%0 Journal Article
%@ 2291-9694
%I JMIR Publications
%V 9
%N 2
%P e23934
%T Electronic Medical Record–Based Case Phenotyping for the Charlson Conditions: Scoping Review
%A Lee,Seungwon
%A Doktorchik,Chelsea
%A Martin,Elliot Asher
%A D'Souza,Adam Giles
%A Eastwood,Cathy
%A Shaheen,Abdel Aziz
%A Naugler,Christopher
%A Lee,Joon
%A Quan,Hude
%+ Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Teaching, Research, & Wellness Building, 3280 Hospital Dr NW, Calgary, AB, T2N 4Z6, Canada, 1 403 210 9317, hquan@ucalgary.ca
%K electronic medical records
%K Charlson comorbidity
%K EMR phenotyping
%K health services research
%D 2021
%7 1.2.2021
%9 Review
%J JMIR Med Inform
%G English
%X Background: Electronic 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. Objective: This review aims to describe and assess the present 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 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. Results: A 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. Conclusions: Recognizing 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. 
%M 33522976
%R 10.2196/23934
%U https://medinform.jmir.org/2021/2/e23934
%U https://doi.org/10.2196/23934
%U http://www.ncbi.nlm.nih.gov/pubmed/33522976