Cross-sectional analysis of accuracy versus interpretability in Medicare Advantage risk adjustment.

ABSTRACT:

Background

Risk adjustment models in Medicare Advantage determine annual payments of over $300 billion in public funds to private companies. Policymakers want risk adjustment models that are both accurate and interpretable to ensure appropriate use of public funds.

Methods

The trade-off between accuracy and interpretability from using standard machine learning (ML) models for risk adjustment was evaluated. A cross-sectional analysis was conducted using 2018-2019 Medicare claims with 3,602,618 beneficiaries. Multiple risk adjustment models were estimated, including traditional and ML-based approaches. Performance was assessed using out-of-sample mean absolute and squared error (MAE and MSE). Interpretability was measured using coefficient count and log-transformed coefficient

SUBMITTER: Lozinski M 

PROVIDER: S-EPMC12811633 | biostudies-literature | 2026 Jun

REPOSITORIES: biostudies-literature