The Medicare Advantage (MA) program allows
Medicare beneficiaries to receive their Part A and B benefits through private
plans that are paid a fixed amount per enrollee (or capitated) by Medicare to
provide all such benefits to their enrolled members. MA plans play an
increasingly important role in the Medicare program, with enrollment growing from
about a quarter of all Medicare beneficiaries (or 11 million) in 2010 to 34
percent of beneficiaries (approximately 20 million) in 2018. And, according to
the Congressional Budget Office, enrollment in MA is projected to exceed 40 percent (32 million) of
the total Medicare population by 2028.
MA’s popularity indicates that it works well,
and one of the reasons the system functions as well as it does is that plan
payments are adjusted for the health risks of enrollees. The approach to risk
adjustment in MA has evolved over time and will likely continue to evolve.
Until now, the Centers for Medicare and Medicaid Services (CMS) has estimated
its MA risk-adjustment models using data from traditional Medicare, but the agency has signaled in
the past its interest in using encounter data from MA to estimate (or
recalibrate) the model. CMS reiterated this interest in its fiscal year 2020 performance budget.
Under such recalibration, CMS would
recalculate the coefficients for the demographic characteristics and health
conditions based on MA encounter data, instead of on traditional
fee-for-service Medicare claims. Encounter data, unlike fee-for-service claims
data, are not necessarily the basis for payment to providers but instead capture
services received by MA beneficiaries, regardless of how the MA plan paid for
those services. Such a move would likely change the risk scores of certain
populations in MA to more closely reflect MA costs and treatment patterns,
although the Medicare Payment Advisory Commission (MedPAC) has questioned whether
encounter data are yet of sufficient quality to rely on them for payment
purposes, including risk adjustment. Likewise Sean Creighton and
colleagues, writing recently in the Health Affairs blog, also explored the challenges that
persist with encounter data.
CMS has released neither a specific proposal
for this recalibration nor a timeline, but development of this new model would
require the agency to address many policy, technical, and operational issues
prior to implementation. The Urban Institute and the American Action Forum
convened a one-day roundtable summit with experts on MA, risk adjustment, and
related areas to discuss the implications of calibrating the model using
encounter data. Attendees included 21 experts from a variety of sectors,
including the insurance and pharmaceutical industries, academia, consultants,
actuaries, and former CMS officials. Here, we highlight some of the key points
that emerged during the summit. The full participant list and further findings are
provided in an issue brief and panel discussion video.
Risk Adjustment In MA
Needs To Balance Payment And Policy Goals
Risk adjustment in MA balances two
governmental goals: paying plans accurately for the health status of their
enrollees and creating appropriate incentives for plans to compete by offering
value instead of avoiding risks. Payment accuracy (that is, the degree of
correspondence between payments and costs) would almost certainly improve by
recalibrating the MA risk model with encounter data because the risk model
would be fit to the patterns of spending and use for particular diagnosis
groups within MA, instead of the cost and use for those diagnosis groups in
traditional Medicare. Prior research has shown lower use and spending in
MA than in the traditional fee-for-service side of Medicare, perhaps reflecting
MA network design, referral requirements, or use management.
However, improved payment accuracy due to
recalibration with encounter data could have implications for various financial
incentives faced by MA plans. For example, if MA plans are, on average, more
efficient at treating diabetes than is the traditional Medicare program, the
calculated relative risk of—and thus the payments for—beneficiaries with
diabetes would likely be lower in a risk-adjustment model calibrated with MA
encounter data than in the current model. Lowering the risk score for diabetes
could affect MA plans’ incentives to coordinate care and control costs for
those patients. On the other hand, if plans are more efficient than
fee-for-service providers because of care coordination programs designed for
enrollees with diabetes—the costs of which are not reflected in encounter
data—payments for these enrollees would be artificially low, and payment
accuracy would be undermined.
If payments for enrollees with particular
health conditions (such as diabetes) became too low because of recalibration,
plans could select against such enrollees or shift costs onto beneficiaries.
Under significant payment pressure, both selection and cost control could be
accomplished by relatively straightforward benefit design changes, such as
narrowing provider networks for specialists or increasing cost sharing for
insulin. It is unclear, however, how feasible condition-specific risk selection
would be. A significant reduction in relative payment for diabetes could also
meaningfully alter the market for chronic condition special needs
plans, which are targeted MA plans that limit enrollment to
Medicare-eligible beneficiaries who have particular chronic conditions, such as
diabetes. This could potentially result in plan exits from that market.
Recalibrating the risk model would have other
implications, too. It would eliminate the need for the coding intensity adjustment currently
applied to MA risk scores and could induce the need for changes to other
aspects of the MA system that are also based on traditional Medicare costs,
such as the benchmarking system for plans
submitting bids, as described in the brief.
Issues With Assigning
Costs To MA Encounters And Completeness Of Encounter Data Must Also Be Addressed
Encounter data do not necessarily provide
comparable, accurate costs across the MA system, particularly for vertically
integrated plans or plans with subcapitated arrangements with provider groups.
Because MA plans largely base provider reimbursements on traditional Medicare
prices, MA costs could be estimated by applying traditional Medicare prices to
MA use data, instead of relying on plan-submitted costs. While this approach
could fix potential problems with encounter data prices, it would not address the
likely underreporting of use data resulting from subcapitated payment
arrangements between providers and plans.
In addition, because MA still makes up only
about a third of the Medicare population, it is unclear if encounter data will
provide sufficient sample sizes to estimate models for all the diagnosis
categories that are currently part of the risk-adjustment model, potentially
requiring CMS to combine some categories. Also, the total cost of care for MA
beneficiaries may be underestimated if it is calculated only from the encounter
data. Plans do not report encounter data for all supplemental benefits,
therefore underestimating the costs of serving the MA enrollee population. MA
encounter data may also not fully capture postacute care received from skilled
nursing facilities and home health agencies because diagnoses from postacute
care are not used in risk adjustment. Postacute care services have been shown
to be underreported in encounter data as
recently as 2015.
Research Should Inform
A Transparent Process For Changing MA Risk Adjustment
Before making major changes, CMS can use MA
encounter data it already has on hand to conduct and disseminate research to
inform eventual recalibration. Such research could promote a deliberative,
public process. For example, analyses exploring how MA plans provide care, how
the provision of care in MA differs from traditional Medicare, and how outcomes
differ between MA and traditional Medicare could help CMS and stakeholders more
fully understand the implications of recalibrating the risk-adjustment model
using encounter data. Such research would support policy making on risk
adjustment and the broader MA and traditional Medicare payment systems.
As CMS considers moving to a risk-adjustment
model calibrated with MA encounter data, conversations between CMS and
stakeholders will help create buy-in and address concerns about potential negative
effects on the MA system. If CMS plans to move forward with recalibrating the
risk-adjustment model with encounter data, it should communicate the policy
goals it expects to achieve and how progress toward those goals will be
measured. It should also establish a transparent process for developing the
revised system with a timeline that allows for stakeholder input along the way.
Overall, the MA experts at the risk-adjustment
summit agreed that encounter data are not a cure-all for all potential problems
with the MA risk-adjustment system. Experts were united in calling for a
careful, deliberative, and transparent process as CMS works to address the
policy, technical, and operational issues raised by recalibrating the
risk-adjustment model with encounter data.
Support for this project was provided by
Anthem Inc. and Eli Lilly and Company. For more information on the Urban
Institute’s funding principles, click here.
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