated a sufficiently 

significant improvement on the 

transplant rate among their attributed 

beneficiaries who are dual eligible or 

receive the LIS. 

Furthermore, we proposed to modify 

the transplant waitlist rate achievement 

and improvement benchmarks by 

incrementally increasing the 

benchmarks every two measurement 

periods by 10 percent relative to ESRD 

facilities and Managing Clinicians not 

selected for participation. Applying the 

preset benchmarks update method 

balanced out the negative impact to 

Medicare savings generated from the 

proposed stratification and the Health 

Equity Incentive, roughly preserving the 

overall savings level estimated at 

baseline for the model parameters 

previously finalized before the changes 

offered in this proposed rule. 

(7) Effects on Kidney Disease Patient 

Education Services and HD Training 

Add-Ons 

The changes in this proposed rule do 

not impact the findings reported for the 

effects of the ETC Model on the Kidney 

Disease Patient education services and 

HD training add-ons described in 

section VI.C.2.b(6) in the Specialty Care 

Models final rule (85 FR 61355). 

b. Effects on Medicare Beneficiaries 

The changes in this proposed rule 

could incentivize ESRD facilities and 

Managing Clinicians serving dual 

eligible or LIS recipient Medicare 

beneficiaries to potentially improve 

access to care for those beneficiaries. 

The changes could also marginally 

improve take-up of the in-center 

nocturnal dialysis treatment modality 

compared to how the model was 

finalized previously since these dialysis 

methods were not directly incentivized 

(that is, accounted for in the home 

dialysis rate and in-center self dialysis 

rate numerator) in the Specialty Care 

Models final rule. 

As previously noted in section 

VI.C.3.B of the Specialty Care Models 

final rule (85 FR 61357), we continue to 

anticipate that the ETC Model would 

have a negligible impact on the cost to 

beneficiaries receiving dialysis. Under 

current policy, Medicare FFS 

beneficiaries are generally responsible 

for 20 percent of the allowed charge for 

services furnished by providers and 

suppliers. This policy will remain the 

same under the ETC Model. However, 

we will waive certain requirements of 

title XVIII of the Act as necessary to test 

the PPA and HDPA under the ETC 

Model and to hold beneficiaries 

harmless from any effect of these 

payment adjustments on cost sharing. In 

addition, the Medicare beneficiary’s 

quality of life has the potential to 

improve if the beneficiary elects to have 

home dialysis as opposed to in-center 

dialysis. Studies have found that home 

dialysis patients experienced improved 

quality of life as a result of their ability 

to continue regular work schedules or 

life plans; as well as better overall, 

physical, and psychological health in 

comparison to other dialysis options. 

c. Alternatives Considered 

Throughout this proposed rule, we 

have identified our policies and 

alternatives that we have considered, 

and provided information as to the 

likely effects of these alternatives and 

the rationale for each of our policies. 

This proposed rule addresses a model 

specific to ESRD. It provides 

descriptions of the requirements that we 

would waive, identifies the performance 

metrics and payment adjustments 

proposed to be tested, and presents 

rationales for our proposals, and where 

relevant, alternatives that we 

considered. We carefully considered the 

alternatives to this proposed rule, 

including the degree that benchmark 

targets should be prospectively updated 

to provide greater transparency to ETC 

Participants while preserving the 

expectation for model net savings for 

the program. For context related to 

alternatives previously considered when 

establishing the ETC Model we refer 

readers to the Specialty Cares Model 

final rule (85 FR 61114) for more 

information on policy-related 

stakeholder comments, our responses to 

those comments, and statements of final 

policy preceding the limited 

modifications proposed here. 

C. Accounting Statement 

As required by OMB Circular A–4 

(available at https://www.whitehouse.

gov/sites/whitehouse.gov/files/omb/ 

circulars/A4/a-4.pdf), in Table 20, we 

have prepared an accounting statement 

showing the classification of the 

transfers and costs associated with the 

various provisions of this proposed rule. 


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Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 36429 

In accordance with the provisions of 

Executive Order 12866, this proposed 

rule was reviewed by the Office of 

Management and Budget. 

D. Regulatory Flexibility Act Analysis 

(RFA) 

The Regulatory Flexibility Act (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. Approximately 11 percent 

of ESRD dialysis facilities are 

considered small entities according to 

the Small Business Administration’s 

(SBA) size standards, which classifies 

small businesses as those dialysis 

facilities having total revenues of less 

than $41.5 million in any 1 year. 

Individuals and states are not included 

in the definitions of a small entity. For 

more information on SBA’s size 

standards, see the Small Business 

Administration’s website at http://

www.sba.gov/content/small-businesssize-standards (Kidney Dialysis Centers 

are listed as 621492 with a size standard 

of $41.5 million). 

When viewed as individual entities, 

as opposed to being a part of an LDO, 

there are approximately 643 (∼12 

percent of total number of ESRD 

facilities) ESRD facilities that provide 

fewer than 4,000 treatments per year. 

With a low volume payment 

adjustment, each facility generates 

revenue from dialysis treatments of 

∼$1.26 million per year per facility. This 

is shown in the Table 21. 


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TABLE 20: Accounting Statement: Classification of Estimated 

Transfers and Costs/Savings 

ESRD PPS and AKI (CY 2022) 

Category Transfers 

Annualized Monetized Transfers $110 million 

From Whom to Whom Federal government to ESRD providers 

Category Transfers 

Increased Beneficiary Co-insurance Payments $30 million 

From Whom to Whom Beneficiaries to ESRD providers 

ESRD QIP for PY 2024 

Category Transfers 

Annualized Monetized Transfers -$1 7 million 

From Whom to Whom Federal government to ESRD providers. 

ESRD QIP for PY 2025 

Category Transfers 

Annualized Monetized Transfers -$1 7 million 

From Whom to Whom Federal government to ESRD providers 

ETC Model for Jan 1, 2023 through June 30, 2027 

Impacts of Changes in the Proposed Rule 

Category Transfers 

Annualized Monetized Transfers -$1.27 million 

From Whom to Whom Federal government to ESRD facilities and 

Managing Clinicians 
36430 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

We do not believe ESRD facilities are 

operated by small government entities 

such as counties or towns with 

populations of 50,000 or less, and 

therefore, they are not enumerated or 

included in this estimated RFA analysis. 

Individuals and states are not included 

in the definition of a small entity. 

For purposes of the RFA, we estimate 

that approximately 11 percent of ESRD 

facilities are small entities as that term 

is used in the RFA (which includes 

small businesses, nonprofit 

organizations, and small governmental 

jurisdictions). This amount is based on 

the number of ESRD facilities shown in 

the ownership category in Table 9. 

Using the definitions in this ownership 

category, we consider 515 facilities that 

are independent and 378 facilities that 

are shown as hospital-based to be small 

entities. The ESRD facilities that are 

owned and operated by Large Dialysis 

Organizations (LDOs) and regional 

chains would have total revenues of 

more than $41.5 million in any year 

when the total revenues for all locations 

are combined for each business 

(individual LDO or regional chain), and 

are not, therefore, included as small 

entities. 

For the ESRD PPS updates proposed 

in this rule, a hospital-based ESRD 

facility (as defined by type of 

ownership, not by type of dialysis 

facility) is estimated to receive a 1.3 

percent increase in payments for CY 

2022. An independent facility (as 

defined by ownership type) is estimated 

to receive a 1.1 percent increase in 

payments for CY 2022. 

