A study on collaborative governance of excessive medical care based on three-way evolutionary game and simulation

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Abstract

Introduction: Although China has made some progress in regulating and governing overtreatment behaviors in healthcare institutions, excessive medical care remains a persistent challenge in the Chinese healthcare sector. Methods: This study adopts a perspective of bounded rationality and employs evolutionary game theory to construct a collaborative governance model involving government regulatory departments, healthcare institutions, and patients. The model analyzes the strategic stability of each participant and examines the impact of various factors, such as fiscal subsidies, government fines, rectification costs, regulatory costs, reasonable treatment income, and overtreatment income, on the strategic choices of the game participants. Parameter sensitivity within the three-party gaming system is also investigated through simulation analysis. Results: The findings indicate that when patients trust treatment outcomes and healthcare institutions are more inclined to provide appropriate care, government regulatory departments tend to adopt a more relaxed regulatory strategy. Simulation results show that increasing government fiscal subsidies, raising reasonable treatment income, and strengthening supervision and rectification efforts are effective in reducing overtreatment behaviors. Discussion: The decision-making of government regulatory departments is influenced by the degree of patient trust. Improving collaborative governance for overtreatment requires establishing comprehensive laws and regulations, leveraging government regulatory functions, strengthening internal constraint mechanisms in healthcare institutions, and raising patients' awareness of their rights and supervisory responsibilities.

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Gong, H., Zhang, T., Wang, X., Wu, B., & Zhao, S. (2025). A study on collaborative governance of excessive medical care based on three-way evolutionary game and simulation. Frontiers in Public Health, 13. https://doi.org/10.3389/fpubh.2025.1593398

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