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

Journal of the American Medical Informatics Association : JAMIA
Huan MoPaul A Harris

Abstract

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). 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. 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 ph...Continue Reading

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Citations

Aug 9, 2016·Journal of the American Medical Informatics Association : JAMIA·Pedro L TeixeiraJoshua C Denny
Sep 16, 2016·BMC Medical Informatics and Decision Making·Harry HochheiserRebecca S Jacobson
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Feb 6, 2020·JMIR Medical Informatics·Emily R PfaffAshok Krishnamurthy
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Datasets Mentioned

BETA
GM105688
GM103859

Software Mentioned

PheKB
Eureka !
PheRM
eMERGE
i2b2
SHARP
Eureka
PhenotypePortal Derivative interface
Konstanz Information Miner ( KNIME )
DXplain

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