Health-risk-assessment tools used to predict costs in defined populations

Area of Science:

• Health Services Research
• Health Economics
• Medical Informatics

Background:

• The Balanced Budget Act of 1997 mandates risk-adjusted payment mechanisms for Medicare managed care plans.
• Healthier beneficiaries tend to enroll in Medicare + Choice plans, necessitating accurate risk adjustment.
• Current HCFA risk adjustment relies on prior year inpatient diagnoses.

Purpose of the Study:

• To review nineteen risk-adjustment research papers relevant to Medicare managed care.
• To evaluate the effectiveness of different risk-adjustment models.
• To identify potential building blocks for future HCFA risk-adjustment methods.

Main Methods:

• Review of nineteen risk-adjustment research papers.
• Analysis of claims-based, survey-based (e.g., SF-36), and demographics-based models.
• Comparison of model power in predicting total healthcare costs.

Main Results:

• Claims-based models demonstrate superior predictive power for total costs compared to survey-based or demographics-based models.
• Survey-based models offer an alternative when claims data are unavailable, despite higher costs and lower predictive power.
• The SF-36 survey tool is gaining importance for quality outcomes measurement in Medicare + Choice plans.

Conclusions:

• Existing risk-adjustment models, despite limitations, serve as foundational elements for future reimbursement strategies.
• Claims-based models are preferred for predicting healthcare costs in capitated payment systems.
• The integration of tools like SF-36 highlights a move towards comprehensive quality outcome assessment in managed care.