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

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

doi:10.2196/23934

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

JMIR Med Inform. 2021 Feb 1;9(2):e23934. doi: 10.2196/23934.

Authors

Seungwon Lee^{1

2

3

4}, Chelsea Doktorchik^{1

2}, Elliot Asher Martin^{1

3}, Adam Giles D'Souza^{1

3}, Cathy Eastwood^{1

2}, Abdel Aziz Shaheen^{1

2

5}, Christopher Naugler^{2

6}, Joon Lee^{1

2

4

7}, Hude Quan^{1

2}

Affiliations

¹ Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
² Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
³ Alberta Health Services, Calgary, AB, Canada.
⁴ Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
⁵ Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
⁶ Department of Pathology and Laboratory Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
⁷ Department of Cardiac Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.

PMID: 33522976
PMCID: PMC7884219
DOI: 10.2196/23934

Abstract

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.

Keywords: Charlson comorbidity; EMR phenotyping; electronic medical records; health services research.

©Seungwon Lee, Chelsea Doktorchik, Elliot Asher Martin, Adam Giles D'Souza, Cathy Eastwood, Abdel Aziz Shaheen, Christopher Naugler, Joon Lee, Hude Quan. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 01.02.2021.

Publication types

Review