澳大利亚初级保健数据中的OMOP共同数据模型:构建一个质量研究准备的统一数据集
Roger Ward1, Christine Mary Hallinan1, David Ormiston-Smith1
1Health & Biomedical Research Information Technology Unit (HaBIC R2), Department of General Practice and Primary Care, Faculty of Medicine, Dentistry & Health Sciences, The University of Melbourne, Parkville, Victoria, Australia.
PloS one
|April 18, 2024
概括
这项研究成功地将初级保健电子健康记录转换为观察医疗结果伙伴关系共同数据模型 (OMOP-CDM). 标准化数据的合格率达到了97%,使得健康研究得以强大.
科学领域:
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
- 临床研究 临床研究
背景情况:
- 经常收集的健康数据,例如来自电子病历 (EMR),对于推进医学研究和改善患者治疗结果至关重要.
- 通过使用观察医学结果伙伴关系共同数据模型 (OMOP-CDM) 标准化EMR数据,可以在各种数据集中进行分析.
- OMOP-CDM协调了术语和编码方案,增强了研究能力,促进了药物监督和跨机构验证.
研究的目的:
- 在PATRON初级保健数据库中调查开源OMOP-CDM的应用和有效性.
- 评估将EMR数据转换为二级研究的标准化格式的可行性.
主要方法:
- 使用结构化查询语言 (SQL) 来提取数据,转换和加载到OMOP-CDM中.
- 开发了一种手动绘制具有挑战性的自由文本术语的策略,重点关注高频率术语,以确保超过95%的词汇链接.
- 使用OHDSI数据质量仪表板 (DQD) 对卡恩框架进行数据可信性,一致性和全面性的评估.
主要成果:
- 成功转换了来自三个初级保健系统的EMR数据,包括203万活跃患者,转换为OMOP CDM版本5.4.
- 在OHDSI数据质量仪表板进行了3,570次评估,对数据进行了预定义值的评估.
- 在PATRON初级保健OMOP数据库中获得了97%的整体数据质量合格率.
结论:
- OMOP-CDM提供了一个完善的数据标准化框架,得到国际合作和培训的支持.
- 它的兼容性促进了分析包的共享,使得研究小组之间能够进行快速和可重复的数据比较.
- 像OHDSI DQD这样的开源工具支持OMOP模型,由于其简单性和基于标准的方法,促进其采用和集成到现有数据流程中.
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