Desiderata for computable representations of electronic health records-driven phenotype algorithms

Huan MO, William K. THOMPSON, Luke V. RASMUSSEN, Jennifer A. PACHECO, Guoqian JIANG, Richard KIEFER, Qian ZHU, Jie XU, Enid MONTAGUE, David S. CARRELL, Todd LINGREN, Frank D. MENTCH, Yizhao NI, Firas H. WEHBE, Peggy L. PEISSIG, Gerard TROMP, Eric B. LARSON, Christopher G. CHUTE, Jyotishman PATHAK, Joshua C. DENNY*Peter SPELTZ, Abel N. KHO, Gail P. JARVIK, Cosmin A. BEJAN, Marc S. WILLIAMS, Kenneth BORTHWICK, Terrie E. KITCHNER, Dan M. RODEN, Paul A. HARRIS

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

91 Citations (Scopus)

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 humanreadable 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.
Original languageEnglish
Pages (from-to)1220-1230
Number of pages11
JournalJournal of the American Medical Informatics Association : JAMIA
Volume22
Issue number6
Early online date5 Sept 2015
DOIs
Publication statusPublished - Nov 2015
Externally publishedYes

Funding

This work was funded primarily by R01 GM105688 from the National Institute of General Medical Sciences. Additional contribution came from the eMERGE Network sites funded by the National Human Genome Research Institute through the following grants: U01 HG006828 (Cincinnati Children''s Hospital Medical Center); U01-HG004610 and U01-HG006375 (Group Health Cooperative/University of Washington); U01-HG004608 (Marshfield Clinic); U01-HG04599 and U01-HG06379 (Mayo Clinic); U01-HG004609 and U01-HG006388 (Northwestern University); U01-HG006389 (Essentia Institute of Rural Health); U01-HG04603 and U01-HG006378 (Vanderbilt University); and U01-HG006385 (Vanderbilt University serving as the Coordinating Center). Additional support came from R01-LM010685 and R01 GM103859.

Keywords

  • Computable representation
  • Data models
  • Electronic health records
  • Phenotype algorithms
  • Phenotype standardization

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