Clinical care site data integration reveals heterogeneity in EHR phenotyping and healthcare utilization patterns
Year: 2026
Type: preprint
Study design: Observational Study
Abstract: Objective: Genomic research using electronic health record (EHR)-linked biobanks is influenced by heterogeneity in the clinical settings (care sites) where encounters occur. We developed two methods leveraging care site data: ClinicScan identifies where phenotype documentation occurs, and ClinicWAS identifies specialty utilization patterns associated with a risk factor. Materials and Methods: We extracted care sites for each clinical encounter at an academic medical center and mapped each to a clinical specialty. ClinicScan summarizes the specialty distribution of a user-specified diagnosis; ClinicWAS fits a logistic regression for each care site to identify specialty encounters associated with a user-specified risk factor. We applied ClinicScan to depression to test whether requiring a psychiatry encounter strengthened the association between a polygenic risk score (PRS) and a depression phenotype, and ClinicWAS to a coronary heart disease (CHD) PRS to identify sites enriched for high-risk patients. Results: Across 64,983,257 encounters, 2,544 care sites mapped to 57 specialties. Most depression diagnoses occurred in primary care (30.3%) and psychiatry (19.8%). Requiring a psychiatry encounter strengthened the PRS-phenotype association (OR=1.30, 95% CI 1.26-1.35) versus two or more diagnosis codes alone (OR=1.21, 95% CI 1.19-1.24). CHD ClinicWAS identified 19 associated care sites, including 5 catheterization labs. Men and women with high genetic risk (PRS≥95th percentile) underwent catheterization for CHD 3.1 (1.5-4.6) and 4.6 (2.5-6.7) years earlier than normal-risk participants, respectively. Discussion: Care site data capture phenotype heterogeneity that otherwise distorts EHR-based phenotypes and obscures high-risk subpopulations. Conclusion: Clinical care site data are an under-utilized resource in EHR-linked biobanks.
Source: medRxiv
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Funders: University of Texas Southwestern Medical Center, National Institutes of Health, National Heart, Lung, and Blood Institute, National Human Genome Research Institute, National Institute of General Medical Sciences, National Institute of Neurological Disorders and Stroke, National Center for Research Resources
Awards: Pharmacogenomics of Arrhythmia Therapy, Improving Prediction of Drug Action, Genetic Epidemiology of Multiple Sclerosis, The Vanderbilt Institute for Clinical and Translational Research (VICTR) UL1, VESPA: Vanderbilt Electronic Systems for Pharmacogenomic Assessment, Vanderbilt Genome Electronic Records Project, Epidemiologic Architecture for Genes Linked to Environment (EAGLE), Leveraging common genetic variation to reduce misclassification of non-diseased individuals and unnecessary health care utilization attributable to surrogate biomarkers, Medical Scientist Training Program
OpenAlex ID: W7165776084