Author:
Holcomb Jennifer,Oliveira Luis C.,Highfield Linda,Hwang Kevin O.,Giancardo Luca,Bernstam Elmer Victor
Abstract
AbstractProviders currently rely on universal screening to identify health-related social needs (HRSNs). Predicting HRSNs using EHR and community-level data could be more efficient and less resource intensive. Using machine learning models, we evaluated the predictive performance of HRSN status from EHR and community-level social determinants of health (SDOH) data for Medicare and Medicaid beneficiaries participating in the Accountable Health Communities Model. We hypothesized that Medicaid insurance coverage would predict HRSN status. All models significantly outperformed the baseline Medicaid hypothesis. AUCs ranged from 0.59 to 0.68. The top performance (AUC = 0.68 CI 0.66–0.70) was achieved by the “any HRSNs” outcome, which is the most useful for screening prioritization. Community-level SDOH features had lower predictive performance than EHR features. Machine learning models can be used to prioritize patients for screening. However, screening only patients identified by our current model(s) would miss many patients. Future studies are warranted to optimize prediction of HRSNs.
Funder
Cullen Trust for Health Care,United States
National Center for Advancing Translational Sciences
Cancer Prevention and Research Institute of Texas
Reynolds and Reynolds Professorship in Clinical Informatics
Publisher
Springer Science and Business Media LLC
Cited by
9 articles.
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