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

Abstract

This study utilizes a simplified coordination model based on inpatient capacity management to categorize three primary modes of artificial intelligence implementation: systems designed to reduce effort, those focused on enhancing observability, and mechanisms that reshape fundamental incentive structures. While technologies that lower effort or improve visibility can boost performance within established behavioral norms, they generally fail to alter the individual rationality of specific actions. Consequently, these types of interventions are usually assimilated into the status quo equilibria. In contrast, approaches that reconfigure the link between local actions and their downstream effects—specifically by redistributing or capping local risk—have the capacity to shift stable system behaviors. These mechanism-level changes are distinguished not by their technical complexity, but by how they engage with institutional incentives. The findings indicate that projections of system-wide benefits from AI must be contingent upon whether a deployment modifies incentives, rather than merely optimizing tasks or information channels. For healthcare administrators and policy makers, this distinction carries significant practical consequences for the acquisition, governance, and assessment of digital health technologies.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC