[Federal Register Volume 86, Number 129 (Friday, July 9, 2021)]
[Proposed Rules]
[Pages 36322-36437]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2021-14250]



[[Page 36321]]

Vol. 86

Friday,

No. 129

July 9, 2021

Part II





 Department of Health and Human Services





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 Centers for Medicare & Medicaid Services





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42 CFR Parts 413 and 512





 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; Proposed Rule

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

[[Page 36322]]


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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.

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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: 
    <a href="/cdn-cgi/l/email-protection#c78294958397a6beaaa2a9b387a4aab4e9afafb4e9a0a8b1"><span class="__cf_email__" data-cfemail="10554342544071697d757e6450737d633e7878633e777f66">[email protected]</span></a>, for issues related to the ESRD PPS and 
coverage and payment for renal dialysis services furnished to 
individuals with AKI.
    <a href="/cdn-cgi/l/email-protection#0e4b5d5c4a4f7e7e62676d6f7a6761607d4e6d637d2066667d20696178"><span class="__cf_email__" data-cfemail="2c697f7e686d5c5c40454f4d584543425f6c4f415f0244445f024b435a">[email protected]</span></a>, for issues related to the 
Transitional Add-On Payment Adjustment for New and Innovative Equipment 
and Supplies (TPNIES).
    Delia Houseal, (410) 786-2724, for issues related to the ESRD QIP.
    <a href="/cdn-cgi/l/email-protection#6d28392e402e2020242d0e001e4305051e430a021b"><span class="__cf_email__" data-cfemail="064352452b454b4b4f46656b75286e6e7528616970">[email protected]</span></a>, 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[supreg] codes and descriptions to refer 
to a variety of services. We note that CPT[supreg] codes and 
descriptions are copyright 2020 American Medical Association (AMA). All 
Rights Reserved. CPT[supreg] 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 dialysis-

[[Page 36324]]

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
    <bullet> 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 budget-neutrality 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 x .999546) x 1.010 = $255.55).
    <bullet> 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.
    <bullet> 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.
    <bullet> 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 x 1.010 = $9.41).
    <bullet> 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 In-Center 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[supreg]) 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.
    <bullet> 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.
    <bullet> Home Dialysis Rate Calculation: To incentivize additional 
alternative renal replacement modalities under the ETC Model, we 
propose adding nocturnal in-center 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.
    <bullet> 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

[[Page 36325]]

treatment with chemotherapy or radiation for, vital solid organ cancers 
from the calculation of the transplant rate.
    <bullet> 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 dually-eligible 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.
    <bullet> 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.
    <bullet> 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.
    <bullet> 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 Sec.  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.
    <bullet> 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

[[Page 36326]]

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 ESRD-related 
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 oral-only 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 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. We have 
codified our definitions of renal dialysis services at Sec.  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) 
(Sec.  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 (Sec.  413.232). The second adjustment 
reflects differences in area wage levels developed from core-based 
statistical areas (CBSAs) (Sec.  413.231). The third payment adjustment 
accounts for ESRD facilities furnishing renal dialysis services in a 
rural area (Sec.  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 (Sec.  413.235(c)); (2) an 
additional payment for high cost outliers due to unusual variations in 
the type or amount of medically necessary care (Sec.  413.237); (3) a 
transitional drug add-on payment adjustment (TDAPA) for certain new 
renal dialysis drugs and biological products (Sec.  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 (Sec.  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

[[Page 36327]]

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 low-volume 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 Labor-Related 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-to-date MFP projection methodology 
is available on the CMS website at https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/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 Sec.  
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

[[Page 36328]]

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\
---------------------------------------------------------------------------

    \1\ We 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.
---------------------------------------------------------------------------

    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 labor-related 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-Renal-Disease-ESRD-Payment-Regulations-and-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-Service-Payment/ESRDpayment/End-Stage-Renal-Disease-ESRD-Payment-Regulations-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 Sec.  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 Sec.  
413.236(a)(2)), that receive the transitional add-on payment adjustment 
as specified in Sec.  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.
---------------------------------------------------------------------------

    \2\ Transmittal 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.
---------------------------------------------------------------------------

    Under Sec.  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)

[[Page 36329]]

plus the fixed-dollar loss (FDL) amount. In accordance with Sec.  
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 Sec.  
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.
[GRAPHIC] [TIFF OMITTED] TP09JY21.000

    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

[[Page 36330]]

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 Sec.  
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 Sec.  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 Sec. Sec.  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 Sec.  413.230, the per-treatment 
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 budget-neutrality 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 budget-neutrality 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 x .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 x 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 
productivity-adjusted 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 Sec.  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

[[Page 36331]]

years. Section Sec.  413.236(f)(3)(v) states that effective January 1, 
2022, CMS will annually update the amount determined in paragraph 
(f)(3)(iv) of Sec.  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 x 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 add-on 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 
Sec.  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 
Sec.  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 Sec.  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 Sec.  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 Sec.  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:
    <bullet> The new renal dialysis equipment or supply offers a 
treatment option for a patient population unresponsive to, or 
ineligible for, currently available treatments; or
    <bullet> 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
    <bullet> 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,
    <bullet> 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 Sec.  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

[[Page 36332]]

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 Sec.  
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 Sec.  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 Sec.  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 Sec.  
413.237.
    Regarding the basis of payment for the TPNIES, in the CY 2020 ESRD 
PPS final rule, we finalized at Sec.  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 
Sec.  413.236. First, we revised the definition of new at Sec.  
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 Sec.  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 
capital-related 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 capital-related 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 capital-related 
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 Sec.  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 capital-related assets that are home dialysis machines when 
used in the home for a single patient and that meet the other 
eligibility criteria in Sec.  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 Sec.  413.236(b) and announce the results in the 
Federal Register as part of our annual updates and changes to the ESRD 
PPS. Per Sec.  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 Sec.  413.236(b)(2).
    In the CY 2021 ESRD PPS final rule, at Sec.  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 Sec.  413.236(d) to reflect that we would pay 65 percent 
of the pre-adjusted 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 Sec.  413.236(d)(2) to reflect that following payment of 
the TPNIES, the ESRD PPS base rate will not be

[[Page 36333]]

modified and the new and innovative renal dialysis equipment or supply 
will be an eligible outlier service as provided in Sec.  413.237, 
except a capital-related asset that is a home dialysis machine will not 
be an eligible outlier service as provided in Sec.  413.237. In 
summary, under the current eligibility requirements in Sec.  
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 Sec.  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 Sec.  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.
---------------------------------------------------------------------------

    \3\ Medicare Benefits Policy Manual (Pub. L. 100-102), available 
at: https://www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/Downloads/bp102c11.pdf.
---------------------------------------------------------------------------

a. Tablo[supreg] System
    Outset Medical, Inc. submitted an application for the TPNIES for 
the Tablo[supreg] System (Tablo[supreg]) for CY 2022. According to the 
applicant, the technology is an HD machine that has been designed for 
patient-driven self-care 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[supreg] System is 
comprised of (1) the Tablo[supreg] 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[supreg] 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[supreg] System's unique 
features combine to provide a significantly differentiated HD solution 
with many benefits. First, the applicant stated that the Tablo[supreg] 
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[supreg] System can 
accommodate treatments at home allowing for flexibility in treatment 
frequencies, durations, and flow rates. Per the applicant, the 
Tablo[supreg] 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[supreg] 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[supreg] Cartridge with user-friendly pre-strung blood, saline, 
and infusion tubing and a series of sensor-receptors 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[supreg] 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.<SUP>5 6</SUP> 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.<SUP>7 8</SUP>
---------------------------------------------------------------------------

    \4\ United 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.
    \5\ Seshasai, 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.
    \6\ Weinhandl, 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.
    \7\ Seshasai, 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.
    \8\ Chan, Christopher T. et al. (2018). Exploring Barriers and 
Potential Solutions in Home Dialysis: An NKF-KDOQI Conference 
Outcomes Report American Journal of Kidney Diseases, Volume 73, 
Issue 3, 363-371.

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[[Page 36334]]

    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[supreg] System was designed to address many system-related 
barriers that result in patients resigning themselves to in-center care 
and/or stopping home modalities due to the burden of self-managed 
therapy.
---------------------------------------------------------------------------

    \9\ Erickson, 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.
---------------------------------------------------------------------------

    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\
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    \10\ Blake, 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.
---------------------------------------------------------------------------

    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\
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    \11\ Ibid.
    \12\ Mukhopadhyay, 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.
    \13\ United 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.
    \14\ Canada 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.
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    The applicant asserted that that the Tablo[supreg] System presents 
a significant clinical improvement over NxStage[supreg] System One 
(NxStage[supreg]), 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[supreg] is the only other mobile HD machine that is approved 
for home use.
(1) Renal Dialysis Service Criterion (Sec.  413.236(b)(1))
    With respect to the first TPNIES eligibility criterion under Sec.  
413.236(b)(1), whether the item has been designated by CMS as a renal 
dialysis service under Sec.  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 Sec.  413.171.
(2) Newness Criterion (Sec.  413.236(b)(2))
    With respect to the second TPNIES eligibility criterion under Sec.  
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[supreg] System received FDA marketing 
authorization for home use on March 31, 2020. Therefore, the 
Tablo[supreg] 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[supreg] Cartridge was reviewed separately from the Tablo[supreg] 
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 (Sec.  413.236(b)(3))
    With respect to the third TPNIES eligibility criterion under Sec.  
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[supreg] System became available for home use on April 1, 2020. 
Therefore, the Tablo[supreg] System is commercially available.
(4) HCPCS Level II Application Criterion (Sec.  413.236(b)(4))
    With respect to the fourth TPNIES eligibility criterion under Sec.  
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 (Sec. Sec.  413.236(b)(5) and 412.87(b)(1))
    With respect to the fifth TPNIES eligibility criterion under Sec.  
413.236(b)(5), that the item is innovative, meaning it meets the SCI 
criteria specified in Sec.  412.87(b)(1), the applicant claimed that 
the Tablo[supreg] System significantly improves clinical outcomes 
relative to the current standard of care for home HD services, which it 
identified as the incumbent NxStage[supreg] 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

[[Page 36335]]

quality of life. The applicant supported these claims with the 
Tablo[supreg] Investigational Device Exemption (IDE) Study \15\ and 
secondary support from four papers <SUP>16 17 18 19</SUP> and two 
posters.<SUP>20 21</SUP> The applicant also provided comparison data 
from three studies directly related to the incumbent 
<SUP>22 23 24</SUP> and an additional study that, based on the 
timeframe of the study, likely involved participants undergoing 
treatment with NxStage[supreg] although the article does not directly 
reference the incumbent.\25\
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    \15\ Clinicaltrials.gov website. https://www.clinicaltrials.gov/ct2/show/NCT02460263. Last Updated July 1, 2020. https://www.clinicaltrials.gov/ProvidedDocs/63/NCT02460263/Prot_000.pdf.
    \16\ Chertow, G.M., Alvarez, L., Plumb, T.J., Prichard, S.S., & 
Aragon, M. (2020). Patient-reported outcomes from the 
investigational device exemption study of the Tablo hemodialysis 
system. Hemodialysis International, 24(4), 480-486.
    \17\ Leypoldt, 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.
    \18\ Safety 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.
    \19\ Plumb, 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).
    \20\ Alvarez, 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.
    \21\ Chahal, 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).
    \23\ Finkelstein, F.O., et al. (2012). At-home short daily 
hemodialysis improves the long-term health-related quality of life. 
Kidney international, 82(5), 561-569.
    \24\ Weinhandl, 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.
    \25\ Suri, R.S., Li, L., & Nesrallah, G.E. (2015). The risk of 
hospitalization and modality failure with home dialysis. Kidney 
international, 88(2), 360-368.
---------------------------------------------------------------------------

