arXiv: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 multi-agent simulator with five strategic provider channels (coding, selection, delay, effort, triage). An incentive sweep recovers classical health-economics findings as adjacent regimes
The increasing sophistication of multi-agent AI simulations and the push towards more efficient, data-driven healthcare systems enable the development of such policy-as-code approaches.
This research introduces a novel, AI-driven method to design and evaluate healthcare policies by simulating strategic provider responses, moving beyond static models to predict real-world outcomes.
Healthcare mechanism design can now be approached as program synthesis for AI, allowing for dynamic evaluation of policies based on induced provider behavior, which changes how policies are conceived and tested.
- · Healthcare AI developers
- · Policy makers
- · Healthcare system administrators
- · Patients (from optimized systems)
- · Healthcare systems with static policy-making processes
- · Providers exploiting policy loopholes
More robust and effective healthcare policies will be designed and implemented due to advanced simulation capabilities.
There will be a shift towards continuous optimization of healthcare mechanisms as 'code' can be iteratively improved and tested.
This could lead to a 'race to the top' among nations to develop the most efficient and equitable AI-driven healthcare systems, influencing global health and economic competitiveness.
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Read at arXiv cs.AI