Tripartite evolutionary game analysis of medical data governance: interactions among government, medical institutions, and third-party assessment agencies

In the era of the digital economy, medical data has emerged as a core production factor, yet revitalizing its value and achieving effective governance remain critical challenges. Based on evolutionary game theory, this paper constructs a three-party game model encompassing the government, medical institutions, and third-party assessment agencies. The study reveals that the system's evolutionary outcome is highly sensitive to initial parameter configurations, with strategic interdependencies among the three stakeholders. Specifically, government regulatory intensity, incentive-punishment mechanisms, and the cost-benefit structures of medical institutions and third-party assessment agencies constitute pivotal determinants of system stability. To facilitate medical data circulation and achieve effective governance, policymakers should increase incentives for truthful data provision by medical institutions, impose penalties against collusive behavior, strengthen incentives and penalties for third-party assessment agencies, and enhance benefits for medical institutions that provide truthful data. This study contributes to expanding the application of evolutionary game theory in medical data governance while offering actionable policy implications.