    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 Sec.  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[supreg] System; three from dialysis 
patients, three from nephrologists, and one from a dialysis clinic 
nurse. These letters emphasized benefits of the Tablo[supreg] 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 in-home HD 
performance using the Tablo[supreg] System. Per the applicant, this 
study is referred to as the Tablo[supreg] 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[supreg], and the remainder had 
previously received conventional in-center HD care. The applicant also 
noted that the Tablo[supreg] 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[supreg]. 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[supreg] 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[supreg] 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).
---------------------------------------------------------------------------

    \26\ Clinicaltrials.gov website. https://www.clinicaltrials.gov/ct2/show/NCT02460263. Last Updated July 1, 2020. https://www.clinicaltrials.gov/ProvidedDocs/63/NCT02460263/Prot_000.pdf.
---------------------------------------------------------------------------

    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[supreg] 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[supreg] 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[supreg] 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\
---------------------------------------------------------------------------

    \27\ Alvarez, 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.
---------------------------------------------------------------------------

    Next, an article from Chertow, et al., described additional data 
from the Tablo[supreg] IDE study (discussed previously), including 
health-related quality of life, to further assess the safety of home HD 
with the Tablo[supreg]

[[Page 36336]]

System. Demographic information identified the mean age as 49.8 <plus-
minus> 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 <plus-minus> 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 in-center 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 well-rested with 
little difficulty falling or staying asleep or feeling tired and worn 
out when using the Tablo[supreg] System in either environment. The 
authors concluded that when using the Tablo[supreg] System in-home, 
patients reported similar time to recovery, general health status, and 
sleep quality compared to using the Tablo[supreg] System in-center.\28\
---------------------------------------------------------------------------

    \28\ Chertow, G.M., Alvarez, L., Plumb, T.J., Prichard, S.S., & 
Aragon, M. (2020). Patient-reported outcomes from the 
investigational device exemption study of the Tablo hemodialysis 
system. Hemodialysis International, 24(4), 480-486.
---------------------------------------------------------------------------

    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\
---------------------------------------------------------------------------

    \29\ Leypoldt, 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.
---------------------------------------------------------------------------

    Next, an article by Plumb, et al., described the Tablo[supreg] IDE 
study (discussed previously). Demographic information reflected the 
mean age as 52.3 <plus-minus> 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[supreg] System for in-center HD and the 8 weeks of the 
Tablo[supreg] System for in-home HD or between using the Tablo[supreg] 
System in-home HD and the treatment provided prior to study enrollment. 
In both settings, patients dialyzed using the Tablo[supreg] 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 in-center 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\
---------------------------------------------------------------------------

    \30\ Safety 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.
---------------------------------------------------------------------------

    Next, a separate article by Plumb et al., reports additional data 
from the Tablo[supreg] IDE study (previously discussed) regarding 
participants' assessment of the Tablo[supreg] 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[supreg] 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 self-care HD or 
conventional in-center HD. For the participants previously on in-center 
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[supreg] 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[supreg] 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

[[Page 36337]]

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 self-care 
dialysis at home experience and 3.9 sessions for participants with only 
conventional in-center HD experience.\31\
---------------------------------------------------------------------------

    \31\ Plumb, 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).
---------------------------------------------------------------------------

    Next, a poster presentation from Chahal et al., reported patient 
device preference of prior in-home HD patients based on data from the 
Tablo[supreg] IDE study (previously discussed). The authors noted that 
13 of the 30 participants in the Tablo[supreg] 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[supreg] System compared to their prior device and 
85.6 percent found that the Tablo[supreg] System was easier to use.\32\
---------------------------------------------------------------------------

    \32\ Chahal, 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.
---------------------------------------------------------------------------

    As stated previously in this section of the proposed rule, the 
applicant submitted several sources pertaining to the incumbent, 
NxStage.[supreg] First, an article from Kraus et al., describes a 
feasibility study to demonstrate the safety of center-based versus 
home-based daily HD with the NxStage[supreg] 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[supreg] 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\
---------------------------------------------------------------------------

    \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).
---------------------------------------------------------------------------

    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[supreg] 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\
---------------------------------------------------------------------------

    \34\ Finkelstein, F.O., et al. (2012). At-home short daily 
hemodialysis improves the long-term health-related quality of life. 
Kidney International, 82(5), 561-569.
---------------------------------------------------------------------------

    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 <plus-
minus> 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[supreg] from 2007 through 2010. Authors reported home HD was 
associated with 20 percent lower risk for all-cause mortality, 8 
percent lower risk for all-cause 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\
---------------------------------------------------------------------------

    \35\ Weinhandl, 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.

---------------------------------------------------------------------------

[[Page 36338]]

    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, access-related 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\
---------------------------------------------------------------------------

    \36\ Suri, R.S., Li, L., & Nesrallah, G.E. (2015). The risk of 
hospitalization and modality failure with home dialysis. Kidney 
International, 88(2), 360-368.
---------------------------------------------------------------------------

(b) Applicant SCI Claims
    Regarding the applicant's first claim that the Tablo[supreg] System 
decreases treatment frequency with adequate dialysis clearance, the 
applicant stated that the Tablo[supreg] 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[supreg] 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[supreg] System had a higher mean weekly standard Kt/V with 
equivalent or better dialysis-related hospitalization rates,\38\ as 
compared to NxStage[supreg] IDE patients prescribed therapy at 6 days 
per week.\39\
---------------------------------------------------------------------------

    \37\ Alvarez, 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.
    \38\ 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). Safety and efficacy of the Tablo hemodialysis 
system for in-center and home hemodialysis. Hemodialysis 
International.
    \39\ NxStage Clearance Calculator. Available at: https://dosingcalculator.nxstage.com/DosingCalculator/. Accessed on Jan 21, 
2021.
---------------------------------------------------------------------------

    The applicant stated that the Tablo[supreg] 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,[supreg] 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[supreg] 
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.
---------------------------------------------------------------------------

    \40\ Tentori 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.
    \41\ Health Management Associates (HMA) analysis of 2018 100% 
Medicare Outpatient file.
---------------------------------------------------------------------------

    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 x 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

[[Page 36339]]

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[supreg] technical specifications.<SUP>45 46</SUP> 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[supreg] system at its maximal dialysate flow rate.
---------------------------------------------------------------------------

    \42\ Leypoldt, 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.
    \45\ Leypoldt, 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.
---------------------------------------------------------------------------

    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[supreg] 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[supreg] System would decrease the number of necessary dialysis 
treatments, without affecting patient outcomes such as clearance or 
hospitalizations.
---------------------------------------------------------------------------

    \47\ United 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.
    \48\ Wilk, 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.
    \49\ Health Management Associates (HMA) analysis of 2018 100 
percent Medicare Outpatient file.
    \50\ Medicare Coverage Database. Retrieved May 24, 2021 from: 
https://www.cms.gov/medicare-coverage-database/details/lcd-details.aspx?LCDId=35014&ver=39&NCDId=79&ncdver=1&SearchType=Advanced&CoverageSelection=Both&NCSelection=NCA%7CCAL%7CNCD%7CMEDCAC%7CTA%7CMCD&ArticleType=Ed%7CKey%7CSAD%7CFAQ&PolicyType=Final&s=-%7C5%7C6%7C66%7C67%7C9%7C38%7C63%7C41%7C64%7C65%7C44&KeyWord=transplant&KeyWordLookUp=Doc&KeyWordSearchType=Exact&kq=true&bc=IAAAADgAAAAA&
.
---------------------------------------------------------------------------

    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.<SUP>51 52</SUP> Per the applicant, home 
use of the Tablo[supreg] 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[supreg] 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.<SUP>54 55</SUP> 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[supreg] IDE study, patients 
using the Tablo[supreg] 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 all-cause 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\
---------------------------------------------------------------------------

    \51\ National Kidney Foundation. KDOQI clinical practice 
guideline for hemodialysis adequacy: 2015 update. Am J Kidney Dis. 
2015;66(5):884-930.
    \52\ Shafi 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.
    \53\ Safety 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.
    \54\ FHN 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 Population-Based Nested Case-Control 
Study. Medicine, 94(26).
    \56\ United 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.
---------------------------------------------------------------------------

    The applicant stated that while NxStage[supreg] 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

[[Page 36340]]

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[supreg] 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[supreg] for 
treatment. The applicant stated that the results from these studies 
suggest that patients receiving treatment at home with NxStage[supreg] 
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[supreg] System's patient population, the 
applicant's results show incremental improvement over the 
hospitalization rate of the current home HD population.
---------------------------------------------------------------------------

    \57\ Weinhandl, 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.
    \58\ Suri, R.S., Li, L., & Nesrallah, G.E. (2015). The risk of 
hospitalization and modality failure with home dialysis. Kidney 
international, 88(2), 360-368.
---------------------------------------------------------------------------

    Regarding the applicant's second claim that the Tablo[supreg] 
System increases adherence to dialysis treatment and retention to home 
therapy, the applicant stated that patients using the Tablo[supreg] 
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\
---------------------------------------------------------------------------

    \59\ Chan, 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.
---------------------------------------------------------------------------

    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[supreg] 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[supreg]. The applicant also stated that 
the Tablo[supreg] 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[supreg] 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[supreg] IDE patients at home had a 
100 percent treatment compliance rate.
---------------------------------------------------------------------------

    \60\ Weinhandl, 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.
    \61\ Safety 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.
---------------------------------------------------------------------------

    The applicant stated that technique failure in home HD, defined as 
reduced retention at home and a return to in-center care, has been high 
with NxStage[supreg]. Per the applicant, real world data show that 
technique failure occurs in 36 percent of home HD patients using 
NxStage[supreg] 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.
---------------------------------------------------------------------------

    \63\ Weinhandl, 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.
---------------------------------------------------------------------------

    The applicant stated that by directly comparing the Tablo[supreg] 
System's retention to that of NxStage[supreg], 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[supreg] 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[supreg] users and prior in-center 
patients.\64\ The applicant stated that in comparison, 81 percent of 
participants completed the NxStage[supreg] IDE study.\65\
---------------------------------------------------------------------------

    \64\ Safety 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.
---------------------------------------------------------------------------

    The applicant stated that the Tablo[supreg] System's ease of use 
contributed to the improved adherence and retention rates and that the 
Tablo[supreg] System is designed to enable patients to become 
proficient and independent in using the Tablo[supreg] System after an 
average of 3.9 days.\66\ Per the applicant, published NxStage[supreg] 
IDE data \67\ reported an average of 14.5 days ``to complete device 
training on NxStage[supreg].'' The applicant stated that, in 
comparison, device-related training time is reduced by at least 50 
percent on the Tablo[supreg] 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.<SUP>69 70</SUP> Per the applicant, the 
28 patients who entered the home phase of the Tablo[supreg] 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[supreg] 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[supreg] 
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

