Symmetric Enhancement of Big Data Utilization and Protection in Healthcare in China from the Perspective of Evolutionary Game Analysis

With the rapid development of data technologies, the high privacy sensitivity of big data in healthcare imposes higher demands on its security supervision. This paper analyzes the interactive dynamics between the behaviors of regulators and regulated entities, aiming to explore the symmetry and balance between the utilization and protection of big data in healthcare in China. A two-party evolutionary game model between regulators and regulated entities is constructed and refined by incorporating herding preference utility coefficients, and simulation analyses are performed using MATLAB. Furthermore, the main models and differences in health data regulation among the United States, the European Union, the United Kingdom, and China are discussed for broader relevance. This study finds that the fine amount imposed on regulated entities during process supervision has a significant impact on their behavior, yet it cannot eliminate unstable fluctuations in the system. Reducing the prevention costs of regulated entities is the fundamental approach for the system to achieve an equilibrium state of maximum social welfare. Herding preference utility enhances system stability, and when this utility is sufficiently strong, it may even eliminate unstable fluctuations in the system. It is suggested that regulators should carefully consider the prevention costs of regulated entities when proposing prevention requirements, implement subsidy policies when necessary, explore a new model of multi-stakeholder collaborative supervision, enhance the risk awareness of relevant organizations, and strengthen publicity and guidance, thereby achieving the goal of big data security supervision in healthcare.