AI Tool for Blood Clots
(VTE-AI RCT Trial)
What You Need to Know Before You Apply
What is the purpose of this trial?
This trial aims to test a new AI tool, VTE-AI, designed to predict and prevent hospital-acquired blood clots. The AI tool calculates a risk score for patients and suggests prevention measures if needed. The study compares this AI method with the current manual process to determine which more effectively reduces blood clots. Patients admitted to specific Vanderbilt hospitals are eligible to participate. As an unphased trial, this study offers patients the opportunity to contribute to innovative research that could enhance hospital care and patient safety.
Will I have to stop taking my current medications?
The trial information does not specify whether you need to stop taking your current medications.
What prior data suggests that this AI method is safe for predicting and preventing HA-VTE?
Research shows that the AI tool, VTE-AI, is designed to help prevent blood clots in hospitals, a significant risk for patients. The safety of VTE-AI primarily involves how it suggests preventive measures for these clots.
Studies have used tools like VTE-AI to predict and prevent clots by analyzing patient data to calculate risk scores, without requiring input from doctors. This assists doctors in selecting the best prevention methods. The AI tool advises on reviewing blood clot prevention for patients who might need it.
There have been no reports of negative effects specifically from using VTE-AI. It is important to note that the tool's main role is to aid in decision-making. Since it does not involve administering new medications or treatments, its safety concerns focus on accurately identifying patients who need preventive measures.
In summary, the VTE-AI tool aims to improve decision-making to prevent blood clots. Studies have not shown safety issues directly related to the tool itself.12345Why are researchers excited about this trial?
Researchers are excited about this trial because it uses an AI tool, VTE-AI, to help prevent blood clots, specifically venous thromboembolism (VTE), in hospitalized patients. Unlike traditional methods that rely on standard care protocols, this AI-driven approach aims to provide personalized risk assessments and clinical decision support (CDS) to healthcare providers. This could lead to more accurate and timely interventions, potentially reducing the incidence of dangerous blood clots and improving patient outcomes.
What evidence suggests that this AI method is effective for reducing hospital-acquired blood clots?
Research has shown that AI tools like VTE-AI, which participants in this trial may receive, can effectively predict and prevent hospital-acquired blood clots. Studies have found that these AI systems calculate risk scores with high accuracy, often surpassing manual methods. The AI recommends preventive measures for patients who might need them, aiding doctors in making informed decisions. This approach has proven safe, effective, and cost-efficient for at-risk patients. Early results indicate that the machine learning models used in these tools are reliable, with accuracy rates often exceeding 70%.13467
Are You a Good Fit for This Trial?
This trial is for patients admitted to specific Vanderbilt hospitals. It's designed to test if an AI tool can better prevent hospital-acquired blood clots compared to the current manual method used by doctors.Inclusion Criteria
Timeline for a Trial Participant
Screening
Participants are screened for eligibility to participate in the trial
Intervention
Participants are randomized to receive either the AI-driven CDS or the standard manual method for HA-VTE prevention
Follow-up
Participants are monitored for safety and effectiveness after discharge, including readmission rates and bleeding events
What Are the Treatments Tested in This Trial?
Interventions
- VTE-AI
Trial Overview
The study compares a new AI-driven Clinical Decision Support (CDS) system against the traditional manual approach for preventing blood clots in hospitalized patients. Participants will be randomly assigned to either use the AI tool or continue with standard care.
How Is the Trial Designed?
2
Treatment groups
Experimental Treatment
Active Control
Hospitalizations randomized to receive risk model-driven CDS
Hospitalizations randomized to receive Standard of Care in a given clinical setting
Find a Clinic Near You
Who Is Running the Clinical Trial?
Vanderbilt University Medical Center
Lead Sponsor
Citations
AI-Driven Clinical Decision Support to Reduce Hospital ...
Meaning The VTE-AI trial is one of the first randomized clinical trials to measure the effectiveness of health care AI–driven decision support ...
Effectiveness of an artificial intelligence clinical assistant ...
Thromboprophylaxis has been determined to be safe, effective and cost-effective for hospitalised patients at venous thromboembolism (VTE) risk.
3.
news.vumc.org
news.vumc.org/2025/11/11/ai-recruited-to-lower-leading-cause-of-preventable-hospital-deaths/AI recruited to lower leading cause of preventable hospital ...
Vanderbilt study will test whether artificial intelligence can help reduce blood clots that form inside blood vessels during hospitalization ...
AI-driven Clinical Decision Support to Reduce Hospital ...
The AI tool, called VTE-AI, calculates a risk score without needing input from doctors. It will suggest reconsidering blood clot prevention measures for ...
Systematic review of a machine learning model for ...
ML models effectively predict venous thromboembolism with high accuracy (AUC >0.7). •. Logistic regression, XGBoost, and random forests dominate model ...
AI-Driven Clinical Decision Support to Reduce Hospital ... - PMC
Importance. Hospital-acquired venous thromboembolism (HA-VTE) remains a leading cause of preventable death among hospitalized adults in the US.
The potential use of artificial intelligence for venous ...
An effective AI/ML tool would search the medical chart for relevant risk factors, weigh the risk of VTE as well as the risk of bleeding, alert ...
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