在眼科中实施一个共同的数据模型:将结构化电子健康记录和眼科检查数据映射到标准词汇中
Justin C Quon1, Christopher P Long1, William Halfpenny2
1Department of Ophthalmology, Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, California.
Ophthalmology science
|February 3, 2025
概括
观察健康数据科学和信息学观察医疗结果伙伴关系 (OMOP) 共同数据模型 (CDM) 在Cerner电子健康记录 (EHR) 中眼科检查数据中存在显著的概念覆盖差距. 解决这些差距对于利用标准化数据增强眼科研究至关重要.
科学领域:
- 眼科医生 眼科 眼科
- 医疗信息学 医疗信息学
- 电子健康记录 (EHR) 是一种电子健康记录.
背景情况:
- 标准化数据模型对于多个地点的观测研究至关重要.
- 观察医学结果伙伴关系 (OMOP) 共同数据模型 (CDM) 旨在标准化健康数据.
- 塞纳千年是临床实践中广泛使用的EHR系统.
研究的目的:
- 识别和描述眼科检查数据元素中的概念覆盖差距.
- 评估Cerner EHR数据与OMOP CDM的协调性.
- 为改善眼科研究的OMOPCDM提供信息.
主要方法:
- 从默认和本地Cerner EHR实现中提取眼科数据元素.
- 分类数据元素分为8个主题类别.
- 将数据元素映射到 OMOP CDM 中,将映射分类为精确,更宽,更窄或不匹配.
主要成果:
- 在两个EHR实施中,在所有8个眼科类别中都发现了重要的概念覆盖差距.
- 与默认模块相比,本地Cerner模块显示了更广泛 (54%) 和不匹配 (26%) 映射的比例较高.
- 视觉敏度,感觉运动测试和折射等特定类别表现出实质性的差距,超过80%的元素没有准确的映射.
结论:
- 在Cerner EHR中,OMOP CDM对眼科检查数据有相当大的覆盖差距.
- 为了有效的眼科研究,需要改善OMOP CDM的细分化和概念标准化.
- 提供了建议,以提高眼科的数据标准.
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