[[Page 36341]]

is the baseline percentage and reflects 9 of the 13 patients with 
previous self-care experience. The applicant stated that patients 
reported needing help with treatment in only 42 percent of treatment 
weeks while using the Tablo[supreg] System, which is a 39 percent 
reduction from baseline NxStage[supreg] use; and only 18 percent of 
these instances related to use of the Tablo[supreg] System, which is a 
61 percent reduction in rate from baseline NxStage[supreg] use.\72\
---------------------------------------------------------------------------

    \66\ Plumb, 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).
    \68\ Seshasai, R.K., et al. (2019) The home hemodialysis patient 
experience: A qualitative assessment of modality use and 
discontinuation. Hemodialysis International, 23: 139-150 (2019).
    \69\ Suri, 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.
    \71\ Plumb, 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).
    \72\ Ibid.
---------------------------------------------------------------------------

    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[supreg] 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[supreg] 
System (a 23 percent reduction in score from baseline NxStage[supreg] 
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[supreg] System correlated to the improved retention and adherence 
rates in the Tablo[supreg] 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[supreg] 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[supreg].
---------------------------------------------------------------------------

    \73\ Chochinov, 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.
    \74\ Chertow, G.M., Alvarez, L., Plumb, T.J., Prichard, S.S., & 
Aragon, M. (2020). Patient-reported outcomes from the 
investigational device exemption study of the Tablo hemodialysis 
system. Hemodialysis International, 24(4), 480-486.
---------------------------------------------------------------------------

    Regarding the applicant's third claim that the Tablo[supreg] System 
improves patient quality of life, the applicant stated that patients on 
the Tablo[supreg] 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[supreg] IDE study assessed several validated Patient-Reported 
Outcome Measures (PROMs) to better understand overall health-related 
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, self-care, 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).
---------------------------------------------------------------------------

    \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.
---------------------------------------------------------------------------

    Per the applicant, while the NxStage[supreg] IDE study did not 
report results for a quality-of-life instrument, HR-QoL was assessed in 
NxStage[supreg] 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[supreg] 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[supreg] 
patients at follow-up. The applicant further noted that mean EQ-5D 
index values for traditional HD and PD patients reported from a meta-
analysis 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\
---------------------------------------------------------------------------

    \77\ Finkelstein, F.O., et al. (2012). At-home short daily 
hemodialysis improves the long-term health-related quality of life. 
Kidney international, 82(5), 561-569.
    \78\ Liem, 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.
---------------------------------------------------------------------------

    Per the applicant, patients in the Tablo[supreg] IDE study reported 
mean EQ-5D index values of 0.821 (SD: <plus-minus>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[supreg] 
patients. The applicant emphasized that participants in the 
Tablo[supreg] IDE trial underwent a reduced treatment frequency as 
compared to participants in the FREEDOM study who were prescribed 6 
treatments per week on NxStage[supreg]. The applicant stated that among 
patients in the Tablo[supreg] IDE study who had previously been using 
NxStage[supreg], the mean EQ-5D score during the in-home phase of the 
study was 0.906 (SD: <plus-minus>0.119) and asserted that this is 
significantly greater than index population values for HD and 
peritoneal dialysis.
---------------------------------------------------------------------------

    \79\ Chertow, G.M., Alvarez, L., Plumb, T.J., Prichard, S.S., & 
Aragon, M. (2020). Patient-reported outcomes from the 
investigational device exemption study of the Tablo hemodialysis 
system. Hemodialysis International, 24(4), 480-486.
---------------------------------------------------------------------------

    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

[[Page 36342]]

applicant, the frequency of sleep-related symptoms among the 
Tablo[supreg] System's patients was assessed by a survey that was 
administered weekly during the Tablo[supreg] 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.<SUP>82 83</SUP> 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 sleep-related symptoms. The 
applicant stated that, while at home, patients on the Tablo[supreg] 
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[supreg] (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[supreg] to 
23 percent of treatment weeks while on the Tablo[supreg] System.
---------------------------------------------------------------------------

    \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.
    \81\ Urquhart-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.
    \82\ Morin, C.M., Belleville, G., B[eacute]langer, L., & Ivers, 
H. (2011). The Insomnia Severity Index: Psychometric indicators to 
detect insomnia cases and evaluate treatment response. Sleep, 34(5), 
601-608.
    \83\ Natale, V., Fabbri, M., Tonetti, L., & Martoni, M. (2014). 
Psychometric goodness of the mini sleep questionnaire. Psychiatry 
and clinical neurosciences, 68(7), 568-573.
    \84\ Chertow, G.M., Alvarez, L., Plumb, T.J., Prichard, S.S., & 
Aragon, M. (2020). Patient-reported outcomes from the 
investigational device exemption study of the Tablo hemodialysis 
system. Hemodialysis International, 24(4), 480-486.
---------------------------------------------------------------------------

    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[supreg] 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[supreg] System. Per the applicant, a total of 8 (26.7 percent) 
subjects reported hypotensive symptoms during the Tablo[supreg] System 
treatments during the in-home treatment period, compared to 27 (90 
percent) subjects reporting hypotensive symptoms at baseline (prior to 
initiating care on the Tablo[supreg] System). The applicant reported a 
70 percent reduction in the rate of patient-reported hypotensive 
symptoms while on the Tablo[supreg] System, though we were unable to 
validate the source of this statement.
---------------------------------------------------------------------------

    \85\ Urquhart-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.
---------------------------------------------------------------------------

    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\
---------------------------------------------------------------------------

    \86\ Liem, 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.
    \87\ Lowrie, 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.
---------------------------------------------------------------------------

    The applicant concluded by asserting that the totality of evidence 
submitted in support of the Tablo[supreg] 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[supreg] patients in the Tablo[supreg] IDE study found 
the Tablo[supreg] System easier to use than their incumbent device and 
preferred to remain on the Tablo[supreg] System at the end of the 
study.\89\
---------------------------------------------------------------------------

    \88\ Food 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/patient-preference-sensitive-areas-using-patientpreference-information-medical-device-evaluation. Accessed Jan 21, 2021.
    \89\ Chahal, 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.
---------------------------------------------------------------------------

    In summary, the applicant claimed that the Tablo[supreg] System 
improves the treatment of Medicare beneficiaries relative to the 
incumbent by focusing on outcomes set forth in Sec.  
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 Sec.  
413.236(c), CMS will announce its final determination regarding whether 
Tablo[supreg] 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[supreg] IDE study did not test whether patients receive 
adequate dialysis on a thrice-weekly schedule. Instead, data published 
from the Tablo[supreg] 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 thrice-weekly schedule. Specifically, the applicant 
referred to a theoretical modeling study based on historical data from 
the USRDS, Medicare claims, and historical outcomes from 
NxStage[supreg] observational studies. The applicant also stated that 
findings from a retrospective review of 29 patients receiving treatment 
with the Tablo[supreg] 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

[[Page 36343]]

self-care dialysis thrice-weekly with the Tablo[supreg] 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.
---------------------------------------------------------------------------

    \90\ Alvarez, 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.
---------------------------------------------------------------------------

    The applicant's second claim was that the Tablo[supreg] 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[supreg] IDE study (28 participants completed the 
study) and the use of historical comparisons to prior studies involving 
the NxStage[supreg] System. The applicant noted that hospitalization 
rates from the Tablo[supreg] IDE trial were lower than rates in the 
general dialysis population and rates reported in two observational 
studies of patients using the NxStage[supreg] device. While the 
applicant cited an all-cause hospitalization rate of 426 per 1000 
patient years in the Tablo[supreg] IDE study, it does not appear that 
the sources <SUP>91 92</SUP> published these hospitalization rates. We 
further note that the applicant relied on historical comparisons in 
asserting that that patients treated with the Tablo[supreg] System 
experience reduced disease burden and improved quality of life.
---------------------------------------------------------------------------

    \91\ Safety 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.
    \92\ Chertow, G.M., Alvarez, L., Plumb, T.J., Prichard, S. S., & 
Aragon, M. (2020). Patient-reported outcomes from the 
investigational device exemption study of the Tablo hemodialysis 
system. Hemodialysis International, 24(4), 480-486.
---------------------------------------------------------------------------

    We note that in the Tablo[supreg] IDE study, the before-after 
comparisons in patients with NxStage[supreg] regarding improved sleep 
compared to prior to the Tablo[supreg] System may be prone to recall 
bias in that participants' experiences with NxStage[supreg] were not 
recorded at the time they were receiving NxStage[supreg] treatments, 
but rather, were based on recall at the time of the Tablo[supreg] 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[supreg] System represents 
an advance that substantially improves, relative to renal dialysis 
services previously available, the treatment of Medicare beneficiaries.
(6) Capital Related Assets Criterion (Sec.  413.236(b)(6))
    With respect to the sixth TPNIES eligibility criterion under Sec.  
413.236(b)(6), whether the item is a capital-related asset and home 
dialysis machine, Sec.  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[supreg] 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[supreg] System is a capital-related asset that is a home dialysis 
machine. We welcome comments on the Tablo[supreg] 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\
---------------------------------------------------------------------------

    \93\ Mayo Clinic Staff, ``Peritonitis,'' June 18, 2020, 
available at: https://www.mayoclinic.org/diseases-conditions/peritonitis/symptoms-causes/syc-20376247.
    \94\ Ibid.
    \95\ Ibid.
---------------------------------------------------------------------------

    PD-related peritonitis is a major complication and challenge to the 
long-term 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 PD-related 
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/[mu]L or >0.1 x 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

[[Page 36344]]

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.
---------------------------------------------------------------------------

    \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.
    \97\ Briggs, 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.
---------------------------------------------------------------------------

    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 PRO<SUP>TM</SUP>, Baxter 
Healthcare AMIA<SUP>TM</SUP> Automated PD System, and Fresenius 
Liberty[supreg] 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 (Sec.  413.236(b)(1))
    Regarding the first TPNIES eligibility criterion in Sec.  
413.236(b)(1), that the item has been designated by CMS as a renal 
dialysis service under Sec.  413.171, monitoring for peritonitis is a 
service that is essential for dialysis, and therefore would be 
considered a renal dialysis service under Sec.  413.171.
(2) Newness Criterion (Sec.  413.236(b)(2))
    With respect to the second TPNIES eligibility criterion in Sec.  
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 (Sec.  413.236(b)(3))
    Regarding the third TPNIES eligibility criterion in Sec.  
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 (Sec.  413.236(b)(4))
    Regarding the fourth TPNIES eligibility criterion in Sec.  
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 (Sec. Sec.  413.236(b)(5) and 412.87(b)(1))
(a) SCI Claims and Sources
    With regard to the fifth TPNIES eligibility criterion under Sec.  
413.236(b)(5), that the item is innovative, meaning it meets the SCI 
criteria specified in Sec.  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/[mu]L, >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 non-infected. 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.
---------------------------------------------------------------------------

