[Federal Register Volume 89, Number 97 (Friday, May 17, 2024)]
[Proposed Rules]
[Pages 43518-43634]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2024-09989]
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Vol. 89
Friday,
No. 97
May 17, 2024
Part II
Department of Homeland Security
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Cybersecurity and Infrastructure Security Agency
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42 CFR Part 512
Medicare Program; Alternative Payment Model Updates and the Increasing
Organ Transplant Access (IOTA) Model; Proposed Rule
Federal Register / Vol. 89 , No. 97 / Friday, May 17, 2024 / Proposed
Rules
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DEPARTMENT OF HEALTH AND HUMAN SERVICES
Centers for Medicare & Medicaid Services
42 CFR Part 512
[CMS-5535-P]
RIN 0938-AU51
Medicare Program; Alternative Payment Model Updates and the
Increasing Organ Transplant Access (IOTA) Model
AGENCY: Centers for Medicare & Medicaid Services (CMS), Department of
Health and Human Services (HHS).
ACTION: Proposed rule.
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SUMMARY: This proposed rule describes a new mandatory Medicare payment
model, the Increasing Organ Transplant Access Model (IOTA Model), that
would test whether performance-based incentive payments paid to or owed
by participating kidney transplant hospitals increase access to kidney
transplants for patients with end-stage renal disease (ESRD) while
preserving or enhancing the quality of care and reducing Medicare
expenditures. This proposed rule also includes standard provisions that
would apply to Innovation Center models whose first performance period
begins on or after January 1, 2025, and also would apply, in whole or
part, to any Innovation Center model whose first performance period
begins prior to January 1, 2025 should such model's governing
documentation incorporate the provisions by reference in whole or in
part. The proposed standard provisions relate to beneficiary
protections; cooperation in model evaluation and monitoring; audits and
records retention; rights in data and intellectual property; monitoring
and compliance; remedial action; model termination by CMS; limitations
on review; miscellaneous provisions on bankruptcy and other
notifications; and the reconsideration review process.
DATES: To be assured consideration, comments must be received at one of
the addresses provided below, by July 16, 2024.
ADDRESSES: In commenting, please refer to file code CMS-5535-P.
Comments, including mass comment submissions, must be submitted in
one of the following three ways (please choose only one of the ways
listed):
1. Electronically. You may submit electronic comments on this
regulation to http://www.regulations.gov. Follow the ``Submit a
comment'' instructions.
2. By regular mail. You may mail written comments to the following
address ONLY: Centers for Medicare & Medicaid Services, Department of
Health and Human Services, Attention: CMS-5535-P, P.O. Box 8013,
Baltimore, MD 21244-8013.
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-5535-
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: [email protected] for
questions related to the Increasing Organ Transplant Access Model.
[email protected] for questions related to the
Standard Provisions for Innovation Center Models.
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 commenter will take actions to harm an individual. CMS
encourages 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. All Rights Reserved. CPT[supreg] is a registered trademark
of the American Medical Association (AMA). Applicable Federal
Acquisition Regulations (FAR) and Defense Federal Acquisition
Regulations (DFAR) apply.
I. Executive Summary
A. Purpose
Section 1115A of the Social Security Act (the Act) gives the
Secretary of Health and Human Services the authority to test innovative
payment and service delivery models to reduce program expenditures in
Medicare, Medicaid, and the Children's Health Insurance Program (CHIP)
while preserving or enhancing the quality of care furnished to
individuals covered by such programs. This proposed rule describes a
new mandatory Medicare payment model to be tested under section 1115A
of the Act--the Increasing Organ Transplant Access Model (IOTA Model)--
which would begin on January 1, 2025 and end on December 31, 2030. In
this proposed rule, we propose payment policies, participation
requirements, and other provisions to test the IOTA Model. We propose
to test whether performance-based incentives (including both upside and
downside risk) for participating kidney transplant hospitals can
increase the number of kidney transplants (including both living donor
and deceased donor transplants) furnished to End Stage Renal Disease
(ESRD) patients, encourage investments in care processes and patterns
with respect to patients who need kidney transplants, encourage
investments in value-based care and improvement activities, and promote
kidney transplant hospital accountability by tying payments to value.
The IOTA Model is also intended to advance health equity by improving
equitable access to the transplantation ecosystem through design
features such as a proposed health equity plan requirement to address
health outcome disparities and a health equity performance adjustment.
This proposed rule also includes proposed standard provisions that
would apply to Innovation Center models whose first performance periods
begin on or after January 1, 2025, unless otherwise specified in a
model's governing documentation, as well as to Innovation Center models
whose first performance periods begin prior to January 1, 2025,
provided the standard provisions are incorporated into such models'
governing documentation. The proposed standard provisions address
beneficiary protections; cooperation in model evaluation and
monitoring; audits and record retention; rights in data and
intellectual property; monitoring and compliance; remedial action;
model termination by CMS; limitations on review; miscellaneous
provisions on bankruptcy and other
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notifications; and the reconsideration review process.
We seek public comment on these proposals, the alternatives
considered, and the request for information (RFI) in section III.D. of
this proposed rule.
B. Summary of the Proposed Provisions
1. Standard Provisions for Innovation Center Models
The proposed standard provisions for Innovation Center models would
be applicable to all Innovation Center models whose first performance
periods begin on or after January 1, 2025, subject to any limitations
specified in a model's governing documentation. The proposed standard
provisions also would apply to all Innovation Center models whose first
performance periods begin prior to January 1, 2025, provided the
standard provisions are incorporated into such models' governing
documentation.
We are proposing to codify these standard provisions to increase
transparency, efficiency, and clarity in the operation and governance
of Innovation Center models, and to avoid the need to restate the
provisions in each model's governing documentation. The proposed
standard provisions include terms that have been repeatedly
memorialized, with minimal variation, in existing models' governing
documentation. The proposed standard provisions are not intended to
encompass all of the terms and conditions that would apply to each
Innovation Center model, because each model embodies unique design
features and implementation plans that may require additional, more
tailored provisions, including with respect to payment methodology,
care delivery and quality measurement, that would continue to be
included in each model's governing documentation. Model-specific
provisions applicable to the IOTA Model proposed herein are described
in section III of this proposed rule.
2. Model Overview--Proposed Increasing Organ Transplant Access Model
a. Proposed IOTA Model Overview
End-Stage Renal Disease (ESRD) is a medical condition in which a
person's kidneys cease functioning on a permanent basis, leading to the
need for a regular course of long-term dialysis or a kidney transplant
to maintain life.\1\ The best treatment for most patients with kidney
failure is kidney transplantation. Nearly 808,000 people in the United
States are living with ESRD, with about 69 percent on dialysis and 31
percent with a kidney transplant.\2\ For ESRD patients, regular
dialysis sessions or a kidney transplant is required for survival.
Relative to dialysis, a kidney transplant can improve survival, reduce
avoidable health care utilization and hospital acquired conditions,
improve quality of life, and lower Medicare expenditures.\3\ \4\
However, despite these benefits, evidence shows low rates of ESRD
patients placed on kidney transplant hospitals' waitlists, a decline in
living donors over the past 20 years, and underutilization of available
donor kidneys, coupled with increasing rates of donor kidney discards,
and wide variation in kidney offer acceptance rates and donor kidney
discards by region and across kidney transplant hospitals.\5\ \6\
Further, there are substantial disparities in both deceased and living
donor transplantation rates among structurally disadvantaged
populations. Strengthening and improving the performance of the organ
transplantation system is a priority for the Department of Health and
Human Services (HHS). Consistent with this priority, and through joint
efforts with HHS' Health Resources and Services Administration (HRSA),
the proposed IOTA Model would aim to reduce Medicare expenditures and
improve performance and equity in kidney transplantation by creating
performance-based incentive payments for participating kidney
transplant hospitals tied to access and quality of care for ESRD
patients on the hospitals' waitlists.
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\1\ End-Stage Renal Disease (ESRD) [verbar] CMS. (n.d.). https://www.cms.gov/medicare/coordination-benefits-recovery/overview/end-stage-renal-disease-esrd.
\2\ United States Renal Data System. 2022 USRDS 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, 2022.
\3\ Tonelli, M., Wiebe, N., Knoll, G., Bello, A., Browne, S.,
Jadhav, D., Klarenbach, S., & Gill, J. (2011). Systematic review:
kidney transplantation compared with dialysis in clinically relevant
outcomes. American Journal of Transplantation: Official Journal of
the American Society of Transplantation and the American Society of
Transplant Surgeons, 11(10), 2093-2109. https://doi.org/10.1111/j.1600-6143.2011.03686.xhttps://doi.org/10.1111/j.1600-6143.2011.03686.
\4\ Cheng, X. S., Han, J., Braggs-Gresham, J. L., Held, P. J.,
Busque, S., Roberts, J. P., Tan, J. C., Scandling, J. D., Chertow,
G. M., & Dor, A. (2022). Trends in Cost Attributable to Kidney
Transplantation Evaluation and Waitlist Management in the United
States, 2012-2017. JAMA network open, 5(3), e221847. https://doi.org/10.1001/jamanetworkopen.2022.184.
\5\ Al Ammary, F., Bowring, M. G., Massie, A. B., Yu, S.,
Waldram, M. M., Garonzik-Wang, J., Thomas, A. G., Holscher, C. M.,
Qadi, M. A., Henderson, M. L., Wiseman, A. C., Gralla, J., Brennan,
D. C., Segev, D. L., & Muzaale, A. D. (2019). The changing landscape
of live kidney donation in the United States from 2005 to 2017.
American journal of transplantation: official journal of the
American Society of Transplantation and the American Society of
Transplant Surgeons, 19(9), 2614-2621. https://doi.org/10.1111/ajt.15368.
\6\ Mohan, S., Yu, M., King, K. L., & Husain, S. A. (2023).
Increasing Discards as an Unintended Consequence of Recent Changes
in United States Kidney Allocation Policy. Kidney international
reports, 8(5), 1109-1111. https://doi.org/10.1016/j.ekir.2023.02.1081.
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The proposed IOTA Model would be a mandatory model that would begin
on January 1, 2025 and end on December 31, 2030, resulting in a 6-year
model performance period (``model performance period'') comprised of 6
individual performance years (each a ``performance year'' or ``PY'').
The proposed IOTA Model would test whether performance-based incentives
paid to, or owed by, participating kidney transplant hospitals can
increase access to kidney transplants for patients with ESRD, while
preserving or enhancing quality of care and reducing Medicare
expenditures. CMS would select kidney transplant hospitals to
participate in the IOTA Model through the methodology proposed in
section III.C.3.d of this proposed rule. As this would be a mandatory
model, the selected kidney transplant hospitals would be required to
participate. CMS would measure and assess the participating kidney
transplant hospitals' performance during each PY across three
performance domains: achievement, efficiency, and quality.
The achievement domain would assess each participating kidney
transplant hospital on the overall number of kidney transplants
performed during a PY, relative to a participant-specific target. The
efficiency domain would assess the kidney organ offer acceptance rates
of each participating kidney transplant hospital relative to the
national rate. The quality domain would assess the quality of care
provided by the participating kidney transplant hospitals across a set
of proposed outcome metrics and quality measures. Each participating
kidney transplant hospital's performance score across these three
domains would determine its final performance score and corresponding
amount for the performance-based incentive payment that CMS would pay
to, or the payment that would be owed by, the participating kidney
transplant hospital. The proposed upside risk payment would be a lump
sum payment paid by CMS after the end of a PY to a participating kidney
transplant hospital with a final performance score of 60 or greater.
Conversely, beginning after PY 2, the downside risk payment would be a
lump sum payment paid to CMS by any participating kidney transplant
hospital
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with a final performance score of 40 or lower. We are not proposing a
downside risk payment for PY 1 of the model.
b. Model Scope
We propose that participation in the IOTA Model would be mandatory
for 50 percent of all eligible kidney transplant hospitals in the
United States. We anticipate that a total of approximately 90 kidney
transplant hospitals will be selected to participate in the IOTA Model.
As discussed in section III.C.3.b. of this proposed rule, we believe
that mandatory participation is necessary to minimize the potential for
selection bias and to ensure a representative sample size nationally,
thereby guaranteeing that there will be adequate data to evaluate the
model test.
We propose that eligible kidney transplant hospitals would be those
that: (1) performed at least eleven kidney transplants for patients 18
years of age or older annually regardless of payer type during the
three-year period ending 12 months before the model's start date; and
(2) furnished more than 50 percent of the hospital's annual kidney
transplants to patients 18 years of age or older during that same
period. We propose to select the kidney transplant hospitals that will
be required to participate in the IOTA Model from the group of eligible
kidney transplant hospitals using a stratified random sampling of
donation service areas (``DSAs'') to ensure that there is a fair
selection process and representative group of participating kidney
transplant hospitals. For the purposes of this proposed rule, a DSA has
the same meaning given to that term at 42 CFR 486.302.
c. Performance Assessment
We propose to assess each IOTA participants' performance across
three performance domains during each PY of the model, with a maximum
possible final performance score of 100 points. The three performance
domains would include: (1) an achievement domain worth up to 60 points,
(2) an efficiency domain worth up to 20 points, and (3) a quality
domain worth up to 20 points.
The achievement domain would assess the number of kidney
transplants performed by each IOTA participant for attributed patients,
with performance on this domain worth up to 60 points. The final
performance score would be heavily weighted on the achievement domain
to align with the IOTA Model's goal to increase access to kidney
transplants. The IOTA Model theorizes that improvement activities,
including those aimed at reducing unnecessary deceased donor discards
and increasing living donors, may help increase access to kidney
transplants.
We propose that CMS would set a target number of kidney transplants
for each IOTA participant for each PY to measure the IOTA participant's
performance in the achievement domain (the ``transplant target''), as
described in section III.C.5.c of this proposed rule. Each IOTA
participant's transplant target for a given PY would be based on the
IOTA participant's historical volume of deceased and living donor
transplants furnished to attributed patients in the relevant baseline
years, adjusted by the national trend rate in the number of kidney
transplants performed and further adjusted by the proportion of
transplants furnished by the IOTA participant to attributed patients
who are low income. Section III.C.5.c. of this proposed rule describes
the variation in the number of kidney transplants performed across
kidney transplant hospitals, which would make it challenging to set
transplant targets on a regional or national basis. The IOTA Model
would therefore set a transplant target that is specific to each IOTA
participant to address this concern, while still accounting for the
national trend rate in the number of kidney transplants performed. It
is expected that IOTA participants' transplant targets may change from
PY to PY because of the way in which the transplant target would be
calculated.
The efficiency domain would assess the kidney organ offer
acceptance rate ratio for each IOTA participant. The kidney organ offer
acceptance rate ratio measures the number of kidneys an IOTA
participant accepts for transplant over the expected value, based on
variables such as kidney quality. Points for the kidney organ offer
acceptance rate ratio would be determined relative to either the kidney
organ offer acceptance rate ratio across all kidney transplant
hospitals, or the IOTA participant's own past kidney organ offer
acceptance rate ratio, with performance on the efficiency domain being
worth up to 20 points.
Finally, the quality domain would assess IOTA participants'
performance on post-transplant outcomes in addition to three quality
measures--the CollaboRATE Shared Decision-Making Score, Colorectal
Cancer Screening, and the 3-Item Care Transition Measure, with
performance on this domain being worth up to 20 points.
Each IOTA participant's final performance score would be the sum of
the points earned for each domain: achievement, efficiency, and
quality. The final performance score in a PY would be determinative of
whether the IOTA participant would be eligible to receive an upside
risk payment from CMS, fall into the neutral zone where no upside or
downside risk payment would apply, or owe a downside risk payment to
CMS for the PY as described in section III.C.6. of this proposed rule.
d. Performance-Based Incentive Payment Formula
Each IOTA participant's final performance score would determine
whether: (1) CMS would pay an upside risk payment to the IOTA
participant; (2) the IOTA participant would fall into a neutral zone,
in which case no performance-based incentive payment would be paid to
or owed by the IOTA participant; or (3) the IOTA participant would owe
a downside risk payment to CMS. For a final performance score above 60,
CMS would apply the formula for the upside risk payment, which we
propose would be equal to the IOTA participant's final performance
score minus 60, then divided by 60, then multiplied by $8,000, then
multiplied by the number of kidney transplants furnished by the IOTA
participant to attributed patients with Medicare as their primary or
secondary payer during the PY. Final performance scores below 60 in PY
1 and final performance scores of 41 to 59 in PYs 2-6 would fall in the
neutral zone where there would be no payment owed to the IOTA
participant or CMS.
We propose to phase-in the downside risk payment beginning in PY2.
We explain in section III.C.5.b. of this proposed rule that new
entrants to value-based payment models may need a ramp up period before
they are able to accept downside risk. Thus, the IOTA Model proposes an
upside risk-only approach for PY 1 as an incentive in each of the three
performance domains. This would give IOTA participants time to
consider, invest in, and implement value-based care and quality
improvement initiatives before downside risk payments would begin.
Beginning in PY 2, for a final performance score of 40 and below, CMS
would apply the formula for the downside risk payment, which would be
equal to the IOTA participant's final performance score minus 40, then
divided by 40, then multiplied by -$2,000, then multiplied by the
number of kidney transplants furnished by the IOTA participant to
attributed patients with Medicare as their primary or secondary payer
during the PY.
CMS would pay the upside risk payment in lump sum to the IOTA
participant after the PY. The IOTA participant would pay the downside
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risk payment to CMS in a lump sum after the PY.
e. Data Sharing
We propose to collect certain quality, clinical, and administrative
data from IOTA participants for model monitoring and evaluation
activities under the authority in 42 CFR 403.1110(b). We would also
share certain data with IOTA participants upon request as described in
section III.C.3.a. of this proposed rule and as permitted by the Health
Insurance Portability and Accountability Act of 1996 (HIPAA) Privacy
Rule and other applicable law. We propose to offer each IOTA
participant the opportunity to request certain beneficiary-identifiable
data for their attributed Medicare beneficiaries for treatment, case
management, care coordination, quality improvement activities, and
population-based activities relating to improving health or reducing
health care costs, as permitted by 45 CFR 164.506(c). The data uses and
sharing would be allowed only to the extent permitted by the HIPAA
Privacy Rule and other applicable law and CMS policies. We also propose
to share certain aggregate, de-identified data with IOTA participants.
f. Other Requirements
We propose several other model requirements for selected transplant
hospitals, including transparency requirements, public reporting
requirements, and a health equity plan requirement which would be
optional for PY1 and required for PY 2 through PY 6, as described in
section III.C.8. of this proposed rule.
(1) Transparency Requirements
Patients are often unsure whether they qualify for a kidney
transplant at a given kidney transplant hospital. We propose that IOTA
participants would be required to publish on a public facing website
the criteria they use when determining whether or not to add a patient
to the kidney transplant waitlist. We also propose to add requirements
to facilitate increased transparency for patients regarding the organ
offers received on the patient's behalf while the patient is on the
waitlist. Specifically, we propose that IOTA participants would be
required to inform patients on the waitlist, on a monthly basis, of the
number of times an organ was declined on each patient's behalf and the
reason(s) why each organ was declined. We believe that notifying
patients of the organs declined on their behalf would encourage
conversations between patients and their providers regarding a
patient's preferences for transplant and facilitate better shared
decision-making.
(2) Health Equity Requirements
We propose that during the model's first PY, each IOTA participant
would have the option to submit a health equity plan (``HEP'') to CMS.
We propose that each IOTA participant would then be required to submit
a HEP to CMS for PY 2 and to update its HEP for each subsequent PY. We
propose that the IOTA participant's HEP would identify health
disparities within the IOTA participant's population of attributed
patients and outline a course of action to address them.
We also considered proposing to require IOTA participants to
collect and report patient-level health equity data to CMS.
Specifically, we considered proposing that IOTA participants would be
required to conduct health related social needs screening for at least
three core areas--food security, housing, and transportation. We
recognize these areas as some of the most common barriers to kidney
transplantation and the most pertinent health related social needs for
the IOTA patient population.\7\ We have included an RFI in this
proposed rule to solicit feedback and comment on such a requirement.
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\7\ Venkataraman, S., & Kendrick, J. (2020). Barriers to kidney
transplantation in ESKD. Seminars in Dialysis, 33(6), 523-532.
https://doi.org/10.1111/sdi.12921.
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g. Medicare Payment Waivers and Additional Flexibilities
We believe it is necessary to waive certain requirements of title
XVIII of the Act solely for purposes of carrying out the testing of the
IOTA Model under section 1115A of the Act. We propose to issue these
waivers using our waiver authority under section 1115A(d)(1) of the
Act. Each of the proposed waivers is discussed in detail in section
III.C.10. of this proposed rule.
h. Overlaps With Other Innovation Center Models and CMS Programs
We expect that there could be situations where a Medicare
beneficiary attributed to an IOTA participant is also assigned,
aligned, or attributed to another Innovation Center model or CMS
program. Overlap could also occur among providers and suppliers at the
individual or organization level, such as where an IOTA participant or
one of their providers would participate in multiple Innovation Center
models. We believe that the IOTA Model would be compatible with
existing models and programs that provide opportunities to improve care
and reduce spending. The IOTA Model would not be replacing any covered
services or changing the payments that participating hospitals receive
through the inpatient prospective payment system (IPPS) or outpatient
prospective payment system (OPPS). Rather, the IOTA Model proposes
performance-based payments separate from what participants would be
paid by CMS for furnishing kidney transplants to Medicare
beneficiaries. Additionally, we would work to resolve any potential
overlaps between the IOTA Model and other Innovation Center models or
CMS programs that could result in duplicative payments for services, or
duplicative counting of savings or other reductions in expenditures.
Therefore, we propose to allow overlaps between the IOTA Model and
other Innovation Center models and CMS programs.
i. Monitoring
We propose to closely monitor the implementation and outcomes of
the IOTA Model throughout its duration consistent with the monitoring
requirements proposed in the Standard Provisions for Innovation Center
models in section II of this proposed rule and the proposed
requirements in section III.C.13. of this proposed rule. The purpose of
this monitoring would be to ensure that the IOTA Model is implemented
safely and appropriately, that the quality and experience of care for
beneficiaries is not harmed, and that adequate patient and program
integrity safeguards are in place.
j. Beneficiary Protections
As proposed in section III.C.10. of this proposed rule, CMS would
not allow beneficiaries or patients to opt out of attribution to an
IOTA participant; however, the IOTA Model would not restrict a
beneficiary's freedom to choose another kidney transplant hospital, or
any other provider or supplier for healthcare services, and IOTA
participants would be subject to the Standard Provisions for Innovation
Center Models outlined in section II. of this proposed rule protecting
Medicare beneficiary freedom of choice and access to medically
necessary services. We also would require that IOTA participants notify
Medicare beneficiaries of the IOTA participant's participation in the
IOTA Model by, at a minimum, prominently displaying informational
materials in offices or facilities where beneficiaries receive care.
Additionally, IOTA participants would be subject to the proposed
Standard Provisions for Innovation Center Models regarding descriptive
model materials and activities in section II. of this proposed rule.
[[Page 43522]]
C. Summary of Costs and Benefits
The IOTA Model aims to incentivize transplant hospitals to overcome
system-level barriers to kidney transplantation. The chronic shortfall
in kidney transplants results in poorer outcomes for patients and
increases the burden on Medicare in terms of payments for dialysis and
dialysis-based enrollment in the program. There is reasonable evidence
that the savings to Medicare resulting from an incremental growth in
transplantation would potentially exceed the payments projected under
the model's proposed incentive structure.
II. Standard Provisions for Innovation Center Models
A. Introduction
Section 1115A of the Act authorizes the Center for Medicare and
Medicaid Innovation (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. We have
designed and tested both voluntary Innovation Center models--governed
by participation agreements, cooperative agreements, and model-specific
addenda to existing contracts with CMS--and mandatory Innovation Center
models that are governed by regulations. Each voluntary and mandatory
model features its own specific payment methodology, quality metrics,
and certain other applicable policies, but each model also features
numerous provisions of a similar or identical nature, including
provisions regarding cooperation in model evaluation; monitoring and
compliance; and beneficiary protections.
On September 29, 2020, we published in the Federal Register a final
rule titled ``Medicare Program; Specialty Care Models To Improve
Quality of Care and Reduce Expenditures'' (85 FR 61114) (hereinafter
the ``Specialty Care Models final rule''), in which we adopted General
Provisions Related to Innovation Center models at 42 CFR part 512
subpart A that apply to the End-Stage Renal Disease Treatment Choices
(ETC) Model and the Radiation Oncology (RO) Model.\8\ The Specialty
Care Models final rule codified general provisions regarding
beneficiary protections, cooperation in model evaluation and
monitoring, audits and record retention, rights in data and
intellectual property, monitoring and compliance, remedial action,
model termination by CMS, limitations on review, and bankruptcy and
other notifications. These general provisions were adopted only for the
ETC and RO Models (and, in practice, applied only to the ETC Model).
However, we now believe the general provisions should apply to
Innovation Center models more broadly. As we note, the Innovation
Center models share numerous similar provisions, and codifying the
general provisions into law to expand their applicability across
models, except where otherwise explicitly specified in a model's
governing documentation, would, we believe, promote transparency,
efficiency, clarity, and ensure consistency across models to the extent
appropriate, while avoiding the need to restate the provisions in each
model's governing documentation.
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\8\ In the autumn of 2020, due to the Secretary of Health and
Human Services' Determination that a Public Health Emergency Exists
for the Coronavirus disease 2019 (COVID-19) (https://aspr.hhs.gov/legal/PHE/Pages/2019-nCoV.aspx), CMS revised the RO Model's
performance period to begin on July 1, 2021, and to end on December
31, 2025, in the CY 2021 Hospital Outpatient Prospective Payment
(OPPS) and Ambulatory Surgical Center (ASC) Payment Systems and
Quality Reporting Programs final rule with comment period (85 FR
85866). Section 133 of the Consolidated Appropriations Act (CAA),
2021 (Pub. L. 116-260) (hereinafter referred to as ``CAA, 2021''),
enacted on December 27, 2020, included a provision that prohibited
implementation of the RO Model before January 1, 2022. This
congressional action superseded the July 1, 2021, start date that we
had established in the CY 2021 OPPS/ASC IFC. To align the RO Model
regulations with the requirements of the CAA, 2021, we proposed to
modify the definition of ``model performance period'' in 42 CFR part
512.205 to provide for a 5-year model performance period starting on
January 1, 2022, unless the RO Model was prohibited by law from
starting on January 1, 2022, in which case the model performance
period would begin on the earliest date permitted by law that is
January 1, April 1, or July 1. We also proposed other modifications
both related and unrelated to the timing of the RO Model in the
proposed rule that appeared in the August 4, 2021, Federal Register
titled ``Medicare Program: Hospital Outpatient Prospective Payment
and Ambulatory Surgical Center Payment Systems and Quality Reporting
Programs; Price Transparency of Hospital Standard Charges; Radiation
Oncology Model; Request for Information on Rural Emergency
Hospitals'' (86 FR 42018). These provisions were finalized in a
final rule with comment period titled ``Medicare Program: Hospital
Outpatient Prospective Payment and Ambulatory Surgical Center
Payment Systems and Quality Reporting Programs; Price Transparency
of Hospital Standard Charges; Radiation Oncology Model'' that
appeared in the November 16, 2021 Federal Register (86 FR 63458)
(hereinafter referred to as the ``CY 2022 OPPS/ASC FC'').
On December 10, 2021, the Protecting Medicare and American
Farmers from Sequester Cuts Act (Pub. L. 117-71) was enacted, which
included a provision that prohibits implementation of the RO Model
prior to January 1, 2023. The CY 2022 OPPS/ASC final rule with
comment period specified that if the RO Model was prohibited by law
from beginning on January 1, 2022, the model performance period
would begin on the earliest date permitted by law that is January 1,
April 1, or July 1. As a result, under the current definition for
model performance period at Sec. 512.205, the RO Model would have
started on January 1, 2023, because that date is the earliest date
permitted by law. However, given the multiple delays to date, and
because both CMS and RO participants must invest operational
resources in preparation for implementation of the RO Model, we have
considered how best to proceed under these circumstances. In a final
rule titled ``Radiation Oncology (RO) Model,'' which appeared in the
Federal Register on August 29, 2022 (87 FR 52698), we delayed the
start date of the RO Model to a date to be determined through future
rulemaking, and modified the definition of the model performance
period at Sec. 512.205 to provide that the start and end dates of
the model performance period for the RO Model would be established
in future rulemaking. We have not undertaken rulemaking to determine
the start date for the RO Model and, thus, the model is not active
at this time.
---------------------------------------------------------------------------
We also propose a new provision pertaining to the reconsideration
review process that would apply to Innovation Center models that waive
the appeals processes provided under section 1869 of the Act.
B. General Provisions Codified in the Code of Federal Regulations That
Would Apply to Innovation Center Models
Each Innovation Center model features many unique aspects that must
be memorialized in its governing documentation, but each model also
includes certain provisions that are common to most or all models. We
believe that codifying these common provisions would facilitate their
uniform application across models (except where the governing
documentation for a particular model dictates otherwise) and promote
program efficiency and consistency that would benefit CMS' program
administration and model participants.
As such, we propose to expand the applicability of the 42 CFR part
512 subpart A ``General Provisions Related to Innovation Center
Models'' to all Innovation Center models whose first performance
periods begin on or after January 1, 2025, unless otherwise specified
in the models' governing documentation, and also to any Innovation
Center models whose first performance periods begin prior to January 1,
2025 if incorporated by reference into the models' governing
documentation. To accomplish this, we propose that the provisions
codified at 42 CFR part 512 subpart A for the ETC and RO Models,
including those with respect to definitions, beneficiary protections,
cooperation in model evaluation and monitoring, audits and record
retention, rights in data and intellectual property, monitoring and
compliance, remedial action, Innovation Center model termination by
CMS, and limitations on review, would be designated as the newly
defined ``standard provisions for Innovation Center models'' and would
apply to all Innovation Center models as described
[[Page 43523]]
above. We propose specific revisions that would be necessary to expand
the scope of several of the current general provisions, but otherwise
propose that the general provisions (which would be referred to as the
``standard provisions for Innovation Center models'') would not change.
In particular, we propose that the substance of the following
provisions would not change, except that they would apply to all
Innovation Center Models as opposed to just the ETC and RO Models:
Sec. 512.120 Beneficiary protections; Sec. 512.130 Cooperation in
model evaluation and monitoring; Sec. 512.135 Audits and record
retention; Sec. 512.140 Rights in data and intellectual property:
Sec. 512.150 Monitoring and compliance; Sec. 512.160 Remedial action;
Sec. 512.165 Innovation center model termination by CMS; Sec. 512.170
Limitations on review; and Sec. 512.180 Miscellaneous provisions on
bankruptcy and other notifications.
C. Proposed Revisions to the Titles, Basis and Scope Provision, and
Effective Date
We propose to amend the title of part 512 to read ``Standard
Provisions for Innovation Center Models and Specific Provisions for the
Radiation Oncology Model and the End Stage Renal Disease Model'' so
that it more closely aligns with the other changes proposed herein and
to ensure that the title indicates that part 512 includes both standard
provisions for Innovation Center models and specific provisions for the
RO and ETC Models. We also propose to amend the title of subpart A to
read ``Standard Provisions for Innovation Center Models'' to use the
term we propose to define the provisions codified at 42 CFR part 512
subpart A.
Additionally, we propose to amend Sec. 512.100(a) and (b) so that
the standard provisions would take effect on January 1, 2025, and would
apply to each Innovation Center model where that model's first
performance period begins on or after January 1, 2025, unless the
model's governing documentation indicates otherwise, as well as any
Innovation Center model that begins testing its first performance
period prior to January 1, 2025, if the model's governing documentation
incorporates the provisions by reference in whole or in part. We
propose to determine on a case-by-case basis, based on each model's
unique features and design, whether the standard provisions would apply
to a particular model, or whether we would specify alternate terms in
the model's governing documentation.
We believe that these standard provisions are necessary for the
testing of the IOTA model, regardless of whether they are finalized as
proposed for all Innovation Center models. As such, as an alternative
to the previous proposal, we would propose making these standard
provisions for Innovation Center models applicable to, and effective
for, the IOTA Model beginning on January 1, 2025, absent extending the
standard provisions to all Innovation Center models. Under such an
alternative, the general provisions in the Specialty Care Models final
rule would also still be applicable to the ETC Model and the RO Model.
These proposed standard provisions would not, except as
specifically noted in this section II. of this proposed rule, affect
the applicability of other provisions affecting providers and suppliers
under Medicare fee-for-service (FFS).
We invite public comment on these proposed changes.
D. Provisions Revising Certain Definitions
We propose to amend the definition of ``Innovation Center model''
at 42 CFR 512.110 by replacing the specific references to the RO and
ETC Models with a definition consistent with section 1115A of the Act
and intended to encompass all Innovation Center models. We propose to
amend the definition for ``Innovation Center model'' to read as
follows: ``an innovative payment and service delivery model tested
under the authority of section 1115A(b) of the Act, including a model
expansion under section 1115A(c) of the Act.''
We propose to add a new definition of the term ``governing
documentation'' at Sec. 512.110 to mean, ``the applicable Federal
regulations, and the model-specific participation agreement,
cooperative agreement, and any addendum to an existing contract with
CMS, that collectively specify the terms of the Innovation Center
model.'' We propose to add a new definition, ``standard provisions for
Innovation Center models,'' at Sec. 512.110 to mean, ``the provisions
codified in 42 CFR 512 Subpart A.'' We propose to add a new definition,
``performance period,'' at Sec. 512.110 to mean, ``the period of time
during which an Innovation Center model is tested and model
participants are held accountable for cost and quality of care; the
performance period for each Innovation Center model is specified in the
governing documentation.''
Further, we propose to amend the definitions of ``Innovation Center
model activities,'' ``model beneficiary,'' and ``model participant'' to
pertain to all ``Innovation Center models,'' as we propose to define
that term, instead of just the models previously implemented under part
512. As such, we propose to define ``Innovation Center model
activities'' to mean ``any activities affecting the care of model
beneficiaries related to the test of the Innovation Center model.'' We
propose to define ``model beneficiary'' to mean ``a beneficiary
attributed to a model participant or otherwise included in an
Innovation Center model.'' We propose to define ``model participant''
to mean ``an individual or entity that is identified as a participant
in the Innovation Center model.''
We invite public comment on these proposed changes to the
definitions of ``Innovation Center model,'' ``Innovation Center model
activities,'' ``model beneficiary,'' and ``model participant'' and the
proposed definitions of ``governing documentation,'' ``standard
provisions for Innovation Center models,'' and ``performance period.''
E. Proposed Reconsideration Review Process
We propose to add a new Sec. 512.190 to part 512 subpart A to
codify a reconsideration review process, based on processes implemented
under current Innovation Center models. The process would enable model
participants to contest determinations made by CMS in certain
Innovation Center models, where model participants would not otherwise
have a means to dispute determinations made by CMS. We propose at Sec.
512.190(a)(1) that such a reconsideration process would apply only to
Innovation Center models that waive section 1869 of the Act, which
governs determinations and appeals in Medicare, or where section 1869
would not apply because model participants are not Medicare-enrolled.
We propose at Sec. 512.190(a)(2) that only model participants may
utilize the dispute resolution process, unless the governing
documentation for the Innovation Center model states otherwise. Such
limitations with respect to such models are, we believe, appropriate,
because with respect to such models, model participants do not have
another means to dispute determinations made by CMS. We propose to
codify a reconsideration review process in regulation in order to have
a transparent and consistent method of reconsideration for model
participants participating in models that do not utilize the standard
reconsideration process outlined in section 1869 of the Act.
This proposed reconsideration review process would be utilized
where a model-specific determination has been made and the affected
model participant
[[Page 43524]]
disagrees with, and wishes to challenge, that determination. Each
Innovation Center model features a unique payment and service delivery
model, and, as such, requires its own model-specific determination
process. Each Innovation Center model's governing documentation details
the model-specific determinations made by CMS, which may include, but
are not limited to, model-specific payments, beneficiary attribution,
and determinations regarding remedial actions. Each Innovation Center
model's governing documentation also includes specific details about
when a determination is final and may be disputed through the model's
reconsideration review processes.
We propose at Sec. 512.190(b) that model participants may request
reconsideration of a determination made by CMS in accordance with an
Innovation Center model's governing documentation only if such
reconsideration is not precluded by section 1115A(d)(2) of the Act,
part 512 subpart A, or the model's governing documentation. A model
participant may challenge, by requesting review by a CMS
reconsideration official, those final determinations made by CMS that
are not precluded from administrative or judicial review. We propose at
Sec. 512.190(b)(i) that the CMS reconsideration official would be
someone who is authorized to receive such requests and was not involved
in the initial determination issued by CMS or, if applicable, the
timely error notice review process. We propose at Sec. 512.190(b)(ii)
that the reconsideration review request would be required to include a
copy of CMS's initial determination and contain a detailed written
explanation of the basis for the dispute, including supporting
documentation. We propose at Sec. 512.190(b)(iii) that the request for
reconsideration would have to be made within 30 days of the date of
CMS' initial determination for which reconsideration is being requested
via email to an address as specified by CMS in the governing
documentation. At Sec. 512.190(b)(2), we propose that requests that do
not meet the requirements of paragraph (b)(1) would be denied.
We propose at Sec. 512.190(b)(3) that the reconsideration official
would send a written acknowledgement to CMS and to the model
participant requesting reconsideration within 10 business days of
receiving the reconsideration request. The acknowledgement would set
forth the review procedures and a schedule that would permit each party
an opportunity to submit position papers and documentation in support
of its position for consideration by the reconsideration official.
We propose to codify at Sec. 512.190(b)(4) that, to access the
reconsideration process for a determination concerning a model-specific
payment where the Innovation Center model's governing documentation
specifies an initial timely error notice process, the model participant
must first satisfy those requirements before submitting a
reconsideration request under this process. Should a model participant
fail to timely submit an error notice with respect to a particular
model-specific payment, we propose that the reconsideration review
process would not be available to the model participant with regard to
that model-specific payment.
We propose to codify standards for reconsideration at Sec.
512.190(c). First, during the course of the reconsideration, we propose
that both CMS and the party requesting the reconsideration must
continue to fulfill all responsibilities and obligations under the
governing documentation during the course of any dispute arising under
the governing documentation. Second, the reconsideration would consist
of a review of documentation timely submitted to the reconsideration
official and in accordance with the standards specified by the
reconsideration official in the acknowledgement at Sec. 512.190(b)(3).
Finally, we propose that the model participant would bear the burden of
proof to demonstrate with clear and convincing evidence to the
reconsideration official that the determination made by CMS was
inconsistent with the terms of the governing documentation.
We propose to codify at Sec. 512.190(d) that the reconsideration
determination would be an on-the-record review. By this, we mean a
review that would be conducted by a CMS reconsideration official who is
a designee of CMS who is authorized to receive such requests under
proposed Sec. 512.190(b)(1)(i), of the position papers and supporting
documentation that are timely submitted and in accordance with the
schedule specified under proposed Sec. 512.190(b)(3)(ii) and that meet
the standards of submission under proposed Sec. 512.190(b)(1) as well
as any documents and data timely submitted to CMS by the model
participant in the required format before CMS made the initial
determination that is the subject of the reconsideration request. We
propose at Sec. 512.190(d)(2) that the reconsideration official would
issue to the parties a written reconsideration determination. Absent
unusual circumstances, in which the reconsideration official would
reserve the right to an extension upon written notice to the model
participant, the reconsideration determination would be issued within
60 days of CMS's receipt of the timely filed position papers and
supporting documentation in accordance with the schedule specified
under proposed Sec. 512.190(b)(3)(ii). Under proposed Sec.
512.190(d)(3), the determination made by the CMS reconsideration
official would be final and binding 30 days after its issuance, unless
the model participant or CMS were to timely request review of the
reconsideration determination by the CMS Administrator in accordance
with Sec. Sec. 512.190(e)(1) and (2).
We propose to codify at Sec. 512.190(e) a process for the CMS
Administrator to review reconsideration determinations made under Sec.
512.190(d). We propose that either the model participant or CMS may
request that the CMS Administrator review the reconsideration
determination. The request to the CMS Administrator would have to be
made via email, within 30 days of the reconsideration determination, to
an email address specified by CMS. The request would have to include a
copy of the reconsideration determination, as well as a detailed
written explanation of why the model participant or CMS disagrees with
the reconsideration determination. The CMS Administrator would promptly
send the parties a written acknowledgement of receipt of the request
for review. The CMS Administrator would send the parties notice of
whether the request for review was granted or denied. If the request
for review is granted, the notice would include the review procedures
and a schedule that would permit each party to submit a brief in
support of the party's positions for consideration by the CMS
Administrator. If the request for review is denied, the reconsideration
determination would be final and binding as of the date of denial of
the request for review by the CMS Administrator. If the request for
review by the CMS Administrator is granted, the record for review would
consist solely of timely submitted briefs and evidence contained in the
record of the proceedings before the reconsideration official and
evidence as set forth in the documents and data described in proposed
Sec. 512.190(d)(1)(ii); the CMS Administrator would not consider
evidence other than information set forth in the documents and data
described in proposed Sec. 512.190(d)(1)(ii). The CMS
[[Page 43525]]
Administrator would review the record and issue to the parties a
written determination that would be final and binding as of the date
the written determination is sent.
We invite public comment on the proposed reconsideration review
process for Innovation Center models.
III. Proposed Increasing Organ Transplant Access (IOTA) Model
A. Introduction
In this proposed rule, we are proposing to test the IOTA Model, a
new mandatory Medicare alternative payment model under the authority of
the Innovation Center, that would begin on January 1, 2025, and end on
December 31, 2030. The IOTA Model would test whether using performance-
based incentive payments in the form of upside risk payments and
downside risk payments to and from select transplant hospitals
increases the number of kidney transplants furnished to patients with
ESRD, thereby reducing Medicare expenditures while preserving or
enhancing quality of care.
The goal of the proposed performance-based payments is: to increase
the number of kidney transplants furnished to ESRD patients placed on a
kidney transplant hospital's waitlist; encourage investments in value-
based care and quality improvement activities, particularly those that
promote an equitable kidney transplant process prior to, during, and
post transplantation for all patients; encourage better use of the
current supply of deceased donor organs and greater provider and
community collaborations to address medical and non-medical needs of
patients; and increased awareness, education, and support for living
donations. The IOTA Model payment structure would also promote IOTA
participant accountability by linking performance-based payments to
quality. We theorize that increasing the number of kidney transplants
furnished to ESRD patients on the participating hospitals' waitlists
would reduce Medicare expenditures by reducing dialysis expenditures
and avoidable health care service utilization and would improve the
quality of life for patients with ESRD.
As discussed in section III.B of this proposed rule, studies show
that kidney transplant hospitals are underutilizing donor kidneys and
have become more conservative in accepting organs for transplantation,
with notable variation by region and across transplant hospitals.\9\
The IOTA Model aims to address these access and equity problems through
financial incentives that reward IOTA participants that improve their
kidney organ offer acceptance rate ratios over time or hold them
financially accountable for not doing so. The IOTA Model's proposed
payment structure would include upside or downside performance-based
incentive payments (``upside risk payment'' or ``downside risk
payment'') for kidney transplant hospitals selected to participate in
the IOTA Model (``IOTA participant''), with these payments being tied
to performance on achievement, efficiency, and quality domains.
---------------------------------------------------------------------------
\9\ Mohan, S., Chiles, M.C., Patzer, R.E., Pastan, S.O., Husain,
S.A., Carpenter, D.J., Dube, G.K., Crew, R.J., Ratner, L.E., &
Cohen, D.J. (2018). Factors leading to the discard of deceased donor
kidneys in the United States. Kidney International, 94(1), 187-198.
https://doi.org/10.1016/j.kint.2018.02.016.
---------------------------------------------------------------------------
The achievement domain would assess the number of kidney
transplants performed relative to a participant-specific target, with
performance on this domain being worth up to 60 points. The efficiency
domain would assess kidney organ offer acceptance rate ratios relative
to a national rate for all kidney transplant hospitals, including those
not selected to participate in the model, with performance on this
domain being worth up to 20 points. The quality domain would assess
performance based on post-transplant outcomes at one-year after
transplant and a proposed set of quality measures, with performance on
this domain being worth up to 20 points. The achievement domain would
be weighted more heavily than the other two domains because increasing
the number of transplants is a key goal of the model and would be a
primary factor in determining the amount of the performance-based
payment.
The final performance score for each IOTA participant would be the
sum of the points earned across the achievement domain, efficiency
domain, and quality domain. The final performance score would determine
whether an upside risk payment or downward risk payment would be owed
and the amount of such payment. Specifically:
For PY 1, if an IOTA participant has a final performance
score between 60 and 100 points, it would qualify for the upside risk
payment in accordance with the proposed calculation methodology
described in section III.C.6.c(a) of this proposed rule (final
performance score minus 60, then divided by 60, then multiplied by
$8,000, then multiplied by the number of kidney transplants furnished
by the IOTA participant to beneficiaries with Medicare as a primary or
secondary payer during the PY).
For PY 1, if an IOTA participant has a final performance
score below 60, it would fall into a neutral zone where no upside risk
payment and no downside risk payment would apply.
For PY 2 and each subsequent PY (PYs 2-6) if an IOTA
participant achieves a final performance score of 41 to 59 points, it
would fall into a neutral zone where no upside risk payment and no
downside risk payment would apply.
For PY 2 and each subsequent PY, if an IOTA participant
achieves a final performance score of 40 points or below, it would
qualify for the downside risk payment in accordance with the proposed
calculation methodology described in section III.C.6.c.(b). of this
proposed rule (final performance score minus 40, then divided by 40,
then multiplied by -$2,000, then multiplied by the number of kidney
transplants furnished by the IOTA participant to beneficiaries with
Medicare as a primary or secondary payer during the PY).
We recognize the complexity of the transplant ecosystem, which
requires coordination between transplant hospitals, other health care
providers, organ procurement organizations (OPOs), patients, potential
donors, and their families. The proposed IOTA Model does not prescribe
or require specific processes or policy approaches that each selected
IOTA participant must implement for purposes of the model test.
We believe the IOTA Model would complement other efforts in
relation to the transplant ecosystem to enhance health and safety
outcomes, increase transparency, increase the number of transplants,
and reduce disparities. We also believe that the proposed payment
methodology would act in concert with measures that are currently under
development by HRSA to increase the numbers of both deceased and living
donor organ transplants.
This proposed model falls within a larger framework of activities
initiated by the Federal Government during the past several years and
planned for the upcoming year to enhance the donation, procurement, and
transplantation of solid organs. This Federal collaborative, called the
Organ Transplantation Affinity Group (OTAG), is a coordinated group
working together to strengthen accountability, equity, and performance
in organ donation, procurement, and transplantation.\10\
---------------------------------------------------------------------------
\10\ Moody-Williams, J.D., & Nair, S. (2023, September 15).
Organ Transplantation Affinity Group (OTAG): Strengthening
accountability, equity, and performance CMS. BLOG. https://www.cms.gov/blog/organ-transplantation-affinity-group-otag-strengthening-accountability-equity-and-performance.
---------------------------------------------------------------------------
[[Page 43526]]
B. Background
A review of the literature on kidney transplantation shows that the
increasing numbers of kidney transplants is unable to keep pace with
the increasing need for organs.\11\ While more people die waiting for a
kidney transplant, the short- and long-term outcomes of patients who
undergo kidney transplantation have improved, despite both recipients
and donors increasing in age and adverse health conditions.\12\ Recent
studies show that transplant hospitals have become more conservative in
accepting organs for transplantation when offered for specific
patients, avoiding the use of less-than-ideal organs on account of
perceived risk.\13\ Wide variation among geographic regions and
transplant hospitals in rates of kidney transplantation, along with
access and equity issues, raises the need to hold kidney transplant
hospitals accountable for performance.\14\ The IOTA Model proposes a
two-sided performance-based payment structure that rewards IOTA
participants for high performance in the achievement, efficiency, and
quality domains, and imposes financial accountability on IOTA
participants that perform poorly on those domains. We propose the IOTA
Model as a complement to wider efforts aimed at transplant ecosystem
performance and equity improvements. Ultimately, we seek a set of
interventions that focus on ESRD patients in need of a kidney
transplant. In this section of the proposed rule, we summarize the
transplant ecosystem and HHS oversight within CMS and HRSA related to
kidney transplantation, highlight related initiatives and priorities
nationally, and outline our rationale for the proposed IOTA Model
informed by literature, data, and studies.
---------------------------------------------------------------------------
\11\ Too Many Donor Kidneys Are Discarded in U.S. Before
Transplantation--Penn Medicine. (2020, December 16).
www.pennmedicine.org. https://www.pennmedicine.org/news/news-releases/2020/december/too-many-donor-kidneys-are-discarded-in-us-before-transplantation.
\12\ Hariharan, S., Israni, A.K., & Danovitch, G. (2021). Long-
Term Survival after Kidney Transplantation. New England Journal of
Medicine, 385(8), 729-743. https://doi.org/10.1056/nejmra2014530.
\13\ Stewart, D.E., Garcia, V.C., Rosendale, J.D., Klassen,
D.K., & Carrico, B.J. (2017). Diagnosing the Decades-Long Rise in
the Deceased Donor Kidney Discard Rate in the United States.
Transplantation, 101(3), 575-587. https://doi.org/10.1097/tp.0000000000001539.
\14\ Mohan, S., Chiles, M.C., Patzer, R.E., Pastan, S.O.,
Husain, S.A., Carpenter, D.J., Dube, G.K., Crew, R.J., Ratner, L.E.,
& Cohen, D.J. (2018). Factors leading to the discard of deceased
donor kidneys in the United States. Kidney International, 94(1),
187-198. https://doi.org/10.1016/j.kint.2018.02.016.
---------------------------------------------------------------------------
1. The Transplant Ecosystem
Kidney transplantation occurs within an overall organ donation and
transplantation system (also known and referred to as the transplant
ecosystem) that comprises a vast network of institutions dedicated to
ensuring that patients are evaluated and, if appropriate, placed onto
the organ transplant waitlist, and that those on the organ transplant
waitlist receive lifesaving organ transplants. Transplantation of
livers, hearts, lungs, and other organs is also well established within
the U.S. health care system. The transplant ecosystem includes the
Organ Procurement and Transplantation Network (OPTN); Organ Procurement
Organizations (OPOs); transplant hospitals and providers;
histocompatibility laboratories that provide blood, tissue, and
antibody testing for the organ matching process; and patients,
including ESRD patients in need of a transplant, their families, and
caregivers.\15\ For kidney transplantation, it also includes ESRD
facilities, commonly known as dialysis facilities.
---------------------------------------------------------------------------
\15\ Moody-Williams, J.D., & Nair, S. (2023, September 15).
Organ Transplantation Affinity Group (OTAG): Strengthening
accountability, equity, and performance [verbar] CMS. BLOG. https://www.cms.gov/blog/organ-transplantation-affinity-group-otag-strengthening-accountability-equity-and-performance.
---------------------------------------------------------------------------
The National Organ Transplant Act of 1984, referred to herein as
NOTA, established the OPTN, with HHS oversight, to manage and operate
the national organ transplantation system (42 U.S.C. 274). The OPTN
coordinates the nation's organ procurement, distribution, and
transplantation systems. The OPTN is a network of clinical experts,
patients, donor families, and community stakeholders who work
collectively to develop, implement, and monitor organ allocation policy
and performance of the organ transplant ecosystem.
Organ Procurement Organizations (OPOs) are non-profit organizations
operating under contract with the Federal Government that are charged,
under section 371(b) of the Public Health Service Act (PHS Act, 42
U.S.C. 273(b)) with activities including, but not limited to,
identifying potential organ donors, providing for the acquisition and
preservation of donated organs, the equitable allocation of donated
organs, and the transportation of donated organs to transplant
hospitals. Section 371(b) of the Public Health Services Act requires
that an OPO must have a defined service area, a concept that is defined
at 42 CFR part 486 subpart G as the Donation Service Area (DSA).
Section 1138(b) of the Act states that the Secretary may not designate
more than one OPO to serve each DSA. There are currently 56 OPOs that
serve the United States and Puerto Rico.
Section 1138(b) of the Act lays out the requirements that an OPO
must meet to have its costs reimbursed by the Secretary. CMS sets out
the components of allowable Medicare organ acquisition costs at 42 CFR
413.402(b). Allowable organ acquisition costs are those costs incurred
in the acquisition of organs intended for transplant, and include, but
are not limited to: costs associated with special care services, the
surgeon's fee for excising the deceased donor organ from the donor
patient (limited to $1,250 for kidneys), operating room and other
inpatient ancillary services provided to the living or deceased donor,
organ preservation and perfusion costs, donor and beneficiary
evaluation, and living donor complications. OPOs and transplant
hospitals may incur organ acquisition costs and include these and some
additional administrative and general costs on the Medicare cost
report.
The CMS conditions for coverage for OPOs at 42 CFR 486.322 require
an OPO to have written agreements with 95 percent of the Medicare and
Medicaid certified hospitals and critical access hospitals in its DSA
that have a ventilator and an operating room and have not been granted
a waiver to work with another OPO. These hospitals, known as donor
hospitals, are required by the CMS conditions of participation for
hospitals at 42 CFR 482.45 to have an agreement with an OPO under which
the donor hospital must notify the OPO of patients who are expected to
die imminently and of patients who have died in the hospital. (Under
the hospital conditions of participation, such an agreement is required
of all hospitals that participate in Medicare.) Also, under the
hospital conditions of participation, donor hospitals are responsible
for informing donor patient families of the option to donate organs,
tissues, and eyes, or to decline to donate; and to work collaboratively
with the OPO to educate hospital staff on donation, improve its
identification of potential donors, and work with the OPO to manage the
potential donor patient while testing and placement of the potential
donor organ occurs.
At 42 CFR 482.70, CMS defines a transplant hospital as ``a hospital
that furnishes organ transplants and other medical and surgical
specialty services
[[Page 43527]]
required for the care of transplant patients,'' and a transplant
program as ``an organ-specific transplant program within a transplant
hospital,'' as so defined. In accordance with 42 CFR 482.98, a
transplant program must have a primary transplant surgeon and a
transplant physician with the appropriate training and experience to
provide transplantation services, who are immediately available to
provide transplantation services when an organ is offered for
transplantation. The transplant surgeon is responsible for providing
surgical services related to transplantation, and the transplant
physician is responsible for providing and coordinating transplantation
care.
In accordance with CMS' Conditions for Coverage (CfC) for ESRD
Facilities at 42 CFR part 494, ESRD facilities are charged with
delivering safe and adequate dialysis to ESRD patients, and, among
other requirements, informing patients of their treatment modalities,
including dialysis and kidney transplantation. The CfCs require ESRD
facilities to conduct a patient assessment that includes evaluation of
suitability for referral for transplantation, based on criteria
developed by the prospective transplantation center and its surgeon(s).
General nephrologists refer patients for evaluation for kidney
transplants.\16\ Candidates for kidney transplant undergo a rigorous
evaluation by a transplant program prior to placement on a waitlist,
involving evaluation by a multidisciplinary team for conditions
pertaining to the potential success of the transplant, the possibility
of recurrence, and surgical issues including frailty, obesity, diabetes
and other causes of ESRD, infections, malignancies, cardiac disease,
pulmonary disease, peripheral arterial disease, neurologic disease,
hematologic conditions, and gastrointestinal and liver disease and an
immunological assessment; a psychosocial assessment; assessment of
adherence behaviors; and tobacco counseling.\17\
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\16\ Virmani, S., & Asch, W.S. (2020). The Role of the General
Nephrologist in Evaluating Patients for Kidney Transplantation: Core
Curriculum 2020. American Journal of Kidney Diseases, 76, 567-579.
https://doi.org/10.1053/j.ajkd.2020.01.001.
\17\ Chadban, S.J., Ahn, C., Axelrod, D.A., Foster, B.J.,
Kasiske, B.L., Kher, V., Kumar, D., Oberbauer, R., Pascual, J.,
Pilmore, H.L., Rodrigue, J.R., Segev, D.L., Sheerin, N.S., Tinckam,
K.J., Wong, G., & Knoll, G.A. (2020). KDIGO Clinical Practice
Guideline on the Evaluation and Management of Candidates for Kidney
Transplantation. Transplantation, 104(4S1), S11. https://doi.org/10.1097/TP.0000000000003136.
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Once placed on the waitlist, potential recipients must maintain
active status to be eligible to receive a deceased donor
transplant.\18\ An individual may receive a status of `inactive' if
they are missing lab results, contact information, or any of the other
requirements that would be necessary for them to receive an organ
transplant if offered. An individual may only receive an organ offer if
they have a status of `active'.\19\ Each transplant hospital has its
own waitlist, and patients can attempt to be placed on multiple
waitlists; OPTN maintains a national transplant waiting list that
encompasses the waitlists for all kidney transplant
hospitals.20 21 Individuals already on dialysis continue to
receive regular dialysis treatments while waiting for an organ to
become available. After surgery, a transplant nephrologist manages the
possible outcomes of organ rejection and infection, and other medical
complications.\22\
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\18\ National kidney Foundation. (2017, February 10). The Kidney
Transplant Waitlist--What You Need to Know. National Kidney
Foundation. https://www.kidney.org/atoz/content/transplant-waitlist.
\19\ The kidney transplant waitlist. (n.d.). Transplant Living.
https://transplantliving.org/kidney/the-kidney-transplant-waitlist/.
\20\ National kidney Foundation. (2019, June 12). Understanding
the transplant waitlist. National Kidney Foundation. https://www.kidney.org/content/understanding-transplant-waitlist.
\21\ National kidney Foundation. (2016, August 4). Multiple
Listing for Kidney Transplant. National Kidney Foundation. https://www.kidney.org/atoz/content/multiple-listing.
\22\ Transplant Nephrology Fellowship. (n.d.).
Www.hopkinsmedicine.org. Retrieved May 30, 2023, from https://
www.hopkinsmedicine.org/nephrology/education/
transplant_fellowship.html#:~:text=Diagnose%20and%20manage%20acute%20
and.
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2. HHS Oversight and Priorities
HRSA, which oversees the OPTN, and CMS play a vital role in
protecting the health and safety of Americans as they engage with the
U.S. health care system.\23\ The OPTN operates a complex network of
computerized interactions whereby specific deceased donor organs get
matched to individual patients on the national transplant waiting list.
The Scientific Registry of Transplant Recipients (SRTR), operated under
contract with HRSA, is responsible for providing statistical and
analytic support to the OPTN. Section 373 of the PHS Act requires the
operation of the SRTR to support ongoing evaluation of the scientific
and clinical status of solid organ transplantation.\24\
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\23\ On March 22, 2023, HRSA announced an initiative that
included several actions to strengthen accountability and
transparency in the OPTN. These actions include modernization of the
OPTN information technology system. HRSA also intends to issue
contract solicitations for multiple awards to support the OPTN.
\24\ Mission, Vision, and Values. (n.d.). Www.srtr.org. https://www.srtr.org/about-srtr/mission-vision-and-values/.
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CMS oversees and evaluates OPO performance. OPOs must meet
performance measures and participate in, and abide by certain rules of,
the OPTN.\25\ The PHS Act requires the Secretary to establish outcome
and process performance measures to recertify OPOs (Part H section 371;
42 U.S.C. 273). CMS has promulgated the OPO CfCs at 42 CFR part 486
subpart G.
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\25\ U.S. Congress. (1934) United States Code: Social Security
Act, 42 U.S.C. 301-Suppl. 4 1934.
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Additionally, the OPTN Bylaws specify that OPOs whose observed
organ yield rates fall below the expected rates by more than a
specified threshold would be reviewed by the OPTN Membership
Professional Standards Committee (MPSC).\26\ CMS also conducts
oversight of transplant programs, located within transplant hospitals,
which must abide by both the hospital and the transplant program
conditions of participation (CoPs). CMS contracts with quality
improvement entities such as the ESRD Networks and Quality Improvement
Organizations to provide technical support to providers and patients
seeking improvements in the transplant ecosystem.
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\26\ Bylaws--OPTN. (n.d.). Optn.transplant.hrsa.gov. Retrieved
May 30, 2023, from https://optn.transplant.hrsa.gov/policies-bylaws/bylaws/.
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Medicare covers certain transplant-related services when provided
at a Medicare-approved facility. Medicare Part A covers the costs
associated with a Medicare kidney transplant procedure received in a
Medicare-certified hospital and any additional inpatient hospital care
needed following the procedure, and organ acquisition costs including
kidney registry fees and laboratory tests associated with the
evaluation of a Medicare transplant candidate. The evaluation or
preparation of a living donor, the living donor's donation of the
kidney, and postoperative recovery services directly related to the
living donor's kidney donation are covered under Medicare. In addition,
deductible and coinsurance requirements do not apply to living donors
for services furnished to an individual in connection with the donation
of a kidney for transplant surgery. Medicare Part B coverage includes
the surgeon's fees for performing the kidney transplant procedure and
perioperative care. Medicare Part B also covers physician services for
the living kidney donor without regard to whether the service would
otherwise be covered by
[[Page 43528]]
Medicare. Part A and Part B share responsibility for covering blood,
including packed red blood cells, blood components and the cost of
processing and receiving blood.
Medicare Part B covers immunosuppressive drugs following an organ
transplant for which payment is made under Title XVIII.
Immunosuppressive drugs following an organ transplant are covered by
Part D when an individual did not have Part A at the time of the
transplant. Beneficiaries who have Medicare due to ESRD alone lose
Medicare coverage 36 months following a successful kidney transplant.
Section 402(a) of the Consolidated Appropriations Act (CAA) of 2021
added section 1836(b) of the Act to provide coverage for
immunosuppressive drugs beginning January 1, 2023, for eligible
individuals whose eligibility for Medicare based on ESRD ends by reason
of section 226A(b)(2) of the Act for those three-years post kidney
transplant. Under section 1833 of the Act, the amounts paid by Medicare
for immunosuppressive drugs are equal to 80 percent of the applicable
payment amount; beneficiaries are thus subject to a 20 percent
coinsurance for immunosuppressive drugs covered by both Part B and the
Medicare Part B Immunosuppressive Drug Benefit (Part B-ID).
3. Federal Government Initiatives To Enhance Organ Transplantation
a. CMS Regulatory Initiatives To Enhance Organ Transplantation
On September 30, 2019, we published the final rule, ``Medicare and
Medicaid Programs; Regulatory Provisions To Promote Program Efficiency,
Transparency, and Burden Reduction; Fire Safety Requirements for
Certain Dialysis Facilities; Hospital and Critical Access Hospital
(CAH) Changes To Promote Innovation, Flexibility, and Improvement in
Patient Care'' (84 FR 51732). The rulemaking, in part, aimed to address
the concern that too many organs are being discarded that could be
transplanted successfully, including hearts, lungs, livers, and
kidneys. This rule implemented changes to the transplant program
regulations, eliminating requirements for re-approval of transplant
programs pertaining to data submission, clinical experience, and
outcomes. We believed that the removal of these requirements aligned
with our goal of increasing access to kidney transplants by increasing
the utilization of organs from deceased donors and reducing the organ
discard rate (84 FR 51749). We sought improved organ procurement,
greater organ utilization, and reduction of burden for transplant
hospitals, while still maintaining the importance of safety in the
transplant process.
On December 2, 2020, we issued 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), which revised the OPO CfCs
by replacing the previous outcome measures with new transparent,
reliable, and objective outcome measures. In modifying the metrics used
for assessing OPO performance, we sought to promote greater utilization
of organs that might not otherwise be recovered or used due to
perceived organ quality.\27\
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\27\ The Organ Procurement Organizations Annual Public
Aggregated Performance Report for 2023 is available at https://www.cms.gov/files/document/opo-annual-public-performance-report-2023.pdf.
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While these regulatory changes recently went into effect with the
goal of improving the performance of transplant hospitals and OPOs and
to promote the procuring of organs and delivering them to prospective
transplant recipients, we acknowledged the need for improvements in
health, safety, and outcomes across the transplant ecosystem, including
in transplant programs, OPOs, and ESRD facilities.28 29 In
particular, we recognize that further action must be taken to address
disparities and inequities observed across transplant hospitals.
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\28\ One study--Doby, B.L., Ross-Driscoll, K., Shuck, M.,
Wadsworth, M., Durand, C.M., & Lynch, R.J. (2021). Public discourse
and policy change: Absence of harm from increased oversight and
transparency in OPO Performance. American Journal of
Transplantation, 21(8), 2646-2652. https://doi.org/10.1111/ajt.16527--showed that deceased donor organ donation increased
during 2019, that is., during the period of public debate about
regulating OPO performance.
\29\ In addition, CMS finalized a policy in the final rule for
FY 2023 for the Medicare Physician Fee Schedule that Medicare Part A
and Part B payment can be made for dental or oral examinations,
including necessary treatment, performed as part of a necessary
workup prior to organ transplant surgery. In the final rule, CMS
describes certain dental services as inextricably linked and
integral to the clinical success of organ transplantation. (87 FR
69671-69675).