For AKI dialysis, we are unable to 

estimate whether patients would go to 

ESRD facilities, however, we have 

estimated there is a potential for $52 

million in payment for AKI dialysis 

treatments that could potentially be 

furnished in ESRD facilities. 

For ETC Model, this proposed rule 

includes as ETC Participants Managing 

Clinicians and ESRD facilities required 

to participate in the Model pursuant to 

§ 512.325(a). We assume for the 

purposes of the regulatory impact 

analysis that the great majority of 

Managing Clinicians are small entities 

and that the greater majority of ESRD 

facilities are not small entities. 

Throughout the proposed rule we 

describe how the adjustments to certain 

payments for dialysis services and 

dialysis-related services furnished to 

ESRD beneficiaries may affect Managing 

Clinicians and ESRD facilities 

participating in the ETC Model. The 

great majority of Managing Clinicians 

are small entities by meeting the SBA 

definition of a small business (having 

minimum revenues of less than $8 

million to $41.5 million in any 1 year, 

varying by type of provider and highest 

for hospitals) with a minimum 

threshold for small business size of 

$41.5 million (https://www.sba.gov/ 

document/support--table-size-standards 

http://www.sba.gov/content/smallbusinesssize-standards). The great 

majority of ESRD facilities are not small 

entities, as they are owned, partially or 

entirely by entities that do not meet the 

SBA definition of small entities. 

The HDPA in the ETC Model is a 

positive adjustment on payments for 

specified home dialysis and home 

dialysis-related services. The PPA in the 

ETC Model, which includes both 

positive and negative adjustments on 

payments for dialysis services and 

dialysis-related services, excludes 

aggregation groups with fewer than 132 

attributed beneficiary-months during 

the relevant year. 

The aggregation methodology groups 

ESRD facilities owned in whole or in 

part by the same dialysis organization 

within a Selected Geographic Area and 

Managing Clinicians billing under the 

same TIN within a Selected Geographic 

Area. This aggregation policy increases 

the number of beneficiary months, and 

thus statistical reliability, of the ETC 

Participant’s home dialysis and 

transplant rate for ESRD facilities that 

are owned in whole or in part by the 

same dialysis organization and for 

Managing Clinicians that share a TIN 

with other Managing Clinicians. 

Taken together, the low volume 

threshold exclusions and aggregation 

policies previously described, coupled 

with the fact that the ETC Model would 

affect Medicare payment only for select 

services furnished to Medicare FFS 

beneficiaries; we have determined that 

the provisions of the proposed rule 

would not have a significant impact on 

spending for a substantial number of 

small entities (defined as greater than 5 

percent impact). 

Therefore, the Secretary has 

determined that this proposed rule 

would not have a significant economic 

impact on a substantial number of small 

entities. The economic impact 

assessment is based on estimated 

Medicare payments (revenues) and 

HHS’s practice in interpreting the RFA 

is to consider effects economically 

‘‘significant’’ only if greater than 5 

percent of providers reach a threshold of 

3 to 5 percent or more of total revenue 

or total costs. We solicit comment on 

the RFA analysis provided. 

In addition, section 1102(b) of the Act 

requires us to prepare a RIA if a rule 

may have a significant impact on the 

operations of a substantial number of 

small rural hospitals. This analysis must 

conform to the provisions of section 603 


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TABLE 21: Revenue Table for Low Volume ESRD Facilities for CY 2022 ESRD PPS 

Proposed Rule 

ESRD Facility size #of low %of ~Individual ESRD facility ~Annual ~ Total annual 

based on # of dialysis volume total revenue per treatment total treatment revenue 

treatments ESRD number (including low volume treatment to all low volume 

Facilities of adjustment) revenue ESRD facilities 

per Table ESRD perESRD 

9 facilities facility 

based on 

3999 

treatments 

or less 

<4000 643 ~12% $311 $1.26M $800M 
Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 36431 

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. We do not believe this proposed 

rule would have a significant impact on 

operations of a substantial number of 

small rural hospitals because most 

dialysis facilities are freestanding. 

While there are 122 rural hospital-based 

dialysis facilities, we do not know how 

many of them are based at hospitals 

with fewer than 100 beds. However, 

overall, the 122 rural hospital-based 

dialysis facilities would experience an 

estimated 1.0 percent increase in 

payments. 

Therefore, the Secretary has 

determined that this proposed rule 

would not have a significant impact on 

the operations of a substantial number 

of small rural hospitals. 

E. Unfunded Mandates Reform Act 

Analysis (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 2021, that 

threshold is approximately $158 

million. This proposed rule does not 

mandate any requirements for state, 

local, or tribal governments in the 

aggregate, or by the private sector. 

Moreover, HHS interprets UMRA as 

applying only to unfunded mandates. 

We do not interpret Medicare payment 

rules as being unfunded mandates, but 

simply as conditions for the receipt of 

payments from the federal government 

for providing services that meet federal 

standards. This interpretation applies 

whether the facilities or providers are 

private, state, local, or tribal. 

F. Federalism 

Executive Order 13132 establishes 

certain requirements that an agency 

must meet when it promulgates a 

proposed rule (and subsequent final 

rule) that imposes substantial direct 

requirement costs on state and local 

governments, preempts state law, or 

otherwise has federalism implications. 

We have reviewed this proposed rule 

under the threshold criteria of Executive 

Order 13132, Federalism, and have 

determined that it would not have 

substantial direct effects on the rights, 

roles, and responsibilities of states, local 

or Tribal governments. 

G. Congressional Review Act 

These proposed rules are subject to 

the Congressional Review Act 

provisions of the Small Business 

Regulatory Enforcement Fairness Act of 

1996 (5 U.S.C. 801 et seq.) and has been 

transmitted to the Congress and the 

Comptroller General for review. 

X. Files Available to the Public via the 

Internet 

The Addenda for the annual ESRD 

PPS proposed and final rulemakings 

will no longer appear in the Federal 

Register. Instead, the Addenda will be 

available only through the internet and 

is posted on the CMS website at http:// 

www.cms.gov/ESRDPayment/PAY/ 

list.asp. In addition to the Addenda, 

limited data set files are available for 

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

Research-Statistics-Data-and-Systems/ 

Files-for-Order/LimitedDataSets/End

StageRenalDiseaseSystemFile.html. 

Readers who experience any problems 

accessing the Addenda or LDS files, 

should contact ESRDPayment@

cms.hhs.gov. 

Chiquita Brooks-LaSure, 

Administrator of the Centers for 

Medicare & Medicaid Services, 

approved this document on June 16, 

2021. 

List of Subjects 

42 CFR Part 413 

Diseases, Health facilities, Medicare, 

Puerto Rico, Reporting and 

recordkeeping requirements. 

42 CFR Part 512 

Administrative practice and 

procedure, Health facilities, Medicare, 

Reporting and recordkeeping 

requirements. 

For the reasons set forth in the 

preamble, the Centers for Medicare & 

Medicaid Services proposes to amend 

42 CFR chapter IV as follows: 

PART 413—PRINCIPLES OF 

REASONABLE COST 

REIMBURSEMENT; PAYMENT FOR 

END-STAGE RENAL DISEASE 

SERVICES; PROSPECTIVELY 

DETERMINED PAYMENT RATES FOR 

SKILLED NURSING FACILITIES; 

PAYMENT FOR ACUTE KIDNEY 

INJURY DIALYSIS 

■ 1. The authority citation for part 413 

continues to read as follows: 

Authority: 42 U.S.C. 1302, 1395d(d), 

1395f(b), 1395g, 1395l(a), (i), and (n), 

1395x(v), 1395hh, 1395rr, 1395tt, and 

1395ww. 