    \100\ Briggs, 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.
---------------------------------------------------------------------------

    The second study the applicant submitted is the Prospective 
Clinical Study to Evaluate the Ability of the CloudCath System to 
Detect Peritonitis

[[Page 36345]]

Compared to Standard of Care during In-Home Peritoneal Dialysis 
(CATCH).\102\ CloudCath initiated this ongoing single-arm, open-label, 
multi-center 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/[mu]L or >0.1 x 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.
---------------------------------------------------------------------------

    \102\ CloudCath, ``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.
    \103\ CloudCath, ``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.
    \106\ CloudCath, ``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.
---------------------------------------------------------------------------

    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-to-contact 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-to-treatment 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 PD-failure 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.<SUP>119 120 121</SUP> 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 
PD-related peritonitis.
---------------------------------------------------------------------------

    \114\ Muthucumarana, 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.
    \120\ Houck, 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.
    \121\ Lodise 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.
---------------------------------------------------------------------------

    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

[[Page 36346]]

with ESRD undergoing PD treatments can quickly detect and treat 
infections.
---------------------------------------------------------------------------

    \122\ Mehrotra, 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.
---------------------------------------------------------------------------

(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 
Sec.  413.236(b)(5) and Sec.  412.87(b)(1). We note that, consistent 
with Sec.  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 Sec.  412.87(b)(1)(ii)(B). Specifically, Sec.  
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 Sec.  
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 Sec.  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.
---------------------------------------------------------------------------

    \123\ CloudCath, ``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.
    \124\ Briggs, et al., ``Early Detection of Peritonitis in 
Patients Undergoing Peritoneal Dialysis: A Device and Cloud-Based 
Algorithmic Solution,'' unpublished report.
    \125\ Muthucumarana, 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.
---------------------------------------------------------------------------

    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 PD-related 
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\
---------------------------------------------------------------------------

    \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.
    \127\ CloudCath, ``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.
---------------------------------------------------------------------------

    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

[[Page 36347]]

clinicians beginning treatment for PD-related peritonitis when not 
necessary in an already vulnerable group of Medicare beneficiaries. We 
welcome public comment on these issues.
---------------------------------------------------------------------------

    \131\ Briggs, et al., ``Early Detection of Peritonitis in 
Patients Undergoing Peritoneal Dialysis: A Device and Cloud-Based 
Algorithmic Solution,'' unpublished report.
---------------------------------------------------------------------------

(6) Capital Related Assets Criterion (Sec.  413.236(b)(6))
    Regarding the sixth TPNIES eligibility criterion in Sec.  
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 capital-related 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\
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    \132\ CMS Provider Reimbursement Manual, Chapter 1, Section 
104.1. Available at: https://www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/Paper-Based-Manuals-Items/CMS021929.
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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 Sec.  413.220, updated by the ESRD bundled market 
basket percentage increase factor minus a productivity adjustment as 
set forth in Sec.  413.196(d)(1), adjusted for wages as set forth in 
Sec.  413.231, and adjusted by any other amounts deemed appropriate by 
the Secretary under Sec.  413.373. We codified this policy in Sec.  
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 Sec.  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 Sec.  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.
---------------------------------------------------------------------------

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

IV. End-Stage Renal Disease Quality Incentive Program (ESRD QIP)

A. Background

    For a detailed discussion of the End-Stage 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:
    <bullet> CY 2011 ESRD PPS final rule (75 FR 49030),
    <bullet> CY 2012 ESRD PPS final rule (76 FR 628),
    <bullet> CY 2012 ESRD PPS final rule (76 FR 70228),
    <bullet> CY 2013 ESRD PPS final rule (77 FR 67450),
    <bullet> CY 2014 ESRD PPS final rule (78 FR 72156),
    <bullet> CY 2015 ESRD PPS final rule (79 FR 66120),
    <bullet> CY 2016 ESRD PPS final rule (80 FR 68968),
    <bullet> CY 2017 ESRD PPS final rule (81 FR 77834),
    <bullet> CY 2018 ESRD PPS final rule (82 FR 50738),
    <bullet> CY 2019 ESRD PPS final rule (83 FR 56922),
    <bullet> CY 2020 ESRD PPS final rule (84 FR 60648), and
    <bullet> 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.

[[Page 36348]]

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.
---------------------------------------------------------------------------

    \134\ CMS, 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/cms-announces-relief-clinicians-providers-hospitals-and-facilities-participating-quality-reporting.
    \135\ CMS, 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-and-extensions-quality-reporting-and-value-based-purchasing-programs.pdf.
---------------------------------------------------------------------------

    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 Sec.  
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 Sec.  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.
---------------------------------------------------------------------------

    \136\ https://mycrownweb.org/2020/11/november-2020-newsletter/.
---------------------------------------------------------------------------

    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/profile-info.
    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.
---------------------------------------------------------------------------

    \137\ https://mycrownweb.org/2021/02/eqrs-data-reporting-update-feb-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

[[Page 36349]]

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\
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    \138\ https://www.cms.gov/files/document/cy-2021-final-technical-specifications-20201130.pdf.
---------------------------------------------------------------------------

    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

[[Page 36350]]

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:
    <bullet> 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.
    <bullet> Factor 2: Clinical proximity of the measure's focus to the 
relevant disease, pathogen, or health impacts of the COVID-19 PHE.
    <bullet> 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.
    <bullet> 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 facility-level 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 decision-making 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

[[Page 36351]]

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:
    <bullet> 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);
    <bullet> 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 facility-level case mix);
    <bullet> 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
    <bullet> 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 all-cause, 
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

[[Page 36352]]

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

[[Page 36353]]

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.
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    \139\ Groupings of questions and composite measures can be found 
at https://ichcahps.org/Portals/0/ICH_Composites_English.pdf.
---------------------------------------------------------------------------

    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: Long-Term 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.

[[Page 36354]]

    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 Sec.  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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    \140\ https://www.cms.gov/files/document/esrd-measures-manual-v61.pdf.
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BILLING CODE 4120-01-P

[[Page 36355]]

[GRAPHIC] [TIFF OMITTED] TP09JY21.001

BILLING CODE 4120-01-C
    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, 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

[[Page 36356]]

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.
---------------------------------------------------------------------------

    \141\ United 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.
    \143\ Office 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.
---------------------------------------------------------------------------

    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 NQF-endorsed 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\
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    \144\ National 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.
    \145\ Measure 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.
    \146\ Measure 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.
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    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.

[[Page 36357]]

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 Sec.  
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.
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    \147\ We note that for most ESRD QIP measures, this partial year 
data would be measure data from July and August 2020.
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    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.
BILLING CODE 4120-01-P

[[Page 36358]]

[GRAPHIC] [TIFF OMITTED] TP09JY21.002

    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.

[[Page 36359]]

[GRAPHIC] [TIFF OMITTED] TP09JY21.003

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.

[[Page 36360]]

[GRAPHIC] [TIFF OMITTED] TP09JY21.004

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 Sec.  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 Sec.  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.

[[Page 36361]]

[GRAPHIC] [TIFF OMITTED] TP09JY21.005

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 Sec.  
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 Sec.  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 Sec.  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

[[Page 36362]]

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:
    <bullet> Background. This section provides information on existing 
statements describing our commitment to health equity, and existing 
initiatives with an emphasis on reducing disparity.
    <bullet> Current CMS Disparity Methods. This section describes the 
methods, measures, and indicators of social risk currently used with 
the CMS Disparity Methods.
    <bullet> 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.
    <bullet> 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.<SUP>149 150 151 152 153 154 155 156</SUP> 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.<SUP>157 158 159 160 161 162</SUP> 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.<SUP>163 164 165 166 167</SUP> 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

[[Page 36363]]

persons relative to white persons.<SUP>171 172 </SUP> 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 ``long-
standing 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.
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    \148\ United States Department of Health and Human Services. 
``Healthy People 2020: Disparities. 2014.'' Available at: https://www.healthypeople.gov/2020/about/foundation-health-measures/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.
    \150\ Lindenauer PK, Lagu T., Rothberg MB, et al. Income 
Inequality and 30 Day Outcomes After Acute Myocardial Infarction, 
Heart Failure, and Pneumonia: Retrospective Cohort Study. British 
Medical Journal. 2013;346.
    \151\ Trivedi 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.
    \152\ Polyakova, 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.
    \153\ Rural Health Research Gateway. Rural Communities: Age, 
Income, and Health Status. Rural Health Research Recap. November 
2018.
    \154\ Polyakova, 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.
    \155\ www.cdc.gov/mmwr/volumes/70/wr/mm7005a1.htm.
    \156\ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7386532/.
    \157\ Martino, 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.
    \159\ Singh 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.
    \160\ Rivera-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.
    \162\ Tsai 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.
    \163\ Rodriguez 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.
    \164\ Centers 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.
    \166\ Prieto-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.
    \168\ https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/ESRD-Infographic.pdf.
    \169\ Ibid.
    \170\ HHS. Heart disease and African Americans. (March 29, 
2021). https://www.minorityhealth.hhs.gov/omh/browse.aspx?lvl=4&lvlid=19.
    \171\ https://www.cms.gov/files/document/medicare-covid-19-data-snapshot-fact-sheet.pdf.
    \172\ Ochieng 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-and-medicare/.
    \173\ Weinhandl 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.
    \174\ https://www.cdc.gov/coronavirus/2019-ncov/community/health-equity/race-ethnicity.html.
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    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.
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    \175\ https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-Quality-Strategy.pdf.
    \176\ https://www.federalregister.gov/documents/2021/01/25/2021-01753/advancing-racial-equity-and-support-for-underserved-communities-through-the-federal-government.
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    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:
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    \177\ Centers 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.
    \178\ Centers 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-equity-cms-omh-progress-report.pdf.
    \179\ Centers for Medicare Services. CMS Quality Strategy. 2016. 
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-Quality-Strategy.pdf.
    \180\ https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/MMF/General-info-Sub-Page.
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    <bullet> 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\
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    \181\ https://www.cms.gov/About-CMS/Agency-Information/OMH/OMH-Mapping-Medicare-Disparities.
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    <bullet> 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\
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    \182\ https://www.cms.gov/About-CMS/Agency-Information/OMH/research-and-data/statistics-and-data/stratified-reporting.
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    <bullet> The Rural-Urban Disparities in Health Care in Medicare 
Report which details rural-urban differences in health care experiences 
and clinical care.\183\
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    \183\ Centers for Medicare & Medicaid Services. Rural-Urban 
Disparities in Health Care in Medicare. 2019. https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Rural-Urban-Disparities-in-Health-Care-in-Medicare-Report.pdf.
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    <bullet> 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).
    <bullet> The CMS Innovation Center's Accountable Health Communities 
Model which includes standardized collection of health-related social 
needs data.
    <bullet> 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\
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    \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.
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    <bullet> 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\
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    \185\ CMS. Chronic Kidney Disease Disparities: Educational Guide 
for Primary Care. February 2020. Available at: https://www.cms.gov/files/document/chronic-kidney-disease-disparities-educational-guide-primary-care.pdf.
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    <bullet> The CMS Disparity Methods which provide hospital-level 
confidential results stratified by dual eligibility for condition-
specific readmission