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We published a request for information in the Federal Register on
December 3, 2021, titled ``Request for Information: Health and Safety
Requirements for Transplant Programs, Organ Procurement Organizations,
and End-Stage Renal Facilities'' (86 FR 68594) (hereafter known as the
``Transplant Ecosystem RFI''). This RFI solicited public comments on
potential changes to the requirements that transplant programs, OPOs,
and ESRD facilities must meet to participate in the Medicare and
Medicaid programs. Specifically, we solicited public comments on ways
to:
Continue to improve systems of care for all patients in
need of a transplant;
Increase the number of organs available for transplant for
all solid organ types;
Encourage the use of dialysis in alternate settings or
modalities over in-center hemodialysis where clinically appropriate and
advantageous;
Ensure that the CMS and HHS policies appropriately
incentivize the creation and use of future new treatments and
technologies; and
Harmonize requirements across government agencies to
facilitate these objectives and improve quality across the organ
donation and transplantation ecosystem.
We also solicited information related to opportunities,
inefficiencies, and inequities in the transplant ecosystem and what can
be done to ensure all segments of our healthcare systems are invested
and accountable in ensuring improvements to organ donation and
transplantation rates (86 FR 68596). The Transplant Ecosystem RFI
focused on questions in the areas of transplantation, kidney health and
ESRD facilities, and OPOs. For transplant programs, specific topics
included transplant program CoPs, patient rights, and equity in organ
transplantation and organ donation (86 FR 68596). For kidney health and
ESRD facilities, topics included maintaining and improving health of
patients, ways to identify those at risk of developing chronic kidney
disease (CKD), improving detection rates of CKD, and ways to close the
CKD detection, education, and care health equity gap (86 FR 68599).
Other topics included home dialysis, dialysis in alternative settings
such as nursing homes and mobile dialysis, and alternate models of care
(86 FR 68600). For OPOs, specific topics included assessment and
recertification, organ transport and tracking, the donor referral
process, organ recovery centers, organ discards, donation after cardiac
death, tissue banks, organs for research, and vascular composite
organs. (86 FR 68601 through 68606)
The Transplant Ecosystem RFI followed three executive orders
addressing health equity that were issued by President Biden on January
20 and January 21, 2021--
Executive Order on Advancing Racial Equity and Support for
Underserved Communities Through the Federal Government (E.O. 13985, 86
FR 7009, January 20, 2021);
[[Page 43529]]
Executive Order on Preventing and Combating Discrimination
on the Basis of Gender Identity or Sexual Orientation (E.O. 13988, 86
FR 7023, January 25, 2021); and
Executive Order on Ensuring an Equitable Pandemic Response
and Recovery (E.O. 13995, 86 FR 7193, January 26, 2021).
The RFI was among several issued by CMS in 2021 to request public
comment on ways to advance health equity and reduce disparities in our
policies and programs.
CMS's regulatory initiatives since 2018 pertaining to organ
donation and transplantation have included final rules modifying CoPs
and CfCs for transplant programs (84 FR 51732) and OPOs (85 FR 77898),
respectively, and our recent RFI on transplant program CoPs, OPO CfCs,
and the ESRD facility CfCs (86 FR 68594). These regulations and RFIs
have sought to foster greater health and safety for patients, greater
transparency for all patients, increases in organ donation and
transplantation, and reduced disparities in organ donation and
transplantation. Through these regulations, we are working to attain
these goals by designing and implementing policies that improve health
for all people affected by the transplant ecosystem.
b. CMS Innovation Center Payment Models
The Innovation Center is currently pursuing complementary
alternative payment model tests--the ESRD Treatment Choices (ETC) Model
and the Kidney Care Choices (KCC) Model--aimed at enhancing kidney
transplantation and improving health-related outcomes for patients with
late-stage CKD and ESRD, thereby reducing costs to the Medicare
program. The impetus for the ETC and KCC Models originated with
evaluation findings for the earlier Comprehensive ESRD Care (CEC)
Model, which ran from October 2015 through March 2021, that showed
large dialysis organizations achieving positive clinical and financial
outcomes relating to services to Medicare beneficiaries receiving
dialysis, though the CEC Model did not achieve net savings to
Medicare.\30\ The CEC Model focused on patients being treated in ESRD
facilities, with no explicit incentives to encourage increases in
kidney transplantation.
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\30\ The results of the CMS-sponsored evaluation of the CEC
Model are available at https://innovation.cms.gov/innovation-models/comprehensive-esrd-care. The 5-year model test reduced Medicare
expenses by $217 million, or 1.3 percent relative to the pre-CEC
period. These results do not account for shared savings payments to
the model participants. There was a 3 percent decrease in the number
of hospitalizations and a 0.4 percent increase in the number of
outpatient dialysis sessions for Medicare beneficiaries in CEC
compared to non-CEC beneficiaries. In addition, the CEC Model
improved key quality outcomes.
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The ETC and KCC Models have engaged a broader range of health care
providers beyond ESRD facilities, including nephrology professionals
and transplant providers, and address transplantation. Each model
includes direct financial incentives for increasing the number of
kidney transplants.
The ETC Model, which began January 1, 2021, and which is scheduled
to end on June 30, 2027, is a mandatory model that tests whether
greater use of home dialysis and kidney transplantation for Medicare
beneficiaries with ESRD reduces Medicare expenditures while preserving
or enhancing the quality of care furnished to those beneficiaries. We
established requirements for the ETC Model in the Medicare Program;
Specialty Care Models to Improve Quality of Care and Reduce
Expenditures final rule (85 FR 61114 through 61381). These requirements
are codified at 42 CFR subpart C. The ETC Model tests the effects of
certain Medicare payment adjustments to participating ESRD facilities
and Managing Clinicians (clinicians who manage ESRD beneficiaries and
bill the Monthly Capitation Payment (MCP)). The payment adjustments are
designed to encourage greater utilization of home dialysis and kidney
transplantation, support beneficiary modality choice, reduce Medicare
expenditures, and preserve or enhance quality of care. Under the ETC
Model, CMS makes upward adjustments to certain payments under the ESRD
Prospective Payment System (PPS) to certain dialysis facilities on home
dialysis claims, and upward adjustments to the MCP paid to certain
Managing Clinicians on home dialysis-related claims (85 FR 61117). In
addition, CMS makes upward and downward adjustments to PPS payments to
participating ESRD facilities and to the MCP paid to participating
Managing Clinicians based on the Participant's home dialysis rate and
transplant waitlisting and living donor transplant rate (85 FR 61117).
The ETC Model's objectives, as described in the final rule, include
supporting paired donations and donor chains, and reducing the
likelihood that potentially viable organs are discarded (85 FR 61128).
The ETC Model was updated by the final rule dated November 8, 2021,
titled ``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'' and the
final rule dated November 7, 2022, titled ``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'' (87 FR 67136). We finalized further
modifications to the ETC Model related to the availability of
administrative review of an ETC Participant's targeted review request
in the final rule issued on November 6, 2023, titled ``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'' (88 FR 76345).
CMS is also operating the ETC Learning Collaborative, which is
focused on increasing the availability of deceased donor organs for
transplantation.\31\ The ETC Learning Collaborative regularly convenes
ETC Participants, transplant hospitals, OPOs, and large donor
hospitals, with the goal of using learning and quality improvement
techniques to systematically spread the best practices of the highest
performing organizations. CMS is employing quality improvement
approaches to improve performance by collecting and analyzing data to
identify the highest performers, and to help others to test, adapt and
spread the best practices of these high performers throughout the
entire national organ recovery system (85 FR 61346).
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\31\ Centers for Medicare & Medicaid Services. https://innovation.cms.gov/innovation-models/esrd-treatment-choices-model.
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The KCC Model, which began its performance period on January 1,
2022, and is scheduled to end on December 31, 2026, is a voluntary
model that also builds upon the CEC Model structure to encourage health
care providers to better manage the care for Medicare beneficiaries
with CKD stages 4 and 5 and ESRD, delay the onset of dialysis, and
incentivize kidney transplantation. Various entities are participating
in the KCC Model, including nephrologists and nephrology practices,
dialysis facilities, and other health care providers. The participating
entities receive a bonus payment for each aligned beneficiary who
receives a
[[Page 43530]]
kidney transplant, so long as the transplant remains successful over a
certain time period. CMS plans to continue to evaluate the
effectiveness of the ETC and KCC Models in achieving clinical goals,
improving quality of care, and reducing Medicare costs.\32\
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\32\ The evaluation report for the first two years (2021, 2022)
of the ETC Model is available at https://www.cms.gov/priorities/innovation/data-reports.
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The IOTA Model proposes to complement the ETC and KCC Models and
expand kidney model participation to kidney transplant hospitals, which
are a key player in the transplant ecosystem, to test whether two-sided
risk payments based on performance increase access to kidney
transplants for ESRD patients placed on the waitlists of participating
transplant hospitals.
c. HRSA Initiatives Involving Kidney Transplants
NOTA established the OPTN almost 40 years ago to coordinate and
operate the nation's organ procurement, allocation, and transplantation
system. There are about 400 member organizations that comprise the
OPTN. Section 372(b)(2)(A) of the PHS Act charges the OPTN with
establishing a national list of individuals who need organs and a
national computer system to match organs with individuals on the
waitlist. HRSA has also undertaken efforts in alignment with CMS
efforts and Federal Government initiatives to improve accountability in
OPTN functions. On March 22, 2023, HRSA launched the OPTN Modernization
Initiative to strengthen accountability, equity, and performance in the
organ donation and transplantation system through a focus on five key
areas: technology, data transparency, governance, operations, and
quality improvement and innovation.\33\ The OPTN Modernization
Initiative was further supported by the Securing the U.S. Organ
Procurement and Transplantation Network Act (Pub. L. 118-14), which
included several key provisions proposed in the President's Fiscal Year
2024 Budget and was signed into law on September 22, 2023.\34\ The new
law expressly authorizes HHS to make multiple awards to different
entities, which could enable the OPTN to benefit from best-in-class
vendors and provide a more efficient system that strengthens oversight
and improves patient safety.
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\33\ HRSA Announces Organ Procurement and Transplantation
Network Modernization Initiative [verbar] HRSA. (n.d.).
Www.hrsa.gov. Retrieved August 20, 2023, from https://www.hrsa.gov/optn-modernization/march-2023.
\34\ The White House. (2023, September 22). Bill Signed: H.R.
2544. The White House. https://www.whitehouse.gov/briefing-room/
legislation/2023/09/22/bill-signed-h-r-2544/
#:~:text=On%20Friday%2C%20September%2022%2C%202023,Organ%20Procuremen
t%20and%20Transplantation%20Network.
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Effective July 14, 2022, revisions to the OPTN Bylaws were made
related to the Transplant Program Performance to establish new criteria
for identification of transplant programs that enter MPSC performance
review based on the following criteria: \35\
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\35\ OPTN. (n.d.). Enhance Transplant Program Performance
Monitoring System, Phase 1 (July 2022) Sponsoring Committee:
Membership and Professional Standards Bylaws Affected. Retrieved
August 20, 2023, from https://optn.transplant.hrsa.gov/media/hgkksfuu/phase-1_tx-prgm-performance-monitoring_dec-2021.pdf.
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The transplant program's 90-day post-transplant graft
survival hazard ratio is greater than 1.75 during the 2.5-year time
period; or
The transplant program's 1-year post-transplant graft
survival conditional on 90-day post-transplant graft survival hazard
ratio is greater than 1.75 during a 2.5-year period.
Transplant programs that meet either of the criteria, as reported
by the SRTR, must participate in the OPTN Membership and Professional
Standards Committee (MPSC) performance review, which may require the
member to take appropriate actions to determine if the transplant
program has demonstrated sustainable improvement, including, but not
limited to--
Providing information about the program structure,
procedures, protocols and quality;
Review processes;
Adopting and implementing a plan for improvement;
Participating in an informal discussion with MPSC members;
and
Participating in a peer visit.
The MPSC would continue to review the transplant program under the
performance review until the MPSC determines that the transplant
program has made sufficient and sustainable improvements to avoid risk
to public health or patient safety. If the MPSC's review determines
that a risk to patient health or public safety exists, the MPSC may
request that a member inactivate or withdraw a designated transplant
program, or a specific component of the program, to mitigate the risk.
Transplant programs that do not participate in the MPSC performance
review process or fail to act to improve their performance are subject
to the policies described in Appendix L of the OPTN Bylaws, Reviews and
Actions, including the declaration of ``Member Not in Good Standing.''
While being designated ``Member Not in Good Standing'' does not
necessarily lead to the closure or removal of that program from
receiving reimbursement from Federal health insurance programs, the
Secretary can, based on a recommendation from the OPTN Board of
Directors, revoke OPTN membership, close an OPTN member, or remove the
ability of the member to receive Federal funding from Medicare or
Medicaid. Additionally, numerous private payers align with the MPSC
metrics and SRTR star rating system that evaluate transplant hospitals
on post-transplant performance to create their Centers of Excellence
programs. Therefore, MPSC reviews and performance on the MPSC
monitoring measures are a powerful regulatory incentive for transplant
programs.
In the final rule, dated September 22, 2020, titled ``Removing
Financial Disincentives to Living Organ Donation'' (85 FR 59438), HRSA
expanded the scope of qualified reimbursable expenses incurred by
living donors under the Living Organ Donation Reimbursement Program to
include lost wages and dependent care (childcare and elder care)
expenses to further the goal of reducing financial barriers to living
organ donation. The program previously only allowed for reimbursement
of travel, lodging, meals, and incidental expenses. In the final
notice, dated September 22, 2020, titled, ``Reimbursement of Travel and
Subsistence Expenses Toward Living Organ Donation Program Eligibility
Guidelines,'' HRSA increased the income eligibility threshold under the
Living Organ Donation Reimbursement Program from 300 percent to 350
percent of the Federal Poverty Guidelines (85 FR 59531).
3. Rationale for the Proposed IOTA Model
a. Alignment With Federal Government Initiatives and Priorities
For decades, patients and health care providers have confronted an
imbalance in the number of transplant candidates and the supply of
acceptable donor organs, including kidneys and other organs. Observed
variation in access to organ transplantation by geography, race/
ethnicity, disability status, and socioeconomic status, as well as the
overall performance of the organ transplantation ecosystem, raised the
need to make performance improvements and address disparities.\36\
Strengthening and improving the
[[Page 43531]]
performance of the organ transplantation ecosystem is a priority for
HHS. To that end, OTAG was established in 2021 by CMS and HRSA and has
expanded interagency coordination and collaboration to ``drive
improvements in donations, clinical outcomes, system improvement,
quality measurement, transparency, and regulatory oversight.'' \37\
Collectively, CMS and HRSA seek to--
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\36\ Moody-Williams, J.D., & Nair, S. (2023, December 13). Organ
Transplantation Affinity Group (OTAG): Strengthening accountability,
equity, and performance [verbar] CMS. BLOG. https://www.cms.gov/blog/organ-transplantation-affinity-group-otag-strengthening-accountability-equity-and-performance.
\37\ Moody-Williams, J.D., & Nair, S. (2023, December 13). Organ
Transplantation Affinity Group (OTAG): Strengthening accountability,
equity, and performance [verbar] CMS. BLOG. https://www.cms.gov/blog/organ-transplantation-affinity-group-otag-strengthening-accountability-equity-and-performance.
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Reduce variation of pre-transplant and referral practices;
\38\
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\38\ Pre-transplant/referral practices are inclusive of the
referring physician's assessment criteria, patient education, and
feedback to the referring physician from the transplant assessment.
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Increase availability and use of donated organs;
Increase accountability for organ procurement and
matching;
Promote equitable access to transplants; and
Empower patients, families, and caregivers to actively
engage in the transplant journey.
We believe the proposed IOTA Model has the potential to
substantially increase the number of kidney transplants in a way that
enhances fairness for all affected individuals, regardless of
socioeconomic status or other factors that limit access to care and
negatively affect health outcomes, thereby improving quality of care,
reducing costs to Medicare, and prolonging lives. The IOTA Model, as
proposed, is complementary to the ETC and KCC Models, and to other CMS
and HRSA initiatives, with the collective goal of achieving
improvements in processes among transplant hospitals that would spur an
increase in both deceased donor and living donor kidney transplantation
and reduce population health disparities. Furthermore, although we are
targeting our proposals to kidney transplant programs, we seek to test
specific modifications for Medicare payment and other programmatic
measures that would establish a framework for potential future
interventions for transplantation relating to the other solid organ
types.
In the following sections of this proposed rule, we review
scientific literature that outlines specific ways that kidney
transplantation can be enhanced. Although not the focus of our
analysis, we also present findings pertaining to the transplantation of
other organs, especially livers. We aim to show how the types of
interventions that we are proposing might also apply for any future
efforts to increase transplant numbers for other organ types, and to
continue to pursue the goal of greater equity. We also describe recent
efforts from CMS and HRSA to enhance organ transplantation that
complement our proposals to use payment incentives as a policy lever to
increase the number of kidney transplants and achieve a fairer
distribution.
b. End Stage Renal Disease Impact
According to the United States Renal Data System (USRDS), in 2021
about 808,536 people in the United States were living with ESRD, almost
double the number in 2001.\39\ Prevalence of ESRD varied by Health
Service Area (HSA) and ESRD Network.\40\ Stratified by age and race/
ethnicity, ESRD was consistently more prevalent among older people (65
and older) and in Black people.\41\ Diabetes and hypertension are most
often the primary cause of ESRD.\42\ According to the National Kidney
Foundation, these diseases disproportionately affect minority
populations, increasing the risk of kidney disease.\43\ Year-over-year,
incidence of ESRD continues to increase, as the number of patients
newly registered increased from 97,856 in 2001 to 134,837 in 2019 and
135,972 in 2021.\44\ Studies show that people with kidney transplants
live longer than those who remain on dialysis.\45\ Despite these
positive outcomes, the percentage of prevalent ESRD patients with a
functioning kidney transplant remained relatively stable over the past
decade, increasing only slightly from 29.7 percent in 2011 to 30.51
percent in 2021.\46\ In 2021, 72,864 patients with ESRD were on the
kidney transplant waitlist, of which 27,413 were listed during that
year.\47\ The IOTA Model proposes to focus on the ESRD patients who are
on the kidney transplant waitlists of the kidney transplant hospitals
that would be required to participate in this Model. ESRD patients
represent a small portion of the U.S. population, but the disease
burden to the patient and to CMS is great in terms of health outcomes,
survival, quality of life, and cost. The ESRD population accounted for
6.1% of total Medicare expenditures in 2020.\48\
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\39\ United States Renal Data System. 2023.End Stage Renal
Disease: Chapter 1. Figure 1.5.
\40\ United States Renal Data System. 2023. End Stage Renal
Disease: Chapter 1. Figure 1.7.
\41\ United States Renal Data System. 2023. End Stage Renal
Disease: Chapter 1. Figure 1.8.
\42\ United States Renal Data System. 2023. End Stage Renal
Disease. Chapter 1. Table 1.3.
\43\ National Kidney Foundation. (2016, January 7). Race,
Ethnicity and Kidney Disease. National Kidney Foundation. https://www.kidney.org/atoz/content/minorities-KD.
\44\ United States Renal Data System. 2023. End Stage Renal
Disease. Chapter 1. Figure 1.1.
\45\ National Kidney Foundation. (2017, February 14). Kidney
Transplant. National Kidney Foundation. https://www.kidney.org/atoz/content/kidney-transplant.
\46\ United States Renal Data System. 2023. End Stage Renal
Disease: Chapter 7. Figure 7.16.
\47\ United States Renal Data System. 2023. End Stage Renal
Disease: Chapter 7. Figures 7.1 and 7.2.
\48\ United States Renal Data System. 2022. End Stage Renal
Disease: Chapter 9.
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Due to wide variability across eligible kidney transplant
hospitals, we are unable to estimate the IOTA Model's attributed
patient population until the IOTA participants are randomly selected.
c. Benefits of Kidney Transplantation
ESRD, when a person's kidney function has declined to the point of
requiring regular dialysis or a transplant for survival, as the
person's kidneys are no longer able to perform life-sustaining
functions, is the final stage of CKD. ESRD is a uniquely burdensome
condition, with uncertain survival and poor quality of life for
patients. The higher mortality and substantially greater expenditures
and hospitalization rates for ESRD beneficiaries compared to the
overall Medicare population suggest the need to explore policy
interventions to enhance patients' survival and life experience, as
well as to reduce the impact to Medicare. The IOTA Model proposes to
improve patient outcomes by incentivizing increased access to kidney
transplantation across IOTA participants. Access to this lifesaving
treatment may delay or avert dialysis, reduce costs to the Medicare
program and to patients, and enhance survival and quality of life.
A kidney transplant involves surgically transplanting a kidney from
a living or deceased donor to a kidney transplant recipient. The
replacement organ is known as a graft. Most kidneys are transplanted
alone, as kidneys transplanted along with other organs are very
rare.\49\ Fewer than 1,000 patients each year receive a simultaneous
kidney-pancreas transplant, which is generally conducted for patients
who have kidney failure related to type 1 diabetes mellitus.\50\ The
kidney in such
[[Page 43532]]
a simultaneous transplant may come from a living or deceased donor, but
other organs mostly come from a deceased donor.
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\49\ According to OPTN data, in 2022, there were 389 kidney-
heart transplants in the U.S, 789 kidney-liver transplants, 22
kidney-lung transplants, and 3 kidney-intestine transplants. See
https://optn.transplant.hrsa.gov/data/view-data-reports/national-data/.
\50\ Health Resources and Services Administration. (2020).
Scientific Registry for Transplant Recipients. OPTN/SRTR 2020 Annual
Data Report: Pancreas. https://srtr.transplant.hrsa.gov/annual_reports/2020/Pancreas.aspx.
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About three-quarters of kidney transplants in the U.S. are deceased
donor kidney transplants.\51\ For deceased donor transplantation, a
patient needs to contact a transplant hospital and arrange for an
evaluation to assess the feasibility of surgery. The patient's name
would then be added to a list of individuals who can receive organ
offers. This is known as the kidney transplant hospital's kidney
transplant waitlist. Living donation occurs when a living person
donates an organ to a family member, friend, or other individual.
People unknown to one another sometimes take part in paired exchanges,
which allow the switching of recipients based on blood type and other
biological factors. The numbers of deceased donor kidney donation have
increased over the past decade, while living donor kidney donation has
remained relatively constant, declining in 2020 with the COVID-19
pandemic.\52\
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\51\ United States Renal Data System. 2022. USRDS Annual Data
Report. Volume 2. End-stage Renal Disease (ESRD) in the United
States, Chapter 7: Transplantation. Figure 7.10b.
\52\ United States Renal Data System. 2022. USRDS Annual Data
Report. Volume 2. End-stage Renal Disease (ESRD) in the United
States, Chapter 7: Transplantation. Figure 7.10b.
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Kidney transplantation is considered the optimal treatment option
for most ESRD patients. Although not a cure for kidney disease, a
transplant can help a person live longer and improve quality of life.
On average, patients experience 14 to 16 years of function from a
kidney from a living kidney donor, while few people survive more than a
decade on dialysis.\53\ According to one source, the majority of
deceased donor kidneys are expected to function for about 9 years, with
high quality organs lasting longer.\54\ A systematic review of studies
worldwide finds significantly lower mortality and risk of
cardiovascular events associated with kidney transplantation compared
with dialysis.\55\ Additionally, this review finds that patients who
receive transplants experience a better quality of life than treatment
with dialysis.\56\ The average dialysis patient is admitted to the
hospital nearly twice a year, often as a result of infection, and more
than 35 percent of dialysis patients who are discharged are re-
hospitalized within 30 days of being discharged.\57\ Among transplant
recipients, there are lower rates of hospitalizations, emergency
department visits, and readmissions compared to those still on
dialysis.\58\ In general, from the standpoint of long-term survival and
quality of life, a living donor kidney transplant is considered the
best among all kidney transplant options for most people with
CKD.59 60
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\53\ Get the Facts on Kidney Transplantation Before You Start
Dialysis--Penn Medicine. (2019, July 24). Www.pennmedicine.org.
https://www.pennmedicine.org/updates/blogs/transplant-update/2019/july/kidney-transplant-facts-before-dialysis.
\54\ Organ Procurement and Transplantation Network. Kidney Donor
Profile Index (KDPI) Guide for Clinicians. https://
optn.transplant.hrsa.gov/professionals/by-topic/guidance/kidney-
donor-profile-index-kdpi-guide-for-clinicians/
#:~:text=Figure%201%20shows%20that%20a,function%20for%20about%209%20y
ears.
\55\ Tonelli, M., Wiebe, N., Knoll, G., Bello, A., Browne, S.,
Jadhav, D., Klarenbach, S., & Gill, J. (2011). Systematic Review:
Kidney Transplantation Compared With Dialysis in Clinically Relevant
Outcomes. American Journal of Transplantation, 11(10), 2093-2109.
https://doi.org/10.1111/j.1600-6143.2011.03686.x.
\56\ Ibid.
\57\ United States Renal Data System. 2022. USRDS Annual Data
Report. 2022. Volume 2. End-stage Renal Disease (ESRD) in the United
States, Chapter 5: Hospitalization. Figures 5.1a, 5.9.
\58\ United States Renal Data System. 2021. USRDS Annual Data
Report. Volume 2. End-Stage Renal Disease (ESRD) in the United
States. Chapter 5: Hospitalization, Figures 5.1a, 5.6a, 5.8.
\59\ Nemati, E., Einollahi, B., Lesan Pezeshki, M., Porfarziani,
V., & Fattahi, M.R. (2014). Does Kidney Transplantation With
Deceased or Living Donor Affect Graft Survival? Nephro-Urology
Monthly, 6(4). https://doi.org/10.5812/numonthly.12182.
\60\ United States Renal Data System. 2022. USRDS Annual Data
Report. Volume 2. End-stage Renal Disease (ESRD) in the United
States, Chapter 7: Hospitalization. Figure 7.20.b.
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A cost advantage also arises with kidney transplantation. Per
person per year Medicare FFS spending for beneficiaries with ESRD with
a transplant is less than half that for either hemodialysis or
peritoneal dialysis.\61\ While the benefits to patient survival and
quality of life from living donor kidney transplantation are more
pronounced, a recent literature review shows that deceased donor kidney
transplantation generally produced better outcomes at a lower cost
compared to dialysis, although old age and a high comorbidity load
among kidney transplant patients may mitigate this advantage.\62\ An
earlier study, based on a single hospital, showed rates of
hospitalization, a substantial factor in health care costs, to be lower
among kidney transplant patients than for those on dialysis.\63\
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\61\ United States Renal Data System. 2022. USRDS Annual Report.
Volume 2. End-stage Renal Disease (ESRD) in the United States,
Chapter 9: Healthcare Expenditures for Persons with ESRD. Figure
9.11.
\62\ Fu, R., Sekercioglu, N., Berta, W., & Coyte, P.C. (2020).
Cost-effectiveness of Deceased-donor Renal Transplant Versus
Dialysis to Treat End-stage Renal Disease. Transplantation Direct,
6(2), e522. https://doi.org/10.1097/txd.0000000000000974.
\63\ Khan, S., Tighiouart, H., Kalra, A., Raman, G., Rohrer,
R.J., & Pereira, B.J.G. (2003). Resource utilization among kidney
transplant recipients. Kidney International, 64(2), 657-664. https://doi.org/10.1046/j.1523-1755.2003.00102.x.
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Despite these outcomes, in 2020, only about 30 percent of prevalent
ESRD patients--those with existing ESRD diagnoses--in the U.S. had a
functioning kidney transplant, or graft.\64\ In 2016, only 2.8 percent
of incident ESRD patients--meaning patients newly diagnosed with ESRD--
received a preemptive kidney transplant, allowing them to avoid
dialysis.\65\ These rates are substantially below those of other
developed nations. The U.S. was ranked 17th out of 42 reporting
countries in kidney transplants per 1,000 dialysis patients in 2020,
with 42 transplants per 1,000 dialysis patients in 2020.\66\ We seek to
test policy approaches aimed at increasing the number of kidney
transplants over current levels given these relatively low numbers and
the overall benefit to patients from transplantation, as well as the
potential savings to Medicare.
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\64\ United States Renal Data System. 2022 Annual Data Report.
Volume 2. End Stage Renal Disease Chapter 7 Transplantation Figure
7.16.
\65\ United States Renal Data System. 2018. Annual Data Report.
Volume 2. Chapter 1: Incidence, Prevalence, Patient Characteristics,
and Treatment Modalities. Figure 1.2. Retrieved from https://www.usrds.org/2018/view/v2_01.aspx.
\66\ United States Renal Data System. 2022. Annual Data Report.
Volume 2. End Stage Renal Disease. Chapter 7. Transplantation.
Figure 11.17b.
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d. Kidney Transplant Rates and Unmet Needs
Annually, more than one hundred thousand individuals in the U.S.
begin treatment for ESRD.\67\ Despite transplantation being widely
regarded as the optimal treatment for people with ESRD, as well as
being more cost-effective in the long term compared to dialysis, only a
minority of people with ESRD (13 percent) are added to the waitlist,
and even fewer receive a transplant. To be added to the kidney
transplant waitlist, a patient must complete an evaluation at a
transplant hospital, and the patient must be found to be a good
candidate for a transplant. Nearly 5,000 patients on the national
kidney transplant waiting list die each year.68 69 70 These
trends have persisted
[[Page 43533]]
for several decades despite increases in the number of kidney
transplants from deceased donors and living donors.
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\67\ United States Renal Data System. 2022. USRDS 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; 2022.Volume 2: End-
stage Renal Disease (ESRD) in the United States, Chapter 1:
Incidence, Prevalence, Patient Characteristics.
\68\ Scientific Registry of Transplant Recipients. Program
Specific Reports. Www.srtr.org. Retrieved June 15, 2023, from
https://www.srtr.org/reports/program-specific-reports/.
\69\ Too Many Donor Kidneys Are Discarded in U.S. Before
Transplantation--Penn Medicine. (2020, December 16).
www.pennmedicine.org. https://www.pennmedicine.org/news/news-releases/2020/december/too-many-donor-kidneys-are-discarded-in-us-before-transplantation.
\70\ United States Renal Data System. 2022 Annual Data Report.
Volume 2. End Stage Renal Disease Chapter 7 Transplantation Figure
7.4.
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From 1996 to 2019, the number of kidneys made available for
transplantation from deceased donors grew steadily, in part because of
organs that became available as a result of the opioid
epidemic.71 72 In 2018 and 2019, the total number of kidney
transplants rose steadily as compared to previous years.\73\ In 2019,
almost one third of patients received a transplant within one year of
being placed on the waitlist (32.9 percent), and the rate reached 51.8
percent within 5 years of being placed on the waitlist.\74\ The number
of kidney transplants increased by 10.2 percent from 2018 to 2019, but
fell by 2.7 percent from 2019 to 2020, from 24,511 to 23,853. The
reduction was precipitated by a 23.6 percent decline in living donor
transplants on account of the COVID-19 pandemic.\75\ The overall number
of patients with a functioning graft continued its upward trend,
reaching 245,846 in 2020, an increase of 2.7 percent from 2019.\76\
Nonetheless, these gains in kidney transplantation in the U.S. have
fallen far short of the prevailing need among individuals with ESRD or
facing the prospect of kidney failure. The number of individuals with
ESRD added to the waitlist for a kidney transplant reached a high of
28,533 in 2019, but dropped slightly to 25,136 in 2020, while rising to
27,413 in 2021.\77\ At the end of 2021, 72,864 individuals were on the
waitlist for a kidney transplant.\78\
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\71\ Hariharan, S., Israni, A. K., & Danovitch, G. (2021). Long-
Term Survival after Kidney Transplantation. New England Journal of
Medicine, 385(8), 729-743. https://doi.org/10.1056/nejmra2014530.
\72\ Durand, C.M., Bowring, M.G., Thomas, A.G., Kucirka, L.M.,
Massie, A.B., Cameron, A., Desai, N.M., Sulkowski, M., & Segev, D.L.
(2018). The Drug Overdose Epidemic and Deceased-Donor
Transplantation in the United States: A National Registry Study.
Annals of Internal Medicine, 168(10), 702-711. https://doi.org/10.7326/M17-2451.
\73\ United States Renal Data System. 2021. Annual Data Report.
Volume 2. End Stage Renal Disease. Chapter 7. Transplantation.
Figure 7.11.
\74\ United States Renal Data System. 2021. Annual Data Report.
Volume 2. End Stage Renal Disease. Chapter 7. Transplantation.
Figure 7.7.
\75\ United States Renal Data System. 2022. Annual Data Report.
Volume 2. End Stage Renal Disease. Chapter 7. Transplantation.
Figure 7.10b.
\76\ United States Renal Data System. 2022. Annual Data Report.
Volume 2. End Stage Renal Disease. Chapter 7. Transplantation.
Figure 7.16.
\77\ United States Renal Data System. 2023. Annual Data Report.
Volume 2. End Stage Renal Disease. Chapter 7. Transplantation.
Figure 7.1.
\78\ United States Renal Data System. 2023. Annual Data Report.
Volume 2. End Stage Renal Disease. Chapter 7. Transplantation.
Figure 7.2.
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The increase in deceased donor kidney transplantation was
accompanied by a gradual but steady decline in the number of living
donor transplants as compared to patients undergoing dialysis. The
total number of living donor transplants per year has risen moderately
over the past two decades, from 5,048 in 2000 to 5,241 in 2020, and
5,971 in 2021.79 80 With the overall dialysis population
growing, the rate of living donor transplants per 100 patient-years on
dialysis declined from 1.4 to 0.8 transplants from 2010 to 2020.\81\ A
report states the proportion of patients undergoing living donor kidney
donation to have decreased from 37 percent in 2010 to 29 percent in
2019.\82\ A study in 2013 of OPTN data found that the decline in living
donation appeared most prominent among men, Black/African Americans,
and younger and lower income adults, potentially leading to longer
waiting times for transplantation, greater dialysis exposure, higher
death rates on the waitlist, lower graft and patient survival for
recipients, and higher overall healthcare costs for the care of
patients with ESRD.\83\
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\79\ United States Renal Data System. 2012. Annual Data Report.
Atlas ESRD. Table 7.1.
\80\ United States Renal Data System. 2023. Annual Data report.
Volume 2. End Stage Renal Disease. Chapter 7. Transplantation.
Figure 7.10a.
\81\ United States Renal Data System. 2022. Annual Data Report.
Volume 2. End Stage Renal Disease. Chapter 7. Transplantation.
Figure 7.10a.
\82\ Charnow, J.A. (2021, June 8). Living Donor Kidney
Transplants Declined in the Last Decade. Renal and Urology News.
https://www.renalandurologynews.com/home/conference-highlights/american-transplant-congress/living-donor-kidney-transplantation-decreased-after-2010-united-states-trends/.
\83\ Rodrigue, J.R., Schold, J.D., & Mandelbrot, D.A. (2013).
The Decline in Living Kidney Donation in the United States.
Transplantation Journal, 96(9), 767-773. https://doi.org/10.1097/tp.0b013e318298fa61.
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e. Disparities
Kidney transplantation research in the U.S. reveals disparities
across a number of different axes including geography, race and
ethnicity, disability, socioeconomic status, neighborhood factors, and
availability of health insurance.84 85 86 87 88 Studies
during the past decade have shown substantial disparities in kidney
transplant rates among transplant programs at a national level, as well
as both among and within donation service areas (DSAs).\89\ A 2020
study examined data from a registry that included all U.S. adult kidney
transplant candidates added to the waitlist in 2011 and 2015,
comprising 32,745 and 34,728 individuals, respectively.\90\ Among
transplant programs nationwide, in 2015, the study found that the
probability of a deceased donor transplant within three years for the
average patient to be up to 16 times greater in some transplant
hospitals as compared to others.\91\ Substantial differences in
probability of deceased donor transplantation were found even within
DSAs, where all transplant programs utilize the same OPO and local
organ supply. For the 2015 cohort, there was a median 2.3-fold
difference between the highest and lowest hospital in each DSA in the
43 of 58 DSAs with more than one transplant hospital. The largest
absolute difference in probability of transplant occurred in a DSA with
seven transplant programs, with a patient on the waitlist at the
transplant program with the highest probability of
[[Page 43534]]
transplant being 9.8 times more likely to receive a transplant than a
patient at the transplant program with the lowest probability of
receiving a transplant.\92\ Factors such as local organ supply, the
characteristics of individuals on the waitlist of a given transplant
program, the size of the waitlist, and the transplant program's volume
of transplants may account for the differences observed nationally
across DSAs. However, the variation among transplant programs across
DSAs is significantly associated with organ offer acceptance patterns
at individual transplant hospitals.\93\ This underscores the need to
address geographic disparities and for more transparency on how
transplant programs make decisions on organ offers for their waitlist
patients.
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\84\ King, K.L., Husain, S.A., Schold, J.D., Patzer, R.E.,
Reese, P.P., Jin, Z., Ratner, L.E., Cohen, D.J., Pastan, S.O., &
Mohan, S. (2020). Major Variation across Local Transplant Centers in
Probability of Kidney Transplant for Wait-Listed Patients. Journal
of the American Society of Nephrology, 31(12), 2900-2911. https://doi.org/10.1681/ASN.2020030335.
\85\ Melanson T., Basu M., Plantiga L., Pastan S., Mohan S.,
Patzer R. (2017). Variation in Living Donor Kidney Transplantation
among U.S. Transplant Centers. American Journal of Transplantation,
17 (suppl 3).
\86\ United States Renal Data System. 2022. Annual Data Report.
Supplements: COVID-19, Racial and Ethnic Disparities Figures 14-4
and 14.15.
\87\ Wesselman, H., Ford, C.G., Leyva, Y., Li, X., Chang, C.-
C.H., Dew, M.A., Kendall, K., Croswell, E., Pleis, J.R., Ng, Y.H.,
Unruh, M.L., Shapiro, R., & Myaskovsky, L. (2021). Social
Determinants of Health and Race Disparities in Kidney Transplant.
Clinical Journal of the American Society of Nephrology, 16(2), 262-
274. https://doi.org/10.2215/cjn.04860420.
\88\ Ng, Y.-H., Pankratz, V.S., Leyva, Y., Ford, C.G., Pleis,
J.R., Kendall, K., Croswell, E., Dew, M.A., Shapiro, R., Switzer,
G.E., Unruh, M.L., & Myaskovsky, L. (2019). Does Racial Disparity in
Kidney Transplant Wait-listing Persist After Accounting for Social
Determinants of Health? Transplantation, 1. https://doi.org/10.1097/tp.0000000000003002.
\89\ With the enactment of NOTA, CMS designated donation service
areas (DSAs); generally, each DSA includes an OPO within its
geographic area. Until March 2021, when OPTN implemented the current
policy for allocation of deceased donor kidneys, the priority for
organs acquired by an OPO was based, among other factors, on an
individual's residence within the DSA extending around the OPO.
\90\ King, K.L., Husain, S.A., Schold, J.D., Patzer, R.E.,
Reese, P.P., Jin, Z., Ratner, L.E., Cohen, D.J., Pastan, S.O., &
Mohan, S. (2020). Major Variation across Local Transplant Centers in
Probability of Kidney Transplant for Wait-Listed Patients. Journal
of the American Society of Nephrology, 31(12), 2900-2911. https://doi.org/10.1681/ASN.2020030335.
\91\ King et al. 2020. 2903.
\92\ King et al., 2020. 2903.
\93\ King et al. 2020. 2903-2904.
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Living donor kidney donation also varies widely among transplant
hospitals. A 2018 report using OPTN data from 2015 showed that while
most transplant hospitals perform few living donor kidney transplants,
certain transplant hospitals have substantially higher rates for their
waitlist patients than the median rate. Differences among transplant
hospitals were correlated with geographic region and the number of
deceased donor kidney transplantations performed.\94\ This underscores
the need for initiatives and processes among transplant hospitals to
encourage living donations to reduce geographic disparities.
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\94\ Melanson T., Basu M., Plantiga L., Pastan S., Mohan S.,
Patzer R. (2017). Variation in Living Donor Kidney Transplantation
among U.S. Transplant Centers. American Journal of Transplantation,
17 (suppl 3).
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Disparities in kidney transplantation rates for various populations
in the U.S. have long been documented. Literature over the past two
decades has focused on Non-Hispanic Black patients, who experience
lower rates of deceased and living donor kidney transplantation as
compared to Non-Hispanic White patients, while being four times more
likely to have kidney failure. Black/African Americans and Hispanics/
Latinos with kidney failure experience lower rates of kidney
transplantation compared with White patients.\95\ Additionally, Black/
African Americans and Hispanics/Latinos, along with Asians, American
Indian/Alaskan Natives, and other minorities, are at a higher risk of
illnesses that may eventually lead to kidney failure, such as diabetes
and high blood pressure.\96\
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\95\ United States Renal Data System. 2022. Annual Data Report.
Supplements: COVID-19, Racial and Ethnic Disparities Figures 14-4
and 14.15.
\96\ National Kidney Foundation. (2016, January 7). Race,
Ethnicity, & Kidney Disease. National Kidney Foundation. https://
www.kidney.org/atoz/content/minorities-
KD#:~:text=Black%20or%20African%20Americans%20are.
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The literature over several decades has also addressed the effect
of differences in age, gender, socioeconomic status (SES), and cultural
aspects.\97\ Recent studies have emphasized poverty and income
differentials in analyzing the interplay of these and other factors
among populations referred for kidney transplantation at several large
transplant hospitals.98 99 100 101 This research extends in
time prior to the Kidney Allocation System (KAS) of 2014, which aimed
to lessen the impact of racial differences on access to kidney
transplantation.
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\97\ Patzer, R.E., & Pastan, S.O. (2020). Policies to promote
timely referral for kidney transplantation. Seminars in Dialysis,
33(1), 58-67. https://doi.org/10.1111/sdi.12860.
\98\ Patzer, R. Perryman, J. Schrager, J. Pastan, S. Amaral, S.
Gazmararian, J. Klein, M. Kutner, N. McClellan, W. 2012. Patzer,
R.E., Perryman, J.P., Schrager, J.D., Pastan, S., Amaral, S.,
Gazmararian, J.A., Klein, M., Kutner, N., & McClellan, W.M. (2012).
The Role of Race and Poverty on Steps to Kidney Transplantation in
the Southeastern United States. American Journal of Transplantation,
12(2), 358-368. https://doi.org/10.1111/j.1600-6143.2011.03927.x.
\99\ Wesselman, H., Ford, C.G., Leyva, Y., Li, X., Chang, C.-
C.H., Dew, M.A., Kendall, K., Croswell, E., Pleis, J.R., Ng, Y.H.,
Unruh, M.L., Shapiro, R., & Myaskovsky, L. (2021). Social
Determinants of Health and Race Disparities in Kidney Transplant.
Clinical Journal of the American Society of Nephrology, 16(2), 262-
274. https://doi.org/10.2215/cjn.04860420.
\100\ Ng, Y.-H., Pankratz, V.S., Leyva, Y., Ford, C.G., Pleis,
J.R., Kendall, K., Croswell, E., Dew, M.A., Shapiro, R., Switzer,
G.E., Unruh, M.L., & Myaskovsky, L. (2019). Does Racial Disparity in
Kidney Transplant Wait-listing Persist After Accounting for Social
Determinants of Health? Transplantation, 1. https://doi.org/10.1097/tp.0000000000003002.
\101\ Schold, J.D., Gregg, J.A., Harman, J.S., Hall, A.G.,
Patton, P.R., & Meier-Kriesche, H.-U. (2011). Barriers to Evaluation
and Wait Listing for Kidney Transplantation. Clinical Journal of the
American Society of Nephrology, 6(7), 1760-1767. https://doi.org/10.2215/cjn.08620910.
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Research findings support the proposition that a broad
interpretation of social determinants of health (SDOH) may
substantially explain racial disparities in both deceased and living
donor kidney transplantation.\102\ Recently, a comprehensive survey of
the literature on disparities in transplantation for kidneys and other
organs found that socioeconomic factors may substantially explain
disproportionately lower transplant rates and longer wait times.\103\
As described in recent literature, a person's SDOH may contribute to
inequities in their prospects for waitlist registration and receipt of
transplantation.104 105 106 SDOH is defined more broadly
than socioeconomic status, to include those conditions in the places
where people live, learn, work, and play that affect a wide range of
health and quality of life risks and outcomes.\107\ More specifically,
SDOH include variations in employment, neighborhood factors, education,
social support systems, and healthcare coverage that impact health
outcomes.
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\102\ Reed, R.D., & Locke, J.E. (2020). Social Determinants of
Health: Going Beyond the Basics to Explore Racial Disparities in
Kidney Transplantation. Transplantation, 104, 1324-1325. https://doi.org/10.1097/tp.0000000000003003.
\103\ National Academies of Science, Engineering, and Medicine.
2022. ``Realizing the Promise of Equity in the Organ Transplantation
System. National Academies Press. Washington DC. 88-93.
\104\ Centers for Disease Control and Prevention. Social
Determinants of Health at CDC. Retrieved June 13, 2023, from https://www.cdc.gov/about/sdoh/index.html.
\105\ Wesselman et al., 2021.
\106\ Ng et al., 2020.
\107\ Centers for Disease Control and Prevention.
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Salient among recent analyses are those of a cohort of patients
initially referred for evaluation for a kidney transplant at a large
urban transplant hospital between 2010 and 2012. These studies showed
lower waitlist registration and transplant rates for Black/African
Americans, regardless of SDOH. However, after the introduction of the
KAS in 2014, racial difference showed weaker associations with rates of
waitlist registration and receipt of a deceased donor transplant, when
controlling for SDOH.108 109 This finding is consistent with
reports showing a decrease nationally in differences in rates of
deceased donor kidney transplants among White patients as compared to
Black/African American patients and Hispanic/Latino patients on
dialysis, following the introduction of the KAS.110 111 The
studies of this patient cohort showed Black/African American race to be
associated with a decrease in probability of kidney transplant, while
still according influence to clinical, social, demographic and cultural
factors. These factors included older age, lower income, public
insurance, having more comorbidities, being transplanted pre-KAS, less
social support, and less transplant knowledge.\112\ Similarly, an
earlier study of a population at a single
[[Page 43535]]
transplant hospital found that socioeconomic factors attenuated the
association between racial difference and placement on the waitlist for
a kidney transplant.\113\ This underscores the need to consider
initiatives and improvement activities aimed at addressing SDOH for
ESRD patients to remove barriers to access to kidney transplantations.
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\108\ Ng Y et al. 2020. 8.
\109\ Wesselman et al., 2021. 271.
\110\ United States Renal Data System. 2022. Annual Data Report.
End Stage Renal Disease Chapter 7 Transplantation. Figures 7.10a,
7.10b.
\111\ OPTN Two Year Analysis shows effects of Kidney Allocation
System https://optn.transplant.hrsa.gov/news/two-year-analysis-shows-effects-of-kidney-allocation-system/.
\112\ Wesselman et al. 2021. 267.
\113\ Schold et al., 2021.
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Living donor transplantation has demonstrated the enduring
influence of racial disparities, but also the importance of SES and
neighborhood factors. The cohort of patients identified previously,
initially referred for evaluation at a large urban hospital between
2010 and 2012, showed that for living donor transplantation, Black/
African American race and lower income held a stronger association with
a lower probability of living donor transplant than for deceased donor
donation.\114\ These results accord with findings nationwide that White
patients are more likely to receive a living donor transplant, followed
by Asian and Hispanic/Latino patients. Black/African American patients
have had lower rates of living donor transplants than other racial or
ethnic groups.\115\ Explanations for these differences have included
disparate rates of diabetes, obesity, and hypertension observed among
minority populations that may contraindicate living donation by a
relative; cultural differences in willingness to donate or ask for a
living donation; concerns about costs among potential donors; and lack
of knowledge about living donor transplantation on the part of
patients, their families, and health care providers.116 117
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\114\ Wesselman et al., 2021. 270.
\115\ United States Renal Data System. 2022. Annual Data Report.
End Stage Renal Disease Chapter 7 Transplantation Figure 7.10a.
\116\ Purnell, T.S., Hall, Y.N., & Boulware, L.E. (2012).
Understanding and Overcoming Barriers to Living Kidney Donation
Among Racial and Ethnic Minorities in the United States. Advances in
Chronic Kidney Disease, 19(4), 244-251. https://doi.org/10.1053/j.ackd.2012.01.008.
\117\ Rodrigue, J.R., Kazley, A.S., Mandelbrot, D.A., Hays, R.,
LaPointe Rudow, D., & Baliga, P. (2015). Living Donor Kidney
Transplantation: Overcoming Disparities in Live Kidney Donation in
the US--Recommendations from a Consensus Conference. Clinical
Journal of the American Society of Nephrology, 10(9), 1687-1695.
https://doi.org/10.2215/cjn.00700115.
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Research over several decades confirms the relation between health
care access and SES factors and disparities in living donor kidney
transplantation receipt for Black/African American and Hispanic/Latino
patients, and, additionally, that these disparities have increased over
time.118 119 120 121 According to one study, between 1995
and 2014, disparities in the receipt of living donor kidney
transplantation grew more for Black/African Americans and Hispanics/
Latinos: (1) living in poorer (versus wealthier) neighborhoods; (2)
without (versus with) a college degree; and (3) with Medicare (versus
private insurance).\122\ The study suggests that delays in the receipt
of kidney care may contribute to reported racial and ethnic differences
in the quality and timing of discussions among patients, families, and
clinicians about living donor kidney transplantation as a treatment
option.\123\
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\118\ Purnell, T.S., Luo, X., Cooper, L.A., Massie, A.B.,
Kucirka, L.M., Henderson, M.L., Gordon, E.J., Crews, D.C., Boulware,
L.E., & Segev, D.L. (2018). Association of Race and Ethnicity With
Live Donor Kidney Transplantation in the United States From 1995 to
2014. JAMA, 319(1), 49. https://doi.org/10.1001/jama.2017.19152.
\119\ Hall, E.C., James, N.T., Garonzik Wang, J.M., Berger,
J.C., Montgomery, R.A., Dagher, N.N., Desai, N.M., & Segev, D.L.
(2012). Center-Level Factors and Racial Disparities in Living Donor
Kidney Transplantation. American Journal of Kidney Diseases, 59(6),
849-857. https://doi.org/10.1053/j.ajkd.2011.12.021.
\120\ Gore, J.L., Danovitch, G.M., Litwin, M.S., Pham, P-T.T., &
Singer, J.S. (2009). Disparities in the Utilization of Live Donor
Renal Transplantation. American Journal of Transplantation, 9(5),
1124-1133. https://doi.org/10.1111/j.1600-6143.2009.02620.x.
\121\ Rodrigue et al. 2015.
\122\ Purnell et al. 2015. 58.
\123\ Purnell et al. 2015. 59.
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One study also established associations between rates of living
donor kidney transplantation for Black/African Americans and transplant
hospital characteristics. While recognizing the potential effect of
clinical factors, the study found that hospitals with high overall
rates of living donor kidney transplantation showed significantly
decreased racial disparities. The authors suggest that such high rates
reveal commitment to living donor kidney transplantation, possibly
shown in better education programs, more formalized procedures to
reduce failure to complete transplant evaluations, increased use of
medically complex and unrelated donors, and more success in reducing
financial barriers to living donor kidney donation.\124\ The study also
notes that hospitals with higher percentages of Black/African American
candidates experience greater racial disparities. The authors surmise
that such a high percentage might indicate an urban setting exhibiting
greater differences in access to health care between Black/African
Americans and other populations.\125\
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\124\ Hall et al. 2012. 855.
\125\ Hall et al. 2012. 855.
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Studies have also shown discrimination on the basis of disability
with regard to organ transplantation, particularly for individuals with
intellectual and developmental disabilities, who are often assumed by
transplant providers to be unable to manage post-transplantation care
requirements.\126\ Discrimination occurs even though individuals'
disabilities that are not related to the need for an organ transplant
generally have little or no impact on the likelihood that the
transplant would be successful.\127\ The American Society of Transplant
Surgeons has recommended that no patient be discriminated against or
precluded from transplant listing solely due to the presence of a
disability, whether physical or psychological.\128\
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\126\ See, for example., Nat'l Council on Disability, Organ
Transplants Discrimination against People with Disabilities: Part of
the Bioethics and Disability Series (2019), https://ncd.gov/sites/default/files/NCD_Organ_Transplant_508.pdf.
\127\ Id. at 38-40.
\128\ Am. Soc'y of Transplant Surgeons, Statement Concerning
Eligibility for Solid Organ Transplant Candidacy (Feb. 12, 2021),
https://asts.org/advocacy/position-statements.
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CMS has kept these concerns in mind when developing the IOTA Model
proposals. The IOTA Model proposes performance-based payments that hold
transplant hospitals selected as the IOTA participants financially
accountable for improvements in access to both deceased and living
donor kidney transplantations. To reduce disparities and promote health
equity, CMS is proposing that the IOTA participants would be required
to develop and submit a Health Equity Plan to CMS in PYs 2 through 6.
This proposed model design feature is aimed at encouraging IOTA
participants to reassess their processes and policies around living and
deceased donor kidneys and promote investments in performance and
quality improvement activities that address barriers to care, including
SDOH. The sequence of steps that patients need to undertake to gain
access to kidney transplantation is complex, and the challenge posed by
this process for potential recipients may be compounded by racial,
socioeconomic and neighborhood factors. Thus, we believe that a unified
framework of interventions to address the distinct social contexts
underlying differences among racial groups in deceased donor kidney
transplantation and living donor kidney transplantation may result in
the desired outcomes of greater overall kidney transplant numbers and
equity.
[[Page 43536]]
f. Post-Transplant Outcomes
While the need for kidney transplants has grown, the rates of
patient and graft survival have increased. Between 2001 and 2020, graft
survival rates at 1 and 5 years showed an increasing trend.\129\
Patient survival at 1 year increased from 97.5 percent in 2001 to 99.2
percent in 2018, but then declined to 98.9 percent in 2019 and 98.4
percent in 2020; patient survival at 5 years rose from 89.8 percent in
2001 to an all-time high of 93.6 percent in 2013, dropping slightly to
93.2 percent in 2016.\130\ For living donor kidney transplants, the
rate of graft failure at 3 years decreased from 3.0 per 100 person
years in 2010 to 2.1 per 100 person years in 2018. The rate of death at
3 years with a functioning graft also decreased from 1.2 to 1.0 per 100
person-years.\131\ For deceased donor kidney transplants, the rate of
graft failure at 3 years decreased from 2010 (6.3 per 100 patient
years) to 2014 (4.9 per 100 patient years), but increased to 5.3 per
100 patient years in 2018. The same pattern was observed for death with
a functioning graft, except that the rate in the 2018 cohort (2.8 per
100 patient years) exceeded that of the 2010 cohort (2.6 per 100
patient years).\132\
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\129\ United States Renal Data System. 2023. Annual Data Report.
Volume 2. End Stage Renal Disease. Transplantation. Figures 7.19a
and 7.19b.
\130\ United States Renal Data System. 2023. Annual Data Report.
Volume 2. End Stage Renal Disease. Chapter 7. Transplantation.
Figures 7.20a and 720.b.
\131\ United States Renal Data System. 2023. Annual Data Report.
Volume 2. End Stage Renal Disease. Chapter 7. Transplantation.
Figure 7.21a.
\132\ United States Renal Data System. 2023. Annual Data Report
Volume 2. End Stage Renal Disease. Chapter 7. Transplantation.
Figure 721.b.
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A study published in the New England Journal of Medicine in 2021
shows the advantage of transplantation using deceased donor organs over
long-term dialysis, even with an increasing trend of adverse conditions
among recipients and donors. Notably, patient survival improved between
the 1990s and the period from 2008 to 2011, despite increases in both
(a) recipients' age, body-mass index (BMI), frequency of diabetes, and
length of time undergoing dialysis, as well as a higher proportion of
recipients with a previous kidney transplant; and (b) donors' age and
in the percentage of donations after circulatory death.\133\ Early
referral of patients for transplants, kidney exchange programs, better
diagnostic tools to identify early acute rejection, innovative
therapies for countering rejection and infection, and optimization of
immunosuppressive medications may be opportunities to enhance kidney
graft survival.\134\
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\133\ Hariharan S, Israni AK, Danovitch G. Long-Term Survival
after Kidney Transplantation. N Engl J Med. 2021 Aug 19;385(8):729-
743. doi: 10.1056/NEJMra2014530. PMID: 34407344.
\134\ Hariharan, S., Israni, A. K., & Danovitch, G. (2021).
Long-Term Survival after Kidney Transplantation. New England Journal
of Medicine, 385(8), 729-743. https://doi.org/10.1056/nejmra2014530.
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g. Non-Acceptance and Discards in Kidney Transplantation
Studies have documented the substantial extent of deceased donor
kidney non-utilization in the U.S. relative to other countries
(although methods of defining these rates differ among countries), as
well as a steady increase in that trend over the past two
decades.135 136 137 138 139 A study in 2018 described donor-
specific factors, such as biopsy findings and donor history, along with
an increasing selectivity among transplant hospitals in accepting
organs for transplant and inability to locate a recipient as
contributing to this increase in non-utilization.\140\ Within the
context of the COVID-19 pandemic, the non-utilization of deceased donor
kidneys in 2020 rose to the highest level up to that time, 21.3
percent, despite the decline in discard of organs from hepatitis C-
positive donors.141 142 An analysis found that the donor
kidney discard rate peaked at 27 percent during the fourth quarter of
2021.\143\
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\135\ Mohan, S., Chiles, M. C., Patzer, R. E., Pastan, S. O.,
Husain, S. A., Carpenter, D. J., Dube, G. K., Crew, R. J., Ratner,
L. E., & Cohen, D. J. (2018). Factors leading to the discard of
deceased donor kidneys in the United States. Kidney International,
94(1), 187-198. https://doi.org/10.1016/j.kint.2018.02.016.
\136\ Aubert, O. Reese. P. Audry, B. Bouatou, B. Raynaud, M.
Viglietti, D. Legendre, C. Glotz, D. Empana, J. Jouben, X.
Lefaucheur, C. Jacquelinet, C. Loupy, A. (2019). Disparities in
Acceptance of Deceased Donor Kidneys Between the United States and
France and Estimated Effects of Increased US Acceptance. JAMA
Internal Medicine, 179(10), 1365-1374. https://doi.org/10.1001/jamainternmed.2019.2322.
\137\ Ibrahim, M., Vece, G., Mehew, J., Johnson, R., Forsythe,
J., Klassen, D., Callaghan, C., & Stewart, D. (2019). An
international comparison of deceased donor kidney utilization: What
can the United States and the United Kingdom learn from each other?
American Journal of Transplantation, 20(5), 1309-1322. https://doi.org/10.1111/ajt.15719.
\138\ Stewart, D. E., Garcia, V. C., Rosendale, J. D., Klassen,
D. K., & Carrico, B. J. (2017). Diagnosing the Decades-Long Rise in
the Deceased Donor Kidney Discard Rate in the United States.
Transplantation, 101(3), 575-587. https://doi.org/10.1097/tp.0000000000001539.
\139\ Health Resources and Services Administration. OPTN.
(2017). Two year analysis shows effects of kidney transplantation
system. Optn.transplant.hrsa.gov. Retrieved May 30, 2023, from
https://optn.transplant.hrsa.gov/news/two-year-analysis-shows-effects-of-kidney-allocation-system/.
\140\ Mohan, Chiles et al. (2018).
\141\ Lentine, K. Smith, J. Hart, A. Miller, J. Skeans, M.
Larkin, L. Robinson, A. Gauntt, K. Israni, A. Hirose, R. Snyder, J.
(2022). OPTN/SRTR 2020 Annual Data Report: Kidney. American Journal
of Transplantation 22(Suppl 2) 21-136.
\142\ Following upon the introduction of certain anti-viral
drugs, transplanting kidneys from donors infected with Hepatitis C
has shown promising outcomes in recent studies. See Penn Medicine
News ``Penn Researchers Continue to Advance Transplantation of
Hepatitis C Virus-infected kidneys into HCV-Negative Recipients''
August 31, 2020 https://www.pennmedicine.org/news/news-releases/2020/august/penn-researchers-advance-transplantation-hepatitis-c-virus-infected-kidneys-hcv-negative-recipients.
\143\ Cron, D. Husain, S. Adler, J. (2022). The new distance-
based kidney allocation system: Implications for patients,
transplant centers, and Organ Procurement Organizations. Current
Transplantation Reports, 9(4), 304. https://doi.org/10.1007/s40472-022-00384-z.
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Since 2014, when the KAS went into effect, OPTN has aimed to
address the high rate of kidneys going unused. The new kidney
allocation system was developed in response to higher than necessary
discard rates of kidneys, variability in access to transplants for
candidates who are harder to match due to biologic reasons, inequities
resulting from the way waiting time was calculated, and a matching
system that results in unrealized life years and high re-transplant
rates.\144\ The KAS also revised the system that matched waitlisted
individuals with available organs.\145\ As part of the KAS, the Kidney
Donor Profile Index (KDPI) was implemented to assess the quality of
kidneys procured for kidney transplants. The KDPI is based on a
preliminary measurement, the Kidney Donor Risk Index (KDRI), which
estimates the relative risk of post-transplant kidney graft failure
based on scores for the deceased donor on a set of 10 demographic and
clinic characteristics, including age, height, weight, ethnicity,
history of hypertension, history of diabetes, cause of death, serum
creatinine, hepatitis C virus status, and donation after circulatory
death status.\146\ This relative risk is determined in relation to the
overall distribution of a grouping of these scores across the overall
deceased donor population for the previous year. The KDPI transforms
the KDRI to a zero-to-100 scale. Lower KDPI scores are associated with
greater expected post-transplant longevity, while higher KDPI
[[Page 43537]]
scores are associated with a worse expected outcome in this
regard.\147\
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\144\ OPTN Kidney Transplantation Committee. (n.d.). The New
Kidney Allocation System (KAS) Frequently Asked Questions. Retrieved
December 6, 2023, from https://optn.transplant.hrsa.gov/media/1235/kas_faqs.pdf. p. 4.
\145\ OPTN. (n.d.) The New Kidney Allocation System (KAS)
Frequently Asked Questions. https://optn.transplant.hrsa.gov/media/1235/kas_faqs.pdf. p. 4.
\146\ OPTN. (n.d.). The New Kidney Allocation System Frequently
Asked Questions. https://optn.transplant.hrsa.gov/media/1235/kas_faqs.pdf. pp. 8-9.
\147\ OPTN. (n.d.). The New Kidney Allocation System Frequently
Asked Questions . https://optn.transplant.hrsa.gov/media/1235/kas_faqs.pdf. p. 4.
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According to these new allocation rules, the KDPI of an available
organ was to be assessed, with donor kidneys with low KDPI scores being
offered to patients scoring high in terms of expected longevity. New
revisions to the KAS also included an individual's time on dialysis
prior to waitlisting to assess waiting time used for determining
priority for an available organ, and new rules that allowed for greater
access for candidates with blood type B to donor kidneys with other
blood types.\148\
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\148\ OPTN. (n.d.). The New Kidney Allocation System Frequently
Asked Questions. https://optn.transplant.hrsa.gov/media/1235/kas_faqs.pdf. p. 4.
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An OPTN data analysis from 2014 to 2016, the first two years after
KAS implementation, showed that despite substantial increases in both
deceased kidney donor transplants and deceased kidney donation, the
kidney discard rate increased to 19.9 percent in 2016.\149\ OPTN linked
the discard rates to KDPI scores, with fewer than 3 percent of donor
kidneys with KDPI between zero and 20 percent discarded, compared with
60 percent of donor kidneys with KDPI between 86 and 100 percent being
discarded.\150\
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\149\ OPTN. (2017, July 9). Two Year Analysis shows effects of
Kidney Allocation System. Retrieved June 9, 2023, from https://optn.transplant.hrsa.gov/news/two-year-analysis-shows-effects-of-kidney-allocation-system/.
\150\ OPTN. (2017, July 9). Two Year Analysis shows effects of
Kidney Allocation System. Retrieved June 9, 2023, from https://optn.transplant.hrsa.gov/news/two-year-analysis-shows-effects-of-kidney-allocation-system/.
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In March 2021, OPTN finalized a newer allocation policy, which
eliminated the use of DSAs and regions from kidney and pancreas donor
distribution. These measures were part of a framework announced in 2019
that also applied to heart, lung, and liver donor distribution, with
the goal of reducing the importance of geography in patients' access to
organs, and, instead, emphasizing medical urgency.151 152
The new system instituted a point system with up to 2 points (equal to
2 years on the wait list) for patients listed at transplant hospitals
within 250 nautical miles of the donor hospital, and the points
decreasing linearly from the donor hospital to the circle perimeter.