■ 2. Section 413.177 is amended by 

revising paragraph (a) introductory text 

to read as follows: 

§ 413.177 Quality incentive program 

payment. 

(a) With respect to renal dialysis 

services as defined under § 413.171, 

except for those renal dialysis services 

furnished during payment year 2022, in 

the case of an ESRD facility that does 

not earn enough points under the 

program described at § 413.178 to meet 

or exceed the minimum total 

performance score (as defined at 

§ 413.178(a)(8)) established by CMS for 

a payment year (as defined at 

§ 413.178(a)(10)), payments otherwise 

made to the facility under § 413.230 for 

renal dialysis services during the 

payment year, will be reduced by up to 

2 percent as follows: 

■ 3. Section 413.178 is amended by 

adding paragraph (h) to read as follows: 

§ 413.178 ESRD quality incentive program. 

* * * * * 

(h) Special rule for payment year 

2022. (1) CMS will calculate a measure 

rate for all measures specified by CMS 

under paragraph (c) of this section for 

the PY 2022 ESRD QIP but will not 

score facility performance on any of 

those measures or calculate a TPS for 

any facility under paragraph (e) of this 

section. 

(2) CMS will not establish a mTPS for 

PY 2022. 

PART 512—RADIATION ONCOLOGY 

MODEL AND END STAGE RENAL 

DISEASE TREATMENT CHOICES 

MODEL 

■ 4. The authority citation for part 512 

continues to read as follows: 

Authority: 42 U.S.C. 1302, 1315(a), and 

1395hh. 

■ 5. Section 512.160 is amended by 

adding paragraph (a)(9), and by revising 

paragraph (b)(6) as follows: 

§ 512.160 Remedial action. 

(a) * * * 

(9) For the ETC Model only, has 

misused or disclosed the beneficiaryidentifiable data in a manner that 

violates any applicable statutory or 

regulatory requirements or that is 

otherwise non-compliant with the 

provisions of the applicable data sharing 

agreement. 

(b) * * * 

(6) In the ETC Model only: 

(i) Terminate the ETC Participant 

from the ETC Model. 

(ii) Suspend or terminate the ability of 

the ETC Participant, pursuant to 

§ 512.397(c), to reduce or waive the 


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36432 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

coinsurance for kidney disease patient 

education services. 

■ 6. Section 512.310 is amended by 

adding the definitions of ‘‘Clinical 

staff’’, ‘‘Health Equity Incentive’’, 

‘‘Kidney disease patient education 

services coinsurance patient incentive’’, 

and ‘‘Qualified staff’’ in alphabetical 

order to read as follows: 

§ 512.310 Definitions. 

* * * * * 

Clinical staff means a licensed social 

worker or registered dietician/nutrition 

professional who furnishes services for 

which payment may be made under the 

physician fee schedule under the 

direction of and incident to the services 

of the Managing Clinician who is an 

ETC Participant. 

* * * * * 

ETC Large Dialysis Organization (ETC 

LDO) means a legal entity that owns, in 

whole or in part, 500 or more ESRD 

facilities. 

* * * * * 

Health Equity Incentive means the 

amount added to the ETC Participant’s 

improvement score, calculated as 

described in § 512.370(c)(1) of this 

chapter, if the ETC Participant’s 

aggregation group demonstrated 

sufficient improvement on the home 

dialysis rate or transplant rate for 

attributed beneficiaries who are dual 

eligible or Medicare Low Income 

Subsidy (LIS) recipients between the 

Benchmark Year and the MY. 

* * * * * 

Qualified staff means both clinical 

staff and any qualified person (as 

defined at § 410.48(a)) who is an ETC 

Participant. 

* * * * * 

■ 7. Section 512.360 is amended by 

revising paragraph (c)(2)(ii) introductory 

text and adding paragraph (c)(2)(iii) to 

read as follows: 

§ 512.360 Beneficiary population and 

attribution. 

* * * * * 

(c) * * * 

(2) * * * 

(ii) For MY1 and MY2, a Pre-emptive 

LDT Beneficiary who is not excluded 

based on the criteria in paragraph (b) of 

this section is attributed to the 

Managing Clinician with whom the 

beneficiary has had the most claims 

between the start of the MY and the 

month in which the beneficiary received 

the transplant for all months between 

the start of the MY and the month of the 

transplant. 

* * * * * 

(iii) For MY3 through MY10, a Preemptive LDT Beneficiary who is not 

excluded based on the criteria in 

paragraph (b) of this section is attributed 

to the Managing Clinician who 

submitted the most claims for services 

furnished to the beneficiary in the 365 

days preceding the date in which the 

beneficiary received the transplant. 

(A) If no Managing Clinician has had 

the most claims for a given Pre-emptive 

LDT Beneficiary such that multiple 

Managing Clinicians each had the same 

number of claims for that beneficiary in 

the 365 days preceding the date of the 

transplant, the Pre-emptive LDT 

Beneficiary is attributed to the 

Managing Clinician associated with the 

latest claim service date at the claim 

line through date during the 365 days 

preceding the date of the transplant. 

(B) If no Managing Clinician had the 

most claims for a given Pre-emptive 

LDT Beneficiary such that multiple 

Managing Clinicians each had the same 

number of claims for that beneficiary in 

the 365 days preceding the date of the 

transplant, and more than one of those 

Managing Clinicians had the latest 

claim service date at the claim line 

through date during the 365 days 

preceding the date of the transplant, the 

Pre-emptive LDT Beneficiary is 

randomly attributed to one of these 

Managing Clinicians. 

(C) The Pre-emptive LDT Beneficiary 

is considered eligible for attribution 

under this paragraph (c)(2)(iii) if the 

Pre-emptive LDT Beneficiary has at least 

1-eligible month during the 12-month 

period that includes the month of the 

transplant and the 11 months prior to 

the month of the transplant. An eligible 

month refers to a month during which 

the Pre-emptive LDT Beneficiary not 

does not meet exclusion criteria in 

paragraph (b) of this section. 

■ 8. Section 512.365 is amended by— 

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

(b)(2)(ii), and 

■ b. Revising paragraphs (c)(1)(i)(A), 

(c)(1)(ii)(A), (c)(2)(i)(A), and 

(c)(2)(ii)(A)(1) and (2). 

The revisions read as follows: 

§ 512.365 Performance assessment. 

* * * * * 

(b) * * * 

(1) * * * 

(ii) For MY1 and MY2, the numerator 

is the total number of home dialysis 

treatment beneficiary years plus one 

half the total number of self dialysis 

treatment beneficiary years for 

attributed ESRD Beneficiaries during the 

MY. For MY3 through MY10, the 

numerator for ESRD facilities owned in 

whole or in part by an ETC LDO is the 

total number of home dialysis treatment 

beneficiary years plus one half the total 

number of self dialysis treatment 

beneficiary years for attributed ESRD 

Beneficiaries during the MY. For MY3 

through MY10, the numerator for ESRD 

facilities not owned in whole or in part 

by an ETC LDO is the total number of 

home dialysis treatment beneficiary 

years, plus one half the total number of 

self dialysis treatment beneficiary years, 

plus one half the total number of 

nocturnal in center dialysis beneficiary 

years for attributed ESRD Beneficiaries 

during the MY. 

