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

How can AI help design effective hospital incentive and resource-allocation policies when providers respond strategically? Yet conventional research that evaluates these policies against fixed provider programs or a single performance measure can miss distortions that shift across behaviors or emerge through further adaptation. We introduce , a closed-loop multi-agent simulator that integrates hospital operations with five dimensions of strategic provider response: billing classification (coding), patient acceptance, treatment deferral, treatment effort, and bed requests. Administrator and provider policies are typed, inspectable programs, which an LLM edits using rollout feedback. We first map behavior under fixed response rules, then test whether gains from joint program design survive unilateral adaptation and responses to retained policy histories. Stronger audits suppress coding inflation while worsening access for complex patients; contract changes can restore effort while coding distortion persists; and further provider search can erode recovered health. We characterize finite-policy convergence under exact response oracles and separate search and payoff-estimation errors in an approximate stopping bound. These results identify pressure migration across response channels and adaptation stages, suggesting that AI-designed mechanisms should be evaluated through the behaviors they induce and the further adaptations they invite.