关键词: 健康医疗数据, 演化博弈, 仿真分析, 数据共享

Abstract:

[Purpose/Significance] The governance of health medical data is fundamentally challenged by the "protection-sharing" paradox: the critical need to safeguard sensitive personal information often conflicts with the desire to utilize these data for public benefit. This issue is particularly pressing under China's "Healthy China" initiative, which promotes data sharing while the rapid expansion of medical APPs has led to increasing data misuse incidents. Existing research has extensively explored technological solutions such as blockchain, but a significant gap remains in understanding the dynamic, strategic interactions among the key stakeholders - government regulators, APP operators, and users - who operate with bounded rationality. This study addresses this gap by constructing a tripartite evolutionary game model. Its primary significance lies in dynamically modeling the co-evolution of strategies to identify critical leverage points, thereby providing a theoretical basis for designing effective collaborative governance mechanisms that can reconcile data protection with utilization and ensure the sustainable development of the health data ecosystem. [Method/Process] This study established a three-party evolutionary game model involving government regulators, medical-health APP operators, and users, based on the core assumption of bounded rationality. The model incorporated a comprehensive set of parameters, including direct benefits, various costs (compliance, regulatory), data risks, and network benefits under different regulatory scenarios. Replicator dynamic equations were derived for each party to mathematically describe the evolution of their strategy choices over time. The stability of the system's equilibrium points was rigorously analyzed using Lyapunov's first method to identify key stability thresholds. To validate the theoretical analysis and explore the dynamic evolutionary paths, numerical simulations were conducted using MATLAB. These simulations tested the impact and sensitivity of critical parameters - such as user-perceived data risk under operator self-discipline, user network benefits under dynamic regulation, government compliance rewards, and penalties for overdevelopment - from various initial strategy combinations. [Results/Conclusions] The analysis yielded several critical findings. First, users' authorization decisions are highly sensitive to the operational context, and they are significantly positively influenced by the perceived level of operator self-discipline and the observed intensity of government dynamic regulation. Enhancing user network benefits under effective regulation and reducing perceived data risks are paramount to encouraging authorization. Second, for APP operators, increasing government penalties for overdevelopment acts as a powerful deterrent, rapidly steering operators towards compliance. In contrast, government financial rewards for compliance, while effective, must be carefully balanced against their potential fiscal burden, which can slow the government's own stabilization into a dynamic regulatory role. Third, the system exhibits strong path dependence, capable of converging towards either an inefficient equilibrium (Non-Authorization, Overdevelopment, Passive Regulation) or the optimal Pareto state (Authorization, Self-discipline, Dynamic Regulation), depending heavily on initial conditions. The study concludes that resolving the paradox requires a multi-faceted strategy: advancing and ensuring robust anonymization technologies, implementing intelligent graded supervision that combines incentives and punishments, and firmly establishing institutional safeguards for user data sovereignty to build essential trust. A key limitation is the omission of data leakage risks from government data openness. Future work will integrate empirical data and consider user heterogeneity to refine the model.

Key words: health data, evolutionary game, simulation analysis, data sharing