WEBINAR WEDNESDAY What is Encounter Data …...2019/02/13 · an encounter data-based model Key...
Transcript of WEBINAR WEDNESDAY What is Encounter Data …...2019/02/13 · an encounter data-based model Key...
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WEBINAR WEDNESDAY
What is Encounter Data Risk Adjustment Methodology and How Do I Prepare for it?
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Introduction
PURPOSE OF WEBINAR CURRENT STATE TRANSITION TIMELINE IMPLICATION FOR PROVIDERS
(DELEGATED AND NON DELEGATED)
ADVOCACY
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Agenda
APG Advocacy and MA Call Letter
Implications for Providers and Plans
Findings from the MA Risk Adjustment Summit
Background on Encounter Data and MA Risk Adjustment
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Background on Encounter Data and Medicare Advantage Risk Adjustment
Laura Skopec – Urban Institute
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Outline
How are Medicare Advantage plans paid?
What is encounter data?
How is encounter data being used?
How could encounter data be used in the
future?
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Overview of MA Plan Payment
CMS Sets Benchmarks
Based on trended TM costs
At county level
MA Plans Submit Bids
Detail Part A, B, and supplemental costs
Bid for average risk enrollee
Compare Bids to Benchmarks
Above the benchmark, must charge premiums.
Below the benchmark, get bid plus rebates
Risk Adjustment
Payments are adjusted to reflect the risk of the plans’
enrollees.
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How MA Risk Adjustment Works
CMS calculates beneficiary-specific risk scores using
diagnoses and demographic data
from the prior year
Payment = bid x beneficiary-
specific risk score
Risk scores are calculated for eight different
beneficiary segments
Data for calculating risk scores comes from two data
sources: the Risk Adjustment Data
Processing System and the
Encounter Data System.
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What Is Encounter Data?• Encounter data ≈ claims data
• MA plans collect encounters from physicians, hospitals, and other providers and provide that data to CMS.
• Encounter data isn’t necessarily used for payment, so may not include price information.
• Encounter data is far more detailed than the previous risk adjustment data – RAPS.
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RAPS vs EDSRAPS Data –
limited diagnosis information
• Beneficiary ID• Date of service• Diagnosis codes• Provider type
EDS Data –Full claims data, including:
• Beneficiary ID• Date of service• Diagnosis codes• Cost• Quantity• Provider NPI• Revenue codes• Procedure codes• Place of service• Type of bill
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Timeline for CMS Collection and Use of MA Encounter Data
CMS clarified authority to
collect encounter data
CMS says it will eventually use
encounter data to estimate the risk
adjustment model
MA plans began submitting
encounter data
CMS began calculating risk scores based in
part on encounter data
Risk scores will be based entirely on encounter data.
2008 2011 2012 2015 2020
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How Could Encounter
Data Be Used in the
Future?
• Exploring differences in utilization between MA and TM
• Exploring differences between the populations that enroll in MA vs TM
• Quality improvement activities in MA or TM
• Estimating a new risk adjustment model
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Findings from the Medicare Advantage Risk Adjustment Summit
Bowen Garrett – Urban Institute
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Urban Institute and American Action Forum Expert Summit
Summit topics:– What are the objectives of risk adjustment in the Medicare Advantage
program?
– What are the strengths and weaknesses of the current Medicare Advantage risk adjustment system?
– What are the goals for using encounter data in the Medicare Advantage risk adjustment system?
– What are the potential benefits and drawbacks of calibrating the risk adjustment model with encounter data?
