AI-driven Clinical Decision Support to Reduce Hospital-Acquired Venous Thromboembolism: Study Protocol for the VTE-AI Randomized Trial.

Recruiting now · Not applicable

Conditions studied: Venothromboembolism

In brief

Hospital-acquired blood clots (HA-VTE) are the leading cause of death in hospitalized patients in the US. Each year, about 900,000 people get blood clots, costing between $7 and $10 billion in medical expenses. HA-VTE is the second leading cause of long-term disability and causes significant health issues and deaths in both adults and children. About 1 in 3 people who get blood clots experience long-term complications. Reducing HA-VTE is a major challenge. This study will test a new AI method to predict and prevent HA-VTE. The goal is to see if this AI tool can reduce the number of HA-VTE cases in the Vanderbilt Health System, which includes both urban and rural hospitals. The AI tool, called VTE-AI, calculates a risk score without needing input from doctors. It will suggest reconsidering blood clot prevention measures for patients who don't have them ordered and have no reasons to avoid them. This suggestion will be made after admission and daily during the hospital stay. Currently, doctors manually calculate a risk score and choose a prevention option. This study will compare the effectiveness of the AI tool against the current manual method in reducing HA-VTE cases. The study will randomly assign half of the patients to use the AI tool and the other half to the standard manual method.

Key facts

Study ID
NCT06939803
Run by
Vanderbilt University Medical Center
People needed
2236
Starts
2025-12-17
Expected to finish
2027-03-31
Last updated by the study team
2026-02-13

Who can join

Age: any. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

You may not qualify if…

Where it is running

Full record on ClinicalTrials.gov

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