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

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.

Figure 1:

Phenotype algorithm for identifying type 2 diabetes mellitus (T2DM) from electronic medical records (EMR or EHR). T1DM: type 1 diabetes mellitus; Dx: diagnoses, defined as recorded using International Classification of Diseases, 9th Revision (ICD-9) codes; med: medication; physcn: physicians; Rx: prescriptions. More details can be found in the appendix and on PheKB.org.

Figure 2:

Schematic of desiderata for computable phenotype electronic health record-driven phenotyping. Numerals 1–9 in the figure correspond to Desiderata 1–9 (Desideratum 10 is not depicted in this Figure).