Machine Learning Prediction of Possible Central Line Associated Blood Stream Infections and Rate of Reduction
Prediction and Reduction of Central Line Associated Blood Stream Infections: A Machine Learning Improvement Study
Swedish Medical Center
17,800 participants
Jul 1, 2025
INTERVENTIONAL
Conditions
Summary
Prospective, multi-center, cluster-randomized trial of a hospital Infection Preventionist (IP)-led quality improvement study to provide clinical teams with just-in-time clinical education and reinforcement of existing best practices recommendations based on the output of a possible Central Line Associated Blood Stream Infection (CLABSI) Machine Learning (ML) prediction model. The objective is to determine whether providing this model to Infection Preventionists will decrease the CLABSI rates versus routine clinical practice.
Eligibility
Inclusion Criteria1
- The top twenty Providence St. Joseph Health Hospitals by CLABSI burden.
Exclusion Criteria1
- Less than 18 years of age
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Interventions
Infection preventionists at each study hospital review a dashboard on a daily basis that contains predictions for the infection preventionist's hospital. If a patient is predicted to have a possible CLABSI by the ML model, the infection preventionist reviews the case and recommends next steps to the care team based on Providence's CLABSI prevention best-practices bundle, which include reviewing the line for necessity and recommending alternate IV access when appropriate. If line-removal isn't possible, the infection preventionist collaborates with the direct care team to ensure that the line maintenance best practices are observed, including maintaining a clean, dry and intact dressing and using daily chlorhexidine baths.
Locations(19)
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NCT07108660