Development of a repository of computable phenotype definitions using the clinical quality language. Issue 4 (3rd December 2021)

• Record Type: Journal Article
• Authors: Brandt, Pascal S; Pacheco, Jennifer A; Rasmussen, Luke V
• Is Part Of: JAMIA open. Volume 4:Issue 4(2021)
• Publication Date: 2021-12-03
• Subjects: FHIR -- CQL -- EHR-driven phenotyping -- cohort identification; Medical informatics -- Periodicals; 610.285
• DOI: 10.1093/jamiaopen/ooab094
• Languages: English
• ISSNs: 2574-2531

Abstract

Abstract: Objective: The objective of this study is to create a repository of computable, technology-agnostic phenotype definitions for the purposes of analysis and automatic cohort identification. Materials and Methods: We selected phenotype definitions from PheKB and excluded definitions that did not use structured data or were not used in published research. We translated these definitions into the Clinical Quality Language (CQL) and Fast Healthcare Interoperability Resources (FHIR) and validated them using code review and automated tests. Results: A total of 33 phenotype definitions met our inclusion criteria. We developed 40 CQL libraries, 231 value sets, and 347 test cases. To support these test cases, a total of 1624 FHIR resources were created as test data. Discussion and Conclusion: Although a number of challenges were encountered while translating the phenotypes into structured form, such as requiring specialized knowledge, or imprecise, ambiguous, and conflicting language, we have created a repository and a development environment that can be used for future research on computable phenotypes. Lay Summary: The process of conducting biomedical research almost always involves extracting patient data from electronic health record systems. Identifying the right patients in these systems is challenging due to the nature of clinical data and the complexity of diseases. Further, the process is usually manual, which is not very efficient and can lead to mistakes. In this work, we present a standard format that can be used to describe the patients of interest. This description can then be used to automatically extract the appropriate patient data. We create a database of 33 descriptions using this standard format and describe a method that can be used by anyone who wants to create additional standardized patient cohort descriptions. We hope that using the proposed format and methods will increase the rate at which clinical research can be conducted, and in turn, the rate of biomedical knowledge generation.