AI-driven Clinical Decision Support to Reduce Hospital-Acquired Venous Thromboembolism
Vanderbilt University Medical Center
Summary
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.
Description
Background Hospital Acquired Venous Thromboembolism (HA-VTE) remains the leading cause of death in hospitalized patients in the US. Approximately 900,000 people experience VTE each year, with incidence-based medical costs estimated between $7 and $10 billion per year. The second leading cause of disability-adjusted life-years, HA-VTE causes significant morbidity and mortality in adult and pediatric patients. Roughly 1 in 3 people experience long-term complications (i.e., post-thrombotic syndrome) following VTE. Reducing HA-VTE presents a major diagnostic challenge. Despite numerous published…
Eligibility
- Age range
- Not specified
- Sex
- All
- Healthy volunteers
- No
Inclusion Criteria: * Inpatient admission to Vanderbilt Adult Hospital, Vanderbilt Tullahoma Harton Hospital, Vanderbilt Bedford County Hospital, or Vanderbilt Wilson County Hospital Exclusion Criteria: * None
Interventions
- OtherRisk model-driven CDS
The CDS intervention will use an automated risk model called "VTE-AI" to add EHR-based prompts in the form of alerts targeting those encounters on which 1) VTE-AI risk is above 5% predicted risk (found to be high risk in prior analyses), 2) no active DVT prophylaxis pharmacologic order is present, 3) no contraindication has been documented in the current admission
Location
- Vanderbilt University Medical CenterNashville, Tennessee