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

TL;DR

This paper uses game theory to analyze how different types of healthcare AI influence incentives and system stability, emphasizing that only incentive-shaping interventions can effectively change outcomes.

Contribution

It introduces three archetypal AI types and demonstrates, through a stylized example, that incentive restructuring is essential for meaningful system-level improvements.

Findings

Task optimization alone does not change outcomes without incentive changes.

Only interventions that reshape risk allocation can shift stable system behavior.

Implications for healthcare leadership and procurement strategies.

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.