Evaluating Alternative Risk Adjusters for Medicare.

Area of Science:

• Health economics
• Health services research
• Biostatistics

Background:

• Medicare capitation payments require accurate risk adjustment to account for beneficiary health status.
• Existing demographic and survey-based models have limitations in predicting healthcare expenditures.

Purpose of the Study:

• To evaluate the predictive performance of alternative risk adjustment models for Medicare capitation.
• To compare demographic, survey, and claims-based risk adjusters.

Main Methods:

• Utilized 3 years of Medicare Current Beneficiary Survey (MCBS) data.
• Developed and compared predictive models based on demographics, survey health status, and claims diagnoses.
• Assessed model performance in predicting average beneficiary expenditures.

Main Results:

• Survey health-status models were 3-4 times more predictive than demographic models.
• Claims-based risk adjustment models showed 75% greater predictive power than comprehensive survey models.
• No single model accurately predicted expenditures for all beneficiary subgroups.

Conclusions:

• Claims-based risk adjustment offers superior predictive accuracy for Medicare payments.
• A combined risk adjustment model may be necessary for comprehensive coverage of beneficiary subgroups.
• Further data are required for stable parameter estimation in risk adjustment models.