Incentives, Equilibria, and the Limits of Healthcare AI: A Game-Theoretic Perspective
For healthcare leaders and policymakers, it highlights the limits of AI in improving system outcomes without addressing incentive structures.
This paper argues that AI deployment in healthcare, when focused solely on task optimization, is unlikely to change system-level outcomes if underlying incentives remain unchanged. Using game-theoretic analysis, it shows that only interventions reshaping risk allocation can shift stable system behavior.
Artificial intelligence (AI) is widely promoted as a promising technological response to healthcare capacity and productivity pressures. Deployment of AI systems carries significant costs including ongoing costs of monitoring and whether optimism of a deus ex machina solution is well-placed is unclear. This paper proposes three archetypal AI technology types: AI for effort reduction, AI to increase observability, and mechanism-level incentive change AI. Using a stylised inpatient capacity signalling example and minimal game-theoretic reasoning, it argues that task optimisation alone is unlikely to change system outcomes when incentives are unchanged. The analysis highlights why only interventions that reshape risk allocation can plausibly shift stable system-level behaviour, and outlines implications for healthcare leadership and procurement.