Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
Division of Health and Biomedical Informatics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
J Am Med Inform Assoc. 2018 Nov 1;25(11):1540-1546. doi: 10.1093/jamia/ocy101.
Electronic health record (EHR) algorithms for defining patient cohorts are commonly shared as free-text descriptions that require human intervention both to interpret and implement. We developed the Phenotype Execution and Modeling Architecture (PhEMA, http://projectphema.org) to author and execute standardized computable phenotype algorithms. With PhEMA, we converted an algorithm for benign prostatic hyperplasia, developed for the electronic Medical Records and Genomics network (eMERGE), into a standards-based computable format. Eight sites (7 within eMERGE) received the computable algorithm, and 6 successfully executed it against local data warehouses and/or i2b2 instances. Blinded random chart review of cases selected by the computable algorithm shows PPV ≥90%, and 3 out of 5 sites had >90% overlap of selected cases when comparing the computable algorithm to their original eMERGE implementation. This case study demonstrates potential use of PhEMA computable representations to automate phenotyping across different EHR systems, but also highlights some ongoing challenges.
电子健康记录 (EHR) 算法常用于定义患者队列，这些算法通常以需要人工干预的自由文本描述形式共享，既需要解释又需要执行。我们开发了 Phenotype Execution and Modeling Architecture (PhEMA，http://projectphema.org)，用于编写和执行标准化的可计算表型算法。使用 PhEMA，我们将电子病历和基因组网络 (eMERGE) 开发的良性前列腺增生算法转换为基于标准的可计算格式。八个站点（eMERGE 内的 7 个）收到了可计算算法，其中 6 个成功地针对本地数据仓库和/或 i2b2 实例执行了该算法。对可计算算法选择的病例进行盲法随机图表审查显示，PPV≥90%，当将可计算算法与他们原始的 eMERGE 实现进行比较时，有 3 个站点的选择病例的重叠率超过 90%。这项案例研究表明，PhEMA 可计算表示形式有可能在不同的 EHR 系统中实现表型自动化，但也突出了一些持续存在的挑战。
