在德国医疗数据集成中心共享可追溯的研究数据,以FAIR (可查找性,可访问性,互操作性和可重复使用性) 为导向的来源实现:概念验证研究研究
Kerstin Gierend1, Dagmar Waltemath2, Thomas Ganslandt3
1Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
JMIR formative research
|December 7, 2023
概括
本研究介绍了德国医疗数据整合中心 (DIC) 的来源跟踪,以提高电子健康记录 (EHR) 数据的质量和可重复使用性. 实施的系统增强了数据完整性和对二次研究使用的信任.
科学领域:
- 生物医学信息学 生物医学信息学
- 数据科学数据科学数据科学
- 医疗信息学 医疗信息学
背景情况:
- 来自德国医疗数据整合中心 (DIC) 的数字健康记录和电子患者数据对于未来的患者护理至关重要.
- 当前系统中关于数据完整性,可追溯性和质量的有限信息减少了对电子健康记录 (EHR) 的信任.
- 遵守FAIR原则需要将数据与来源元数据进行丰富,以改善数据管理和治理.
研究的目的:
- 提高医疗DIC中临床常规数据的可重复使用性,用于二级研究.
- 建立来源跟踪,验证数据完整性,可靠性和对电子健康记录的信任.
- 通过数据来源来提高医疗DIC的问责制.
主要方法:
- 开发了一个定制的来源框架路线图,分析跨ETL阶段的数据集成.
- 基于到期模型的来源库的衍生要求.
- 实施了一个概念验证来源类,整合了W3C来源标准,并与快速医疗互操作资源 (FHIR) 保持一致.
主要成果:
- 在德国医疗DIC中,成功地在数据元素层面实施了集成的来源跟踪.
- 设计了一种结合质量和健康标准指导的元数据管理的实用方法.
- 证明了临床常规数据的准确和可靠处理,具有高效的管道执行时间,确保DIC的问责制.
结论:
- 开发的概念验证来源类促进生物医学数据的有效数据管理实践.
- 将数据与来源相结合,提高了其对研究和社会的价值,促进了数据的简化重用.
- 这些原则在科学领域普遍适用,减轻了与没有来源和质量知识的数据分析相关的风险.
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