The more points an individual has, the higher their position on the
waitlist and the more likely they are to receive an organ offer. If
there is no candidate within the designated radius, the kidney is
offered to patients listed at hospitals outside the fixed circle, based
on separate proximity points that decrease linearly as the location of
a patient approaches 2,500 nautical miles from the donor hospital.\153\
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\151\ Potluri, V. S., & Bloom, R. D. (2021). Effect of Policy on
Geographic Inequities in Kidney Transplantation. American Journal of
Kidney Diseases, 79(6), 897-900. https://doi.org/10.1053/j.ajkd.2021.11.005.
\152\ Penn Medicine. (2021, November 17). Update: Change in
Organ Allocation Designed to Increase Equity in US Kidney and
Pancreas Transplantation. Penn Medicine Physician Blog. https://www.pennmedicine.org/updates/blogs/penn-physician-blog/2021/november/change-in-organ-allocation-designed-to-increase-equity-in-us-kidney-and-pancreas-transplantation.
\153\ Potluri, Bloom. (2021). 897-898.
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Interested parties within the transplant ecosystem commented that
the new policy might further contribute to the increasing rate of donor
organ non-acceptance. According to one review, sharing kidneys over a
broader geographic region means that OPOs would need to work with
transplant hospitals with which there was no prior relationship.\154\
Concern was also expressed about increased transportation time and
procurement costs, risk associated with air transport, and a greater
number of interactions between transplant hospitals and
OPOs.155 156 157 One study notes that policymakers would
need to assess the extent to which the new kidney allocation policy
might affect organ offer acceptance patterns, organ recovery and
utilization rates, and wait times both for the transplant hospital and
broader geographic areas.\158\ Another report cited unpublished SRTR
data, saying that preliminary results suggest an increase in transplant
rate overall, but a trend toward higher donor kidney discard and
increased cold ischemia time.\159\ A study at a single transplant
hospital showed that the number of organ offers--for livers and
kidneys--grew by 140 percent between May 1, 2019, and July 31, 2021,
while the number of transplanted organs remained stable, suggesting
less efficient allocation of organs after the new change in allocation
policy.\160\
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\154\ Potluri, Bloom. (2021) 898.
\155\ Gentry, S.E., Chow, E.K.H., Wickliffe, C.E., Massie, A.B.,
Leighton, T., & Segev, D.L. (2014). Impact of broader sharing on the
transport time for deceased donor livers. Liver Transplantation,
20(10), 1237-1243. https://doi.org/10.1002/lt.23942.
\156\ Chow, E.M., DiBrito, S.R., Luo, X., Wickliffe, C., Massie,
A.B., Locke, J.E., Gentry, S.E., Garonzik-Wang, J., & Segev, D.L.
(2018). Long Cold Ischemia Times in Same Hospital Deceased Donor
Transplants. Transplantation, 102(3), 471-477. https://doi.org/10.1097/tp.0000000000001957.
\157\ Adler, J.T., Husain, S.A., King, K.L., & Mohan, S. (2021).
Greater complexity and monitoring of the new Kidney Allocation
System: Implications and unintended consequences of concentric
circle kidney allocation on network complexity. American Journal of
Transplantation, 21(6), 2007-2013. https://doi.org/10.1111/ajt.16441.
\158\ Adler et al., 2021. 2012.
\159\ Cron, D.C., S. Ali Husain, & Adler, J. T. (2022). The New
Distance-Based Kidney Allocation System: Implications for Patients,
Transplant Centers, and Organ Procurement Organizations. Current
Transplantation Reports, 9(4), 302-307. https://doi.org/10.1007/s40472-022-00384-z.
\160\ Reddy, V., Briget da Graca, Martinez, E., Ruiz, R.,
Asrani, S.K., Testa, G., & Wall, A. (2022). Single-center analysis
of organ offers and workload for liver and kidney allocation.
American Journal of Transplantation, 22(11), 2661-2667. https://doi.org/10.1111/ajt.17144.
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A similar study assessing deceased donor kidney discards from 2000
to 2015 found that 17.3 percent of 212,305 procured deceased donor
kidneys were discarded, representing a 91.5 percent increase in
deceased donor kidney discards during the same time period. The
increase in donor kidney discards outpaced the number of organs
recovered for transplantation, adversely impacting transplantation
rates and waitlist times. Kidneys with higher KDPIs and from donors
with more disadvantageous characteristics were more likely to be
discarded. The estimated 5-year graft survival for even the lowest
quality kidneys substantially exceeds the average 5-year dialysis
survival rate, making discard patterns concerning.\161\ The study
indicates a significant overlap in the quality of discarded and
transplanted deceased donor kidneys, and substantial geographical
variation in the odds of donor kidney discards, which, as seen
previously, would continue to be observed in SRTR data for following
years.\162\ The study also found patterns that indicate factors beyond
organ quality, including biopsy findings, donor history and poor organ
function, and inability to locate a kidney donor recipient, may factor
into deceased organ acceptance decisions. Other factors may be driving
the deceased donor organ discard rates, as the study found that
``discarded organs were more likely to come from older, heavier donors
who were Black, female, diabetic, hypertensive, with undesirable social
behavior and higher terminal creatinine.'' \163\ This finding accords
with observed discard patterns from earlier studies whereby recipients
of marginal kidneys, in terms of advanced donor age, hypertension,
diabetes, or greater cold ischemia time, showed lower mortality and
greater survival benefit for many candidates as
[[Page 43538]]
compared to staying on the transplant wait list.164 165 166
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\161\ Mohan, Chiles et al. 2018. p. 192.
\162\ Mohan et al. 2018. p. 195.
\163\ Mohan et al. 2018. 192.
\164\ Ojo, A.O., Hanson, J.A., Herwig Ulf Meier-Kriesche, Chike
Nathan Okechukwu, Wolfe, R.R., Leichtman, A.B., Agodoa, L.Y.,
Kaplan, B., & Port, F.K. (2001). Survival in Recipients of Marginal
Cadaveric Donor Kidneys Compared with Other Recipients and Wait-
Listed Transplant Candidates. Journal of the American Society of
Nephrology, 12(3), 589-597. https://doi.org/10.1681/asn.v123589.
\165\ Massie, A.B., Luo, X., Chow, E.K.H., Alejo, J.L., Desai,
N.M., & Segev, D.L. (2014). Survival Benefit of Primary Deceased
Donor Transplantation With High-KDPI Kidneys. American Journal of
Transplantation, 14(10), 2310-2316. https://doi.org/10.1111/ajt.12830.
\166\ Cohen, J.B., Eddinger, K.C., Locke, J.E., Forde, K.A.,
Reese, P.P., & Sawinski, D. (2017). Survival Benefit of
Transplantation with a Deceased Diabetic Donor Kidney Compared with
Remaining on the Waitlist. Clinical Journal of the American Society
of Nephrology, 12(6), 974-982. https://doi.org/10.2215/cjn.10280916.
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Research at this time suggests that CMS regulatory requirements and
OPTN policies may have been contributing to transplant hospitals
growing more selective in choosing organs for their waitlisted
patients. A study from 2017 examined OPTN registry data for deceased
donors from 1987 to 2015, showing that changes in the donor pool and
certain clinical practices explained about 80 percent of the increase
in non-utilization of deceased donor kidneys.\167\ However, according
to the study, the remainder of kidney discards, not accounted for by
these factors, suggests that increased risk aversion was leading
transplant hospitals to be more selective about the kidneys they
accept, regardless of the actual risk profile. Furthermore, increasing
reliance on the part of OPTN, CMS, and private insurers on program-
specific reports that assessed the performance of transplant hospitals
on transplant graft and recipient survival rates might have been
contributing to the overall trend of organs going unused.\168\
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\167\ Stewart et al. (2017). 575.
\168\ Stewart et al. (2017). 585.
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The finding of high rates of non-use of organs that could
potentially be transplanted with positive outcomes has led to closer
examination of trends among transplant hospitals in declining the
possible use of organs for specific patients. Information on each organ
that is recovered by an OPO is shared with the OPTN, which runs the
matching system that determines which organ should be offered to which
recipient. If an organ is determined to be a good match for a
particular patient, then OPTN would offer that organ to the transplant
hospital at which the patient is waitlisted on the patient's
behalf.\169\ A transplant hospital can decline an offer without
informing the candidate of the offer or the reason it was
declined.\170\ A study in 2019 focused on patient outcomes associated
with declines in offers of organs by transplant hospitals. Using OPTN
data, the study identified a cohort of 280,041 adults on the kidney
transplant waitlist (out of 367,405 candidates on the waitlist from
2008 through 2015, the study period) who received one or more offers
for a deceased donor kidney during that period. More than 80 percent of
deceased donor kidneys were declined on behalf of one or more
candidates before being accepted for transplant, and a mean of 10
candidates who previously received an offer died every day during the
study period.\171\ As reported by transplant hospitals, organ or donor
quality concerns accounted for 92.6 percent of all declined offers,
whereas 2.6 percent of offers were refused because of patient-related
factors, and an even smaller number for logistical limitations or other
concerns. While organ or donor quality concerns remained the primary
reason for declined offers across all KDPI ranges, the study observed
marked State-level variability in the interval between first offer and
death or transplant and in the likelihood of dying while having
remained on the wait list after receiving an offer.\172\
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\169\ National Kidney Foundation. (2017, February 10). The
Kidney Transplant Waitlist--What You Need to Know. National Kidney
Foundation. https://www.kidney.org/atoz/content/transplant-waitlist.
\170\ Husain, S.A., King, K.L., Pastan, S., Patzer, R.E., Cohen,
D.J., Radhakrishnan, J., & Mohan, S. (2019). Association Between
Declined Offers of Deceased Donor Kidney Allograft and Outcomes in
Kidney Transplant Candidates. JAMA Network Open, 2(8), e1910312.
https://doi.org/10.1001/jamanetworkopen.2019.10312.
\171\ Husain et al. 2019.
\172\ Husain et al. 2019.
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The methodology and findings of this study are notable since they
draw a correlation between the specific patterns among transplant
hospitals of organ non-acceptance and the longevity of patients on the
wait list. The tendency among certain hospitals to choose to not use
kidneys for specific patients is shown apart from the distinct finding
of organs going unused and being discarded. The study shows the
potential for a similar effect on patient survival from organ offer
non-acceptance as for organ non-use. The authors of an earlier study
commented that low acceptance rates of organ offers lead to
inefficiency, longer ischemia time, unequal access to donated kidneys,
and perhaps to higher rates of discarded organs.\173\ The findings in
the 2019 study of a wide range of organ offer acceptance rates among
transplant hospitals nationwide, as well as of the relation between
organ offer declines and patient deaths, suggest the need for
incentives for transplant hospitals to accept earlier offers for their
patients, which, in turn, could reduce cold ischemia time, and, on the
whole, increase patient survival.
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\173\ Wolfe, R.A., Laporte, F., Rodgers, A.M., Roys, E., Fant,
G., & Leichtman, A.B. (2007). Developing Organ Offer and Acceptance
Measures: When ``Good'' Organs Are Turned Down. American Journal of
Transplantation, 7, 1404-1411. https://doi.org/10.1111/j.1600-6143.2007.01784.x.
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h. Non-Acceptance and Discards in Transplantation for Other Solid Organ
Types
SRTR has also tracked the non-use, or discard rate, of other solid
organ types. In 2020, 9.5 percent of livers recovered were not
transplanted, with livers from older donors less likely to be
transplanted.\174\ The discard rate for pancreases was 23.4 percent in
2020; organs from obese donors were highly likely not to be
transplanted.\175\ The discard rate for hearts in 2020 was one percent,
having stayed similar over the previous decade.\176\
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\174\ OPTN/SRTR 2020 Annual Data Report. 2020. Liver. Figures LI
49, 50.
\175\ OPTN/SRTR 2021 Annual Data Report. Pancreas. Figures PA
39, 43.
\176\ OPTN/SRTR 2021 Annual Data Report. Heart. Figure HR 52.
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Liver transplantation shows survival benefits for individuals with
chronic liver disease, but liver transplantation suffers from a severe
shortage of donor organs.177 178 A study from 2012 shows
organ offer non-acceptance on the part of transplant programs to affect
mortality for individuals with end-stage liver disease in a similar
manner as for ESRD patients. According to the study, most candidates
for a liver transplant who died or were removed from the wait list had
received at least one organ offer, suggesting that a substantial
portion of waitlist mortality results in part from declined organ
offers.\179\ As we propose for kidney transplantation, understanding
and addressing why livers, and possibly other organs, are not chosen
for specific patients also has the
[[Page 43539]]
potential to lead to improved outcomes and longer lives.
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\177\ Merion, R.M., Schaubel, D.E., Dykstra, D.M., Freeman,
R.B., Port, F.K., & Wolfe, R.A. (2005). The Survival Benefit of
Liver Transplantation. American Journal of Transplantation, 5(2),
307-313. https://doi.org/10.1111/j.1600-6143.2004.00703.x.
\178\ Ross, K., Patzer, R.E., Goldberg, D.S., & Lynch, R.J.
(2017). Sociodemographic Determinants of Waitlist and Posttransplant
Survival Among End-Stage Liver Disease Patients. American Journal of
Transplantation, 17(11), 2879-2889. https://doi.org/10.1111/ajt.14421.
\179\ Lai, J.C., Feng, S., & Roberts, J.P. (2012). An
Examination of Liver Offers to Candidates on the Liver Transplant
Wait-List. Gastroenterology, 143(5), 1261-1265. https://doi.org/10.1053/j.gastro.2012.07.105.
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i. Organ Transplant Affinity Group
On September 15, 2023, CMS published a blog post entitled ``Organ
Transplantation Affinity Group (OTAG): Strengthening accountability,
equity, and performance.'' \180\ This blog discussed the formation of
OTAG, a Federal collaborative with staff from CMS and HRSA working
together to strengthen accountability, equity, and performance to
improve access to organ donation, procurement, and transplantation for
patients, donors, families and caregivers, and providers. The proposed
IOTA Model is a part of this coordinated effort from the OTAG and
relies on input from across CMS and HRSA.
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\180\ Moody-Williams, J, Nair, S. Organ Transplantation Affinity
Group (OTAG): Strengthening accountability, equity, and performance.
CMS Blog, September 15, 2023. https://www.cms.gov/blog/organ-transplantation-affinity-group-otag-strengthening-accountability-equity-and-performance.
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C. Provisions of the Proposed Regulation
1. Proposal To Implement the IOTA Model
In this section of the proposed rule, we propose our policies for
the IOTA Model, including model-specific definitions and the general
framework for implementation of the IOTA Model. The proposed upside
risk payment to the IOTA participants and the proposed downside risk
payment from IOTA participants to CMS, are designed to increase access
to kidney transplants for patients with ESRD on the IOTA participant's
waitlist. As described in section I of this proposed rule, access to
kidney transplants widely varies by region and across transplant
hospitals and disparities by demographic characteristics are pervasive,
raising the need to strengthen and improve performance. We theorize
that the IOTA Model financial structure would promote improvement
activities across selected transplant hospitals that address access
barriers, including SDOH, thereby increasing the number of transplants,
quality of care, and cost-effective treatment. Selected transplant
hospitals may be motivated to revisit processes and policies around
deceased and living donor organ acceptance to identify opportunities
for improvement. The IOTA model payments may also require selected
transplant hospitals to engage in care delivery transformation to
better coordinate and manage patient care and needs, invest in
infrastructure, improve the patient, family, and caregiver experience,
and engage a care delivery team that is tasked with holistic patient
care.
a. Proposal for Model Performance Period
We are proposing a 6-year ``model performance period.'' We are
proposing to define the model performance period as the 72-month period
from the model start date, comprised of 6 individual PYs. During the
model performance period, the IOTA participants' performance would be
measured and assessed for purposes of determining their performance-
based payments, as proposed in this rule. We propose to define the
``performance year'' (PY) as a 12-month calendar year during the model
performance period. We are proposing to define the start of the model
performance period as the ``model start date,'' and we propose a model
start date of January 1, 2025, meaning that PY 1 would be January 1,
2025 to December 31, 2025, and the model performance period would end
on December 31, 2030. We are proposing a 6-year model performance
period to allow sufficient time for selected transplant hospitals to
invest in care delivery transformation and realize returns on
investments.
We alternatively considered a 3- or 5-year model performance
period; however, we believe that a 3-year model performance period
would be too short to allow adequate time for selected transplant
hospitals to invest in care delivery transformations. Additionally, our
analyses detailed in section III.D. of this proposed rule project that
considerable savings to Medicare would be achieved after the fifth PY,
which is another reason why we are proposing a 6-year model performance
period. We also considered a 10-year model performance period similar
to some more recent Innovation Center models; however, given that this
would be a mandatory model, we believe it important to limit the
duration of the initial test to a shorter period.
We alternatively considered proposing to begin the IOTA Model on
April 1, 2025 or July 1, 2025, to allow selected transplant hospitals
more time to prepare to implement the model and to better align the
model performance periods with that of our data sources, as detailed in
section III.C. of this proposed rule. However, we are proposing a
January 1, 2025 start date because we believe that there will be
sufficient time for IOTA participants to prepare for the model. A
proposed start date of January 1st also aligns with other CMS calendar
year rules. We propose that in the event the model start date is
delayed from the proposed start date, the model performance period for
the entire model would be 6 PYs with each PY being a 12-month period
that begins on the model start date. For example, if the IOTA Model
were to begin April 1, 2025, ``performance year'' would still be
defined as a 12-month period beginning on the model start date, meaning
April 1, 2025, to March 31, 2026. As a result, the model performance
period end date would also shift to include a 72-month period from the
model start date In the previous example, the model performance period
would be April 1, 2025, to March 31, 2031.
We seek comment on the proposed model performance period of 6 years
and the proposed model start date. We also seek comment on the
alternative model performance periods that we considered of 3, 5, and
10 years. We also seek comment on the alternative start dates (April 1,
2025, and July 1, 2025), and the subsequent adjustments to the model
performance period if the model start date were to change.
b. Other Proposals
We are also proposing additional policies for the IOTA Model,
including the following: (1) the method for selecting transplant
hospitals for participation; (2) the schedule and methodologies for the
performance-based payments, and waivers of certain Medicare payment
requirements solely as necessary to test these payment methodologies
under the model; (3) the performance assessment methodology for
selected transplant hospitals, including the proposed methodologies for
patient attribution, target setting and scoring, and calculation of
performance across the achievement domain, efficiency domain, and
quality domain; (4) monitoring and evaluation; and (5) overlap with
other Innovation Center models and CMS programs.
We propose that IOTA participants would be subject to the general
provisions for Innovation Center models specified in 42 CFR part 512
subpart A and in 42 CFR part 403 subpart K, effective January 1, 2025.
The general provisions at subpart A of part 512 are also the subject of
proposed revisions in this proposed rule. As described in section II.B.
of this proposed rule, we are proposing to expand the applicability of
the general provisions for Innovation Center models to provide a set of
standard provisions for Innovation Center models that are applicable
more broadly across Innovation Center models. We believe that this
approach would promote transparency, efficiency, and clarity in
Innovation Center models and avoid the need to restate the provisions
in each
[[Page 43540]]
model's governing documentation. We believe that applying these
provisions to the IOTA Model would promote these purposes.
We seek comment on our proposal to apply the general provisions for
Innovation Center models, or the proposed standard provisions for
Innovation Center models, to the IOTA Model.
2. Definitions
We propose at Sec. 512.402 to define certain terms for the IOTA
Model. We describe these proposed definitions in context throughout
section III. of this proposed rule. We propose to codify the
definitions and policies of the IOTA Model at 42 CFR part 512 subpart D
(proposed Sec. Sec. 512.400 through 512.460). In addition, we propose
that the definitions contained in the general provision related to
Innovation Center models at subpart A of part 512, and the revisions to
those provisions proposed in this notice of proposed rulemaking, would
also apply to the IOTA Model. We seek comment on these proposed
definitions for the IOTA Model.
3. IOTA Participants
a. Proposed Participants
We propose to define ``IOTA participant'' as a kidney transplant
hospital, as defined at Sec. 512.402, that is required to participate
in the IOTA Model pursuant to Sec. 512.412. In addition, we note that
the definition of ``model participant'' contained in 42 CFR part
512.110, as well as the proposed revisions to that definition, would
include an IOTA participant.
We propose to define ``transplant hospital'' as a hospital that
furnishes organ transplants as defined in 42 CFR 121.2. We propose this
definition to align with the definition used by Medicare. We propose to
define ``kidney transplant hospital'' as a transplant hospital with a
Medicare approved kidney transplant program. Under Sec. 482.70, a
transplant program is ``an organ-specific transplant program within a
transplant hospital (as defined in this section).'' Kidney transplants
are the most common form of transplants, but not all transplant
hospitals have a kidney transplant program. As the focus of the IOTA
Model is kidney transplants, we propose this definition of kidney
transplant hospital to refer specifically to transplant hospitals that
perform kidney transplants. We propose to define ``kidney transplant''
as the procedure in which a kidney is surgically transplanted from a
living or deceased donor to a transplant recipient, either alone or in
conjunction with any other organ(s). As described in section III.B.4.b.
of this proposed rule, the vast majority of kidney transplants are
performed alone. However, we believe that it is necessary to include in
the definition of kidney transplant those kidney transplants that occur
in conjunction with other organ transplants to avoid creating a
disincentive for multi-organ transplants within the IOTA Model.
Kidney transplant hospitals are the focus of the proposed IOTA
Model because they are the entities that furnish kidney transplants to
ESRD patients on the waitlist and ultimately decide to accept donor
recipients as transplant candidates. Kidney transplant hospitals play a
key role in managing transplant waitlists and patient, family, and
caregiver readiness. They are also responsible for the coordination and
planning of kidney transplantation with the OPO and donor facilities,
staffing and preparation for kidney transplantation, and oversight of
post-transplant patient care, and they are largely responsible for
managing the living donation process. The proposed model is intended to
promote improvement activities across selected transplant hospitals
that reduce access barriers, including SDOH, thereby increasing the
number of transplants, quality of care, and cost-effective treatment.
The IOTA Model would also aim to improve quality of care for ESRD
patients on the waitlist pre-transplant, during transplant, and during
post-transplant care. As described in section III.B.4.e. of this
proposed rule, kidney transplant access and acceptance rates vary
nationally across kidney transplant hospitals by geography and other
demographic and socioeconomic factors. The Innovation Center has
implemented models targeting dialysis facilities and nephrology
providers, including in the CEC, ETC, and KCC Models. CMS has also
implemented changes to the OPO CfCs to strengthen performance
accountability for OPOs. However, kidney transplant hospitals have not
been the principal focus of any Innovation Center models to date.
Expanding accountability to kidney transplant hospitals, key players in
the transplantation ecosystem for ESRD patients, aligns with the larger
efforts across CMS and HRSA to improve performance and address
disparities in kidney transplantation.
We alternatively considered having the IOTA participants be
accountable care organizations (ACOs), such as a kidney transplant
ACOs, instead of individual kidney transplant hospitals. In this
alternative conception, a kidney transplant ACO would form as a
separate legal entity, potentially including kidney transplant
hospitals, OPOs, transplant surgeons, and other provider types. The
kidney transplant ACO would assume accountability for the number of
kidney transplants, equity in the distribution of transplants, and the
quality of transplant services from the point of a patient being
waitlisted to after a transplant recipient's condition stabilizes
following transplantation. This alternative would potentially carry
some advantages in the potential for improved coordination among
individual providers and suppliers in the kidney transplant ACO, but we
believe that it would be administratively burdensome, as it would
require the formation of an ACO governing board distinct from the
governing boards of individual providers. In addition, such an ACO
arrangement possibly would be subject to additional Federal, State, and
tribal laws with respect to grievance, licensure, solvency, and other
regulations, as well as considerable overlap with other ACO-based
Innovation Center models. We therefore believe that the ``IOTA
participant'' should be defined as a kidney transplant hospital, as
defined at Sec. 512.402, that is required to participate in the IOTA
Model pursuant to Sec. 512.412.
We further alternatively considered requiring OPO participation in
the IOTA Model as the entity charged with identifying eligible donors
and securing organs from deceased donors. However, in 2020, CMS issued
a final rule that updated OPO CfC requirements to receive Medicare and
Medicaid payment. This final rule focuses on holding OPOs in the
transplant ecosystem accountable for improving performance, and the
Innovation Center does not plan further interventions regarding OPOs at
this time.
We seek public comment on the proposal that the IOTA Model
participants would be kidney transplant hospitals.
b. Proposed Mandatory Participation
We propose that all kidney transplant hospitals that meet the
eligibility requirements as discussed in section III.C.3.c. of this
proposed rule, and that are selected through the participation
selection process discussed in section III.C.3.d. of this proposed
rule, must participate in the IOTA Model. We believe that a mandatory
model is necessary to ensure that a sufficient number of kidney
transplant hospitals participate in the IOTA Model such that CMS will
be able to conduct a sound evaluation of the model's effects on cost
and quality of care in accordance with
[[Page 43541]]
section 1115A(b)(4) of the Act. A mandatory model would also minimize
the potential for selection bias, thereby ensuring that the model
participants are a representative sample of kidney transplant
hospitals. We believe a mandatory model is necessary to obtain relevant
information about the effects of the model's proposed policies on
Medicare savings, kidney transplant volume, kidney transplant
acceptance rates, health equity, and quality of care.
Nationally, kidney transplant hospitals serve diverse patient
populations, operate in varied organizational and market contexts, and
differ in size, staffing, and capability. There is also wide variation
across kidney transplant hospitals on performance on kidney transplant
access and organ offer acceptance rate ratios by geography and other
demographic and socioeconomic factors. We believe that selection bias
would be a challenge in a voluntary model because we are proposing that
the IOTA Model would include financial accountability on performance on
access to kidney transplants and quality of care, and downside risk for
poor performers. A mandatory model would address these selection bias
concerns and ensure that our model reaches ESRD patients residing in
underserved communities.
We alternatively considered making participation in the IOTA Model
voluntary. However, we would be concerned that a voluntary model would
not be evaluable, would result in insufficient numbers of kidney
transplant hospital participants, and would not be representative of
kidney transplant hospitals and ESRD patients nationally. These
concerns reflect our expectation that the proposed payment approach
would disproportionately attract kidney transplant hospitals already
performing well in kidney transplant volume, organ offer acceptance
rate ratios, and quality of care pre- and post-transplantation. Kidney
transplant hospitals already positioned to score high in the IOTA
Model's achievement, efficiency, and quality domains may be more likely
to join the model than other kidney transplant hospitals, as they would
expect to receive upside risk payments. This may be especially true for
kidney transplant hospitals that would stand the most to benefit from a
model that rewards an increase in the number of kidney transplants. We
believe that selection bias in a voluntary model would also limit our
ability to assess systematic differences in the IOTA Model's effects on
kidney transplant disparities, and may further widen disparity gaps for
underserved communities that stand to lose if the model does not reach
them. We therefore propose that the IOTA Model would be mandatory for
all eligible kidney transplant hospitals selected for participation in
the model, as we believe this would minimize the risk of potential
distortions in the model's effects on outcomes resulting from hospital
self-selection.
We seek public comment on our proposal to make participation in the
IOTA Model mandatory.
c. Participant Eligibility
We are proposing kidney transplant hospital participant eligibility
criteria that would increase the likelihood that: (1) individual kidney
transplant hospitals selected as IOTA participants represent a diverse
array of capabilities across the performance domains as discussed in
section III.C.5. of this proposed rule; and (2) the results of the
model test would be statistically valid, reliable, and generalizable to
kidney transplant hospitals nationwide should the model test be
successful and considered for expansion under section 1115A(c) of the
Act.
We are proposing that eligible kidney transplant hospitals would be
those that: (1) performed 11 or more transplants for patients aged 18
years or older annually, regardless of payer type, each of the baseline
years (the ``low volume threshold''); and (2) furnished more than 50
percent of its kidney transplants annually to patients over the age of
18 during each of the baseline years. We propose to define ``baseline
year'' as a 12-month period within a 3-year historical baseline period
that begins 48 months (or 4 years) before the start of each model PY
and ends 12 months (or 1 year) before the start of each model PY. For
example, if the IOTA Model were to start on January 1, 2025, the
baseline years for PY 1 would be the 12-month period that begins
January 1, 2021, and ends on December 31, 2023. We propose to define
``non-pediatric facility'' as a kidney transplant hospital that
furnishes over 50 percent of their kidney transplants annually to
patients 18 years of age or older. CMS would select approximately half
of all DSAs nationwide using a stratified sampling methodology, and all
eligible kidney transplant hospitals in the selected DSAs would be
required to participate in the IOTA Model.
The proposed low volume threshold of 11 or more kidney transplants
for ESRD patients aged 18 years or older during each of the three
baseline years (as described in section I.B.2.b. of this proposed rule)
would exclude low volume kidney transplant hospitals from the IOTA
Model. We believe that these kidney transplant hospitals should be
excluded from the model because they may not have the capacity to
comply with the model's policies, and because the inclusion of this
group of kidney transplant hospitals in the model would be unlikely to
significantly alter the overall rates of kidney transplantation. We are
also proposing a low volume threshold of 11 adult kidney transplants
because it is consistent with the minimum thresholds for the display of
CMS data to protect the confidentiality of Medicare and Medicaid
beneficiaries by avoiding the release of information that can be used
to identify individual beneficiaries. We alternatively considered using
a higher threshold, such as 30 adult kidney transplants or 50 adult
kidney transplants during each of the three baseline years. However, we
have found that many kidney transplant hospitals consistently perform
between 11 and 50 transplants per year. We further believe that using a
higher threshold would decrease the number, size and location of kidney
transplant hospitals eligible to be selected for participation in the
IOTA Model, thereby limiting the generalizability of the model test. We
also recognize that the number of kidney transplants performed by a
kidney transplant hospital may fluctuate from year to year, and looking
back three years would help determine if a kidney transplant hospital
has the capacity to consistently perform 11 or more transplants per
year. We seek feedback on this approach for determining which kidney
transplant hospitals would be eligible for selection under the model.
We considered including pediatric kidney transplant hospitals as
eligible participants in the IOTA Model. However, pediatric kidney
transplantation has significantly different characteristics,
considerations, and processes from adult kidney transplantation. The
number of pediatric kidney transplants performed each year is also
exceedingly small, which would present difficulties in reliably
determining the effects to the model in the pediatric population.
Additionally, a much larger proportion of pediatric kidney transplants
are living donor transplants than in the adult population. As such, we
do not believe the proposed IOTA Model would function in the same way
for both kidney transplant hospitals serving primarily adults and those
serving primarily children, and we believe it is necessary to include
only non-pediatric
[[Page 43542]]
kidney transplant hospitals in the IOTA Model.
We seek comment on our proposed participant eligibility criteria
for kidney transplant hospitals, including the requirement that a
kidney transplant hospital perform 11 or more kidney transplants
annually on patients aged 18 years or older during the baseline years.
We also seek comment on the proposal to include only kidney transplant
hospitals that meet the proposed definition for a non-pediatric
facility during the baseline years.
d. Participant Selection
(1) Overview and Process for Participant Selection
We propose to select eligible kidney transplant hospitals for
participation in the IOTA Model using a stratified sampling of
approximately half of all DSAs nationwide. All kidney transplant
hospitals that meet the proposed participant eligibility criteria
described in section III.C.3.c. of this proposed rule and are located
in the selected DSAs would be required to participate in the IOTA
Model. As defined in 42 CFR 486.302, a ``Donation Service Area (DSA)''
means a geographical area of sufficient size to ensure maximum
effectiveness in the procurement and equitable distribution of organs
and that either includes an entire metropolitan statistical area (MSA)
or does not include any part of such an area and that meets the
standards of subpart G. A DSA is designated by CMS, is served by one
OPO, contains one or more transplant hospitals, and one or more donor
hospitals. There are currently 56 DSAs as of January 1, 2024. A map of
the DSAs can be found on the SRTR website.\181\ CMS would use the list
of DSAs as it appears on January 1, 2024 to select the DSAs, and
therefore the eligible kidney transplant hospitals that would be
required to participate in the IOTA Model.
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\181\ https://www.srtr.org/reports/opo-specific-reports/interactive-report.
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We propose this approach for selecting IOTA participants to obtain
a group of eligible kidney transplant hospitals that is representative
of kidney transplant hospitals from across the country in terms of
geography and kidney transplant volume. We propose to stratify the DSAs
into groups based on each DSA's Census Division and the total number of
adult kidney transplants performed annually across all eligible kidney
transplant hospitals in each DSA during the baseline years for the
first PY. Selecting eligible kidney transplant hospitals from these
groups of DSAs would ensure that the IOTA participants are
representative of eligible kidney transplant hospitals from across the
nation in terms of geography and the volume of adult kidney
transplants.
A second aim of our proposal to select eligible kidney transplant
hospitals from stratified groups of DSAs is to prevent distortions on
the effects of the model's policies and features on outcomes. Our
analysis of kidney transplant hospital data shows that selecting only
some eligible kidney transplant hospitals within a selected DSA to
participate in the IOTA Model may shift the supply of deceased donor
organs from non-IOTA participants to IOTA participants within the same
DSA. The resulting distortions would make it difficult to attribute
changes in outcomes to the model and would limit its evaluability.
Our proposed approach for selecting IOTA participants would involve
stratifying DSAs into groups based on the average number of adult
kidney transplants performed by all eligible transplant hospitals
located in the DSA during the baseline years of PY 1. We propose using
this variable to stratify the DSAs into groups because increasing the
total number of adult kidney transplants is the primary metric that we
propose to use to evaluate the IOTA participants' performance in the
model.
The proposed approach for IOTA participant selection is as follows:
Assign all DSAs to a Census Division.\182\ The Census
Bureau subdivides the United States into four Census Regions
(Northeast, Midwest, South, and West) which are in turn divided into
nine Census Divisions. CMS would assign each DSA to a single Census
Division. Due to the New England region being both a DSA and a Census
Division, CMS would combine the Middle Atlantic and New England Census
Divisions for a total of eight Census Divisions. If CMS were to keep
the New England Census Division separate, the New England DSA would be
guaranteed participation in the model in subsequent steps. As such, we
are proposing to combine the Middle Atlantic and New England Census
Divisions for the purposes of this selection methodology. Some DSAs may
span several Census Divisions, but most DSAs will be assigned to the
Census Division where the majority of the DSA's population resides
according to the 2020 Census data. Puerto Rico is the only DSA which
exists outside of a Census Division. This DSA would be assigned to the
South Atlantic Census Division as it is the closest geographically.
This step would create eight Census Division groups, one for each
Census Division (with the exception of the combined Middle Atlantic and
New England Census Divisions, which would be grouped together to create
one Census Division group).
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\182\ A complete list of DSAs in the United States as of 2022-
2023 can be obtained using the data reporting tool found on the SRTR
website (https://optn.transplant.hrsa.gov/data/view-data-reports/build-advanced/).
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Determine the kidney transplant hospitals located within
each DSA. CMS would list out the kidney transplant hospitals located
within each DSA and assigned Census Division group.
Identify the eligible kidney transplant hospitals located
within each DSA. CMS would use the criteria noted in section III.C.3.c.
of this proposed rule to identify the eligible kidney transplant
hospitals within each DSA. This step is expected to yield approximately
180 to 200 eligible kidney transplant hospitals total across the eight
Census Division Groups.
For each DSA, determine the average number of adult kidney
transplants performed annually across all eligible kidney transplant
hospitals during the baseline years for PY 1. CMS would use data from
the baseline years for PY 1 (2021-2023) to determine the average number
of adult kidney transplants performed annually across all of the
eligible transplant hospitals located in each DSA. CMS would sum the
number of adult kidney transplants performed by all of the eligible
kidney transplant hospitals in a DSA during each of the baseline years
for PY 1 and divide each DSA's sum by three to determine the average
number of adult kidney transplants furnished annually during the
baseline years by the eligible kidney transplant hospitals located
within each DSA.
Within each Census Division group, create two mutually
exclusive groups of DSAs using the average number of adult kidney
transplants performed annually across the baseline years for PY 1. CMS
would separate DSAs assigned to a Census Division group into two
mutually exclusive groups of DSAs based on the average number of adult
kidney transplants performed annually across the baseline years for PY
1. The two groups within each Census Division group would be: (1) DSAs
having higher numbers of adult kidney transplants across the baseline
years; and (2) DSAs having lower numbers of adult kidney transplants
across the baseline years. Since the average number of adult kidney
transplants will be different across each DSA, each Census Division
group will have a different cut off to create these two groups. To
ensure each DSA has a 50 percent chance of being chosen in step 7, each
DSA group
[[Page 43543]]
within a Census Division group should have the same number of DSAs.
However, in the event of an odd number of DSAs within a Census Division
group, CMS would proceed to step six.
For groups within a Census Division group that contain an
odd number of DSAs, CMS would randomly select one DSA from the group.
Each of these individual selected DSAs would have a 50 percent
probability of being selected for the IOTA Model. For groups within a
Census Division group that contain an odd number of DSAs, CMS would
randomly select one DSA from the group and determine that individual
DSA's chance of selection for inclusion in the IOTA Model with 50
percent probability. Following this step, each group within a Census
Division group would have an even number of DSAs.
Randomly select 50 percent of remaining DSAs in each
group. CMS would then take a random sample, without replacement, of 50
percent of the remaining DSAs in each group (the groups being DSAs
having higher numbers of adult kidney transplants across the baseline
years and DSAs having lower numbers of adult kidney transplants across
the baseline years) within each Census Division group. All of the
eligible transplant hospitals located within the selected DSAs would be
required to participate in the IOTA Model.
We propose that CMS would notify IOTA participants of their
selection to participate in the IOTA Model in a form and manner chosen
by CMS, such as public notice and email, at least 3 months prior to the
start of the model performance period. As described in section
III.C.3.b. of this proposed rule, we are proposing that participation
in the IOTA Model would be mandatory. As such, if an IOTA eligible
transplant hospital is located within one of the DSAs that CMS randomly
selects for the IOTA Model, the eligible kidney transplant hospital
would not be able to decline participation in this model, nor would it
be able to terminate its participation in the model once selected.
Model termination policies are further discussed in section III.C.16.
of this proposed rule.
(2) Consideration of Alternatives to Proposed Participant Selection
Approach
We considered using other geographic units for stratified random
sampling to choose IOTA participants, such as Core Based Statistical
Areas (CBSAs), Metropolitan Statistical Areas (MSAs), Hospital Referral
Regions (HRRs), or States. CBSAs, MSAs, HRRs, and States are commonly
known geographic units, and have been used as part of participant
selection for other Innovation Center models. We believe selecting
participants by DSA significantly mitigates behavior that would
artificially inflate the model's effects on kidney transplant volume
for the reasons described in the preceding section. OPOs associated
with selected DSAs would be expected to benefit from consistency in
rules across most or all of their transplant hospitals. The Innovation
Center found that selecting participants by DSA improved the ability to
detect changes in kidney transplant volume to a level consistent with
the anticipated change in kidney transplant volume associated with the
model's payment rules. Participants from the same DSA are, for the most
part, subject to similar levels of kidney supply, and, with the
exception of kidneys from another DSA, the same rules for kidney
allocation apply. While OPTN recently updated its organ allocation
methodology to allow organs to go outside of the DSA in which an organ
was procured, many kidney transplant hospitals still receive a
plurality of kidneys from the local OPO in their DSA, ensuring that
this is still a meaningful method to group kidney transplant hospitals.
Using alternative geographic units would negate these advantages.
We also considered other random sampling techniques, including
simple random sampling of transplant hospitals, simple random sampling
of DSAs, and cluster sampling of DSAs. Simple random sampling of
hospitals risks oversampling regions of the country where transplant
hospitals are concentrated and under sampling areas with fewer eligible
transplant hospitals. Using simple random sampling of DSAs may result
in an unrepresentative sample of DSAs with a greater risk of
oversampling regions where DSAs cover small geographic areas. We
considered cluster random sampling where half of all DSAs would be
sampled in a first step and half of eligible kidney transplant
hospitals within selected DSAs would be sampled. However, because this
approach would retain half of eligible kidney transplant hospitals in
selected DSAs, we expect the model's effects on kidney transplant
volume would be overstated because kidney supply flowing towards non-
participant hospitals prior to the start of the model would be
redirected towards IOTA participants. In addition, CMS's analyses of
these alternative sampling approaches indicated the model would not be
evaluable because these approaches were associated with lower precision
in detecting changes in kidney transplant volumes due to the model
compared to the increase in transplant volume anticipated from the
model's payment rules.
As an alternative we also considered other variables to create DSA
groups for stratified sampling of DSAs. Specifically, after assigning
each DSA to a Census Division, we considered stratifying DSAs using the
following DSA level variables:
Number of eligible transplant hospitals in DSA.
Annual adult kidney transplants per eligible transplant
hospital in DSA.
Average organ/offer acceptance rate ratio across eligible
kidney transplant hospitals in DSA.
Average percent of Medicare kidney transplant recipients
dually eligible for Medicare and Medicaid or who are LIS recipients.
Percent of eligible transplant hospitals in DSA
participating in the Kidney Care Choices or ESRD Treatment Choices
Models.
Average percent of kidney transplants from a living donor
among eligible kidney transplant hospitals in DSA.
These variables were given consideration in the stratified
selection approach because their use would create groups of DSAs whose
eligible transplant hospitals are more similar to each other on the
listed characteristics instead of only adult kidney transplant volume
and Census Division. However, we opted to use the simpler stratified
participant selection approach to provide greater transparency in the
model's participant selection approach.
We also considered stratified random sampling of individual kidney
transplant hospitals using similar variables as those described in the
preceding paragraph. Although this approach provided representativeness
of sampled transplant hospitals along dimensions important for the
model, it would be expected to result in a subset of eligible kidney
transplant hospitals in at least a portion of DSAs being designated as
participants. As we have described previously, we expect that allowing
a portion of DSA kidney transplant hospitals to be model participants
would result in an overstatement of the model's effects on kidney
transplant volume and other outcomes of interest. As with the sampling
approaches considered in the preceding paragraph, CMS's analyses
indicated the IOTA Model would not be evaluable if stratified sampling
of individual kidney transplant hospitals were used in participant
selection for the reasons described previously.
[[Page 43544]]
CMS expects that no additional participant selections would be made
for the IOTA Model after its start date unless 10 percent or more of
selected participants are terminated from the model during the model
performance period. If this were to occur, we would address the
selection of new participants in future rulemaking.
We seek comment on our proposed approach for selecting IOTA
participants and on the alternative approaches considered, including
perceived advantages and disadvantages of our proposed participant
selection approach relative to alternatives.
4. Patient Population and Attribution
a. Proposed Attributed Patient Population
We propose that the following patients who are alive at the time
CMS conducts attribution would be attributed to an IOTA participant:
(1) A kidney transplant waitlist patient, as defined in section
III.C.4.a. of this proposed rule, regardless of payer type and waitlist
status, who is alive, 18 years of age or older, and is registered on a
waitlist, as defined in section III.C.4.a. of this proposed rule, to
one or more IOTA participants, as identified by the OPTN computer match
program (``IOTA waitlist patient,''); and (2) A kidney transplant
patient who receives a kidney transplant at the age of 18 years or
older from an IOTA participant at any time during the model performance
period (``IOTA transplant patient''). These patients would be referred
to as IOTA waitlist patients and IOTA transplant patients,
respectively, for purposes of assessing each IOTA participant's
performance across the achievement domain, efficiency domain, and
quality domain as discussed in section III.C.5. of this proposed rule.
IOTA waitlist patients and IOTA transplant patients would factor into
the model's performance-based payments to IOTA participants.
For the purpose of this model, we propose to define ``waitlist'' as
a list of transplant candidates, as defined in 42 CFR 121.2, registered
to the waiting list, as defined in Sec. 121.2, and maintained by a
transplant hospital in accordance with 42 CFR 482.94(b). We propose to
define ``kidney transplant waitlist patient'' as a patient who is a
transplant candidate, as defined in Sec. 121.2, and who is registered
to a waitlist for a kidney at one or more kidney transplant hospitals.
We understand that many patients on the waiting list are registered
at multiple transplant hospitals. Therefore, we propose attributing
each of these waitlisted patients to every IOTA participant where they
are registered on a waitlist during a given month in the applicable
quarter. However, ``kidney transplant patient,'' defined as a patient
who is a transplant candidate, as defined in Sec. 121.2, and received
a kidney transplant furnished by a kidney transplant hospital,
regardless of payer type, would be attributed to the IOTA participant
that furnished the kidney transplant.
We propose attributing kidney transplant waitlist patients and
kidney transplant recipients to IOTA participants for two reasons.
First, we believe that by attributing these patients to IOTA
participants it would ensure the full population of potential and
actual kidney transplant candidates is represented when measuring
participant performance. The waiting list captures most candidates
except some living donor recipients. Transplant recipients include
those who received deceased or living donor transplants. Second,
because CMS is proposing to hold IOTA participants accountable for
furnishing kidney organ transplants; focusing on kidney transplant
waitlist patients and kidney transplant patients, and attributing them
to IOTA participants, aligns with the model's goals of improving access
to, and quality of, kidney transplantation, including post-transplant.
CMS is proposing to determine an IOTA participant's performance
across the achievement domain, efficiency domain, and quality domain
based on all IOTA waitlist patients and IOTA transplant patients,
regardless of payer type, as described in section III.C.5. of this
proposed rule. That is, an IOTA participant's performance in terms of
both Medicare beneficiaries and non-Medicare patients would be used to
determine whether the IOTA participant would receive an upside risk
payment from CMS, or owe a downside risk payment to CMS. As described
in section III.C.5. of this proposed rule, demand for kidney
transplants far exceeds supply, raising concerns that if the IOTA Model
were limited to Medicare beneficiaries only, the model may
inadvertently incentivize inappropriate diversion of donor organs to
Medicare beneficiaries to improve their performance in the model,
thereby limiting access to non-Medicare beneficiaries and potentially
disincentivizing pre-emptive kidney transplants for patients not
already covered by Medicare because their CKD has not progressed to
ESRD. We believe that the change in care patterns that IOTA
participants may undertake to be successful in the IOTA Model are
unlikely to apply solely to Medicare beneficiaries under their care.
We considered limiting IOTA waitlist patients and IOTA transplant
patients to Medicare beneficiaries only, as Medicare covers more than
50 percent of all kidney transplants from both deceased and living
donors. However, we believe it is necessary to include all patients,
regardless of payer type, in the IOTA participant's performance
calculations to protect against unintended consequences and problematic
financial incentives. Moreover, the group of eligible waitlist and
transplant patients that would be attributed to each IOTA participant
is already relatively small, both in terms of transplant candidates and
transplant recipients. Limiting the IOTA Model performance assessment,
as described in section III.C.5. of this proposed rule, to Medicare
beneficiaries would further limit the patient sample size, potentially
affecting our ability to detect changes in performance due to model
payments. Therefore, we are proposing that the IOTA Model reflect both
Medicare beneficiaries and non-Medicare patients for performance
assessment, with Medicare beneficiaries just being a subset of the
patient population attributed to each model participant.
We seek public comment on our proposals to include: (1) all kidney
transplant waitlist patients, regardless of payer type and waitlist
status, who are alive, 18 years of age or older, and registered on a
waitlist to an IOTA participant, as identified by the OPTN computer
match program; and (2) all kidney transplant patients who receive a
kidney transplant, at 18 years of age or older, from an IOTA
participant at any time during the model performance period, in each
IOTA participant's population of attributed patients. We also seek
public comment on our proposal to attribute IOTA waitlist patients and
IOTA transplant patients, respectively, to IOTA participants for the
purposes of assessing each IOTA participant's performance across the
achievement domain, efficiency domain, and quality domain, and to
determine performance-based payments to and from IOTA participants.
b. Patient Attribution Process
As described in section III.C.4.a. of this proposed rule, we
propose to define ``attribution'' as the process by which CMS
identifies patients for whom each IOTA participant is accountable
during the model performance period. CMS would identify and assign a
set of Medicare and non-Medicare patients to the IOTA participant
through attribution. We propose to define
[[Page 43545]]
``attributed patient'' as an IOTA waitlist patient or an IOTA
transplant patient, as described in section III.C.4.a. of this proposed
rule. We propose that a patient may not opt out of attribution to an
IOTA participant under the model.
Section III.C.4.b.(1). of this proposed rule outlines in more
detail the attribution criteria to identify attributable kidney
transplant waitlist patients and kidney transplant patients during
initial attribution, quarterly attribution, and at annual attribution
reconciliation using Medicare claims data, Medicare administrative
data, and OPTN data. In advance of the model start date, we propose to
attribute patients to IOTA participants through an initial attribution
process described in section III.C.4.b.(2). of this proposed rule;
quarterly attribution would be conducted thereafter to update the
patient attribution list as described in section III.C.4.b.(3). of this
proposed rule, to include the dates in which patient attribution
changes occur. After the fourth quarter of each PY, we propose to
finalize each IOTA participant's annual attribution reconciliation list
for that PY, including removing certain attributed patients, as
described in section III.C.4.b(4) of this proposed rule. We propose
that once a patient is attributed to an IOTA participant, that
attributed patient would remain attributed to the IOTA participant for
the duration of the model, unless the patient is removed from the IOTA
participant's list of attributed patients during the annual attribution
reconciliation process, as described in section III.C.4.b.(4). of this
proposed rule.
We also considered proposing that once a patient is attributed to
an IOTA participant, either through the initial attribution process or
through quarterly attribution, that the patient would remain attributed
only through the end of the PY. Initial attribution would then occur
prior to the beginning of each PY. However, we choose to align with the
attribution processes of our other kidney models to simplify
operations.
We propose to identify kidney waitlist patients and kidney
transplant patients using SRTR data, OPTN data, Medicare claims data,
and Medicare administrative data.
We seek comment on our patient attribution process proposals and
alternatives considered.
(1) Attribution and De-attribution Criteria
(i) IOTA Waitlist Patient Attribution
We propose that kidney transplant waitlist patients would be
attributed as IOTA waitlist patients to one or more IOTA participants
based on where the patient is registered on a kidney transplant
waitlist, regardless of payer type and waitlist status, as identified
by the OPTN computer match program. We propose that CMS would conduct
attribution on a quarterly basis, before each quarter of the model
performance period. CMS is proposing to attribute a kidney transplant
waitlist patient as an IOTA waitlist patient to an IOTA participant if
the patient meets all of the following criteria:
The patient is registered to one or more IOTA
participant's kidney transplant waitlist during a month in the
applicable quarter.
The patient is 18 years or older at the time of
attribution.
The patient is alive at the time of attribution.
For purposes of attributing IOTA waitlist patients to IOTA
participants, the proposed criteria must be met on the date that CMS
runs attribution, as described in section III.C.4.b.(1).(i). of this
proposed rule.
As described in section III.C.4.b.(1). of this proposed rule, a
kidney transplant waitlist patient may be registered to more than one
waitlist, which is why we propose to attribute kidney transplant
waitlist patients as IOTA waitlist patients to IOTA participants in a
way that accurately reflects their waitlist registrations. A kidney
transplant hospital should be actively engaged in coordinating the
transplant process for kidney transplant waitlist patients on their
waitlist, as they are responsible for accepting donor organs and
furnishing transplants. As such, if a kidney transplant waitlist
patient is registered on the waitlist of multiple IOTA participants,
CMS would attribute that kidney transplant waitlist patient as an IOTA
waitlist patient to all of the IOTA participants that have the kidney
transplant waitlist patient on their waitlists.
We alternatively considered limiting IOTA waitlist patient
attribution to only one IOTA participant based on ``active'' waitlist
status. That is, the IOTA waitlist patient would be attributed to each
IOTA participant where the patient is registered to a kidney transplant
waitlist with an ``active'' status in a given quarter. A kidney
transplant hospital designates patients on its waitlist with an
``active'' status to signal their readiness to receive a donor kidney
offer when one becomes available. However, we anticipate that there
would be operational challenges if CMS were to base patient attribution
on waitlist ``active'' status, as doing so would require real-time and
accurate information regarding each patient's waitlist status. There
may be a time delay when changing a waitlist status from provisionally
inactive to active once minor issues have been resolved. A kidney
transplant waitlist patient may be made inactive or ineligible to
receive an organ offer if, for example, they have an incomplete
transplant evaluation to assess medical readiness, their BMI exceeds
the transplant hospital's established threshold, due to infection or
patient choice, or because of complications presented by other medical
issues. Additionally, due to our inability to recognize differences in
the contributions between kidney transplant hospitals in maintaining a
patient's transplant readiness, we believe attributing kidney
transplant waitlist patients as IOTA waitlist patients to all the IOTA
participants where a kidney transplant waitlist patient is registered
is the most appropriate approach to IOTA waitlist patient attribution,
regardless of waitlist status.
As indicated in section III.C.3.c. of this proposed rule, we are
only proposing to include non-pediatric facilities as eligible
participants in the IOTA Model. In alignment with this proposal, we
propose to exclude pediatric patients under 18 years of age from the
population of attributed patients. According to national data from the
OPTN, children under the age of 18 make up a small proportion of the
kidney transplant candidates registered on the waiting list. However,
pediatric patients have greater access to both deceased and living
donor kidney transplant relative to adults and are more likely to
receive a kidney transplant than adults over the age of 18. Pediatric
patients under 18 years of age are also more likely to receive a living
donor transplant than adults over the age of 18, and are infrequently
the recipient of organs at high risk for non-use.\183\ Thus, CMS is not
proposing to include pediatric patients under the age of 18 as part of
the population that would be identified and attributed to IOTA
participants. We alternatively considered including pediatric patients
under the age of 18 in the IOTA model patient population, but believe
focusing on adults, given their unique challenges
[[Page 43546]]
accessing kidney transplants, is a priority.
---------------------------------------------------------------------------
\183\ Lentine, K. L., Smith, J. M., Miller, J. M., Bradbrook,
K., Larkin, L., Weiss, S., Handarova, D. K., Temple, K., Israni, A.
K., & Snyder, J. J. (2023). OPTN/SRTR 2021 Annual Data Report:
Kidney. American journal of transplantation: official journal of the
American Society of Transplantation and the American Society of
Transplant Surgeons, 23(2 Suppl 1), S21-S120. https://doi.org/10.1016/j.ajt.2023.02.004.
---------------------------------------------------------------------------
The waiting list often has a delay between when a patient's
waitlist status changes and when that change is reflected in the data.
For example, patients who have died are ineligible for transplant and
must be removed from the waiting list, but there may be a time delay
between a patient's death and their removal. Thus, we are proposing to
limit IOTA waitlist patient attribution to patients who are alive at
the time of attribution.
We seek comments on our proposed criteria for identifying and
attributing kidney transplant waitlist patients to one or more IOTA
participants and alternatives considered.
(ii) IOTA Transplant Patient Attribution
We propose that kidney transplant patients would be attributed as
IOTA transplant patients to the IOTA participant that furnished a
kidney transplant during the model performance period, if they meet the
following criteria:
The patient was 18 years of age or older at the time of
their transplant; and
The patient was alive at the time of attribution.
We note that an IOTA transplant patient who experiences transplant
failure and is then de-attributed from an IOTA participant, as
described in section III.C.4.b.(1).(iii). of this proposed rule, could
become attributed to an IOTA participant again at any point during the
model performance period if they rejoined a kidney transplant waitlist
for, or received a kidney transplant from, any IOTA participant and
satisfied all of the criteria for attribution as described in section
III.C.4.b.(1).(i). or section III.C.4.b.(1).(ii). of this proposed
rule.
We propose to attribute kidney transplant patients to the IOTA
participant that furnished the transplant to hold the IOTA participant
accountable for patient transplant and post-transplant outcomes. We
alternatively considered attributing kidney transplant patients based
on the plurality of post-transplant services, as identified in Medicare
claims, because it would still result in attributing kidney transplant
patients to only one IOTA participant and would base attribution on
where the majority of services were furnished. We recognize that
patients may choose to receive their pre-and post-transplant care from
multiple IOTA participants in addition to the IOTA participant that
performed their kidney transplant. However, the model's incentives do
not support shifting accountability for post-transplant outcomes away
from the IOTA participant that furnished the transplant. We believe
that the IOTA participant that performed the transplant should remain
accountable for any surgery related outcomes, both successes and
failures.
We propose not to attribute patients who are younger than 18 years
of age at the time of their kidney transplant or who are deceased at
the time of attribution due to the same reasons described in section
III.C.4.b.(1).(i). of this proposed rule.
We seek comments on our proposed criteria for identifying and
attributing kidney transplant patients as IOTA transplant patients to
the IOTA participant that furnished their kidney transplant during the
model performance period. We also seek comment on the alternative
considered.
(iii) De-Attribution Criteria
We propose that CMS would only de-attribute attributed patients
from an IOTA participant during annual attribution reconciliation, as
described in section III.C.4.b.(4). of this proposed rule. We propose
that CMS would de-attribute any attributed patient from an IOTA
participant that meets any of the following criteria as of the last day
of the PY being reconciled, in accordance with the annual attribution
reconciliation list as described in section III.C.4.c. of this proposed
rule:
The IOTA waitlist patient was not registered on an IOTA
participant's kidney transplant waitlist on the last day of the PY
being reconciled.
The IOTA waitlist patient died at any point during the PY.
We propose that an IOTA waitlist patient who has died during the PY
would be removed from the list of attributed IOTA waitlist patients
effective on the last day of the PY that the death occurred.
The IOTA transplant patient has died at any point during
the PY. We propose that an IOTA transplant patient who has died during
the PY would be de-attributed from the list of attributed IOTA
transplant patients effective on the last day of the PY that the death
occurred.
The IOTA transplant patient's kidney failed during the PY,
and the patient is not included on the IOTA participant's waitlist. We
propose that an IOTA transplant patient who experiences transplant
failure at any point during the PY and does not rejoin an IOTA
participant's kidney transplant waitlist or receive another transplant
from an IOTA participant before the last day of the same PY would be
listed as de-attributed in the annual attribution reconciliation list.
This IOTA transplant patient would no longer be attributed to the IOTA
participant effective the last day of the PY in which the IOTA
transplant patient's kidney transplant has failed.
We seek comment on our proposed methodology and criteria for
identifying and de-attributing attributed patients from an IOTA
participant.
(2) Initial Attribution
We propose that before the model start date, CMS would conduct an
``initial attribution'' to identify and prospectively attribute
waitlist patients to an IOTA participant pursuant to Sec. 512.414. The
list of IOTA waitlist patients identified through initial attribution,
namely the initial attribution list, would prospectively apply to the
first quarter of PY 1, effective on the model start date. The purpose
of this initial attribution list would be to prospectively provide IOTA
participants with a list of their IOTA waitlist patients for the
upcoming quarter.
We considered attributing patients to IOTA participants at
different points in time, such as the day that a kidney transplant
waitlist patient was added to the IOTA participant's kidney transplant
waitlist, or the day that a kidney transplant patient received their
kidney transplant. This approach would be more precise than considering
all attributed patients to be attributed as of the start of the
quarter. However, due to the limitations of data sources and the
frequency with which these data are updated, we did not see this as a
viable alternative.
We seek comment on our proposal to conduct initial attribution
before the model start date and alternatives considered.
(3) Quarterly Attribution
We propose that CMS would attribute patients to IOTA participants
in advance of each quarter, after initial attribution, and distribute a
``quarterly attribution list'' to each IOTA participant that includes
all their attributed patients, including newly attributed patients, on
a quarterly basis throughout the model performance period, except in
the event of termination as described in section III.C.16.(b). of this
proposed rule.
We considered monthly attribution for more frequent updates to the
initial attribution list, but believe it would be operationally
burdensome. We also considered annual attribution for less frequent
updates to the initial attribution list, which would be less
operationally burdensome than monthly or quarterly attribution. Annual
[[Page 43547]]
attribution is common in other Innovation Center models and CMS
programs where the participant is managing total cost of care for a
population. The benefits of annual attribution would include
prospectively providing participants a stable list of patients for whom
they would be held accountable, and, as the process would occur only
once a year, would be associated with lower administrative burden. The
downside of annual attribution, however, is that IOTA participants
would have less frequent updates and understanding of their attributed
population, potentially making it hard to plan and budget accordingly.
We do not believe annual attribution would be appropriate for the IOTA
Model's goal of improving access to kidney transplants and quality of
care for a patient population that changes frequently. For example,
kidney transplant hospitals add patients to their kidney transplant
waitlist throughout the year. Were we to limit attribution to once a
year, kidney transplant waitlist patients added during the year would
not be attributed to an IOTA participant until the following year,
delaying our ability to meet the minimum number of patients required to
evaluate a model test. As such, we believe more frequent attribution
would be necessary.
We seek comment on our proposal to conduct attribution on a
quarterly basis during the model performance period and on the
alternatives considered.
(4) Annual Attribution Reconciliation
We propose that after the end of each PY, CMS would conduct annual
attribution reconciliation. We propose to define ``annual attribution
reconciliation'' as the yearly process by which CMS would: (1) create
each IOTA participant's final list of attributed patients for the PY
being reconciled by retrospectively de-attributing from each IOTA
participant any attributed patients that satisfied a criterion for de-
attribution pursuant to Sec. 512.414(c); and (2) create a final list
of each IOTA participant's attributed patients who would remain
attributed for the PY being reconciled, subject to the attribution
criteria in Sec. 512.414(b)(1) and (2). For the purposes of this
model, we propose to define ``annual attribution reconciliation list''
as the final cumulative record of attributed patients that would be
generated annually for whom each IOTA participant was accountable for
during the applicable PY.
For example, after PY 1, CMS would rerun attribution for the entire
PY to finalize the list of attributed patients that met the criteria
specified in sections III.C.4.b.(1). and (2). of this proposed rule.
Once the fourth quarter is complete, CMS would use the fourth quarter
attribution list to determine and de-attribute any attributed patients
that meet a criterion for de-attribution, as described in section
II.C.4.b.(1).(iii). of this proposed rule, from the IOTA participant,
as described in section III.C.4.b.(1).(iii). of this proposed rule, and
remove those attributed patients from the quarterly attribution list to
create the annual attribution reconciliation list. Before the second
quarter of the following PY, CMS would distribute the annual
attribution reconciliation list to IOTA participants. We propose that
these lists, at a minimum, would identify each attributed patient,
identify reasons for de-attribution in the previous PY, and the dates
in which attribution began, changed, or ended, where applicable.
We seek comment on our proposal to conduct annual attribution
reconciliation.
c. IOTA Patient Attribution Lists
We propose that no later than 15 days prior to the start of the
first model performance period, CMS would provide the IOTA participant
the ``initial attribution list.'' For the purposes of the model, we
propose to define ``days'' as calendar days, as defined in 42 CFR
512.110, unless otherwise specified by CMS. On a quarterly basis
thereafter, CMS would provide the IOTA participant the ``quarterly
attribution list'' no later than 15 days prior to the start of the next
quarter. The annual attribution reconciliation list for a given PY
would be provided to the IOTA participants after the conclusion of the
PY, before the second quarter of the following PY.
We propose that the initial, quarterly, and annual attribution
reconciliation lists would be provided in a form and manner determined
by CMS.
We seek comment on our proposed attribution list policies.
5. Performance Assessment
a. Goals and Proposed Data Sources
As described in section III.B. of this proposed rule, CMS and the
OPTN each have roles in assessing the performance of kidney transplant
hospitals. CMS' regulations in 42 CFR part 482 subpart E require
certain conditions of participation for kidney transplant hospitals to
receive approval to perform Medicare transplant services. Under 42 CFR
part 121, the OPTN is required to implement a peer review process by
which OPOs and transplant hospitals are periodically reviewed for
compliance with the bylaws of the OPTN and the OPTN final rule (63 FR
16332). The OPTN MPSC is charged with performing these evaluations;
including the identification of threats to patient safety and public
health.\184\
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\184\ https://optn.transplant.hrsa.gov/about/committees/membership-professional-standards-committee-mpsc/.
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CMS and the OPTN have each acknowledged the limitations of
transplant hospital performance assessment based on the one-year
patient and transplant survival measure alone. In 2018, CMS eliminated
its assessment of one year patient and transplant survival for the
purposes of transplant hospital re-approval in the final rule,
``Medicare and Medicaid Programs; Regulatory Provisions To Promote
Program Efficiency, Transparency, and Burden Reduction; Fire Safety
Requirements for Certain Dialysis Facilities; Hospital and Critical
Access Hospital (CAH) Changes To Promote Innovation, Flexibility, and
Improvement in Patient Care'' (84 FR 51732), leaving assessment of the
one year patient and transplant survival measure only for initial
Medicare approval, due to concerns that the measure was causing
conservative behavior in transplant hospitals.\185\ In 2021, the OPTN
disseminated a proposal to enhance the MPSC's performance monitoring
process by expanding the number of measures used to identify transplant
hospital underperformance.\186\ In that proposal, the OPTN acknowledged
the potential for transplant hospital risk aversion due to the MPSC's
evaluations of performance based on the one year patient and transplant
survival metric alone and proposed transplant hospital assessment based
on a holistic set of measures encompassing aspects of care across the
transplant journey.\187\
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\185\ Medicare and Medicaid Programs; Regulatory Provisions To
Promote Program Efficiency, Transparency, and Burden Reduction.
Federal Register. https://www.federalregister.gov/d/2018-19599/p-215.
