Designing Insurance for Uncertainty: Incentivizing Value in High-Variance Risk Pools

Oskam et al. (2023) challenge the assumption that eliminating predictable mean differences in costs removes selection incentives, highlighting the role of variance (uncertainty) in residual spending. Building on this, the proposed research would design new payment models or risk adjustment schemes that reward insurers and providers not just for controlling average costs, but also for effectively managing high-variance patient groups. For example, bonus payments could be tied to reductions in both mean and variance of spending for complex cases, or to improved outcomes for patients with unpredictable needs. This idea breaks from the tradition of focusing solely on mean risk equalization and could significantly reduce selection against high-variance groups, leading to fairer and more efficient insurance markets.

References:

1. Heteroscedasticity of residual spending after risk equalization: a potential source of selection incentives in health insurance markets with premium regulation. Michel Oskam, R. V. van Kleef, R. Douven (2023). The European journal of health economics : HEPAC : health economics in prevention and care.
2. Heteroscedasticity of residual spending after risk equalization: a potential source of selection incentives in health insurance markets with premium regulation. Michel Oskam, R. V. van Kleef, R. Douven (2023). The European journal of health economics : HEPAC : health economics in prevention and care.