[[Page 36364]]

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.
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    \186\ National 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.
    \187\ https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs.
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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 
Across-Hospital 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.
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    \188\ https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs.
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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.
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    \189\ Centers for Medicare Services. CMS Quality Strategy. 2016. 
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-Quality-Strategy.pdf.
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(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.
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    \190\ National 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.
    \191\ https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs.
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    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 non-dual 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

[[Page 36365]]

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.
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    \192\ https://www.kff.org/medicare/report/racial-and-ethnic-health-inequities-and-medicare/.
    \193\ United 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.
    \194\ United States Renal Data System. 2018 Annual Data Report, 
Vol 2, Figure 1.12. Available online at https://www.usrds.org/2018/view/Default.aspx.
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    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.
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    \195\ Executive 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.
    \196\ https://www.census.gov/topics/population/hispanic-origin/about.html.
    \197\ https://phinvads.cdc.gov/vads/ViewValueSet.action?id=67D34BBC-617F-DD11-B38D-00188B398520.
    \198\ ONC criteria for certified health IT products: https://www.healthit.gov/isa/representing-patient-race-and-ethnicity.
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    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.<SUP>200 201</SUP> 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.
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    \199\ Eicheldinger, C., & Bonito, A. (2008). More accurate 
racial and ethnic codes for Medicare administrative data. Health 
Care Financing Review, 29(3), 27-42.
    \200\ Filice CE, Joynt KE. Examining Race and Ethnicity 
Information in Medicare Administrative Data. Med Care. 
2017;55(12):e170-e176. doi:10.1097/MLR.0000000000000608.
    \201\ Eicheldinger, C., & Bonito, A. (2008). More accurate 
racial and ethnic codes for Medicare administrative data. Health 
Care Financing Review, 29(3), 27-42.
    \202\ Centers 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/Agency-Information/OMH/Downloads/Data-Collection-Resources.pdf.
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    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\
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    \203\ Centers 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/Agency-Information/OMH/Downloads/Data-Collection-Resources.pdf.
    \204\ https://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.
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    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\
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    \205\ https://aspe.hhs.gov/pdf-report/second-impact-report-to-congress.
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    While development of sustainable and consistent programs to collect 
data on social determinants of health can be considerable undertakings, 
we recognize

[[Page 36366]]

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 Census-linked 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 self-identified data are not available.\207\
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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.
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    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\
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    \208\ Eicheldinger, C., & Bonito, A. (2008). More accurate 
racial and ethnic codes for Medicare administrative data. Health 
Care Financing Review, 29(3), 27-42.
    \209\ Haas, 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.
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    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.<SUP>211 212</SUP> The 
algorithms under consideration are considerably less accurate for 
individuals who self-identify as American Indian or Alaskan Native as 
well as for those who self-identify as multiracial.\213\
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    \210\ https://www.cms.gov/About-CMS/Agency-Information/OMH/research-and-data/statistics-and-data/stratified-reporting.
    \211\ Haas, 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.
    \212\ https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf.
    \213\ Haas, 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 Race-Ethnicity 
Codes and Socioeconomic Status (SES) Indicators for Medicare 
Beneficiaries. Final Report, Sub-Task 2. (Prepared by RTI 
International for the Centers for Medicare and Medicaid Services 
through an interagency agreement with the Agency for Healthcare 
Research and Policy, under Contract No. 500-00-0024, Task No. 21) 
AHRQ Publication No. 08-0029-EF. Rockville, MD, Agency for 
Healthcare Research and Quality. January 2008.
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    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 self-reported 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

[[Page 36367]]

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.
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    \214\ The Centers for Medicare & Medicaid Services. CMS Quality 
Strategy. 2016. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-Quality-Strategy.pdf.
    \215\ The 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.
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    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.
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    \216\ https://minorityhealth.hhs.gov/assets/pdf/checked/1/Fact_Sheet_Section_4302.pdf.
    \217\ https://www.healthit.gov/sites/default/files/2020-08/2015EdCures_Update_CCG_USCDI.pdf.
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(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\
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    \218\ Agniel 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.
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    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 risk-adjusted 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:
    <bullet> Summarize facility performance across multiple social risk 
factors (initially dual eligibility and indirectly estimated race and 
ethnicity, as described above).
    <bullet> 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
    <bullet> 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;
    <bullet> The potential future application of indirect estimation of 
race and ethnicity information to permit stratification of

[[Page 36368]]

measure data for reporting ESRD facility-level disparity results;
    <bullet> 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.
    <bullet> 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.
    <bullet> 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.
    <bullet> Recommendations for types of quality measures or 
measurement domains to prioritize for stratified reporting by dual 
eligibility, race and ethnicity, and disability.
    <bullet> 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
    <bullet> 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 decision-making and care delivery, and the potential 
challenges and benefits of collecting more granular, structured 
demographic information, such as the ``Race & Ethnicity--CDC'' code 
system.
    <bullet> 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
    <bullet> The possible creation and confidential reporting of an 
ESRD Facility Equity Score to synthesize results across multiple social 
risk factors and disparity measures.
    <bullet> 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\
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    \219\ U.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.
    \220\ Centers 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.
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    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\
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    \222\ Centers for Disease Control and Prevention. (2020). CDC 
COVID Data Tracker. Available at: https://covid.cdc.gov/covid-data-tracker/#cases_casesper100klast7days.
    \223\ Associated Press. Tired to the Bone. Hospitals Overwhelmed 
with Virus Cases. November 18, 2020. Accessed on December 16, 2020, 
at https://apnews.com/article/hospitals-overwhelmed-coronavirus-cases-74a1f0dc3634917a5dc13408455cd895. 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-new-data-paints-an-alarming-picture.html.
    \224\ U.S. Currently Hospitalized [bond] The COVID Tracking 
Project. Accessed January 31, 2021 at: https://covidtracking.com/data/charts/us-currently-hospitalized.
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    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\
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    \225\ Centers 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.
    \230\ Centers 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.
    \231\ Centers 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.
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    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-

[[Page 36369]]

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\
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    \233\ The 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/speeches-remarks/2021/03/29/remarks-by-president-biden-on-the-covid-19-response-and-the-state-of-vaccinations/.
    \234\ The 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/speeches-remarks/2021/04/21/remarks-by-president-biden-on-the-covid-19-response-and-the-state-of-vaccinations-2/.
    \235\ The 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/statements-releases/2021/05/04/fact-sheet-president-biden-to-announce-goal-to-administer-at-least-one-vaccine-shot-to-70-of-the-u-s-adult-population-by-july-4th/.
    \236\ Health 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-for-distributing-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-states-prioritizing-who-will-get-the-covid-19-vaccine-first/.
    \239\ Associated 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-coronavirus-vaccine-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-covid-19-vaccine-priority-line-continues-to-change-as-states-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.
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    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\
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    \242\ Centers 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.
    \243\ The 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/speeches-remarks/2021/04/06/remarks-by-president-biden-marking-the-150-millionth-covid-19-vaccine-shot/.
    \244\ The White House. FACT SHEET: Biden Administration 
Announces Historic $10 Billion Investment to Expand Access to COVID-
19 Vaccines and Build Vaccine Confidence in Hardest-Hit and Highest-
Risk Communities. March 25, 2021. Available at: https://www.whitehouse.gov/briefing-room/statements-releases/2021/03/25/fact-sheet-biden-administration-announces-historic-10-billion-investment-to-expand-access-to-covid-19-vaccines-and-build-vaccine-confidence-in-hardest-hit-and-highest-risk-communities/.
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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.
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    \245\ https://www.cdc.gov/nhsn/hps/weekly-covid-vac/index.html.
    \246\ https://www.cdc.gov/nhsn/dialysis/pt-covid-vac/index.html.
    \247\ https://www.cdc.gov/nhsn/pdfs/hps/covidvax/weekly-covid-guidance-508.pdf.
    \248\ https://www.cdc.gov/nhsn/pdfs/dialysis/covidvax/getting-started-508.pdf.
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    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

[[Page 36370]]

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.''
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    \249\ Centers for Disease Control and Prevention. Overview of 
Influenza Vaccination among Health Care Personnel. October 2020. 
(2020) Accessed March 16, 2021 at: https://www.cdc.gov/flu/toolkit/long-term-care/why.htm.
    \250\ Measure 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.
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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.
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    \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.
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    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\
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    \253\ National 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.
    \254\ Measure 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.
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    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.
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    \256\ Ibid.
    \257\ Ibid.
    \258\ Measure Applications Partnership. 2020-2021 MAP Final 
Recommendations. Accessed on February 3, 2021 at: http://www.qualityforum.org/Setting_Priorities/Partnership/Measure_Applications_Partnership.aspx.
    \259\ Measure Applications Partnership. 2020-2021 MAP Final 
Recommendations. Accessed on February 23, 2021 at: http://www.qualityforum.org/Project_Pages/MAP_Hospital_Workgroup.aspx.
    \260\ Measure Applications Partnership. 2020-2021 MAP Final 
Recommendations. Accessed on February 23, 2021 at: http://www.qualityforum.org/Project_Pages/MAP_Hospital_Workgroup.aspx.
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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\
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    \261\ Specifications for both measures available at: https://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=94650.
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    We are also interested in public comment on data collection,

[[Page 36371]]

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:
    <bullet> 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.
    <bullet> Definition of Digital Quality Measures (dQMs). This part 
provides a potential definition for dQMs. Specific requests for input 
are included in the section.
    <bullet> 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.
    <bullet> 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 value-based 
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.
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    \262\ What are patient generated health data: https://www.healthit.gov/topic/otherhot-topics/what-are-patient-generated-health-data.
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    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[supreg]) 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 high-quality 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

[[Page 36372]]

Certification Program until December 31, 2022 (85 FR 70064).\265\
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    \263\ Application 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.
    \264\ https://www.healthit.gov/isa/united-states-core-data-interoperability-uscdi.
    \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.
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    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 long-standing 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.
---------------------------------------------------------------------------

    \266\ The 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.
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    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 
re-specifying 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.
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    \267\ eCQI Resource Center, https://ecqi.healthit.gov/.
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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 self-contained 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

[[Page 36373]]

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 standards-based 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 non-standardized 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:
    <bullet> Have the flexibility to support calculation of single or 
multiple quality measure(s).
    <bullet> 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).
    <bullet> Be compatible with any data source systems that implement 
standard interoperability requirements.
    <bullet> Exist separately from digital data source(s) and respect 
the limitations of the functionality of those data sources.
    <bullet> Be tested and updated independently of the data source 
systems.
    <bullet> Operate in accordance with health information protection 
requirements under applicable laws and comply with governance functions 
for health information exchange.
    <bullet> 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.

