Yao Han, Liu Yuanli
School of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
NPJ Digit Med. 2025 Sep 1;8(1):562. doi: 10.1038/s41746-025-01950-2.
This study constructs a tripartite evolutionary game model involving healthcare data management authorities (DMAs), healthcare data operating departments (DODs), and data-related entities (DEs) within a triple principal-agent framework. We analyze dynamic interactions among these stakeholders in healthcare data governance, focusing on privacy security, moral hazard, and interest alignment. Results indicate that strategic instability arises under conditions of ambiguous data property rights and asymmetric risk responsibilities. However, the system converges to compliance-oriented equilibria when critical thresholds are surpassed. Notably, DMAs' strong incentive strategies are pivotal in resolving regulatory paradoxes, while DODs' risk behaviors exhibit nonlinear sensitivity to penalty intensity and revenue levels. We further demonstrate that a dual "penalty-compensation" mechanism mitigates economic losses from data breaches, and synergistic government reputation mechanisms with financial incentives reduce regulatory costs. Policy implications include a tiered dynamic regulatory system, a revenue-risk linked distribution mechanism, and a collaborative governance ecosystem driven by technology and credibility constraints.
本研究在三重委托代理框架内构建了一个涉及医疗数据管理机构（DMA）、医疗数据运营部门（DOD）和数据相关实体（DE）的三方演化博弈模型。我们分析了这些利益相关者在医疗数据治理中的动态互动，重点关注隐私安全、道德风险和利益协调。结果表明，在数据产权模糊和风险责任不对称的情况下会出现战略不稳定。然而，当超过临界阈值时，系统会收敛到以合规为导向的均衡状态。值得注意的是，DMA的强激励策略在解决监管悖论方面至关重要，而DOD的风险行为对惩罚强度和收入水平表现出非线性敏感性。我们进一步证明，双重“惩罚 - 补偿”机制减轻了数据泄露造成的经济损失，并且具有财政激励的协同政府声誉机制降低了监管成本。政策启示包括分层动态监管系统、收入 - 风险挂钩分配机制以及由技术和信誉约束驱动的协同治理生态系统。
