LLM-Based Program Synthesis for Healthcare Mechanism Design
A recent preprint on arXiv (2605.30680) presents Medi-Sim, a multi-agent simulator designed to analyze strategic responses from providers within healthcare systems. This framework reinterprets hospital mechanism design through program synthesis tailored for language models, utilizing typed, inspectable rule programs executed in Medi-Sim. The simulation encompasses five strategic provider channels: coding, selection, delay, effort, and triage. An incentive sweep confirms traditional health-economics insights, such as up-coding and the selection of low-complexity patients when profit is at stake, alongside a Goodhart-style drift where performance metrics inversely relate to actual outcomes. Notably, a single audit lever demonstrates pressure migration, indicating that restricting the coding channel significantly increases low-complexity selections. The study underscores that current healthcare AI benchmarks do not assess mechanisms based on the equilibrium they generate, as they keep provider responses constant.
Key facts
- arXiv:2605.30680v1
- Medi-Sim is a multi-agent simulator with five strategic provider channels
- Channels: coding, selection, delay, effort, triage
- Incentive sweep recovers up-coding and low-complexity-patient selection under profit pressure
- Goodhart-style drift observed where measured performance becomes anti-correlated with true outcomes
- Closing the coding channel more than doubles low-complexity selection
- LLM-guided evolutionary code search synthesizes an inspectable mixed mechanism
- Existing healthcare AI benchmarks hold provider response fixed
Entities
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