(A) Home dialysis treatment 

beneficiary years included in the 

numerator are composed of those 

months during which attributed ESRD 

Beneficiaries received maintenance 

dialysis at home, such that 1-beneficiary 

year is comprised of 12-beneficiary 

months. Months in which an attributed 

ESRD Beneficiary received maintenance 

dialysis at home are identified by claims 

with Type of Bill 072X and condition 

codes 74 or 76. 

(B) Self dialysis treatment beneficiary 

years included in the numerator are 

composed of those months during 

which attributed ESRD Beneficiaries 

received self dialysis in center, such 

that 1-beneficiary year is comprised of 

12-beneficiary months. Months in 

which an attributed ESRD Beneficiary 

received self dialysis are identified by 

claims with Type of Bill 072X and 

condition code 72. 

(C) Nocturnal in center dialysis 

beneficiary years included in the 

numerator are composed of those 

months during which attributed ESRD 

Beneficiaries received nocturnal in 

center dialysis, such that 1-beneficiary 

year is comprised of 12-beneficiary 

months. Months in which an attributed 

ESRD Beneficiary received nocturnal in 

center dialysis are identified by claims 

with Type of Bill 072X and modifier UJ. 

* * * * * 

(2) * * * 

(ii) For MY1 and MY2, the numerator 

is the total number of home dialysis 

treatment beneficiary years for 

attributed ESRD Beneficiaries during the 

MY plus one half the total number of 

self dialysis treatment beneficiary years. 

For MY3 through MY10, the numerator 

is the total number of home dialysis 

treatment beneficiary years, plus one 

half the total number of self dialysis 

treatment beneficiary years, plus one 

half the total number of nocturnal in 

center dialysis beneficiary years for 

attributed ESRD Beneficiaries during the 

MY. 

(A) Home dialysis treatment 

beneficiary years included in the 

numerator are composed of those 

months during which attributed ESRD 

Beneficiaries received maintenance 


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dialysis at home, such that 1-beneficiary 

year is comprised of 12-beneficiary 

months. Months in which an attributed 

ESRD Beneficiary received maintenance 

dialysis at home are identified by claims 

with CPT codes 90965 or 90966. 

(B) Self-dialysis treatment beneficiary 

years included in the numerator are 

composed of those months during 

which attributed ESRD Beneficiaries 

received self dialysis in center, such 

that 1-beneficiary year is comprised of 

12-beneficiary months. Months in 

which an attributed ESRD Beneficiary 

received self dialysis are identified by 

claims with Type of Bill 072X and 

condition code 72. 

(C) Nocturnal in center dialysis 

beneficiary years included in the 

numerator are composed of those 

months during which attributed ESRD 

Beneficiaries received nocturnal in 

center dialysis, such that 1-beneficiary 

year is comprised of 12-beneficiary 

months. Months in which an attributed 

ESRD Beneficiary received nocturnal in 

center dialysis are identified by claims 

with Type of Bill 072X and modifier UJ. 

* * * * * 

(c) * * * 

(1) * * * 

(i) * * * 

(A) The denominator is the total 

dialysis treatment beneficiary years for 

attributed ESRD Beneficiaries during the 

MY. Dialysis treatment beneficiary years 

included in the denominator are 

composed of those months during 

which an attributed ESRD beneficiary 

received maintenance dialysis at home 

or in an ESRD facility, such that 1- 

beneficiary year is comprised of 12- 

beneficiary months. For MY1 and MY2, 

months during which an attributed 

ESRD Beneficiary received maintenance 

dialysis are identified by claims with 

Type of Bill 072X, excluding claims for 

beneficiaries who were 75 years of age 

or older at any point during the month. 

For MY3 through MY10, months during 

which an attributed ESRD Beneficiary 

received maintenance dialysis are 

identified by claims with Type of Bill 

072X, excluding claims for beneficiaries 

who were 75 years of age or older at any 

point during the month, or had a vital 

solid organ cancer diagnosis and were 

receiving treatment with chemotherapy 

or radiation for vital solid organ cancer 

during the MY. 

(1) An attributed ESRD Beneficiary 

had a diagnosis of vital solid organ 

cancer in an MY if the beneficiary had 

any of the following diagnosis codes on 

any claim during the MY or the 6 

months prior to the start of the MY: 

C22.0–C22.9, C34.10–C34.12, C34.2, 

C34.30–C34.32, C34.80–C34.82, C34.90– 

C34.92, C38.0, C38.8, C46.50–C46.52, 

C64.1, C64.2, C64.2, C78.00–C78.02, 

C78.7, C79.00–C79.02, C7A.090, 

C7A.093, or C7B.02. 

(2) Months in which an attributed 

ESRD Beneficiary received treatment 

with chemotherapy or radiation for vital 

solid organ cancer are months during 

which the beneficiary had a claim with 

any of the following procedure codes: 

(i) CPT® 96401–96402, 96405–96406, 

96409, 96411, 96413, 96415–96417, 

96420, 96422–26423, 96425, 96440, 

96446, 96549, 77373, 77401–77402, 

77407, 77412, 77423, 77424–77425, 

77520, 77522–77523, 77525, 77761– 

77763, 77770–77772, 77778, 77789, 

77799, 79005, 79101, 79200, 79300, 

79403, 79440, 79445, 79999. 