[[Page 36374]]

    <bullet> Be designed to enable easy installation for supplemental 
uses by medical professionals and other non-technical end-users, such 
as local calculation of quality measure scores or quality improvement.
    <bullet> Have the flexibility to employ current and evolving 
advanced analytic approaches such as natural language processing.
    <bullet> Be designed to support pro-competitive 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 Sec.  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.
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    \268\ Calendar Year (CY) 2021 Physician Fee Schedule Final Rule: 
Finalized (New and Updated) Qualified Clinical Data Registry (QCDR) 
and Qualified Registry Policies, https://qpp-cm-prod-content.s3.amazonaws.com/uploads/1362/QCDR%20and%20QR%20Updates%202021%20Final%20Rule%20Fact%20Sheet.pdf.
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    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 patient-centeredness. 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.
---------------------------------------------------------------------------

    \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.
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    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:
    <bullet<ls-thn-eq> 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

[[Page 36375]]

also welcome more specific comments on the attributes or functions to 
support such an approach of deploying dQMs.
    <bullet> 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.
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    \270\ ZIP code \TM\ is a trademark of the United States Postal 
Service.
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    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

[[Page 36376]]

for attributing Pre-emptive LDT Beneficiaries to Managing Clinicians to 
better reflect the care relationship between beneficiaries who receive 
pre-emptive 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 Sec.  
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 
Sec.  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

[[Page 36377]]

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 Sec.  512.360(c)(2)(ii), a Pre-emptive LDT 
Beneficiary is generally attributed to the Managing Clinician with whom 
the Pre-emptive 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 Pre-emptive LDT Beneficiary 
such that multiple Managing Clinicians each had the same number of 
claims for that beneficiary during the MY, the Pre-emptive 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 Sec.  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 Pre-
emptive LDT Beneficiary is randomly attributed to one of these Managing 
Clinicians, as described in Sec.  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 Pre-emptive 
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 Sec.  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 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 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 Pre-emptive LDT Beneficiary 
would be considered eligible for attribution to a Managing Clinician 
under this proposed new Sec.  512.360(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 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 Sec.  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 Pre-
emptive LDT Beneficiary attribution to Managing Clinicians beginning 
for MY3 in proposed new Sec.  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 in-center 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 self-dialysis 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 Sec. Sec.  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 in-center 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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    \271\ 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.

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[[Page 36378]]

    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.<SUP>272 273 274 275 276</SUP>
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    \272\ Burton, 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.
    \274\ Nesrallah, 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.
    \275\ Wong, B., Collister, D., Muneer, M., Storie, D., Courtney, 
M., Lloyd, A., . . . Pauly, R. P. (2017). In-center 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.
    \276\ Lacson 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.
---------------------------------------------------------------------------

    In addition to the clinical benefits, nocturnal in-center dialysis 
also provides an alternative to traditional in-center 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 
in-center dialysis during the day.<SUP>277 278</SUP>
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    \277\ Bugeja 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.
    \278\ Lacson 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.
---------------------------------------------------------------------------

    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 self-dialysis 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 in-
center 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\
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    \279\ Ibid.
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c. Proposed Inclusion of Nocturnal In-Center 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

[[Page 36379]]

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.<SUP>281 282</SUP> 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.
---------------------------------------------------------------------------

    \280\ United States Renal Data System. 2015. ``2015 Researcher's 
Guide to the USRDS Database.'' https://usrds.org/media/2219/2015_usrds_researchers_guide_15.pdf.
    \281\ Mehrotra 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.
    \283\ Medicare Payment Advisory Commission. 2021. Report to the 
Congress: Medicare and the health care delivery system. Washington, 
DC: MedPAC. http://www.medpac.gov/docs/default-source/reports/mar21_medpac_report_to_the_congress_sec.pdf.
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    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 in-center 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 Sec.  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 Sec. Sec.  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 in-center 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 Sec.  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.

[[Page 36380]]

    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 Sec. Sec.  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, Pre-emptive 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 Pre-
emptive 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, Pre-
emptive 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:
    <bullet> C22.0-C22.9 (malignant neoplasm of liver and intrahepatic 
bile ducts),
    <bullet> C34.10-C34.12 (malignant neoplasm of upper lobe, bronchus 
or lung),
    <bullet> C34.2 (malignant neoplasm of middle lobe, bronchus or 
lung),
    <bullet> C34.30-C34.32 (malignant neoplasm of lower lobe, bronchus 
or lung),
    <bullet> C34.80-C34.82 (malignant neoplasm of overlapping sites of 
bronchus and lung),
    <bullet> C34.90-C34.92 (malignant neoplasm of unspecified part of 
bronchus or lung),
    <bullet> C38.0 (malignant neoplasm of heart),
    <bullet> C38.8 (malignant neoplasm of overlapping sites of heart, 
mediastinum and pleura),
    <bullet> C46.50-C46.52 (Kaposi's sarcoma of lung),

[[Page 36381]]

    <bullet> C64.1, C64.2, C64.9 (malignant neoplasm of kidney, except 
renal pelvis),
    <bullet> C78.00-C78.02 (secondary malignant neoplasm of lung),
    <bullet> C78.7 (secondary malignant neoplasm of liver and 
intrahepatic bile duct),
    <bullet> C79.00-C79.02 (secondary malignant neoplasm of kidney and 
renal pelvis),
    <bullet> C7A.090 (malignant carcinoid tumor of the bronchus and 
lung),
    <bullet> C7A.093 (malignant carcinoid tumor of the kidney), or
    <bullet> 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:
    <bullet> CPT[supreg] 96401-96402, 96405-96406, 96409, 96411, 96413, 
96415-96417, 96420, 96422-26423, 96425, 96440, 96446 (chemotherapy 
administration);
    <bullet> CPT[supreg] 96549 (unlisted chemotherapy procedure);
    <bullet> CPT[supreg] 77373 (stereotactic body radiation therapy);
    <bullet> CPT[supreg] 77401-77402, 77407, 77412 (radiation treatment 
delivery);
    <bullet> CPT[supreg] 77423 (high energy neutron radiation treatment 
delivery);
    <bullet> CPT[supreg] 77424-77425 (intraoperative radiation 
treatment delivery);
    <bullet> CPT[supreg] 77520, 77522-77523, 77525 (proton treatment 
delivery);
    <bullet> CPT[supreg] 77761-77763 (intracavitary radiation source 
application);
    <bullet> CPT[supreg] 77770-77772, 77778, 77789, 77799 (clinical 
brachytherapy radiation treatment);
    <bullet> CPT[supreg] 79005, 79101, 79200, 79300, 79403, 79440, 
79445, 79999 (radiopharmaceutical therapy);
    <bullet> 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);
    <bullet> 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);
    <bullet> 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);
    <bullet> 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);
    <bullet> 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
    <bullet> 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 Sec.  512.365(c) to 
exclude ESRD Beneficiaries and, if applicable, Pre-emptive 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 Sec.  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 Sec. Sec.  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 Sec.  512.370(b). Table 7 
details the achievement score scale described in Sec.  512.370(b).

[[Page 36382]]

[GRAPHIC] [TIFF OMITTED] TP09JY21.006

    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 dual-eligible or LIS recipients.
c. Proposed Achievement Benchmarking and Scoring
(1) Achievement Benchmarking and Scoring for MY3 Through MY10
    We propose to modify the percentile-based 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 Sec.  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

[[Page 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.
[GRAPHIC] [TIFF OMITTED] TP09JY21.007

    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

[[Page 36384]]

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 Sec.  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 Sec.  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 dual-eligible 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 dual-eligible 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 Sec.  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 
Sec.  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:

<bullet> Greater than 10 percent improvement relative to the Benchmark 
Year rate: 1.5 points
<bullet> Greater than 5 percent improvement relative to the Benchmark 
Year rate: 1 point
<bullet> Greater than 0 percent improvement relative to the Benchmark 
Year rate: 0.5 points
<bullet> 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 dual-
eligible 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 Sec.  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

[[Page 36385]]

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 Sec.  512.310 as the 
amount added to the ETC Participant's improvement score calculated as 
described in Sec.  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 dual-eligible 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 Sec.  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 Sec.  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 Sec.  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 Sec.  512.370(c) such that the improvement 
benchmarking and scoring methodology for MY1 and MY2 would be specified 
at Sec.  512.370(c)(1), and the improvement benchmarking and scoring 
methodology for MY3 through MY10, described above, would be specified 
at Sec.  512.370(c)(2). We seek comment on the proposal to modify Sec.  
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 
dual-eligible 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.

[[Page 36386]]

    We propose that ETC Participants in aggregation groups that fall 
below a low-volume 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 Sec.  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 low-
volume 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 Sec.  512.370(d) such that the calculation of the MPS 
for MY1 and MY2 is specified at Sec.  512.370(d)(1), and the 
calculation of the MPS for MY3 through MY10 is specified at Sec.  
512.370(d)(2). We propose that the formula for the MPS for MY3 through 
MY10 would be the following:

Modality Performance Score
= 2
x (Higher of the home dialysis achievement or (home dialysis 
improvement score + Health Equity Bonus [dagger]))
+ (Higher of the transplant achievement or (transplant improvement 
score + Health Equity Bonus [dagger]))

    [dagger] 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 Sec.  512.370(c)(2)(iii).
    We seek comment on our proposed definition for the Health Equity 
Incentive at Sec.  512.310 and our proposal to amend Sec.  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, Pre-emptive 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 Sec.  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 Sec.  
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 
Sec.  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 Beneficiary-Identifiable Data
    We propose to establish a process in new Sec.  512.390(b)(1) under 
which CMS would share certain beneficiary-identifiable 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 Sec.  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 Sec.  
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 beneficiary-identifiable 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-treatment-choices-model.
    Regarding the specific beneficiary-identifiable data that CMS would 
be required to share with ETC Participants,

[[Page 36387]]

we are proposing in Sec.  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 Sec.  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, self-dialysis, 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 (beneficiary-specific) 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 Sec.  
512.390(b)(1) for CMS to share this data with ETC Participants.
---------------------------------------------------------------------------

    \284\ Under 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.''
---------------------------------------------------------------------------

    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 user-friendly. 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

[[Page 36388]]

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 Sec.  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 
Sec.  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 Sec.  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 
beneficiary-identifiable 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-to-date 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

[[Page 36389]]

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 Sec.  
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 beneficiary-identifiable data, and on our 
proposal in Sec.  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 beneficiary-identifiable 
data.
(2) Content of ETC Data Sharing Agreement Provisions for Beneficiary-
Identifiable Data
    We are proposing in new Sec.  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 beneficiary-identifiable 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 beneficiary-identifiable 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 beneficiary-identifiable 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 
beneficiary-identifiable 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 in-center, 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

[[Page 36390]]

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 
beneficiary-identifiable 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 beneficiary-identifiable 
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 beneficiary-identifiable 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 beneficiary-identifiable 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 
Sec.  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 Sec.  
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 Sec.  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 Sec.  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 Sec.  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 beneficiary-identifiable 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 
Beneficiary-Identifiable 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 beneficiary-
identifiable 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 
beneficiary-identifiable data, in the same form and manner (which we 
expect to be a web-based platform).
    In the alternative, we considered providing the beneficiary-
identifiable data to ETC Participants via paper mail rather than 
through a web-based

[[Page 36391]]

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 web-
based 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 beneficiary-identifiable 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 web-based 
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 beneficiary-identifiable 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 Sec.  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, de-identified 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 beneficiary-identifiable 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 two-factor 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 beneficiary-identifiable 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 Sec.  410.48 of our 
regulations, Medicare Part B covers outpatient, face-to-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

[[Page 36392]]

individuals and entities that furnish and bill for kidney disease 
patient education services. We codified these waivers at Sec.  
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 Sec.  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 Sec.  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 Sec.  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 Sec.  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 Sec.  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 Sec.  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 
Sec.  410.78(b)(3) of our regulations under this proposed waiver, if 
finalized.

