ArticleJournal of the American Medical Informatics Association : JAMIA2018
A case study evaluating the portability of an executable computable phenotype algorithm across multiple institutions and electronic health record environments.
Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Real-World Use of Controlled Terminologies, Ontologies, and Vocabularies for Evidence Generation Across a Large International Observational Network: Challenges and Lessons Learned From a Mixed Method Study.JMIR medical informatics · 2026Article
- A platform for generating clinical intelligence from routine health data in hospitals: PICTURE.Future healthcare journal · 2026Article
- Decoding Immunodeficiencies with Artificial Intelligence: A New Era of Precision Medicine.Biomedicines · 2025Review
- Equivalence of electronic health record data for measuring hypertension prevalence: a retrospective comparison to BRFSS with data from two Indiana health systems, 2021.BMC public health · 2025Article
- Phenotype execution and modeling architecture to support disease surveillance and real-world evidence studies: English sentinel network evaluation.JAMIA open · 2024Article
- A computable case definition for patients with SARS-CoV2 testing that occurred outside the hospital.JAMIA open · 2023Article
- Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network.Scientific reports · 2023Article
- Not all phenotypes are created equal: covariates of success in e-phenotype specification.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- A Framework for Augmented Intelligence in Allergy and Immunology Practice and Research-A Work Group Report of the AAAAI Health Informatics, Technology, and Education Committee.The journal of allergy and clinical immunology. In practice · 2022Article
- Applying computable phenotypes within a common data model to identify heart failure patients for an implantable cardiac device registry.International journal of cardiology. Heart & vasculature · 2022Article
- The U.S. National Library of Medicine's impact on precision and genomic medicine.Information services & use · 2022Article
- Desiderata for the development of next-generation electronic health record phenotype libraries.GigaScience · 2021Article
- Harnessing electronic health records to study emerging environmental disasters: a proof of concept with perfluoroalkyl substances (PFAS).NPJ digital medicine · 2021Article
- Development of a Portable Tool to Identify Patients With Atrial Fibrillation Using Clinical Notes From the Electronic Medical Record.Circulation. Cardiovascular quality and outcomes · 2020Article
- Toward cross-platform electronic health record-driven phenotyping using Clinical Quality Language.Learning health systems · 2020Article
- Review of Clinical Research Informatics.Yearbook of medical informatics · 2020Review
- Development and validation of phenotype classifiers across multiple sites in the observational health data sciences and informatics network.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Developing a FHIR-based EHR phenotyping framework: A case study for identification of patients with obesity and multiple comorbidities from discharge summaries.Journal of biomedical informatics · 2019Article
- Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE network.Journal of biomedical informatics · 2019Article
- Identifying Opioid Use Disorder in the Emergency Department: Multi-System Electronic Health Record-Based Computable Phenotype Derivation and Validation Study.JMIR medical informatics · 2019Article
Corrections and comments
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Authors and funding
24 authors.
Funding
Abstract
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.
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Registered trials
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