A new paradigm for medical data circulation: research on medical data sharing based on evolutionary game theory.

To address the challenge of redundant testing in medical data sharing and mutual recognition, this paper constructs an evolutionary game model involving three parties: government, medical institutions, and patients. By integrating three scenarios-pessimistic (high costs, low acceptance), baseline (cost-benefit equilibrium), and optimistic (low costs, high coordination)-the study explores the system's dynamic evolution through payoff matrix derivation, stability analysis, and MATLAB simulation. Findings indicate: Under the pessimistic scenario, the system lacks stable equilibrium, requiring external intervention to break the deadlock. In the benchmark scenario, the system converges toward weak government regulation, proactive sharing by medical institutions, and active acceptance by patients, reflecting a transition toward coordinated government regulation and market autonomy. Under the optimistic scenario, the system converges toward an ideal state of tripartite collaborative participation, fully unlocking the shared value of medical examination data. Parameter sensitivity analysis indicates that reducing sharing costs for medical institutions, regulatory costs for governments, and privacy costs for patients, while enhancing institutional network effects, are core variables driving medical examination data sharing. Based on these findings, recommendations include a tiered implementation strategy, diversified incentive mechanisms, technological empowerment, and regulatory refinement, providing theoretical foundations and practical pathways for medical examination data sharing and mutual recognition.