[[Page 36393]]

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 Sec.  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 Sec.  
512.390(b)(1)--namely licensed social workers and registered 
dieticians/nutrition professionals. The remaining clinicians currently 
specified in Sec.  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 Sec.  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 Sec.  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 Sec.  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.
---------------------------------------------------------------------------

    \285\ Table 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-kidney-disease/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).
    \286\ Morton, 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.
---------------------------------------------------------------------------

    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, self-
dialysis, or nocturnal in-center dialysis, rather than traditional in-
center dialysis.
    Accordingly, beginning January 1, 2022, we propose at Sec.  
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 anti-
kickback statute safe harbor for CMS-sponsored model patient incentives 
(42 CFR 1001.952(ii)(2)) would be available to protect cost-sharing 
support that is furnished in compliance with ETC

[[Page 36394]]

Model requirements with respect to kidney disease patient education 
services. If CMS makes such a determination, the safe harbor for CMS-
sponsored model patient incentives would protect an ETC Participant, as 
that term is defined at Sec.  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 Sec.  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 Sec.  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 cost-
sharing 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 
cost-sharing 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 Sec.  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 Sec.  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 Sec.  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 Sec.  
512.397(c)(1). We propose that, if we make this determination, we would 
specify in regulation text at Sec.  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

[[Page 36395]]

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 cost-sharing 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 Sec.  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 Sec.  512.310, to offer 
coinsurance support for kidney disease patient education services to 
beneficiaries who are eligible for such services, including those 
eligible under Sec.  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 Sec.  512.397(b)(1) through (4) in their 
entirety to accomplish a few goals. First, we propose to make 
conforming changes throughout Sec.  512.397(b) to the manner in which 
CMS discusses kidney disease patient education services. Currently, 
Sec.  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 
Sec.  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 Sec.  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 Sec.  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 Sec.  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 Sec.  
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 Sec.  512.397(b)(1) of 
this part.
    We solicit public comments on these proposed changes to Sec.  
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 Sec.  
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 Sec.  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.
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    \287\ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2924406/#B38.
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    Based on these issues, we seek feedback about how CMS can test and

[[Page 36396]]

use Medicare payment policy, under the ETC model, to promote placement 
of PD catheters. Specifically, we are seeking feedback on the following 
questions:
    <bullet> What are the key barriers to increased placement of PD 
catheters?
    <bullet> How can CMS promote placement of PD catheters in a more 
timely manner?
    <bullet> 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)[supreg] 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:
    <bullet> What domains of a patient experience of care with home 
dialysis would be the most useful to assess and why?
    <bullet> 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?
    <bullet> How would a patient experience measure be best used to 
further the purpose of the ETC Model?
    <bullet> How should CMS use a patient experience measure to assess 
the quality of care of beneficiaries?
    <bullet> 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:
    <bullet> What is the frequency with which CMS should disseminate 
the results?
    <bullet> 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 facility-level 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, oral-only 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

[[Page 36397]]

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-Service-Payment/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 low-volume under Sec.  
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 Sec.  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 Sec.  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 Sec.  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

[[Page 36398]]

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 Medicare-
eligible 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 Sec.  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\
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    \288\ http://www.medpac.gov/docs/default-source/reports/jun20_ch7_reporttocongress_sec.pdf?sfvrsn=0.
    \289\ https://www.cms.gov/files/document/end-stage-renal-disease-prospective-payment-system-technical-expert-panel-summary-report-april-2021.pdf.
    \290\ https://www.cms.gov/files/document/end-stage-renal-disease-prospective-payment-system-technical-expert-panel-summary-report-april-2021.pdf.
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    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.
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    \291\ https://www.cms.gov/files/document/end-stage-renal-disease-prospective-payment-system-technical-expert-panel-summary-report-april-2021.pdf.
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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 non-viable 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.
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    \292\ https://www.cms.gov/files/document/end-stage-renal-disease-prospective-payment-system-technical-expert-panel-summary-report-april-2021.pdf.
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    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 low-volume 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 two-regression 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.
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    \293\ http://www.medpac.gov/docs/default-source/reports/jun20_ch7_reporttocongress_sec.pdf?sfvrsn=0.
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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.
    <bullet> Should a distinction other than census tract information 
be considered?
    <bullet> 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)?
    <bullet> What are the concerns for facilities that would lose the 
LVPA under the LVI methodology?
    <bullet> What are the concerns about the potential for gaming 
within the LVI methodology?

[[Page 36399]]

    <bullet> 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:
    <bullet> The current adult case-mix adjustors were calculated using 
old data (that is, 2012-2013 claims and cost report data);
    <bullet> current adjustors may not align with resource-intensive 
patient-level services such as isolation rooms, behavioral issues, or 
neurocognitive issues;
    <bullet> apportioned composite rate costs (such as labor and 
capital related costs), from the cost reports, used in the case-mix 
adjustment are currently only observable at the facility level and do 
not include patient or treatment level variations; and
    <bullet> 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 single-equation 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.
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    \294\ http://medpac.gov/docs/default-source/comment-letters/medpac-comment-on-cmss-/proposed-rule-on-the-esrd-prospective-payment-/system-and-the-dmepos-competiti.pdf?sfvrsn=0.
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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

[[Page 36400]]

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.
---------------------------------------------------------------------------

    \295\ https://www.cms.gov/files/document/mm11871.pdf.
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    During the 2020 TEP, the data contractor for CMS presented and the 
panelists discussed potential refinement to concerns regarding the 
current case-mix 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 beneficiary-facility-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 case-mix adjusters.\296\ The 
suggested case-mix 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.
---------------------------------------------------------------------------

    \296\ https://www.cms.gov/files/document/end-stage-renal-disease-prospective-payment-system-technical-expert-panel-summary-report-april-2021.pdf.
    \297\ https://www.cms.gov/files/document/end-stage-renal-disease-prospective-payment-system-technical-expert-panel-summary-report-april-2021.pdf.
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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.
    <bullet> 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?
    <bullet> 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?
    <bullet> 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?
    <bullet> What challenges would be encountered in reporting 
treatment duration on claims, using one of the options discussed?
    <bullet> 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

[[Page 36401]]

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 Sec.  413.237. 
Under Sec.  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.
---------------------------------------------------------------------------

    \298\ The 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.
---------------------------------------------------------------------------

    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 loss-sharing 
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 Sec.  
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 
Sec.  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\
---------------------------------------------------------------------------

    \299\ Outlier 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.
---------------------------------------------------------------------------

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.

[[Page 36402]]

    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-Service-Payment/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.
    <bullet> 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\
---------------------------------------------------------------------------

    \300\ The 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.
---------------------------------------------------------------------------

    ++ How many years of data should be included in calculation of this 
trend to best capture changes in treatment patterns?
    <bullet> 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?
    <bullet> 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.
    <bullet> Adoption of the suggested methodology may account for 
systematic changes in the use of high-cost 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 hospital-based 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 Sec. Sec.  
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 Sec.  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

[[Page 36403]]

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 case-mix 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 
pediatric-specific 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 per-treatment is 
higher in pediatric facilities and pediatric comorbidities require more 
(specialized) staffing. Dialysis for

[[Page 36404]]

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 per-treatment 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 pediatric-specific 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.
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    \301\ https://www.cms.gov/files/document/end-stage-renal-disease-prospective-payment-system-technical-expert-panel-summary-report-april-2021.pdf.
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    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.
    <bullet> 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?
    <bullet> 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?
    <bullet> If duration of treatment is not a valid proxy for 
composite rate costs per treatment, what are alternative proxies to 
consider?
    <bullet> What, if any, are the specific concerns about 
incorporating pediatric patients into the estimation of multipliers for 
both the adult and pediatric populations?
    <bullet> 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?
    <bullet> Are there additional costs factors for pediatric patients 
that are not adequately captured on the 72X claim?

[[Page 36405]]

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 (CMS-Form-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; related-party 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 non-
allowable 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/Cost-Reports/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.

[[Page 36406]]

    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:
    <bullet> 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.
    <bullet> 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.
    <bullet> 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 intermediate-level 
providers, as appropriate.
d. Managerial and Administrative Labor Allocation
    The data contractor and TEP panelists discussed Medicare cost 
report's non-direct 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,

[[Page 36407]]

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.
    <bullet> 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?
    <bullet> 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?
    <bullet> 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.
    <bullet> 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.
    <bullet> 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)?
    <bullet> What challenges would hospital-based facilities face were 
the hospital-based 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?
    <bullet> 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?
    <bullet> 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.
---------------------------------------------------------------------------

    \302\ The 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.
---------------------------------------------------------------------------

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

[[Page 36408]]

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 two-thirds 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.
    <bullet> What degree of specificity is needed in the reporting of 
pediatric dialysis costs?
    <bullet> 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.
    <bullet> 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?
    <bullet> 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)?
    <bullet> What obstacles do providers face in reporting pediatric 
specific costs of dialysis treatment? How can these obstacles be 
overcome?
    <bullet> 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 non-
specialized 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 Sec.  413.220, updated by the ESRD 
bundled market basket percentage increase factor minus a productivity 
adjustment as set forth in Sec.  413.196(d)(1), adjusted for wages as 
set forth in Sec.  413.231, and adjusted by any other amounts deemed 
appropriate by the Secretary under Sec.  413.373. We codified this 
policy in Sec.  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

[[Page 36409]]

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-emergency-declaration-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:
    <bullet> The need for the information collection and its usefulness 
in carrying out the proper functions of our agency.
    <bullet> The accuracy of our estimate of the information collection 
burden.
    <bullet> The quality, utility, and clarity of the information to be 
collected.
    <bullet> 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

[[Page 36410]]

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.
---------------------------------------------------------------------------

    \304\ https://www.bls.gov/oes/current/oes292098.htm. Accessed on 
June 7, 2021.
---------------------------------------------------------------------------

    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 x $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 x 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 x $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 patient-year 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

[[Page 36411]]

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 x 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 x $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

[[Page 36412]]

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 re-estimated 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 x 
$110.74). Therefore, we estimate that the total cost of reviewing this 
regulation is $ 78,903 ($692.13 x 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

[[Page 36413]]

payments compared to estimated payments to ESRD facilities in CY 2021.
[GRAPHIC] [TIFF OMITTED] TP09JY21.008


[[Page 36414]]


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

[[Page 36415]]

[GRAPHIC] [TIFF OMITTED] TP09JY21.010


[[Page 36416]]


[GRAPHIC] [TIFF OMITTED] TP09JY21.011

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 case-mix 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.

