Healthcare Mechanisms from Policy-as-Code Search under Strategic Provider Response
Quick Answer
The study introduces a novel approach to healthcare mechanism design using program synthesis for language models, specifically through Medi-Sim, which evaluates strategic provider responses.
Quick Take
The study introduces a novel approach to healthcare mechanism design using program synthesis for language models, specifically through Medi-Sim, which evaluates strategic provider responses. Findings reveal that closing the coding channel significantly increases low-complexity patient selection, while LLM-guided code synthesis can eliminate up-coding and maintain profitability.
Key Points
- Medi-Sim simulates five strategic provider channels for healthcare mechanism evaluation.
- Incentive sweep reveals classical health-economics findings like Goodhart-style drift.
- Closing the coding channel more than doubles low-complexity patient selection.
- LLM-guided code synthesis eliminates up-coding and halves rejection rates.
- The mixed-objective program retains most of the baseline's profit-oriented funds.
Paper Resources
Article Excerpt
From source RSS / original summaryarXiv:2605. 30680v1 Announce Type: new 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 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.
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