Design and validation of a FHIR-based EHR-driven phenotyping toolbox.

TitleDesign and validation of a FHIR-based EHR-driven phenotyping toolbox.
Publication TypeJournal Article
Year of Publication2022
AuthorsBrandt PS, Pacheco JA, Adekkanattu P, Sholle ET, Abedian S, Stone DJ, Knaack DM, Xu J, Xu Z, Peng Y, Benda NC, Wang F, Luo Y, Jiang G, Pathak J, Rasmussen LV
JournalJ Am Med Inform Assoc
Date Published2022 Aug 16
KeywordsElectronic Health Records, Humans, Language, Phenotype, Polyhydroxyethyl Methacrylate

OBJECTIVES: To develop and validate a standards-based phenotyping tool to author electronic health record (EHR)-based phenotype definitions and demonstrate execution of the definitions against heterogeneous clinical research data platforms.

MATERIALS AND METHODS: We developed an open-source, standards-compliant phenotyping tool known as the PhEMA Workbench that enables a phenotype representation using the Fast Healthcare Interoperability Resources (FHIR) and Clinical Quality Language (CQL) standards. We then demonstrated how this tool can be used to conduct EHR-based phenotyping, including phenotype authoring, execution, and validation. We validated the performance of the tool by executing a thrombotic event phenotype definition at 3 sites, Mayo Clinic (MC), Northwestern Medicine (NM), and Weill Cornell Medicine (WCM), and used manual review to determine precision and recall.

RESULTS: An initial version of the PhEMA Workbench has been released, which supports phenotype authoring, execution, and publishing to a shared phenotype definition repository. The resulting thrombotic event phenotype definition consisted of 11 CQL statements, and 24 value sets containing a total of 834 codes. Technical validation showed satisfactory performance (both NM and MC had 100% precision and recall and WCM had a precision of 95% and a recall of 84%).

CONCLUSIONS: We demonstrate that the PhEMA Workbench can facilitate EHR-driven phenotype definition, execution, and phenotype sharing in heterogeneous clinical research data environments. A phenotype definition that integrates with existing standards-compliant systems, and the use of a formal representation facilitates automation and can decrease potential for human error.

Alternate JournalJ Am Med Inform Assoc
PubMed ID35799370
PubMed Central IDPMC9382394
Grant ListR01 MH121907 / MH / NIMH NIH HHS / United States
R01 MH121922 / MH / NIMH NIH HHS / United States
R01 LM013337 / LM / NLM NIH HHS / United States
R01 MH119177 / MH / NIMH NIH HHS / United States
R01 GM105688 / GM / NIGMS NIH HHS / United States
Institute of Artificial Intelligence for Digital Health
Faculty Publication