Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review
Open Access
- 1 February 2021
- journal article
- review article
- Published by JMIR Publications Inc. in JMIR Public Health and Surveillance
- Vol. 9 (2), e23934
- https://doi.org/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.This publication has 289 references indexed in Scilit:
- Identification of acute myocardial infarction from electronic healthcare records using different disease coding systems: a validation study in three European countriesBMJ Open, 2013
- Derivation and Validation of Automated Electronic Search Strategies to Extract Charlson Comorbidities From Electronic Medical RecordsMayo Clinic Proceedings, 2012
- Electronic Health Record Use to Classify Patients with Newly Diagnosed versus Preexisting Type 2 Diabetes: Infrastructure for Comparative Effectiveness Research and Population Health ManagementPopulation Health Management, 2012
- The accuracy of using integrated electronic health care data to identify patients with undiagnosed diabetes mellitusJournal of Evaluation in Clinical Practice, 2011
- Electronic medical records for discovery research in rheumatoid arthritisArthritis Care & Research, 2010
- Prediction Modeling Using EHR DataMedical Care, 2010
- Robust Replication of Genotype-Phenotype Associations across Multiple Diseases in an Electronic Medical RecordAmerican Journal of Human Genetics, 2010
- Electronic Health Record-Based Cardiac Risk Assessment and Identification of Unmet Preventive NeedsMedical Care, 2009
- Presentation adapting a clinical comorbidity index for use with ICD-9-CM administrative data: Differing perspectivesJournal of Clinical Epidemiology, 1993
- A new method of classifying prognostic comorbidity in longitudinal studies: Development and validationJournal of Chronic Diseases, 1987