Medicare Risk Adjustment Update Rebecca Paul, Division of Payment Policy Medicare Plan Payment Group November 3, 2015

Objectives Purposes and Uses of Risk Adjustment Risk Adjustment Basics Overview of CMS-HCC Model Measuring Model Performance Recent Model Research & Findings Next Steps

Purposes and Uses of Risk Adjustment Minimize the incentive for selection Pay plans accurately for the risk of the beneficiaries they enroll Predict costs due to chronic conditions Pay appropriately for subpopulations For payment: Used to standardize plan bids and county rates CMS multiplies the standardized rate (bids for MA plans, county rates for demonstrations or PACE) by the risk score that we calculated for the beneficiary

Risk Adjustment Basics A risk score reflects a beneficiary’s risk, relative to the average Medicare cost. A risk score of 1.2 = the beneficiary is expected to cost 20% more than average A risk score of 0.8 = the beneficiary is expected to cost 20% less than average Each beneficiary’s risk score is based on their own demographic characteristics and health conditions Risk scores are using diagnoses reported from MA plans and/or Medicare FFS providers Risk scores are produced by various models: Part C, Part D, ESRD Risk scores produced by each model are distinct, and are predictive of expenditures for a specific program or set of beneficiaries

Overview of the CMS-HCC Model Prospective model Separately predicts costs of different subgroups: Community, Long Term Institutional, New Enrollee Diagnoses are grouped together when they are similar clinically and with respect to cost 79 disease categories for community and long term institutional residents Hierarchies are imposed among related conditions, so that a beneficiary is coded for only the most severe manifestation among related diseases Demographic factors Age/sex factors, originally disabled Medicaid Status Defined as one month of Medicaid eligibility during data collection period New enrollees use concurrent Medicaid Each demographic factor and disease group has an associated coefficient – coefficients are converted into relative values for each demographic characteristic or health condition. Model is additive – relative factors are added across each beneficiary’s demographic and disease characteristics.

Measuring Model Performance Insurance function R2 Predictive Ratios Calculated as: mean predicted expenditures for the payment year mean actual expenditures in the payment year A predictive ratio greater than 1.0 indicates that the risk adjustment model overpredicts expenditures for the validation group A predictive ratio less than 1.0 indicates underprediction.

Predictive Ratios by Decile Validation groups CMS-HCC model ratio predicted to actual Sorted by CMS-HCC model predicted expenditures   First (lowest) decile 0.892 Second decile 0.929 Third decile 0.960 Fourth decile 0.974 Fifth decile 0.990 Sixth decile 0.998 Seventh decile 1.014 Eighth decile 1.032 Ninth decile 1.036 Tenth (highest) decile 0.999

Dually Eligible Beneficiaries The CMS-HCC model incorporates dual status. Medicaid factors: Vary based on the beneficiary’s gender, whether they are aged or disabled, and whether they live in the community or in an institution Are prospective, similar to diagnoses Full risk beneficiaries – Medicaid any month in the data collection period.

Recent Model Research Explored model performance for dually eligible beneficiaries Looked at dual status in the payment year Focused on community segment Institutional Segment For long-term institutionalized population Predominantly dual eligible (83.5%) Full benefit dual eligible (83.3%). Predictive ratio for all dual eligible beneficiaries is 0.998 Predictive ratio for full benefit dual eligible beneficiaries is 0.999

Predictive Ratios by Dual Status Community Population, 2014 Model FFS population 1.000 Non-dual 1.015 Dual 0.957 Full benefit duals 0.914 Partial benefit duals 1.092

Model Development Findings Reviewed a wide variety of possible versions of the community portion of the model Dual status in payment year Treat full and partial benefit duals separately Key findings: We found significant differences in regression coefficients for the age-sex factors and HCCs across aged and disabled populations Predictive ratios indicate that the Non-Dual, Full Benefit Dual, and Partial Benefit Dual, as well as the aged and disabled populations are distinct

Community Beneficiaries FFS, 2012 Group Mean Actual Costs Proportion of the model sample Full benefit dual – aged $15,147 7.7% Full benefit dual – disabled $10,418 7.4% Partial benefit dual – aged $10,635 3.6% Partial benefit dual – disabled $9,239 2.9% Non-dual – aged $8,932 70.9% Non-dual – disabled $7,829 7.6% Notes: Dual status is as of the payment year (i.e. concurrent status)

Predictive Ratios by Deciles *Deciles were created by dividing each subpopulation equally among 10 groups (sorted by predicted expenditures)

Predictive Ratios by Deciles *Deciles were created by dividing each subpopulation equally among 10 groups (sorted by predicted expenditures)

Predictive Ratios by Deciles *Deciles were created by dividing each subpopulation equally among 10 groups (sorted by predicted expenditures)

Next Steps Recalibrate CMS-HCC model using 2013-2014 data that will include up to six separate community segments based on dual and aged/disabled status in the payment year. Explore minor updates to the institutional and new enrollee segments of the model to distinguish between full and partial benefit duals. We will also explore whether updating the Medicaid factors to reflect concurrent (payment year) dual status improves the predictive ratios of the institutional segment of the model. CMS is planning to propose the revised model in the 2017 Advance Notice, to be published in February 2016. 

Resources Comments on the memo and risk adjustment questions can be sent to RiskAdjustment@cms.hhs.gov. Submit comments by November 25, 2015 with subject heading “Proposed Updates to the CMS-HCC Risk Adjustment Model” Other Risk Adjustment Resources Manual Chapter https://www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/Downloads/mc86c07.pdf Training materials and operational documents http://csscoperations.com