JMIR Medical Informatics (Feb 2021)

Electronic Medical Record–Based Case Phenotyping for the Charlson Conditions: Scoping Review

  • Lee, Seungwon,
  • Doktorchik, Chelsea,
  • Martin, Elliot Asher,
  • D'Souza, Adam Giles,
  • Eastwood, Cathy,
  • Shaheen, Abdel Aziz,
  • Naugler, Christopher,
  • Lee, Joon,
  • Quan, Hude

DOI
https://doi.org/10.2196/23934
Journal volume & issue
Vol. 9, no. 2
p. e23934

Abstract

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BackgroundElectronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research. ObjectiveThis review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions. MethodsA scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines. ResultsA total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance. ConclusionsRecognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.