ArticleJournal of the American Medical Informatics Association : JAMIA2017
Assessing electronic health record phenotypes against gold-standard diagnostic criteria for diabetes mellitus.
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 2 of them syntheses that pooled it.
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Who cites it
47 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Systematic Review of Crohn's Disease Case Definitions in Administrative or Claims Databases.Inflammatory bowel diseases · 2023Pooled it
- Characteristics of diabetes mellitus patients in Turkey: An analysis of national electronic health records.Turkish journal of medical sciences · 2023Pooled it
- Computable Phenotype for Identifying Undiagnosed Hypermobile Ehlers-Danlos Syndrome: Protocol for a Development and Validation Study.JMIR research protocols · 2026Article
- Linking "Big" Geospatial and Health Data: Implications for Research in Environmental Epidemiology.Environmental health perspectives · 2026Article
- Measuring the Impact of Data Quality and Computable Phenotypes on Potential Racial Disparities in Predicting Healthcare Utilization Among Type 2 Diabetes Populations.Journal of racial and ethnic health disparities · 2026Article
- Article
- Using clinical notes to identify children with speech-language delay and understand differences in diagnostic timing.JAMIA open · 2026Article
- Association Between Therapeutic Inertia and Future Hypoglycemia Among Patients with Type 2 Diabetes.Diabetes epidemiology and management · 2026Article
- Long-Term Glycemic Control and Pharmacotherapy in Type 2 Diabetes Mellitus: A Descriptive Analysis of 10-Year Trends in a Statewide Sample.Journal of diabetes research · 2026Article
- Unsupervised Coverage Sampling to Enhance Clinical Chart Review Coverage for Computable Phenotype Development: Simulation and Empirical Study.JMIR medical informatics · 2025Article
- Equivalence of Type 2 Diabetes Prevalence Estimates: Comparative Study of Similar Phenotyping Algorithms Using Electronic Health Record Data.JMIR public health and surveillance · 2025Article
- Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system.Clinical research in cardiology : official journal of the German Cardiac Society · 2024Article
- Social Determinants of Health Mediate Racial Disparities in Cardiovascular Disease in Men With Prostate Cancer.JACC. CardioOncology · 2024Article
- Defining diabetes status using medication groups in Medicare data: Trends in prescribing diabetes medications to patients without a diabetes diagnosis over time.Diabetes, obesity & metabolism · 2024Article
- SEED: A novel web-based data visualization platform to visualize, communicate, and explore social, environmental, and equity drivers of health.Journal of clinical and translational science · 2024Article
- Automated Type 2 Diabetes Case and Control Identification from the MIMIC-IV Database.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Association between Gentrification and Health and Healthcare Utilization.Journal of urban health : bulletin of the New York Academy of Medicine · 2022Article
- Development and validation of algorithms to identify patients with chronic kidney disease and related chronic diseases across the Northern Territory, Australia.BMC nephrology · 2022Article
- Design and Evaluation of a Computational Phenotype to Identify Patients With Metastatic Breast Cancer Within the Electronic Health Record.JCO clinical cancer informatics · 2022Article
- Article
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Authors and funding
34 authors.
Funding
Abstract
objectiveWe assessed the sensitivity and specificity of 8 electronic health record (EHR)-based phenotypes for diabetes mellitus against gold-standard American Diabetes Association (ADA) diagnostic criteria via chart review by clinical experts. MATERIALS AND
methodsWe identified EHR-based diabetes phenotype definitions that were developed for various purposes by a variety of users, including academic medical centers, Medicare, the New York City Health Department, and pharmacy benefit managers. We applied these definitions to a sample of 173 503 patients with records in the Duke Health System Enterprise Data Warehouse and at least 1 visit over a 5-year period (2007-2011). Of these patients, 22 679 (13%) met the criteria of 1 or more of the selected diabetes phenotype definitions. A statistically balanced sample of these patients was selected for chart review by clinical experts to determine the presence or absence of type 2 diabetes in the sample.
resultsThe sensitivity (62-94%) and specificity (95-99%) of EHR-based type 2 diabetes phenotypes (compared with the gold standard ADA criteria via chart review) varied depending on the component criteria and timing of observations and measurements. DISCUSSION AND
conclusionsResearchers using EHR-based phenotype definitions should clearly specify the characteristics that comprise the definition, variations of ADA criteria, and how different phenotype definitions and components impact the patient populations retrieved and the intended application. Careful attention to phenotype definitions is critical if the promise of leveraging EHR data to improve individual and population health is to be fulfilled.
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