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

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

3. 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

4. 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

5. 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.

6. 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.

7. 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

8. 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.

9. 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

10. 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

11. 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

12. 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)

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

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

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

16. Next Steps
Recalibrate CMS-HCC model using 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. 

17. Resources
Comments on the memo and risk adjustment questions can be sent to
Submit comments by November 25, 2015 with subject heading “Proposed Updates to the CMS-HCC Risk Adjustment Model”
Other Risk Adjustment Resources
Manual Chapter
Training materials and operational documents