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利用 EPIC 的原生 ETL 基础设施来实现 OMOP CDM:一个协作体验.

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科学领域:

  • 医疗信息学 医疗信息学
  • 生物医学研究生物医学研究
  • 数据管理数据管理

背景情况:

  • 跨机构的合作研究对于推进医学知识至关重要.
  • 标准化电子健康记录 (EHR) 数据对于多机构研究至关重要.
  • 观察医学结果伙伴关系 (OMOP) 共同数据模型 (CDM) 促进了数据协调.

研究的目的:

  • 使用OMOP CDM与Epic EHR数据实现可复制的研究管道.
  • 为了在OHDSI (观察健康数据科学和信息学) 网络中实现协作研究.
  • 评估使用Epic本地ETL系统用于OMOPCDM映射的可行性和数据质量.

主要方法:

  • 将德克萨斯大学西南医疗中心 (UTSW) 和德克萨斯州卫生资源 (THR) EHR数据映射到25个OMOP CDM表中.
  • 利用Epic现有的提取,转换和加载 (ETL) 系统将数据传输到共享的OMOP数据库.
  • 在THR和UTSW分别进行每周和每日ETL流程.

主要成果:

  • 获得了高数据质量评估分数:THR 97%和UTSW 98%.
  • 成功建立了一个功能和可重复的数据管道.
  • 扩大了OHDSI研究网络,包括了来自UTSW和THR的统一数据.

结论:

  • 与Epic的原生ETL框架集成的OMOP CDM提供了一个强大的协作研究解决方案.
  • 这种方法提高了数据质量和可用于未来观测研究的概括性.
  • 已建立的管道支持更广泛地参与OHDSI网络,加速医学发现.