\186\ https://optn.transplant.hrsa.gov/media/4777/transplant_program_performance_monitoring_public_comment_aug2021.pdf.
\187\ Ibid.
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Strengthening and improving the performance of the organ
transplantation system is a priority for HHS, including CMS and HRSA.
In accordance with this priority and joint efforts with HRSA, the IOTA
Model would aim to improve performance and equity in kidney
transplantation by testing whether performance-based payments to IOTA
participants increases access to kidney transplants for kidney
transplant waitlist and kidney transplant patients attributed to
[[Page 43548]]
IOTA participants in the model, thereby reducing Medicare program
expenditures while preserving or enhancing quality of care. For the
IOTA Model, we are proposing a broader set of metrics which aligns with
the trends that we believe would encourage IOTA participants to meet
the model goals as described in section III.A of this proposed rule.
The IOTA Model would assess performance on a broad set of metrics
that were selected to align with all of the following model goals:
Increase number of, and access to, kidney transplants.
Improve utilization of available deceased donor organs.
Support more donors through the living donation process.
Improve quality of care and equity.
We propose using Medicare claims and administrative data about
beneficiaries, providers, suppliers, and data from the OPTN, which
contains comprehensive information about transplants that occur
nationally, to measure IOTA participant performance in the three model
domains: (1) achievement domain; (2) efficiency domain; and (3) quality
domain. Medicare administrative data refers to non-claims data that
Medicare uses as part of regular operations. This includes information
about beneficiaries, such as enrollment information, eligibility
information, and demographic information. Medicare administrative data
also refers to information about Medicare-enrolled providers and
suppliers, including Medicare enrollment and eligibility information,
practice and facility information, and Medicare billing information.
We solicit comment on our proposal for selecting performance
metrics and performance domains. We also solicit comment on our
proposed use of Medicare claims data, Medicare administrative data, and
OPTN data to calculate the performance across the three proposed
domains, as described in section III.C.5. of this proposed rule.
b. Method and Scoring Overview
In accordance with our proposed goals of the IOTA performance
assessment, as described in section III.C.5.a. of this proposed rule,
we propose to assess performance across three domains: (1) achievement
domain; (2) efficiency domain; and (3) quality domain. We propose to
use one or more metrics within each domain to assess IOTA participant
performance. We propose that CMS would assign each set of metrics
within a domain a maximum point value, with the total possible points
awarded to an IOTA participant being 100 points. We propose to define
``final performance score'' as the sum total of the scores earned by
the IOTA participant across the achievement domain, efficiency domain,
and quality domain for a given PY. We also propose that the combined
sum of total possible points would determine whether and how the IOTA
Model performance-based payments, as described in section III.C.6.c. of
this proposed rule, would apply and be calculated. We propose the
following point allocations for each of these three domains:
The achievement domain would make up 60 of 100 maximum
points. The achievement domain would measure the number of kidney
transplants performed relative to a participant-specific target, as
described in section III.C.5.c. of this proposed rule. The achievement
domain would represent a large portion (60 percent) of the maximum
total performance score. We weighted the achievement domain performance
score more than the efficiency and quality domain because we believe it
aligns with the primary goal of the IOTA Model, to increase the overall
number of kidney transplants. Additionally, because increasing the
number of kidney transplants performed is the primary goal of the
model, we believe weighing performance on this measure more than the
efficiency domain and quality domain is necessary to directly
incentivize participants to meet their target.
The efficiency domain would make up 20 of 100 maximum
points. The efficiency domain would measure performance on a kidney
organ offer acceptance rate ratio.
The quality domain would make up 20 of 100 maximum points.
As described in section III.C.5.e. of this proposed rule, the quality
domain would measure performance on a set of quality metrics, including
post-transplant outcomes, and on three proposed quality measures--
CollaboRATE Shared Decision-Making Score, Colorectal Cancer Screening,
and 3-Item Care Transition Measure.
We believe that many prospective IOTA participants may already be
familiar with the approach of assigning points up to a maximum in
multiple domains. This structure is similar to other CMS programs,
including the Merit-based Incentive Payment System (MIPS) track of the
Quality Payment Program. For MIPS, we assess the performance of MIPS
eligible clinicians (as defined in 42 CFR 414.1305) across four
performance categories--one of which is quality--and then determine a
positive, neutral, or negative MIPS payment adjustment factor that
applies to the clinician's Medicare Part B payments for professional
services. Similar to MIPS, we are proposing that the IOTA Model would
use a performance scoring scale from zero to 100 points across
performance domains, and apply a specific weight for each domain. We
believe using wider scales of 0 to 100 points would allow us to
calculate more granular performance scores for IOTA participants and
provide greater differentiation between IOTA participants' performance.
In the future, we believe this methodology for assessing performance
could be applied with minimal adaptation to future IOTA participants if
CMS adds other types of organs transplants to the model through
rulemaking. We believe that the approach of awarding points in the
achievement, efficiency, and quality domains for a score out of 100
points represents the best combination of flexibility and comparability
that would allow us to assess participant performance in the IOTA
Model.
The proposed performance domains and scoring structure would also
allow us to combine more possible metric types within a single
framework. We believe that this approach allows for more pathways to
success than performance measurement based on relative or absolute
quintiles, which were also alternatively considered, as it would reward
efforts made towards achievable targets.
We considered more than three domains to assess performance, which
would potentially offer IOTA participants more opportunity to succeed
due to the ability to maximize points in different combinations of
domains. The more domains there are, the more the maximum points
possible in each domain are spread out. However, we limited the number
of domains to three to ensure the model is focused and goal-oriented,
thus promoting, encouraging, and driving improvement activity and care
delivery transformation across IOTA participants that evidence suggest
may help achieve desired outcomes. Desired outcomes include delaying or
avoiding dialysis, improving access to kidney transplantation by
reducing barriers and disparities, reducing unnecessary deceased donor
discards, increasing living donors, and improving care coordination and
quality of care pre and post transplantation. We believe that the three
domains and the proposed performance scoring structure would offer IOTA
participants multiple paths to succeed in the proposed IOTA Model due
to the ability to maximize points in different combinations of domains.
[[Page 43549]]
We also considered not using the three performance domains and
scoring structure, instead opting for alternative methods. We
considered a performance assessment methodology in which an IOTA
participant's performance on a metric would be divided by an expected
value for each metric, which would indicate whether an IOTA participant
is performing better or worse on a given measure than expected. We
would then calculate a weighted average of all performance scores to
reach a final score. However, we believe that setting appropriate
targets of expected performance for each IOTA participant for each
metric would be unrealistic to implement. The additional methodological
complexity necessary for this approach would be difficult for an IOTA
participant to incorporate into its operations and data systems,
thereby limiting an IOTA participant's ability to understand the care
practice changes it would need to make to succeed in the IOTA Model.
We also considered assessing IOTA participant performance solely on
magnitude of increased transplants over expected transplants. Under
this approach, an IOTA participant's number of transplants furnished in
a given PY subtracted from expected transplants would show a numeric
net gain or loss in total transplants. This net value would be
multiplied by an IOTA participant's kidney transplant survival rate to
generate a total score for each IOTA participant. This option would
reward successfully completed transplants. This methodology reflects
the goals of the IOTA Model and acknowledges that kidney transplant
failures are an undesirable outcome. In addition, the methodology is
simple to evaluate and understand, requiring only two inputs and a
simple calculation. However, this approach does not account for
efficiency and quality domain metrics, as proposed in section
III.C.5.d. and e. of this proposed rule, which we believe to be
important goals of the model. Thus, we are not proposing this method to
assess IOTA participant performance.
We also considered directly translating the benefits of a kidney
transplant by measuring the net effect of increased transplants and
post-transplant care at the IOTA participant level. In a performance
scoring methodology focused on the net effect of increased transplants
and post-transplant care, the number of kidney transplants performed in
a given PY would be compared to a benchmark year for the IOTA
participant. Each additional kidney transplant would then be multiplied
by the expected number of years of dialysis treatment the transplant
averted, based on organ quality. Post-transplant care would analyze
observed versus expected kidney transplant failures. For IOTA
participants that achieved fewer kidney transplant failures than
expected, the difference in volumes would be translated into life-
years. Each marginal additional year of averted dialysis care would be
used to determine the performance-based payment. Because calculating
expected transplant failures is a complicated calculation with
assumptions based on organ quality, donor age, and donor health
conditions, a scoring system of this type would require us to make
multiple broad assumptions about individual transplants or average
scores across all transplants performed by the IOTA participant to
create an accurate estimate of the total number of years of dialysis
treatment the kidney transplant averted. This level of complexity would
also introduce operational risks and burden. This approach would be
aligned with the goals of the IOTA Model as it relates to increasing
the number and access to kidney transplants but would still require CMS
to separately assess performance on proposed performance measures for
the IOTA Model, as discussed in section III.C.5.c., d., and e. of this
proposed rule.
We are soliciting feedback from the public on our proposal to
assess IOTA participant performance in three domains: (1) achievement
domain; (2) efficiency domain; and (3) quality domain. We are also
seeking feedback on our proposed performance scoring approach that
would weigh the achievement domain higher than the efficiency and
quality domain, and our proposed use of a 0 to 100 performance scoring
approach to determine if and how performance-based payments would
apply. Additionally, we invite feedback on the alternatives considered.
c. Achievement Domain
As stated in section III.C.5.b. of this proposed rule, we propose
measuring IOTA participant performance across three domains, one of
which is the achievement domain. We propose to define ``achievement
domain'' as the performance assessment category in which CMS assesses
the IOTA participant's performance based on the number of transplants
performed on patients 18 years of age or older, relative to a target,
subject to a health equity performance adjustment, as described in
section III.C.5.c.(3). of this proposed rule, during a PY. We propose
to use OPTN data, regardless of payer, and Medicare claims data to
calculate the number of kidney transplants performed during a PY by an
IOTA participant on patients 18 years of age or older at the time of
transplant, as described in section III.C.5.c.(2). of this proposed
rule.
We propose to set the participant-specific target for the
achievement domain based on each IOTA participant's historic number of
transplants. A central goal of the proposed IOTA Model test is to
increase the number of kidney transplants furnished by IOTA
participants, which we believe would be possible via care delivery
transformation and improvement activities, including donor acceptance
process improvements to reduce underutilization and discards of donor
kidneys. We believe IOTA participants may also increase the number of
kidney transplants furnished to patients by improving or implementing
greater education and support for living donors.
We considered constructing and using a transplant waitlisting rate
measure or using SRTR's transplant rate \188\ rather than measuring
number of transplants performed relative to a participant-specific
target for the achievement domain. Research has suggested that
including such a metric could demonstrate the need for both living and
deceased donor organs for a particular transplant hospital and be less
reliant on organ availability for a particular geographical area.\189\
Research also suggests that the inclusion of a pretransplant measure,
such as waitlisting rate, may allow for a more complete assessment of
transplant hospital performance and provide essential information for
patient decision-making.\190\ However, for the IOTA Model, we propose
to test the effectiveness of the model's incentives to change outcomes,
rather than on processes. The relevant outcome for purposes of the IOTA
Model is the
[[Page 43550]]
receipt of a kidney transplant, not getting on and remaining on the
kidney transplant waitlist. Additionally, the SRTR transplant rate
measure calculates the number of those transplanted as a share of the
kidney transplant hospital's waitlist, which we believe does not
reflect the variety of ways that kidney transplant hospitals construct
their waitlist practices. For example, for some kidney transplant
hospitals, the number of kidneys transplanted as a share of their
``active'' waitlist transplant candidates may be a more accurate
representation of their waitlist practices. Thus, we did not believe
this was appropriate to propose for the IOTA Model.
---------------------------------------------------------------------------
\188\ For additional information on SRTR's transplant rate
measure, please see https://www.srtr.org/about-the-data/technical-methods-for-the-program-specific-reports#figurea2.
\189\ Paul, S., Melanson, T., Mohan, S., Ross-Driscoll, K.,
McPherson, L., Lynch, R., Lo, D., Pastan, S.O., & Patzer, R.E.
(2021). Kidney transplant program waitlisting rate as a metric to
assess transplant access. American Journal of Transplantation:
Official Journal of the American Society of Transplantation and the
American Society of Transplant Surgeons, 21(1), 314-321. https://doi.org/10.1111/ajt.16277.
\190\ Paul, S., Melanson, T., Mohan, S., Ross-Driscoll, K.,
McPherson, L., Lynch, R., Lo, D., Pastan, S.O., & Patzer, R.E.
(2021). Kidney transplant program waitlisting rate as a metric to
assess transplant access. American Journal of Transplantation:
Official Journal of the American Society of Transplantation and the
American Society of Transplant Surgeons, 21(1), 314-321. https://doi.org/10.1111/ajt.16277.
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We seek comment on our proposed achievement domain performance
metric and alternative methodologies considered for assessing
transplant rates.
(1) Calculation of Transplant Target
We propose that for each model PY, CMS would calculate a
``transplant target'' for each IOTA participant, which would determine
performance in the achievement domain. For the purposes of the model,
we propose to define ``transplant target'' as the target number of
transplants set for each IOTA participant to measure performance in the
achievement domain as described in section III.C.5.c. of this proposed
rule. We propose that CMS would notify each IOTA participant of their
transplant target by the first day of each PY, in a form and manner
determined by CMS.
For each PY, we propose that CMS would calculate the transplant
target for the achievement domain by first determining the highest
number of deceased donor kidney transplants and living donor kidney
transplants furnished to patients 18 years of age or older in a single
year during the baseline years, as defined in section III.C.3.c. of
this proposed rule. CMS would then sum the highest number of deceased
donor kidney transplants and living donor kidney transplants furnished
in a single year during the baseline years calculate the transplant
target for an IOTA participant, even if those transplant numbers were
achieved during different baseline years. We believe that choosing the
highest transplant numbers during the baseline years would illustrate
the capabilities and capacities of the IOTA participant, and, when
combined, would be an appropriate target for number of transplants
performed during the PY. We also understand that living donation and
deceased donor donation involve different processes by the IOTA
participant, so we are choosing each of those numbers separately to
recognize the potential capacity for each IOTA participant for both
living and deceased donor transplantation.
We propose that the sum of the highest number of deceased donor and
living donor transplants across the baseline years of the IOTA
participant would then be projected forward by the national growth
rate, as described in section III.C.5.c.(1). of this proposed rule, or
zero should the national growth rate be negative, resulting in the
transplant target for a given PY. We propose to define ``national
growth rate'' as the percentage increase or decrease in the number of
kidney transplants performed over a twelve-month period by all kidney
transplant hospitals except for pediatric kidney transplant hospitals
and kidney transplant hospitals that fall below the low volume
threshold described in section III.C.3. of this proposed rule. We
propose to define ``pediatric kidney transplant hospitals'' as a kidney
transplant hospital that performs 50 percent or more of its transplants
in a 12-month period on patients under the age of 18. We are also
proposing that the low volume threshold to be 11 kidney transplants
performed for the purposes of calculating the national growth rate. We
also propose this approach for calculating the national growth rate to
account for and reflect the growth in organ procurement by OPOs that
has occurred, indicating potential growth in the number of available
organs.
We propose that CMS would calculate the national growth rate by
determining the percent increase or decrease of all kidney transplants
furnished to patients 18 years of age or older from two years prior to
the PY to one year prior to the PY. Because the proposed national
growth rate includes IOTA participants and non-IOTA participant kidney
transplant hospitals, we acknowledge that it could make achieving the
transplant target number harder. This is why, if the national growth
rate becomes negative for a PY, we propose treating it as zero and CMS
would not apply the national growth rate to project forward the sum of
the highest number of deceased and living donor kidney transplants
furnished in a single year during the baseline years. In other words,
an IOTA participant's transplant target would equal the sum of its own
highest deceased and living donor transplants furnished across the
baseline years if the national growth rate were to be negative for a
PY. We also want to be able to share model performance targets with
IOTA participants before the start of each PY and are prioritizing
ensuring prospectivity over ensuring the most up-to-date trend figures.
We also propose that if the model begins on an any date after January
1, 2025, the trend would also be adjusted.
For example, to calculate the national growth rate for PY 1 using
the proposed model start date of January 1, 2025, CMS would first
subtract the total number of kidney transplants furnished to patients
18 years of age or older in 2022 from the total number of kidney
transplants furnished to patients 18 years of age or older in 2023.
Next, CMS would then divide that number by the total number of kidney
transplants furnished to patients 18 years of age or older in 2022 to
determine national growth rate. To create the transplant target for
each IOTA participant for PY 1 CMS would do the following:
If the national growth rate is positive, CMS would trend
the national growth rate forward for an IOTA participant by multiplying
the national growth rate by the sum of the highest number of deceased
donor and living donor transplants furnished to patients 18 years of
age or older across the baseline years for the IOTA participant.
CMS would take the product of step 1 and add it to the sum
of the highest living donor and deceased donor kidney transplants
furnished to patients 18 years of age or old across the baseline years
for an IOTA participant.
The sum of step 2 would be the transplant target for an
IOTA participant. However, if the national growth rate were negative,
CMS would not trend the growth rate forward for PY 1 and the transplant
target would be the sum of the highest living donor and deceased donor
kidney transplants across the baseline years.
We propose that when calculating the national growth rate for each
PY, CMS would look to the relevant baseline years for that PY, as
depicted in Table 1. This approach would mitigate our concern that a
static baseline may reward a one-time investment, rather than
continuous improvement. The model PYs, as proposed, would not factor
into an IOTA participant's transplant target calculation until PY 3 of
the model (January 1, 2027, to December 31, 2027) and the baseline
years would not be based exclusively on PYs until PY 5 of the model
(January 1, 2029, to December 31, 2029), which may represent an
effective phase-in approach to drive improved performance and savings
for the Medicare trust fund. We believe that using baseline years to
calculate the transplant targets would also account for kidney
transplant hospitals that experience changes in strategy or staffing
that may affect their
[[Page 43551]]
capacity to perform transplants at the level that they did in previous
years.
[GRAPHIC] [TIFF OMITTED] TP17MY24.000
Should we finalize a model start date other than January 1, 2025,
we propose that the baseline years, as defined in section III.B.2.c. of
this proposed rule, would shift accordingly, as illustrated in Table 2.
[GRAPHIC] [TIFF OMITTED] TP17MY24.001
We believe that IOTA participants could improve on this metric in
several ways. For example, IOTA participants could increase the number
of kidney organ offers they accept, which would also potentially lead
to greater efficiency domain scores. IOTA participants could also
invest in a living donation program or modify their OR schedules to
facilitate fewer discards due to physician scheduling.
We considered basing the transplant target on the total number of
all organ transplants performed by the IOTA participant over the
baseline years. However, we did not believe this was appropriate
because the total would not reflect the specific capabilities of the
IOTA participant's kidney transplant program. We also considered
adjusting the transplant target by IOTA participant revenue from
hospital cost reports. In this scenario, our consideration was to look
at historical kidney transplant data as the best predictor, since this
reveals the demonstrated capacity for each IOTA
[[Page 43552]]
participant to complete kidney transplants.
We also considered setting each IOTA participant's transplant
target by determining the IOTA participant's average total kidney
transplant volume from the three previous years instead of using the
sum of the highest living and deceased donor kidney transplant volumes
during the baseline years. We believe this methodology would be simpler
and result in a transplant target that is potentially more attainable
for IOTA participants, assuming that the average kidney transplant
volume is lower than the sum of the highest volumes of deceased and
living donor kidney transplants. However, we do not believe that this
would reflect the potential highest capacity for transplant that we
would otherwise like the target to reflect.
We alternatively considered a static or fixed baseline approach for
purposes of determining the transplant target for each IOTA
participant, as it would minimize operational burden for CMS due to
less frequent updates to the transplant target and ensure that the
model does not set a moving target year-over-year. However, we believe
that a fixed baseline may reward a one-time investment, rather than
continuous improvement, and may not account for kidney transplant
hospitals that experience changes in strategy or staffing that may
affect their capacity to perform transplants at the level that they did
in historical years. The rolling baseline approach we are instead
proposing uses historical kidney transplant volumes pre-dating the
model start date through the first two model PYs, ensuring a phased-in
approach before any improvements made during the model performance
period are accounted for in the baseline.
We also considered setting the transplant target for IOTA
participants based on two baseline years, rather than the proposed
methodology of three. For the proposed model start date of January 1,
2025, this approach would look at the highest living and deceased
volumes from 2022 and 2023, trended by the national growth rate from
2024, to set the transplant target for PY 1. We believe this
methodology would be more reflective of recent transplantation volume
and account for the changes to the kidney allocation system that were
implemented in 2021. However, we believe that using two baseline years
to set a transplant target would be more susceptible to temporary
market disruptions or fluctuations that may impact IOTA participants
capability or capacity to furnish kidney transplants, such as: if the
transplant hospital experiences a shortage in transplant surgeons or
other critical staff; if the transplant hospital is acquired; or, the
occurrence of a natural disaster, pandemic, or other public health
emergency or other extreme and uncontrollable circumstance that would
require the transplant hospital to temporarily suspend operations. Any
of these disruptions or fluctuations could result in an inaccurate
transplant target that would not accurately reflect an IOTA
participant's volume capability.
We considered determining the national growth rate by calculating
separately; (1) the growth rate of the deceased donor target number by
the growth in organs procured, and (2) the living donor target number
by the national growth rate in living donor transplants. However,
procurement rates vary nationally depending on variables unique to each
geography and local OPO policies.\191\ Because we want the model to
inspire kidney transplant hospitals to expand living donor programs,
not just match national growth rates, we did not believe this
alternative methodology was appropriate to propose.
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\191\ Potluri, V.S., & Bloom, R.D. (2021). Effect of Policy on
Geographic Inequities in Kidney Transplantation. https://doi.org/10.1053/j.ajkd.2021.11.005; Hanaway, M.J., MacLennan, P.A., & Locke,
J.E. (2020). Exacerbating Racial Disparities in Kidney Transplant.
JAMA Surgery, 155(8), 679. https://doi.org/10.1001/jamasurg.2020.1455.
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We also considered determining the national growth rate using the
following information: (1) the total growth rate in kidney transplants;
(2) the change in rate of organs procured by OPOs; (3) the growth rate
in kidney transplants in the non-selected portions of the country; and
(4) calculating the average growth rate across multiple baseline years.
However, we believe that the national growth rate in kidney transplants
makes the most sense to use as the basis for the model's growth factor
because it best reflects volume trends in the kidney transplant
ecosystem overall, as it considers all kidney transplant hospitals, not
just IOTA participants.
Finally, we also considered a performance assessment methodology
for IOTA participants already achieving higher rates of kidney
transplantation by assessing each such IOTA participant's total
transplant volume as compared to all IOTA participants, rather than on
an IOTA participant specific transplant target. We believe this
methodology is both easy to understand and simple to administer because
it rewards IOTA participants for the total number of transplants
performed. However, we believe this methodology would not be fair to
IOTA participants that are smaller in size or achieving lower rates of
kidney transplantation.
We solicit comment on our proposal to set unique transplant targets
for each IOTA participant, the methodology for setting transplant
targets, and any alternatives considered.
(2) Calculation of Points
We propose that the achievement domain would be worth 60 points. We
chose this domain for the highest number of points because we believe
that driving an increase in the number of transplants should be the
main incentive for change in the model. We considered allocating fewer
points to this domain, such as 50 points, but we believe that
performance in this domain should impact the overall performance score
more than the other domains given its centrality to the model.
We propose that an IOTA participant's performance would be assessed
relative to their transplant target, with those performing at less than
75 percent of the transplant target receiving no points and those
performing at 150 percent of the transplant target or above receiving
the maximum number of points (60 points). That is, at the highest end
of the scale, IOTA participants performing at or above 150 percent of
the transplant target would earn the maximum 60 points, while at the
lowest end of the scale, IOTA participants performing at less than 75
percent of the transplant target would earn no points for the
achievement domain; performance that falls in between 75 percent and
150 percent of the transplant target may earn the IOTA participant 45,
30, or 15 points in the achievement domain. Table 3 illustrates our
proposal for how an IOTA participant's performance would be assessed
against its transplant target. We chose 150 percent as the maximum
performance level based on the theoretical capability of growth in one
year and analysis in trends of transplant over time. We recognize that
an IOTA participant might exceed 150 percent of its transplant target,
but this is not expected given the investment needed for substantiable
transplant infrastructure to consistently support that number of
transplants over time.
[[Page 43553]]
[GRAPHIC] [TIFF OMITTED] TP17MY24.002
We believe that a methodology based on performance improvement
relative to historical performance is important and would allow us to
test whether the model's performance based payments drive increased
behavior from IOTA participant, as opposed to just rewarding IOTA
participants based on the status quo. IOTA participants that are
achieving a high rate of kidney transplantation, and already have
robust transplant programs at the start, can more easily scale up to
achieve the additional growth required for excellent performance under
the model. Also, given our statutory requirements to achieve savings,
the CMS Office of the Actuary (OACT) estimates, as described in section
VI of this proposed rule, suggest that savings would be driven by the
effects of increased transplants. We believe that the model's
performance based payments need to be tied to a policy that aims to
create and drive Medicare savings.
We considered offering differential credit for transplants by type.
With this methodology, IOTA participants would receive bonus points and
score higher for transplants that fit into categories that lead to more
savings, such as living donor kidney transplants (LDK), high KDPI
donors, or pre-emptive transplants, compared to other transplants.
However, we believe that counting all transplants the same, except for
transplants furnished to underserved populations, would maximize
flexibility for IOTA participants in meeting their targets and minimize
the potential harm and unintended consequences the alternative system
would create.
As an alternative, we considered including gradient points instead
of points based on bands (that is, between X and Y). Scoring closer to
a performance minimum would result in increased points rather than
remaining static throughout the band. We considered the following
formula: Percent Performance Relative to Transplant Target * (100/2.5),
not to exceed 60 points. However, we decided that a narrower range of
results would better differentiate performance among IOTA participants
and allow for easier comparison across IOTA participants.
We also considered smaller point brackets of improvement, requiring
IOTA participants to achieve a flat number increase of kidney
transplants, such as to a 140 percent, 125 percent, or 120 percent, to
achieve the highest performance in this category, and asymmetric point
brackets that would make the magnitude of performance required to
achieve the highest performance rate a flat number increase in addition
to a percentage increase. However, we wanted the percentage of the
transplant target necessary to achieve the highest number of points to
be large enough to incentivize behavior while still being achievable.
We also considered improvement-only scoring, based on year-over-
year IOTA participant transplant growth, without inclusion of national
rates. In this methodology, positive improvement rates less than 5
percent would be scored 15 points, rates over 5 percent would be scored
30 points, rates over 20 percent would be scored 45 points, and rates
over 50 percent would be scored 60 points. We also considered using
combinations of potential transplant target or scoring methods, with
the final score being whichever score was highest to ensure low-volume
IOTA participants are not penalized and to mitigate unrealistic
transplant targets. We considered an improvement-only scoring
methodology to reflect the historical performance of each IOTA
participant. However, because we want a methodology that sets more of a
national standard for expected growth rate to assess volume trends in
the transplant space overall, we chose not to propose improvement-only
scoring. As organ supply continues to increase year-over-year, we wish
to set the expectation for IOTA participants to grow their transplant
volumes at least at the cadence of the national growth rate.
We solicit comment on our proposed achievement domain scoring
methodology and alternative methodologies considered.
(3) Health Equity Performance Adjustment
Socioeconomic factors impact patient access to kidney transplants.
Patients with limited resources or access to care may require more
assistance from kidney transplant hospitals to overcome barriers to
transplantation. To incentivize IOTA participants to decrease
disparities in the overall transplant rate among patients of various
income levels, we propose to include a health equity performance
adjustment in the methodology for calculating the overall number of
transplants furnished to patients attributed to an IOTA participant
during the PY. We propose to define the ``health equity performance
adjustment'' as the multiplier applied to each kidney transplant
furnished to a low-income population IOTA transplant patient when
calculating the transplant target as described in Sec. 512.424). For
purposes of the model, we propose to define the ``low-income
population'' to mean an IOTA transplant patient in one or more of the
following groups:
The uninsured.
Medicaid beneficiaries.
Medicare-Medicaid dually eligible beneficiaries.
Recipients of the Medicare LIS.
Recipients of reimbursements from the Living Organ
Donation Reimbursement Program administered by the National Living
Donor Assistance Center (NLDAC).
We propose to apply a health equity performance adjustment, a 1.2
multiplier, to each kidney transplant furnished by an IOTA participant
to a patient, 18 years of age or older at the time of transplant, that
meets the low-income population definition. That is, each kidney
transplant that is furnished to a patient who meets the low-income
population definition would be multiplied by 1.2, thus counting that
transplant as 1.2 instead of 1. The resulting count of the overall
number of
[[Page 43554]]
kidney transplants performed during the PY, after the health equity
performance adjustment is applied, would then be compared to the
transplant target. In effect, the health equity performance adjustment
would be a reward-only adjustment to the performance score in the
achievement domain. We also considered basing the multiplier on the
difference between rates of transplantation for Medicare beneficiaries
with ESRD who are dual eligible and those who are not. In 2019, 47
percent of Medicare beneficiaries with ESRD were dually eligible for
Medicare. However, only 41 percent of Medicare transplants recipients
were dually eligible, which would yield a multiplier of 1.1.\192\
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\192\ Gillen, E.M., Ganesan, N., Kyei-Baffour, B., & Gooding, M.
(2021, August 30). Avalere analysis of disparities in Kidney Care
Service Utilization. Avalere Health. https://avalere.com/insights/avalere-analysis-of-disparities-in-kidney-care-service-utilization.
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We chose 1.2 as the health equity performance adjustment multiplier
because, according to USRDS data, 78.6 percent of patients living with
ESRD have some form of Medicare and or Medicaid coverage; however only
65.1 percent of patients who received transplants in 2020 were on
Medicare, Medicaid, or both.193 194 The 1.2 multiplier
represents the ratio of those living with ESRD and those who received
transplants. We theorize that providing this incentive for IOTA
participants to increase their transplant rate among low-income
populations would ultimately reduce disparities in access to kidney
transplants, as it would encourage IOTA participants to address access
barriers low-income patients often face, such as transportation,
remaining active on the kidney transplant waiting list, and making
their way through the living donation process.
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\193\ United States Renal Data System. (2020). 2020 USRDS 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.
\194\ Lentine, K. L., Smith, J. M., Hart, A., Miller, J.,
Skeans, M. A., Larkin, L., Robinson, A., Gauntt, K., Israni, A. K.,
Hirose, R., & Snyder, J. J. (2022). OPTN/SRTR 2020 Annual Data
Report: Kidney. American Journal of Transplantation, 22(S2), 21-136.
https://doi.org/10.1111/ajt.16982 https://doi.org/10.1111/ajt.16982.
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We believe the health equity performance adjustment would be a
strong incentive to promote health equity, as the multiplier earned
would help IOTA participants meet or exceed their kidney transplant
target, thereby potentially resulting in upside risk payments given the
heavy weighted scoring applied to the achievement domain. We also
believe it would ensure IOTA participants that serve disproportionately
high numbers of low-income populations are not penalized in the
achievement performance scoring.
We considered not applying a health equity performance adjustment
to the achievement performance scoring, which would ensure all kidney
transplants, regardless of the low-income status of individual
patients, are counted as one transplant. The concern with the health
equity performance adjustment may be that it may incentivize shifting
of kidney transplants from one type of patient to another. However, we
believe the incentive is to promote improvement activities that would
increase access to all patients while recognizing that low-income
patients may face more barriers to care outside of the IOTA
participants' control. It also recognizes that disparities already
exist in access to kidney transplants for low-income patients, so, by
addressing inequities, IOTA participants would focus efforts on
tackling inequities for patients outside the Medicare population.
For purposes of the health equity performance adjustment, we also
considered using the area deprivation index (ADI) to define the low-
income population. ADI ranks neighborhoods based on socioeconomic
disadvantage in the areas of income, education, employment, and housing
quality. Areas with greater disadvantage are ranked higher, and they
correlate with worse health outcomes in measures such as life
expectancy.\195\ The areas used in the ADI are defined by Census Block
Group, which presents a number of challenges.\196\ However, because
address information for Medicare beneficiaries may be incomplete, and
not available at all for patients who have private insurance or the
uninsured, we opted to not use ADI to define the low-income population.
We believe that this would leave an incomplete picture of the
transplant population for a given IOTA participant. Furthermore, the
socioeconomic status of individuals within a given ADI can vary
greatly. Those that are underserved in a Census Block Group with a low
ADI may be overlooked.
---------------------------------------------------------------------------
\195\ Neighborhood Atlas--Home. (2018). Wisc.edu. https://www.neighborhoodatlas.medicine.wisc.edu/.
\196\ https://www2.census.gov/geo/pdfs/reference/GARM/Ch11GARM.pdf.
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We also considered including ``rural resident'' as one of the
groups that define a low-income population in the IOTA Model, as rural
transplant patients face numerous barriers to care, including
transportation, food, housing, and income insecurity, and no or limited
access to kidney transplant hospitals within or close to their rural
communities. We considered defining rural beneficiaries consistent with
the criteria used for identifying a rural area when determining CAH
eligibility at 42 CFR part 485.610(b)(1)(i), that is beneficiaries
living outside an MSA. However, we were unsure if it was appropriate to
include this group to define a low-income population to determine if a
health equity adjustment would apply to the achievement performance
score, particularly as the proposed low-income definition may already
capture the majority of rural kidney transplant patients.
We seek comment on our proposed health equity performance
adjustment, including on the adjustment multiplier and calculation
method, the definition of low-income population and alternatives
considered, including consideration of ADI as an alternative
definition, or including rural resident in the low-income population
definition.
d. Efficiency Domain
We propose to define the ``efficiency domain'' as the performance
assessment category in which CMS assesses the IOTA participant's
performance a metric intended to improve the transplant process, as
described in section III.C.5.d.(1). of this proposed rule, during a PY.
The efficiency domain is focused on improving the overall efficiency of
the transplant ecosystem.
We propose including OPTN's organ offer acceptance rate measure in
the efficiency domain. The organ offer acceptance rate ratio measure is
a ratio of observed organ offer acceptances versus expected organ offer
acceptances, as described in section III.C.5.d.(1). of this proposed
rule.
(1) Organ Offer Acceptance Rate Ratio
With over 90,000 unique patients on the waitlist for a kidney
transplant, the need to effectively use every available donor organ is
critical. However, despite the new allocation system introduced in
2021, and more organs being offered over a wider geographic area, the
kidney discard rate has risen to over 24.6 percent and continues to
trend upwards.\197\ There is a significant shortage of organs available
for transplantation, and many patients die waiting for a kidney
transplant. Moreover, there are large disparities in organ offer
acceptance ratio performance. A 2020 national registry
[[Page 43555]]
study found that the probability of receiving a deceased donor kidney
transplant within three years of placement on the waiting list varied
16-fold between different kidney transplant hospitals across the
U.S.\198\ The study also found that large variations were still present
between kidney transplant hospitals that utilized the same OPO and that
the probability of transplant was significantly associated with
transplant hospitals' offer acceptance rates.\199\ By incentivizing
kidney organ offer acceptance, we aim to optimize the use of available
organs, thereby reducing underutilization and discards of quality donor
organs.
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\197\ MN, 1Scientific R. of T. R., Hennepin Healthcare Research
Institute, Minneapolis. (n.d.). Kidney. Srtr.transplant.hrsa.gov.
Retrieved June 19, 2023, from https://srtr.transplant.hrsa.gov/annual_reports/2021/Kidney.aspx.
\198\ King, K. L., Husain, S. A., Schold, J. D., Patzer, R. E.,
Reese, P. P., Jin, Z., Ratner, L. E., Cohen, D. J., Pastan, S. O., &
Mohan, S. (2020). Major Variation across Local Transplant Centers in
Probability of Kidney Transplant for Wait-Listed Patients. Journal
of the American Society of Nephrology, 31(12), 2900-2911. https://doi.org/10.1681/ASN.2020030335.
\199\ King, K. L., Husain, S. A., Schold, J. D., Patzer, R. E.,
Reese, P. P., Jin, Z., Ratner, L. E., Cohen, D. J., Pastan, S. O., &
Mohan, S. (2020). Major Variation across Local Transplant Centers in
Probability of Kidney Transplant for Wait-Listed Patients. Journal
of the American Society of Nephrology, 31(12), 2900-2911. https://doi.org/10.1681/ASN.2020030335.
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For purposes of assessing the performance of IOTA participants in
the achievement domain, we propose to include the organ offer
acceptance rate ratio as one of the two metrics of performance. We
believe that including this measure in the efficiency domain would
encourage IOTA participants to increase the utilization of available
organs. We also believe that this measure would encourage IOTA
participants to improve efficiency in the organ offer process, improve
acceptance practices for offers received, and allow for maximal
utilization of available organs. We believe that the organ offer
acceptance rate ratio is an important system-wide metric, as improved
performance by an IOTA participant would also improve opportunities for
other kidney transplant hospitals that would not have to wait as long
for an available donor kidney. We recognize that all kidney transplant
hospitals are already assessed on the organ offer acceptance rate ratio
metric under the OPTN, however, we believe that the IOTA Model sets a
higher bar for performance, as discussed in section III.C.5.d.(1).(a).
of this proposed rule, rather than clearing the threshold that the OPTN
sets at 0.30.\200\
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\200\ Enhance Transplant Program Performance Monitoring System
OPTN Membership and Professional Standards Committee. (n.d.).
https://optn.transplant.hrsa.gov/media/4777/transplant_program_performance_monitoring_public_comment_aug2021.pdf.
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In the United States, kidney transplant waitlist candidates face
considerable disparities in access to kidney transplant, such as in who
is referred and placed on the waiting list, who remains ``active'' on
the waiting list, and how waitlisted patients are managed by kidney
transplant hospitals.\201\ Additionally, kidney transplant hospital
performance is commonly measured by post-transplant outcomes. We
recognize that including pre-transplant measures could allow for a more
thorough evaluation of transplant hospital performance and provide
insight for patient decision-making.
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\201\ Schold, J.D., Gregg, J.A., Harman, J.S., Hall, A.G.,
Patton, P.R., & Meier-Kriesche, H.U. (2011). Barriers to Evaluation
and Wait Listing for Kidney Transplantation. Clinical Journal of the
American Society of Nephrology, 6(7), 1760-1767. https://doi.org/10.2215/cjn.08620910; Hod, T., & Goldfarb-Rumyantzev, A.S. (2014).
The role of disparities and socioeconomic factors in access to
kidney transplantation and its outcome. Renal Failure, 36(8), 1193-
1199. https://doi.org/10.3109/0886022x.2014.934179; Stolzmann, K.L.,
Bautista, L.E., Gangnon, R.E., McElroy, J.A., Becker, B.N., &
Remington, P.L. (2007). Trends in kidney transplantation rates and
disparities. Journal of the National Medical Association, 99(8),
923-932. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2574300/;
Paul, S., Melanson, T., Mohan, S., Ross-Driscoll, K., McPherson, L.,
Lynch, R., Lo, D., Pastan, S.O., & Patzer, R.E. (2021). Kidney
transplant program waitlisting rate as a metric to assess transplant
access. American Journal of Transplantation: Official Journal of the
American Society of Transplantation and the American Society of
Transplant Surgeons, 21(1), 314-321. https://doi.org/10.1111/ajt.16277; Cheng, X.S., Busque, S., Lee, J., Discipulo, K., Hartley,
C., Tulu, Z., Scandling, J. ., & Tan, J.C. (2018). A new approach to
kidney wait-list management in the kidney allocation system era:
Pilot implementation and evaluation. Clinical Transplantation,
32(11), e13406. https://doi.org/10.1111/ctr.13406.
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We considered several waitlist management metrics for assessing
performance in the efficiency domain, such as the number of patients
registered to a waitlist, the number or percentage of attributed
patients registered on a waitlist with an active waitlist status, or
the number or percentage of attributed patients on a waitlist with
active waitlist status to inactive waitlist status. Metrics focused on
the waitlist could help assess how effectively kidney transplant
hospitals are managing their kidney transplant waitlist patients. Organ
offers to waitlist kidney transplant patients are made directly to the
kidney transplant hospital where they are waitlisted. Once a kidney
transplant hospital receives an organ offer for one of their kidney
transplant waitlist patients, it is ultimately its decision to accept
or decline an organ offer on the patient's behalf. Kidney transplant
hospitals are not required to inform kidney transplant waitlist
patients for whom an offer was received when an organ offer was
received or why an organ offer was declined. While we understand the
importance of a transplant surgeon's clinical decision-making and
respect the clinical judgement of transplant surgeons, declining an
offer without involving the affected patient in the decision-making can
be detrimental to the patient, as additional time on the waitlist can
negatively impact the patient's quality of life.\202\
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\202\ Husain, S.A., King, K.L., Pastan, S., Patzer, R.E., Cohen,
D.J., Radhakrishnan, J., & Mohan, S. (2019). Association Between
Declined Offers of Deceased Donor Kidney Allograft and Outcomes in
Kidney Transplant Candidates. JAMA Network Open, 2(8), e1910312.
https://doi.org/10.1001/jamanetworkopen.2019.10312.
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We also considered including a waitlist mortality metric for
assessing efficiency domain performance, so as to incentivize
improvements in mortality outcomes of attributed patients on a
waitlist. On average, as many as 20 patients on the waitlist for a
kidney transplant die each day waiting for a kidney transplant in the
United States.\203\ While a waitlist mortality metric may help assess
patient outcomes and experience while waiting for an organ offer,\204\
and provide insight into differences in waitlist management practices
across kidney transplant hospitals, we recognize that waitlist
mortality rate is also influenced by the insufficient supply of
available donor organs available for transplantation. We also recognize
that IOTA participants may not have a direct effect on, or ability to
improve, mortality metrics, as nephrologists are also closer to the
direct care of waitlist patients and would have a greater ability to
affect their care and mortality rate. Furthermore, we believe that we
are already testing the ability of nephrologists to manage care for
Medicare beneficiaries with ESRD or CKD via the KCC Model.
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\203\ Delmonico, F.L., & McBride, M.A. (2008). Analysis of the
Wait List and Deaths Among Candidates Waiting for a Kidney
Transplant. Transplantation, 86(12), 1678-1683. https://doi.org/10.1097/tp.0b013e31818fe694.
\204\ Shepherd, S., & Formica, R.N. (2021). Improving Transplant
Program Performance Monitoring. 8(4), 293-300. https://doi.org/10.1007/s40472-021-00344-z; Wey, A., Gustafson, S.K., Salkowski, N.,
Kasiske, B.L., Skeans, M., Schaffhausen, C.R., Israni, A.K., &
Snyder, J.J. (2019). Association of pretransplant and posttransplant
program ratings with candidate mortality after listing. 19(2), 399-
406. https://doi.org/10.1111/ajt.15032.
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We also considered several other metrics for assessing efficiency
domain performance related to time to transplant, such as--
Time from initial evaluation to transplant;
[[Page 43556]]
Time from initial referral to transplant;
Time from initial placement on a waitlist to transplant;
and
Time from when a patient was initially referred to time of
initial evaluation to time of initial placement on a waitlist to time
to transplant.
Before a patient can be considered for, and placed on, the waiting
list for a kidney transplant, they must first be referred by either a
nephrologist or dialysis facility, at which point they undergo a
comprehensive evaluation process by a transplant hospital.\205\ Studies
have shown long-standing barriers and disparities to access to
transplantation by patient demographics, such as racial/ethnic, sex,
socioeconomic, and insurance factors.\206\ Disparities are driven by
various factors, but we recognize that delays or lack of referrals for
evaluation, evaluation criteria that may unintentionally deem a patient
not eligible to be placed on a waitlist, and organ acceptance rate
variations across kidney transplant hospitals, may exacerbate
disparities. Thus, measuring time to transplant was considered an
appropriate potential performance metric that could incentivize IOTA
participants to improve. However, we chose not to propose this type of
measure due to concerns about how to properly measure start and end
points and unintended consequences that may harm patients, as it may
create opportunities for kidney transplant hospitals to manipulate
average times by only adding patients to the waitlist when they are
certain of imminent transplant, which could exacerbate waitlist
inequities.
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\205\ Paul, S., Plantinga, L.C., Pastan, S.O., Gander, J.C.,
Mohan, S., & Patzer, R.E. (2018). Standardized Transplantation
Referral Ratio to Assess Performance of Transplant Referral among
Dialysis Facilities. Clinical Journal of the American Society of
Nephrology, 13(2), 282-289. https://doi.org/10.2215/cjn.04690417;
Redeker, S., Massey, E.K., van Merweland, R.G., Weimar, W., Ismail,
S.Y., & Busschbach, J.J.V. (2022). Induced demand in kidney
replacement therapy. Health Policy, 126(10), 1062-1068. https://doi.org/10.1016/j.healthpol.2022.07.011; Knight, R.J., Teeter, L.D.,
Graviss, E.A., Patel, S.J., DeVos, J.M., Moore, L.W., & Gaber, A.O.
(2015). Barriers to Preemptive Renal Transplantation.
Transplantation, 99(3), 576-579. https://doi.org/10.1097/tp.0000000000000357; Schold, J.D., Patzer, R.E., Pruett, T.L., &
Mohan, S. (2019). Quality Metrics in Kidney Transplantation: Current
Landscape, Trials and Tribulations, Lessons Learned, and a Call for
Reform. American Journal of Kidney Diseases, 74(3), 382-389. https://doi.org/10.1053/j.ajkd.2019.02.020.
\206\ Shepherd, S., & Formica, R.N. (2021). Improving Transplant
Program Performance Monitoring. 8(4), 293-300. https://doi.org/10.1007/s40472-021-00344-z; Ernst, Z., Wilson, A., Pe[ntilde]a, A.,
Love, M., Moore, T., & Vassar, M. (2023). Factors associated with
health inequities in access to kidney transplantation in the USA: A
scoping review. Transplantation Reviews, 100751. https://doi.org/10.1016/j.trre.2023.100751.
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We also considered including a transplantation referral to
evaluation conversion rate measure. For patients with ESRD, access to
transplantation is influenced by both referral patterns of pre-
transplantation providers and transplant hospital processes of care and
evaluation criteria.\207\ Additionally, some studies found considerable
variation in referral rates to transplantation by dialysis facilities,
proposing significant regional and facility-level variation in
care.\208\ However, because dialysis facilities are often the primary
referrer and are not IOTA participants, we did not propose this
measure. We also have concerns about how this data would be collected.
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\207\ Schold, J.D., Patzer, R.E., Pruett, T.L., & Mohan, S.
(2019). Quality Metrics in Kidney Transplantation: Current
Landscape, Trials and Tribulations, Lessons Learned, and a Call for
Reform. American Journal of Kidney Diseases, 74(3), 382-389. https://doi.org/10.1053/j.ajkd.2019.02.020.
\208\ Ibid; Alexander, G. Caleb., & Sehgal, A.R. (2002).
Variation in access to kidney transplantation across dialysis
facilities: Using process of care measures for quality improvement.
American Journal of Kidney Diseases, 40(4), 824-831. https://doi.org/10.1053/ajkd.2002.35695; Patzer, R.E., Plantinga, L.C.,
Paul, S., Gander, J., Krisher, J., Sauls, L., Gibney, E.M., Mulloy,
L., & Pastan, S.O. (2015). Variation in Dialysis Facility Referral
for Kidney Transplantation Among Patients With End-Stage Renal
Disease in Georgia. JAMA, 314(6), 582. https://doi.org/10.1001/jama.2015.8897.
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Finally, we also considered a living donor rate as one of the
metrics used to assess performance in the efficiency domain to measure
percentage of potential living donors who are evaluated to donate a
kidney and that actually donated a kidney. This metric could help
assess success towards addressing living donor concerns and
improvements in education on the living donor process. However, we did
not propose this metric because we have concerns about our ability to
access data needed for measurement.
Ultimately, we chose not to propose to include waitlist management
metrics when assessing IOTA participant performance in the efficiency
domain because we believe that costs are already accounted for in the
Medicare cost report. Transplant waitlist measures also do not capture
living donation, which is an additional path to a successful kidney
transplant that CMS already incentivizes living donations in the ETC
Model. Moreover, studies have shown that organ acquisition costs have
been rising and were not solely attributable to the cost of
procurement, suggesting that an increased focus on the waiting list
could further increase Medicare expenditures.\209\ Also, for some of
the measures considered (that is, waitlist mortality, transplantation
referral to evaluation rate), nephrologists and dialysis facilities
play large roles in maintaining the patient's health, and we do not
believe it is appropriate to include a measure that would depend
largely upon the behavior and actions of physicians and facilities
other than the IOTA participant. We also believe this type of measure
could distract from increasing rates of transplant and provide false
expectations for time to transplant for kidney transplant waitlist
patients. We are also concerned that a waitlist measure could have
unintended consequences and potentially lead to those most in need of
transplant not being listed to receive a transplant.
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\209\ Cheng, X.S., Han, J., Braggs-Gresham, J.L., Held, P.J.,
Busque, S., Roberts, J.P., Tan, J.C., Scandling, J.D., Chertow,
G.M., & Dor, A. (2022). Trends in Cost Attributable to Kidney
Transplantation Evaluation and Waitlist Management in the United
States, 2012-2017. JAMA Network Open, 5(3), e221847. https://doi.org/10.1001/jamanetworkopen.2022.1847.
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We solicit comment on our proposed organ offer acceptance rate
ratio metric for purposes of assessing performance in the efficiency
domain, and the alternatives considered.
(a) Calculation of Metric
We propose calculating organ offer acceptance rates for an IOTA
participant using OPTN's offer acceptance rate ratio performance metric
(see Equation 1). Per OPTN's new offer acceptance rate ratio, a rate
ratio for a kidney transplant hospital that is greater than 1 indicates
that the kidney transplant hospital usually accepts more offers than
expected. A rate ratio that is less than 1 conveys a kidney transplant
hospital's tendency to accept fewer offers than expected compared to
national offer acceptance practices.\210\ The OPTN MPSC has reported
that this metric assesses kidney transplant hospitals' rate of observed
organ offer acceptances to expected acceptances and is intended to
answer the following question: Given the types of offers received to
the specific candidates, does this program accept offers at a rate
higher/lower than national experience for similar offers to similar
candidates.\211\
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\210\ OPTN. (2022). OPTN Enhanced Transplant Program Performance
Metrics. https://optn.transplant.hrsa.gov/media/r5lmmgcl/mpsc_performancemetrics_3242022b.pdf.
\211\ Mpsc-enhance-transplant-program-performance-monitoring-
system_srtr-metrics.pdf. (n.d.). Retrieved December 28, 2022, from
https://optn.transplant.hrsa.gov/media/qfuj3osi/mpsc-enhance-transplant-program-performance-monitoring-system_srtr-metrics.pdf.
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[[Page 43557]]
Expected acceptances are based solely on kidneys that are accepted
and transplanted by a kidney transplant hospital, so unsuitable kidneys
are excluded from this measure, and are calculated using logistic
regression models to determine the probability that a given organ offer
will be accepted. The measure, as specified by SRTR methodology, is
inherently risk adjusted as it only counts organs that are ultimately
accepted by a kidney transplant hospital.\212\ We propose to use SRTR
data to calculate the OPTN organ offer acceptance rate ratio, as
described in section III.C.5.d.(1).(b). of this proposed rule.
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\212\ Scientific Registry of Transplant Recipients. (n.d.). Risk
Adjustment Model: Offer Acceptance. Offer acceptance. https://www.srtr.org/tools/offer-acceptance/.
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Per the SRTR measure, we propose dividing the number of kidney
transplant organs accepted by each IOTA participant (numerator) by the
risk-adjusted number of expected organ offer acceptances
(denominator).\213\ This measure utilizes a logistic regression and
risk adjusts for the following: donor quality and recipient
characteristics; donor-candidate interactions, such as size and age
differences; number of previous offers; and, distance of potential
recipient from the donor.\214\ We propose to use SRTR's adult kidney
model strata risk adjustment methodology and most recently available
set of coefficients to calculate the number of expected organ offer
acceptances.
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\213\ Ibid.
\214\ SRTR. (2023). Srtr.org. https://tools.srtr.org/OAModelApp_2205/; Ibid.
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For example, suppose we have a model for predicting the probability
a kidney offer will be accepted, and this model adjusts for the number
of years the candidate has been on dialysis, whether the kidney was
biopsied, and the distance between the donor hospital and the
candidate's transplant center. Consider the offer of a biopsied kidney
150 nautical miles (NM) away to a candidate who has been on dialysis
for 2 years. To calculate the probability of acceptance, we would first
multiply these values by their respective model coefficients and then
sum up those products with the model's intercept, as illustrated in
Table 4.\215\
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\215\ CMS notes that some risk adjustment factors in the SRTR
models may only apply in certain ranges of a continuous variable.
For example, a term that applies if the patient's age at the time of
listing is >35 may be named
``can_age_at_listing_right_spline_knot_35''. In these cases, obtain
the product using this formula if the patient's age at listing was
>35: product = (Age-35)*(model coefficient). Others may apply if the
value is less than (<) a specified value. For example, for a term
like ``can_age_at_listing_left_spline_knot_18'', obtain the product
for a patient younger than 18 as: product = (18-Age)*(model
coefficient).
[GRAPHIC] [TIFF OMITTED] TP17MY24.003
We would then plug that total into the following equation (see
Equation 2) to get that the probability of acceptance is approximately
0.119 (that is, 11.9% chance of acceptance).
Equation 2: Probability of Organ Offer Acceptance
[GRAPHIC] [TIFF OMITTED] TP17MY24.004
To determine the number of offers a transplant program was expected
to accept, we would add up the probability of acceptance for every
offer that transplant program received The final organ offer acceptance
rate ratio (OAR) is then constructed from the observed (O) number of
acceptances and the expected (e) number of acceptances using equation 1
to paragraph (b)(1) of Sec. 512.426. In this example we showed a
simple logistic regression model that only included three risk-
adjusters. The actual models used by the SRTR adjust for many more
variables, but the process demonstrated here is the same.
A kidney may be transplanted into a candidate who did not appear on
the match run, usually to avoid discard if the intended recipient is
unable to undergo transplant. If the eventual recipient was not a
multi-organ transplant candidate and was blood type compatible per
kidney allocation policy, then these transplants would be included in
the organ offer acceptance rate. For purposes of the IOTA Model, we
propose to define ``match run'' as a computerized ranking of transplant
candidates based upon donor and candidate medical compatibility and
criteria defined in OPTN policies.
Per OPTN's new organ offer acceptance rate ratio, Table 5
summarizes the types of organ offers that we propose be included and
excluded in the calculation of this metric. For the purposes of organ
offers excluded from the organ offer acceptance rate ratio, we propose
to define ``missing responses'' as organ offers that the kidney
transplant hospital received from the OPO but did not submit a response
(accepting or rejecting) in the allotted time frame from the time the
offer was made per OPTN policy 5.6.B.\216\ For purposes of organ offers
excluded from the organ offer acceptance rate ratio measure, we
[[Page 43558]]
propose to define ``bypassed response'' as an organ offer not received
due to expedited placement \217\ or a decision by a kidney transplant
hospital to have all of its waitlisted candidates skipped during the
organ allocation process based on a set of pre-defined filters matching
the characteristics of the potential organ to be transplanted.\218\
---------------------------------------------------------------------------
\216\ OPTN. (2023). OPTN Policies. https://optn.transplant.hrsa.gov/media/eavh5bf3/optn_policies.pdf.
\217\ Expedited placement has the potential to minimize delays
in organ allocation by directing organs that may not be ideal to
transplant centers that have demonstrated a willingness to utilize
such organs. Currently, expedited placement, also known as
``accelerated placement'' or ``out-of-sequence'' allocation, permits
OPOs to deviate from the standard match run, which determines the
priority of patients on the waiting list for organ offers, under
exceptional circumstances. This discretionary tool of expedited
placement is employed by OPOs when there are suboptimal donor
characteristics associated with donor disease or recovery-related
issues, in order to prevent the organ from going unused. For
numerous years, expedited organ placement has played a crucial role
in organ allocation, enabling OPOs to promptly allocate organs that
they believe are at risk of not being utilized for transplantation.
\218\ King, K.L., S Ali Husain, Cohen, D.J., Schold, J.D., &
Mohan, S. (2022). The role of bypass filters in deceased donor
kidney allocation in the United States. American Journal of
Transplantation, 22(6), 1593-1602. https://doi.org/10.1111/ajt.16967; Transplant Quality Corner [verbar] The New MPSC Metric.
(n.d.). The Organ Donation and Transplantation Alliance. Retrieved
February 23, 2024, from https://www.organdonationalliance.org/insights/quality-corner/new-mpsc-metric/.
[GRAPHIC] [TIFF OMITTED] TP17MY24.005
We believe that IOTA participants could improve on the organ offer
acceptance rate ratio metric in at least two ways. First, IOTA
participants could increase the number of organ offers they accept,
which would also potentially lead to greater performance scores in the
achievement domain. Second, IOTA participants could also decrease the
number of expected acceptances by adding better filters so that they
are only receiving offers that they are likely to accept. Stricter
filters may help ensure that an IOTA participant is not delaying the
allocation of organs that they are uninterested in that could otherwise
be accepted by another kidney transplant hospital. Since there are
multiple ways to improve the offer acceptance ratio, the model is not
requiring increased utilization of higher KDPI kidneys that some
centers may not want to use due to their clinical protocols.
Additionally, the IOTA Model is not prescribing or requiring specific
care delivery transformation or improvement activities of IOTA
participants, so as to allow for flexibility and innovation.
---------------------------------------------------------------------------
\219\ OPTN. (2022). OPTN Enhanced Transplant Program Performance
Metrics. https://optn.transplant.hrsa.gov/media/r5lmmgcl/mpsc_performancemetrics_3242022b.pdf; For Transplant Center
Professionals. (n.d.). Www.srtr.org. Retrieved February 22, 2023,
from https://www.srtr.org/faqs/for-transplant-center-professionals/#oaconsideration.
---------------------------------------------------------------------------
We considered calculating the organ offer acceptance rate by
dividing the number of organs each IOTA participant accepts by the
number offered to that transplant hospital's patients that are
ultimately accepted elsewhere; however, the lack of risk adjustment in
this metric may be unfair to some IOTA participants.
We considered calculating the organ offer acceptance rate by
dividing the number of organs each IOTA participant accepts by the
number offered to that transplant hospital's patients that are
ultimately accepted elsewhere; however, the lack of risk adjustment in
this metric may be unfair to some IOTA participants.
We also considered updating the calculation for organ offer
acceptance rate ratio to account for the benefits of living donation by
increasing the number of organs in the system because the proposed
organ offer acceptance rate ratio only shows improvement in deceased
donor utilization. This modification would add a single 1 in the
numerator and a single 1 in the denominator for each living donation a
transplant hospital completes. However, we did not propose updating the
organ offer acceptance rate ratio because we decided to focus on
deceased donor acceptance to remain aligned with the SRTR calculation.
We also did not believe this was appropriate to propose because we
believe that IOTA participants with an established or high performing
living donation program would be able to gain points more easily in the
achievement domain, which has a larger percent of overall points, which
we believe may be unfair to IOTA participants that do not.
We seek comment on our proposal to use and calculate the OPTN organ
offer acceptance rate ratio in accordance with OPTN's measure
specifications and SRTR's methodology as the metrics that would
determine IOTA participants' performance on the efficiency domain. We
also seek comments on the alternatives we considered. Additionally, we
seek comment on our proposed definitions.
(b) Calculation of Points
As described in section III.C.5.b. of this proposed rule, we
propose that performance on the efficiency domain would be worth up to
20 points of 100 maximum points. As indicated in section III.C.5.c(2)
of this proposed rule, the efficiency domain is weighted lower than the
achievement domain but equal to the quality domain to ensure
performance measurement is primarily
[[Page 43559]]
focused on increasing number of kidney transplants, while still
incentivizing efficiency and quality. Within the efficiency domain, we
propose that the OPTN organ offer acceptance rate ratio would account
for the entirety of the 20 allocated points in that domain.
We propose applying a two-scoring system to award up to 20 points
to the IOTA participant based on its performance on the OPTN organ
offer acceptance rate ratio. Under this two-scoring system, we would
determine two separate scores for an IOTA participant: an ``achievement
score'' reflecting its current level of performance, and an
``improvement score'' reflecting changes in its performance over time.
We propose that the IOTA participant would be awarded points equal to
the higher of the two scores, up to a maximum of 20 points. We believe
that this approach would recognize both high achievement among high
performing IOTA participants as well as IOTA participants that make
marked improvement in their performance. We believe that average or
low-performing IOTA participants would likely require multiple years of
transformation to catch up with those who have a high organ offer
acceptance rate ratio.
For achievement scoring, we propose that points earned would be
based on the IOTA participants' performance on the organ offer
acceptance rate ratio ranked against a national target, inclusive of
all eligible kidney transplant hospitals, both those selected and not
selected as IOTA participants. Currently, there is a large disparity in
organ offer acceptance ratio performance. As previously noted, a 2020
national registry study found that the probability of receiving a
deceased donor kidney transplant within 3 years of waiting list
placement varied 16-fold between different kidney transplant hospitals
across the U.S.\220\ Large variations were still present between kidney
transplant hospitals that utilized the same OPO.\221\ The probability
of transplant was significantly associated with transplant hospitals'
offer acceptance rates.\222\
---------------------------------------------------------------------------
\220\ King, K.L., Husain, S.A., Schold, J.D., Patzer, R.E.,
Reese, P.P., Jin, Z., Ratner, L.E., Cohen, D.J., Pastan, S.O., &
Mohan, S. (2020). Major Variation across Local Transplant Centers in
Probability of Kidney Transplant for Wait-Listed Patients. Journal
of the American Society of Nephrology, 31(12), 2900-2911. https://doi.org/10.1681/ASN.2020030335.
\221\ King, K.L., Husain, S.A., Schold, J.D., Patzer, R.E.,
Reese, P.P., Jin, Z., Ratner, L.E., Cohen, D.J., Pastan, S.O., &
Mohan, S. (2020). Major Variation across Local Transplant Centers in
Probability of Kidney Transplant for Wait-Listed Patients. Journal
of the American Society of Nephrology, 31(12), 2900-2911. https://doi.org/10.1681/ASN.2020030335.
\222\ Ibid.
---------------------------------------------------------------------------
We propose that achievement scoring points be awarded based on the
national quintiles, as outlined in Table 6. Utilizing quintiles aligns
with the calculation of the upside and downside risk payments in
relation to the final performance score, as detailed in section
III.C.6.c.(2). of this proposed rule, where average performance yields
half the number of points. The scoring is normalized, meaning an
average performing IOTA participant earns 10 points out of 20, 50
percent of the total possible points. We recognize that there is an
upper limit to the benefits of efficiency, and quintiles combine the
highest 20 percent of performers in a point band. Due to the current
disparity among kidney transplant hospitals on this metric, we do not
expect every IOTA participant to reach top-level performance.
We propose the following Organ Offer Acceptance Rate Achievement
point allocation for IOTA participants, as illustrated in Table 6:
IOTA participants in the 80th percentile and above, 20
points.
IOTA participants in the 60th to below the 80th percentile
of performers, 15 points.
IOTA participants in the 40th to the 60th percentile of
performers, 10 points.
IOTA participants in the 20th to below the 40th percentile
of performers, 6 points.
IOTA participants who are below the 20th percentile of
performers, 0 points.
[GRAPHIC] [TIFF OMITTED] TP17MY24.006
We considered the approach used by the MPSC, that would yield
maximum points if transplant hospitals have at least a .35 organ offer
acceptance rate ratio. However, we do not believe that this approach
fits with the IOTA Model's goals. MPSC metrics are more focused on
highlighting and improving performance for the lowest performers,
whereas the model seeks to improve performance across the board, not
just avoid poor performance.
For improvement scoring, we propose that points earned would be
based on the IOTA participants' performance on organ offer acceptance
rate ratio during a PY relative to their performance during the third
baseline year for the PY that is being measured. We propose to use the
same baseline year definition used for participant eligibility, as
described in section III.C.3 of this proposed rule, including the
rationale for doing so. We separately propose to calculate an
``improvement benchmark rate,'' defined as 120 percent of the IOTA
participants' performance on the organ offer acceptance rate ratio
during the third baseline year for each PY. We would award points by
comparing the IOTA participant's organ offer acceptance rate ratio
during the PY to the IOTA participant's improvement benchmark rate to
determine the improvement scoring points earned. Specifically:
IOTA participants whose organ offer acceptance rate ratio
during a PY
[[Page 43560]]
is at or above the improvement benchmark rate would receive 12 points.
IOTA participants whose organ offer acceptance rate ratio
during a PY is at or below the organ offer acceptance rate ratio during
the third baseline year for that respective PY would receive no points.
IOTA participants whose organ offer acceptance rate ratio
during a PY is greater than the organ offer acceptance rate ratio
during the third baseline year for that respective PY, but less than
the improvement benchmark rate, would earn a maximum of 12 points in
accordance with equation 1 to paragraph (c)(1)(ii)(B)(1) of Sec.
512.426.