(ii) ICD–10–PCS® DB020ZZ, 

DB021ZZ, DB022ZZ, DB023Z0, 

DB023ZZ, DB024ZZ, DB025ZZ, 

DB026ZZ, DB1297Z, DB1298Z, 

DB1299Z, DB129BZ, DB129CZ, 

DB129YZ, DB12B6Z, DB12B7Z, 

DB12B8Z, DB12B9Z, DB12BB1, 

DB12BBZ, DB12BCZ, DB12BYZ, 

DB22DZZ, DB22HZZ, DB22JZZ, 

DBY27ZZ, DBY28ZZ, DBY2FZZ, 

DBY2KZZ, DB070ZZ, DB071ZZ, 

DB072ZZ, DB073Z0, DB073ZZ, 

DB074ZZ, DB075ZZ, DB076ZZ, 

DB1797Z, DB1798Z, DB1799Z, 

DB179BZ, DB179CZ, DB179YZ, 

DB17B6Z, DB17B7Z, DB17B8Z, 

DB17B9Z, DB17BB1, DB17BBZ, 

DB17BCZ, DB17BYZ, DB27DZZ, 

DB27HZZ, DB27JZZ, DBY77ZZ, 

DBY78ZZ, DBY7FZZ, DBY7KZZ, 

DF000ZZ, DF001ZZ, DF002ZZ, 

DF003Z0, DF003ZZ, DF004ZZ, 

DF005ZZ, DF006ZZ, DF1097Z, 

DF1098Z, DF1099Z, DF109BZ, 

DF109CZ, DF109YZ, DF10B6Z, 

DF10B7Z, DF10B8Z, DF10B9Z, 

DF10BB1, DF10BBZ, DF10BCZ, 

DF10BYZ, DF0DZZ, DF20HZZ, 

DF20JZZ, DFY07ZZ, DFY08ZZ, 

DFY0CZZ, DFY0FZZ, DFY0KZZ, 

DT000ZZ, DT001ZZ, DT002ZZ, 

DT003Z0, DT003ZZ, DT004ZZ, 

DT005ZZ, DT006ZZ, DT1097Z, 

DT1098Z, DT1099Z, DT109BZ, 

DT109CZ, DT109YZ, DT10B6Z, 

DT10B7Z, DT10B8Z, DT10B9Z, 

DT10BB1, DT10BBZ, DT10BCZ, 

DT10BYZ, DT20DZZ, DT20HZZ, 

DT20JZZ, DTY07ZZ, DTY08ZZ, 

DTY0CZZ, DTY0FZZ, DW020ZZ, 

DW021ZZ, DW022ZZ, DW023Z0, 

DW023ZZ, DW024ZZ, DW025ZZ, 

DW026ZZ, DW1297Z, DW1298Z, 

DW1299Z, DW129BZ, DW129CZ, 

DW129YZ, DW12B6Z, DW12B7Z, 

DW12B8Z, DW12B9Z, DW12BB1, 

DW12BBZ, DW12BCZ, DW12BYZ, 

DW22DZZ, DW22HZZ, DW22JZZ, 

DWY27ZZ, DWY28ZZ, DWY2FZZ, 

DW030ZZ, DW031ZZ, DW032ZZ, 

DW033Z0, DW033ZZ, DW034ZZ, 

DW035ZZ, DW036ZZ, DW1397Z, 

DW1398Z, DW1399Z, DW139BZ, 

DW139CZ, DW139YZ, DW13B6Z, 

DW13B7Z, DW13B8Z, DW13B9Z, 

DW13BB1, DW13BBZ, DW13BCZ, 

DB13BYZ, DW23DZZ, DW23HZZ, 

DW23JZZ, DWY37ZZ, DWY38ZZ, 

DWY3FZZ, DW050ZZ, DW051ZZ, 

DW052ZZ, DW053Z0, DW053ZZ, 

DW054ZZ, DW055ZZ, DW056ZZ, 

DWY57ZZ, DWY58ZZ, DWY5FZZ, 

DWY5GDZ, DWY5GFZ, DWY5GGZ, 

DWY5GHZ, DWY5GYZ. 

* * * * * 

(ii) * * * 

(A) The denominator is the total 

dialysis treatment beneficiary years for 

attributed ESRD Beneficiaries during the 

MY. Dialysis treatment beneficiary years 

included in the denominator are 

composed of those months during 

which an attributed ESRD Beneficiary 

received maintenance dialysis at home 

or in an ESRD facility, such that 1- 

beneficiary year is comprised of 12- 

beneficiary months. For MY1 and MY2, 

months during which an attributed 

ESRD Beneficiary received maintenance 

dialysis are identified by claims with 

Type of Bill 072X, excluding claims for 

beneficiaries who were 75 years of age 

or older at any point during the month. 

For MY3 through MY10, months during 

which an attributed ESRD Beneficiary 

received maintenance dialysis are 

identified by claims with Type of Bill 

072X, excluding claims for beneficiaries 

who were 75 years of age or older at any 

point during the month, or had a vital 

solid organ cancer diagnosis and were 

receiving treatment with chemotherapy 

or radiation for vital solid organ cancer 

during the MY. Months in which an 

attributed ESRD Beneficiary had a 

diagnosis of vital solid organ cancer are 

identified as described in paragraph 

(c)(1)(i)(A)(1) of this section. Months in 

which an attributed ESRD Beneficiary 

received treatment with chemotherapy 

or radiation for vital solid organ cancer 

are identified as described in paragraph 

(c)(1)(i)(A)(2) of this section. 

* * * * * 

(2) * * * 

(i) * * * 

(A) The denominator is the total 

dialysis treatment beneficiary years for 

attributed ESRD Beneficiaries during the 

MY. Dialysis treatment beneficiary years 

included in the denominator are 

composed of those months during 

which an attributed ESRD Beneficiary 

received maintenance dialysis at home 

or in an ESRD facility, such that 1- 

beneficiary year is comprised of 12- 

beneficiary months. For MY1 and MY2, 

months during which an attributed 


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ESRD Beneficiary received maintenance 

dialysis are identified by claims with 

CPT codes 90957, 90958, 90959, 90960, 

90961, 90962, 90965, or 90966, 

excluding claims for beneficiaries who 

were 75 years of age or older at any 

point during the month. For MY3 

through MY10, months during which an 

attributed ESRD Beneficiary received 

maintenance dialysis are identified by 

claims with CPT codes 90957, 90958, 

90959, 90960, 90961, 90962, 90965, or 

90966, excluding claims for 

beneficiaries who were 75 years of age 

or older at any point during the month, 

or had a vital solid organ cancer 

diagnosis and were receiving treatment 

with chemotherapy or radiation for vital 

solid organ cancer during the MY. 

Months in which an attributed ESRD 

Beneficiary had a diagnosis of vital solid 

organ cancer are identified as described 

in paragraph (c)(1)(i)(A)(1) of this 

section. Months in which an attributed 

ESRD Beneficiary received treatment 

with chemotherapy or radiation for vital 

solid organ cancer are identified as 

described in paragraph (c)(1)(i)(A)(2) of 

this section. 

* * * * * 

(ii) * * * 

(A) * * * 

(1) Dialysis treatment beneficiary 

years included in the denominator are 

composed of those months during 

which an attributed ESRD Beneficiary 

received maintenance dialysis at home 

or in an ESRD facility, such that 1- 

beneficiary year is comprised of 12- 

beneficiary months. For MY1 and MY2, 

months during which an attributed 

ESRD Beneficiary received maintenance 

dialysis are identified by claims with 

CPT codes 90957, 90958, 90959, 90960, 

90961, 90962, 90965, or 90966, 

excluding claims for beneficiaries who 

were 75 years of age or older at any 

point during the month. For MY3 

through MY10, months during which an 

attributed ESRD Beneficiary received 

maintenance dialysis are identified by 

claims with CPT codes 90957, 90958, 

90959, 90960, 90961, 90962, 90965, or 

90966, excluding claims for 

beneficiaries who were 75 years of age 

or older at any point during the month, 

or had a vital solid organ cancer 

diagnosis and were receiving treatment 

with chemotherapy or radiation for vital 

solid organ cancer during the MY. 

Months in which an attributed ESRD 

Beneficiary had a vital solid organ 

cancer diagnosis are identified as 

described in paragraph (c)(1)(i)(A)(1) of 

this section. Months in which an 

attributed ESRD Beneficiary received 

treatment with chemotherapy or 

radiation for vital solid organ cancer are 

identified as described in paragraph 

(c)(1)(i)(A)(2) of this section. 

(2) MY1 and MY2, Pre-emptive LDT 

beneficiary years included in the 

denominator are composed of those 

months during which a Pre-emptive 

LDT Beneficiary is attributed to a 

Managing Clinician, from the beginning 

of the MY up to and including the 

month of the living donor transplant. 

For MY3 through MY10, Pre-emptive 

LDT beneficiary years included in the 

denominator are composed of those 

months during which a Pre-emptive 

LDT Beneficiary is attributed to a 

Managing Clinician, from the beginning 

of the MY up to and including the 

month of the living donor transplant, 

excluding beneficiaries who had a vital 

solid organ cancer diagnosis and were 

receiving treatment with chemotherapy 

or radiation for vital solid organ cancer 

during the MY. Months in which an 

attributed ESRD Beneficiary had a vital 

solid organ cancer diagnosis are 

identified as described in paragraph 

(c)(1)(i)(A)(1) of this section. Months in 

which an attributed ESRD Beneficiary 

received treatment with chemotherapy 

or radiation for vital solid organ cancer 

are identified as described in paragraph 

(c)(1)(i)(A)(2) of this section. Preemptive LDT Beneficiaries are identified 

using information about living donor 

transplants from the SRTR Database and 

Medicare claims data. 

* * * * * 

■ 9. Section 512.370 is amended by 

revising paragraphs (b), (c), and (d) to 

read as follows: 

§ 512.370 Benchmarking and scoring. 

* * * * * 

(b) Achievement scoring. CMS 

assesses ETC Participant performance at 

the aggregation group level on the home 

dialysis rate and transplant rate against 

achievement benchmarks constructed 

based on the home dialysis rate and 

transplant rate among aggregation 

groups of ESRD facilities and Managing 

Clinicians located in Comparison 

Geographic Areas during the Benchmark 

Year. Achievement benchmarks are 

calculated as described in paragraph 

(b)(1) of this section and, for MY3 

through MY10, are stratified as 

described in paragraph (b)(2) of this 

section. 

(1) Achievement benchmarks. CMS 

uses the following scoring methodology 

to assess an ETC Participant’s 

achievement score. 

TABLE 1 TO § 512.370(B)(1)—ETC MODEL SCHEDULE OF PPA ACHIEVMENT BENCHMARKS BY MEASUREMENT YEAR

MY1 and MY2 MY3 and MY4 MY5 and MY6 MY7 and MY8 MY9 and MY10 Points 

90th+ Percentile of benchmark rates for Comparison 

Geographic Areas during 

the Benchmark Year.

1.1 * (90th+ Percentile of 

benchmark rates for Comparison Geographic Areas 

during the Benchmark 

Year).

1.2 * (90th+ Percentile of 

benchmark rates for Comparison Geographic Areas 

during the Benchmark 

Year).

1.3 * (90th+ Percentile of 

benchmark rates for Comparison Geographic Areas 

during the Benchmark 

Year).

1.4 * (90th+ Percentile of 

benchmark rates for Comparison Geographic Areas 

during the Benchmark 

Year).

2 

75th+ Percentile of benchmark rates for Comparison 

Geographic Areas during 

the Benchmark Year.

1.1 * (75th+ Percentile of 

benchmark rates for Comparison Geographic Areas 

during the Benchmark 

Year).

1.2 * (75th+ Percentile of 

benchmark rates for Comparison Geographic Areas 

during the Benchmark 

Year).

1.3 * (75th+ Percentile of 

benchmark rates for Comparison Geographic Areas 

during the Benchmark 

Year).

1.4 * (75th+ Percentile of 

benchmark rates for Comparison Geographic Areas 

during the Benchmark 

Year).

1.5 

50th+ Percentile of benchmark rates for Comparison 

Geographic Areas during 

the Benchmark Year.

1.1 * (50th+ Percentile of 

benchmark rates for Comparison Geographic Areas 

during the Benchmark 

Year).

1.2 * (50th+ Percentile of 

benchmark rates for Comparison Geographic Areas 

during the Benchmark 

Year).

1.3 * (50th+ Percentile of 

benchmark rates for Comparison Geographic Areas 

during the Benchmark 

Year).

1.4 * (50th+ Percentile of 

benchmark rates for Comparison Geographic Areas 

during the Benchmark 

Year).

1 

30th+ Percentile of benchmark rates for Comparison 

Geographic Areas during 

the Benchmark Year.

1.1 * (30th+ Percentile of 

benchmark rates for Comparison Geographic Areas 

during the Benchmark 

Year).

1.2 * (30th+ Percentile of 

benchmark rates for Comparison Geographic Areas 

during the Benchmark 

Year).

1.3 * (30th+ Percentile of 

benchmark rates for Comparison Geographic Areas 

during the Benchmark 

Year).

1.4 * (30th+ Percentile of 

benchmark rates for Comparison Geographic Areas 

during the Benchmark 

Year).

0.5 


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TABLE 1 TO § 512.370(B)(1)—ETC MODEL SCHEDULE OF PPA ACHIEVMENT BENCHMARKS BY MEASUREMENT YEAR— 

Continued 

MY1 and MY2 MY3 and MY4 MY5 and MY6 MY7 and MY8 MY9 and MY10 Points 

<30th Percentile of benchmark rates for Comparison 

Geographic Areas during 

the Benchmark Year.

1.1 * (<30th Percentile of 

benchmark rates for Comparison Geographic Areas 

during the Benchmark 

Year).

1.2 * (<30th Percentile of 

benchmark rates for Comparison Geographic Areas 

during the Benchmark 

Year).

1.3 * (<30th Percentile of 

benchmark rates for Comparison Geographic Areas 

during the Benchmark 

Year).

1.4 * (<30th Percentile of 

benchmark rates for Comparison Geographic Areas 

during the Benchmark 

Year).

0 

(2) Stratifying achievement 

benchmarks. For MY3 through MY10, 

CMS stratifies achievement benchmarks 

based on the proportion of beneficiary 

years attributed to the aggregation group 

for which attributed beneficiaries are 

dual eligible or LIS recipients during the 

MY. An ESRD Beneficiary or Preemptive LDT Beneficiary is considered 

to be dual eligible or an LIS recipient for 

a given month if at any point during the 

month the beneficiary was dual eligible 

or an LIS recipient based on Medicare 

administrative data. CMS stratifies the 

achievement benchmarks into the 

following two strata: 

(i) Stratum 1: 50 percent or more of 

attributed beneficiary years during the 

MY are for beneficiaries who are dual 

eligible or LIS recipients. 

(ii) Stratum 2: Less than 50 percent of 

attributed beneficiary years during the 

MY are for beneficiaries who are dual 

eligible or LIS recipients. 

(c) Improvement scoring. CMS 

assesses ETC Participant improvement 

on the home dialysis rate and transplant 

rate against benchmarks constructed 

based on the ETC Participant’s 

aggregation group’s historical 

performance on the home dialysis rate 

and transplant rate during the 

Benchmark Year to calculate the ETC 

Participant’s improvement score, as 

specified in paragraph (c)(1) of this 

section. For MY3 through MY10, CMS 

assesses ETC Participant improvement 

on the home dialysis rate and transplant 

rate for ESRD Beneficiaries and, if 

applicable, Pre-emptive LDT 

Beneficiaries, who are dual eligible or 

LIS recipients to determine whether to 

add the Health Equity Incentive to the 

ETC Participant’s improvement score, as 

specified in paragraph (c)(2) of this 

section. 

(1) Improvement score calculation. 

CMS uses the following scoring 

methodology to assess an ETC 

Participant’s improvement score. 

(i) Greater than 10 percent 

improvement relative to the Benchmark 

Year rate: 1.5 points 

(ii) Greater than 5 percent 

improvement relative to the Benchmark 

Year rate: 1 point 

(iii) Greater than 0 percent 

improvement relative to the Benchmark 

Year rate: 0.5 points 

(iv) Less than or equal to the 

Benchmark Year rate: 0 points 

(v) For MY3 through MY10, when 

calculating improvement benchmarks 

constructed based on the ETC 

Participant’s aggregation group’s 

historical performance on the home 

dialysis rate and transplant rate during 

the Benchmark Year, CMS adds one 

beneficiary month to the numerator of 

the home dialysis rate and adds one 

beneficiary month to the numerator of 

the transplant rate, such that the 

Benchmark Year rates cannot be equal 

to zero. 

