The realization of the value of health big data relies on the coordinated cooperation among patients, the government, and data users. Enhancing the symmetry and balance between patient participation and the compliant use of data by data users is a critical link. This paper constructs a tripartite evolutionary game model and employs MATLAB R2023a simulation to analyze the impact of factors such as initial willingness, compliance costs, and penalties for violations on the strategic choices of the game players and the evolution of the system. The findings reveal that: (1) Patient participation is a key condition for achieving an ideal equilibrium in the system. (2) The data service income from participating in data provision and the costs associated with privacy breaches are critical factors influencing patients’ strategic choices. (3) Penalties for violations are a crucial factor in ensuring that data users choose compliant utilization; however, when compliance costs are high, their constraining effect may be somewhat diminished. (4) Enhancing regulatory efficiency is the future direction for government departments. Based on these findings, countermeasures and suggestions are proposed, including trust building, technological innovation and differentiated supervision, and constructing trusted data spaces, to provide references for health big data governance.

The value of personal data can only be unlocked through efficient circulation. This study explores a multi-party collaborative mechanism for personal-data trading, aiming to improve data quality and market vitality via incentive-compatible institutional design, thereby supporting the high-quality development of the digital economy. Symmetry enhancement refers to the use of strategies and mechanisms to narrow the information gap among data controllers, operators, and demanders, enabling all parties to facilitate personal-data transactions on relatively equal footing. Drawing on evolutionary-game theory, we construct a tripartite dynamic-game model that incorporates data controllers, data operators, and data demanders. We analyze how initial willingness, payoff structures, breach costs, and risk factors (e.g., data leakage) shape each party’s strategic choices (cooperate vs. defect) and their evolutionary trajectories, in search of stable equilibrium conditions and core incentive mechanisms for a healthy market. We find that (1) the initial willingness to cooperate among participants is the foundation of a virtuous cycle; (2) the net revenue of data products significantly influences operators’ and demanders’ propensity to cooperate; and (3) the severity of breach penalties and the potential losses from data leakage jointly affect the strategies of all three parties, serving as key levers for maintaining market trust and compliance. Accordingly, we recommend strengthening contract enforcement and trust-building; refining the legal and regulatory framework for data rights confirmation, circulation, trading, and security; and promoting stable supply–demand cooperation and market education to enhance awareness of data value and compliance, thereby stimulating individuals’ willingness to authorize the use of their data and maximizing its value.

Against the backdrop of artificial intelligence (AI) empowering the medical industry, achieving symmetric coordination between patients and medical intelligent systems has emerged as a key factor in enhancing the efficacy of medical human–computer collaborative diagnosis. This study systematically identified the factors influencing the effectiveness of human–machine collaborative diagnosis in healthcare by combining literature analysis with expert interviews, based on the Socio-technical Systems Theory. It constructed a symmetric evaluation framework consisting of 19 indicators across four dimensions: user, technology, task, and environment. An integrated DEMATEL method incorporating symmetric logic was employed to quantitatively analyze the interdependent relationships among factors and identify 18 key factors. Subsequently, ISM was applied to analyze the dependency relationships between these key factors, thereby constructing a clear multi-level hierarchical structure model. Through hierarchical construction of a multi-level hierarchical structure model, four core paths driving diagnostic effectiveness were revealed. The research shows that optimizing user behavior mechanisms and technology adaptability and strengthening dynamic coordination strategies between tasks and the environment can effectively achieve the two-way symmetric mapping of the medical human–machine system from fuzzy decision-making to precise output. This has not only improved the efficacy of medical human–computer collaborative diagnosis, but also provided a theoretical basis and practical guidance for optimizing the practical application of medical human–computer collaborative diagnosis.