From Prediction to Prevention: How the VA Uses Analytics to Reduce Chronic Disease and Mental Health Risk

From Prediction to Prevention: How the VA Uses Analytics to Reduce Chronic Disease and Mental Health Risk

Alvin Sherman Library

Predictive analytics has become an essential component of preventive care strategies within the Veterans Health Administration (VHA), which serves a medically and psychosocially complex population at elevated risk for chronic disease and mental health conditions. This research examines how the VHA has leveraged predictive analytics to improve early identification, prevention, and targeted intervention for chronic illnesses and mental health disorders. Specifically, it explores established initiatives such as the Care Assessment Needs (CAN) score for identifying veterans at high risk of hospitalization or mortality, the Reach Out, Stay Strong, Essentials for Veterans (REACH VET) program for suicide risk prediction and prevention, and population health tools supporting chronic disease management programs including diabetes and cardiovascular risk reduction. These initiatives integrate electronic health record data, utilization patterns, and social determinants of health to support proactive, risk-stratified care delivery. Existing evidence suggests that such predictive models enhance clinical decision-making, improve care coordination, and enable timely preventive interventions for high-risk veterans. By analyzing these initiatives, this research highlights how predictive analytics strengthens disease prevention and mental health outcomes within the VA and offers insights for broader application of data-driven preventive care models across large healthcare systems.