统一数据模型协调电子健康记录数据的数据质量和实用性兼容的匿名化:范围审查协议
Gaetan Kamdje Wabo1, Fabian Prasser2, Kerstin Gierend1
1Department of Biomedical Informatics, Center for Preventive Medicine and Digital Health Baden-Württemberg, Mannheim Medical Faculty of the University of Heidelberg, Mannheim, Germany.
JMIR research protocols
|August 11, 2023
概括
本范围审查检查了共同数据模型 (CDM) 转换的电子健康记录 (EHR) 数据的匿名化方法. 它评估了如何管理数据质量和效用,这对于保护隐私的健康研究至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 数据 隐私 数据 隐私 数据
- 电子健康记录 (EHR) 是一种电子医疗记录.
背景情况:
- 匿名化共同数据模型 (CDM) 转换的电子健康记录 (EHR) 数据对于协调健康数据研究中的隐私至关重要.
- 当前的匿名化技术可能会影响数据属性,可能会影响研究结果.
- 限量审查解决了在CDM格式的健康数据匿名化中管理数据实用性和质量方面的问题.
研究的目的:
- 识别和描述用于CDM转换健康数据的正式匿名化方法.
- 评估这些匿名化过程中如何解决数据质量和实用性的问题.
- 为了比较不同CDM关于它们适合存储匿名数据的情况.
主要方法:
- 根据Arksey和O'Malley框架进行范围审查,包括英语文章.
- 文献搜索将数据匿名化,CDM标准和数据质量关键字结合起来,由图书馆员验证.
- 系统选,由两个独立审稿人进行全文审查,并使用标准化模板进行数据提取,以PRISMA-ScR检查清单为指导.
主要成果:
- 在Web of Science的初步搜索结果显示了507篇非重复的文章,这表明了大量的相关文献.
- 进一步的分析和选择将完善用于审查的文献集合.
- 该研究预计将在2023年第四季度末完成.
结论:
- 了解CDM格式化EHR数据的匿名化方法对于识别研究缺口和未来方向至关重要.
- 本范围审查协议提供了一个结构化的方法来调查匿名化方法,数据质量和实用性.
- 这些发现将为使用CDM的隐私保护健康数据研究提供最佳实践的见解.
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