We propose using equation 1 to paragraph (c)(1)(ii)(B)(1) of Sec.
512.426 to mirror the methodology used in the Hospital Value Based
Purchasing (VBP) Program, with the only modification being the number
of points available for this metric. Equation 3 would also allow for a
maximum of 12 points to be earned by IOTA participants whose organ
offer acceptance rate ratio during the PY is greater than the baseline
year organ offer acceptance rate ratio but less than the improvement
benchmark rate. We did not want the improvement score to be worth more
than, or equal to, the achievement score, as proposed for the organ
offer acceptance rate ratio performance scoring, so as to reserve the
highest number of points (15 points) for top performers in the metric.
Once both the achievement score and the improvement score are
calculated, we propose comparing the two scores and applying the higher
of the two values as the performance score or points earned (of 20
possible points) for the organ offer acceptance rate ratio metric
within the efficiency domain.
We considered setting the improvement benchmark rate to be 200
percent of the IOTA participant's third baseline year for a given PY to
measure performance on the organ offer acceptance rate ratio. The
scoring structure would be the same, with 12 or 0 points to be awarded
depending on whether the benchmark is met. However, we believed this
would be too strict and risk penalizing already high-achieving IOTA
participants.
We considered simplifying the performance scoring for the organ
offer acceptance rate ratio metric within the efficiency domain by only
awarding performance points based on the proposed achievement scoring
methodology, rather than also calculating an improvement score for the
IOTA participant and comparing the scores. However, given the variation
that is present amongst kidney transplant hospitals, we believed it
might be difficult for some IOTA participants to achieve top tier
points for the first two model PYs. Thus, incorporating an improvement
scoring method would ensure that IOTA participants are still rewarded
for improvements made towards the efficiency domain goal.
We considered using the scoring method proposed for the post-
transplant outcomes metric within the quality domain, as described in
section III.C.5.e.(1).(b). of this proposed rule, as it would award
full points if the hazard ratio or confidence interval of the metric
includes the number one or higher. We believe this scoring method would
honor the intent of the organ offer acceptance rate ratio metric, which
is to determine if an IOTA participant is accepting more organs than
expected. However, given the variation in performance on this metric
across all kidney transplant hospitals, we believe improvement
opportunities exist in this metric. We also believe that our proposed
approach rewards both achievement and improvements and is a more
rigorous scoring methodology.
We considered a continuous scoring range from zero to 15, where
IOTA participants may earn a score of any point value instead of bands.
We believe a continuous scoring range could provide more flexibility
for IOTA participants and greater variety of scores. However, we
believe grading using bands provides a more favorable scoring system
for IOTA participants by grouping performance. We also recognize there
is diminishing marginal efficiency for higher and higher organ offer
acceptance rate ratios.
We considered using the lower and upper bounds of the offer
acceptance odds ratio within a confidence interval, like we are
proposing in the quality domain for post-transplant outcomes, as
described in section III.C.5.e.(1).(b). of this proposed rule. However,
the organ offer acceptance rate ratio metric, unlike post-transplant
outcomes, has wider disparity in performance than in post-transplant
outcomes. We believe that there is a clear benefit to patients and the
transplantation ecosystem overall by continuing to increase performance
on this metric and promoting better performance than the national
average. Under this alternative, IOTA participants would be evaluated
based on whether the lower bound, acceptance ratio, and upper bound all
crossed 1. Doing so would indicate the IOTA participant's true offer
acceptance ratio with 95 percent probability. We are not proposing this
approach, however, as our analyses using SRTR data indicate that the
majority of kidney transplant hospitals had either all three bounds
cross 1 or all three never cross 1. Thus, scoring would largely not
have differed from utilizing the offer acceptance ratio alone.
Finally, we also considered stratifying offer acceptance by KDRI
status, with different score targets based on KDRI status ranges, such
as KDRI of less than 1.05, between 1.05 and 1.75, and more than 1.75.
We believe this scoring method may potentially prevent IOTA
participants from narrowing their criteria to only receive selected
offers. However, we believe that it is already risk adjusted for organ
status inherently in the measure because only organs that are
ultimately transplanted are counted in the denominator.
We seek comment on our proposed organ offer acceptance rate ratio
performance scoring methodology for purposes of assessing efficiency
domain performance for each IOTA participant, including on the
achievement and improvement score calculation and point allocation
method. We also seek comments on alternatives considered.
e. Quality Domain
We propose to define ``quality domain'' as the performance
assessment category in which CMS assesses the IOTA participant's
performance using a performance measure and quality measure set focused
on improving the quality of transplant care, as described in section
III.C.5.e. of this proposed rule. We propose that performance on the
quality domain would be worth up to 20 points out of the proposed 100
points. The quality domain is focused on monitoring post-transplant
care and quality of life for IOTA transplant patients.
Our goal for the quality domain within the IOTA Model is to achieve
acceptable post-transplant outcomes while incentivizing increased
kidney transplant volume. We believe that transplant hospital
accountability for patient-centricity and clinical outcomes continues
post-transplantation. While transplant outcomes have historically
received the most attention, often at the exclusion of other factors,
we seek to encourage a better balance in the system to offer the
benefits of transplant to more patients. Therefore, we are proposing to
include one post-transplant outcome measure, as described in section
III.C.5.e.(1). of this proposed rule, and a quality measure set that
includes two patient-reported outcome-based performance measures (PRO-
PM) and one process measure, as described in section III.C.5.e.(2). of
this proposed rule.
[[Page 43561]]
(1) Post-Transplant Outcomes
We propose using an unadjusted rolling ``composite graft survival
rate,'' defined as the total number of functioning grafts relative to
the total number of adult kidney transplants performed, as described in
section III.C.5.e.(1).(a). of this proposed rule, to assess IOTA
participant performance on post-transplant outcomes. In this measure,
the numerator (observed functioning grafts) and denominator (number of
kidney transplants completed) would increase each PY of the IOTA Model
to include a cumulative total.
Over the past few decades, advances in immunosuppressive therapies,
surgical techniques, and organ preservation methods have resulted in
significant improvements in kidney transplantation outcomes.\223\
According to the OPTN, the overall 1-year survival rate for kidney
transplantation recipients in the United States is over 90 percent, and
the 5-year survival rate is around 75 percent. However, even with the
advances that have been made to improve kidney outcomes, the success of
kidney transplantation is still dependent upon factors such as the age
and health of the donor and recipient, the presence of comorbidities
(for example, diabetes), and the effectiveness of the immunosuppressive
regimen. Kidney transplant outcomes can also be affected by possible
post-transplant complications, including infection, cardiovascular
disease, and kidney failure.\224\
---------------------------------------------------------------------------
\223\ Stewart, D.E., Garcia, V.C., Rosendale, J.D., Klassen,
D.K., & Carrico, B.J. (2017). Diagnosing the Decades-Long Rise in
the Deceased Donor Kidney Discard Rate in the United States.
Transplantation, 101(3), 575-587. https://doi.org/10.1097/tp0000000000001539;. Vinson, A., Kiberd, B.A., & Karthik Tennankore.
(2021). In Search of a Better Outcome: Opting Into the Live Donor
Paired Kidney Exchange Program. 8, 205435812110174-205435812110174.
https://doi.org/10.1177/20543581211017412; Shepherd, S., & Formica,
R. N. (2021). Improving Transplant Program Performance Monitoring.
8(4), 293-300. https://doi.org/10.1007/s40472-021-00344-z.
\224\ Gioco, R., Sanfilippo, C., Veroux, P., Corona, D.,
Privitera, F., Brolese, A., Ciarleglio, F., Volpicelli, A., &
Veroux, M. (2021). Abdominal wall complications after kidney
transplantation: A clinical review. Clinical Transplantation,
35(12), e14506. https://doi.org/10.1111/ctr.14506; Wei, H., Guan,
Z., Zhao, J., Zhang, W., Shi, H., Wang, W., Wang, J., Xiao, X., Niu,
Y., & Shi, B. (2016). Physical Symptoms and Associated Factors in
Chinese Renal Transplant Recipients. Transplantation Proceedings,
48(8), 2644-2649. https://doi.org/10.1016/j.transproceed.2016.06.052; Mehrabi, A., Fonouni, H., Wente, M.,
Sadeghi, M., Eisenbach, C., Encke, J., Schmied, B.M., Libicher, M.,
Zeier, M., Weitz, J., Buchler, M.W., & Schmidt, J. (2006). Wound
complications following kidney and liver transplantation. Clinical
Transplantation, 20(s17), 97-110. https://doi.org/10.1111/j.1399-0012.2006.00608.x.
---------------------------------------------------------------------------
More recently, CMS received feedback from transplant hospitals,
patient advocacy groups, and transplant societies, including on the
recent rule making (``Medicare and Medicaid Programs; Regulatory
Provisions To Promote Program Efficiency, Transparency, and Burden
Reduction,'' 83 FR 47686), that the 1-year measure was causing
transplant centers to be risk averse about the patients and organs they
would transplant while being simultaneously topped out (83 FR
47706).\225\ Notably, even the lowest ranked programs, as measured by
the SRTR, achieved a result of 90 percent of transplanted patients have
a functioning graft at one year.\226\
---------------------------------------------------------------------------
\225\ Medicare and Medicaid Programs; Regulatory Provisions To
Promote Program Efficiency, Transparency, and Burden Reduction
(September, 20, 2018) https://www.federalregister.gov/documents/2018/09/20/2018-19599/medicare-and-medicaid-programs-regulatory-provisions-to-promote-program-efficiency-transparency-and.
\226\ Scientific Registry of Transplant Recipients. Request for
Information. Requested on 05/02/2023. https://www.srtr.org./.
---------------------------------------------------------------------------
To safeguard patient outcomes under the IOTA Model, we are
proposing to include this measure as a checkpoint. Because there is
significant variation in post-transplant outcomes across kidney
transplant hospitals, we believe the IOTA Model should promote
improvement in outcomes for the benefit of attributed patients. We also
believe that this measure would build upon, and complement, existing
OPTN and SRTR measures to the maximum extent possible. Additionally, we
believe that this approach could be applied with minimal adaptation to
other organs were they to be added to the model through future
rulemaking. Furthermore, we believe that this measure would enhance
patient understanding of clinically important post-transplant outcomes
beyond existing 90-day, 1-year and 3-year post transplant outcomes.
We considered measuring post-transplant outcomes using SRTR's
methodology at 90 days,\227\ and constructing 5-year and 10-year post-
transplant measures. However, we did not select these measures because
post-transplant outcomes are already measured at 90-days by SRTR.
Additionally, because the IOTA Model as proposed spans only 6 years, we
did not believe we could appropriately measure post-transplant outcomes
at 5 or 10 years.
---------------------------------------------------------------------------
\227\Mpsc-enhance-transplant-program-performance-monitoring-
system_srtr-metrics.pdf (n.d.). Retrieved December 28, 2022, from
https://optn.transplant.hrsa.gov/media/qfuj3osi/mpsc-enhance-transplant-program-performance-monitoring-system_srtr-metrics.pdf.
---------------------------------------------------------------------------
We considered constructing an ongoing post-transplant outcome
measure that would continuously evaluate post-transplant outcomes at 1-
year throughout the model performance period of the IOTA Model. In this
measure the numerator (observed graft failures) and denominator (number
of transplants completed) would increase each PY of the model to a
cumulative total. For example, in PY 1 of the model an IOTA participant
could have five 1-year observed graft failures and complete 20
transplants, resulting in a graft failure rate of 0.25. In PY 2 of the
model, the same IOTA participant could have eight 1-year observed graft
failures and complete 30 transplants. To calculate the IOTA
participant's graft failure rate for PY 2 of the model, we would divide
the cumulative total of 13 1-year observed graft failures by the
cumulative total of 50 completed transplants. However, we believed it
was important to measure post-transplant outcomes in terms of graft
survival rather than in terms of graft failure. We acknowledge that for
the purposes of measuring graft survival using OPTN data, use of either
concept would generate the same outcome measurement because OPTN data
identify graft status as either functioning or failed. However, we aim
to convey the importance of ongoing management to preserve the health
of the transplanted graft and the health and quality of life of the
attributed patients.
We considered constructing a continuous patient survival measure
that would evaluate patient survival throughout the entirety of the
IOTA Model. Similar to the considered measure mentioned in the previous
paragraph, the numerator (number of patients alive) and denominator
(number of received kidney organ offers) would increase each PY of the
model to a cumulative total. For the denominator, we considered only
including organ offers where the sequence number was less than 100 or
less than 50. In other words, under that rationale we would only
include offers that came within a certain point of time that could have
potentially benefited the patient or should not have been turned down.
We believe that this type of measure would not disincentivize
waitlisting and could potentially increase equity within this
population. Additionally, we believe that this type of measure would
indirectly encourage living donor transplants because those would only
hit the numerator (number of people alive) but not the denominator
(number of kidney organ offers received). However, we believe this
measure
[[Page 43562]]
would be somewhat duplicative of other parts of the model where we are
already evaluating organ offer acceptance. We also chose not to propose
this measure due to logistical concerns, and believed it could be
difficult to determine how many people were offered a specific organ
and determining what an appropriate sequence number cutoff should be.
We considered measuring estimated glomerular filtration rate (eGFR)
at the 1-year anniversary of the date of transplant. Glomerular
filtration rate (GFR) is a way to assess renal function, and eGFR is
the test used to assess renal function in primary clinical care.\228\
Despite the fact that studies indicate eGFR's potential as a reliable
predictor of long-term post-transplant prognosis, our goal is to adopt
a measure that resonates more with the transplant community's
evaluation of post-transplant outcomes.\229\ We recognize that the
equation for calculating eGFR was revised in 2021 to not include race,
but we still have some concerns over the potential for bias and
inaccurate results and the limitations that still exist with the
updated equation and did not feel it was appropriate to propose.\230\
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\228\ Mayne, T.J., Nordyke, R.J., Schold, J.D., Weir, M.R., &
Mohan, S. (2021). Defining a minimal clinically meaningful
difference in 12-month estimated glomerular filtration rate for
clinical trials in deceased donor kidney transplantation. Clinical
Transplantation, 35(7), e14326. https://doi.org/10.1111/ctr.14326.
\229\ Ibid; Wu, J., Li, H., Huang, H., Wang, R., Wang, Y., He,
Q., & Chen, J. (2010). Slope of changes in renal function in the
first year post-transplantation and one-yr estimated glomerular
filtration rate together predict long-term renal allograft survival.
Clinical Transplantation, 24(6), 862-868. https://doi.org/10.1111/j.1399-0012.2009.01186.x; Schold, J.D., Nordyke, R.J., Wu, Z.,
Corvino, F., Wang, W., & Mohan, S. (2022). Clinical events and renal
function in the first year predict long-term kidney transplant
survival. Kidney360, 10.34067/KID.0007342021. https://doi.org/10.34067/kid.0007342021; Hariharan, S., Mcbride, M.A., Cherikh,
W.S., Tolleris, C.B., Bresnahan, B.A., & Johnson, C.P. (2002). Post-
transplant renal function in the first year predicts long-term
kidney transplant survival. Kidney International, 62(1), 311-318.
https://doi.org/10.1046/j.1523-1755.2002.00424.x.
\230\ Majerol, M., & Hughes, D.L. (2022, July 5). CMS Innovation
Center Tackles Implicit Bias. Health Affairs. Retrieved January 16,
2024, from https://www.healthaffairs.org/content/forefront/cms-innovation-center-tackles-implicit-bias.
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We considered constructing several hospital-based post-transplant
outcome measures such as those that measure: the number of days spent
out of the hospital post-transplant, how many days spent at home post-
transplant before returning to work, and number of hospital
readmissions post-transplant. However, we do not want to penalize the
use of moderate-to-high KDPI kidneys, as we recognize that utilizing
these organs carries an increased risk of transplant recipient
hospitalizations. Additionally, we had concerns over how we would
assess and measure this type of metric.
We considered proposing a phased-in approach to measuring post-
transplant outcomes, in which no post-transplant outcome metrics would
be included until PY 3 of the model. In this alternative methodology,
the quality domain for the first two PYs would only include our
proposed quality measure set, as described in section III.C.5.e.(2). of
this proposed rule. Starting PY 3 of the model, IOTA participants would
be evaluated on two post-transplant outcome measures (SRTR's 1-year
post-transplant outcome conditional on 90-day survival measure and 3-
year post-transplant outcome measure) in addition to our proposed
quality measure set. This approach incorporates a time delay, allowing
us to assess the post-transplant outcomes of IOTA participants using
SRTR's measures. Because we believed it was critical to include a post-
transplant measure from the onset of the model to check for unintended
consequences throughout the entirety of the model performance period,
we did not believe this alternative was appropriate to propose.
We also considered using SRTR's new ``1-year post-transplant
outcome conditional on 90-day graft survival'' measure and including a
3-year post-transplant outcome measure, such as the one currently used
by SRTR. We also considered constructing our own 3-year post-transplant
outcome measure conditional on 1-year survival. However we chose not to
propose SRTR's conditional 1-year or 3-year post-transplant outcome
measures or our own measure for the following reasons: (1) because
SRTR's conditional 1-year metric has a 2.5 year lookback period, it
would require us to evaluate IOTA participants on post-transplant
outcomes prior to starting the model for at least the first two PYs;
(2) because SRTR does not currently have a 3-year conditional post-
transplant outcome measure, we would not be in alignment with SRTR if
we constructed our own; (3) including SRTR's 3-year post-transplant
outcome measure would include time outside of the model for at least
the first three PYs and we want to evaluate IOTA participants based on
their performance within the model; and (4) we recognize there may be
some logistical issues and difficulty in measuring performance in that
time. We may consider incorporating a 3-year post-transplant outcome
measure into the model in the future, through rulemaking.
We seek public comment on our proposal to evaluate IOTA
participants on post-transplant outcomes using our new composite graft
survival rate metric, as well as on the alternatives we considered. We
are also interested in public comment on how we may be able to use OPTN
data to characterize different clinical manifestations of graft
survival, as we understand that not all surviving grafts are clinically
equivalent or have the same impact on the patient and graft health. We
would further be interested to hear from the public on which factors
involved in graft survival are modifiable by the care team.
(a) Calculation of Metric
We propose that for each model PY, CMS would calculate a composite
graft survival rate for each IOTA participant, as defined in section
III.C.5.e.(1). of this proposed rule, to measure performance in the
quality domain as described in section III.C.5.e. of this proposed
rule.
We propose to use our own unadjusted composite graft survival rate
equation to evaluate post-transplant outcomes. We propose to calculate
the composite graft survival rate by taking the total number of
functioning grafts an IOTA participant has and dividing that by the
total number of kidney transplants furnished to patients 18 years of
age or older at the time of the transplant in PY 1 and all subsequent
PYs as specified in Equation 1 to paragraph (b)(1) of Sec. 512.428 to
evaluate post-transplant outcomes during the IOTA Model performance
period.
For example, if in PY 1 of the model, an IOTA participant had 20
observed functioning grafts and furnished 25 kidney transplants to
patients 18 years of age or older at the time of transplant, the
composite graft survival rate for that IOTA participant would be 0.8
(20 from PY 1 divided by 25 from PY 1). Continuing this example, for
PY2 of the model if the same IOTA participant had 30 observed
functioning grafts and furnished 35 kidney transplants to patients 18
years of age or older at the time of transplant, and two functioning
kidney grafts failed from PY 1, CMS would calculate its composite graft
survival rate for PY 2 as follows. CMS would divide the cumulative
total of 48 observed functioning grafts (30 from PY 2 + 20 from PY 1-2
from PY 1) by the cumulative total of 60 completed kidney transplants
(35 from PY 2 + 25 from PY 1), resulting in a composite graft survival
rate of 0.8 (48 divided by 60).
In the proposed equation, the numerator (number of functioning
grafts) is defined as the total number of living adult kidney
transplant patients with a functioning graft. The numerator,
[[Page 43563]]
functioning grafts, would exclude grafts that have failed, as defined
by SRTR. SRTR counts a graft as failed when follow-up information
indicates that one of the following occurred before the reporting time
point: (1) graft failure (except for heart and liver, when re-
transplant dates are used instead); (2) re-transplant (for all
transplants except heart-lung and lung); or 3) death.\231\ OPTN follow-
up forms are used to identify graft failure and re-transplant
dates.\232\ We also propose to use OPTN adult kidney transplant
recipient follow-up forms \233\ to identify graft failure and re-
transplant dates for all transplant furnished to kidney transplant
patients 18 years of age or older at the time of the transplant. In the
proposed equation, we note that the numerator and denominator would not
be limited to the attributed IOTA transplant patients. By this, we mean
that it could include IOTA transplant patients who have been de-
attributed from an IOTA participant due to transplant failure. We
believe that IOTA participants could improve on this metric by working
with IOTA collaborators to coordinate post-transplant care.
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\231\ Technical Methods for the Program-Specific Reports.
(n.d.). Www.srtr.org. Retrieved December 3, 2022, from https://www.srtr.org/about-the-data/technical-methods-for-the-program-specific-reports/; OPTN. (2022). OPTN Enhanced Transplant Program
Performance Metrics. https://optn.transplant.hrsa.gov/media/r5lmmgcl/mpsc_performancemetrics_3242022b.pdf.
\232\ Technical Methods for the Program-Specific Reports.
(n.d.). Www.srtr.org. Retrieved December 3, 2022, from https://www.srtr.org/about-the-data/technical-methods-for-the-program-specific-reports/ reports/.
\233\ https://unos.org/wp-content/uploads/Adult-TRF-Kidney.pdf.
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We considered incorporating a risk adjustment methodology to our
proposed composite graft survival equation, such as the one used by
SRTR for 1-year post-transplant outcomes conditional on 90-day survival
or constructing our own. While we recognize that risk adjustment
methodologies may help account for patient and donor traits, we could
not find a risk adjustment approach that has consensus agreement within
the kidney transplant community. We also believe that our proposed
measure is inherently risk adjusted as it only counts organs that are
ultimately transplanted to patients 18 years of age or older by a
kidney transplant hospital.
We invite public comment on our proposed methodology to calculate
post-transplant outcomes in the IOTA Model, and on alternatives
considered. Although we are proposing an unadjusted composite graft
survival rate to measure post-transplant outcomes, we are interested in
comments on whether risk risk-adjustments are necessary, and which
ones, such as donor demographic characteristics (race, gender, age,
disease condition, geographic location), would be significant and
clinically appropriate in the context of our proposed approach.
(b) Calculation of Points
As described in section III.C.5.e. of this proposed rule,
performance on the quality domain would be worth up to 20 points.
Within the quality domain, we propose that the composite graft survival
rate would account for 10 of the 20 allocated points. We propose that
the points earned would be based on the IOTA participants' performance
on the composite graft survival rate metric ranked against a national
target, inclusive of all eligible kidney transplant hospitals, both
those selected and not selected as IOTA participants. We believe that
using percentiles would create even buckets of scores among the
continuum of IOTA participants.
We propose that points would be awarded based on the national
quintiles, as outlined in Table 7, such that IOTA participants that
perform--
At or above the 80th percentile would earn 10 points;
In the 60th percentile to below the 80th percentile would
earn 8 points;
In the 40th to below the 60th percentile would earn 5
points;
In the 20th percentile to below the 40th percentile would
earn 3 points; and
Below the 20th percentile would receive no points for the
composite graft survival rate.
[GRAPHIC] [TIFF OMITTED] TP17MY24.007
Utilizing quintiles aligns with the calculation of the upside and
downside risk payments in relation to the final performance score as
detailed in section III.C.6.c.(2). of this proposed rule, where average
performance yields half the number of points. The scoring is
normalized, meaning an average performing IOTA participant earns 5
points out of 10, or about 50 percent of possible points. We recognize
that there is an upper limit to the benefits of efficiency, and
quintiles combine the highest 20 percent of performers in a point band.
Due to the current disparity among kidney transplant hospitals, we do
not expect every IOTA participant to reach top-level performance on
this metric.
We considered a strategy similar to the proposed organ offer
acceptance methodology which would apply a two-scoring system in which
we would determine an achievement score and improvement score and award
the point equivalent to the higher value between the two scores. We
also considered proposing just an improvement score, in which we would
evaluate IOTA participants' performance on composite graft survival
during a PY relative to their performance the previous CY. We
considered both approaches because we recognize that if an IOTA
participant does not do well one year in our proposed methodology, that
it may be difficult for it to improve during the model performance
period. However, we chose not to propose either of these other
methodologies (achievement and improvement or just improvement scoring)
because we had concerns over
[[Page 43564]]
our ability to measure improvement year over year due to potentially
small numbers.
We seek public comment on the proposed point allocation and
calculation methodology for post-transplant outcomes within the quality
domain for the IOTA Model and alternatives considered.
(2) Quality Measure Set
We propose to select and use quality measures to assess IOTA
participant performance in the quality domain. Performance on the
proposed IOTA Model quality measure set would be used to assess the
performance of an IOTA participant on aspects of care that we believe
contribute to a holistic and patient-centered journey to receiving a
kidney transplant.
We propose the following three measures for inclusion in the IOTA
Model quality measure set: (1) CollaboRATE Shared Decision-Making Score
(CBE ID: 3327), (2) Colorectal Cancer Screening (COL) (CBE ID: 0034),
and (3) the 3-Item Care Transition Measure (CTM-3) (CBE ID:
0228).234 235 236 The quality measures that we are proposing
share common features. We are proposing measures that have been or are
currently endorsed by the CMS Consensus-Entity (CBE) through the CMS
Consensus-Based Process. This ensures that the measures proposed have
been assessed against established evaluation criteria of importance,
acceptability of measure properties, feasibility, usability, and
competing measures.\237\ Our proposed measure set is patient-centered,
reflecting areas that we have heard from patients are important and for
which there is significant variation in performance among transplant
hospitals. We are proposing measures that would incentivize
improvements in care that we would otherwise not expect to improve
based on the financial incentives in the model alone. We are also
proposing a measure set that would allow us to make a comprehensive
assessment of post-transplant outcomes. The composite graft survival
rate that we are proposing in section III.C.5.e.(1). of this proposed
rule would provide an essential, albeit limited, assessment of the
success of a kidney transplant. Finally, we are proposing measures that
we believe would incentivize improvement in aspects of post-transplant
care that are important to patients and modifiable by IOTA
participants.
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\234\ collaboRATE. (2019). Glyn Elwyn. http://www.glynelwyn.com/collaborate.html.
\235\ Colorectal Cancer Screening--NCQA. (2018). NCQA. https://www.ncqa.org/hedis/measures/colorectal-cancer-screening/ https://www.ncqa.org/hedis/measures/colorectal-cancer-screening/.
\236\ THE NATIONAL QUALITY FORUM Specifications for the Three-
Item Care Transition Measure-CTM-3. (n.d.). Retrieved May 28, 2023,
from https://mhdo.maine.gov/_pdf/NQF_CTM_3_%20Specs_FINAL.pdf.
\237\ Supplemental Material to the CMS Measures Management
System (MMS) Hub CMS Consensus-Based Entity (CBE) Endorsement and
Maintenance. (2022). https://www.cms.gov/files/document/blueprint-nqf-endorsement-maintenance.pdf.
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On March 2, 2023, Jacobs et al. published Aligning Quality Measures
across CMS--The Universal Foundation, which describes CMS leadership's
vision for a set of foundational quality measures known as the
Universal Foundation. This measure set would be used by as many CMS
value-based and quality programs as possible, with other measures added
based on the population or healthcare setting.\238\ CMS selected
measures for the Universal Foundation that are meaningful to a broad
population, reduce burden by aligning measures, advance equity, support
automatic and digital reporting, and have minimal unintended
consequences.\239\
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\238\ Jacobs, D. B., Schreiber, M., Seshamani, M., Tsai, D.,
Fowler, E., & Fleisher, L.A. (2023). Aligning quality measures
across CMS--the Universal Foundation. New England Journal of
Medicine, 388(9), 776-779. https://doi.org/10.1056/nejmp2215539.
\239\ Jacobs, D.B., Schreiber, M., Seshamani, M., Tsai, D.,
Fowler, E., & Fleisher, L.A. (2023). Aligning quality measures
across CMS--the Universal Foundation. New England Journal of
Medicine, 388(9), 776-779. https://doi.org/10.1056/nejmp2215539.
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We considered only including two measures in the initial quality
measure set and pre-measure development because we were concerned about
the potential added reporting burden placed on IOTA participants.
However, we chose to propose three measures and pre-measure development
because we want to use them to incentivize and improve patient care. We
seek additional feedback on which of the proposed measures have the
highest potential to impact changes in behavior, while minimizing
provider burden.
We also considered only including COL in the quality measure set
and allotting this measure 4 points, with the remaining 16 points
allotted to the composite graft survival rate. It is worth noting that
if we choose fewer measures, then we propose allocating the points
accordingly within the remaining measures.
We considered several alternative measures for the quality domain
performance assessment. We considered the Hospital Consumer Assessment
of Healthcare Providers and Systems (HCAHPS) survey because hospitals
are already required to report that survey in the Hospital VBP Program,
thereby reducing or limiting burden to IOTA participants burden since
it is already in use. We are not proposing the HCAHPS measure for the
IOTA Model because HCAHPS data is based on survey results from a random
sample of adult patients across medical conditions. We believe that the
HCAHPS would present sample size issues for purposes of calculation.
We considered the Gains in Patient Activation Measure (PAM[supreg])
(CBE ID: 2483). The PAM[supreg] measure is being used in the voluntary
KCC Model and was included on the 2022 Measures Under Consideration
(MUC) List for the ESRD Quality Incentive Program (QIP) and MIPS.\240\
We considered whether the PAM[supreg] Measure could encourage IOTA
participants and IOTA Collaborators, as defined in section III.C.11.d.
of this proposed rule, to activate IOTA waitlist patients to work in
collaboration with IOTA participants to complete requirements to
maintain active waitlist status; however, we were unable to locate any
peer-reviewed literature to support this hypothesis.
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\240\ Pre-Rulemaking [verbar] The Measures Management System.
(n.d.). Mmshub.cms.gov. Retrieved May 12, 2023, from https://mmshub.cms.gov/measure-lifecycle/measure-implementation/pre-rulemaking/overview.
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We also considered the Depression Remission at 12 Months measure
(CBE ID: 0710e). Studies have shown that depression and anxiety are
common amongst people on dialysis and suggested that incorporating
patient reported outcome measures (PROs) that focus on depression can
improve health-related quality of life in patients with ESRD.\241\ One
study found that, at the time of kidney evaluation, over 85 percent of
patients exhibited at least minimal depressive symptoms and that
patients with depressive symptoms were less likely to gain access to
the waitlist.\242\ Although the waitlist offers
[[Page 43565]]
some hope to patients, being waitlisted for a kidney transplant is also
psychologically distressing, with patients reporting disillusionment,
moral distress, unmet expectations, increasing vulnerability, and
deprivation.\243\ These factors are likely contributors to high rates
of stress and anxiety observed among waitlisted patients.\244\ The
conditions of participation (CoPs) for transplant hospitals require
that prospective transplant candidates receive a psychosocial
evaluation prior to placement on a waitlist (42 CFR part 482.90(a)(1)),
if possible, and OPTN bylaws specify that transplant hospitals must
include team members to coordinate a transplant candidate's
psychosocial needs; however, neither the CoP nor the OPTN bylaws
require specific assessment of, or intervention into, patients'
behavioral health. The ESRD QIP measure set includes the Clinical
Depression Screening and Follow-Up measure; however, performance on the
measure requires only documentation that an attempt at screening and
follow up was made.\245\ Additionally, this measure is already being
used in the KCC Model.
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\241\ Feroze, U., Martin, D., Kalantar-Zadeh, K., Kim, J.C.,
Reina-Patton, A., & Kopple, J.D. (2012). Anxiety and depression in
maintenance dialysis patients: Preliminary data of a cross-sectional
study and brief literature review. Journal of Renal Nutrition,
22(1), 207-210. https://doi.org/10.1053/j.jrn.2011.10.009; Mclaren,
S., Jhamb, M., & Unruh, M. (2021). Using Patient-Reported Measures
to Improve Outcomes in Kidney Disease. Blood Purification, 1-6.
https://doi.org/10.1159/000515640; Cukor, D., Donahue, S.,
Tummalapalli, S.L., Bohmart, A., & Silberzweig, J. (2022). Anxiety,
comorbid depression, and dialysis symptom burden. Clinical Journal
of the American Society of Nephrology, 17(8), 1216-1217. https://doi.org/10.2215/cjn.01210122.
\242\ Chen, X., Chu, N.M., Basyal, P.S., Vihokrut, W., Crews,
D., Brennan, D.C., Andrews, S.R., Vannorsdall, T.D., Segev, D.L., &
McAdams-DeMarco, M. A. (2022). Depressive symptoms at kidney
transplant evaluation and access to the kidney transplant waitlist.
Kidney International Reports, 7(6), 1306-1317. https://doi.org/10.1016/j.ekir.2022.03.008.
\243\ Tong, A., Hanson, C.S., Chapman, J.R., Halleck, F., Budde,
K., Josephson, M.A., & Craig, J.C. (2015). `suspended in a paradox'-
patient attitudes to wait-listing for Kidney Transplantation:
Systematic review and thematic synthesis of qualitative studies.
Transplant International, 28(7), 771-787. https://doi.org/10.1111/tri.12575.
\244\ Ibid.
\245\ CMS ESRD Measures Manual for the 2023 Performance Period.
(2022). https://www.cms.gov/files/document/esrd-measures-manual-v81.pdf.
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While we understand the importance of including measures focused on
depression, we believe that IOTA participants may have limited
experience diagnosing and treating depression and may struggle to make
referrals due to limited behavioral health providers. We also believe
that this measure may be duplicative with other policies in this model
that strive to improve the health and post-transplant outcomes of
attributed patients. Additionally, based on the KCC Model experience,
the Depression Remission measure is operationally complex due to the
10-month reporting period and novel collection and reporting processes.
We believe that IOTA participants would experience similar challenges
due to the mandatory nature of the model and unfamiliarity with
reporting quality measure data to the Innovation Center.
We considered the Depression Remission at 12 Months measure (CBE
ID: 0710e) because major depression is prevalent in the dialysis
population and most kidney transplant recipients spend some time on a
dialysis modality.\246\ Depression measures are included in the
Universal Foundation because successfully treating depression can
improve physical health outcomes, in addition to behavioral health
outcomes.\247\ A depression measure would align with the behavioral
health domain of Meaningful Measures 2.0. We considered a depression
remission measure over a depression screening measure because we
believed a depression remission measure would incentivize IOTA
participants to work with the other clinicians and providers involved
in the care of attributed patients to resolve or improve the depressive
symptoms rather than only identifying them. Our review of the
literature found that presence of behavioral health symptoms affected
the ability of patients to get on the kidney transplant waitlist, but
did not affect likelihood of receiving a kidney transplant.\248\ We are
not proposing the Depression Remission at 12 Months Measure because we
were unable to locate any publications that found depression remission
affected access to a kidney transplant. We also chose not to propose
this type of measure because the IOTA Model does not target pre-
waitlist patients for attribution to model participants. We also
believe that IOTA participants may have limited experience in diagnosis
and treating depression and may struggle to make referrals due to
limited behavioral health providers. Additionally, behavioral health
management is not under the purview of a kidney transplant hospital
that might see a kidney transplant waitlist patient perhaps only a
handful of times, but may be more appropriate for the patient's
nephrologist or dialysis center.
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\246\ Cukor, D., Donahue, S., Tummalapalli, S.L., Bohmart, A., &
Silberzweig, J. (2022). Anxiety, comorbid depression, and dialysis
symptom burden. Clinical Journal of the American Society of
Nephrology, 17(8), 1216-1217. https://doi.org/10.2215/cjn.01210122
https://doi.org/10.2215/cjn.01210122.
\247\ Jacobs, D.B., Schreiber, M., Seshamani, M., Tsai, D.,
Fowler, E., & Fleisher, L.A. (2023). Aligning quality measures
across CMS--the Universal Foundation. New England Journal of
Medicine, 388(9), 776-779. https://doi.org/10.1056/nejmp2215539.
\248\ Szeifert, L., Bragg-Gresham, J.L., Thumma, J., Gillespie,
B.W., Mucsi, I., Robinson, B.M., Pisoni, R.L., Disney, A., Combe,
C., & Port, F.K. (2011). Psychosocial variables are associated with
being wait-listed, but not with receiving a kidney transplant in the
dialysis outcomes and Practice Patterns Study (dopps). Nephrology
Dialysis Transplantation, 27(5), 2107-2113. https://doi.org/10.1093/ndt/gfr568; Chen, X., Chu, N.M., Basyal, P.S., Vihokrut, W., Crews,
D., Brennan, D.C., Andrews, S.R., Vannorsdall, T.D., Segev, D.L., &
McAdams-DeMarco, M.A. (2022). Depressive symptoms at kidney
transplant evaluation and access to the kidney transplant waitlist.
Kidney International Reports, 7(6), 1306-1317. https://doi.org/10.1016/j.ekir.2022.03.008.
---------------------------------------------------------------------------
We seek comment on our proposed quality measure set that includes
two PRO-PMs (CollaboRATE Shared Decision-Making Score and 3-Item Care
Transition Measure) and one process measure (Colorectal Cancer
Screening) for purposes of measuring performance in the quality domain.
We also seek comment on alternative quality measures considered.
(a) Quality Measure Set Selection, Reporting and Changes
As proposed in section III.C.5.e.(2). of this proposed rule, we are
proposing that CMS select and use quality measures to assess IOTA
participant performance in the quality domain. We propose that each PY,
IOTA participants would be required to report quality measure data
during survey and reporting windows to CMS in a form and manner, and at
times, established by CMS. We also propose that, where applicable, IOTA
participants would be required to administer any surveys or screenings
relevant to the quality measures selected for inclusion in the IOTA
Model to attributed patients. We propose to define ``survey and
reporting windows'' as two distinct periods where IOTA participants
would be required to administer a quality measure-related survey or
screening to attributed patients or submit attributed patient responses
to CMS pursuant to Sec. 512.48(b)(2)(ii). We propose that CMS would
notify, in a form and manner as determined by CMS, IOTA participants of
the survey and reporting window for applicable quality measures by the
first day of each PY.
We propose that CMS would use future rulemaking to make
substantiative updates to the specifications of any of the quality
measures in the IOTA Model. Additionally, we propose that the quality
measures finalized for inclusion in the IOTA Model would remain in the
quality measure set unless CMS, through future rulemaking, removed or
replaced them.
We propose that CMS could remove or replace a quality measure based
on one of the following factors:
A quality measure does not align with current clinical
guidelines or practice.
Performance on a quality measure among IOTA participants
is so high and unvarying that meaningful distinctions and improvement
in performance can no longer be made (``topped out'' measure), as
defined in 42 CFR 412.140(g)(3)(i)(A).
[[Page 43566]]
Performance or improvement on a quality measure does not
result in better patient outcomes.
The availability of a more broadly applicable quality
measure (across settings or populations) or the availability of a
quality measure that is more proximal in time to desired patient
outcomes for the particular topic.
The availability of a quality measure that is more
strongly associated with desired patient outcomes for the particular
topic.
Collection or public reporting of a quality measure leads
to negative unintended consequences other than patient harm.
It is not feasible to implement the quality measure
specifications.
The costs associated with a quality measure outweigh the
benefit of its continued use in the IOTA Model.
We propose that CMS would assess the benefits of removing or
replacing a quality measure from the IOTA Model on a case-by-case
basis. We propose that CMS would use the future rulemaking process to
add, remove, suspend, or replace quality measures in the IOTA Model to
allow for public comment, unless a quality measure raises specific
safety concerns. We propose that if CMS determines that the continued
requirement for IOTA participants to submit data on a quality measure
raises specific patient safety concerns, CMS could elect to immediately
remove the quality measure from the IOTA Model quality measure set.
Finally, we propose that CMS would, upon removal of a quality measure,
and in a form and manner determined by CMS, do the following:
Provide notice to IOTA participants and the public at the
time CMS removes the quality measure, along with a statement of the
specific patient safety concerns that would be raised if IOTA
participants continued to submit data on the quality measure.
Provide notice of the removal in the Federal Register.
We seek comment on the requirement that IOTA participants report
quality measure data to CMS. We additionally seek comment on our
proposed process for adding, removing, or replacing quality measures in
the IOTA Model.
(b) CollaboRATE Shared Decision-Making Score
The CollaboRATE Shared Decision-Making Score is a patient-reported
measure of shared decision-making. The measure provides a performance
score representing the percentage of adults 18 years of age and older
who experience a high degree of shared decision making. The CollaboRATE
Shared Decision-Making Score is based on three questions that assess
the degree to which effort was made to inform the patient of his or her
health issues, to listen to the patient's priorities, and the extent to
which the patient's priorities were included in determining next steps.
The measure is generic and applies to all clinical encounters,
irrespective of the condition or the patient group. We propose that
IOTA participants would be required to administer the CollaboRATE
Shared Decision-Making Score to attributed patients once per PY, at
minimum, and report quality measure data to CMS during survey and
reporting windows, as defined in section III.C.5.e.(2).(a). of this
proposed rule, that would be established by CMS.
We believe that incentivizing shared decision-making is critical to
ensuring the model centers the patient experience and treatment choice
to meet the IOTA desired goals of improving equity, increasing the
number of kidney transplants, and reducing kidney non-utilization.
Patients needing a kidney transplant often face many challenges when
making healthcare decisions, as they must first decide between
treatment options (such as dialysis versus transplantation, living
donor versus deceased-donor transplantation) and where they wish to be
evaluated for transplantation. Research findings demonstrate the
importance and impact of shared decision-making throughout the entire
transplant process for patients because of the types of complex
decisions they must make, and the dynamic factors involved in each
patient's decision.\249\ Research studies have found that shared
decision-making shifts the patient-physician relationship past
traditional practices and contributes to better health outcomes,
increased quality of life, increased patient knowledge and medication
adherence, and lower healthcare expenditures.\250\ Furthermore,
research findings support that shared decision-making with the patient
could reduce kidney non-utilization, improve equity,
[[Page 43567]]
and increase the number of kidney transplants.\251\
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\249\ Jones, E.L., Shakespeare, K., McLaughlin, L., & Noyes, J.
(2023). Understanding people's decisions when choosing or declining
a kidney transplant: a qualitative evidence synthesis. BMJ Open,
13(8), e071348. https://doi.org/10.1136/bmjopen-2022-071348;
Stephenson, M.D., & Bradshaw, W. (2018). Shared decision making in
chronic kidney disease. Renal Society of Australasia Journal, 14(1),
26-32. http://mutex.gmu.edu/login?url=https://www.proquest.com/scholarly-journals/shared-decision-making-chronic-kidney-disease/docview/2283078287/se-2; Gordon, E.J., Butt, Z., Jensen, S.E., Lok-
Ming Lehr, A., Franklin, J., Becker, Y., Sherman, L., Chon, W.J.,
Beauvais, N., Hanneman, J., Penrod, D., Ison, M.G., & Abecassis,
M.M. (2013). Opportunities for Shared Decision Making in Kidney
Transplantation. American Journal of Transplantation, 13(5), 1149-
1158. https://doi.org/10.1111/ajt.12195; Salter, M.L., Babak Orandi,
McAdams-DeMarco, M.A., Law, A., Meoni, L.A., Jaar, B.G., Sozio,
S.M., Hong, W., Parekh, R.S., & Segev, D.L. (2014). Patient- and
Provider-Reported Information about Transplantation and Subsequent
Waitlisting. Journal of the American Society of Nephrology, 25(12),
2871-2877. https://doi.org/10.1681/asn.2013121298; Schold, J.D.,
Huml, A.M., Poggio, E.D., Reese, P.P., & Mohan, S. (2022). A tool
for decision-making in kidney transplant candidates with poor
prognosis to receive deceased donor transplantation in the United
States. Kidney International. https://doi.org/10.1016/j.kint.2022.05.025; Schaffhausen, C.R., Bruin, M.J., McKinney, W.T.,
Snyder, J.J., Matas, A.J., Kasiske, B.L., & Israni, A.K. (2019). How
patients choose kidney transplant centers: A qualitative study of
patient experiences. 33(5), e13523-e13523. https://doi.org/10.1111/ctr.13523; Hart, A., Bruin, M., Chu, S., Matas, A., Partin, M.R., &
Israni, A.K. (2019). Decision support needs of kidney transplant
candidates regarding the deceased donor waiting list: A qualitative
study and conceptual framework. Clinical Transplantation, 33(5),
e13530. https://doi.org/10.1111/ctr.13530; S. Ali Husain, Brennan,
C., Michelson, A., Tsapepas, D., Patzer, R.E., Schold, J.D., &
Mohan, S. (2018). Patients prioritize waitlist over posttransplant
outcomes when evaluating kidney transplant centers. 18(11), 2781-
2790. https://doi.org/10.1111/ajt.14985; Patzer, R.E., McPherson,
L., Basu, M., Mohan, S., Wolf, M., Chiles, M., Russell, A., Gander,
J.C., Friedewald, J.J., Ladner, D., Larsen, C.P., Pearson, T., &
Pastan, S. (2018). Effect of the iChoose Kidney decision aid in
improving knowledge about treatment options among transplant
candidates: A randomized controlled trial. American Journal of
Transplantation: Official Journal of the American Society of
Transplantation and the American Society of Transplant Surgeons,
18(8), 1954-1965. https://doi.org/10.1111/ajt.14693.
\250\ Stephenson, M.D., & Bradshaw, W. (2018). Shared decision
making in chronic kidney disease. Renal Society of Australasia
Journal, 14(1), 26-32. http://mutex.gmu.edu/login?url=https://www.proquest.com/scholarly-journals/shared-decision-making-chronic-kidney-disease/docview/2283078287/se-2; Gordon, E.J., Butt, Z.,
Jensen, S.E., Lok-Ming Lehr, A., Franklin, J., Becker, Y., Sherman,
L., Chon, W.J., Beauvais, N., Hanneman, J., Penrod, D., Ison, M.G.,
& Abecassis, M.M. (2013). Opportunities for Shared Decision Making
in Kidney Transplantation. American Journal of Transplantation,
13(5), 1149-1158. https://doi.org/10.1111/ajt.12195; Schold, J.D.,
Huml, A.M., Poggio, E.D., Reese, P.P., & Mohan, S. (2022). A tool
for decision-making in kidney transplant candidates with poor
prognosis to receive deceased donor transplantation in the United
States. Kidney International. https://doi.org/10.1016/j.kint.2022.05.025; Schaffhausen, C.R., Bruin, M.J., McKinney, W.T.,
Snyder, J.J., Matas, A.J., Kasiske, B.L., & Israni, A.K. (2019). How
patients choose kidney transplant centers: A qualitative study of
patient experiences. 33(5), e13523-e13523. https://doi.org/10.1111/ctr.13523; Hart, A., Bruin, M., Chu, S., Matas, A., Partin, M.R., &
Israni, A.K. (2019). Decision support needs of kidney transplant
candidates regarding the deceased donor waiting list: A qualitative
study and conceptual framework. Clinical Transplantation, 33(5),
e13530. https://doi.org/10.1111/ctr.13530; Patzer, R.E., McPherson,
L., Basu, M., Mohan, S., Wolf, M., Chiles, M., Russell, A., Gander,
J.C., Friedewald, J.J., Ladner, D., Larsen, C.P., Pearson, T., &
Pastan, S. (2018). Effect of the iChoose Kidney decision aid in
improving knowledge about treatment options among transplant
candidates: A randomized controlled trial. American Journal of
Transplantation: Official Journal of the American Society of
Transplantation and the American Society of Transplant Surgeons,
18(8), 1954-1965. https://doi.org/10.1111/ajt.14693.
\251\ Kucirka, L.M., Grams, M.E., Balhara, K.S., Jaar, B.G., &
Segev, D.L. (2011). Disparities in Provision of Transplant
Information Affect Access to Kidney Transplantation. American
Journal of Transplantation, 12(2), 351-357. https://doi.org/10.1111/j.1600-6143.2011.03865.x; Patzer, R.E., Retzloff, S., Buford, J.,
Gander, J., Browne, T., Jones, H., Ellis, M., Canavan, K., Berlin,
A., Mulloy, L., Gibney, E., Sauls, L., Muench, D., Reeves-Daniel,
A., Zayas, C., DuBay, D., Mutell, R., & Pastan, S.O. (2021).
Community Engagement to Improve Equity in Kidney Transplantation
from the Ground Up: the Southeastern Kidney Transplant Coalition.
Current Transplantation Reports, 8(4), 324-332. https://doi.org/10.1007/s40472-021-00346-x; Schold, J.D., Huml, A.M., Poggio, E.D.,
Reese, P.P., & Mohan, S. (2022). A tool for decision-making in
kidney transplant candidates with poor prognosis to receive deceased
donor transplantation in the United States. Kidney International.
https://doi.org/10.1016/j.kint.2022.05.025; Patzer, R.E., McPherson,
L., Basu, M., Mohan, S., Wolf, M., Chiles, M., Russell, A., Gander,
J.C., Friedewald, J.J., Ladner, D., Larsen, C.P., Pearson, T., &
Pastan, S. (2018). Effect of the iChoose Kidney decision aid in
improving knowledge about treatment options among transplant
candidates: A randomized controlled trial. American Journal of
Transplantation: Official Journal of the American Society of
Transplantation and the American Society of Transplant Surgeons,
18(8), 1954-1965. https://doi.org/10.1111/ajt.14693.
---------------------------------------------------------------------------
By pairing the CollaboRATE Shared Decision-Making Score measure
with the proposed achievement domain number of kidney transplants
metric, as described in section III.C.5.c. of this proposed rule, and
the proposed quality domain post-transplant outcomes metrics, as
described in section III.C.5.e.(1). of this proposed rule, we aim to
incentivize care delivery transformation and improvement activity
across IOTA participants that would center attributed patients and
their family and caregiver as a critical decision-maker in treatment
choices that align with their preferences and values. This may include
greater transparency on donor organ offers and reasons for non-
acceptance, and increased education and support on the living donor
process. We also believe that this would support attributed patients in
receiving a kidney that may be at higher risk of non-use, but that may
offer a survival and quality of life advantage over remaining on
dialysis, dying while waitlisted, or being de-listed.\252\
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\252\ Massie, A.B., Luo, X., Chow, E.K.H., Alejo, J.L., Desai,
N.M., & Segev, D.L. (2014). Survival benefit of primary deceased
donor transplantation with high-KDPI kidneys. American Journal of
Transplantation, 14(10), 2310-2316. https://doi.org/10.1111/ajt.12830.
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We acknowledge that the instrument used for the CollaboRATE Shared
Decision-Making Score is generic; however, we have not been able to
identify alternative measures of shared decision-making that are
specific to kidney transplant that have been endorsed by the CBE.
Similarly, while there may be value in an instrument that measures
shared decision-making regarding the types of kidney organ offers
attributed patients are willing to accept, no such measure exists. We
believe the CollaboRATE Shared Decision-Making Score would capture
variation in the presence and quality of shared decision-making among
IOTA participants and that the instrument need not be specific to
kidney transplant to incentivize meaningful improvements in patient-
centricity and the patient experience, equity, and reducing kidney non-
use.
We seek comment on our proposal to include the CollaboRATE Shared
Decision-Making Score as a quality measure for purposes of quality
domain performance assessment.
(c) Colorectal Cancer Screening
The Colorectal Cancer Screening (COL) measure identifies the
percentage of patients 50-75 years of age who had guideline concordant
screening for colorectal cancer. Kidney transplant recipients are at
higher risk for cancer than the general population, due in part to
long-term immunosuppression.\253\ Kidney transplant recipients have a
higher incidence of colorectal cancer and advanced adenomas and may
have worse prognoses than the general population, both of which support
improved screening and prophylactic care for kidney transplant
recipients.254 255 256
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\253\ Rama, I., & Griny[oacute], J.M. (2010). Malignancy after
renal transplantation: The role of immunosuppression. Nature Reviews
Nephrology, 6(9), 511-519. https://doi.org/10.1038/nrneph.2010.102.
\254\ Komaki, Y., Komaki, F., Micic, D., Ido, A., & Sakuraba, A.
(2018). Risk of colorectal cancer in chronic kidney disease. Journal
of Clinical Gastroenterology, 52(9), 796-804. https://doi.org/10.1097/mcg.0000000000000880.
\255\ Privitera, F., Gioco, R., Civit, A.I., Corona, D.,
Cremona, S., Puzzo, L., Costa, S., Trama, G., Mauceri, F., Cardella,
A., Sangiorgio, G., Nania, R., Veroux, P., & Veroux, M. (2021).
Colorectal cancer after Kidney Transplantation: A screening
colonoscopy case-control study. Biomedicines, 9(8), 937. https://doi.org/10.3390/biomedicines9080937.
\256\ Farrugia, D., Mahboob, S., Cheshire, J., Begaj, I.,
Khosla, S., Ray, D., & Sharif, A. (2014). Malignancy-related
mortality following kidney transplantation is common. Kidney
International, 85(6), 1395-1403. https://doi.org/10.1038/ki.2013.458.
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The COL measure is a Universal Foundation measure in the CMS
Meaningful Measures 2.0 Wellness and Prevention Domain. By nature of
its inclusion in the Universal Foundation measure set, the COL measure
addresses a condition associated with significant morbidity and
mortality and incentivizes action on high-value preventive care.\257\
The COL measure is also aligned with the goals of the President's
Cancer Moonshot to reduce the death rate from cancer by 50 percent over
the next 25 years and improve the experience of people living with
cancer and those who have survived it.\258\
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\257\ Jacobs, D.B., Schreiber, M., Seshamani, M., Tsai, D.,
Fowler, E., & Fleisher, L.A. (2023). Aligning quality measures
across CMS--the Universal Foundation. New England Journal of
Medicine, 388(9), 776-779. https://doi.org/10.1056/nejmp2215539.
\258\ Cancer Moonshot. (n.d.). The White House. https://www.whitehouse.gov/cancermoonshot/.
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We are proposing the COL measure for inclusion in our assessment of
quality domain performance in the model because we believe it would
provide a signal of the importance of ongoing post-transplant care and
reduce variation in the screening and prophylactic care of kidney
transplant recipients by transplant hospital. We propose that IOTA
participants would be required to administer the COL measure yearly to
all attributed IOTA transplant patients who are Medicare beneficiaries.
The COL measure would work in concert with the proposed composite graft
survival metric to increase the likelihood that attributed patients in
the IOTA Model would receive comprehensive post-transplant care that
would account not only for the attributed patient and graft survival,
but also complications and comorbidities associated with receiving a
kidney transplant.
We seek comment on our proposal to include the COL measure as a
quality measure for purposes of quality domain performance assessment.
(d) 3-Item Care Transition Measure (CTM-3)
The 3-Item Care Transition Measure (CTM-3) is a hospital-level,
patient-reported measure of readiness for self-care at time of
discharge from an acute care hospital. The CTM-3 is based on data from
a three-question instrument that assesses whether the patient and
family's preferences were accounted for in the care plan; whether
patients understood their role in self-management; and, whether
appropriate medication education was provided. A higher score on the
CTM-3 reflects a higher quality transition of care. We propose that
IOTA participants would be required to administer the CTM-3 to
attributed patients once per PY, at minimum, and report quality measure
data to CMS during survey and reporting windows, as defined in section
III.C.5.e.(2).(a). of this proposed rule, that would be established by
CMS.
[[Page 43568]]
Transitions of care after kidney transplant are common and indicate
elements of modifiable transplant hospital quality. One study found
that 30-day hospital readmissions after an organ transplant were
significantly associated with graft loss and death.\259\ Poor
understanding of and adherence to immunosuppressive drugs were
identified as key elements associated with an increased risk for early
hospital readmission.\260\ Mitigating readmission risk may be of
special importance given that IOTA participants may choose to increase
their number of transplants by transplanting more kidneys that may have
clinical value to patients. Simultaneously, there may also be increased
healthcare utilization needs due to delayed graft function (DGF), which
could require longer hospital stays, readmissions, and more complex
care coordination.\261\ We have also heard from interested parties
about the need for patient-reported measures to contribute to the
assessment of post-transplant outcomes.
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\259\ Covert, K.L., Fleming, J.N., Staino, C., Casale, J.P.,
Boyle, K.M., Pilch, N.A., Meadows, H.B., Mardis, C.R., McGillicuddy,
J.W., Nadig, S., Bratton, C.F., Chavin, K.D., Baliga, P.K., & Taber,
D.J. (2016). Predicting and preventing readmissions in Kidney
Transplant Recipients. Clinical Transplantation, 30(7), 779-786.
https://doi.org/10.1111/ctr.12748.
\260\ Covert, K.L., Fleming, J.N., Staino, C., Casale, J.P.,
Boyle, K.M., Pilch, N.A., Meadows, H.B., Mardis, C.R., McGillicuddy,
J.W., Nadig, S., Bratton, C.F., Chavin, K.D., Baliga, P.K., & Taber,
D.J. (2016). Predicting and preventing readmissions in Kidney
Transplant Recipients. Clinical Transplantation, 30(7), 779-786.
https://doi.org/10.1111/ctr.12748.
\261\ Jadlowiec, C.C., Frasco, P., Macdonough, E., Wagler, J.,
Das, D., Budhiraja, P., Mathur, A.K., Katariya, N., Reddy, K.,
Khamash, H., & Heilman, R. (2022). Association of DGF and early
readmissions on outcomes following Kidney Transplantation.
Transplant International, 35. https://doi.org/10.3389/ti.2022.10849.
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The CTM-3 is a patient-reported measure and would measure
transplant hospital performance on an aspect of care that we understand
to be important to the patient experience, modifiable by transplant
hospitals, and that may not otherwise improve based on the financial
incentives in the model targeted towards 1- and 3-year outcomes, but
not directly at perioperative transitions of care and readmission risk.
The CTM-3 is a domain of the HCAHPS (CBE ID: 0166). We believe that
IOTA participants would have some familiarity with the HCAHPS survey
and that the hospital systems of which IOTA participants would be a
part would have an infrastructure in place for the administration of
HCAHPS that could be leveraged to support administration of the CTM-3.
We seek comment on our proposal to include the CTM-3 measure as a
quality measure as a quality measure for purposes of quality domain
performance assessment.
(e) Calculation of Points
We propose that the IOTA participant would receive up to 10 points
for performance on our three proposed measures within the quality
domain--the CollaboRATE Shared Decision-Making Score, COL, and CTM-3
measures. For purposes of quality measure set performance scoring, we
propose that IOTA participants may receive up to 4 points for
performance on the CollaboRATE Shared Decision-Making Score measure, up
to 2 points on the COL measure, and up to 4 points on the CTM-3
measure. Lower weight in terms of scoring points was given to the COL
measure because it is a claims-based measure that does not require
reporting from IOTA participants. Because the CTM-3 and CollaboRATE are
PRO-PMs we believe it is important to allot more points to them, to
recognize the additional operational activities necessary for IOTA
participants.
We propose to phase-in quality performance benchmarks for the three
quality measures selected for the IOTA quality measure set, such that
we would reward reporting for the first two years of the model
performance period (``pay-for-reporting''), at minimum, before we
reward performance against quality performance benchmarks for each
measure (``pay-for-performance''). Thus, performance for each of these
three quality measures would be measured against a ``response rate
threshold'' applicable to our proposed ``pay-for-reporting'' method for
PY 1-PY 2, while performance would be measured against quality
performance benchmarks calculated by CMS applicable to our proposed
``pay-for-performance'' method for PY 3-PY 6. Table 8 illustrates our
proposed pay-for-reporting and pay-for-performance timeline. We note
that we anticipate establishing a quality performance benchmarks and
minimum attainment levels for quality measures in future rule making.
[GRAPHIC] [TIFF OMITTED] TP17MY24.008
We propose that CMS would determine and share with IOTA
participants the response rate threshold by the first day of each PY in
a form and manner chosen by CMS. This approach to assessing IOTA
participant quality performance would serve four key purposes. First,
it would promote measure implementation, uptake, and data collection by
IOTA participants through a rewards-only scoring system. Second, it
would build experience over the first two model PYs, giving IOTA
participants more time to prepare and build capacity to meet
performance benchmarks. Third, it would allow CMS to collect data
needed to develop measure benchmarks. Finally, it would focus model
incentives on care delivery transformation and improvement activity
directly aimed at meeting quality performance goals, as to ensure the
patient is centered in this approach. Ultimately, we considered the
pay-for-reporting approach to be a reasonable approach. We also believe
that some IOTA participants may be familiar with this as it is similar
to the format within the KCC Model. We recognize that these measures
already exist, but, because they are used in a much broader population,
there are no benchmarks that are applicable for the model.
We propose to define the ``response rate threshold'' as the level
of complete and accurate reporting for each quality measure, within the
quality measure set of the quality domain, that the IOTA participant
must meet to earn points on the quality domain during a performance
year as described in Sec. 512.428(c) and (e). For the CTM-3 and
CollaboRATE measures, we propose that
[[Page 43569]]
points be awarded based on response rate thresholds, as illustrated in
Table 9, such that IOTA participants with a response rate threshold
of--
90-100 percent of attributed patients would receive 4
points;
50-89 percent of attributed patients would receive 2
points; or
Under 50 percent of attributed patients would receive 0
points.
We propose for the COL measure that a completion rate of 50 percent
or greater would result in the IOTA participant receiving two points,
and a completion rate of less than 50 percent would result in the IOTA
participant receiving zero points, as illustrated in Table 9.
[GRAPHIC] [TIFF OMITTED] TP17MY24.009
We recognize that the proposed response rate thresholds are high,
but we want to make sure that we have enough data to set appropriate
and meaningful benchmarks in PY 3 through PY 6. We considered setting a
higher maximum measure completion rate; however, given that each IOTA
participant may have different levels of engagement with kidney
transplant waitlist patients, we believe a higher threshold may be
difficult for IOTA participants to achieve. We also believe that a
higher response rate would incentivize IOTA participants to collect the
data. We considered the following variations to the response rate
threshold for each of the proposed quality measure:
Response rate threshold of 100 percent would receive 10
points, if not 100 percent 0 points would be awarded.
Response rate threshold of 80-100 percent would receive 10
points, 50-79 percent would receive 5 points, and 49-0 percent would
receive 0 points.
50-100 percent would receive 10 points; under 50 percent
would receive 0 points.
We considered mirroring the point structure under which an IOTA
participant would receive either all possible points, or, if data was
not collected from all their attributed patients, none of the possible
points. We believe this could incentivize IOTA participants to
administer the surveys associated with the proposed quality measures,
which would allow us to create meaningful benchmarks for future model
years. However, because there would be some additional burden placed
onto IOTA participants to administer the surveys associated with the
proposed quality measures, we believe this point structure would be
difficult for some and wanted to provide more attainable response rate
thresholds. We also considered lowering the response rate thresholds
for the same reasons mentioned earlier, but, because there are
currently no benchmarks for these measures in this specific population,
we believed the response rate threshold needed to be higher but still
attainable.
We also considered achievement and improvement scoring for the
proposed quality measures. However, because none of the measures
included in the proposed quality measure set, as described in section
III.C.5.e.(2). of this proposed rule, currently have benchmarks, we did
not believe it was appropriate to propose achievement and improvement
scoring for the proposed quality measures at this time.
We seek comment on our proposed calculation of points for the
quality measure set, as well as the proposal to reward IOTA participant
reporting for the first two PYs (``pay-for-reporting''), before
rewarding IOTA participant performance against quality performance
benchmarks. We seek comment on the proposed response rate thresholds
and point allocations for measures included in the proposed quality
measure set within the quality domain.
6. Payment
a. Purpose and Goals
We believe that risk-based payment arrangements in Innovation
Center models drive healthcare innovation and transform the healthcare
payment system by rewarding value over volume. Risk-based payment
models hold participants financially accountable, as these payments are
structured to incentivize value-based care that improves quality and
reduces total cost of care for beneficiaries. Risk-based payment models
may be upside-risk only, or have two-sided, upside and downside, risk.
Under these risk-based arrangements, model participants may receive a
payment from CMS if performance goals are met or exceeded, and, if the
model features downside risk, may owe a payment to CMS for failing to
meet performance goals.\262\
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\262\ https://www.cms.gov/priorities/innovation/key-concepts/risk-arrangements-health-care.
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For the IOTA Model, we propose an alternative payment model (APM)
structure that incorporates both upside and downside risk to existing
Medicare fee-for-service (FFS) payments for kidney transplantations as
described in section III.C.6.b. of this proposed rule.
The IOTA Model would test whether performance-based payments,
including an upside risk payment and downside risk payment, to IOTA
participants increases access to kidney transplants for attributed
patients while preserving or enhancing quality of care and reducing
program expenditures. As described in section III.C.5. of this proposed
rule, IOTA participants would be assessed against proposed metrics to
assess performance for each PY relative to specified targets,
threshold, or benchmarks proposed and determined by CMS. The final
performance score, not to exceed a maximum of 100 points, would
determine if and how upside and downside risk payments are applied, as
described in section III.C.6.c. of this proposed rule. We believe this
upside and downside risk approach would be a strong incentive to
promote performance improvement.
