In risk-adjusted payment models, which practices help mitigate selection bias?

• A. Risk adjustment alone.
• B. Quality benchmarking alone.
• C. Risk adjustment, quality benchmarking, and credible measurement.
• D. None of the above.

Answer: (C)

In risk-adjusted payment models, balancing incentives to treat all patients fairly with accurate performance signals is the goal. Risk adjustment addresses differences in patient health status and complexity, so providers aren’t rewarded for avoiding high-risk patients or penalized for sicker populations. But adjusting for risk alone can fall short if the payment signals aren’t benchmarked against external standards or if the data used to compute risk and outcomes aren’t trustworthy. Quality benchmarking adds an external yardstick, helping to prevent narrow focus on a few metrics and encouraging consistent performance across patient groups. Yet benchmarks are only meaningful when the underlying data are solid. Credible measurement ensures data are accurate, complete, and verifiable, which supports reliable risk adjustment and trustworthy benchmarks. Put together, risk adjustment, quality benchmarking, and credible measurement create a robust framework that reduces selection bias and aligns payments with true value and outcomes.