(2) Health Equity Incentive. CMS 

calculates the ETC Participant’s 

aggregation group’s home dialysis rate 

and transplant rate as specified in 

§§ 512.365(b) and 512.365(c), 

respectively, using only attributed 

beneficiary years comprised of months 

during the MY in which ESRD 

Beneficiaries and, if applicable, Preemptive LDT Beneficiaries, are dual 

eligible or LIS recipients. CMS also 

calculates the threshold for earning the 

Health Equity Incentive based on the 

ETC Participant’s aggregation group’s 

historical performance on the home 

dialysis rate and transplant rate during 

the Benchmark Year, using only 

attributed beneficiary years comprised 

of months during the Benchmark Year 

in which ESRD Beneficiaries and, if 

applicable, Pre-emptive LDT 

Beneficiaries, are dual eligible or LIS 

recipients. An ESRD Beneficiary or Preemptive LDT Beneficiary is considered 

to be dual eligible or an LIS recipient for 

a given month if at any point during the 

month the beneficiary was dual eligible 

or an LIS recipient. CMS determines 

whether a beneficiary was dual eligible 

or an LIS recipient based on Medicare 

administrative data. 

(i) The ETC Participant earns the 

Health Equity Incentive for the home 

dialysis rate improvement score if the 

home dialysis rate for the MY, 

calculated as specified in paragraph 

(c)(2) of this section, is at least 5- 

percentage points higher than the home 

dialysis rate for the Benchmark Year, 

calculated as specified in paragraph 

(c)(2) of this section. If the ETC 

Participant earns the Health Equity 

Incentive for the home dialysis rate 

improvement score, CMS adds 0.5 

points to the ETC Participant’s home 

dialysis rate improvement score, 

calculated as specified in paragraph 

(c)(1) of this section, unless the ETC 

Participant is ineligible to receive the 

Home Equity Incentive as specified in 

paragraph (c)(2)(iii) of this section. 

(ii) The ETC Participant earns the 

Health Equity Incentive for the 

transplant rate improvement score if the 

home dialysis rate for the MY, 

calculated as specified in paragraph 

(c)(2) of this section, is at least 5- 

percentage points higher than the 

transplant rate for the Benchmark Year, 

calculated as specified in paragraph 

(c)(2) of this section. If the ETC 

Participant earns the Health Equity 

Incentive for the transplant rate 

improvement score, CMS adds 0.5 

points to the ETC Participant’s 

transplant rate improvement score, 

calculated as specified in paragraph 

(c)(1) of this section, unless the ETC 

Participant is ineligible to receive the 

Home Equity Incentive as specified in 

paragraph (c)(2)(iii) of this section. 

(iii) An ETC Participant in an 

aggregation group with fewer than 11- 

attributed beneficiary years comprised 

of months in which ESRD Beneficiaries 

and, if applicable, Pre-emptive LDT 

Beneficiaries, are dual eligible or LIS 

recipients, during either the Benchmark 

Year or the MY is ineligible to earn the 

Health Equity Incentive. 

(d) Modality Performance Score. (1) 

For MY1 and MY2, CMS calculates the 

ETC Participant’s MPS as the higher of 

ETC Participant’s achievement score or 

improvement score for the home 

dialysis rate, together with the higher of 

the ETC Participant’s achievement score 

or improvement score for the transplant 

rate, weighted such that the ETC 

Participant’s score for the home dialysis 

rate constitutes 2⁄3 of the MPS and the 

ETC Participant’s score for the 

transplant rate constitutes 1⁄3 of the 

MPS. 

CMS uses the following formula to 

calculate the ETC Participant’s MPS for 

MY1 and MY2: 

Modality Performance Score 


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36436 Federal Register / Vol. 86, No. 129 / Friday, July 9, 2021 / Proposed Rules 

= 2 × (Higher of the home dialysis 

achievement or improvement score) 

+ (Higher of the transplant achievement 

or improvement score) 

(2) For MY3 through MY10, CMS 

calculates the ETC Participant’s MPS as 

the higher of the ETC Participant’s 

achievement score for the home dialysis 

rate or the sum of the ETC Participant’s 

improvement score for the home 

dialysis rate calculated as specified in 

paragraph (c)(1) of this section and, if 

applicable, the Health Equity Incentive, 

calculated as described in paragraph 

(c)(2)(i) of this section, together with the 

higher of the ETC Participant’s 

achievement score for the transplant 

rate or the sum of the ETC Participant’s 

improvement score for the transplant 

rate calculated as specified in paragraph 

(c)(1) of this section and, if applicable, 

the Heath Equity Incentive, calculated 

as described in paragraph (c)(2)(ii) of 

this section, weighted such that the ETC 

Participant’s score for the home dialysis 

rate constitutes 2⁄3 of the MPS and the 

ETC Participant’s score for the 

transplant rate constitutes 1⁄3 of the 

MPS. 

CMS uses the following formula to 

calculate the ETC Participant’s MPS for 

MY3 through MY10: 

Modality Performance Score 

= 2 

× (Higher of the home dialysis 

achievement or (home dialysis 

improvement score + Health Equity 

Bonus †)) 

+ (Higher of the transplant achievement 

or (transplant improvement score 

+ Health Equity Bonus †)) 

† The Health Equity Incentive is applied 

to the home dialysis improvement 

score or transplant improvement 

score only if earned by the ETC 

Participant. 

■ 10. Section 512.390 is amended by 

revising the section heading, 

redesignating paragraph (b) as (c) and 

adding new paragraph (b) to read as 

follows: 

§ 512.390 Notification, data sharing, and 

targeted review. 

* * * * * 

(b) Data sharing with ETC 

Participants. CMS shares certain 

beneficiary-identifiable data as 

described in paragraph (b)(1) of this 

section and certain aggregate data as 

described in paragraph (b)(2) of this 

section with ETC Participants regarding 

their attributed beneficiaries and 

performance under the ETC Model. 

(1) Beneficiary-identifiable data. CMS 

shares beneficiary-identifiable data with 

ETC Participants as follows: 

(i) CMS will make available certain 

beneficiary-identifiable data for retrieval 

by ETC Participants no later than one 

month before the start of each PPA 

Period, in a form and manner specified 

by CMS. ETC Participants may retrieve 

this data at any point during the 

relevant PPA Period. 

(ii) This beneficiary-identifiable data 

includes, when available, the following 

information for each PPA Period: 

(A) The ETC Participant’s attributed 

beneficiaries’ names, Medicare 

Beneficiary Identifiers, dates of birth, 

dual eligible status, and LIS recipient 

status. 

(B) Data regarding the ETC 

Participant’s performance under the 

ETC Model, including, for each 

attributed beneficiary, as applicable: 

The number of months the beneficiary 

was attributed to the ETC Participant, 

home dialysis months, self-dialysis 

months, nocturnal in-center dialysis 

months, transplant waitlist months, and 

months following a living donor 

transplant. 

(iii) CMS shares this beneficiaryidentifiable data on the condition that 

the ETC Participants observe all relevant 

statutory and regulatory provisions 

regarding the appropriate use of data 

and the confidentiality and privacy of 

individually identifiable health 

information as would apply to a covered 

entity under the Health Insurance 

Portability and Accountability Act of 

1996 (HIPAA) regulations, and comply 

with the terms of the data sharing 

agreement described in paragraph 

(b)(1)(iv) of this section. 

