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. 2023 Feb 16;30(3):427-437.
doi: 10.1093/jamia/ocac235.

Characterizing variability of electronic health record-driven phenotype definitions

Pascal S Brandt  1 , Abel Kho  2 , Yuan Luo  2 , Jennifer A Pacheco  2 , Theresa L Walunas  2 , Hakon Hakonarson  3 , George Hripcsak  4 , Cong Liu  4 , Ning Shang  4 , Chunhua Weng  4 , Nephi Walton  5 , David S Carrell  6 , Paul K Crane  7 , Eric B Larson  7   8 , Christopher G Chute  9 , Iftikhar J Kullo  10 , Robert Carroll  11 , Josh Denny  12 , Andrea Ramirez  11 , Wei-Qi Wei  13 , Jyoti Pathak  14 , Laura K Wiley  15 , Rachel Richesson  16 , Justin B Starren  2 , Luke V Rasmussen  2
Affiliations

Characterizing variability of electronic health record-driven phenotype definitions

Pascal S Brandt et al. J Am Med Inform Assoc. .

Abstract

Objective: The aim of this study was to analyze a publicly available sample of rule-based phenotype definitions to characterize and evaluate the variability of logical constructs used.

Materials and methods: A sample of 33 preexisting phenotype definitions used in research that are represented using Fast Healthcare Interoperability Resources and Clinical Quality Language (CQL) was analyzed using automated analysis of the computable representation of the CQL libraries.

Results: Most of the phenotype definitions include narrative descriptions and flowcharts, while few provide pseudocode or executable artifacts. Most use 4 or fewer medical terminologies. The number of codes used ranges from 5 to 6865, and value sets from 1 to 19. We found that the most common expressions used were literal, data, and logical expressions. Aggregate and arithmetic expressions are the least common. Expression depth ranges from 4 to 27.

Discussion: Despite the range of conditions, we found that all of the phenotype definitions consisted of logical criteria, representing both clinical and operational logic, and tabular data, consisting of codes from standard terminologies and keywords for natural language processing. The total number and variety of expressions are low, which may be to simplify implementation, or authors may limit complexity due to data availability constraints.

Conclusions: The phenotype definitions analyzed show significant variation in specific logical, arithmetic, and other operators but are all composed of the same high-level components, namely tabular data and logical expressions. A standard representation for phenotype definitions should support these formats and be modular to support localization and shared logic.

Keywords: CQL; EHR-driven phenotyping; FHIR; cohort identification.

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Figures

Figure 1.
Figure 1.
Histograms of medical vocabulary code and code system usage. (A) The number of phenotype definitions using a number of code systems. (B) The number of phenotype definitions using a specific code system. (C) The number of distinct vocabulary codes used for a given code system for all phenotype definitions. (D) The number of phenotype definitions using a number of distinct codes. AMT: Australian Medicines Terminology; BDPM: Public Database of Medications; CIEL: Columbia International eHealth Laboratory; CPT: Current Procedural Terminology; dm + d: Dictionary of Medicines and Devices; ICD-9-CM: International Classification of Diseases, Ninth Revision, Clinical Modification; ICD-10-CM: International Classification of Diseases, Tenth Revision, Clinical Modification; ICD-9-Proc: International Classification of Diseases, Ninth Revision, Procedures; ICD-10-PCS: International Classification of Diseases, Tenth Revision, Procedure Coding System; HCPCS: Healthcare Common Procedure Coding System; LOINC: Logical Observation Identifiers Names and Codes; MedDRA: Medical Dictionary for Regulatory Activities; MeSH: Medical Subject Headings; SNOMED: Systematized Nomenclature of Medicine.
Figure 2.
Figure 2.
Cumulative CQL expression counts per category. CQL: Clinical Quality Language.
Figure 3.
Figure 3.
Total numbers of individual CQL expression types by category (excluding literal expressions). CQL: Clinical Quality Language.
Figure 4.
Figure 4.
Number of phenotype definitions utilizing various data types within literal expressions.
Figure 5.
Figure 5.
Data retrieval expressions and data types. (A) The number of phenotype definitions using a number of distinct data types. (B) The number of phenotype definitions using specific data types. (C) The number of phenotype definitions using ranges of retrieve statements.
Figure 6.
Figure 6.
Example of expression depth (A), along with histograms of total (B) and where clause (C) expression depths.
See this image and copyright information in PMC

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