Description
With the increased adoption of electronic health records, data collected for routine clinical care is used for health outcomes and population sciences research, including the identification of phenotypes. In recent years, research networks, such as eMERGE, OHDSI and PCORnet, have been able to increase statistical power and population diversity by combining patient cohorts. These networks share phenotype algorithms that are executed at each participating site. Here we observe experiences with phenotype algorithm portability across seven research networks and propose a generalizable framework for phenotype algorithm portability. Several strategies exist to increase the portability of phenotype algorithms, reducing the implementation effort needed by each site. These include using a common data model, standardized representation of the phenotype algorithm logic, and technical solutions to facilitate federated execution of queries. Portability is achieved by tradeoffs across three domains: Data, Authoring and Implementation, and multiple approaches were observed in representing portable phenotype algorithms. Our proposed framework will help guide future research in operationalizing phenotype algorithm portability at scale.
Learning Objective: Understand the concept of portability with respect to EHR-based phenotype algorithms, and the application of a novel framework to the concept.
Authors:
Luke Rasmussen (Presenter)
Northwestern University
Pascal Brandt, University of Washington
Guoqian Jiang, Mayo Clinic
Richard Kiefer, Mayo Clinic
Jennifer Pacheco, Northwestern University
Prakash Adekkanattu, Weill Cornell Medicine
Jessica Ancker, Weill Cornell Medicine
Fei Wang, Weill Cornell Medicine
zhenxing xu, Weill Cornell Medicine
Jyotishman Pathak, Weill Cornell Medicine
Yuan Luo, Northwestern University
Presentation Materials: