Desiderata for computable representations of electronic health records-driven phenotype algorithms. (5th September 2015)

• Record Type: Journal Article
• Authors: Mo, Huan; Thompson, William K; Rasmussen, Luke V; Pacheco, Jennifer A; Jiang, Guoqian; Kiefer, Richard; Zhu, Qian; Xu, Jie; Montague, Enid; Carrell, David S; Lingren, Todd; Mentch, Frank D; Ni, Yizhao; Wehbe, Firas H; Peissig, Peggy L; Tromp, Gerard; Larson, Eric B; Chute, Christopher G; Pathak, Jyotishman; Denny, Joshua C; Speltz, Peter; Kho, Abel N; Jarvik, Gail P; Bejan, Cosmin A; Williams, Marc S; Borthwick, Kenneth; Kitchner, Terrie E; Roden, Dan M; Harris, Paul A
• Is Part Of: Journal of the American Medical Informatics Association. Volume 22:Number 6(2015:Nov.)
• Page Start: 1220
• Page End: 1230
• Publication Date: 2015-09-05
• Subjects: electronic health records -- phenotype algorithms -- computable representation -- phenotype standardization -- data models; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocv112
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Background Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM). Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms. Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility. Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.