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

doi:10.1093/jamia/ocv112

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

J Am Med Inform Assoc. 2015 Nov;22(6):1220-30. doi: 10.1093/jamia/ocv112. Epub 2015 Sep 5.

Authors

Huan Mo¹, William K Thompson², Luke V Rasmussen³, Jennifer A Pacheco⁴, Guoqian Jiang⁵, Richard Kiefer⁵, Qian Zhu⁶, Jie Xu⁷, Enid Montague⁷, David S Carrell⁸, Todd Lingren⁹, Frank D Mentch¹⁰, Yizhao Ni⁹, Firas H Wehbe³, Peggy L Peissig¹¹, Gerard Tromp¹², Eric B Larson⁸, Christopher G Chute¹³, Jyotishman Pathak¹⁴, Joshua C Denny¹⁵, Peter Speltz¹, Abel N Kho⁷, Gail P Jarvik¹⁶, Cosmin A Bejan¹, Marc S Williams¹⁷, Kenneth Borthwick¹⁸, Terrie E Kitchner¹¹, Dan M Roden¹⁹, Paul A Harris¹

Affiliations

¹ Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
² Center for Biomedical Research Informatics, NorthShore University HealthSystem, Evanston, IL, USA.
³ Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
⁴ Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
⁵ Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
⁶ Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, USA.
⁷ Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
⁸ Group Health Research Institute, Seattle, WA, USA.
⁹ Division of Biomedical Informatics, Cincinnati Children's Hospital, Cincinnati, OH, USA.
¹⁰ Center for Applied Genomics, the Children's Hospital of Philadelphia, Philadelphia, PA, USA.
¹¹ Marshfield Clinic Research Foundation, Marshfield Clinic, Marshfield, WI, USA.
¹² Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, University of Stellenbosch, Cape Town, South Africa.
¹³ Division of General Internal Medicine, Johns Hopkins University, Baltimore, MD, USA.
¹⁴ Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.
¹⁵ Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA Department of Medicine, Vanderbilt University, Nashville, TN, USA josh.denny@vanderbilt.edu.
¹⁶ Department of Medicine (Medical Genetics), University of Washington, Seattle, WA, USA Department of Genome Sciences, University of Washington, Seattle, WA, USA.
¹⁷ Department of Genome Sciences, University of Washington, Seattle, WA, USA.
¹⁸ The Sigfried and Janet Weis Center for Research, Geisinger Health System, Danville, PA, USA.
¹⁹ Department of Medicine, Vanderbilt University, Nashville, TN, USA Department of Pharmacology, Vanderbilt University, Nashville, TN, USA.

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.

Keywords: computable representation; data models; electronic health records; phenotype algorithms; phenotype standardization.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Algorithms*
Diagnosis, Computer-Assisted*
Electronic Health Records*
Humans
Phenotype

Abstract

Publication types

MeSH terms

Grants and funding