Eos和OMOCL:将开放EHR记录无整合到OMOP共同数据模型中
Severin Kohler1, Diego Boscá2, Florian Kärcher3
1Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Digital Health Center, Kapelle-Ufer 2, 10117 Berlin, Germany.
Journal of biomedical informatics
|July 13, 2023
概括
为了研究,将电子健康记录 (EHR) 标准化至关重要. 我们开发了OMOP转换语言 (OMOCL) 和Eos工具,将开放EHR数据自动转换为观察医学结果伙伴关系 (OMOP) 共同数据模型 (CDM),提高数据可访问性.
科学领域:
- 医疗信息学 医疗信息学
- 生物医学研究数据管理数据管理
背景情况:
- 电子健康记录 (EHR) 为研究提供了有价值的数据,但受到结构和语义异质性的困扰.
- 观察医学结果伙伴关系 (OMOP) 共同数据模型 (CDM) 标准化了研究数据,但EHR转换涉及复杂的提取转换和负载 (ETL) 过程.
- 由于其语义精度,openEHR正在获得采用,但将openEHR记录集成到OMOP CDM中需要采用标准化的方法.
研究的目的:
- 开发一个标准化流程,将开放的EHR记录集成到OMOP CDM中,以降低EHR数据在研究中的重用障碍.
- 为研究目的应对EHR数据异质性和语义不精确性的挑战.
主要方法:
- 分析并比较openEHR和OMOPCDM模型以确定潜在的数据映射.
- 定义了将openEHR记录转换为OMOP CDM表的流程.
- 开发了OMOP转换语言 (OMOCL) 用于声明性原型到CDM映射,并实施了Eos工具用于自动化的ETL.
主要成果:
- OMOP转换语言 (OMOCL) 允许定义openEHR转换为OMOP的通用映射.
- 通过使用OMOCL文件,Eos工具成功地将ETL过程从现实世界和样本EHR自动化到OMOP CDM中.
- 拟议的方法促进了临床和研究数据领域之间的互操作性.
结论:
- Eos和OMOCL提供了一个可行的方法,可以将开放的EHR记录集成到OMOP CDM中,从而提高研究数据的可访问性.
- 这种整合是实现临床和研究数据领域之间的互操作性的重要一步.
- 如果转换为表达力较低的OMOP CDM,可能会导致语义数据丢失,则存在限制.
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