Post Andrew, Chappidi Nityananda, Gunda Dileep, Deshpande Nita
Department of Biomedical Informatics, Emory University, Atlanta, GA.
AMIA Jt Summits Transl Sci Proc. 2019 May 6;2019:92-101. eCollection 2019.
Electronic health record (EHR) data is valuable for finding patients for clinical research and analytics but is complex to query. EHR phenotyping involves the curation and dissemination of best practices for querying commonly studied populations. Phenotyping software computes patterns in clinical and administrative data and may add the found patterns as derived variables to a database that researchers can query. This paper describes a method for managing EHR phenotypes in a data warehouse as the warehouse is incrementally updated with new and changed data. We have implemented this method in proof-of-concept form as an extension to the Eureka! Clinical Analytics phenotyping software system and evaluated the implementation's performance. The method shows promise for realizing the efficient addition, modification, and removal of derived variables representing phenotypes in a data warehouse.
电子健康记录（EHR）数据对于寻找临床研究和分析的患者很有价值，但查询起来很复杂。EHR表型分析涉及整理和传播查询常见研究人群的最佳实践。表型分析软件计算临床和管理数据中的模式，并可能将找到的模式作为派生变量添加到研究人员可以查询的数据库中。本文描述了一种在数据仓库中管理EHR表型的方法，该数据仓库会随着新数据和变更数据的增量更新而更新。我们已经以概念验证的形式实现了此方法，作为对Eureka！临床分析表型分析软件系统的扩展，并评估了该实现的性能。该方法有望在数据仓库中高效地添加、修改和删除表示表型的派生变量。
