用程序搜索设计医疗机制，揭示医生策略响应对政策效果的影响
Healthcare Mechanisms from Policy-as-Code Search under Strategic Provider Response
- 将医院机制设计转化为语言模型的程序合成问题
- 发现利润压力下存在过度编码和低复杂度患者选择等异常行为
- 通过审计杠杆暴露激励转移，用AI生成可解释的新规则
医疗机制与其引发的医疗机构策略响应密不可分：现有医疗AI评估基准固定了响应行为，无法衡量机制在均衡状态下的真实效果。本文将医院机制设计重构为语言模型的程序合成任务，使用带有五类战略响应渠道（编码、选择、延迟、努力、分诊）的多智能体模拟器Medi-Sim执行并评分类型化、可检查的规则程序。通过激励扫描重现经典健康经济学结论——在利润压力下出现上编码和低复杂度患者选择，以及表现指标与真实结果反向关联的古德哈特式漂移；单一审计杠杆显示关闭编码通道使低复杂度选择行为翻倍。随后，在同一规则程序空间中采用大模型引导的进化代码搜索，生成了一个可解释的混合目标程序，消除上编码现象，使拒绝率减半，并保留了原利润导向基线的大部分资金。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。