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October 23 SummitJonathan Blum, Health Management Associates
Kristina Cournoyer, Anthem
Sean Creighton, Avalere
Errin Crowell, Aetna
Frank Cunningham, Eli Lilly & Company
Randall Ellis, Boston University
Doug Fearrington, Anthem
Michael Geruso, University of Texas at Austin
Gretchen Jacobson, Kaiser Family Foundation
Lisa Joldersma, Pharmaceutical Research and Manufacturers of America
Tom Kornfield, America’s Health Insurance Plans (AHIP)
Julia Lambert, Wakely Consulting Group
David Lipschutz, Center for Medicare Advocacy
Thomas McGuire, Harvard University
Mark Miller, Laura and John Arnold Foundation
Rob Pipich, Milliman
Marc Russo, Anthem
Valinda Rutledge, America’s Physician Groups
Doug Stoss, Humana
Cori Uccello, American Academy of Actuaries
Jonathan Weiner, Johns Hopkins University
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Funding
Support for this project was provided by Anthem, Inc., and Eli Lilly and Company. For more information on the Urban Institute’s funding principles, go to:
http://www.urban.org/about/funding-and-annual-reports
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General Objectives
of Risk Adjustment
• Risk adjustment encourages plans to compete by offering value.
• If insurers are paid per enrollee without risk adjustment, they have an incentive to focus on enrolling healthy beneficiaries and avoiding sick beneficiaries.
• Attempts to draw favorable selection can occur by discouraging initial enrollment and by encouraging sicker beneficiaries to disenroll.
• Risk adjustment supports plans’ efforts to invest in innovative benefit designs and care management of chronic conditions and other costly beneficiaries.
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What would it mean to
calibrate the risk model
with encounter
data?
• Currently, traditional Medicare spending and utilization data are used to estimate the coefficients for the risk adjustment model.
• In the future, CMS may use MA encounter data to estimate these coefficients so that they reflect MA spending and utilization patterns—rather than create an entirely new model.
• CMS may also need to rethink other design choices which current risk factors to include in an encounter data-based model
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Key points from summit
MA risk adjustment system balances the
goals of payment accuracy and creating appropriate incentives
for plans to compete by offering value, rather than by avoiding risk
Recalibrating the risk adjustment model with
MA encounter data does not, by itself, solve
most of the problems stakeholders identified
with the current risk adjustment system
Recalibration could create new problems in
the risk adjustment system
Risk adjustment is only one piece of the MA payment and policy
system, and recalibrating the risk
adjustment model with encounter data could
have ripple effects
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Other materials from summit
Issue brief: “Using Encounter Data in Medicare Advantage Risk Adjustment”
Webcast from January 15 release event, including panel discussion led by Douglas Holtz-Eakin
Additional background reading on MA risk adjustment
Available at: https://www.urban.org/events/using-encounter-data-medicare-advantage-risk-adjustment
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Implications of EDS Based Risk Adjustment
Sean Creighton – Avalere
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General Implications
22
The premium for cost efficiency relative to FFS will decrease, but more efficient
health plans will still be advantaged
The CMS-HCC model will show very different relationships
between conditions (diseases) and incremental cost
(coefficients)
Relationship between treatment cost and revenue
will shift, making some categories of patients less
economically sustainable/profitable to treat
Could have profound effects for certain plan types
including Special Needs Plans (SNPs) and plans that treat
sicker patients
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Providers and Plans
23
Health plans and provider practices often share revenue based on risk adjusted payments from CMS
Changes to the model may impact certain types of provider practices more dramatically, as revenue shifts from certain disease groups (e.g. coefficients for diabetes and heart/lung disease) may decrease
Physician practices will need to be aware of these changes in order to accurately anticipate the business consequences
CMS has not announced plans to calibrate RA models with Encounter Data and due to the development timeline, the earliest date for an EDS model would be 2021
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CMS and Other Entities
24
Encounter Data based RA may mean some shifts in the county rates (benchmarks) and health plans will need time to assess potential impacts
This means that CMS will have to coordinate internally (with the Office of the Actuary) to publish new proposed models and adjusted ratebooks so plans can accurately simulate bids
Historically, CMS has not adjusted proposed rates in a timely fashion for the public to conduct such detailed impact analysis
The CMS-HCC model is also used to risk adjust a variety of other programs (ACOs, MMPs etc.) – the downstream implications of new models will have to be evaluated
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APG Advocacy and MA Call Letter
Margaret Peterson & Valinda Rutledge – APG
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Questions?• Laura Skopec | [email protected]• Bowen Garrett | [email protected]• Sean Creighton | [email protected]• Margaret Peterson | [email protected]• Valinda Rutledge | [email protected]