Walsh, Colin G.; Long, Yufei; Novak, Laurie Lovett; Salwei, Megan E.; Tillman, Benjamin F.; French, Benjamin C.; Mixon, Amanda S.; Law, Michelle E.; Franklin, Jacob; Embi, Peter J. (2025). AI-Driven Clinical Decision Support to Reduce Hospital-Acquired Venous Thromboembolism: A Trial Protocol. JAMA Network Open, 8(10), e2535137. https://doi.org/10.1001/jamanetworkopen.2025.35137
Hospital-acquired venous thromboembolism (HA-VTE), or blood clots that develop in the veins during or after a hospital stay, remains one of the leading preventable causes of death among hospitalized adults in the United States. Although many models have been created to predict which patients are most at risk, none have clearly proven to be more effective than others, and it is still uncertain whether these models actually improve doctors’ decisions about preventive treatment. Testing these systems in both urban and rural hospitals may help determine how well they work across different healthcare environments.
This study is a randomized clinical trial designed to test whether an artificial intelligence (AI)–based clinical decision support (CDS) tool can reduce the number of HA-VTE cases among adult hospital patients. The trial will be conducted by Vanderbilt University Medical Center from October 2025 through September 2027, including adults aged 18 and older who are hospitalized in medical, surgical, or intensive care units and are at high risk for blood clots but do not currently have one or a condition that prevents preventive treatment. Participants will be drawn from Vanderbilt Adult Hospital in Nashville and three partner hospitals serving rural communities in Middle Tennessee.
Within the hospital’s electronic health record system, patients will be randomly assigned to receive either AI-supported care, which uses an alert system to prompt clinicians about clot prevention, or standard care based on traditional risk assessment tools. The main goal of the study is to determine whether the AI tool reduces the number of hospital-acquired blood clots. Additional measures will include hospital length of stay, readmission rates, safety outcomes, and bleeding events.
This study will be one of the first to examine whether an AI-driven decision support system can safely and effectively lower the risk of hospital-acquired blood clots without increasing side effects. It will also assess whether the same AI model performs equally well in both urban and rural hospitals. The results and supporting data will be shared publicly through peer-reviewed publications and ClinicalTrials.gov.
Figure 1. Intervention OurPractice Advisories Logic
BPA indicates best practice advisory; CDS, clinical decision support; DVT, deep vein thrombosis; VTE-AI, Venous Thromboembolism Using Artificial Intelligence.