Linking EMR and Exposome Data for Risk Prediction and Interventions: A Translational Approach

Precision medicine (PM) is a medical model that proposes the customization of healthcare, with medical decisions, treatments, practices, or products being tailored to the individual patient.  An individual’s “Social Determinants of Health” (SDOH) have been demonstrated as a key factor in obtaining successful clinical outcomes for individual patients which necessitate individualized interventions.  Two major problems exist in addressing SDOH within a clinical setting.  First, interventions that have shown to be successful in addressing challenges presented by various Social Determinants of Health often scarce and span outside of those services that are available and/or reimbursed within a healthcare setting.  Because of this, it is critical that these resources can be deployed to the patients who will benefit the most.  Second, identifying those patients who will most benefit from those interventions is problematic because of the limited information on SDOH contained in a typical Electronic Medical Record (EMR).  This talk will walk through a proposed research design to use geo-spatial SDOH data, the exposome, as a proxy for individual-level SDOH data.  I propose to test the hypothesis that exposome data, linked with EMR data, can better predict risk of hypertension.  I will also propose a method for external validation and discuss how this might be applied to specific care interventions.