数据湖的丰富以支持人口健康结果使用社会决定因素的研究 相关的EHR数据
Md Kamruz Zaman Rana1, Xing Song1,2, Humayera Islam1,2
1Department of Health Management and Informatics, University of Missouri, Columbia, Missouri.
概括
这项研究引入了一个安全的框架,将非识别的电子健康记录与健康数据的社会决定因素联系起来. 它使人口健康研究能够强大,同时保护隐私并减少安全风险.
科学领域:
- 医疗信息学 医疗信息学
- 人口健康研究 人口健康研究
- 数据安全 数据安全
背景情况:
- 整合电子健康记录 (EHR) 与健康的社会决定因素 (SDoH) 对于人口健康研究至关重要.
- 为此集成收集可识别信息带来了重大安全风险.
- 现有的方法往往难以平衡数据实用性与隐私保护.
研究的目的:
- 提出一个新的框架,以安全地整合非识别的临床数据与隐私保护的地理编码的SDoH数据.
- 为传输数据开发和评估重新识别风险检测算法.
- 为了证明框架在人口健康结果研究中的实用性.
主要方法:
- 在数据湖中开发了一个框架,以促进与隐私保护的地理编码SDoH数据相关的非识别的EHR数据.
- 实施了重新识别风险检测算法,以评估数据传输风险.
- 进行了一项人口健康结果研究,研究了社会经济地位和慢性疾病之间的联系.
主要成果:
- 该框架成功地将非识别的临床数据与SDoH数据集成在一起,同时保持隐私.
- 重新识别风险算法提供了对数据传输安全性的评估.
- 该研究表明,社会经济地位与患慢性病的风险之间存在相关性,展示了该框架的实用性.
结论:
- 拟议的框架有效地减少了研究人员的计算和管理负担以及安全风险.
- 它可以对与SDoH相关的临床数据进行快速,可靠的研究,支持基于证据的干预措施.
- 这种方法对于理解复杂的SDoH-健康结果关系至关重要,同时保持数据隐私.
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