设计中的隐私:使用混合人机系统进行交互记录链接的案例研究
Hye-Chung Kum1, Eric Ragan2, Mahin Ramezani1
1Population Informatics Lab, Department of Health Policy and Management, School of Public Health, Texas A&M University, College Station, TX, USA; Department of Computer Science and Engineering; Texas A&M University, College Station, TX, USA.
像MiNDFIRL这样的交互记录链接 (RL) 系统可以提高患者匹配的准确性. 这种混合人机方法尽量减少数据披露,同时减少现实世界健康数据链接中的错误.
科学领域:
- 卫生信息学
- 数据链接
- 保护隐私的技术
背景情况:
- 如果没有一个共同的标识符, 准确匹配不同数据源的患者是具有挑战性的.
- 使用混合人机系统的交互记录链接 (RL) 对于高质量的患者匹配至关重要.
- 在RL期间尽量减少信息披露对于患者的隐私至关重要.
研究的目的:
- 介绍和评估混合原型软件系统MiNDFIRL (互动记录链接的最低必要披露).
- 证明MiNDFIRL能够最大限度地提高连接准确度,同时尽量减少信息披露.
- 在实例研究中评估MiNDFIRL的有效性.
主要方法:
- 进行了两项用户研究,涉及10,000对电子病历数据和18,240个患者生成的数据.
- 使用自动化RL,然后由12名审查员使用MiNDFIRL进行人工审查.
- 员工达成共识以解决审查员的分歧,并进行半结构面试以获得系统反.
主要成果:
- 随机森林算法发现了388个 (EHR) 和539个 (患者生成的) 匹配,另外还有303个和187个对需要手动审查.
- 手动审查证实了232个 (EHR) 和84个 (患者生成的) 不确定对的额外真实联系.
- MiNDFIRL仅使用30%的可用识别信息来正确分类77% (EHR) 和45% (患者生成) 的不确定的联系,其中姓名和电子邮件最常见.
结论:
- 具有按需访问和掩盖的混合人机系统减少了RL中的披露风险.
- 在真实世界患者数据链接中,MiNDFIRL有效地减少了假阳性和假阴性.
- 风险量化和互动审查是记录链接中准确性和隐私平衡的关键.
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