[[Page 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.
[GRAPHIC] [TIFF OMITTED] TP09JY21.012

    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.
[GRAPHIC] [TIFF OMITTED] TP09JY21.013

    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

[[Page 36418]]

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.
[GRAPHIC] [TIFF OMITTED] TP09JY21.014

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

[[Page 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.
[GRAPHIC] [TIFF OMITTED] TP09JY21.015

    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.
[GRAPHIC] [TIFF OMITTED] TP09JY21.016

    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

[[Page 36420]]

ESRD QIP, the actual impact of the PY 2025 ESRD QIP may vary 
significantly from the values provided here.
BILLING CODE 4120-01-P
[GRAPHIC] [TIFF OMITTED] TP09JY21.017

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 Hospital-Acquired 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.

[[Page 36421]]

[GRAPHIC] [TIFF OMITTED] TP09JY21.018

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 
Sec. Sec.  512.340 and 512.350, and to certain home dialysis-related 
claims for Managing Clinicians, as described in Sec. Sec.  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 Sec. Sec.  512.375(a) and 512.380 for ESRD facilities and 
Sec. Sec.  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 Sec.  
512.365(b), and transplant rate, as described in Sec.  512.365(c), for 
each MY. The ETC Participant's transplant rate will be aggregated, as 
described in Sec.  512.365(e), and the ETC Participant's home dialysis 
rate will be aggregated, as described in Sec.  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 Sec.  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

[[Page 36422]]

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:
    <bullet> 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.
    <bullet> Exclude beneficiaries with a diagnosis of and who are 
receiving chemotherapy or radiation for vital solid organ cancer from 
the transplant rate calculation.
    <bullet> 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).
    <bullet> 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 risk-adjustment 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 (Sec.  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 self-dialysis, condition code 72 (Sec.  512.365) and in-
center nocturnal dialysis, based on any of the claims' lines 1-5 HCPCS 
codes equal to the ``UJ'' modifier. Self-care 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 Sec.  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[supreg] codes 90965 and 90966 (Sec.  512.345). Beneficiaries 
receiving in-center dialysis were identified by outpatient claims with 
CPT[supreg] codes 90957, 90958, 90959, 90960, 90961, and 90962 (Sec.  
512.360). Last, following the low-volume threshold described in Sec.  
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

[[Page 36423]]

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 one-third 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 in-center 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*(in-center self-dialysis beneficiary months); 
therefore, not including nocturnal in-center 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*(in-center self-dialysis 
beneficiary months) + 0.5*(nocturnal in-center dialysis beneficiary 
months).
    The number of beneficiaries on in-center 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:
    <bullet> MYs 3 and 4: Comparison Geographic Area percentiles*1.10,
    <bullet> MYs 5 and 6: Comparison Geographic Area percentiles*1.20,
    <bullet> MYs 7 and 8: Comparison Geographic Area percentiles*1.30, 
and
    <bullet> 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 dual-eligible 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

[[Page 36424]]

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 pre-pandemic 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.
---------------------------------------------------------------------------

    \305\ UNOS. 2021. COVID-19 and Solid Organ Transplants. 
Transplant and Waitlist Data Visualizations. https://unos.org/covid/
.
---------------------------------------------------------------------------

    No changes were proposed to the payment structure for the HDPA 
calculation described in the final rule (Sec.  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[supreg] 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
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[[Page 36425]]

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[[Page 36426]]


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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 cost-sharing. 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

[[Page 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 one-third 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

[[Page 36428]]

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.
---------------------------------------------------------------------------

    \306\ United States Renal Data System. 2020. ``ADR Reference 
Table E6 Renal Transplants by Donor Type.'' https://adr.usrds.org/2020/reference-tables.
    \307\ Organ Procurement and Transplantation Network. 2021. 
``Current US Waiting List, Overall by Organ.'' https://optn.transplant.hrsa.gov/data/view-data-reports/national-data/#.
---------------------------------------------------------------------------

(6) Effects of the Transplant Waitlist Rate
    This proposed rule includes the transplant waitlist rate described 
in the Specialty Care Models final rule (Sec.  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.

[[Page 36429]]

[GRAPHIC] [TIFF OMITTED] TP09JY21.021

    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-business-size-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.

[[Page 36430]]

[GRAPHIC] [TIFF OMITTED] TP09JY21.022

    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 Sec.  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/small-businesssize-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

[[Page 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/EndStageRenalDiseaseSystemFile.html. Readers who 
experience any problems accessing the Addenda or LDS files, should 
contact <a href="/cdn-cgi/l/email-protection#773224253327160e1a12190337141a04591f1f0459101801"><span class="__cf_email__" data-cfemail="c98c9a9b8d99a8b0a4aca7bd89aaa4bae7a1a1bae7aea6bf">[email protected]</span></a>.
    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

0
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.

0
2. Section 413.177 is amended by revising paragraph (a) introductory 
text to read as follows:


Sec.  413.177   Quality incentive program payment.

    (a) With respect to renal dialysis services as defined under Sec.  
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 Sec.  413.178 to meet or 
exceed the minimum total performance score (as defined at Sec.  
413.178(a)(8)) established by CMS for a payment year (as defined at 
Sec.  413.178(a)(10)), payments otherwise made to the facility under 
Sec.  413.230 for renal dialysis services during the payment year, will 
be reduced by up to 2 percent as follows:
0
3. Section 413.178 is amended by adding paragraph (h) to read as 
follows:


Sec.  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

0
4. The authority citation for part 512 continues to read as follows:

    Authority: 42 U.S.C. 1302, 1315(a), and 1395hh.

0
5. Section 512.160 is amended by adding paragraph (a)(9), and by 
revising paragraph (b)(6) as follows:


Sec.  512.160   Remedial action.

    (a) * * *
    (9) For the ETC Model only, has misused or disclosed 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.
    (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 Sec.  512.397(c), to reduce or waive the

[[Page 36432]]

coinsurance for kidney disease patient education services.
0
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:


Sec.  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 Sec.  
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 Sec.  410.48(a)) who is an ETC Participant.
* * * * *
0
7. Section 512.360 is amended by revising paragraph (c)(2)(ii) 
introductory text and adding paragraph (c)(2)(iii) to read as follows:


Sec.  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 Pre-emptive 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.
0
8. Section 512.365 is amended by--
0
a. Revising paragraphs (b)(1)(ii) and (b)(2)(ii), and
0
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:


Sec.  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

[[Page 36433]]

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[supreg] 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[supreg] 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

[[Page 36434]]

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. Pre-emptive LDT 
Beneficiaries are identified using information about living donor 
transplants from the SRTR Database and Medicare claims data.
* * * * *
0
9. Section 512.370 is amended by revising paragraphs (b), (c), and (d) 
to read as follows:


Sec.  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 Sec.   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   1.1 * (90th+       1.2 * (90th+      1.3 * (90th+      1.4 * (90th+            2
 rates for Comparison            Percentile of      Percentile of     Percentile of     Percentile of
 Geographic Areas during the     benchmark rates    benchmark rates   benchmark rates   benchmark rates
 Benchmark Year.                 for Comparison     for Comparison    for Comparison    for Comparison
                                 Geographic Areas   Geographic        Geographic        Geographic
                                 during the         Areas during      Areas during      Areas during
                                 Benchmark Year).   the Benchmark     the Benchmark     the Benchmark
                                                    Year).            Year).            Year).
75th+ Percentile of benchmark   1.1 * (75th+       1.2 * (75th+      1.3 * (75th+      1.4 * (75th+          1.5
 rates for Comparison            Percentile of      Percentile of     Percentile of     Percentile of
 Geographic Areas during the     benchmark rates    benchmark rates   benchmark rates   benchmark rates
 Benchmark Year.                 for Comparison     for Comparison    for Comparison    for Comparison
                                 Geographic Areas   Geographic        Geographic        Geographic
                                 during the         Areas during      Areas during      Areas during
                                 Benchmark Year).   the Benchmark     the Benchmark     the Benchmark
                                                    Year).            Year).            Year).
50th+ Percentile of benchmark   1.1 * (50th+       1.2 * (50th+      1.3 * (50th+      1.4 * (50th+            1
 rates for Comparison            Percentile of      Percentile of     Percentile of     Percentile of
 Geographic Areas during the     benchmark rates    benchmark rates   benchmark rates   benchmark rates
 Benchmark Year.                 for Comparison     for Comparison    for Comparison    for Comparison
                                 Geographic Areas   Geographic        Geographic        Geographic
                                 during the         Areas during      Areas during      Areas during
                                 Benchmark Year).   the Benchmark     the Benchmark     the Benchmark
                                                    Year).            Year).            Year).
30th+ Percentile of benchmark   1.1 * (30th+       1.2 * (30th+      1.3 * (30th+      1.4 * (30th+          0.5
 rates for Comparison            Percentile of      Percentile of     Percentile of     Percentile of
 Geographic Areas during the     benchmark rates    benchmark rates   benchmark rates   benchmark rates
 Benchmark Year.                 for Comparison     for Comparison    for Comparison    for Comparison
                                 Geographic Areas   Geographic        Geographic        Geographic
                                 during the         Areas during      Areas during      Areas during
                                 Benchmark Year).   the Benchmark     the Benchmark     the Benchmark
                                                    Year).            Year).            Year).

[[Page 36435]]

 
<30th Percentile of benchmark   1.1 * (<30th       1.2 * (<30th      1.3 * (<30th      1.4 * (<30th            0
 rates for Comparison            Percentile of      Percentile of     Percentile of     Percentile of
 Geographic Areas during the     benchmark rates    benchmark rates   benchmark rates   benchmark rates
 Benchmark Year.                 for Comparison     for Comparison    for Comparison    for Comparison
                                 Geographic Areas   Geographic        Geographic        Geographic
                                 during the         Areas during      Areas during      Areas during
                                 Benchmark Year).   the Benchmark     the Benchmark     the Benchmark
                                                    Year).            Year).            Year).
----------------------------------------------------------------------------------------------------------------

    (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 Pre-emptive 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 Sec. Sec.  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, Pre-emptive 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 Pre-emptive 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

[[Page 36436]]

= 2 x (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
x (Higher of the home dialysis achievement or (home dialysis 
improvement score + Health Equity Bonus [dagger]))
+ (Higher of the transplant achievement or (transplant improvement 
score
+ Health Equity Bonus [dagger]))

[dagger] The Health Equity Incentive is applied to the home dialysis 
improvement score or transplant improvement score only if earned by the 
ETC Participant.
0
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:


Sec.  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 beneficiary-identifiable 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 
beneficiary-identifable 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 
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 data sharing agreement, the ETC Participant 
will no longer be eligible to retrieve beneficiary-identifiable 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, de-identified 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.
* * * * *
0
11. Section 512.397 is amended by revising the section heading and 
paragraph (b) and adding paragraph (c) to read as follows:


Sec.  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 Sec.  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

[[Page 36437]]

services to be provided by clinical staff (as defined at Sec.  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 Sec.  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 Sec.  
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 
Sec.  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 
Sec.  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 Sec.  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 Sec.  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 Sec.  
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 Sec.  410.48(b) or Sec.  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 Sec.  410.48 and Sec.  
512.397(b).
    (2) The ETC Participant must maintain and provide the government 
with access to records of the following information in accordance with 
Sec.  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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