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

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.

Keywords: collaborative governance; evolutionary game; excessive medical care; healthcare regulation; simulation analysis.

Figure 1

Simulation of equilibrium point (1,0,0) parameters (evolved 50 times). The x-axis represents the proportion of patients recognizing treatment outcomes, the y-axis denotes the proportion of healthcare institutions providing reasonable treatment, and the z-axis refers to the proportion of government regulators adopting strict supervision. Different colored trajectories represent different initial probability settings, all converging toward the equilibrium under the baseline scenario. The figure visually demonstrates the dynamic stability and convergence tendency of the tripartite system.

Figure 2

Simulation of equilibrium point (0,1,0) parameters (50 times of evolution). The axes are defined as in Figure 1. Each colored line indicates a unique combination of initial strategy probabilities for the three stakeholders. This figure illustrates how varying initial attitudes still lead to the same or similar equilibrium points, highlighting the model's robustness to initial conditions.

Figure 3

Sensitivity analysis of government subsidies. Each subplot traces the time evolution of strategy proportions for the three participant groups. The distinct lines indicate outcomes under varying levels of government subsidies. Results show that increased subsidies incentivize reasonable medical practice by institutions and accelerate convergence toward cooperative equilibria.

Figure 4

Sensitivity analysis of government fines on medical institutions. Each curve displays how strategy adoption among stakeholders shifts over time in response to changing penalty strength. The figure illustrates that enhanced penalties discourage overtreatment and encourage compliance, while also affecting the government's supervisory intensity.

Figure 5

Sensitivity analysis of government remediation costs. The subplots reveal how changes in rectification costs alter the pace and direction of strategy evolution among the three groups. Higher rectification costs strengthen internal and external constraints, reducing overtreatment tendencies in healthcare institutions.

Figure 6

Sensitivity analysis of government regulatory costs. The plots show how changes in income from providing reasonable care affect strategic evolution over time. Greater rewards for reasonable treatment motivate providers to prioritize patient-centered care, with little effect on regulator behavior.

Figure 7

Sensitivity analysis of reasonable treatment revenue of medical institutions. These curves demonstrate that as the incentives for overtreatment increase, providers become more likely to pursue such strategies, while patient approval declines. The figure underscores the importance of aligning provider incentives with rational medical practice.

Figure 8

Sensitivity analysis of excessive medical care in medical institutions. The graphs show the effect of varying supervisory costs on the speed and final distribution of stakeholder strategies. When supervision is more costly, regulatory efforts decrease, leading to higher risk of overtreatment by healthcare institutions.