A Method for EHR Phenotype Management in an i2b2 Data Warehouse.

• Authors: Andrew Post, Emory University; Nityananda Chappidi, Emory University; Dileep Gunda, Emory University; Nita Deshpande, Emory University
• Journal Title: AMIA Joint Summits on Translational Science proceedings AMIA Summit on Translational Science
• Volume: Volume 2019
• Publisher: American Medical Informatics Association | 2019, Pages 92-101
• Type of Work: Article | Final Publisher PDF
• Subjects: Engineering, Biomedical

Abstract

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.