Healthcare Mechanisms from Policy-as-Code Search under Strategic Provider Response
Existing healthcare AI benchmarks fix provider responses, failing to evaluate mechanisms by their resulting equilibrium. This research recasts hospital mechanism design as program synthesis for language models, using a multi-agent simulator (Medi-Sim) and LLM-guided evolutionary code search to synthesize an inspectable mixed-objective program that eliminates up-coding, halves rejection, and retains most baseline funds.
[2605.30680] Healthcare Mechanisms from Policy-as-Code Search under Strategic Provider Response
[Submitted on 29 May 2026]
Title:Healthcare Mechanisms from Policy-as-Code Search under Strategic Provider Response
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Abstract:Healthcare mechanisms are inseparable from the strategic provider response they induce: existing healthcare AI benchmarks hold this response fixed and so cannot evaluate mechanisms by the equilibrium they produce. We recast hospital mechanism design as program synthesis for language models: typed, inspectable rule programs are executed and scored by Medi-Sim, a multi-agent simulator with five strategic provider channels (coding, selection, delay, effort, triage). An incentive sweep recovers classical health-economics findings as adjacent regimes -- up-coding and low-complexity-patient selection under profit pressure, and Goodhart-style drift where measured performance becomes anti-correlated with true outcomes -- and a single audit lever exposes pressure migration: closing the coding channel more than doubles low-complexity selection. LLM-guided evolutionary code search over the same rule-program space then synthesizes an inspectable mixed-objective program that eliminates up-coding, halves rejection, and retains most of the profit-oriented baseline's funds.
Comments: 32 pages, 18 figures, 4 tables
Subjects:
Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
Cite as: arXiv:2605.30680 [cs.AI]
(or arXiv:2605.30680v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.30680
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Wenhao Li [view email] [v1] Fri, 29 May 2026 00:21:54 UTC (4,753 KB)
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