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

Pascal S. Brandt, Jennifer A. Pacheco, Prakash Adekkanattu, Evan T. Sholle, Sajjad Abedian, Daniel J. Stone, David M. Knaack, Jie Xu, Zhenxing Xu, Yifan Peng, Natalie C. Benda, Fei Wang, Yuan Luo, Guoqian Jiang, Jyotishman Pathak, Luke V. Rasmussen

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)1449-1460
Number of pages12
JournalJournal of the American Medical Informatics Association
Issue number9
StatePublished - Sep 1 2022


  • CQL
  • EHR-driven phenotyping
  • FHIR
  • cohort identification

ASJC Scopus subject areas

  • Health Informatics


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