We seek comment on our proposed two-sided risk payment design to
incentivize model performance goals.
b. Alternative Payment Design Overview
There are two payment components in the current Medicare FFS
program for organ transplantation. Under the
[[Page 43570]]
Medicare Inpatient Prospective Payment System (IPPS), kidney transplant
hospitals are paid a prospective payment system rate based on the MS-
DRG for the organ transplant. Payment for organ acquisition costs as
described at 42 CFR 413.402, which include costs associated with
beneficiary and donor evaluation, is made on a reasonable cost basis.
To remain active on the transplant waitlist, candidates must meet a
variety of criteria, including annual screenings for cardiovascular
diseases and cancers.
In the IOTA Model, CMS is proposing two-sided performance-based
payments for ``Medicare kidney transplants,'' defined as kidney
transplants furnished to attributed patients whose primary or secondary
insurance is Medicare FFS, as identified in Medicare FFS claims with
MS-DRGs 008, 019, 650, 651 and 652, and as illustrated in Table 10.
This APM design aligns with the Health Care Payment Learning & Action
Network (LAN) Category 3 APM framework in which model participants
continue to be paid on the basis of Medicare FFS, but a retrospective
annual attribution reconciliation and performance assessment after the
end of each model PY is conducted to determine performance-based
payments.\263\
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\263\ https://hcp-lan.org/workproducts/apm-refresh-whitepaper-final.pdf.
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The IOTA Model's performance-based payments are linked to existing
Medicare Part A and Part B services for kidney transplants, and align
with other Innovation Center models' payment structure, including the
ETC Model where upward and downward adjustments are made to certain
Medicare payments under the ESRD Prospective Payment System and
Physician Fee Schedule depending on a n ETC Participant's performance
at the aggregation group level under the model. The difference between
ETC and the IOTA Model, for example, is how these retrospective
adjustments would be paid or recouped by CMS. CMS is not proposing to
adjust existing Medicare IPPS payments for kidney transplants furnished
to Medicare beneficiaries. Instead, CMS is proposing to make
performance-based payments to IOTA participants separate from claims-
based payments.
[GRAPHIC] [TIFF OMITTED] TP17MY24.010
We propose to base performance-based payments on increasing the
number of transplants and other metrics of efficiency and quality
because: (1) we believe it would be a strong proxy for total cost; (2)
it directly aligns with the model's focused goal of increasing access
and volume of kidney transplantations; (3) acknowledges kidney waitlist
and transplant patients are high-cost and high-need, making performance
based on total cost of care unfair for IOTA participants with lower
volume and fewer capabilities and resources given increased opportunity
for outliers; and (4) may safeguard against unintended consequences
introduced by defining value based on cost for an attributed patient
population already at high-risk, such as inappropriate cost shifting
and widening access to care disparities. We theorize that increasing
the number of, and access to, kidney transplants alone would result in
better quality. As indicated in our estimates presented in section IV
of this proposed rule, it would also result in savings to Medicare.
While we propose to assess model performance for each IOTA
participant for all attributed patients regardless of payer type, as
described in section III.C.6.c of this proposed rule, we propose model
performance-based payments that would only be based on kidney
transplants furnished to attributed patients with Medicare FFS as the
primary or secondary insurance.
We considered also basing the model performance-based payments on
kidney transplants furnished to attributed patients enrolled in
Medicare Advantage (MA), as kidney transplants are a Medicare-covered
service that MA plans must also cover. As these payments would be made
to transplant hospitals, a potential waiver of section 1851(i)(2) of
the Act, which provides that only the MA plan shall be entitled to
payments for services furnished to the beneficiary, may have been
necessary to apply the payments to attributed patients enrolled in MA.
Because further consideration is needed for the implications of such a
potential waiver, we are not proposing to apply model performance-based
payments performed on attributed patients enrolled in MA.
We believe that the benefits of applying model performance-based
payments to transplants furnished to attributed patients enrolled in MA
would be recognizing the growth in MA enrollment relative to Medicare
FFS enrollment, strengthening the model test through aligned payment
incentives across payers, and protecting against unintended
consequences of incentivizing inappropriate organ offer acceptance
based on payer type. However, we are not proposing to base payments on
attributed patients enrolled in MA, because of concerns about
potentially waiving section 1851(i)(2) of the Act. This provision
states that only the MA plan is entitled to payments for services
provided to the beneficiary. Waiving this requirement would be
unprecedented and the effects are unknown. We do recognize that the
proposed incentives in the IOTA Model would have a larger effect if
transplant hospitals were receiving performance-based payments based on
their entire panel of attributed beneficiaries who receive transplants,
and not just based on transplants for attributed beneficiaries with
Medicare FFS as their primary or secondary insurance. To that end, the
IOTA Model would encourage multi-payer alignment with the goal of
aligning on goals, incentives, and quality. CMS intends to engage with
the payer community, including MA,
[[Page 43571]]
Medicaid, and commercial payers, to discuss opportunities and
approaches for alignment.
We request comment and feedback, especially from MA plans, on our
decision not to calculate model performance-based payments to
transplants furnished to attributed patients enrolled in MA. We are
especially interested in comments that address how the Innovation
Center should generally approach the growing MA population with the
design of its models, which have traditionally been focused on the fee-
for-service Medicare population.
While kidney transplant hospitals are subject to value-based
payment programs, some IOTA participants may have limited APM
experience, resources, and capacity to meet model goals. We considered
an upside-risk payment only framework that would still base model
payments on kidney transplant utilization and other metrics of
efficiency and quality. However, we believed that two-sided risk
payments would be stronger incentives to achieve desired goals. We also
recognized this in the model design by proposing a phased-in approach
to two-sided risk, with upside-only applied to the first model PY. We
also considered other APM frameworks that would link performance to
quality, such as pay-for-reporting and pay-for-performance. We did not
propose these frameworks, as they did not align with our goals of
establishing two-sided risk accountability for IOTA participants.
Recognizing the benefits of a rewards-focused approach, particularly as
it relates to quality performance, we did incorporate a rewards-focused
performance scoring structure designed as pay-for-reporting and pay-
for-performance within the quality domain performance assessment.
Another alternative we considered was a flat positive adjustment to
the Medicare FFS payment for a kidney transplant based on the number of
completed kidney transplants that an IOTA participant performs.
Increasing the amount paid for completed kidney transplants through a
FFS adjustment is the simplest policy and aligns with a main focus of
the IOTA Model; that is, increasing the number of kidney transplants.
Additionally, adjusting the FFS payment would directly incentivize an
increase in the number of kidney transplants performed by IOTA
participants. Under this approach, eligible claims would be identified
utilizing Medicare claims data with Medicare Severity Diagnosis Related
Groups (MS-DRGs) 008 (simultaneous pancreas-kidney transplant) and 652
(kidney transplant); and claims with ICD-10 procedure codes 0TY00Z0
(transplantation of right kidney, allogeneic, open approach), 0TY00Z1
(transplantation of right kidney, syngeneic, open approach), 0TY00Z2
(transplantation of right kidney, zooplastic, open approach) 0TY10Z0
(transplantation of left kidney, allogeneic, open approach), 0TY10Z1
(transplantation of left kidney, syngeneic, open approach), and 0TY10Z2
(transplantation of left kidney, zooplastic, open approach).
We are not proposing a performance methodology based solely on
adjusting the DRG payment for a kidney transplant, because this option
would not encourage IOTA participants to focus on issues other than
transplant volume, including equity, increased utilization of donor
kidneys, quality of care, and patient outcomes, all of which are all
important parts of the transplant process where we believe performance
is variable and can be improved. We further believe that the claims-
only approach would limit IOTA participant responsiveness to the model
because IOTA participants that already have high kidney transplant
volumes would be rewarded through increased reimbursements whether they
improved year-over-year or not. Finally, we do not believe that this
approach would provide any additional encouragement for IOTA
participants to manage post-transplant care.
We also considered establishing a payment for transplant waitlist
management to encourage additional investment in the transplant
process, but decided to focus more on the outcomes described in section
III.C.5 of this proposed rule. Additionally, given that IOTA
participants are already reimbursed at cost for efforts to manage
beneficiaries on the waitlist, we did not believe an explicit
additional payment would be necessary in this area.
We seek feedback on our proposed alternative payment model design,
data source to identify kidney transplants, and proposal to only apply
model performance-based payments, both upside and downside, to Medicare
kidney transplants. We also seek feedback on alternative approaches
considered, including consideration of MA inclusion. We welcome input
on how CMS may be able to work with multiple payers to ensure alignment
with the IOTA Model.
c. Performance-Based Payment Method
We are proposing that the final performance score as described in
section III.C.5. of this proposed rule would determine if and how an
IOTA participant qualifies for an upside risk payment, falls in the
neutral zone, or qualifies for a downside risk payment, proposed using
a two-step process. First, we would determine if an IOTA participant's
final performance score qualifies the IOTA participant for upside risk
payments, downside risk payments, or the neutral zone, as described in
section III.C.6.c.(1). of this proposed rule. Second, we would apply
the proposed calculation formula for each of type of payment, as
described in section III.C.6.c.(2). of this proposed rule. Ultimately,
we are proposing a performance-based payment method that prioritizes
the following principles:
Significant weight should be given to performance in the
achievement domain, representing up to 60 points relative to a 100
maximum performance score, in alignment with the primary goals of the
model to increase number of kidney transplants.
The magnitude of performance-based payments should be tied
to relative number of kidney transplants, given significant
differentials across kidney transplant hospitals nationally.
The largest performance-based payments amount in total
dollars should go to IOTA participants that perform the most
transplants because they are removing the most people from dialysis and
creating the largest quality improvement and cost savings for the
Medicare Trust Fund.
The payments need to be calibrated to provide an incentive
to IOTA participants, but still ensure net savings to Medicare based on
the analysis performed by OACT in section IV of this proposed rule.
The mechanisms should recognize that CMS has not
previously offered kidney transplant hospitals a value-based care
payment model around transplantation and should provide a transition to
any form of downside risk to allow for an opportunity to become
familiar with the value-based care process.
Limit operational complexity for both IOTA participants
and CMS to avoid any potential for errors.
(1) Determine Final Performance Score Range Category
We propose to establish three final performance score range
categories, as illustrated in Table 11, that dictate which type of
performance-based payment would apply to an IOTA participant for a
given PY.
We propose to define ``upside risk payment'' as a lump sum payment
that CMS would make to an IOTA participant if the IOTA participant's
final performance score for a PY falls
[[Page 43572]]
within the payment range specified in section III.C.6.c(2)(a) of this
proposed rule. As proposed and indicated in Table 11, if in PY 1-6, an
IOTA participant's final performance score is greater than or equal to
60 points, the IOTA participant would qualify for an upside risk
payment.
We propose to define ``neutral zone'' as the final performance
score range in which the IOTA participant would not owe a downside risk
payment to CMS or receive an upside-risk payment from CMS if the IOTA
participant's final performance score falls within the ranges specified
in section III.C.6.c.(2).(c). of this proposed rule. In the first year
of the model, we propose that the neutral zone would apply for final
performance scores below 60. As such, only upside payments and the
neutral zone would exist in PY 1. We are also proposing the neutral
zone in PYs 2-6 would apply for final performance scores of 41-59
(inclusive). We believe that average performance should yield no upside
or downside risk payment.
We propose to define ``downside risk payment'' as a lump sum
payment the IOTA participant would be required to pay to CMS after a PY
if the IOTA participant's final performance score falls within the
ranges specified in section III.C.6.c.(2).(b). of this proposed rule.
We propose that there will be no downside risk payment in the PY 1. We
are proposing no downside risk payment in the first PY to allow IOTA
participants time to implement changes to improve performance prior to
facing downside risk. In PYs 2-6, we are proposing to introduce
downside risk payments. We propose that an IOTA participant's final
performance score of 40 or below in PYs 2-6, would result in a downside
risk payment. We believe that below average performance should yield a
downside risk payment.
The performance assessment scoring method, as described in section
III.C.5. of this proposed rule, was designed such that IOTA
participants with limited experience in APMs would still be likely to
achieve a sufficient final performance score that would result in no
downside risk payment. For example, it is expected that most IOTA
participants would earn around 30 of 60 possible points in the
achievement domain. We believe that average performance should be
neither rewarded nor penalized. We also considered eliminating the
neutral zone and only applying upside and downside performance
payments, narrowing the neutral zone score range (that is, 44-55), or
applying a wider-to-narrower phased-in approach over the model
performance period. We believed these alternative options would be less
flexible and more penalty-focused, with some IOTA participants more
likely to be penalized due to varying degrees of capabilities and
capacity that would limit their ability to achieve performance targets
as they progress and evolve over the model performance period. Thus, we
are opting to propose a neutral zone that would allow for more
opportunities and incentives to achieve improvements over time without
a large probability of downside risk.
[GRAPHIC] [TIFF OMITTED] TP17MY24.011
We seek feedback on the use of the final performance scores to
determine the upside risk payment, the downside risk payment, and the
neutral zone.
(2) Apply Payment Calculation Formula to Final Performance Score
We propose that after determining if an IOTA participant's final
performance score qualifies the IOTA participant for an upside risk
payment, downside risk payment, or the neutral zone, as described in
section III.C.6.c.(1). of this proposed rule, we would apply a
calculation formula unique to each PY to the final performance score,
as specified in sections III.C.6.c.(2).(a). through (c). of this
proposed rule.
(a) Upside Risk Payment
If, in PYs 1-6, an IOTA participant's final performance score is
greater than or equal to 60 points, we propose that the IOTA
participant would qualify for an upside risk payment. If an IOTA
participant's final performance score would qualify them for the upside
risk payment, we propose a methodology to calculate their upside risk
payment using the formula in equation 2, where:
$8,000 is a fixed, risk-based payment amount within the
calculation formula, estimated to be about 33 percent of the average
Medicare FFS kidney transplant MS-DRG cost. We aimed to create a strong
financial incentive with significant earning opportunity for IOTA
participants that meet or exceed model performance expectations. We
believe this amount or proportion of the MS-DRG to be a large financial
incentive to promote behavior changes while maintaining expectations of
net savings to Medicare. We calibrated this based on projection of the
incentive effects that would encourage the necessary support and
infrastructure investment needed to achieve high performance and
produce overall model savings and have the effects that we are looking
for.
The final performance score is the sum of points earned
from the achievement domain, efficiency domain, and quality domain in a
PY, as described in section III.C.5. of this proposed rule.
Medicare kidney transplants is the number of Medicare
kidney transplants furnished by the IOTA participant in a PY.
Equation 2: Proposed Upside Risk Payment Calculation Formula
Upside Risk Payment = $8,000 * ((Final Performance Score-60)/40) *
Medicare Kidney Transplants
We also considered calculating the maximum positive multiplier per
Medicare kidney transplant claim based on the Kidney Transplant Bonus
in the KCC Model. In 2019, the Kidney Transplant Bonus for entities
participating in the KCC Model was set to $15,000. Adjusted for
inflation, this is roughly $18,000, which would be the maximum
allowable positive bonus payment per transplant. The Kidney Transplant
Bonus was originally calculated based on the difference in spending
between a beneficiary who went on to get a transplant and the average
ESRD beneficiary cost.
[[Page 43573]]
However, we believe that the maximum positive adjustment may be too
large in relation to current Medicare payments for kidney transplants
for the model to yield net savings.
We also considered using a system similar to the Hospital VBP
Program under which CMS withholds 2 percent of participating's
hospitals Medicare payments and uses the sum of these reductions to
fund value-based incentive payments to hospitals based on their
performance under the program. However, we wished to have equal upside
and downside multipliers across IOTA participants.
We also considered adjusting the maximum upside multiplier in PYs
2-6; however, we felt making that decision prior to the start of the
model would be premature and wish to understand IOTA participant
performance before making such a decision.
We seek comment on our proposed methodology to calculate the upside
risk payment and alternatives considered.
(b) Downside Risk Payment
If an IOTA participant's final performance score is at or below 40
points in PYs 2--6, the IOTA participant would qualify for a downside
risk payment. If an IOTA participant qualifies for a downside risk
payment, we describe the methodology to calculate their downside risk
payment risk using the formula in equation 3:
Equation 3: Proposed Downside Risk Payment Calculation Formula
Downside Risk Payment = $2,000 * ((40-Final Performance Score)/40) *
Medicare Kidney Transplants
$2,000 is a fixed, risk-based payment amount within the
calculation formula, estimated to be about one-twelfth, or 8 percent,
of the average Medicare FFS kidney transplant MS-DRG cost. We are
proposing a lower downside-risk value relative to the upside-risk value
proposed for the upside risk payments (about one-fourth lower) because
we wanted to maintain a greater rewards approach, while still holding
IOTA participants accountable for poor performance. We also believe
that this approach is more flexible and accommodating to IOTA
participants with no, or limited, APM experience, or that are more
limited in terms of resources and capabilities.
The final performance score is the sum of points earned
from the achievement domain, efficiency domain, and quality domain, as
described in section III.C.5. of this proposed rule.
Medicare kidney transplants is the count of furnished
Medicare kidney transplants during the PY.
We also considered applying the same fixed amount to both the
upside and downside risk payment ($8,000 or $2,000 in both) or having
the downside risk payment be 50 percent of the fixed amount of the
upside risk payment ($4,000) but opted against it to maintain lower
levels of risk given the fact that this model would be mandatory for
eligible kidney hospitals. As discussed in section III.C.6.b of this
proposed rule, we considered an upside-risk only payment framework,
thus eliminating the application of downside-risk payments. Recognizing
the potential for volatility in performance year-over-year, we also
considered requiring IOTA participants to owe downside-risk payments to
CMS if their final performance score was at or below 40 for more than
one PY, starting from PY 1, potentially giving IOTA participants a
similar phased-in, or, rather, ramp-up, opportunity to adjust and
improve before downside-risk payments kick in. We considered this
option to be unnecessary and operationally complex, particularly as it
would function in a similar way as our proposed approach from a
phasing-in standpoint. We also considered adjusting the $2,000 fixed,
risk-based payment amount for PYs 2--6; however, we believe a fixed
amount would provide greater transparency to IOTA participants on
financial risk and model implementation experience would better inform
if this approach would be necessary.
We seek comment on our proposed downside risk payment calculation
formula, and alternatives considered.
(c) Neutral Zone
If, in PY 1, an IOTA participant's final performance score was
below 60 points, or if, in PYs 2-6, an IOTA participant's final
performance score was between 41 and 59 (inclusive), we propose that
the final performance score, as described in section III.C.6.c.(1). of
this proposed rule, would qualify the IOTA participant for the neutral
zone, where no upside risk payment or downside risk payment would
apply. As such, in a PY where an IOTA participant's final performance
score falls in the neutral zone, no money would be paid to the IOTA
participant by CMS, nor would money be owed by the IOTA participant to
CMS.
We seek comment on our proposed neutral zone.
(3) Payments Operations and Timelines
After the end of each PY, CMS would assess each IOTA participant's
performance in accordance with section III.C.5. of this proposed rule
and calculate performance-based payments in accordance with the
methodology specified in section III.C.6.c. of this proposed rule. We
propose to define this process as ``preliminary performance assessment
and payment calculations.''
We propose that CMS would conduct and calculate preliminary
performance assessment and payment calculations at least 3 to 6 months
after the end of each PY to allow for sufficient Medicare kidney
transplant claims runout. We propose that CMS would notify IOTA
participants of their preliminary model performance assessment,
including the IOTA participant's score for each metric within the
achievement domain, efficiency domain, and quality domain and the final
performance score, and payment calculations with respect to any
applicable upside risk payment or downside risk payment, at least 5 to
9 months after the end of each PY, allowing for a two-to-three month
period for CMS to conduct calculations after the claims runout period.
We propose that a 30-day notification period between preliminary and
final calculations would apply, giving IOTA participants 30 days to
review preliminary data and calculations and request targeted reviews,
as described in section III.C.6.c.(4). of this proposed rule. This 30-
day notification period would also be intended to provide IOTA
participants with advance notice of forthcoming performance-based
payments before upside risk payments or demand letters for downside
risk payments would be issued by CMS. We also propose that CMS would
notify IOTA participants of their model performance assessment and
payment calculations in a form and manner determined by CMS, such as
letters, email, or model dashboard. We propose that CMS would notify
the IOTA participant of their final performance score and any
associated upside risk payment or downside risk payment at least 30
days after notifying the IOTA participant of their preliminary model
performance assessment and payment calculations.
We propose that after CMS notifies the IOTA participant of their
final performance score and any associated upside risk payment and by a
date determined by CMS, CMS would issue the upside risk payment to the
tax identification number (TIN) on file for the IOTA participant in the
Medicare Provider Enrollment, Chain, and Ownership System (PECOS).
We propose that after CMS notifies the IOTA participant of their
final
[[Page 43574]]
performance score and any associated downside risk payment and by a
date determined by CMS, CMS would issue a demand letter to the TIN on
file in PECOS for the IOTA participant for downside risk payments owed
to CMS, with a payment due date of at least 60 days after the date on
which the demand letter is issued. We propose that the demand letter
would include details on model performance, the downside risk payment,
and how payments would be made to CMS.
Rather than the proposed lump-sum payment and demand letter
approach, we also considered making the upside risk payments and
downside risk payments to IOTA participants in the form of Medicare FFS
claim adjustments. The benefit of this approach would be that upside
risk payments and downside risk payments, which are retrospective,
would be applied prospectively and spread out over a 12-month period,
so that a transplant hospital would not need to pay back to CMS a large
sum of monies owed all at once. However, we believe that this approach
would delay model payments and collection of monies owed to CMS. We
also consider this approach to be disruptive to standard claims
processing systems and operationally complex, with more opportunities
for error and less flexibility to correct errors in a timely manner.
We seek comment on our proposed payment operations and timeline and
alternative considered.
(4) Targeted Review
We believe that CMS calculation errors are possible, and therefore
IOTA participants should be able to dispute the results of
calculations.
Thus, upon receipt of CMS issued notifications of preliminary
performance assessment and payment calculations, as described in
section III.C.6.c.(3). of this proposed rule, we propose that IOTA
participants may appeal via a ``targeted review process,'' defined as
the process in which an IOTA participant could dispute performance
assessment and payment calculations made, and issued, by CMS.
We propose that an IOTA participant would be able to request a
targeted review for one or more calculations made and issued by CMS
within the preliminary performance assessment and payment calculations.
We propose that an IOTA participant would be able to request a targeted
review for CMS consideration if--
The IOTA participant believes an error occurred in
calculations due to data quality or other issues; or
The IOTA participant believes an error occurred in
calculations due to misapplication of methodology.
We propose that an IOTA participant would be required to submit a
targeted review request within 30 days, or another time period as
specified by CMS, of receiving its preliminary performance assessment
and payment calculations from CMS. We also propose the request would
require supporting information from the IOTA participant, in a form and
manner specified by CMS. The 30-day window to appeal generally aligns
with the length of time we have finalized for submitting appeals in
other CMS models, such as the ETC Model, as well as under the Hospital
VBP Program, and we believe would allow ample time for IOTA
participants to separately review CMS calculations.
We propose that the targeted review process would not provide IOTA
participants the ability to dispute policy and methodology, as it would
be limited to the dispute of calculations. Specifically, we propose
that CMS will not consider targeted review requests regarding, without
limitation, the following:
The selection of the kidney transplant hospital to be an
IOTA participant.
The attribution of IOTA waitlist patients and the
attribution of IOTA transplant patients to the IOTA participant, or to
any other kidney transplant hospital selected for participation in the
IOTA Model, or to any kidney transplant hospital not selected for
participation in the IOTA Model.
The methodology used for determining the achievement
domain, efficiency domain, and quality domain.
The methodology used for calculating and assigning points
for each metric within the achievement domain, efficiency domain, and
quality domain.
The methodology used for calculating the payment amount
per Medicare kidney transplant paid to an IOTA participant.
We propose that a targeted review request that includes one or more
of the exclusions under Sec. 512.434(c)(1) could still be reviewed by
CMS, given that all remaining considerations of the request meet all
other criteria for consideration by CMS.
Upon receipt of a targeted review request from an IOTA participant,
we propose that CMS would conduct an initial assessment and final
assessment of the targeted review. We believe that this proposal would
be in line with other CMS models.
The CMS targeted review initial assessment would determine if the
targeted review request met the targeted review requirements and
contained sufficient information to substantiate the request. If the
request was not compliant with the requirements or required additional
information, CMS would follow up with IOTA participants to request
additional information in a form and manner determined by CMS. Any
additional information that CMS requests from an IOTA participant would
be due to CMS within 30 days of CMS's request, also in a form and
manner determined by CMS. An IOTA participant's non-responsiveness to
the request for additional information from CMS could result in the
closure of the targeted review request.
In a final assessment, CMS would determine whether it erred in a
calculation, as disputed by the IOTA participant.
CMS's correction of an error may delay the date of payment of an
IOTA participant's upside risk payments or downside risk payments.
Were a calculation error to be found as a result of an IOTA
participant's targeted review request, we would notify the IOTA
participant within 30 days of any findings in a form and manner
determined by CMS and resolve and correct the error and discrepancy in
the amount of the upside risk payment or downside risk payment in a
time and manner as determined by CMS.
We propose that targeted review decisions made by CMS would be
final, unless submitted by the IOTA participant or CMS for a CMS
Administrator review. We are also proposing to include the
reconsideration determination process as outlined in proposed Sec.
512.190 in the IOTA Model.
We note that if an IOTA participant has regular Medicare FFS claims
issues or decisions that it wishes to appeal (that is, issues during
the model performance period with Medicare FFS that are unrelated to
the model performance and payment calculations and payments), then the
IOTA participant should continue to use the standard CMS procedures.
Section 1869 of the Act provides for a process for Medicare
beneficiaries, providers, and suppliers to appeal certain claims and
decisions made by CMS.
We seek comment on our proposals regarding the process by which an
IOTA participant could request a targeted review of CMS calculations.
[[Page 43575]]
(5) Extreme and Uncontrollable Circumstances
Events may occur outside the purview and control of the IOTA
participant that may affect their performance in the model. In the
event of extreme and uncontrollable circumstances, such as a public
health emergency, we propose that CMS may reduce the downside risk
payment, if any, prior to recoupment by an amount determined by
multiplying the downside risk payment by the percentage of total months
during the PY affected by an extreme and uncontrollable circumstance,
by the percentage of attributed patients who reside in an area affected
by the extreme and uncontrollable circumstance. We are proposing to
address only the downside risk payment under this policy, as we wish to
mitigate the harm to entities due to extreme and uncontrollable
circumstances. We considered applying this policy to upside risk
payments and final performance scores in the neutral zone, but we
believe that IOTA participants that have been able to achieve model
success do not need to be made whole by this policy.
We propose to apply determinations made under the Quality Payment
Program with respect to whether an extreme and uncontrollable
circumstance has occurred, and the affected areas, during the PY. We
chose the Quality Payment Program to align across Innovation Center
models and CMS policy. We propose that CMS has the sole discretion to
determine the time period during which an extreme and uncontrollable
circumstance occurred and the percentage of attributed patients
residing in affected areas for the IOTA participant.
We request comment on our extreme and uncontrollable circumstances
policy and whether the determinations by the Quality Payment Program
that an extreme and uncontrollable circumstance has occurred should
apply to IOTA participants.
7. Data Sharing
a. General
We expect that IOTA participants would work toward independently
identifying and producing their own data, through electronic health
records, health information exchanges, or other means that they believe
are necessary to best evaluate the health needs of their patients,
improve health outcomes, and produce efficiencies in the provision and
use of services.
To assist IOTA participants in this process, we propose to provide
IOTA participants with certain beneficiary-identifiable data for their
Medicare beneficiaries who are attributed patients, upon request. We
anticipate that IOTA participants would use this data to better assess
transplant readiness and post-transplant outcomes. We also propose to
provide certain aggregate data that has been de-identified in
accordance with the HIPAA Privacy Rule, 45 CFR 164.514(b), as discussed
below, for the purposes of helping IOTA participants understand their
progress towards the model's performance metrics.
Specifically, subject to the limitations discussed in this proposed
rule, and in accordance with applicable law, including the HIPAA
Privacy Rule, we propose that CMS may offer an IOTA participant an
opportunity to request certain Medicare beneficiary-identifiable data
and reports as discussed in section III.C.7.b of this proposed rule. We
propose that CMS would share beneficiary identifiable data with IOTA
participants on the condition that the IOTA participants, their IOTA
collaborators, and other individuals or entities performing functions
or services related to the IOTA participant's activities observe all
relevant statutory and regulatory provisions regarding the appropriate
use of data and the confidentiality and privacy of individually
identifiable health information, and comply with the terms of the data
sharing agreement described in this section of the proposed rule.
We propose that the beneficiary-identifiable claims data described
in section III.C.7.b of this proposed rule would omit individually
identifiable data for Medicare beneficiaries who have opted out of data
sharing with the IOTA participant, as described in section III.C.7.c of
this proposed rule. We also note that, for the beneficiary-identifiable
claims data, we would exclude information that is subject to the
regulations governing the confidentiality of substance use disorder
patient records (42 CFR part 2) from the data shared with an IOTA
participant.
b. Beneficiary-Identifiable Data
(1) Legal Authority To Share Beneficiary-Identifiable Data
We believe that an IOTA participant may need access to certain
Medicare beneficiary-identifiable data for the purposes of evaluating
its performance, conducting quality assessment and improvement
activities, conducting population-based activities relating to
improving health or reducing health care costs, or conducting other
health care operations listed in the first or second paragraph of the
definition of ``health care operations'' under the HIPAA Privacy Rule,
45 CFR 164.501.
We propose that, subject to providing the beneficiary with the
opportunity to decline data sharing as described in section III.C.10.a
of this proposed rule, and subject to having a valid data sharing
agreement in place, an IOTA participant may request from CMS certain
beneficiary identifiable claims for attributed patients who are
Medicare beneficiaries.
We recognize there are sensitivities surrounding the disclosure of
individually identifiable (beneficiary-specific) health information,
and several 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 the
disclosure. Here, the HIPAA Privacy Rule would allow for the proposed
disclosure of individually identifiable health information by CMS.
Under the HIPAA Privacy Rule, covered entities (defined in 45 CFR
160.103 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. IOTA 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. In light of these relationships, we believe
that the proposed disclosure of the beneficiary-identifiable data under
the IOTA model would be permitted by the HIPAA Privacy Rule under the
provisions that permit disclosures of PHI for ``health care
operations'' purposes. Under those provisions, a covered entity is
permitted to disclose PHI to another covered entity for the recipient's
health care operations purposes if both covered entities have or had a
relationship with the subject of the PHI to be disclosed, the PHI
pertains to that relationship, and the recipient will use the PHI for a
``health care
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operations'' function that falls within the first two paragraphs of the
definition of ``health care operations'' in the HIPAA Privacy Rule (45
CFR 164.506(c)(4)).
The first paragraph of the definition of health care operations
includes ``conducting quality assessment and improvement activities,
including outcomes evaluation and development of clinical guidelines,''
and ``population-based activities relating to improving health or
reducing health costs, protocol development, case management and care
coordination.'' The second paragraph of the definition of health care
operations includes ``evaluating practitioner and provider
performance'' (45 CFR 164.501).
Under our proposal, IOTA participants would be using the data on
their patients to evaluate the performance of the IOTA participant and
other providers and suppliers that furnished services to the patient,
conduct quality assessment and improvement activities, and conduct
population-based activities relating to improved health for their
patients. When done by or on behalf of a covered entity, these are
covered functions and activities that would qualify as ``health care
operations'' under the first and second paragraphs of the definition of
health care operations at 45 CFR 164.501. Hence, as previously
discussed, we believe that this provision is extensive enough to cover
the uses we would expect an IOTA participant to make of the
beneficiary-identifiable data and would be permissible under the HIPAA
Privacy Rule. Moreover, our proposed disclosures would be made only to
HIPAA covered entities that have (or had) a relationship with the
subject of the information, the information we would disclose would
pertain to such relationship, and those disclosures would be for
purposes listed in the first two paragraphs of the definition of
``health care operations.'' Finally, the proposed disclosures would be
limited to beneficiary-identifiable data that we believe would meet
HIPAA requirements in 45 CFR 164.502(b) to limit PHI to the minimum
necessary to accomplish the intended purpose of the use, disclosure, or
request.
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, would 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) and the Health Resources
and Services Administration (HRSA) Organ Procurement and
Transplantation Network (OPTN)/Scientific Registry of Transplant
Recipients (SRTR) Data System. We believe that the proposed data
disclosures are consistent with the purposes for which the data
discussed in the proposed rule were collected and may be disclosed in
accordance with the routine uses applicable to those records.
We propose that CMS would share the following beneficiary-
identifiable lists and data with IOTA participants that have submitted
a formal request for the data. Under our proposal, the request must be
submitted on an annual basis in a manner and form and by a date
specified by CMS. The request also would need to identify the data
being requested and include an attestation that (A) the IOTA
participant is requesting this beneficiary-identifiable data as a HIPAA
covered entity or as a business associate, as those terms are defined
at 45 CFR 160.103, to the IOTA participant's providers and suppliers
who are HIPAA covered entities; and (B) the IOTA participant's request
reflects the minimum data necessary for the IOTA participant to conduct
health care operations work that falls within the first or second
paragraph of the definition of health care operations at 45 CFR
164.501. In addition, IOTA participants who request this data must have
a valid and signed data sharing agreement in place, as described in
more detail later in this section. We propose that we would make
available beneficiary-identifiable data as described in section
III.C.8.b. of this proposed rule for IOTA participants to request for
purposes of conducting health care operations that falls within the
first or second paragraph of the definition of health care operations
at 45 CFR 164.501 on behalf of their attributed patients who are
Medicare beneficiaries. We believe that access to beneficiary-
identifiable claims data would improve care coordination between IOTA
participants and other health care providers. Patients can spend months
in between their visits to the kidney transplant hospital at which they
are listed, and the post-transplant period is critical to transplant
success. We believe that improved care coordination would improve
outcomes and keep patients engaged in their care.
We also propose that IOTA participants limit the request for
beneficiary-identifiable claims data to Medicare beneficiaries whose
name appears on the quarterly attribution list who have been notified
in compliance with section III.C.10.a. of this proposed rule, and who
did not decline having their claims data shared with the IOTA
participant, as proposed in section III.C.7.d. of this proposed rule.
Finally, we propose that CMS would share beneficiary identifiable data
with an IOTA participant on the condition that the IOTA participant,
its IOTA collaborators, and other individuals or entities performing
functions or services related to the IOTA participant's activities,
observe all relevant statutory and regulatory provisions regarding the
appropriate use of data and the confidentiality and privacy of
individually identifiable health information and comply with the terms
of the data sharing agreement described in section III.C.7.f. of this
proposed rule.
(2) Quarterly Attribution Lists
We propose that this data would include, for the relevant PY, a
beneficiary attribution report, shared quarterly, that would include a
list of attributed patients and patients who have been de-attributed
from the IOTA participant. We propose that the report would include at
least the following information for each attributed patient: the
attribution year the attributed patient became attributed to the IOTA
participant; the effective date of the attributed patient's attribution
to the IOTA participant; the effective date of the patient's de-
attribution from the IOTA participant and the reason for such removal
(if applicable); and the attributed patient's data sharing preferences
made pursuant to section III.C.7.d. of this proposed rule. We propose
that CMS may include additional information at its discretion in any of
the quarterly attribution reports as data becomes available. Such
[[Page 43577]]
data may include information from the SRTR or OPTN on waitlist status
or transplant status.
We request comment on whether such additional information would be
beneficial to IOTA participants or whether this information is best
accessed by the IOTA participant through other means.
(3) Beneficiary-Identifiable Claims Data
We propose to offer certain beneficiary-identifiable claims data to
IOTA participants no later than 1 month after the start of each PY, in
a form and manner specified by CMS. We propose that IOTA participants
may retrieve this data at any point during the relevant PY and that it
would include, at a minimum--
Three years of historical Parts A, B, and D claims data
files for attributed patients who are Medicare beneficiaries for 36
months immediately preceding the effective date of the Medicare
beneficiary's attribution to the IOTA participant;
Monthly Parts A, B, and D claims data files specified for
attributed patients who are Medicare beneficiaries; and
Monthly Parts A, B, and D claims data files for Medicare
beneficiaries who have been de-attributed from the IOTA participant for
claims with a date of service prior to the date the Medicare
beneficiary was removed from attribution to the IOTA participant.
We propose that CMS would omit from the beneficiary-identifiable
claims data any substance use disorder patient records subject to 42
U.S.C. 290dd-2 and the implementing regulations at 42 CFR part 2.
We believe these data elements would consist of the minimum data
element necessary for IOTA participants to effectively manage the care
of Medicare beneficiaries who are attributed patients. Specifically,
this data would allow IOTA participants to coordinate care across the
continuum as Medicare beneficiaries who are attributed patients
transition from IOTA waitlist patients to IOTA transplant patients.
c. Minimum Necessary Data
We propose IOTA participants must limit their beneficiary-
identifiable data requests to the minimum necessary to accomplish a
permitted use of the data. We propose the minimum necessary Parts A and
B data elements may include, but are not limited to, the following data
elements:
Beneficiary Identification (ID).
Procedure code.
Gender.
Diagnosis code.
Claim ID.
The from and through dates of service.
The provider or supplier ID.
The claim payment type.
Date of birth and death, if applicable.
Tax Identification Number (TIN).
National Provider Identification (NPI).
We propose the minimum necessary Part D data elements may include,
but are not limited to, the following data elements:
Beneficiary ID.
Prescriber ID.
Drug service date.
Drug product service ID.
Quantity dispensed.
Days supplied.
Brand name.
Generic name.
Drug strength.
TIN.
NPI.
Indication if on formulary.
Gross drug cost.
We request comment and feedback on the minimum beneficiary-
identifiable claims data necessary for IOTA participants to request for
purposes of conducting permissible health care operations purposes
under this model.
d. Medicare Beneficiary Opportunity To Decline Data Sharing
As described in section III.C.10.a. of this proposed rule, we
propose that Medicare beneficiaries must receive notification about the
IOTA model. We also propose that Medicare beneficiaries must be given
the opportunity to decline claims data sharing, and instructions on how
to inform CMS directly of their preference.
We propose that Medicare beneficiaries would be notified about the
opportunity to decline claims data sharing through the notifications
proposed in section III.C.10.a. of this proposed rule. We propose that
these notifications must state that the IOTA participant may have
requested beneficiary identifiable claims data about the Medicare
beneficiary for purposes of its care coordination and quality
improvement work and/or population-based activities relating to
improving health or reducing health care costs, and inform the Medicare
beneficiary how to decline having his or her claims information shared
with the IOTA participant in the form and manner specified by CMS. We
propose that Medicare beneficiary requests to decline claims data
sharing would remain in effect unless and until a beneficiary
subsequently contacts CMS to amend that request to permit claims data
sharing with IOTA participants.
We propose that Medicare beneficiaries may not decline to have the
aggregate, de-identified data proposed in section III.C.7.f. of this
proposed rule shared with IOTA participants. We also propose that
Medicare beneficiaries may not decline to have the: initial attribution
lists, quarterly attribution lists, and annual attribution
reconciliation list as proposed in section III.C.4.b.(2)., b.(3). and
b.(4). of this proposed rule shared with IOTA participants. We note
that, in accordance with 42 U.S.C. 290dd-2 and its implementing
regulations at 42 CFR part 2, CMS does not share beneficiary
identifiable claims data relating to the diagnosis and treatment of
substance use disorders under this model.
We note that the proposed opt out provisions discussed in this
section would relate only to the proposed sharing of beneficiary-
identifiable data between the Medicare program and the IOTA participant
under the IOTA Model, and are in no way intended to impede existing or
future data sharing under other authorities or models.
We request comment and feedback on our proposed policies to enable
Medicare beneficiaries to decline data sharing.
e. Data Sharing Agreement
(1) General
As noted in section III.C.7.a. of this proposed rule, we propose
that, prior to receiving any beneficiary-identifiable data, IOTA
participants would be required to first complete, sign, and submit--and
thereby agree to the terms of--a data sharing agreement with CMS. We
propose that under the data sharing agreement, the IOTA participant
would be required to comply with the limitations on use and disclosure
that are imposed by HIPAA, the applicable data sharing agreement, and
the statutory and regulatory requirements of the IOTA Model. We also
propose that the data sharing agreement would include certain
protections and limitations on the IOTA participant's use and further
disclosure of the beneficiary-identifiable data and would be provided
in a form and manner specified by CMS. Additionally, we propose that an
IOTA Participant that wishes to retrieve the beneficiary-identifiable
data would be required to complete, sign, and submit to CMS a signed
data sharing agreement at least annually. We believe that it is
important for the IOTA Participant to complete and submit a signed data
sharing agreement at least annually so that CMS has up-to-date
information that the IOTA participant wishes to retrieve the
[[Page 43578]]
beneficiary-identifiable data and information on the designated data
custodian(s). As described in greater detail later in this section, we
propose that a designated data custodian would be the individual(s)
that an IOTA 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 IOTA participant to first
complete and submit a signed 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
IOTA participant. As noted 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 IOTA participants would be further protected
in an appropriate fashion.
We solicit public comment on our proposal to require that the IOTA
participant agree to comply with all applicable laws and terms of the
data sharing agreement as a condition of retrieving beneficiary-
identifiable data, and on our proposal that the IOTA participant would
need to submit the signed data sharing agreement at least annually if
the IOTA participant wishes to retrieve the beneficiary-identifiable
data.
(2) Content of the Data Sharing Agreement
We propose that CMS would share the following beneficiary-
identifiable data with IOTA participants that have requested the data
and have a valid data sharing agreement in place, as described in more
detail later in this section. We propose that an IOTA participant that
wishes to receive beneficiary-identifiable data for its attributed
patients who are Medicare beneficiaries must also 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 at 45 CFR part 160 and part
164, subparts A and E, and the requirements of the proposed IOTA model;
(2) to comply with additional privacy, security, breach notification,
and data retention requirements specified by CMS in the data sharing
agreement; (3) to contractually bind each downstream participant of the
beneficiary-identifiable data that is a business associate of the IOTA
participant, including all IOTA collaborators, to the same terms and
conditions with the IOTA participant is itself bound in its data
sharing agreement with CMS as a condition of the business associate's
receipt of the beneficiary-identifiable data retrieved by the IOTA
participant under the IOTA model; and (4) that if the IOTA 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, CMS may: (A) deem the IOTA participant ineligible to
retrieve the beneficiary-identifiable data under paragraph (b) of this
section for any amount of time; (B) terminate the IOTA participant's
participation in the IOTA model under Sec. 512.466; and (C) subject
the IOTA participant to additional sanctions and penalties available
under the law.
CMS believes that these proposed terms for sharing beneficiary-
identifiable data with IOTA participants are appropriate and important,
as CMS must ensure to the best of its ability that any beneficiary-
identifiable data that it shares with IOTA participants would be
further protected by the IOTA participant, and any business associates
of the IOTA participant, in an appropriate fashion.
CMS seeks public comment on the additional privacy, security,
breach notification, and other requirements that we would include in
the 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
data sharing agreement provisions would not prohibit the IOTA
participant from making any disclosures of the data otherwise required
by law.
CMS also seeks public comment on what specific disclosures of the
beneficiary identifiable data might be appropriate to permit or
prohibit under the data sharing agreement. For example, CMS is
considering prohibiting, in the data sharing agreement, any further
disclosure, not otherwise required by law, of the beneficiary-
identifiable data 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 attributed patient
who is a Medicare 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 certain beneficiary-identifiable data with model participants
for their health care operations.
CMS is considering these possibilities because there exist
important legal and policy limitations on the sharing of the
beneficiary-identifiable data and CMS must carefully consider the ways
in which and reasons for which we would provide access to this data for
purposes of the IOTA model. CMS believes that some IOTA 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 IOTA participant's review of their
care management and coordination, quality improvement activities, or
clinical treatment of IOTA 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 IOTA beneficiary.
We seek public comment on how an IOTA participant might need to,
and want to, disclose the beneficiary-identifiable data to other
individuals and entities to accomplish the goals of the IOTA model, in
accordance with applicable law.
Under our proposal, the 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 data sharing agreement, a requirement that the IOTA
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 data
sharing agreement; various security requirements like those found in
participation agreements for other models tested under section 1115A of
the Act, but no less restrictive than those provided in the relevant
Privacy
[[Page 43579]]
Act system of records notices; how and when beneficiary-identifiable
data could be retained by the IOTA 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 data
sharing agreement.
We solicit public comment on this proposal to impose certain
requirements in the IOTA data sharing agreement related to privacy,
security, data retention, breach notification, and data destruction.
f. Aggregate Data
We propose that CMS would share certain aggregate performance data
with IOTA participants in a form and manner to be specified by CMS.
This aggregate data would be de-identified in accordance with HIPAA
requirements at 45 CFR 164.514(b) and would include, when available,
transplant target data.
We propose that, for the relevant PY, CMS would provide aggregate
data to the IOTA participant detailing the IOTA participant's
performance against the transplant target, as described in section
III.C.5.c.(2). of this proposed rule.
We seek comment and feedback on our proposal to share aggregate
data with IOTA participants.
8. Other Requirements
a. Transparency Requirements
(1) Publication of Patient Selection Criteria for Kidney Transplant
Evaluations
Transplant hospitals are currently required to use written patient
selection criteria in determining a patient's suitability for placement
on the waitlist or a patient's suitability for transplantation per the
CoP (see 42 CFR part 482.90). If the transplant hospital performs
living donor transplants, the transplant hospital must use written
donor selection criteria to determine the suitability of candidates for
donation.\264\ The patient selection criteria must ensure fair and non-
discriminatory distribution of organs, and the program must document in
the patient's medical record the patient selection criteria used.\265\
Prior to placement on the transplant hospital's waitlist, a prospective
transplant candidate must receive a psychosocial evaluation, if
possible.\266\ Before a transplant hospital places a transplant
candidate on its waitlist, the candidate's medical record must contain
documentation that the candidate's blood type has been determined.\267\
In addition, when a patient is placed on a hospital's waitlist or is
selected to receive a transplant, the transplant hospital must document
in the patient's medical record the patient selection criteria
used.\268\ Currently, the transplant hospital must also provide a copy
of its patient selection criteria to a transplant patient, or a
dialysis facility, as requested by the patient or a dialysis facility.
For living donor selection, the transplant hospital's living donor
selection criteria must be consistent with the general principles of
medical ethics.269 270 Transplant hospitals must also ensure
that a prospective living donor receives a medical and psychosocial
evaluation, document in the living donor's medical records the living
donor's suitability for donation, and document that the living donor
has given informed consent.\271\
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\264\ https://www.ecfr.gov/current/title-42/section-482.90.
\265\ Ibid.
\266\ Ibid.
\267\ Ibid.
\268\ Ibid.
\269\ OPTN. (n.d.). OPTN Policies--Living Donation, Chapter 14.
https://optn.transplant.hrsa.gov/media/eavh5bf3/optn_policies.pdf.
\270\ AMA Council on Ethical and Judicial Affairs. (2019). AMA
Code of Medical Ethics' Opinions on Organ Transplantation. AMA
Journal of Ethics, 14(3), 204-214. https://doi.org/10.1001/virtualmentor.2012.14.3.coet1-1203.
\271\ https://www.ecfr.gov/current/title-42/section-482.90.
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Available data and studies demonstrate that disparities exist for
patients in underserved communities who seek or are referred for, or
are evaluated for a transplant and who eventually are placed on a
transplant waitlist and receive an organ transplant.\272\ For instance,
the data has shown that White patients are more likely than Black
patients to be referred for organ transplant, while Black patients are
less likely than White patients to be referred for transplant
evaluation.\273\ Racial disparities also exist in transplant wait
listing, even after correcting for SDOH.\274\ In addition, there are
sex and gender disparities in access to the kidney transplant waitlist,
with men more likely to have access compared to women.\275\ Finally, a
recent article in the Journal of the American Medical Association
considers how transplant programs factor patient financial resources
into waitlist decisions.\276\ The authors' review of several studies
suggest that socioeconomically deprived patients were proportionally
less likely to be selected for placement on a waitlist for an organ
transplant. They suggest, based on the strong and consistent
associations between race and poverty, that ``withholding transplants
from those with inadequate financial resources equates to an example of
structural racism in the health care system.'' We refer readers to the
numerous additional studies regarding disparities in organ
transplantation and organ donation that are cited throughout this
proposed rule.
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\272\ Park, C., Jones, M.-M., Kaplan, S., Koller, F.L., Wilder,
J.M., Boulware, L.E., & McElroy, L.M. (2022). A scoping review of
inequities in access to organ transplant in the United States.
International Journal for Equity in Health, 21(1). https://doi.org/10.1186/s12939-021-01616-x.
\273\ Epstein, A.M., Ayanian, J.Z., Keogh, J.H., Noonan, S.J.,
Armistead, N., Cleary, P.D., Weissman, J.S., David-Kasdan, J.A.,
Carlson, D., Fuller, J., Marsh, D., & Conti, R.M. (2000). Racial
Disparities in Access to Renal Transplantation--Clinically
Appropriate or Due to Underuse or Overuse? New England Journal of
Medicine, 343(21), 1537-1544. https://doi.org/10.1056/nejm200011233432106.
\274\ Ng, Y.-H., Pankratz, V.S., Leyva, Y., Ford, C.G., Pleis,
J.R., Kendall, K., Croswell, E., Dew, M.A., Shapiro, R., Switzer,
G.E., Unruh, M.L., & Myaskovsky, L. (2019). Does Racial Disparity in
Kidney Transplant Wait-listing Persist After Accounting for Social
Determinants of Health? Transplantation, 1. https://doi.org/10.1097/tp.0000000000003002.
\275\ Ahern, Patrick et al. Sex Disparity in Deceased-Donor
Kidney Transplant Access by Cause of Kidney Disease. 2021. Clinical
Journal of the American Society of Nephrology. 16 (2) 241-250,
https://cjasn.asnjournals.org/content/16/2/241.
\276\ Wadhwani, S.I., Lai, J.C., & Gottlieb, L.M. (2022).
Medical Need, Financial Resources, and Transplant Accessibility.
JAMA, 327(15), 1445. https://doi.org/10.1001/jama.2022.5283.
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To improve transparency for those looking to gain access to a
transplant waitlist in the transplant program evaluation processes, we
propose to require IOTA participants to publicly post, on a website,
their patient selection criteria for evaluating patients for addition
to their kidney transplant waitlist by the end of PY 1. We propose to
finalize this requirement only if it is not redundant with other HHS
guidance. We also considered requiring that IOTA participants update
their selection criteria at a certain frequency to ensure that
attributed patients have the most up to date information. However, we
are unsure what cadence of update would be most appropriate.
We solicit public comments on this proposal and on how often the
selection criteria should be updated by the IOTA participant.
(2) Transparency Into Kidney Transplant Organ Offers
Those active on a kidney transplant waitlist may receive organ
offers at any time. However, there is currently no
[[Page 43580]]
requirement for providers to discuss organ offers with their patients.
A provider may decline an organ offer for any number of reasons;
however, declining without disclosing the rationale with the patient
may miss an important opportunity for shared decision-making.
We propose to add requirements to increase transparency for IOTA
waitlist patients who are Medicare beneficiaries regarding the volume
of organ offers received on their behalf while on the waitlist.
Specifically, we propose that for each month an organ is offered for an
IOTA waitlist patient who is a Medicare beneficiary, an IOTA
participant must inform the Medicare beneficiary, on a monthly basis,
of the number of times an organ is declined on the Medicare
beneficiary's behalf and the reason(s) for the decline. We are not
proposing to prescribe the method of this notification, but would
require that the medical record reflect that the patient received this
information and the method by which it was delivered (for example,
mail, email, medical appointment, internet portal/dashboard, etc.). We
propose that this information must be shared with the IOTA waitlist
patient who is a Medicare beneficiary, and should be shared, where
deemed appropriate, with their nephrologist or nephrology professional,
to provide the opportunity for questions and clarification of
information.
Organ offer filters are a tool that transplant programs can use to
bypass organ offers they would not accept. Offer filters were tested
during two pilot programs and released nationally in January 2022.\277\
We propose that IOTA participants would be required to review
transplant acceptance criteria and organ offer filters with their IOTA
waitlist patients who are Medicare beneficiaries at least once every 6
months that the Medicare beneficiary is on their waitlist. We propose
that this review may be done on an individual basis in a patient visit,
via phone, email, or mail. We believe that sharing this information
with the patient would offer an opportunity for shared decision-making
between the patient and IOTA participants and may increase the
patient's quality of care. We propose that Medicare beneficiaries would
be able to decline this review with the IOTA participant, as some may
not wish to have this information. We anticipate that the Medicare
beneficiary may decline this review during their next provider visit or
over the phone.
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\277\ Optimizing Usage of Kidney Offer Filters--OPTN. (n.d.).
Optn.transplant.hrsa.gov. Retrieved March 11, 2023, from https://optn.transplant.hrsa.gov/policies-bylaws/public-comment/optimizing-usage-of-kidney-offer-filters/.
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We solicit public comment on whether an alternative frequency of
sharing of organ offers with the Medicare beneficiary is more
appropriate. We also solicit comment on whether there is a more
suitable timeframe and frequency for addressing acceptance criteria
with attributed patients. Per 42 CFR 482.94(c), and 482.102(a) and (c),
kidney transplant hospitals currently review these criteria with
patients upon patient request. Our goal is to provide a balance of
transparency and patient engagement in this process without being
overly prescriptive or burdensome. We also recognize that there are
beneficiaries on the waitlist who may not be eligible to receive an
organ offer for multiple years, so we seek feedback on whether this
requirement should be limited to beneficiaries who have received or are
likely to receive an organ offer in the next year.
(3) Publication of IOTA Participant Results
In the Specialty Care Models final rule (85 FR 61114), CMS
established certain general provisions in 42 CFR part 512 subpart A
that apply to all Innovation Center models. One such general provision
pertains to rights in data. Specifically, in the Specialty Care Models
final rule, we stated that to enable CMS to evaluate the Innovation
Center models as required by section 1115A(b)(4) of the Act and to
monitor the Innovation Center models pursuant to Sec. 512.150, in
Sec. 512.140(a) we would use any data obtained in accordance with
Sec. Sec. 512.130 and 512.135 to evaluate and monitor the Innovation
Center models (85 FR 61124). We also stated that, consistent with
section 1115A(b)(4)(B) of the Act, CMS would disseminate quantitative
and qualitative results and successful care management techniques,
including factors associated with performance, to other providers and
suppliers and to the public. We stated that the data to be disseminated
would include, but would not be limited to, patient de-identified
results of patient experience of care and quality of life surveys, as
well as patient de-identified measure results calculated based upon
claims, medical records, and other data sources. We finalized these
policies in 42 CFR part 512.140(a).
Consistent with these provisions, we propose to publish results
from all PYs of the IOTA Model. Specifically, for each PY, we intend to
post performance across the achievement domain, efficiency domain, and
quality domain for each IOTA participant. We would also identify each
IOTA participant for the PY. The results would be published on the IOTA
Model website. Given that we have proposed that the IOTA Model would
include a process for IOTA participants to request a targeted review of
the calculation of performance score which is calculated based on the
various rates we intend to publish, CMS anticipates that it would
publish these rates only after they have been finalized and CMS has
resolved any targeted review requests timely received from IOTA
participants under section II.E. of this proposed rule. We believe that
the release of this information would inform the public about the cost
and quality of care and about IOTA participants' performance in the
IOTA Model. This would supplement, not replace, the annual evaluation
reports that CMS is required to conduct and release to the public under
section 1115A(b)(4) of the Act.
We considered requiring IOTA participants to publish their
performance results on their own websites as well to increase
transparency; however, we did not want to place additional reporting
burden on IOTA participants, particularly because we propose that CMS
would publish the performance results, which should be adequate.
We seek comment on our intent to post this information to our
website, as well as the information we intend to post and the manner
and timing of the posting.
b. Health Equity Data Reporting
(1) Demographic Data Reporting
As previously discussed in section III.B. of this proposed rule,
and throughout this proposed rule, disparities exist throughout the
transplant process. These circumstances highlight the importance of
data collection and analysis that includes race, ethnicity, language,
disability, sexual orientation, gender identity, and sex
characteristics or other demographics by health care facilities. Such
data are necessary for integration of health equity in quality
programs, because the data permits stratification by patient
subpopulation.278 279 Stratified data can produce meaningful
measures that can be used to expose
[[Page 43581]]
health disparities, develop focused interventions to reduce them, and
monitor performance to ensure interventions to improve care do not have
unintended consequences for certain patients.\280\ Furthermore, quality
programs are carried out with well-known and widely used standardized
procedures, including but not limited to, root cause analysis, plan-do-
study-act (PDSA) cycles, health care failure mode effects analysis, and
fish bone diagrams. These are common approaches in the health care
industry to uncover the causes of problems, show the potential causes
of a specific event, test a change that is being implemented, prevent
failure by correcting a process proactively, and identify possible
causes of a problem and sort ideas into useful categories,
respectively.281 282 283 284 Adding a health equity prompt
to these standardized procedures integrates a health equity lens within
the quality structure and cues considerations of the patient
subpopulations who receive care and services from a transplant
hospital.\285\
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\278\ IOM (Institute of Medicine). 2009. Race, Ethnicity, and
Language Data: Standardization for Health Care Quality Improvement
(p.287). The National Academies Press https://www.ahrq.gov/sites/default/files/publications/files/iomracereport.pdf.
\279\ Sivashanker, K., & Gandhi, T.K. (2020). Advancing Safety
and Equity Together. New England Journal of Medicine, 382(4), 301-
303. https://doi.org/10.1056/nejmp1911700.
\280\ Weinick, R.M., & Hasnain-Wynia, R. (2011). Quality
Improvement Efforts Under Health Reform: How To Ensure That They
Help Reduce Disparities--Not Increase Them. Health Affairs, 30(10),
1837-1843. https://doi.org/10.1377/hlthaff.2011.0617.
\281\ American Society for Quality. (2019). What is root cause
analysis (RCA)? Asq.org. https://asq.org/quality-resources/root-cause-analysis.
\282\ Agency for Healthcare Research and Quality. (2020). Plan-
Do-Study-Act (PDSA) directions and examples. www.ahrq.gov. https://www.ahrq.gov/health-literacy/improve/precautions/tool2b.html.
\283\ Failure Modes and Effects Analysis (FMEA) Tool [verbar]
IHI--Institute for Healthcare Improvement. (2017). www.ihi.org.
https://www.ihi.org/resources/Pages/Tools/FailureModesandEffectsAnalysisTool.aspx.
\284\ Kane, R. (2014). How to Use the Fishbone Tool for Root
Cause Analysis. https://www.cms.gov/medicare/provider-enrollment-and-certification/qapi/downloads/fishbonerevised.pdf.
\285\ Sivashanker, K., & Gandhi, T.K. (2020). Advancing Safety
and Equity Together. New England Journal of Medicine, 382(4), 301-
303. https://doi.org/10.1056/nejmp1911700.
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To align with other Innovation Center efforts, we considered
proposing that, beginning with the first PY and each PY thereafter,
each IOTA participant would be required to collect and report to CMS
demographic and SDOH data pursuant to 42 CFR part 403.1110(b) for the
purposes of monitoring and evaluating the model. We considered
proposing that, in conducting the collection required under this
section, the IOTA participant would make a reasonable effort to collect
demographic and social determinants of health data from all attributed
patients but, in the case the IOTA participant attributed patient
elects not to provide such data to the IOTA participant, the IOTA
participant would indicate such election by the attributed patient in
its report to CMS.
We decided not to propose the collection of demographic data as
this data is already collected by OPOs and the SRTR, thereby making
such a requirement for purposes of this model potentially duplicative
and unnecessarily burdensome. We wish to minimize reporting burden on
IOTA participants where possible to ensure sufficient time and effort
is spent adjusting to the requirements of a mandatory model.
We solicit public comment on the decision not to propose the
collection of this data and potential applications.
(2) Health Related Social Needs (HRSN) Data Reporting
The Innovation Center is charged with testing innovations that
improve quality and reduce the cost of health care. There is strong
evidence that non-clinical drivers of health are the largest
contributor to health outcomes and are associated with increased health
care utilization and costs.286 287 These individual-level,
adverse social conditions that negatively impact a person's health or
healthcare are referred to as ``health-related social needs'' or
HRSNs.\288\ CMS aims to expand the collection, reporting, and analysis
of standardized HRSNs data in its efforts to drive quality improvement,
reduce health disparities, and better understand and address the unmet
social needs of patients. Standardizing HRSN Screening and Referral as
a practice can inform larger, community-wide efforts to ensure the
availability of and access to community services that are responsive to
the needs of Medicare beneficiaries.
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\286\ Booske, B.C., Athens, J.K., Kindig, D.A., Park, H., &
Remington, P.L. (2010). County Health Rankings (Working Paper).
https://www.countyhealthrankings.org/sites/default/files/differentPerspectivesForAssigningWeightsToDeterminantsOfHealth.pdf.
\287\ ROI Calculator for Partnerships to Address the Social
Determinants of Health Review of Evidence for Health-Related Social
Needs Interventions. (2019). https://www.commonwealthfund.org/sites/default/files/2019-07/COMBINED-ROI-EVIDENCE-REVIEW-7-1-19.pdf.
\288\ 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 NPRM (citing A Guide to Using the Accountable Health
Communities Health-Related Social Needs Screening Tool) 87 FR 38554
(June 28, 2022).
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HRSN screening is becoming increasingly common nationally, but
implementation is not uniform across geography or health care setting.
A literature review of national surveys measuring prevalence of social
screening found that almost half of State Medicaid agencies have
established managed care contracting requirements for HRSN screening in
Medicaid.\289\ It also found that health care payers and/or delivery
organizations reported a screening prevalence of 55-77 percent, with
``the highest estimate reported among American Hospital Association
member hospitals.'' \290\ Despite screening proliferation and generally
positive views toward screening among both patients and health care
providers, implementation of screening and referral policies for
beneficiaries of CMS programs with similar health--and even
demographic--profiles may be inconsistent, potentially exacerbating
disparities in the comprehensiveness and quality of care.
---------------------------------------------------------------------------
\289\ De Marchis, E., Brown, E., Aceves, B., Loomba, V., Molina,
M., Cartier, Y., Wing, H., Ma, L., & Gottlieb. (n.d.). State of the
Science of Screening in Healthcare Settings siren State of the
Science on Social Screening in Healthcare Settings Summer 2022.
https://sirenetwork.ucsf.edu/sites/default/files/2022-06/final%20SCREEN%20State-of-Science-Report%5B55%5D.pdf.
\290\ Ibid.
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One of the goals stated in the Innovation Center Strategy Refresh
for advancing system transformation is to require all new models to
collect and report demographic and SDOH data. Thus, in addition to the
proposed health equity requirements in section III.C.8.b. of this
proposed rule, we considered proposing a requirement that IOTA
participants conduct HRSN screening for at least four core areas--food
security, housing, transportation, and utilities. We recognize these
areas as some of the most common barriers to kidney transplantation and
the most pertinent for the IOTA participant patient population.
However, given the need for a psychosocial evaluation prior to addition
to the waitlist, we understand that such a requirement may be redundant
given current clinical practices, we have refrained from making such a
proposal.
We seek comment on whether we should include a requirement for IOTA
participants to conduct HRSN screening and report HRSN data in a form
and manner specified by CMS each PY for their attributed patients. We
are seeking input on following the questions in this section, and
comment on any aspect of the psychosocial evaluation of waitlisted
patients and how this compares to HRSN screenings for the four
domains--food security, housing, transportation, and utilities. Even if
CMS were to adopt an HRSN screening and reporting requirement in the
final rule, CMS might consider delaying the implementation of such a
requirement.
[[Page 43582]]
When evaluating a patient for potential addition to the
kidney transplant waitlist, what questions are asked as part of the
psychosocial evaluation?
How might a psychosocial evaluation compare to an HRSN
screening? What HRSNs are identified as part of a psychosocial
evaluation?
What data is collected from the psychosocial evaluation on
HRSNs?
If HRSNs are identified as part of the evaluation process,
what, if any, steps are taken to assist the patient in addressing these
needs and improving their transplant readiness?
If HRSNs are identified of a patient already on the
transplant waitlist, how might this affect their status on the
transplant waitlist? Could a patient be removed from the transplant
waitlist if HRSNs are identified that may impact transplant readiness?
What, if any, follow-up is conducted with waitlist
patients that have identified HRSNs?
Are there any concerns with HRSN screening and data
collection requirements?
c. Health Equity Plans
To further align with other Innovation Center models and promote
health equity across the transplant process, we propose that, for PY 2
through PY 6, each IOTA participant must submit to CMS, in a form and
manner and by the date(s) specified by CMS, a health equity plan. Given
that this would be a mandatory model, we propose that the health equity
plan be voluntary in the first PY of the model to allow IOTA
participants time to adjust to model requirements. We propose that the
health equity plan must:
Identify target health disparities. We propose to define
``target health disparities'' as health disparities experienced by one
or more communities within the IOTA participant's population of
attributed patients that the IOTA participant would aim to reduce.
