JMIR Publications

ABSTRACT

Background:

Electronic medical records (EMR) 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.

Objective:

To describe and assess the current landscape of EMR-based case phenotyping for the Charlson conditions.

Methods:

A scoping review of EMR-based algorithms for defining the Charlson Comorbidity Index conditions has been completed. This work covered articles published between January 2000 and April 2020, inclusive. EMBASE and MEDLINE/PubMed were searched using keywords developed in three domains: 1) terms related to EMR; 2) terms related to case-finding; 3) disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR).

Results:

A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the USA (n= 181/299, 60.5%), followed by the UK (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 algorithms developed. Data-driven and clinical-rule-based approaches were identified. EMR-based phenotype and algorithm development reflects the data access allowed by respective health systems and algorithms varied in their performance.

Conclusions:

Recognizing similarities and differences in 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 are proposed.