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

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Developing electronic medical record (EMR) case definitions, or EMR phenotyping, is crucial for research. This review assesses EMR phenotyping algorithms for Charlson conditions, highlighting variations in development and performance across health systems.

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Charlson comorbidityEMR phenotypingelectronic medical recordshealth services research

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Area of Science:

  • Health Informatics
  • Clinical Epidemiology
  • Biomedical Data Science

Background:

  • Electronic medical records (EMRs) offer rich clinical data for research.
  • EMR phenotyping is vital for epidemiology, clinical care, and health services research.

Purpose of the Study:

  • To review and assess current EMR-based case phenotyping algorithms for Charlson conditions.
  • To understand the landscape of EMR phenotyping for comorbidity index conditions.

Main Methods:

  • Scoping review of EMR-based algorithms for Charlson comorbidity index conditions (Jan 2000-Apr 2020).
  • Searches of Embase and MEDLINE using keywords for EMR, case finding, and diseases.
  • Adherence to PRISMA-Scoping guidelines for systematic reviews.

Main Results:

  • 274 articles and 299 algorithms were analyzed.
  • Most studies originated in the US (60.5%), UK (14.0%), and Canada (5.0%).
  • Algorithms were developed primarily in inpatient (56.2%) and primary care (34.4%) settings, with high focus on diabetes, CHF, MI, and rheumatology.

Conclusions:

  • Algorithm development must consider health system variations, data strategies, and clinical pathways.
  • Strategies exist to aid phenotype-based case definition development.