简化电子医疗记录数据提取和验证在数字医院:系统性审查以确定最佳方法和方法
Han Chang Lim1, Howard Wong1, Reji Philip1
1Queensland Digital Health Centre, Centre for Health Services Research, Faculty of Medicine The University of Queensland Herston Queensland Australia.
Learning health systems
|October 31, 2025
概括
标准化电子病历 (EMR) 数据提取至关重要. 这次审查确定了使用OMOP共同数据模型改进EMR数据提取和验证的关键挑战和方法.
科学领域:
- 医疗信息学 医疗信息学
- 临床数据管理 临床数据管理
- 观测研究方法 观测研究方法
背景情况:
- 从大型临床信息系统中提取和整理数据存在重大挑战.
- 电子医疗记录 (EMR) 数据提取的最佳方法通常不清楚.
- 标准化数据模型,如观察医学结果伙伴关系 (OMOP) 共同数据模型 (CDM) 越来越多地用于数据比较.
研究的目的:
- 系统地调查和评估关于医疗机构从EMR中提取数据的现有方法的研究文献.
- 评估从医疗机构的EMR数据提取经验中吸取的经验教训,使用OMOP CDM作为标准化用例.
主要方法:
- 在多个数据库 (PubMed,Web of Science,Embase,Google Scholar) 进行了系统的文献搜索,寻找2017年6月至2022年12月期间发表的研究.
- 在审查了316篇候选文章后,有9篇文章符合纳入标准.
- 用专题综合和数据分析来检查普遍的技术和挑战.
主要成果:
- 在EMR数据提取和验证中发现的最常见的挑战包括缺乏标准化流程,数据结构问题和技术人员不足.
- 包括的9项研究中,有5项显示出高质量 (在评估中超过70%).
- 使用的具体工具和框架包括观察健康数据科学和信息学套件以及临床文本框架的策划,规范化和注释的分阶段优化.
结论:
- 标准化统一的方法,一致的流程和适当的工具对于有效的EMR数据提取和验证至关重要.
- 已识别的方法和技术有可能简化EMR数据提取流程.
- 未来的工作将涉及与医疗保健组织合作对这些方法的经验评估.
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