Incentives, Equilibria, and the Limits of Healthcare AI: A Game-Theoretic Perspective

2026-03-29

AI Summary

This study investigates whether deploying AI can genuinely enhance overall healthcare system performance without altering existing incentive structures. Drawing on game theory and mechanism design, the authors develop a simplified signaling model of inpatient capacity to analyze how three categories of AI technologies—task-automation AI, observability-enhancing AI, and incentive-restructuring AI—affect equilibrium behaviors within the system. The analysis reveals that AI systems which merely improve task efficiency, without realigning risk allocation or incentive schemes, fail to shift the system’s steady state, thereby exposing the limitations of technological solutionism. The findings underscore the necessity of mechanism-level interventions and offer healthcare administrators critical insights into the bounded value of AI in complex institutional settings.

Abstract

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.

Problem

• healthcare AI
• incentives
• game theory
• system-level behaviour
• risk allocation

Innovation

• game theory
• healthcare AI
• incentive design
• risk allocation
• system equilibrium