Collaborative governance mechanism of healthcare data in medical consortia based on a tripartite evolutionary game model

Against the backdrop of digital health development and the construction of tightly integrated medical consortia, collaborative governance of healthcare data has become a critical foundation for improving the efficiency of regional healthcare resource allocation and advancing the hierarchical diagnosis and treatment system. However, due to factors such as unequal benefit distribution, insufficient collaborative incentives, and data security risks, significant disparities in collaborative behavior persist among government regulators, leading hospitals, and affiliated member institutions. Drawing upon collaborative governance theory and evolutionary game theory, this study constructs a tripartite evolutionary game model involving government authorities, leading hospitals, and affiliated member institutions. Key variables, including benefit distribution, indirect benefit transformation, policy incentives, and risk losses, are incorporated to examine the dynamic evolutionary mechanism of collaborative governance in healthcare data sharing within medical consortia. Furthermore, MATLAB-based system stability simulations and parameter sensitivity analyses are conducted. The findings indicate that: (1) the system exhibits multiple stable states, including low-level collaboration, asymmetric collaboration, and high-level collaboration; (2) expansion of the overall system benefits constitutes the fundamental prerequisite for collaborative upgrading, whereas isolated improvements in local incentives for individual stakeholders are insufficient to achieve overall collaborative transition; (3) the evolutionary threshold for member institutions to shift from passive participation to active participation is higher than that for leading hospitals to evolve from passive collaboration to proactive collaboration; and (4) coordinated optimization of multiple parameters is more likely to trigger the transition from localized collaboration to bilateral active collaboration than single-parameter adjustments. Based on these findings, a closed-loop collaborative governance mechanism characterized by “state identification–constraint diagnosis–strategy adjustment–feedback updating” is proposed, with the aim of providing theoretical insights and practical pathways for collaborative governance of healthcare data and the development of digital governance systems within medical consortia.