Identify the data sources used to inform the
identification of target health disparities.
Describe the health equity plan intervention. We propose
to define ``health equity plan intervention'' as the initiative(s) the
IOTA participant would create and implement to reduce target health
disparities.
Include a resource gap analysis. We propose to define
``resource gap analysis'' as the resources needed to implement the
health equity plan interventions and identifies any gaps in the IOTA
participant's current resources and the additional resources that would
be needed.
Include a health equity project plan. We propose to define
``health equity project plan'' as the timeline for the IOTA participant
to implement the IOTA participant's the health equity plan.
Identify health equity plan performance measure(s). We
propose to define ``health equity performance plan measure(s)'' as one
or more quantitative metrics that the IOTA participant would use to
measure the reductions in target health disparities arising from the
health equity plan interventions.
Identify health equity goals and describes how the IOTA
participant would use the health equity goals to monitor and evaluate
progress in reducing targeted health disparities. We propose to define
``health equity goals'' as targeted outcomes relative to the health
equity plan performance measures for the first PY and all subsequent
PYs.
We propose that once an IOTA participant submits their health
equity plan to CMS, CMS will use reasonable efforts to approve or
reject the health equity plan within 60 business days. We propose that
if CMS approves the IOTA participant's health equity plan, the IOTA
participant must engage in activities related to the execution of the
IOTA participant's health equity plan, including implementing health
equity plan interventions and monitoring and evaluating progress in
reducing target health disparities. Discrimination on the basis of
race, ethnicity, national origin, religion, or gender in activities
related to the execution of the IOTA participant's health equity plan
would be prohibited.
Should CMS determine that the IOTA participant's health equity plan
does not satisfy the proposed requirements and is inconsistent with the
applicable CMS Health Equity Plan guidance, does not provide sufficient
evidence or documentation to demonstrate that the health equity plan is
likely to accomplish the IOTA participant's intended health equity
goals, or is likely to result in program integrity concerns or
negatively impact beneficiaries' access to quality care, we propose
that CMS may reject the health equity plan or require amendment of the
health equity plan at any time, including after its initial submission
and approval.
We propose that if CMS rejects the IOTA participant's health equity
plan, in whole or in part, the IOTA participant must not, and must
require its IOTA collaborators to not, conduct health equity activities
identified in the health equity plan that have been rejected by CMS.
We propose that in PY 3, and each subsequent PY, in a form and
manner and by the date(s) specified by CMS, each IOTA participant would
be required to submit to CMS an update on its progress in implementing
its health equity plan. This update would be required to include all of
the following:
Updated outcomes data for the health equity plan
performance measure(s).
Updates to the resource gap analysis.
Updates to the health equity project plan.
We propose that if an IOTA participant fails to meet the
requirements of the heath equity plan described in this section of the
proposed rule, the IOTA participant would be subject to remedial action
as specified in section III.C.16. of this proposed rule. Such remedial
actions could include: corrective action such as recoupment of any
upside risk payments; or termination from the model.
We solicit feedback on these proposals. We also solicit comment on
the potential impact of creation of a health equity plan, whether such
plans should be voluntary, and whether health equity plans should only
be a requirement in later PYs of the IOTA Model.
9. Overlap With Other Innovation Center Models, CMS Programs, and
Federal Initiatives
a. Other Innovation Center Models and CMS Programs
We propose that IOTA participants would be allowed to
simultaneously participate in IOTA and other CMS programs and models.
The IOTA Model would overlap with several other CMS programs and models
and Departmental regulatory efforts, and we seek comment on our
proposals to account for overlap.
KCC Model--The KCC Model is a voluntary Innovation Center model for
nephrologists, dialysis facilities, transplant providers, and other
providers and suppliers that are focused on beneficiaries with CKD and
beneficiaries with ESRD. The KCC Model performance period began on
January 1, 2022, and is scheduled to end December 31, 2026. As such,
the KCC Model would run concurrently for 2 years with the IOTA Model,
which would have a proposed start date of January 1, 2025. The KCC
Model includes a payment incentive called the Kidney Transplant Bonus
(KTB). KCC participants are eligible for up to $15,000 for every
aligned beneficiary
[[Page 43583]]
with CKD or ESRD who receives a kidney transplant, whether from a
living or deceased donor, provided the transplant remains successful.
Kidney Contracting Entities (KCEs) participating in the KCC Model are
also required to include a transplant provider, defined as a transplant
program that provides kidney transplants, a transplant hospital that
provides kidney transplants, a transplant surgeon who provides kidney
transplants, a transplant nephrologist, a transplant nephrology
practice, an OPO, or another Medicare-enrolled provider or supplier
that provides kidney transplant related covered services to Medicare
beneficiaries.
Though transplant hospitals are one of the types of health care
provider eligible to serve as a transplant provider, CMS has found
relatively low participation by transplant hospitals in the KCC Model.
Across the 100 KCEs participating in the model in 2023, there were only
10 kidney transplant hospitals participating in the model and serving
as the transplant provider for the relevant KCE. In discussions with
participants and with kidney transplant hospitals, CMS heard a few
reasons for this relatively low rate of participation. CMS heard that
it was difficult administratively for kidney transplant hospitals to
participate as they are part of corporate entities that may have a
larger organizational focus on broader shared savings efforts, rather
than just for the kidney population.
We propose that any providers or suppliers participating in the KCC
Model that meet the proposed IOTA participant eligibility requirements
would still be required to participate in the IOTA Model. We believe
that granting an exemption to the IOTA Model for these providers or
suppliers could disrupt the patterns of care being tested in the KCC
Model. We also believe that a prohibition on dual participation could
prevent enough KCEs from having a transplant provider and meeting model
requirements, which could undermine participation in the KCC model.
We considered proposing that any transplant hospitals participating
in the IOTA Model would not be able to participate in the KCC Model and
be able to receive any portion of a Kidney Transplant Bonus payment.
However, we did not believe this was necessary given that there are
currently only 10 transplant hospitals participating in the KCC Model,
meaning that dual participation should not substantially affect the
evaluation of either model. We also considered proposing that any
kidney transplant for an aligned beneficiary that results in a Kidney
Transplant Bonus being paid out in the KCC Model would not be counted
for calculating an upside risk payment or downside risk payment in the
IOTA Model. We decided not to propose this policy because of potential
disruption to the KCC Model, which would be in its fourth performance
year when the proposed IOTA Model would likely begin in 2025.
Additionally, the Kidney Transplant Bonus payment in the KCC Model
serves multiple functions within that model, as it also incentivizes
post-transplant care for up to 3 years post-transplant.
We believe that it is important to test both the IOTA Model and the
KCC Model, to test the effectiveness of payment incentives for kidney
transplants at different points of the care coordination process. The
IOTA Model would test the effect of upside and downside risk payments
for kidney transplant hospitals, while the KCC Model tests how
nephrologists and other providers and suppliers can support
transplantation in the overall care coordination process. Upside risk
payment and downside risk payment from the IOTA Model would not be
counted as expenditures for purposes of the KCC Model, as they would
not be adjustments to claims for individual beneficiaries, but would be
paid out in a lump sum based on aggregate performance directly tied to
individual beneficiary level claims. Additionally, we do not want to
potentially hurt KCC participants that have beneficiaries who could
benefit from the KCC participant's potential high performance in the
IOTA Model.
Both the KCC Model and the IOTA Model would include explicit
incentives for participants when aligned beneficiaries receive kidney
transplants; and a transplant hospital participating in both models
would be eligible to receive a portion of a Kidney Transplant Bonus
from a KCE under the KCC Model and an upside risk payment or downside
risk payment under the IOTA Model. Kidney transplants represent the
most desired and cost-effective treatment for most beneficiaries with
ESRD, but providers and suppliers may currently have insufficient
financial incentives to assist beneficiaries through the transplant
process because dialysis generally results in higher reimbursement over
a more extended period of time than a transplant. As a result, CMS
believes it would be appropriate to allow a transplant hospital to
receive both an upside risk payment or downside risk payment from the
IOTA Model and portion of a Kidney Transplant Bonus from the KCC Model
and the IOTA Model simultaneously to assess their effects on the
transplant rate.
ETC Model--The ETC Model is a mandatory Innovation Center model
that includes as participants certain clinicians who manage dialysis
patients (referred to as Managing Clinicians) and ESRD facilities and
provides incentives for increasing rates of home dialysis, transplant
waitlisting, and living donor transplantation. The ETC Model began on
January 1, 2021, and the model performance period is scheduled to end
December 31, 2025, and it would have one year of overlap with the
proposed model performance period of the IOTA Model beginning January
1, 2025. The ETC Model includes an upward or downward payment
adjustment called the Performance Payment Adjustment (PPA) that is
calculated in part based on the rates of transplant waitlisting and
living donor transplants for the population of beneficiaries aligned to
a participating Managing Clinician or ESRD facility.
We believe that the goals of the ETC Model and the goals of the
proposed IOTA Model are aligned. As CMS described in the 2020 rule
finalizing the ETC Model (85 FR 61114), ``[t]he ETC Model [is] a
mandatory payment model focused on encouraging greater use of home
dialysis and kidney transplants.'' We believe that the IOTA Model would
then test a corresponding incentive on the transplant hospital side to
further assist beneficiaries in moving through the transplant process
to get a transplant. CMS believes it is appropriate to test both models
as the ETC Model does not include direct incentives for transplant
hospitals and we believe that transplant hospitals play a very
important role in the transplant process.
We note for the ETC Model, participants are selected based on their
location in a Selected Geographic Area, which are randomly selected
Hospital Referral Regions (HRR), stratified by census region,
representing approximately one third of the country, as well as HRRs
predominately comprised of ZIP codes in Maryland. This is a different
randomization strategy than is being proposed for the IOTA Model. It is
our intent to look at the effects of each model and its randomization
strategy on the transplant rate as part of our model evaluation, which
is discussed in section III.C.12 of this proposed rule.
Additionally, we note that the ETC Model includes the ETC Learning
Collaborative as part of its model test. This is further discussed in
section III.C.13. of this proposed rule, where we seek feedback about
the experience of
[[Page 43584]]
kidney transplant hospitals, OPOs, ETC Participants, and other
interested parties engaged in the ETC Learning Collaborative, as we
consider how to best promote shared learning in the IOTA Model.
Other Medicare Alternative Payment Models (APMs)--For the Medicare
Shared Savings Program (the Shared Savings Program) and the ACO
Realizing Equity, Access, and Community Health (ACO REACH) Model, which
focus on total cost of care, payment adjustments made under the IOTA
Model would not be counted as program expenditures. The Medicare Shared
Savings Program regulations address payments under a model,
demonstration, or other time-limited program when defining program
expenditures. Specifically, when calculating Shared Savings and Shared
Losses for an ACO in the Shared Savings Program, CMS considers only
``individually beneficiary identifiable final payments made under a
demonstration, pilot, or time limited program'' to be a part of the
ACO's Medicare Parts A and B fee-for-service expenditures (see, for
example, 42 CFR 425.605(a)(5)(ii)). Similarly, in the ACO REACH Model,
an ACO's performance year expenditure is defined to include the total
payment that has been made by Medicare fee-for-service for services
furnished to REACH Beneficiaries (see ACO REACH Model First Amended and
Restated Participation Agreement (Dec. 1, 2023)). Payments under the
IOTA Model are not directly tied to any specific beneficiary. Instead,
they are made on a lump sum basis based on aggregate performance across
transplant patients seen by the center during the performance year.
IOTA Model payments, therefore, would not be considered by the Shared
Savings Program as an amount included in Part A or B fee-for-service
expenditures or by the ACO REACH Model as an amount included in payment
for REACH Beneficiaries' Medicare fee-for-service services.
Hospital VBP Program--CMS adjusts payments to hospitals under the
Inpatient Prospective Payment System (IPPS) based on their performance
under the Hospital VBP Program. However, the Hospital VBP Program does
not currently include any measures related to transplant services. In
addition, transplant services are only offered by a subset of
hospitals. Given the different focuses between the Hospital VBP Program
and the IOTA Model, we are not proposing any changes to the Hospital
VBP Program and believe it is appropriate to test the IOTA Model
alongside the existing Hospital VBP Program.
b. Overlap With Departmental Regulatory Efforts
December 2020 OPO Conditions for Coverage--In December 2020, CMS
issued a final rule entitled ``Organ Procurement Organizations
Conditions for Coverage: Revisions to the Outcome Measure Requirements
for Organ Procurement Organizations; Final Rule'' (85 FR 77898). The
final rule revised the OPO CfCs and was intended to increase donation
rates and organ transplantation rates by replacing the previous outcome
measures. In general, the new outcome measures improve on the prior
measures by using objective, transparent, and reliable data, rather
than OPO self-reported data, to establish the donor potential in the
OPO's DSA. The rule also permits CMS to begin decertifying
underperforming OPOs beginning in 2026.
We believe that the proposed IOTA Model supports the policies set
out in that final rule. We note that we have received feedback from
OPOs and other interested parties that OPOs are required to procure
more organs, while there is not a corresponding incentive on the
transplant hospital side to transplant more organs into beneficiaries.
We also note that the number of discarded organs has risen from 21
percent to 25 percent from 2018 to 2022.\291\ Though there have been
other changes during that time, including the updated organ allocation
system and the effects of the COVID-19 pandemic, this rise in discarded
organs is highly concerning, and we believe that the IOTA Model can
help to mitigate this troubling rise by giving transplant hospitals an
incentive to accept more offers that they may not have accepted without
that incentive.
---------------------------------------------------------------------------
\291\ Sumit Mohan, Miko Yu, Kristen L. King, S. Ali Husain,
Increasing Discards as an Unintended Consequence of Recent Changes
in United States Kidney Allocation Policy, Kidney International
Reports, Volume 8, Issue 5, 2023, Pages 1109-1111, ISSN 2468-0249,
https://doi.org/10.1016/j.ekir.2023.02.1081.
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In September 2019, CMS finalized a rule entitled ``Medicare and
Medicaid Programs; Regulatory Provisions to Promote Program Efficiency,
Transparency, and Burden Reduction; Fire Safety Requirements for
Certain Dialysis Facilities; Hospital and Critical Access Hospital
(CAH) Changes To Promote Innovation, Flexibility, and Improvement in
Patient Care'' (84 FR 51732). This rule was in part motivated by a
commitment across CMS and HHS to ``the vision of creating an
environment where agencies incorporate and integrate the ongoing
retrospective review of regulations into Department operations to
achieve a more streamlined and effective regulatory framework.''
One of the major provisions finalized in this rule was the removal
of data submission, clinical experience, and outcomes requirements for
Medicare re-approval that were previously required of transplant
hospitals participating in the Medicare program. As described in the
rule, CMS had put in place additional CoPs in the March 2007 final rule
(72 FR 15198) in an effort to increase the quality of care by
specifying minimal health and safety standards for transplant
hospitals. In addition, outcome metrics (1 year graft and patient
survival) were included in the regulation and mirrored the OPTN
outcomes metrics as calculated by the SRTR.
CMS removed the outcomes requirements for a few key reasons. First,
the concern was that transplant centers were also subject to OPTN
policies, so parallel regulation on the CMS side was duplicative.
Additionally, the concern was that ``increased emphasis on organ and
patient survival rates, as key metrics of transplant performance,
created incentives for transplant programs to select organs most likely
to survive after transplant without rejection, and to select recipients
most likely to survive after the transplant.'' This focus had the
effect of creating ``performance standards that focused only on organ
and patient survival rates for those who received a transplant, not on
survival rates of patients awaiting transplant.'' \292\
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\292\ https://www.federalregister.gov/d/2019-20736/p-87.
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In December 2021, CMS published an RFI entitled ``Health and Safety
Requirements for Transplant Programs, Organ Procurement Organizations,
and End-Stage Renal Disease Facilities'' (86 FR 68594).\293\ In this
RFI, CMS asked questions about the overall transplant ecosystem, with
goal of helping ``to inform potential changes that would create system-
wide improvements, which would further lead to improved organ donation,
organ transplantation, quality of care in dialysis facilities, and
improved access to dialysis services.''
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\293\ Request for Information; Health and Safety Requirements
for Transplant Programs, Organ Procurement Organizations, and End-
Stage Renal Disease Facilities. https://www.federalregister.gov/documents/2021/12/03/2021-26146/request-for-information-health-and-safety-requirements-for-transplant-programs-organ-procurement.
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We noted that we were seeking ways to harmonize policies across the
[[Page 43585]]
primary HHS agencies (CMS, HRSA, and the Food and Drug Administration
(FDA)) that are involved in regulating stakeholders in the transplant
ecosystem so that our requirements are not duplicative, conflicting, or
overly burdensome. We asked if there any current requirements for
transplant programs, ESRD facilities, or OPOs that are unnecessarily
duplicative of, or in conflict with, OPTN policies or policies that are
covered by other government agencies. We also asked about the impacts
of these duplicative requirements on organ utilization and transplant
program/ESRD facility/OPO quality and efficiency (86 FR 68596).
Given the concerns described in these past efforts, the OPTN has
been in part responsive to concerns from interested parties about their
metrics and effects and has expanded which metrics they are evaluating
transplant centers for their performance. In December 2021, the OPTN
approved four new risk-adjusted metrics to be used to monitor
transplant program performance, including 90-day graft survival hazard
ratio, 1-year conditional graft survival hazard ratio, pre-transplant
mortality rate ratio, and offer acceptance ratio.\294\ This added two
new metrics for areas beyond simply looking at transplant survival, and
looked at a more holistic view of patient care for beneficiaries on the
transplant list. There is a critical role for both the Department and
the OPTN with regard to the transplant ecosystem. The final rule
governing the operation of the OPTN from 1996 (63 FR 16296) stated the
following:
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\294\ OPTN Board adopts new transplant program performance
metrics--OPTN. (2021, December 16). Optn.transplant.hrsa.gov.
Retrieved May 30, 2023, from https://optn.transplant.hrsa.gov/news/optn-board-adopts-new-transplant-program-performance-metrics/.
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The Department believes that the transplantation network must be
operated by professionals in the transplant community, and that both
allocation and other policies of the OPTN should be developed by
transplant professionals, in an open environment that includes the
public, particularly transplant patients and donor families. It is not
the desire or intention of the Department to interfere in the practice
of medicine. This rule does not alter the role of the OPTN to use its
judgment regarding appropriate medical criteria for organ allocation
nor is it intended to circumscribe the discretion afforded to doctors
who must make the difficult judgments that affect individual patients.
At the same time, the Department has an important and constructive role
to play, particularly on behalf of patients. Human organs that are
given to save lives are a public resource and a public trust.
We believe that the proposed IOTA Model recognizes the goals of the
Department on behalf of the public and the medical judgment exhibited
by the OPTN. We believe that constructing this as a model test would
enable the Department to test out a different approach to incentivize
certain behavior for transplant centers, while also acknowledging the
role of the OPTN and transplant professionals in this area.
We note the concern put forward by kidney transplant hospitals that
they would not be able to increase their number of transplants without
potentially affecting their performance 90 day and 1-year graft
survival rate metrics used by the MPSC. However, we believe that there
are several different ways that IOTA participants would ultimately be
able to succeed under the IOTA Model and OPTN policies:
The MPSC standard represents a standard far below the
national average of performance that should be able to be met by member
transplant centers. The MPSC describes this as meaning that to be
identified for outcomes review in a document describing their
Performance Reviews,\295\ ``[t]he adult criteria is based on the
likelihood that the program's performance was at least 75 percent worse
than an average program, accounting for differences in the types of
recipients and donor organs transplanted. The pediatric criterion is
based on the likelihood that the program's performance was at least 60
percent worse than an average program, accounting for differences in
the types of recipients and donor organs transplanted. Even if a
program meets one or both of the criteria for graft survival, the MPSC
may not send the program an inquiry based on various situations, such
as recent release from review for outcomes or program membership
status.'' This represents a minimum standard of care and only a small
percentage were flagged for not meeting those standards.
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\295\ https://optn.transplant.hrsa.gov/media/5j5dov5s/what_to_expect_performance_reviews.pdf.
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The IOTA Model incentivizes investment in both living and
deceased donor transplants. Living donor transplantation has rates that
have been relatively flat for 20 years and has recipients of those
organs with better post-transplant outcomes.
MPSC outcomes metrics are risk adjusted based on organ
quality and can account for the use of organs that are currently being
discarded.
Many organs currently being discarded are quality organs.
Though the median KDRI of discarded kidneys was higher for discarded
kidneys than transplanted kidneys, there is a large overlap in the
quality of discarded and transplanted kidneys.\296\
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\296\ Mohan, S., Chiles, M.C., Patzer, R.E., Pastan, S.O.,
Husain, S.A., Carpenter, D.J., Dube, G.K., Crew, R.J., Ratner, L.E.,
& Cohen, D.J. (2018). Factors leading to the discard of deceased
donor kidneys in the United States. Kidney International, 94(1),
187-198. https://doi.org/10.1016/j.kint.2018.02.016.
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Per 42 CFR 121.10(c)(1), the reviews conducted by the OPTN
result in an advisory opinion to the Secretary of a recommended course
of action. The Secretary then has the option under 42 CFR 121.10(c)(2)
of requesting additional information, declining to accept the
recommendation, accepting the recommendation, or taking such other
action as the Secretary deems necessary. Given the enforcement
discretion given to the Secretary, the Secretary may take into account
performance on the metrics evaluated in the IOTA Model as part of a
holistic evaluation of transplant hospital performance.
Additionally, CMS also considered, but is not proposing, a limited
waiver of section 1138(a)(1)(B) of the Act as part of the IOTA Model,
which requires that a hospital be a member and abide by the rules and
requirements of the OPTN. We considered retaining transplant hospitals'
membership obligations to the OPTN with the exception of their required
responsiveness to MPSC transplant hospital performance reviews and the
potential for adverse actions that may risk a transplant hospital's
operations and reimbursement by Federal health insurance programs.
However, we do not believe that this waiver is necessary for testing
the model, and that a transplant hospital can perform on both the
metrics put forward by the MPSC and demonstrate successful performance
in the IOTA Model.
We invite public comments on our proposals to account for overlaps
with other CMS programs and models.
10. Beneficiary Protections
a. Beneficiary Notifications
We propose to require IOTA participants to provide notice to
attributed patients that the IOTA participant is participating in the
IOTA Model. We believe it would be important for IOTA participants to
provide attributed patients with a standardized, CMS-developed,
beneficiary notice to limit the potential for fraud and abuse,
including patient steering. We intend to provide a notification
template that IOTA
[[Page 43586]]
participants would be required to use. This template would, at minimum,
indicate content that the IOTA participant would not be permitted to
change and would indicate where the IOTA participant could insert its
own content. It would also include information regarding the attributed
patient's ability to opt-out of data sharing with IOTA participants and
how they may opt out if they choose to do so.
We propose requiring IOTA participants to display a notice
containing these rights and protections prominently at each office or
facility locations where an attributed patient may receive treatment,
in a clear manner on its public facing website, and to each attributed
patient in a paper format. This would increase the probability that the
attributed patients would receive and take note of this information.
We seek comment on the proposed requirements for beneficiary
notifications.
b. Availability of Services and Beneficiary Freedom of Choice
If finalized, we propose the Standard Provisions for Innovation
Center Models relating to availability of services and beneficiary
freedom of choice would apply to the IOTA Model. These provisions were
originally finalized as general provisions in the Code of Federal
Regulations (42 CFR part 512 subpart A) that applied to specific
Innovation Center models, but are separately proposed in this proposed
rulemaking in section II.B of this proposed rule for expansion to all
Innovation Center Models with performance periods that begin on or
after January 1, 2025. Consistent with these proposed provisions, IOTA
participants would need to preserve beneficiary freedom of choice and
continue to make medically necessary covered services available to
beneficiaries to the extent required by applicable law.
11. Financial Arrangements and Attributed Patient Engagement Incentives
a. Background
We believe it is necessary to provide IOTA participants with
flexibilities that could support their performance in the IOTA Model
and allow for greater support for the needs of attributed patients.
These flexibilities are outlined in this section and include the
ability to engage in financial arrangements to share IOTA upside risk
payments and responsibility for paying Medicare for IOTA downside risk
payments with providers and suppliers making contributions to the IOTA
participants' performance against model metrics, and the availability
of the provision of attributed patient engagement incentives. Such
flexibilities would allow IOTA participants to share all or some of the
payments they may be eligible to receive from CMS and to share the
responsibility for the funds needed to pay CMS providers and suppliers
engaged in caring for attributed patients, if those providers and
suppliers have a role in the IOTA participant's spending or quality
performance. Additionally, we believe that IOTA participants caring for
attributed patients may want to offer attributed patient engagement
incentives to encourage adherence to recommended treatment and active
patient engagement in recovery. These incentives may help an IOTA
participant reach their quality and efficiency goals for the model,
while also benefitting beneficiaries' health and the Medicare Trust
Fund if the IOTA participant improves the quality and efficiency of
care that results in the Medicare beneficiary's reductions in hospital
readmissions, complications, days in acute care, and mortality, while
recovery continues uninterrupted or accelerates.
b. Overview of IOTA Model Financial Arrangements
We believe that IOTA participants may wish to enter into financial
arrangements with providers and suppliers caring for attributed
patients to share model upside risk payments or downside risk payments,
to align the financial incentives of those providers and suppliers with
the IOTA Model goals of increasing the number of kidney transplants
furnished to attributed patients to lower costs and to improve their
quality of life. To do so, we expect that IOTA participants would
identify key providers and suppliers caring for attributed patients in
their communities and DSAs. The IOTA participants could establish
partnerships with these providers and suppliers to promote
accountability for the quality, cost, and overall care for attributed
patients, including managing and coordinating care; encouraging
investment in infrastructure, enabling technologies, and redesigning
care processes for high quality and efficient service delivery; and
carrying out other obligations or duties under the IOTA Model. These
providers and suppliers may invest substantial time and other resources
in these activities, yet they would neither be the direct recipients of
any model upside risk payments from Medicare, nor directly responsible
for paying to CMS any downside risk payments incurred. Therefore, we
believe it is possible that an IOTA participant that may receive an
upside risk payment from Medicare or may need to pay a downside risk
payment to Medicare may want to enter into financial arrangements with
other providers or suppliers to share these performance adjustments
with the IOTA participant.
We expect that all financial relationships established between IOTA
participants and providers or suppliers for purposes of the IOTA Model
would only be those permitted under applicable law and regulations,
including the applicable fraud and abuse laws and all applicable
payment and coverage requirements. As discussed in section III.C.3 of
this proposed rule, CMS expects to finalize the proposal that the anti-
kickback statute safe harbor for CMS-sponsored model arrangements (42
CFR 1001.952(ii)(1)) is available to protect the financial arrangements
proposed in this section when arrangements with eligible providers and
suppliers are in compliance with this policy and the conditions for use
of the anti-kickback statute safe harbor set out at Sec.
1001.952(ii)(1), if the proposed arrangements are finalized.
We recognize that there are numerous arrangements that IOTA
participants may wish to enter other than the financial arrangements
described in the regulations for which safe harbor protection may be
extended that could be beneficial to the IOTA participants. For
example, IOTA participants may choose to engage with organizations that
are neither providers nor suppliers to assist with matters such as data
analysis; local provider and supplier engagement; care redesign
planning and implementation; beneficiary outreach; beneficiary care
coordination and management; monitoring IOTA participants' compliance
with the model's terms and conditions; or other model-related
activities. Such organizations may play important roles in an IOTA
participant's plans to implement the model based on the experience
these organizations may bring, such as prior experience with living
donation initiatives, care coordination expertise, familiarity with a
particular local community, or knowledge of SRTR data. We expect that
all relationships established between IOTA participants and these
organizations for purposes of the model would be those permitted only
under existing law and regulation, including any relationships that
would include
[[Page 43587]]
the IOTA participant's sharing of model upside risk payments or
downside risk payments with such organizations. We would expect these
relationships to be solely based on the level of engagement of the
organization's resources to directly support the participants' model
implementation.
c. IOTA Collaborators
Given the financial incentives of the IOTA performance-based
payments, as described in section III.C. of this proposed rule, an IOTA
participant may want to engage in financial arrangements with providers
and suppliers making contributions to the IOTA participant's
performance across the achievement domain, efficiency domain, and
quality domain. Such arrangements would allow the IOTA participant to
share monies earned from the upside risk payments. Likewise, such
arrangements could allow the IOTA participant to share the
responsibility for the funds needed to repay CMS the downside risk
payments. We propose to use the term ``IOTA collaborator'' to refer to
these providers and suppliers.
Because attributed patients include both those on the kidney
transplant waitlist and those who have received a kidney transplant, as
described in section III.C.4.a of this proposed rule, many providers
and suppliers other than the IOTA participant would furnish related
services to attributed patients during the model performance period. As
such, for purposes of the anti-kickback statute safe harbor for CMS-
sponsored model arrangements (42 CFR part 1001.952(ii)), we propose
that the following types of providers and suppliers that are Medicare-
enrolled and eligible to participate in Medicare may be IOTA
collaborators:
Nephrologist.
ESRD Facility.
Skilled Nursing Facility (SNF).
Home Health Agency (HHA).
Long-Term Care Hospital (LTCH).
Inpatient Rehabilitation Facility (IRF).
Physician.
Nonphysician practitioner.
Therapist in a private practice.
Comprehensive Outpatient Rehabilitation Facility (CORF).
Provider or supplier of outpatient therapy services.
Physician Group Practice (PGP).
Hospital.
Critical Access Hospital (CAH).
Non-physician provider group practice (NPPGP).
Therapy Group Practice (TGP).
We seek comment on the proposed definition of IOTA collaborators
and any additional Medicare-enrolled providers or suppliers that should
be included in this definition.
d. Sharing Arrangements
(1) General
Similar to the Comprehensive Care for Joint Replacement Payment
Model (CJR) (42 CFR part 510), we propose that certain financial
arrangements between an IOTA participant and an IOTA collaborator be
termed ``sharing arrangements.'' For purposes of the anti-kickback
statute safe harbor for CMS-sponsored model arrangements (Sec.
1001.952(ii)(1)), we propose that a sharing arrangement would be a
financial arrangement to share only--(1) the upside risk payment; and
(2) the downside risk payment.
Where a payment from an IOTA participant to an IOTA collaborator is
made pursuant to a sharing arrangement, we define that payment as a
``gainsharing payment,'' which is discussed in section III.C.11.d.(3).
of this proposed rule. Where a payment from an IOTA collaborator to an
IOTA participant is made pursuant to a sharing arrangement, we define
that payment as an ``alignment payment,'' which is discussed in section
III.C.11.d.(3). of this proposed rule.
(2) Requirements
We propose several requirements for sharing arrangements to help
ensure that their sole purpose is to create financial alignment between
IOTA participants and IOTA collaborators toward the goals of the model
while maintaining adequate program integrity safeguards. An IOTA
participant must not make a gainsharing payment or receive an alignment
payment except in accordance with a sharing arrangement. We propose
that a sharing arrangement must comply with the provisions of Sec.
512.452 and all other applicable laws and regulations, including the
applicable fraud and abuse laws and all applicable payment and coverage
requirements.
We propose that the IOTA participant must develop, maintain, and
use a set of written policies for selecting providers and suppliers to
be IOTA collaborators. To safeguard against potentially fraudulent or
abusive practices, we propose that the selection criteria must include
the quality of care delivered by the potential IOTA collaborator. We
also propose that the selection criteria cannot be based directly or
indirectly on the volume or value of referrals or business otherwise
generated by, between, or among the IOTA participant, any IOTA
collaborator, any collaboration agent, or any individual or entity
affiliated with an IOTA participant, IOTA collaborator, or
collaboration agent. Additionally, we propose that IOTA participants
must consider the selection of IOTA collaborators based on criteria
related to, and inclusive of, the anticipated contribution to the
performance of the IOTA participant across the achievement domain,
efficiency domain, and quality domain by the potential IOTA
collaborator to ensure that the selection of IOTA collaborators takes
into consideration the likelihood of their future performance.
It is necessary that IOTA participants have adequate oversight over
sharing arrangements to ensure that all arrangements meet the
requirements of this section. Therefore, we propose that the board or
other governing body of the IOTA participant have responsibility for
overseeing the IOTA participant's participation in the model,
including, but not limited to: its arrangements with IOTA
collaborators, its payment of gainsharing payments, its receipt of
alignment payments, and its use of beneficiary incentives (as discussed
in III.C.11.h of this proposed rule).
Finally, we propose that if an IOTA participant enters a sharing
arrangement, its compliance program must include oversight of sharing
arrangements and compliance with the applicable requirements of the
model. Requiring oversight of sharing arrangements to be included in
the compliance program provides a program integrity safeguard.
We seek comment about all provisions described in the preceding
discussion, including whether additional or different safeguards would
be needed to ensure program integrity, protect against abuse, and
ensure that the goals of the model are met.
We propose that the sharing arrangement must be in writing, signed
by the parties, and entered into before care is furnished to attributed
patients during the PY under the sharing arrangement. In addition,
participation in the sharing arrangement must require the IOTA
collaborator to comply with the requirements of this model, as those
pertain to their actions and obligations. Participation in a sharing
arrangement must be voluntary and without penalty for nonparticipation.
It is important that providers and suppliers rendering items and
services to attributed patients during the model performance period
have the freedom to provide medically necessary items and services to
attributed patients without any requirement that they participate in a
sharing arrangement to safeguard
[[Page 43588]]
beneficiary freedom of choice, access to care, and quality of care. The
sharing arrangement must set out the mutually agreeable terms for the
financial arrangement between the parties to guide and reward model
care redesign for future performance across the achievement domain,
efficiency domain, and quality domain, rather than reflect the results
of model PYs that have already occurred and where the financial outcome
of the sharing arrangement terms would be known before signing.
We propose that the sharing arrangement must require the IOTA
collaborator and its employees, contractors (including collaboration
agents), and subcontractors to comply with certain requirements that
are important for program integrity under the arrangement. We note that
the terms contractors and subcontractors, respectively, include
collaboration agents as defined later in this section. The sharing
arrangement must require all of the individuals and entities in this
group to comply with the applicable provisions of Sec. Sec. 512.450-
512.466 of this proposed rule, including requirements regarding
beneficiary notifications, access to records, record retention, and
participation in any evaluation, monitoring, compliance, and
enforcement activities performed by CMS or its designees, because these
individuals and entities all would play a role in model care redesign
and be part of financial arrangements under the model. The sharing
arrangement must also require all individuals and entities in the group
to comply with the applicable Medicare provider enrollment requirement
at Sec. 424.500 et seq., including having a valid and active TIN or
NPI, during the term of the sharing arrangement. This is to ensure that
these individuals and entities have the required enrollment
relationship with CMS under the Medicare program, although we note that
they are not responsible for complying with requirements that do not
apply to them. Finally, the sharing arrangement must require these
individuals and entities to comply with all other applicable laws and
regulations.
We propose that the sharing arrangement must not pose a risk to
beneficiary access, beneficiary freedom of choice, or quality of care
so that financial relationships between IOTA participants and IOTA
collaborators do not negatively impact beneficiary protections under
the model. The sharing arrangement must require the IOTA collaborator
to have, or be covered by, a compliance program that includes oversight
of the sharing arrangement and compliance with the requirements of the
IOTA Model that apply to its role as an IOTA collaborator, including
any distribution arrangements, just as we require IOTA participants to
have a compliance program that covers oversight of the sharing
arrangement for this purpose as a program integrity safeguard. We seek
comment on the anticipated effect of the proposed compliance program
requirement for IOTA collaborators, particularly with regard to
individual physicians and nonphysician practitioners, small PGPs,
NPPGPs, and TGPs and whether alternative compliance program
requirements for all or a subset of IOTA collaborators should be
adopted to mitigate any effect of the proposal that could make
participation as an IOTA collaborator infeasible for any provider,
supplier, or other entity on the proposed list of types of IOTA
collaborators.
For purposes of sharing arrangements under the model, we propose to
define activities related to promoting accountability for the quality,
cost, and overall care for attributed patients and performance across
the achievement domain, efficiency domain, and quality domain,
including managing and coordinating care; encouraging investment in
infrastructure and redesigned care processes for high quality and
efficient service delivery; the provision of items and services pre or
post-transplant in a manner that reduces costs and improves quality; or
carrying out any other obligation or duty under the model as ``IOTA
activities.'' In addition to the quality of episodes of care, we
believe the activities that would fall under this proposed definition
could encompass the totality of activities upon which it would be
appropriate for sharing arrangements to value the contributions of
collaborators and collaboration agents toward meeting the performance
goals of the model. We seek comment on the proposed definition of IOTA
activities as an inclusive and comprehensive framework for capturing
direct care and care redesign that contribute to performance across the
achievement domain, efficiency domain, and quality domain.
We propose that the written sharing arrangement agreement must
specify the following parameters of the arrangement:
The purpose and scope of the sharing arrangement.
The identities and obligations of the parties, including
specified IOTA activities and other services to be performed by the
parties under the sharing arrangement.
The date of the sharing arrangement.
Management and staffing information, including type of
personnel or contractors that would be primarily responsible for
carrying out IOTA activities.
The financial or economic terms for payment, including all
of the following:
++ Eligibility criteria for a gainsharing payment.
++ Eligibility criteria for an alignment payment.
++ Frequency of gainsharing or alignment payment.
++ Methodology and accounting formula for determining the amount of
a gainsharing payment that is substantially based on performance across
the achievement domain, efficiency domain and quality domain, and the
provision of IOTA Model activities.
++ Methodology and accounting formula for determining the amount of
an alignment payment.
Finally, we propose to require that the terms of the sharing
arrangement must not induce the IOTA participant, IOTA collaborator, or
any employees, contractors, or subcontractors of the IOTA participant
or IOTA collaborator to reduce or limit medically necessary services to
any attributed patient or restrict the ability of an IOTA collaborator
to make decisions in the best interests of its patients, including the
selection of devices, supplies, and treatments. These requirements are
to ensure that the quality of care for attributed patients is not
negatively affected by sharing arrangements under the model.
The proposals for the requirements for sharing arrangements under
the model are included in Sec. 512.452.
We seek comment about all of the requirements set out in the
preceding discussion, including whether additional or different
safeguards would be needed to ensure program integrity, protect against
abuse, and ensure that the goals of the model are met.
(3) Gainsharing Payments and Alignment Payments
We propose several conditions and limitations for gainsharing
payments and alignment payments as program integrity protections for
the payments to and from IOTA collaborators. We propose to require that
gainsharing payments be derived solely from upside risk payments; that
they be distributed on an annual basis, not more than once per calendar
year; that they not be a loan, advance payment, or payment for
referrals or other business; and that they
[[Page 43589]]
be clearly identified as a gainsharing payment at the time they are
paid.
We believe that gainsharing payment eligibility for IOTA
collaborators should be conditioned on two requirements--(1)
contributing to performance across the achievement domain, efficiency
domain or quality domain; and (2) rendering items and services to
attributed patients during the model performance period--as safeguards
to ensure that eligibility for gainsharing payments is solely based on
aligning financial incentives for IOTA collaborators with the
performance metrics of the model. With respect to the first
requirement, we propose that to be eligible to receive a gainsharing
payment, an IOTA collaborator must contribute to the performance of the
IOTA participant across the achievement domain, efficiency domain or
quality domain during the PY for which the IOTA participant earned the
upside risk payment that comprises the gainsharing payment. We also
propose that the contribution to performance across the achievement
domain, efficiency domain, or quality domain criteria must be
established by the IOTA participant and directly related to the care of
attributed patients. With regard to the second requirement, to be
eligible to receive a gainsharing payment, or to be required to make an
alignment payment, an IOTA collaborator other than a PGP, NPPGP, or TGP
must have directly furnished a billable item or service to an
attributed patient during the same PY for which the IOTA participant
earned the upside risk payment that comprises the gainsharing payment
or incurred a downside risk payment. For purposes of this requirement,
we consider a hospital, CAH or post-acute care provider to have
``directly furnished'' a billable service if one of these entities
billed for an item or service for an attributed patient in the same PY
for which the IOTA participant earned the upside risk payment that
comprises the gainsharing payment or incurred a downside risk payment.
The phrase ``PY for which the IOTA participant earned the upside risk
payment that comprises the gainsharing payment or incurred a downside
risk payment'' does not mean the year in which the gainsharing payment
was made. These requirements ensure that there is a required
relationship between eligibility for a gainsharing payment and the
direct care for attributed patients during PY for these IOTA
collaborators. We believe the provision of direct care is essential to
the implementation of effective care redesign, and the requirement
provides a safeguard against payments to IOTA collaborators other than
a PGP, NPPGP, or TGP that are unrelated to direct care for attributed
patients during the model performance period.
We propose to establish similar requirements for IOTA
collaborator's that are PGPs, NPPGPs and TGPs that vary because these
entities do not themselves directly furnish billable services. To be
eligible to receive a gainsharing payment or required to make an
alignment payment, a PGP, NPPGP or TGP must have billed for an item or
service that was rendered by one or more members of the PGP, NPPGP or
TGP to an attributed patient the same PY for which the IOTA participant
earned an upside risk payment that comprises the gainsharing payment or
incurred a downside risk payment. Like the proposal for IOTA
collaborators that are not PGPs, NPPGPs or TGPs, these proposals also
require a link between the IOTA collaborator that is the PGP, NPPGP or
TGP and the provision of items and services to attributed patients
during the PY by PGP, NPPGP or TGP members.
Moreover, we further propose that, because PGPs, NPPGPs and TGPs do
not directly furnish items and services to patients, to be eligible to
receive a gainsharing payment or be required to make an alignment
payment, the PGP, NPPGP or TGP must have contributed to IOTA activities
and been clinically involved in the care of attributed patients during
the same PY for which the IOTA participant earned the upside risk
payment that comprises the gainsharing payment or incurred a downside
risk payment. For example, a PGP, NPPGP, or TGP could have contributed
to IOTA activities and been clinically involved in the care of
attributed patients if they--
Provided care coordination services to attributed patients
during and after inpatient admission;
Engaged with an IOTA participant in care redesign
strategies, and performed a role in the implementation of such
strategies, that were designed to improve the quality of care for
attributed patients; or
In coordination with other providers and suppliers (such
as PGP members, NPPGP members, or TGP members; the IOTA participant;
and post-acute care providers), implemented strategies designed to
address and manage the comorbidities of attributed patients.
We propose to limit the total amount of gainsharing payments for a
PY to IOTA collaborators that are physicians, nonphysician
practitioners, PGPs, NPPGPs or TGPs. For IOTA collaborators that are
physicians or nonphysician practitioners, that limit is 50 percent of
the Medicare-approved amounts under the PFS for items and services
furnished by that physician or nonphysician practitioner to the IOTA
participant's attributed patients during the same PY for which the IOTA
participant earned the upside risk payment that comprises the
gainsharing payment being made. For IOTA collaborators that are PGPs,
NPPGPs or TGPs that limit is 50 percent of the Medicare-approved
amounts under the PFS for items and services billed by the PGP, NPPGP
or TGP and furnished to the IOTA participant's attributed patients by
members of the PGP, NPPGP or TGP during the same PY for which the IOTA
participant earned the upside risk payment that comprises the
gainsharing payment being made. These limits are consistent with those
in the CJR model.
We propose that the amount of any gainsharing payments must be
determined in accordance with a methodology that is substantially based
on contribution to performance across the achievement domain,
efficiency domain, and quality domain and the provision of IOTA
activities. The methodology may take into account the amount of such
IOTA activities provided by an IOTA collaborator relative to other IOTA
collaborators. While we emphasize that financial arrangements may not
be conditioned directly or indirectly on the volume or value of
referrals or business otherwise generated by, between or among the IOTA
participant, any IOTA collaborator, any collaboration agent, or any
individual or entity affiliated with an IOTA participant, IOTA
collaborator, or collaboration agent so that their sole purpose is to
align the financial incentives of the IOTA participant and IOTA
collaborators toward the model, we believe that accounting for the
relative amount of IOTA activities by IOTA collaborators in the
determination of gainsharing payments does not undermine this
objective. Rather, the proposed requirement allows flexibility in the
determination of gainsharing payments where the amount of an IOTA
collaborator's provision of IOTA activities (including direct care) to
attributed patients during the model performance period may contribute
to the IOTA participant's upside risk payment that may be available for
making a gainsharing payment. Greater contributions of IOTA activities
by one IOTA collaborator versus that result in greater differences in
the funds available for gainsharing payments may be
[[Page 43590]]
appropriately valued in the methodology used to make gainsharing
payments to those IOTA collaborators to reflect these differences in
IOTA activities among them. For example, a physician who is an IOTA
collaborator who treats 20 attributed patients during the PY that
result in high quality, less costly care could receive a larger
gainsharing payment than a physician who is an IOTA collaborator who
treats 10 attributed patients during episodes that similarly result in
high quality, less costly care.
However, we do not believe it would be appropriate to allow the
selection of IOTA collaborators or the opportunity to make or receive a
gainsharing payment or an alignment payment to take into the account
the amount of IOTA activities provided by a potential or actual IOTA
collaborator relative to other potential or actual IOTA collaborators
because these financial relationships are not to be based directly or
indirectly on the volume or value of referrals or business otherwise
generated by, between, or among the IOTA participant, any IOTA
collaborator, any collaboration agent, or any individual or entity
affiliated with an IOTA participant, IOTA collaborator, or
collaboration agent. Specifically, with respect to the selection of
IOTA collaborators or the opportunity to make or receive a gainsharing
payment or an alignment payment, we do not believe that the amount of
model activities provided by a potential or actual IOTA collaborator
relative to other potential or actual IOTA collaborators could be taken
into consideration by the IOTA participant without a significant risk
that the financial arrangement in those instances could be based
directly or indirectly on the volume or value of referrals or business
generated by, between or among the parties. Similarly, if the
methodology for determining alignment payments was allowed to take into
the account the amount of IOTA activities provided by an IOTA
collaborator relative to other IOTA collaborators, there would be a
significant risk that the financial arrangement could directly account
for the volume or value of referrals or business generated by, between,
or among the parties and, therefore, we propose that the methodology
for determining alignment payments may not directly take into account
the volume or value of referrals or business generated by, between or
among the parties.
We seek comment on this proposal for gainsharing payments, where
the methodology could take into account the amount of IOTA activities
provided by an IOTA collaborator relative to other IOTA collaborators.
We are particularly interested in comments about whether this standard
would provide sufficient additional flexibility in the gainsharing
payment methodology to allow the financial reward of IOTA collaborators
commensurate with their level of effort that achieves model goals. In
addition, we are interested in comment on whether additional safeguards
or a different standard is needed to allow for greater flexibility to
provide certain performance-based payments consistent with the goals of
program integrity, protecting against abuse and ensuring the goals of
the model are met.
We propose that for each PY, the aggregate amount of all
gainsharing payments that are derived from an upside risk payment must
not exceed the amount of the upside risk payment paid by CMS. In
accordance with the prior discussion, no entity or individual, whether
a party to a sharing arrangement or not, may condition the opportunity
to make or receive gainsharing payments or to make or receive alignment
payments, directly or indirectly, on the volume or value of referrals
or business otherwise generated by, between, or among the IOTA
participant, any IOTA collaborator, any collaboration agent, or any
individual or entity affiliated with an IOTA participant, IOTA
collaborator, or collaboration agent. We propose that an IOTA
participant must not make a gainsharing payment to an IOTA collaborator
that is subject to any action for noncompliance with this part or the
fraud and abuse laws, or for the provision of substandard care to
attributed patients or other integrity problems. Finally, the sharing
arrangement must require the IOTA participant to recoup any gainsharing
payment that contained funds derived from a CMS overpayment on an
upside risk payment or was based on the submission of false or
fraudulent data. These requirements provide program integrity
safeguards for gainsharing under sharing arrangements.
With respect to alignment payments, we propose that alignment
payments from an IOTA collaborator to an IOTA participant may be made
at any interval that is agreed upon by both parties. We propose that
alignment payments must not be issued, distributed, or paid prior to
the calculation by CMS of a payment amount reflected in a notification
of the downside risk payment; loans, advance payments, or payments for
referrals or other business; or assessed by an IOTA participant if the
IOTA participant does not owe a downside risk payment. The IOTA
participant must not receive any amounts under a sharing arrangement
from an IOTA collaborator that are not alignment payments.
We also propose certain limitations on alignment payments that are
consistent with the CJR Model. For a PY, the aggregate amount of all
alignment payments received by the IOTA participant must not exceed 50
percent of the IOTA participant's downside risk payment. Given that the
IOTA participant would be responsible for developing and coordinating
care redesign strategies in response to its IOTA participation, we
believe it is important that the IOTA participant retain a significant
portion of its responsibility for payment to CMS. For example, upon
receipt of a notification indicating that the IOTA participant owes a
downside risk payment of $100 to CMS, the IOTA participant would be
permitted to receive no more than $50 in alignment payments, in the
aggregate, from its IOTA collaborators. In addition, the aggregate
amount of all alignment payments from a single IOTA collaborator to the
IOTA participant may not be greater than 25 percent of the IOTA
participant's downside risk payment over the course of a single PY for
an IOTA collaborator. We seek comment on our proposed aggregate and
individual IOTA collaborator limitations on alignment payments.
We propose that all gainsharing payments and any alignment payments
must be administered by the IOTA participant in accordance with
generally accepted accounting principles (GAAP) and Government Auditing
Standards (The Yellow Book). Additionally, we propose that all
gainsharing payments and alignment payments must be made by check,
electronic funds transfer (EFT), or another traceable cash transaction.
We seek comment on the effect of this proposal.
The proposals for the conditions and restrictions on gainsharing
payments and alignment payments under the model are included in Sec.
512.452.
We seek comment about all of the conditions and restrictions set
out in the preceding discussion, including whether additional or
different safeguards would be needed to ensure program integrity,
protect against abuse, and ensure that the goals of the model are met.
(4) Documentation Requirements
To ensure the integrity of the sharing arrangements, we propose
that IOTA participants must meet a variety of documentation
requirements for these arrangements. Specifically, the IOTA participant
must--
[[Page 43591]]
Document the sharing arrangement contemporaneously with
the establishment of the arrangement;
Maintain accurate current and historical lists of all IOTA
collaborators, including IOTA collaborator names and addresses.
Specifically, the IOTA participant must--
++ Update such lists on at least a quarterly basis; and
++ Publicly report the current and historical lists of IOTA
collaborators and any written policies for selecting individuals and
entities to be IOTA collaborators required by the IOTA participant on a
web page on the IOTA participants website; and
Maintain and require each IOTA collaborator to maintain
contemporaneous documentation with respect to the payment or receipt of
any gainsharing payment or alignment payment that includes at a minimum
the--
++ Nature of the payment (gainsharing payment or alignment
payment);
++ Identity of the parties making and receiving the payment;
++ Date of the payment;
++ Amount of the payment;
++ Date and amount of any recoupment of all or a portion of an IOTA
collaborator's gainsharing payment; and
++ Explanation for each recoupment, such as whether the IOTA
collaborator received a gainsharing payment that contained funds
derived from a CMS overpayment of an upside risk payment, or was based
on the submission of false or fraudulent data.
In addition, we propose that the IOTA participant must keep records
for all of the following:
Its process for determining and verifying its potential
and current IOTA collaborators' eligibility to participate in Medicare;
A description of current health information technology,
including systems to track upside risk payments and downside risk
payments; and
Its plan to track gainsharing payments and alignment
payments.
Finally, we propose that the IOTA participant must retain and
provide access to, and must require each IOTA collaborator to retain
and provide access to, the required documentation in accordance with
Sec. 512.460 and Sec. 1001.952(ii).
The proposals for the requirements for documentation of sharing
arrangements under the model are included in Sec. 512.452(d).
We seek comment about all of the requirements set out in the
preceding discussion, including whether additional or different
safeguards would be needed to ensure program integrity, protect against
abuse, and ensure that the goals of the model are met.
e. Distribution Arrangements
(1) General
Similar to the CJR Model, we propose that certain financial
arrangements between IOTA collaborators and other individuals or
entities called ``collaboration agents'' be termed ``distribution
arrangements.'' For purposes of the anti-kickback statute safe harbor
for CMS-sponsored model arrangements (Sec. 1001.952(ii)(1)), we
propose to define ``distribution arrangement'' as a financial
arrangement between an IOTA collaborator that is a PGP, NPPGP or TGP
and a collaboration agent for the sole purpose of sharing a gainsharing
payment received by the PGP, NPPGP or TGP. We propose to define
``collaboration agent'' as an individual or entity that is not an IOTA
collaborator and that is a member of a PGP, NPPGP, or TGP that has
entered into a distribution arrangement with the same PGP, NPPGP, or
TGP in which he or she is an owner or employee, and where the PGP,
NPPGP, or TGP is an IOTA collaborator. Where a payment from an IOTA
collaborator that is an PGP, NPPGP, or TGP is made to a collaboration
agent, under a distribution arrangement, composed only of gainsharing
payments, we propose to define that payment as a ``distribution
payment.'' We propose that a collaboration agent could only make a
distribution payment in accordance with a distribution arrangement that
complies with the provisions of Sec. 512.454 and all other applicable
laws and regulations, including the fraud and abuse laws.
The proposals for the general provisions for distribution
arrangements under the model are included in Sec. 512.454.
We seek comment about all of the provisions set out in the
preceding discussion, including whether additional or different
safeguards would be needed to ensure program integrity, protect against
abuse, and ensure that the goals of the model are met.
(2) Requirements
We propose a number of specific requirements for distribution
arrangements as a program integrity safeguard to help ensure that their
sole purpose is to create financial alignment between IOTA
collaborators and collaboration agents and performance across the
achievement domain, efficiency domain, and quality domain. These
requirements largely parallel those proposed in Sec. 512.452 for
sharing arrangements and gainsharing payments based on similar
reasoning for these two types of arrangements and payments. We propose
that all distribution arrangements must be in writing and signed by the
parties, contain the date of the agreement, and be entered into before
care is furnished to attributed patients under the distribution
arrangement. Furthermore, we propose that participation must be
voluntary and without penalty for nonparticipation, and the
distribution arrangement must require the collaboration agent to comply
with all applicable laws and regulations.
Like our proposal for gainsharing payments, we propose that the
opportunity to make or receive a distribution payment must not be
conditioned directly or indirectly on the volume or value of referrals
or business otherwise generated by, between or among the IOTA
participant, any IOTA collaborator, any collaboration agent, or any
individual or entity affiliated with an IOTA participant, IOTA
collaborator, or collaboration agent. We propose more flexible
standards for the determination of the amount of distribution payments
from PGPs, NPPGPs and TGPs for the same reasons we propose this
standard for the determination of gainsharing payments.
We note that for distribution payments made by a PGP to PGP
members, by NPPGPs to NPPGP members, or TGPs to TGP members, the
requirement that the amount of any distribution payments must be
determined in accordance with a methodology that is substantially based
on performance across the achievement domain, efficiency domain, and
quality domain and the provision of IOTA Model activities may be more
limiting in how a PGP pays its members than is allowed under existing
law. Therefore, to retain existing flexibility for distribution
payments by a PGP to PGP members, we propose that the amount of the
distribution payment from a PGP to PGP members must be determined in a
manner that complies with Sec. 411.352(g). This proposal would allow a
PGP the choice either to comply with the general standard that the
amount of a distribution payment must be substantially based on
contribution to the performance across the achievement domain,
efficiency domain, and quality domain and the provision of IOTA Model
activities or to provide its members a financial benefit through the
model without consideration of the PGP member's individual contribution
to the performance across the achievement
[[Page 43592]]
domain, efficiency domain and quality domain. In the latter case, PGP
members that are not collaboration agents (including those who
furnished no services to attributed patients) would be able receive a
share of the profits from their PGP that includes the monies contained
in a gainsharing payment. We believe this is an appropriate exception
to the general standard for determining the amount of distribution
payment under the model from a PGP to a PGP member, because CMS has
determined under the physician self-referral law that payments from a
group practice as defined under Sec. 411.352 to its members that
comply with Sec. 411.352(g) are appropriate.
We seek comment on this proposal and specifically whether there are
additional safeguards or a different standard is needed to allow for
greater flexibility in calculating the amount of distribution payments
that would avoid program integrity risks and whether additional or
different safeguards are reasonable, necessary, or appropriate for the
amount of distribution payments from a PGP to its members, a NPPGP to
its members or a TGP to its members.
Similar to our proposed requirements for sharing arrangements for
those IOTA collaborators that furnish or bill for items and services,
except for a distribution payment from a PGP to a PGP member that
complies with Sec. 411.352(g), we propose that a collaboration agent
is eligible to receive a distribution payment only if the collaboration
agent furnished or billed for an item or service rendered to an
attributed patients during the same PY for which the IOTA participant
earned the upside risk payment. We note that all individuals and
entities that fall within our proposed definition of collaboration
agent may either directly furnish or bill for items and services
rendered to attributed patients. This proposal ensures that, absent the
alternative safeguards afforded by a PGP's distribution payments in
compliance with Sec. 411.352(g), there is the same required
relationship between direct care for attributed patients during the PY
and distribution payment eligibility that we require for gainsharing
payment eligibility. We believe this requirement provides a safeguard
against payments to collaboration agents that are unrelated to direct
care for attributed patients during the PY when the amount of the
distribution payment is not determined in a manner that complies with
Sec. 411.352(g).
Except for a distribution payment from a PGP to a PGP member that
complies with Sec. 411.352(g), we propose the same limitations on the
total amount of distribution payments to physicians, nonphysician
practitioners, PGPs, NPPGPs and TGPs as we propose for gainsharing
payments. In the case of a collaboration agent that is a physician or
nonphysician practitioner, we propose to limit the total amount of
distribution payments paid for a PY to the collaboration agent to 50
percent of the total Medicare-approved amounts under the PFS for items
and services furnished by the collaboration agent to the IOTA
participant's attributed patients during the same PY for which the IOTA
participant earned the upside risk payment that comprises the
gainsharing payment being distributed. In the case of a collaboration
agent that is a group practice, we propose that the limit would be 50
percent of the total Medicare-approved amounts under the PFS for items
and services billed by the group practice for items and services
furnished by members of the group practice to the IOTA participant's
attributed patients during the same PY for which the IOTA participant
earned the upside risk payment that comprises the gainsharing payment
being distributed. We believe that, absent the alternative safeguards
afforded by a group practice's distribution payments in compliance with
Sec. 411.352(g), these proposed limitations on distribution payments,
which are the same as those for gainsharing payments to physicians,
nonphysician practitioners, and group practices, are necessary to
eliminate any financial incentives for these individuals or entities to
engage in a financial arrangement as an IOTA collaborator versus as a
collaboration agent. Furthermore, we believe that group practices
should be able to choose whether to engage in financial arrangements
directly with IOTA participants as IOTA collaborators without having a
different limit on their maximum financial gain from one arrangement
versus another.
We further propose that with respect to the distribution of any
gainsharing payment received by a PGP, NPPGP or TGP, the total amount
of all distribution payments must not exceed the amount of the
gainsharing payment received by the IOTA collaborator from the IOTA
participant. Like gainsharing and alignment payments, we propose that
all distribution payments must be made by check, electronic funds
transfer, or another traceable cash transaction. The collaboration
agent must retain the ability to make decisions in the best interests
of the patient, including the selection of devices, supplies, and
treatments. Finally, the distribution arrangement must not induce the
collaboration agent to reduce or limit medically necessary items and
services to any Medicare beneficiary or reward the provision of items
and services that are medically unnecessary.
We propose that the IOTA collaborator must maintain contemporaneous
documentation regarding distribution arrangements in accordance with
Sec. 512.454, including--
The relevant written agreements;
The date and amount of any distribution payment(s);
The identity of each collaboration agent that received a
distribution payment; and
A description of the methodology and accounting formula
for determining the amount of any distribution payment.
We propose that the IOTA collaborator may not enter into a
distribution arrangement with any individual or entity that has a
sharing arrangement with the same IOTA participant. This proposal
ensures that the proposed separate limitations on the total amount of
gainsharing payment and distribution payment to PGPs, NPPGPs, TGPs,
physicians, and nonphysician practitioners that are substantially based
on performance across the achievement domain, efficiency domain, and
quality domain and the provision of IOTA activities are not exceeded in
absolute dollars by a PGP, NPPGP, TGP, physician, or nonphysician
practitioner's participation in both a sharing arrangement and
distribution arrangement for the care of the same IOTA beneficiaries
during the PY. Allowing both types of arrangements for the same
individual or entity for care of the same attributed patients during
the PY could also allow for duplicate counting of the individual or
entity's same contribution to the achievement domain, efficiency
domain, and quality domain and provision of IOTA Model activities in
the methodologies for both gainsharing and distribution payments,
leading to financial gain that is disproportionate to the contribution
to the achievement domain, efficiency domain and quality domain and
provision of IOTA Model activities by that individual or entity.
Finally, we propose that the IOTA collaborator must retain and provide
access to, and must require collaboration agents to retain and provide
access to, the required documentation in accordance with Sec. 512.460.
The proposals for requirements for distribution arrangements under
the model are included in Sec. 512.454.
We seek comment about all of the requirements set out in the
preceding
[[Page 43593]]
discussion, including whether additional or different safeguards would
be needed to ensure program integrity, protect against abuse, and
ensure that the goals of the model are met. In addition, we seek
comment on how the regulation of the financial arrangements under this
proposal may interact with how these or similar financial arrangements
are regulated under the Medicare Shared Savings Program.
f. Enforcement Authority
OIG authority is not limited or restricted by the provisions of the
model, including the authority to audit, evaluate, investigate, or
inspect the IOTA participant, IOTA collaborators, collaboration agents,
or any other person or entity or their records, data, or information,
without limitations. Additionally, no model provisions limit or
restrict the authority of any other Government Agency to do the same.
The proposals for enforcement authority under the model are included in
Sec. 512.455.
We seek comment about all of the requirements set out in the
preceding discussion, including whether additional or different
safeguards would be needed to ensure program integrity, protect against
abuse, and ensure that the goals of the model are met.
h. Attributed Patient Engagement Incentives
We believe it is necessary and appropriate to provide additional
flexibilities to IOTA participants for purposes of testing the IOTA
Model to give IOTA participants additional access to the tools
necessary to improve attributed patients' access to kidney transplants
and ensure attributed patients receive comprehensive and patient-
centered post-transplant care. As discussed in section III.C.11.i. of
this proposed rule, CMS expects to make a determination that the anti-
kickback statute safe harbor for CMS-sponsored model patient incentives
is available to protect Part B and Part D immunosuppressive drug cost
sharing support and attributed patient engagement incentives proposed
in this section when the incentives are offered in compliance with this
policy, specifically the conditions for use of the anti-kickback
statute safe harbor set out at Sec. 1001.952(ii)(2), if the proposed
Part B and Part D immunosuppressive drug cost sharing support policy
and attributed patient engagement incentives are finalized.
(1) Part B and Part D Immunosuppressive Drug Cost Sharing Support
The cost of immunosuppressive drugs is a financial burden for many
transplant recipients, particularly those without sufficient health
insurance coverage.\297\ A person's ability to pay for
immunosuppressive drugs, among other services needed in the
perioperative and postoperative periods, is a factor used by transplant
hospitals to assess suitability for the transplant waitlist.\298\
Studies have found that low income status decreases the likelihood of
waitlisting.\299\ One survey of a transplant programs found that 67.3
percent of programs surveys reported frequent or occasional failure to
list patients due to concerns regarding ability to pay for
immunosuppressive medications.\300\ In assessing the financial
implications of extending Medicare coverage of immunosuppressive drugs
for the lifetime of the patient, the Assistant Secretary for Planning
and Evaluation (ASPE) assumed a non-adherence graft failure rate of
10.7 percent and assessed that factors outside of affordability had
minimal impact on non-adherence to immunosuppressive drugs.\301\
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\297\ James, A., & Mannon, R.B. (2015). The Cost of Transplant
Immunosuppressant Therapy: Is This Sustainable? Current
Transplantation Reports, 2(2), 113-121. https://doi.org/10.1007/s40472-015-0052-y.
\298\ The kidney transplant waitlist. (n.d.). Transplant Living.
https://transplantliving.org/kidney/the-kidney-transplant-waitlist/.
\299\ Park, C., Jones, M.-M., Kaplan, S., Koller, F.L., Wilder,
J.M., Boulware, L.E., & McElroy, L.M. (2022). A scoping review of
inequities in access to organ transplant in the United States.
International Journal for Equity in Health, 21(1). https://doi.org/10.1186/s12939-021-01616-x.
\300\ Evans, R.W., Applegate, W.H., Briscoe, D.M., Cohen, D.J.,
Rorick, C.C., Murphy, B.T., & Madsen, J.C. (2010). Cost-related
immunosuppressive medication nonadherence among kidney transplant
recipients. Clinical Journal of the American Society of Nephrology,
5(12), 2323-2328. https://doi.org/10.2215/cjn.04220510.
\301\ Assessing the Costs and Benefits of Extending Coverage of
Immunosuppressive Drugs under Medicare. (n.d.). ASPE. https://aspe.hhs.gov/reports/assessing-costs-benefits-extending-coverage-immunosuppressive-drugs-under-medicare.
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Between 2016 and 2019, immunosuppressive drugs represented the
greatest proportion of drug expenditures in the year following kidney
transplant in Medicare Parts B and D.\302\ Between 2016 and 2019, the
Per-Patient-Per-Year expenditure in the year following transplant in
Medicare Parts B and D was $6,947.\303\ Medicare beneficiaries whose
immunosuppressive drugs are covered by Part B are responsible for 20
percent of these costs. The cost sharing obligation of Medicare
beneficiaries whose immunosuppressive drugs are covered by Part D can
vary depending on the benefit structure of the Part D plan.
---------------------------------------------------------------------------
\302\ United States Renal Data System. (2022). 2022 USRDS 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. https://usrds-adr.niddk.nih.gov/2022.
\303\ Ibid.
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We propose to allow IOTA participants to subsidize, in whole or in
part, the cost sharing associated with immunosuppressive drugs covered
by Part B, the Part B-ID benefit, and Part D (``Part B and Part D
immunosuppressive drug cost sharing support'') incurred by attributed
patients. As discussed in section III.C.11.i. of this proposed rule, if
this rule is finalized, CMS expects to make a determination that the
anti-kickback statute safe harbor for CMS-sponsored model patient
incentives (Sec. 1001.952(ii)(2)) is available to protect the
reduction of cost sharing obligations that are made in compliance with
this policy and the conditions for use of the anti-kickback statute
safe harbor set out at Sec. 1001.952(ii)(2).
We expect that a large proportion of an IOTA participant's
attributed patient population would be Medicare ESRD beneficiaries,
covered either by traditional Medicare or by MA. Most ESRD
beneficiaries covered by traditional Medicare receive immunosuppressive
drug coverage through Part B. A proportion of ESRD beneficiaries who
are not eligible for Part A at the time of the kidney transplant or who
receive a kidney transplant in a non-Medicare approved facility receive
immunosuppressive drugs through Medicare Part D. ESRD beneficiaries
covered by MA receive Part B immunosuppressive drugs through the plan
in which the beneficiary is enrolled.
To be eligible for Part B and Part D immunosuppressive drug cost
sharing support, we are proposing to define eligible attributed patient
as an attributed patient that receives immunosuppressive coverage
through Part B or Part D but that does not have secondary insurance
that could provide cost sharing support. An IOTA participant's
attributed patient population could include several subsets of eligible
attributed patients. One subset of eligible attributed patients could
be ESRD beneficiaries who are not able to purchase secondary insurance
due to State laws that do not require insurers to sell Medigap plans to
Medicare Beneficiaries under the age of 65. Another subset of eligible
attributed
[[Page 43594]]
patients could, under certain conditions, be ESRD beneficiaries whose
eligibility for Medicare only due to ESRD ends 36 months following a
kidney transplant. Attributed patients whose eligibility for Medicare
due to ESRD ends 36 months following a kidney transplant may be
eligible for the Medicare Part B Immunosuppressive Drug Benefit (Part
B-ID) depending on the availability of other health coverage options
such as Medicaid, plans purchased via a State health exchange, or the
TRICARE for Life program. Other attributed patients whose Medicare
eligibility due to ESRD concludes 36 months following a transplant
could choose to return to work and receive immunosuppressive drug
coverage through an Employer Group Health Plan (EGHP), enroll in a
Qualified health plan (QHP) under the Affordable Care Act as defined by
45 CFR 155.20, or receive coverage through Medicaid. These attributed
patients would not be eligible for Part B and Part D immunosuppressive
drug cost sharing support. We believe that Part B and Part D
immunosuppressive drug cost sharing support would have special value
for attributed patients whose Medicare eligibility due only to ESRD
ends after 36 months and who are eligible for Medicare Savings Programs
(MSPs) but who live in States that have not expanded Medicaid
eligibility for adults to include certain individuals with incomes up
to 138 percent of the Federal Poverty Level (FPL). These individuals
may have incomes that are too high to qualify for Medicaid, but too low
to qualify for advance premium tax credits (APTCs) or cost-sharing
reductions (CSRs) that would allow them to purchase a QHP. We are not
proposing that Part B and Part D immunosuppressive drug cost sharing
support would count towards an eligible attributed patients' Part D
True Out-of-Pocket (TrOOP). Part B and Part D immunosuppressive drug
cost sharing support would be reported on the Prescription Drug Event
(PDE) record as Patient Liability Reduction due to Other Payer Amount
(PLRO).
We are proposing to allow IOTA participants to subsidize, in whole
or in part, the cost sharing associated with immunosuppressive drugs
covered by Part B, the Part B-ID benefit, and Part D because we believe
cost sharing associated with medically necessary immunosuppressive
drugs would represent a significant out-of-pocket cost burden to
attributed patients who receive immunosuppressive coverage through Part
B, the Part B-ID benefit, or Part D, and because we believe an IOTA
participant's attributed patient population would include beneficiaries
whose immunosuppressive drugs are covered through each of these avenues
(that is, Part B, the Part B-ID benefit, and Part D).
We are proposing several safeguards for the proposed Part B and
Part D immunosuppressive drug cost sharing support policy. First, an
attributed patient must be eligible to receive cost sharing support
under the Part B and Part D cost sharing support policy. IOTA
participants must provide a written policy for Part B and Part D
immunosuppressive drug cost sharing support in a form and manner
determined by CMS that is approved by CMS prior to the PY in which the
cost sharing support would be available and prior to offering
attributed patients the incentive. An IOTA participant would be
required to revalidate the written policy with CMS in a form and manner
determined by CMS prior to each PY in which Part B and Part D
immunosuppressive drug cost sharing support would be offered
subsequently. The initial written policy and the policy that would be
revalidated by CMS must establish and justify the criteria that qualify
an eligible attributed patient to receive Part B and Part D
immunosuppressive drug cost sharing support. In providing the written
policy and the revalidation of the written policy for Part B and Part D
immunosuppressive drug cost sharing support, the IOTA participant must
attest that the IOTA participant will not, in providing Part B and Part
D immunosuppressive drug cost sharing support, take into consideration
the type, cost, generic status, or manufacturer of the
immunosuppressive drug(s) or limit an eligible attributed patient's
choice of pharmacy. We believe these policies are necessary to ensure
that an IOTA participant would have a sound basis for determining
eligibility requirements for Part B and Part D immunosuppressive drug
cost sharing support.
We are proposing safeguards to protect against an IOTA participant
preferentially providing cost sharing support for certain
immunosuppressive drugs. An IOTA participant must not take into
consideration the type, cost, generic status, or manufacturer of the
immunosuppressive drug(s) or limit an eligible attributed patients'
choice of pharmacy when providing Part B and Part D immunosuppressive
drug cost sharing support. In addition, IOTA participant must not
accept financial or operational support for the Part B and Part D
immunosuppressive drug cost sharing support from pharmacies and
pharmaceutical manufacturers. Immunosuppressive drug regimens are
adjusted to an individual's unique clinical characteristics to achieve
a balance between preserving the health of the transplanted organ and
reducing morbidity associated with long-term immunosuppression. We do
not believe that the anti-kickback statute safe harbor for CMS-
sponsored model patient incentives should be used to protect
arrangements that could limit or influence attributed patients' access
to the most clinically appropriate immunosuppressive drugs. Finally, to
facilitate compliance monitoring, we are proposing that IOTA
participants must maintain documentation regarding this beneficiary
incentive. At minimum, the IOTA participant must maintain
contemporaneous documentation that includes the identity of the
eligible attributed patient to whom Part B and Part D immunosuppressive
drug cost sharing support was provided, the date or dates on which Part
B and Part D immunosuppressive drug cost sharing support was provided,
and the amount or amounts of Part B and Part D immunosuppressive drug
cost sharing support that was provided. IOTA participants must retain
and provide access to the required documentation consistent with
section III.C.12 of this proposed rule and Sec. 1001.952(ii)(2).
We considered alternative safeguards for the Part B and Part D
immunosuppressive drug cost sharing support policy. We considered
requiring that an IOTA participant that wishes to offer Part B and Part
D immunosuppressive drug cost sharing support must offer it to every
attributed patient whose immunosuppressive drugs are covered by Part B
or Part D and who does not have secondary insurance. Ultimately, we
believe such a policy would run counter to our intention to offer IOTA
participants flexibility to meet the needs of their attributed patient
populations.
We also considered alternatives to the entirety of the proposed
Part B and Part D immunosuppressive cost sharing support policy. We
considered waiving Medicare payment requirements such that CMS would
pay the full amount of the Part B or Part B-ID coinsurance for
immunosuppressive drugs that are medically necessary for preventing or
treating the rejection of a transplanted organ or tissue. If we were to
pay 100 percent of the cost of immunosuppressive drugs for attributed
patients who are Medicare beneficiaries whose immunosuppressive drugs
are covered by Part B and attributed patients whose immunosuppressive
drugs are covered by the Part B-ID
[[Page 43595]]
benefit, such attributed patients would have no cost sharing
obligation. However, we believed that this policy would represent too
large an impact to the IOTA Model savings estimates, and thus would
potentially jeopardize our ability to continue to test the IOTA Model,
if such a policy were finalized.
We also considered waiving the premium for the Part B-ID benefit.
Under section 402(d) of the CAA and the implementing regulations at 42
CFR part 407 subpart D 408.20(f), the Secretary determines and
promulgates a monthly premium rate for individuals enrolled in the Part
B-ID benefit that is 15 percent of the monthly actuarial rate for
beneficiaries who are age 65 and older. The Part B premium for 2024 for
individuals enrolled in the Part B-ID benefit who file individual or
joint tax returns with a modified adjusted gross income of less than or
equal to $103,000 or $206,000 respectively, is $103.00. The Part B-ID
premium is subject to income-related adjustments based on modified
adjusted gross income. We believe the Part B-ID benefit monthly premium
may represent a substantial out-of-pocket expenditure for individuals
enrolled in the benefit given that it is prudent for the individual to
acquire additional health insurance to cover other necessary health
care services outside of immunosuppressive drugs. A premium waiver for
the Part B-ID benefit is authorized by section 1115A(d)(1) of the Act,
under which the Secretary may waive provisions of Title XVIII of the
Act, including provisions of section 1836(b) of the Act, as may be
necessary solely for purposes of carrying out section 1115A of the Act.
We believe, however, that waiving the premium for the Part B-ID benefit
would have too significant an impact on the IOTA Model savings
estimates; therefore, we are not proposing to waive it for purposes of
the IOTA Model.
We seek feedback on the proposal to allow an IOTA participant to
subsidize the 20 percent coinsurance on immunosuppressive drugs covered
by Part B or the Part B-ID benefit and the cost sharing associated with
immunosuppressive drugs covered by Part D, when an attributed patient
is eligible, meaning the attributed patient does not have secondary
insurance and meets the eligibility criteria defined by the IOTA
participant and approved by CMS prior to the PY in which the cost
sharing support is provided. We are also soliciting input from
interested parties on additional patient-centered safeguards that we
may consider to protect cost sharing subsidies made under the proposed
Part B and Part D immunosuppressive drug cost sharing support policy,
if finalized.
(2) Attributed Patient Engagement Incentives
We believe that providing additional flexibilities under the IOTA
Model would allow IOTA participants to support attributed patients in
overcoming challenges associated with remaining active on the kidney
transplant waitlist and adhering to comprehensive post-transplant care.
Thus, we propose that IOTA participants may offer the following
attributed patient engagement incentives under certain circumstances:
Communication devices and related communication services
directly pertaining to communication with an IOTA participant or IOTA
collaborator to improve communication between an attributed patient and
an IOTA participant or IOTA collaborator;
Transportation to and from a transplant hospital that is
an IOTA participant and between other providers and suppliers involved
in the provision of ESRD care;
Mental health services to address an attributed patient's
behavioral health symptoms pre- and post-transplant; and
In-home care to support the health of the attributed
patient or the kidney transplant in the post-transplant period.
For the purposes of the proposed attributed patient engagement
incentives, we are defining post-transplant period to mean the 90-day
period following an attributed patient's receipt of a kidney
transplant. We are proposing a 90-day post-transplant period because it
may take up to 3 months for many individuals to fully recover from a
kidney transplant.\304\ We are proposing that attributed patient
engagement incentives that are communication devices and related
communication services, transportation to and from an IOTA participant
and between other providers and suppliers involved in the provision of
ESRD care, and mental health services to address an attributed
patient's behavioral health symptoms could, under certain circumstances
described in this section, be offered while an attributed patient is on
a waitlist, after an attributed patient receives a transplant, or both.
In-home care to support the health of the attributed patient or the
kidney transplant may only be offered in the post-transplant period.
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\304\ Recovery after transplant surgery [verbar] American Kidney
Fund. (2021, December 14). Www.kidneyfund.org. https://www.kidneyfund.org/kidney-donation-and-transplant/life-after-transplant-rejection-prevention-and-healthy-tips/recovery-after-transplant-surgery.
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A mixed methods study of transplant providers' assessment of
barriers to accessing a kidney transplant found that transportation was
the most reported impediment to transplant.\305\ Interested parties
have informed us that transportation to medical appointments pre- and
post-transplant, as well as to and from the dialysis center for
treatments pre-transplant, is an important factor in maintaining active
status on the list and the health of an individual and the graft after
the transplant. Interested parties have also communicated with us about
the importance of communication with waitlisted patients. We understand
it can be common for an individual to not receive important information
about the kidney transplant process when transplant hospitals and
dialysis facilities do not communicate with one another about a
patient's status. We believe we may be able to overcome this challenge
by providing IOTA participants with greater flexibility to communicate
directly with attributed patients about their status in the kidney
transplant process.306 307 We understand that attributed
patients who face communication and transportation barriers while on
the kidney transplant waitlist may be inactivated, meaning that the
attributed patient cannot receive organ offers. An attributed patient
that cannot receive organ offers is misaligned with the IOTA Model's
proposed performance assessment methodology, which would encourage an
IOTA participant to increase its number of transplants. An attributed
patient that cannot receive organ offers represents a missed
opportunity for transplant, which is inconsistent with the goals of the
proposed IOTA Model. Accordingly, we are interested in providing a
framework under which an IOTA participant would be able to offer
attributed patient engagement incentives in the form of communication
devices and related communication services may increase the number of
attributed patients who achieve and maintain active status on
[[Page 43596]]
the kidney transplant waitlist. We believe the availability of
transportation to and from an IOTA participant and between other
providers and suppliers involved in the provision of ESRD care and
mental health services to address an attributed patient's behavioral
health symptom may also act in service of assisting more attributed
patients in overcoming barriers to achieving or maintaining active
status on a waitlist, among other challenges in the kidney transplant
process prior to and after receiving a kidney transplant.
---------------------------------------------------------------------------
\305\ Browne, T., McPherson, L., Retzloff, S., Darius, A., Wilk,
A.S., Cruz, A., Wright, S., Pastan, S.O., Gander, J.C., Berlin,
A.A., & Patzer, R.E. (2021). Improving access to kidney
transplantation: Perspectives from Dialysis and Transplant Staff in
the Southeastern United States. Kidney Medicine, 3(5). https://doi.org/10.1016/j.xkme.2021.04.017.
\306\ Ibid.
\307\ Gillespie, A. (2021). Communication breakdown: Improving
communication between transplant centers and dialysis facilities to
improve access to kidney transplantation. Kidney Medicine, 3(5),
696-698. https://doi.org/10.1016/j.xkme.2021.08.003.
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For example, we are also interested in providing greater
flexibility to IOTA participants to support improved adherence to
processes of care pre- and post-transplant that may support the ability
of an attributed patient to accept an organ offer and the outcomes of
the attributed patient and the graft after receiving a kidney
transplant. Anxiety and depression may increase as attributed patients
spend time on the kidney transplant waitlist.\308\ Prevalence of
depression is reported to decrease after kidney transplant, but may
still exceed 20 percent.\309\ Interested parties have reported that
behavioral health symptoms interfere with adherence to care
recommendations, including activities that support remaining active on
the transplant waitlist and behaviors that support positive clinical
outcomes for the patient and the graft after the kidney transplant
procedure. Interested parties have also informed us of the importance
of a transplant recipient having the support of another person in the
home for a short period in the post-transplant period to enhance
recovery.
---------------------------------------------------------------------------
\308\ Corruble, E., Durrbach, A., Charpentier, B., Lang, P.,
Amidi, S., Dezamis, A., Barry, C., & Falissard, B. (2010).
Progressive increase of anxiety and depression in patients waiting
for a kidney transplantation. Behavioral Medicine, 36(1), 32-36.
https://doi.org/10.1080/08964280903521339.
\309\ Szeifert, L., Molnar, M.Z., Ambrus, C., Koczy, A.B.,
Kovacs, A.Z., Vamos, E.P., Keszei, A., Mucsi, I., & Novak, M.
(2010). Symptoms of depression in kidney transplant recipients: A
cross-sectional study. American Journal of Kidney Diseases, 55(1),
132-140. https://doi.org/10.1053/j.ajkd.2009.09.022.
---------------------------------------------------------------------------
We also believe providing the option for flexibility to offer
attributed patient engagement incentives under the auspices of the IOTA
Model would allow IOTA participants to provide attributed patients with
tools to overcome barriers in the process of receiving a kidney
transplant, thereby increasing adherence to the kidney transplant
process, improving post-transplant outcomes, and supporting patient-
centricity in the IOTA Model. As stated in section III.C.11.i. of this
proposed rule, we expect to make the determination that the anti-
kickback statute safe harbor for CMS-sponsored model patient incentives
(Sec. 1001.952(ii)(2)) is available to protect the attributed patient
engagement incentives proposed in this section when the incentives are
offered or given to the attributed patient solely when the remuneration
is exchanged between an IOTA participant and an attributed patient in
compliance with this proposed rule and the conditions of the safe
harbor for CMS-sponsored model patient incentives.
We are proposing programmatic requirements for the attributed
patient engagement incentives. First, an IOTA participant must provide
a written policy in a form and manner determined by CMS for the
provision of attributed patient engagement incentives. The IOTA
participant's written policy must be approved by CMS before the PY in
which an attributed patient engagement incentive is first made
available, and must be revalidated by CMS, in a form and manner
specified by CMS, prior to each PY in which an IOTA participant wishes
to offer an attributed patient engagement incentive subsequently. The
IOTA participant's written policy must describe the items or services
the IOTA participant plans to provide, an explanation of how each item
or service that would be an attributed patient engagement incentive has
a reasonable connection to, at minimum, one of the following: (1)
achieving or maintaining active status on a kidney transplant waitlist;
(2) accessing the kidney transplant procedure; or (3) the health of the
attributed patient or the kidney transplant in the post-transplant
period, and a justification for the need for the attributed patient
engagement incentives that is specific to the IOTA participant's
attributed patient population. The IOTA participant's written policy
must also include an attestation that items that are attributed patient
engagement incentives would be provided directly to an attributed
patient, meaning that third parties would be precluded from providing
an item that is an attributed patient engagement incentive to an
attributed patient. We are not requiring an IOTA participant to provide
any such attestation pertaining to services that are attributed patient
engagement incentives because we acknowledge that services such as
communication services, mental health services and in-home care
services are generally provided by third parties. The IOTA participant
would, however, be required to attest in its written policy that the
IOTA participant would pay the service provider directly for services.
Finally, the IOTA participant's written policy must also include an
attestation that any items or services acquired by the IOTA participant
that would be furnished as attributed patient engagement incentives
would be acquired for the minimum amount necessary to for an attributed
patient to achieve or maintain active status on the waitlist, access
the kidney transplant procedure, or support the health of the
attributed patient or the kidney transplant in the post-transplant
period.
We are proposing the following restrictions on the provision of
attributed patient engagement incentives. An IOTA participant must
include in the written policy approved by CMS prior to offering an
attributed patient engagement incentive, items that are attributed
patient engagement incentives must be provided directly to an
attributed patient and an IOTA participant must pay a service provider
directly for any services that are offered as attributed patient
engagement incentives. An IOTA participant must not offer attributed
patient engagement incentives that are tied to the receipt of items of
services from a particular provider or supplier or advertise or promote
items or services that are attributed patient engagement incentives,
except to make an attributed patient aware of the availability of the
items or services at the time an attributed patient could reasonably
benefit from them. An IOTA participant must not receive donations
directly or indirectly to purchase attributed patient engagement
incentives. Finally, items that are attributed patient engagement
incentives must be retrieved from the attributed patient when the
attributed patient is no longer eligible for that item or at the
conclusion of the IOTA Model, whichever is earlier. Documented,
diligent, good faith attempts to retrieve items that are attributed
patient engagement incentives are deemed to meet the retrieval
requirement.
We are proposing the following, additional restrictions pertaining
to attributed patient engagement incentives that are communication
devices, because we believe that such items may be especially
susceptible to abuse. An IOTA participant's purchase of items that are
communication devices must not exceed $1000 in retail value for any one
attributed patient in any one PY. Items that are communication devices
must remain the property of the IOTA participant. An IOTA participant
must retrieve the item that is a communication device either when the
attributed patient is no longer eligible for the communication device
or at the conclusion of the IOTA Model,
[[Page 43597]]
whichever is earlier. Items that are communication devices must be
retrieved from an attributed patient before another communication
device may be provided to the same attributed patient. This restriction
applies across PYs. In other words, an IOTA participant may not offer
another communication device to the same attributed patient across all
IOTA model years until the first communication device has been
retrieved. We believe these additional restrictions on communication
devices that are offered under the attributed patient engagement
incentive policy are necessary to ensure that IOTA participants are not
providing communication devices for purposes that are not aligned with
the goals of the IOTA Model.
We are also proposing documentation requirements that pertain to
the provision of attributed patient engagement incentives. The IOTA
participant must maintain contemporaneous documentation of items and
services furnished as attributed patient engagement incentives that
includes, at minimum, the date an attributed patient engagement
incentive is provided and the identity of the attributed patient to
whom the item or service was provided. In accordance with the retrieval
requirements for items that attributed patient engagement incentives,
IOTA participants must document all retrieval attempts of items that
are attributed patient engagement incentives, including the ultimate
date of retrieval. IOTA participants must retain all records pertaining
to the furnishing of attributed patient engagement incentives and make
those records available to the Federal Government in accordance with
section III.C.12. of this proposed rule.
Taken together, we believe the safeguards described in this section
are necessary to ensure that attributed patient engagement incentives
offered by an IOTA participant are provided in compliance with the
intent of the proposed policy and the anti-kickback statute safe harbor
for CMS-sponsored model patient incentives (Sec. 1001.952(ii)(2)).
We considered not allowing IOTA participants to offer attributed
patient engagement incentives for attributed patients in the IOTA
Model, which would simplify the IOTA Model. Further, having no
attributed patient engagement incentive policy would allow IOTA
participants to direct available resources to the proposed Part B and
Part D immunosuppressive drug cost sharing support policy described in
section III.C.h.(2). of this proposed rule. We took these
considerations into account; however, we believe allowing for the
maximum amount of flexibility possible for IOTA participants to meet
the needs of attributed patients that relate to accessing a kidney
transplant is consistent with the model's goals. In addition, we were
unable to find any literature to suggest that one type of item or
service, for example, cost sharing subsidies under Part B and Part D
immunosuppressive drug cost sharing, is of greater value to an
individual waiting for a kidney transplant or having received a kidney
transplant than another, for example, an attributed patient engagement
incentive. We also considered including dental services as a service
that may be offered as an attributed patient engagement incentive.
Sources of oral infection must be resolved before an individual can
receive a kidney transplant because post-transplant immunosuppression
puts a kidney transplant recipient at greater risk for oral infections
that can spread to the rest of the body.\310\ We did not include dental
services as an allowable attributed patient engagement incentive
because we understand that sources of oral infection must be resolved
before an individual can be waitlisted for a kidney transplant; in
other words, prior to the ability of an individual to be attributed to
the IOTA Model. We are interested in receiving comments on the extent
to which dental issues emerge once an individual has been listed for a
kidney transplant and whether we should consider dental services as an
attributed patient engagement incentive under the auspices of the IOTA
Model.
---------------------------------------------------------------------------
\310\ Kwak, E.-J., Kim, D.-J., Choi, Y., Joo, D.J., & Park, W.
(2020). Importance of oral health and dental treatment in organ
transplant recipients. International Dental Journal, 70(6), 477-481.
https://doi.org/10.1111/idj.12585.
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We are soliciting feedback on our proposal to allow IOTA
participants to offer attributed patient engagement incentives in a
manner that complies with the restrictions and safeguards in this
section. We are further soliciting feedback on other barriers to
remaining active on the kidney transplant waitlist, receiving organ
offers, and adhering to pre- and post-transplant care that we may be
able to address by expanding the attributed patient engagement
incentives available to attributed patients through future rulemaking.
i. Fraud and Abuse Waiver and OIG Safe Harbor Authority
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 with respect to testing models
described in section 1115A(b) of the Act.
For this model and consistent with the authority under section
1115A(d)(1) of the Act, the Secretary may consider issuing waivers of
certain fraud and abuse provisions in sections 1128A, 1128B, and 1877
of the Act. No fraud or abuse waivers are being issued in this
document; fraud and abuse waivers, if any, would be set forth in
separately issued documentation. Any such waiver would apply solely to
the IOTA Model and could differ in scope or design from waivers granted
for other programs or models. Thus, notwithstanding any provision of
this proposed rule, IOTA participants and IOTA collaborators must
comply with all applicable laws and regulations, except as explicitly
provided in any such separately documented waiver issued pursuant to
section 1115A(d)(1) of the Act specifically for the IOTA Model.
In addition to or in lieu of a waiver of certain fraud and abuse
provisions in sections 1128A and 1128B of the Act, CMS proposes to
waive sections 1881(b) and 1833(a) and 1833(b) of the Act only to the
extent necessary to make payments under the IOTA Model. CMS further
expects to make a determination, if this rule is finalized, that the
anti-kickback statute safe harbor for CMS-sponsored model arrangements
and CMS-sponsored model patient incentives (Sec. 1001.952(ii)(1) and
(2)) is available to protect remuneration exchanged pursuant to certain
financial arrangements and patient incentives that may be permitted
under the final rule, if issued. Specifically, we expect to determine
that the CMS-sponsored models safe harbor would be available to protect
the following financial arrangements and incentives: the IOTA Model
Sharing Arrangement's gainsharing payments and alignment payments, the
Distribution Arrangement's distribution payments, the Part B and Part D
immunosuppressive drug cost sharing support policy and attributed
patient engagement incentives.
We considered not allowing use of the safe harbor provisions.
However, we determined that use of the safe harbor would encourage the
goals of the model. We believe that a successful model requires
integration and coordination among IOTA participants and other health
care providers and suppliers. We believe the use of the safe harbor
would encourage and improve beneficiary experience of care and
coordination of
[[Page 43598]]
care among providers and suppliers. We also believe these safe harbors
offer flexibility for innovation and customization. The safe harbors
allow for emerging arrangements that reflect up-to-date understandings
in medicine, science, and technology.
We seek comment on this proposal, including that the anti-kickback
safe harbor for CMS-sponsored model arrangements (Sec.
1001.952(ii)(1)) be available to IOTA participants and IOTA
collaborators.
12. Audit Rights and Record Retention
By virtue of their participation in an Innovation Center model,
IOTA participants and IOTA collaborators may receive model-specific
payments, access to Medicare payment waivers, or some other model-
specific flexibility, such as the ability to provide cost sharing
support to eligible attributed patients for the proposed Part B and
Part D immunosuppressive drug cost sharing support policy. It is
therefore necessary and appropriate for CMS to audit, inspect,
investigate, and evaluate records and other materials related to
participation in the IOTA Model. CMS must be able to audit, inspect,
investigate, and evaluate records and materials related to
participation in the IOTA Model to allow us to ensure that IOTA
participants are in no way denying or limiting the coverage or
provision of benefits for beneficiaries as part of their participation
in the IOTA Model. We propose to define ``model-specific payment'' to
mean a payment made by CMS only to IOTA participants, or a payment
adjustment made only to payments made to IOTA participants, under the
terms of the IOTA Model that is not applicable to any other providers
or suppliers; the term ``model-specific payment'' would include, unless
otherwise specified, the model upside risk payment and downside risk
payment, described in section III.C.6 of this proposed rule. It is
necessary to propose this definition to distinguish payments and
payment adjustments applicable to IOTA participants as part of their
participation in the IOTA Model, from payments and payment adjustments
applicable to IOTA participants as well as other providers and
suppliers, as certain provisions of proposed part 512 would apply only
to the former category of payments and payment adjustments.
There are audit and record retention requirements under the
Medicare Shared Savings Program (see 42 CFR 425.314) and in other
models being tested under section 1115A of the Act (see, for example,
42 CFR 510.110 and Sec. 512.135).
We are proposing to adopt audit and record retention requirements
for the IOTA Model. Specifically, as a result of our proposal to revise
the scope of the general provisions of 42 CFR Part 512 Subpart A to
include the IOTA Model, see proposed 42 CFR 512.100, we are proposing
to apply Sec. 512.135(a) through (c) to each IOTA participant and its
IOTA collaborators. In applying Sec. 512.135(a) to the IOTA Model, the
Federal Government, including, but not limited to, CMS, HHS, and the
Comptroller General, or their designees, would have a right to audit,
inspect, investigate, and evaluate any documents and other evidence
regarding implementation of an Innovation Center model. In applying
existing Sec. 512.135(b) and (c) to the IOTA model, an IOTA
participant and its IOTA collaborators would be required to:
Maintain and give the Federal Government, including, but
not limited to, CMS, HHS, and the Comptroller General, or their
designees, access to all documents (including books, contracts, and
records) and other evidence sufficient to enable the audit, evaluation,
inspection, or investigation of the IOTA Model, including, without
limitation, documents and other evidence regarding all of the
following:
++ Compliance by the IOTA participant and its IOTA collaborators
with the terms of the IOTA Model, including proposed new subpart A of
proposed part 512.
++ The accuracy of model-specific payments made under the IOTA
Model.
++ The IOTA participant's downside risk payments owed to CMS under
the IOTA Model.
++ Quality measure information and the quality of services
performed under the terms of the IOTA Model, including proposed new
subpart A of proposed part 512.
++ Utilization of items and services furnished under the IOTA
Model.
++ The ability of the IOTA participant to bear the risk of
potential losses and to repay any losses to CMS, as applicable.
++ Where cost sharing support is furnished under the Part B and
Part D immunosuppressive drug cost sharing support policy, the IOTA
participant must maintain contemporaneous documentation that includes
the identity of the eligible attributed patient to whom Part B and Part
D immunosuppressive drug cost sharing support was provided, the date or
dates on which Part B and Part D immunosuppressive drug cost sharing
support was provided, and the amount or amounts of Part B and Part D
immunosuppressive drug cost sharing support that was provided.
++ Contemporaneous documentation of items and services furnished as
attributed patient engagement incentives in accordance with Sec.
512.458 that includes, at minimum, the date the attributed patient
engagement incentive is provided and the identity of the attributed
patient to whom the item or service was provided.
++ Patient safety.
++ Any other program integrity issues.
Maintain the documents and other evidence for a period of
6 years from the last payment determination for the IOTA participant
under the IOTA Model or from the date of completion of any audit,
evaluation, inspection, or investigation, whichever is later, unless--
++ CMS determines there is a special need to retain a particular
record or group of records for a longer period and notifies the IOTA
participant at least 30 days before the normal disposition date; or
++ There has been a termination, dispute, or allegation of fraud or
similar fault against the IOTA participant or its IOTA collaborators,
in which case the records must be maintained for an additional 6 years
from the date of any resulting final resolution of the termination,
dispute, or allegation of fraud or similar fault.
If CMS notifies the IOTA participant of a special need to retain a
record or group of records at least 30 days before the normal
disposition date, the IOTA participant would be required to maintain
the records for such period of time determined by CMS. If CMS notifies
the IOTA participant of a special need to retain records or there has
been a termination, dispute, or allegation of fraud or similar fault
against the IOTA participant or its IOTA collaborators, the IOTA
participant would be required to notify its IOTA collaborators of the
need to retain records for the additional period specified by CMS. This
provision would ensure that that the government has access to the
records.
We note that we previously adopted a rule at 42 CFR 512.110
defining the term ``days,'' as used in 42 CFR 512.135, to mean calendar
days.
We invite public comment on these proposed provisions regarding
audits and record retention.
13. Monitoring
a. General
We propose that CMS, or its approved designees, would conduct
compliance
[[Page 43599]]
monitoring activities to ensure compliance by the IOTA participant and
IOTA collaborators with the terms of the IOTA Model, including to
understand IOTA participants' use of model-specific payments and to
promote the safety of attributed patients and the integrity of the IOTA
Model. Such monitoring activities would include, but not be limited
to--
Documentation requests sent to the IOTA participant and
its IOTA collaborators, including surveys and questionnaires;
Audits of claims data, quality measures, medical records,
and other data from the IOTA participant and its IOTA collaborators;
Interviews with the IOTA participant, including leadership
personnel, medical staff, other associates and its IOTA collaborators;
Interviews with attributed patients and their caregivers;
Site visits to the IOTA participant, which would be
performed in accordance with Sec. 512.462, described below in section
b of this proposed rule;
Monitoring quality outcomes and attributed patient data;
Tracking beneficiary complaints and appeals;
Monitor the definition of and justification for the
subpopulation of the IOTA participant's eligible attributed patients
that may receive Part B and Part D Immunosuppressive Drug Cost Sharing
Support in accordance with Sec. 512.456; and
Monitor the provision of attributed patient engagement
incentives provided in accordance with Sec. 512.458.
Additionally, CMS is concerned about IOTA participants bypassing
the match run, as defined in section III.C.5.d.(1).(a). of this
proposed rule, the rank order list of transplant candidates to be
offered an organ. This practice, known as ``list diving,'' can improve
efficiency in placing organs, but may undermine the mechanisms
promoting fairness in rationing this scarce resource, if overused. We
propose that CMS would monitor out of sequence allocation of kidneys by
assessing how often top-ranked attributed patients receive the organ
that was offered to them and if they did not receive it, what the
reason for that was.
We believe these specific monitoring activities, which align with
those currently used in other models being tested by the Innovation
Center, are necessary to ensure compliance with the terms of the IOTA
Model and can protect attributed patients from potential harm that may
result from the activities of the IOTA participant or its IOTA
collaborators, such as attempts to reduce access to or the provision of
medically necessary covered services.
We propose that when CMS is conducting compliance monitoring and
oversight activities, CMS or its designees would be authorized to use
any relevant data or information, including without limitation Medicare
claims submitted for items or services furnished to attributed patients
who are Medicare beneficiaries. We believe that it is necessary to have
all relevant information available to CMS during compliance monitoring
and oversight activities, including any information already available
to CMS through the Medicare program.
IOTA participants would remain subject to all existing requirements
and conditions for Medicare participation as set out in Federal
statutes and regulations and provider and supplier agreements, unless
waived under the authority of section 1115A(d)(1) of the Act solely for
purposes of testing the IOTA Model.
We seek feedback on how CMS should implement this monitoring
proposal and any additional concerns regarding the overall monitoring
approach.
b. Site Visits
We propose that IOTA participants would be required to cooperate in
periodic site visits conducted by CMS or its designee. Such site visits
would be conducted to facilitate the model evaluation performed
pursuant to section 1115A(b)(4) of the Act and to monitor compliance
with the IOTA Model requirements. We further propose that CMS or its
designee would provide the IOTA participant with no less than 15 days
advance notice of a site visit, to the extent practicable. Furthermore,
we propose that, to the extent practicable, CMS would attempt to
accommodate a request that a site visit be conducted on a particular
date, but that the IOTA participant would be prohibited from requesting
a date that was more than 60 days after the date of the initial site
visit notice from CMS. We believe the 60-day period would reasonably
accommodate IOTA participant schedules while not interfering with the
operation of the IOTA Model. Further, we propose to require the IOTA
participant to ensure that personnel with the appropriate
responsibilities and knowledge pertaining to the purpose of the site
visit be available during any and all site visits. We believe this
proposal is necessary to ensure an effective site visit and prevent the
need for unnecessary follow-up site visits.
Further, we propose that nothing in the previous sections would
limit CMS from performing other site visits as allowed or required by
applicable law. We believe that CMS must retain the ability to timely
investigate concerns related to the health or safety of attributed
patients or program integrity issues, and to perform functions required
or authorized by law. In particular, we believe that it is necessary
for CMS to monitor, and for IOTA participants to be compliant with our
monitoring efforts, to ensure that they are not denying or limiting the
coverage or provision of medically necessary covered services to
attributed patients in an attempt to change model results or their
model-specific payments, including discrimination in the provision of
services to at-risk patients (for example, due to eligibility for
Medicare based on disability).
In the alternative, we considered allowing unannounced site visits
for any reason. However, we determined that giving advanced notice for
site visits for routine monitoring would allow the IOTA participant to
ensure that the personnel with the applicable knowledge is available
and would allow the IOTA participant the flexibility to arrange these
site visits around their operations. However, we propose that if there
is a concern regarding issues that may pose risks to the health or
safety of attributed patients or to the integrity of the IOTA Model,
unannounced site visits would be warranted. We believe this would allow
us to address any potential concerns in a timely manner without a delay
that may increase those potential risks.
c. Reopening of Payment Determinations
To protect the financial integrity of the IOTA Model, we propose in
Sec. 512.462(d) that if CMS discovers that it has made or received a
request from the IOTA participant about an incorrect model payment, CMS
may make payment to, or demand payment from, the IOTA participant.
CMS' interests include ensuring the integrity and sustainability of
the IOTA Model and the underlying Medicare program, from both a
financial and policy perspective, as well as protecting the rights and
interests of Medicare beneficiaries. For these reasons, CMS or its
designee needs the ability to monitor IOTA participants to assess
compliance with model terms and with other applicable Medicare program
laws and policies. We believe our monitoring efforts help ensure that
IOTA participants are furnishing medically necessary covered services
and are not
[[Page 43600]]
falsifying data, increasing program costs, or taking other actions that
compromise the integrity of the IOTA Model or are not in the best
interests of the IOTA Model, the Medicare program, or Medicare
beneficiaries.
We invite public comment on these proposed provisions regarding
monitoring of the IOTA Model and alternatives considered.
14. Evaluation
Section 1115A(b)(4) of the Act requires the Secretary to evaluate
each model tested under the authority of section 1115A of the Act and
to publicly report the evaluation results in a timely manner. The
evaluation must include an analysis of the quality of care furnished
under the model and the changes in program spending that occurred due
to the model. Models tested by the Innovation Center are rigorously
evaluated. For example, when evaluating models tested under section
1115A of the Act, we require the production of information that is
representative of a wide and diverse group of model participants and
includes data regarding potential unintended or undesirable effects.
The Secretary must take the evaluation into account if making any
determinations regarding the expansion of a model under section
1115A(c) of the Act. In addition to model evaluations, the CMS
Innovation Center regularly monitors model participants for compliance
with model requirements.
For the reasons described in section III.C.11. of this proposed
rule, these compliance monitoring activities are an important and
necessary part of the model test. Therefore, we note that IOTA
participants and their IOTA collaborators must comply with the
requirements of 42 CFR 403.1110(b) (regarding the obligation of
entities participating in the testing of a model under section 1115A of
the Act to report information necessary to monitor and evaluate the
model), and must otherwise cooperate with CMS' model evaluation and
monitoring activities as may be necessary to enable CMS to evaluate the
Innovation Center model in accordance with section 1115A(b)(4) of the
Act. This participation in the evaluation may include, but is not
limited to, responding to surveys and participating in focus groups.
15. Learning
In the Specialty Care Models final rule (85 FR 61114), we
established the voluntary ETC Learning Collaborative (ETCLC). The goals
of the ETCLC are to increase the supply and use of deceased donor
kidneys by convening OPOs, transplant hospitals, donor hospitals, and
patients and families to reduce the variation in OPO and transplant
hospital performance and reduce kidney non-use.\311\ The ETCLC is
addressing three national aims over a 5-year period: (1) achieve a 28
percent absolute increase in the number of deceased donor kidneys with
a KDPI greater than or equal to 60 recovered for transplant from the
2021 OPTN/SRTR baseline of 11,284; (2) decrease the current national
non-use rate of all procured kidneys with a KDPI >=60 by 20 percent;
and (3) decrease the current national discard rate of all procured
kidneys with a KDPI <60 by 4 percent. The ETCLC has developed Quality
Improvement (QI) Teams that are identifying and implementing best
practices based on the ETCLC Kidney Donation and Utilization Change
Package. As of June 2023, 54 OPOs and 181 transplant hospitals were
enrolled in ETCLC.\312\
---------------------------------------------------------------------------
\311\ End Stage Renal Disease Treatment Choices Learning
Collaborative--End Stage Renal Disease Treatment Choices Learning
Collaborative--QualityNet Confluence. (n.d.).
Qnetconfluence.cms.gov. Retrieved May 30, 2023, from https://qnetconfluence.cms.gov/display/ETCLC/End+Stage+Renal+Disease+Treatment+Choices+Learning+Collaborative.
\312\ Ibid.
---------------------------------------------------------------------------
While we considered continuing the ETCLC under the auspices of the
IOTA Model, we are proposing to conclude the ETCLC at the end of the
ETC Model test and implement a learning system specific to the IOTA
Model. An IOTA Model learning system would deal only with issues
specific to the IOTA Model and would have neither national aims nor
include other providers in the transplant ecosystem such as OPOs or
donor hospitals as regular participants. The advantages of this
approach are that CMS could provide a forum for IOTA participants to
discuss elements of the model, share experiences implementing IOTA
Model provisions, and solicit support from peers in overcoming
challenges that may arise. Since most transplant hospitals have less
experience with Innovation Center models than other provider types, we
believe an independent learning system would provide unique value to
IOTA participants.
We also considered continuing ETCLC under the aegis of the IOTA
Model. We believe many IOTA participants would already be enrolled in
the ETCLC and dedicating staff and time to participating in QI Teams
and engaging with the Kidney Donation and Utilization Change Package.
We also believe that there may be overlap between the QI work being
undertaken by ETCLC participants and the issues that would be of
interest to IOTA participants. We further considered whether the ETCLC
needs more time to achieve its national aims that could be provided by
continuing the ETCLC under the IOTA Model.
We are soliciting feedback on our proposal to conclude the ETCLC
with the ETC Model and implement a new learning system specific to the
IOTA Model. We are also seeking feedback on the following questions:
What are specific examples of how ETCLC is supporting
transplant hospital QI to increase access to kidney transplant?
What features of a new learning system would be important
for IOTA participants?
Could the ETCLC meet IOTA participants' need for QI
support to succeed in the model?
16. Remedial Action and Termination
a. Remedial Action
We propose the Standard Provisions for Innovation Center Models
relating to remedial actions, originally finalized as general
provisions in the Code of Federal Regulations (42 CFR part 512 subpart
A) that applied to specific Innovation Center models but that we are
proposing for expansion to all Innovation Center Models with model
performance periods that begin on or after January 1, 2025, in section
II.B. of this proposed rule would apply to the IOTA Model. We propose
that CMS could impose one or more remedial actions on the IOTA
participant if CMS determines that--
The IOTA participant has failed to furnish 11 or more
transplants during the PY or any baseline years;
The IOTA participant or its IOTA collaborator has failed
to comply with any of the terms of the IOTA Model;
The IOTA participant has failed to comply with
transparency requirements as listed in section III.C.8.a. of this
proposed rule;
The IOTA participant or its IOTA collaborator has failed
to comply with any applicable Medicare program requirement, rule, or
regulation;
The IOTA participant or its IOTA collaborator has taken
any action that threatens the health or safety of an attributed
patient;
The IOTA participant or its IOTA collaborator has
submitted false data or made false representations, warranties, or
certifications in connection with any aspect of the IOTA Model;
The IOTA participant or its IOTA collaborator has
undergone a Change in Control, as described in section III.C.17.b of
this proposed rule, that presents a program integrity risk;
[[Page 43601]]
The IOTA participant or its IOTA collaborator is subject
to any sanctions of an accrediting organization or a Federal, State, or
local government agency;
The IOTA participant or its IOTA collaborator is subject
to investigation or action by HHS (including the HHS-OIG or CMS) or the
Department of Justice due to an allegation of fraud or significant
misconduct, including being subject to the filing of a complaint or
filing of a criminal charge, being subject to an indictment, being
named as a defendant in a False Claims Act qui tam matter in which the
Federal Government has intervened, or similar action;
The IOTA participant or its IOTA collaborator has failed
to demonstrate improved performance following any remedial action
imposed by CMS; or
The IOTA participant 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.
We propose that CMS may take one or more of the following remedial
actions if CMS determines that one or more of the grounds for remedial
action described in section III.C.16.a. of this proposed rule had taken
place:
Notify the IOTA participant and, if appropriate, require
the IOTA participant to notify its IOTA collaborators of the violation;
Require the IOTA participant to provide additional
information to CMS or its designees;
Subject the IOTA participant to additional monitoring,
auditing, or both;
Prohibit the IOTA participant from distributing model-
specific payments, as applicable;
Require the IOTA participant to terminate, immediately or
by a deadline specified by CMS, its sharing arrangement with an IOTA
collaborator with respect to the IOTA Model;
Terminate the IOTA participant from the IOTA Model;
Suspend or terminate the ability of the IOTA participant
to provide cost sharing support for Part B and Part D immunosuppressive
drugs, or attributed patient engagement incentives in accordance with
III.C.11.h(1).
Require the IOTA participant to submit a corrective action
plan (CAP) in a form and manner and by a deadline specified by CMS;
Discontinue the provision of data sharing and reports to
the IOTA participant;
Recoup model-specific payments;
Reduce or eliminate a model specific payment otherwise
owed to the IOTA participant, as applicable; or
Such other action as may be permitted under the terms of
the IOTA Model.
As part of the Innovation Center's monitoring and assessment of the
impact of models tested under the authority of section 1115A of the
Act, CMS has a special interest in ensuring that these model tests do
not interfere with the program integrity interests of the Medicare
program. For this reason, CMS monitors actions of IOTA participants for
compliance with model terms, as well as other Medicare program rules.
When CMS becomes aware of noncompliance with these requirements, it is
necessary for CMS to have the ability to impose certain administrative
remedial actions on a noncompliant model participant.
In the alternative, we considered a policy where the IOTA
participant would remain in the IOTA Model regardless of any
noncompliance. However, if there are circumstances in which the IOTA
participant has engaged, or is engaged in, egregious actions, we are
proposing that CMS may terminate the IOTA participant, as further
described in section III.C.16.b. of this proposed rule. In addition, we
considered allowing IOTA participants access to their data and reports
regardless of their compliance with the requirements of the IOTA Model
however we are proposing to discontinue data sharing and reports as a
potential remedial action if there are grounds for doing so.
We seek comment on these proposed provisions regarding the proposed
grounds for remedial actions, remedial actions generally, and whether
additional types of remedial action would be appropriate.
b. Termination of IOTA Participant From the IOTA Model by CMS
We propose that CMS may immediately or with advance notice
terminate an IOTA participant from participation in the IOTA Model if:
CMS determines that it no longer has the funds to support
the IOTA Model;
CMS modifies or terminates the model pursuant to section
1115A(b)(3)(B) of the Act;
CMS determines that the IOTA participant--
++ Has failed to comply with any model requirement or any other
Medicare program requirement, rule, or regulation;
++ Has failed to comply with a monitoring or auditing plan or both;
++ Has failed to submit, obtain approval for, implement or fully
comply with the terms of a CAP;
++ Has failed to demonstrate improved performance following any
remedial action;
++ Has taken any action that threatens the health or safety of a
Medicare beneficiary or other patient;
++ Has submitted false data or made false representations,
warranties, or certifications in connection with any aspect of the IOTA
Model; or
++ Assigns or purports to assign any of the rights or obligations
under the model, voluntarily or involuntarily, whether by merger,
consolidation, dissolution, operation of law, or any other manner,
without the written consent of CMS;
Poses significant program integrity risks, including but
not limited to:
++ Is subject to sanctions or other actions of an accrediting
organization or a Federal, State or local government agency; or
++ Is subject to investigation or action by HHS (including OIG or
CMS) or the Department of Justice due to an allegation of fraud or
significant misconduct, including being subject to the filing of a
complaint, filing of a criminal charge, being subject to an indictment,
being named as a defendant in a False Claims Act qui tam matter in
which the government has intervened, or similar action.
We request comment and feedback on the proposal for termination of
an IOTA participant from participating in the IOTA Model.
c. Termination of Model Participation by IOTA Participant
Given the mandatory nature of this model, we propose that an IOTA
participant would not be able to terminate its own participation in the
model. Maintaining a cohort of participants as close to 50 percent of
eligible kidney transplant hospitals across the country is critical to
evaluation of IOTA Model. As such, while we are proposing CMS may
terminate an IOTA participant for reasons such as failure to meet
eligibility criteria or change in kidney transplant hospital status, as
described in section III.C.16.b. of this proposed rule, we are not
proposing voluntary termination by the IOTA participant.
We considered allowing an IOTA participant to voluntarily terminate
their participation in the model; however, we believe this went against
the mandatory nature of the model and jeopardized our ability to
evaluate model success and savings.
[[Page 43602]]
We solicit comment and feedback on our proposal not to allow IOTA
participants to terminate their participation in the IOTA Model.
d. Financial Settlement Upon Termination
We propose that if CMS terminates the IOTA participant's
participation in the IOTA Model or CMS terminates the IOTA Model, CMS
would calculate the final performance score and any upside risk payment
or downside risk payment, if applicable, for the entire PY in which the
IOTA participant's participation in the model or the IOTA Model was
terminated.
We propose that if CMS terminates an IOTA participant for any
reason listed in section III.C.16.b of this proposed rule, CMS shall
not make any payments of upside risk payment for the PY in which the
IOTA participant was terminated and the IOTA participant shall remain
liable for payment of any downside risk payment up to and including the
PY in which termination becomes effective. We propose that CMS would
determine the IOTA participant's effective date of termination.
We considered that in the event of termination, CMS would not pay
any upside risk payments for the year in which the IOTA participant was
terminated, but also only keep the IOTA participant liable for paying
CMS any downside risk payments for completed PYs and not the year in
which the IOTA participant is terminated. However, to deter poor or
non-compliant performance, we believe it necessary to also keep the
IOTA participant liable for paying to CMS any downside risk payment for
the PY in which the IOTA participant is terminated.
We solicit comment on this proposal and alternative considered.
e. Termination of the IOTA Model
We are proposing that the general provisions relating to
termination of the model by CMS in 42 CFR 512.165 would apply to the
IOTA Model. Consistent with these provisions, in the event we terminate
the IOTA Model, we would provide written notice to IOTA participants
specifying the grounds for termination and the effective date of such
termination or ending. As provided by section 1115A(d)(2) of the Act
and Sec. 512.170(e), termination of the model under section
1115A(b)(3)(B) of the Act would not be subject to administrative or
judicial review. We propose that in the event of termination of the
model, financial settlement terms would be the same as those proposed
in section III.C.16.d. of this proposed rule.
17. Miscellaneous Provisions on Bankruptcy and Other Notifications
a. Notice of Bankruptcy
We propose that if an IOTA participant has filed a bankruptcy
petition, whether voluntary or involuntary, the IOTA participant must
provide written notice of the bankruptcy to CMS and to the U.S.
Attorney's Office in the district where the bankruptcy was filed,
unless final payment has been made by either CMS or the IOTA
participant under the terms of each model tested under section 1115A of
the Act in which the IOTA participant is participating or has
participated and all administrative or judicial review proceedings
relating to any payments under such models have been fully and finally
resolved. We propose the notice of bankruptcy must be sent by certified
mail no later than 5 days after the petition has been filed and must
contain a copy of the filed bankruptcy petition (including its docket
number), and a list of all models tested under section 1115A of the Act
in which the IOTA participant is participating or has participated.
This list would not need to identify a model tested under section 1115A
of the Act in which the IOTA participant participated if final payment
has been made under the terms of the model and all administrative or
judicial review proceedings regarding model-specific payments between
the IOTA participant and CMS have been fully and finally resolved with
respect to that model. The notice to CMS would be addressed to the CMS
Office of Financial Management, Mailstop C3-01-24, 7500 Security
Boulevard, Baltimore, Maryland 21244 or to such other address as may be
specified on the CMS website for purposes of receiving such notices.
b. Change in Control
We propose that CMS could terminate an IOTA participant from the
model if the IOTA participant undergoes a Change in Control. We propose
that the IOTA participant shall provide written notice to CMS at least
90 days before the effective date of any Change in Control. For
purposes of this rule, we propose a ``Change in Control'' would mean at
least one of the following: (1) the acquisition by any ``person'' (as
such term is used in Sections 13(d) and 14(d) of the Securities
Exchange Act of 1934) of beneficial ownership (within the meaning of
Rule 13d-3 promulgated under the Securities Exchange Act of 1934),
directly or indirectly, of voting securities of the IOTA participant
representing more than 50 percent of the IOTA participant's outstanding
voting securities or rights to acquire such securities; (2) the
acquisition of the IOTA participant by any individual or entity; (3)
any merger, division, dissolution, or expansion of the IOTA participant
(4) the sale, lease, exchange or other transfer (in one transaction or
a series of transactions) of all or substantially all of the assets of
the IOTA participant; or (5) the approval and completion of a plan of
liquidation of the IOTA participant, or an agreement for the sale or
liquidation of the IOTA participant.
c. Prohibition on Assignment
We propose that except with the prior written consent of CMS, an
IOTA participant shall not transfer, including by merger (whether the
IOTA participant is the surviving or disappearing entity),
consolidation, dissolution, or otherwise: (1) any discretion granted it
under the model; (2) any right that it has to satisfy a condition under
the model; (3) any remedy that it has under the model; or (4) any
obligation imposed on it under the model. We propose that the IOTA
participant provide CMS 90 days advance written notice of any such
proposed transfer. We propose this obligation remains in effect after
the expiration or termination of the model or the IOTA participant's
participation in the model and until final payment by the IOTA
participant under the model has been made. We propose CMS may condition
its consent to such transfer on full or partial reconciliation of
upside risk payments and downside risk payments. We propose that any
purported transfer in violation of this requirement is voidable at the
discretion of CMS.
D. Requests for Information (RFIs) on Topics Relevant to the IOTA 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 would not accept unsolicited proposals. Respondents are advised
that the U.S. Government would not pay for any information or
administrative costs incurred in response to this RFI; all costs
associated with responding to these RFIs would be solely at the
respondent's expense. Failing to
[[Page 43603]]
respond to any of the RFIs would not preclude participation in any
future procurement, if conducted.
Please note that CMS would 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 would not be returned. CMS may publicly
post the comments received, or a summary thereof.
1. Patient-Reported Outcome Performance Measures (PRO-PM)
Chronic kidney disease is both complex and multifaceted and demands
inclusive and thorough medical management, even after transplantation.
Thus, when taking into consideration the lasting impact of CKD, symptom
burden, and its correlation to mental health and psychosocial
difficulties, it is important that the patient perspective and voice be
included through the use of patient-reported outcome measures (PROMs)
to truly grasp how CKD impacts their lives.\313\
---------------------------------------------------------------------------
\313\ Schick-Makaroff, K., Thummapol, O., Thompson, S., Flynn,
R., Karimi-Dehkordi, M., Klarenbach, S., Sawatzky, R., & Greenhalgh,
J. (2019). Strategies for incorporating patient-reported outcomes in
the care of people with chronic kidney disease (PRO kidney): a
protocol for a realist synthesis. Systematic Reviews, 8(1). https://doi.org/10.1186/s13643-018-0911-6; Brett, K.E., Ritchie, L.J.,
Ertel, E., Bennett, A., & Knoll, G.A. (2018). Quality Metrics in
Solid Organ Transplantation. Transplantation, 102(7), e308-e330.
https://doi.org/10.1097/tp.0000000000002149; Mendu, M.L.,
Tummalapalli, S.L., Lentine, K.L., Erickson, K.F., Lew, S.Q., Liu,
F., Gould, E., Somers, M., Garimella, P.S., O'Neil, T., White, D.L.,
Meyer, R., Bieber, S.D., & Weiner, D.E. (2020). Measuring Quality in
Kidney Care: An Evaluation of Existing Quality Metrics and Approach
to Facilitating Improvements in Care Delivery. Journal of the
American Society of Nephrology, 31(3), 602-614. https://doi.org/10.1681/ASN.2019090869; Tang, E., Bansal, A., Novak, M., & Mucsi, I.
(2018). Patient-Reported Outcomes in Patients with Chronic Kidney
Disease and Kidney Transplant--Part 1. Frontiers in Medicine, 4.
https://doi.org/10.3389/fmed.2017.00254; Anderson, N.E., Calvert,
M., Cockwell, P., Dutton, M., Aiyegbusi, O.L., & Kyte, D. (2018).
Using patient-reported outcome measures (PROMs) to promote quality
of care in the management of patients with established kidney
disease requiring treatment with haemodialysis in the UK (PROM-HD):
a qualitative study protocol. BMJ Open, 8(10), e021532. https://doi.org/10.1136/bmjopen-2018-021532.
---------------------------------------------------------------------------
Patient-reported measures are those measures where data comes
directly from the patient. Broadly, patient-reported data includes
patient-reported outcomes (PROs) and ePROs, which is the electronic
capture of this data; patient-reported outcome measures (PROMs), which
is the structure of how the PRO data is reported (for example, a survey
instrument); and patient-reported outcome-based performance measures
(PRO-PMs), which are reliable and valid quality measures of aggregated
PRO data reported through a PROM and potentially used for performance
assessment. PROMs include aspects pertaining health-related quality of
life (HRQOL) and symptoms, both of which are essential measures in
renal care. HRQOL can vary over time and course of an illness and these
types of measures seek to examine the functioning and well-being in
physical, mental, and social dimensions of life. It is also impacted by
a variety of factors such as treatment, level of health, condition,
culture, age, and psychosocial elements.\314\
---------------------------------------------------------------------------
\314\ Pagels, A.A., Stendahl, M., & Evans, M. (2019). Patient-
reported outcome measures as a new application in the Swedish Renal
Registry: Health-related quality of life through Rand-36. Clinical
Kidney Journal, 13(7), 442-449. https://doi.org/10.1093/ckj/sfz084;
Broadbent, E., Petrie, K.J., Main, J., & Weinman, J. (2006). The
Brief Illness Perception Questionnaire. Journal of Psychosomatic
Research, 60(6), 631-637. https://doi.org/10.1016/j.jpsychores.2005.10.020; Mclaren, S., Jhamb, M., & Unruh, M.
(2021). Using patient-reported measures to improve outcomes in
kidney disease. Blood Purification, 50(4-5), 649-654. https://doi.org/10.1159/000515640.
---------------------------------------------------------------------------
Using PROMs or PRO-PMs are two ways to include the patient
experience and has been acknowledged as a way for patients to provide
critical insight about their symptoms, patient experience and quality
of life.\315\ In spite of the growing recognition over the past two
decades that this is paramount to advancing the quality of care at both
the patient and policy levels, there remains significant information
gaps in understanding how PROMs are, and can be utilized across
different domains, especially within nephrology to enrich patient-
centered care, and measure other important quality components, such as
access to transplantation, shared-decision making and quality of life
post-transplantation, to provide a comprehensive understanding.\316\
---------------------------------------------------------------------------
\315\ Schick-Makaroff, K., Thummapol, O., Thompson, S., Flynn,
R., Karimi-Dehkordi, M., Klarenbach, S., Sawatzky, R., & Greenhalgh,
J. (2019). Strategies for incorporating patient-reported outcomes in
the care of people with chronic kidney disease (PRO kidney): a
protocol for a realist synthesis. Systematic Reviews, 8(1). https://doi.org/10.1186/s13643-018-0911-6; Brett, K.E., Ritchie, L.J.,
Ertel, E., Bennett, A., & Knoll, G.A. (2018). Quality Metrics in
Solid Organ Transplantation. Transplantation, 102(7), e308-e330.
https://doi.org/10.1097/tp.0000000000002149; Mendu, M.L.,
Tummalapalli, S.L., Lentine, K.L., Erickson, K.F., Lew, S.Q., Liu,
F., Gould, E., Somers, M., Garimella, P.S., O'Neil, T., White, D.L.,
Meyer, R., Bieber, S.D., & Weiner, D.E. (2020). Measuring Quality in
Kidney Care: An Evaluation of Existing Quality Metrics and Approach
to Facilitating Improvements in Care Delivery. Journal of the
American Society of Nephrology, 31(3), 602-614. https://doi.org/10.1681/ASN.2019090869; Tang, E., Bansal, A., Novak, M., & Mucsi, I.
(2018). Patient-Reported Outcomes in Patients with Chronic Kidney
Disease and Kidney Transplant--Part 1. Frontiers in Medicine, 4.
https://doi.org/10.3389/fmed.2017.00254; Anderson, N.E., Calvert,
M., Cockwell, P., Dutton, M., Aiyegbusi, O.L., & Kyte, D. (2018).
Using patient-reported outcome measures (PROMs) to promote quality
of care in the management of patients with established kidney
disease requiring treatment with haemodialysis in the UK (PROM-HD):
a qualitative study protocol. BMJ Open, 8(10), e021532. https://doi.org/10.1136/bmjopen-2018-021532.
\316\ Ibid.
---------------------------------------------------------------------------
In addition to the proposed measures the IOTA Model proposes would
be used, as described in section III.C.5.e.(2) of this proposed rule,
we would consider incorporating a measure of HRQOL and access to
waitlist.
We seek comments on the inclusion of a HRQOL patient-reported
outcome measure in the IOTA Model, as well as on the inclusion of an
access to waitlist measure. We are seeking input to the questions later
in this section, and comment on any aspect of a kidney transplant
recipient patient experience measure that should be included in a new
measure or existing and validated measurement tools and instruments
appropriate for use in the IOTA Model.
For a meaningful evaluation of transplant program outcomes
from the recipient point of view, are there currently any validated
PROMs of quality of life that are appropriate for use in the IOTA
Model?
Are there specific aspects of quality of life (QOL) that
are particularly important to include for these populations? Why are
these aspect(s) of QOL a high priority for inclusion in a survey? What
should these metrics be (that is, measurement tools, instruments,
concepts)? How should they be measured?
For kidney transplant recipients: What other topic area(s)
should be included in a new patient-reported outcome measure or
performance measure assessing quality of life?
For kidney transplant recipients: What domains of HRQOL
can be influenced or improved by actions taken by transplant hospital
and thus may be appropriate for performance measurement?
In addition, we are seeking input on the questions later in this
section on
[[Page 43604]]
existing PROMs and quality measures that are currently being used by
transplant hospitals.
Which patient-reported outcomes measure(s) that assess
quality of life in kidney transplant recipients are currently being
used?
++ What information is collected in these PROMs? How well do these
surveys perform? What are the strengths of the survey(s) currently in
use?
++ What content area(s) are missing from these survey(s) that are
currently in use?
++ Which content area(s) are low priority or not useful in these
currently used survey(s)? Why are they not useful?
++ How are the results and findings of these current survey(s) used
to evaluate and improve quality of life/care? Are the results and
findings of these current survey(s) used for other purposes?
Are there any other PROMs or PRO-PMs that CMS should
consider using to measure a transplant program's performance?
Are there any other quality measures in general that CMS
should consider using to measure a transplant program's performance?
For transplant hospitals: Can PROs be effectively used to
assess performance?
For transplant hospitals: Does a reporting requirement
effectively incentivize a transplant hospital to improve patient
quality of life without tying payment to performance?
The integration and implementation of PROMs can be challenging for
transplant hospitals as it requires additional resources (that is,
appropriate infrastructure with regard to technological capability or
data security), time, and there may be uncertainty about how to
interpret and use the data to improve patient care.\317\ We are also
seeking information on implementation challenges and support.
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\317\ Ju, A., Cazzolli, R., Howell, M., Scholes-Robertson, N.,
Wong, G., & Jaure, A. (n.d.). Novel Endpoints in Solid Organ
Transplantation: Targeting Patient-reported Outcome Measures.
Transplantation, 10.1097/TP.0000000000004537. https://doi.org/10.1097/TP.0000000000004537; Aiyegbusi, O.L., Kyte, D., Cockwell,
P., Anderson, N., & Calvert, M. (2017). A patient-centred approach
to measuring quality in kidney care. Current Opinion in Nephrology
and Hypertension, 26(6), 442-449. https://doi.org/10.1097/mnh.0000000000000357; MacLean, C.H., Antao, V.C., Fontana, M.A.,
Sandhu, H.S., & McLawhorn, A.S. (2021). PROMs: Opportunities,
Challenges, and Unfinished Business. NEJM Catalyst, 2(11). https://doi.org/10.1056/cat.21.0280.
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When is the appropriate time to measure HRQOL post-
transplantation?