Incorporating Prescription Drugs Into Affordable Care Act Risk Adjustment.

Area of Science:

• Health economics
• Health services research
• Predictive modeling

Background:

• The Patient Protection and Affordable Care Act (ACA) established risk adjustment programs to ensure fair competition in health insurance markets.
• The Department of Health and Human Services Hierarchical Condition Category (HHS-HCC) models were developed to measure enrollee risk.
• Selected prescription drug classes were incorporated as risk markers into the HHS-HCC models starting in 2018.

Purpose of the Study:

• To detail the rationale, methodology, and outcomes of integrating prescription drug utilization into the HHS-HCC risk adjustment models.
• To assess the impact of prescription drug data on the predictive accuracy of HHS-HCC models.

Main Methods:

• Defined and incorporated 10 prescription drug classes (RXCs) into the adult HHS-HCC models.
• Estimated separate HHS-HCC models based on enrollee age and plan actuarial value.

Main Results:

• Inclusion of selected RXCs alongside demographic and diagnostic data resulted in a modest improvement in the predictive power of HHS-HCC models.
• The addition of RXCs effectively captures the higher costs associated with enrollees using specific high-cost medications.
• RXCs facilitate the imputation of diagnoses for enrollees who use a drug but lack a corresponding diagnosis code.

Conclusions:

• Incorporating selected prescription drugs into risk adjustment enhances model predictive accuracy.
• The inclusion of drugs may mitigate insurer strategies to avoid enrolling patients using high-cost medications (e.g., for HIV, multiple sclerosis, rheumatoid arthritis), potentially improving access.
• Adding RXCs can improve risk measurement for plans with less comprehensive diagnosis reporting.