在中国的实验数据交易试点中,对医疗数据治理的进化游戏分析
1School of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
NPJ digital medicine
|September 1, 2025
概括
这项研究模拟了医疗数据治理,揭示了数据管理机构 (DMA) 的明确法规和激励措施稳定了系统. 罚款补偿机制和声誉激励措施可以减少数据泄露的损失和监管成本.
科学领域:
- 卫生信息学
- 游戏理论
- 公共政策
背景情况:
- 医疗数据治理面临隐私,道德风险和利益相关方利益的挑战.
- 现有的框架在数据权利和风险责任方面存在模两可.
研究的目的:
- 构建和分析医疗数据治理的三方进化游戏模型.
- 调查利益相关者互动对隐私,安全和利益对齐的影响.
- 为监管机构和数据运营部门确定最佳策略.
主要方法:
- 与医疗保健数据管理机构 (DMA),数据操作部门 (DOD) 和与数据相关的实体 (DE) 共同开发三方进化游戏模型.
- 在三重主体-代理框架内分析动态相互作用.
- 模拟战略稳定,平衡趋同以及对政策干预的敏感性.
主要成果:
- 战略性不稳定性发生在数据财产权和不对称风险方面.
- 在超越关键值时实现以合规为导向的平衡.
- 在解决监管悖论方面,DMA的激励策略至关重要.
- 国防部的风险行为显示出对罚款和收入的非线性敏感性.
- 双重罚款补偿机制可以减少数据泄露造成的经济损失.
- 政府的声誉机制与金融激励相结合,降低了监管成本.
结论:
- 实施强有力的激励策略对于医疗保健数据治理至关重要.
- 建议采用分层,动态的监管体系和与收入风险相关的分配机制.
- 通过技术和可信度促进协作治理生态系统可以提高数据安全性和合规性.
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