(iv) Data sharing agreement. If an ETC 

Participant wishes to retrieve the 

beneficiary-identifiable data specified in 

paragraph (b)(1)(ii) of this section, the 

ETC Participant must complete and 

submit, on at least an annual basis, a 

signed data sharing agreement, to be 

provided in a form and manner 

specified by CMS, under which the ETC 

Participant agrees: 

(A) To comply with the requirements 

for use and disclosure of this 

beneficiary-identifiable data that are 

imposed on covered entities by the 

HIPAA regulations and the 

requirements of the ETC Model set forth 

in this part. 

(B) To comply with additional 

privacy, security, breach notification, 

and data retention requirements 

specified by CMS in the data sharing 

agreement. 

(C) To contractually bind each 

downstream recipient of the beneficiaryidentifable data that is a business 

associate of the ETC Participant or 

performs a similar function for the ETC 

Participant, to the same terms and 

conditions to which the ETC Participant 

is itself bound in its data sharing 

agreement with CMS as a condition of 

the downstream recipient’s receipt of 

the beneficiary-identifiable data 

retrieved by the ETC Participant under 

the ETC Model. 

(D) That if the ETC Participant 

misuses or discloses the beneficiaryidentifiable data in a manner that 

violates any applicable statutory or 

regulatory requirements or that is 

otherwise non-compliant with the 

provisions of the data sharing 

agreement, the ETC Participant will no 

longer be eligible to retrieve beneficiaryidentifiable data under paragraph 

(b)(1)(i) of this section and may be 

subject to additional sanctions and 

penalties available under the law. 

(2) Aggregate data. CMS shares 

aggregate performance data with ETC 

Participants as follows: 

(i) CMS will make available certain 

aggregate data for retrieval by the ETC 

Participant, in a form and manner to be 

specified by CMS, no later than one 

month before each PPA Period. 

(ii) This aggregate data includes, 

when available, the following 

information for each PPA Period, deidentified in accordance with 45 CFR 

164.514(b): 

(A) The ETC Participant’s 

performance scores on the home 

dialysis rate, transplant waitlist rate, 

living donor transplant rate, and the 

Health Equity Incentive. 

(B) The ETC Participant’s aggregation 

group’s scores on the home dialysis rate, 

transplant waitlist rate, and living donor 

transplant rate, and the Health Equity 

Incentive. 

(C) Information on how the ETC 

Participant’s and ETC Participant’s 

aggregation group’s scores relate to the 

achievement benchmark and 

improvement benchmark. 

(D) The ETC Participant’s MPS and 

PPA for the corresponding PPA Period. 

* * * * * 

■ 11. Section 512.397 is amended by 

revising the section heading and 

paragraph (b) and adding paragraph (c) 

to read as follows: 

§ 512.397 ETC Model Medicare program 

waivers and additional flexibilities. 

* * * * * 

(b) CMS waives the following 

requirements of title XVIII of the Act 

solely for purposes of testing the ETC 

Model: 

(1) CMS waives the requirement 

under section 1861(ggg)(2)(A)(i) of the 

Act and § 410.48(a) of this chapter that 

only doctors, physician assistants, nurse 

practitioners, and clinical nurse 

specialists can furnish kidney disease 

patient education services to allow 

kidney disease patient education 


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services to be provided by clinical staff 

(as defined at § 512.310) under the 

direction of and incident to the services 

of the Managing Clinician who is an 

ETC Participant. The kidney disease 

patient education services may be 

furnished only by qualified staff (as 

defined at § 512.310). 

(2) CMS waives the requirement that 

kidney disease patient education 

services are covered only for Stage IV 

chronic kidney disease (CKD) patients 

under section 1861(ggg)(1)(A) of the Act 

and § 410.48(b)(1) of this chapter to 

permit beneficiaries diagnosed with 

CKD Stage V or within the first 6 

months of starting dialysis to receive 

kidney disease patient education 

services. 

(3) CMS waives the requirement that 

the content of kidney disease patient 

education services include the 

management of co-morbidities, 

including for the purpose of delaying 

the need for dialysis, under 

§ 410.48(d)(1) of this chapter when such 

services are furnished to beneficiaries 

with CKD Stage V or ESRD, unless such 

content is relevant for the beneficiary. 

(4) CMS waives the requirement that 

an outcomes assessment designed to 

measure beneficiary knowledge about 

CKD and its treatment be performed as 

part of a kidney disease patient 

education service under 

§ 410.48(d)(5)(iii) of this chapter, 

provided that such outcomes 

assessment is performed by qualified 

staff within one month of the final 

kidney disease patient education 

service. 

(5) Beginning January 1, 2022, CMS 

waives the geographic and site of 

service originating site requirements in 

sections 1834(m)(4)(B) and 

1834(m)(4)(C) of the Act and 

§ 410.78(b)(3) and (4) of this chapter for 

purposes of kidney disease patient 

education services furnished by 

qualified staff via telehealth in 

accordance with this section, regardless 

of the location of the beneficiary or 

qualified staff. Beginning January 1, 

2022, CMS also waives the requirement 

in section 1834(m)(2)(B) of the Act and 

§ 414.65(b) of this chapter that CMS pay 

a facility fee to the originating site with 

respect to telehealth services furnished 

to a beneficiary in accordance with this 

section at an originating site that is not 

one of the locations specified in 

§ 410.78(b)(3). 

(c)(1) Beginning January 1, 2022, an 

ETC Participant may reduce or waive 

the 20 percent coinsurance requirement 

under section 1833 of the Act if all of 

the following conditions are satisfied: 

(i) The individual or entity that 

furnished the kidney disease patient 

education services is qualified staff. 

(ii) The kidney disease patient 

education services were furnished to a 

beneficiary described in § 410.48(b) or 

§ 512.397(b)(2) who did not have 

secondary insurance on the date the 

services were furnished. 

(iii) The kidney disease patient 

education services were furnished in 

compliance with the applicable 

provisions of § 410.48 and § 512.397(b). 

(2) The ETC Participant must 

maintain and provide the government 

with access to records of the following 

information in accordance with 

§ 512.135(b) and (c) of this part: 

(i) The identity of the qualified staff 

who furnished the kidney disease 

patient education services for which the 

coinsurance was reduced or waived and 

the date such services were furnished. 

(ii) The identity of the beneficiary 

who received the kidney disease patient 

education services for which the 

coinsurance was reduced or waived. 

(iii) Evidence that the beneficiary who 

received the kidney disease patient 

education services coinsurance waiver 

was eligible to receive the kidney 

disease patient education services under 

the ETC Model and did not have 

secondary insurance. 

(iv) The amount of the kidney disease 

patient education coinsurance reduction 

or waiver provided by the ETC 

Participant. 

(3) The Federal anti-kickback statute 

safe harbor for CMS-sponsored model 

patient incentives (42 CFR 

1001.952(ii)(2)) is available to protect 

the kidney disease patient education 

coinsurance waivers that satisfy the 

requirements of such safe harbor and 

paragraph (c)(1) of this section. 

Xavier Becerra, 

Secretary, Department of Health and Human 

Services. 

[FR Doc. 2021–14250 Filed 7–1–21; 4:15 pm] 

BILLING CODE 4120–01–P 


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