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Computable phenotypes for research using real-world data: experiences from the NIH pragmatic trials collaboratory
Marisa L Conte1, Judith M Schlaeger2, Guilherme Del Fiol3
1Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI 48109, United States.
JAMIA Open
|August 6, 2026
Summary
Developing computable phenotypes for embedded pragmatic clinical trials (ePCTs) requires multidisciplinary teams and careful validation. Sharing phenotype creation details and modifications is crucial for successful real-world data utilization in clinical research.
Area of Science:
- Clinical Informatics
- Health Services Research
- Biomedical Data Science
Background:
- Embedded pragmatic clinical trials (ePCTs) leverage real-world data from routine care, such as electronic health records (EHRs).
- Computable phenotypes are essential for identifying patient cohorts based on specific clinical criteria within these data sources.
Purpose of the Study:
- To capture and analyze investigator experiences in developing and applying computable phenotypes for ePCTs.
- To identify best practices and challenges in computable phenotype creation and utilization.
Main Methods:
- The Electronic Health Record (EHR) Core Working Group of the NIH Pragmatic Trials Collaboratory surveyed investigators.
- Four case studies were analyzed to illustrate diverse approaches to computable phenotype development and application.
Main Results:
- Investigator experiences highlight the complexity of computable phenotype development and adaptation.
- Different strategies exist for creating and implementing computable phenotypes in pragmatic trials.
Conclusions:
- Recommendations include forming multidisciplinary teams, rigorous validation, transparent dissemination of phenotype details, and tracking modifications.
- Understanding data context and potential biases is vital for effective phenotype development and validation in ePCTs.
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