Healthcare Mechanisms from Policy-as-Code Search under Strategic Provider Response

Researchers developed Medi-Sim, a multi-agent simulator that models strategic responses by healthcare providers to policy incentives, and used it with LLM-guided code search to design healthcare mechanisms that reduce gaming behavior. The approach synthesizes inspectable rule programs that eliminate up-coding fraud while maintaining financial viability, addressing a critical gap in healthcare AI evaluation.

This research tackles a fundamental problem in healthcare mechanism design: existing AI benchmarks treat provider behavior as static, missing how hospitals and clinics respond strategically to financial incentives. The authors reframe policy design as program synthesis, enabling systematic exploration of how rule changes propagate through provider decision-making. Medi-Sim models five distinct gaming channels—coding manipulation, patient cherry-picking, service delays, effort reduction, and triage abuse—that realistically capture how providers optimize for measured metrics rather than patient outcomes. The incentive sweep validates classical health-economics theory by recovering well-known phenomena like Goodhart's law, where performance measures become divorced from true objectives. This validation strengthens confidence in the simulation's fidelity. The key innovation emerges when evolutionary code search discovers mechanisms that simultaneously reduce fraud, maintain access, and preserve profitability. By exposing how audit interventions cause gaming pressure to migrate between channels rather than disappear, the work reveals system-level dynamics invisible to single-lever policy analysis. For healthcare administrators and policymakers, this demonstrates that effective mechanisms must account for strategic provider responses across multiple dimensions. The ability to inspect synthesized rule programs—rather than relying on black-box optimization—creates transparency essential for policy adoption. The finding that a mixed-objective approach halves rejections while eliminating up-coding suggests room for win-win policy design. This research bridges AI mechanism design with practical healthcare constraints, offering a replicable methodology for evaluating policy robustness against gaming behavior.

• →Existing healthcare benchmarks fail to account for strategic provider responses, limiting their utility for real-world policy evaluation.
• →Medi-Sim's multi-channel simulation reveals how providers migrate gaming behavior when single policy levers are tightened rather than stopping it entirely.
• →LLM-guided evolutionary search discovered rule programs that eliminate up-coding fraud while reducing patient rejection and maintaining profitability.
• →Inspectable, rule-based policy synthesis offers transparency advantages over black-box optimization for healthcare stakeholder acceptance.
• →The approach validates classical health-economics predictions like Goodhart's law while enabling discovery of previously unknown mechanism designs.