Prediction and Reduction of Central Line Associated Blood Stream Infections: A Machine Learning Improvement Study
Swedish Medical Center
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.
Description
Central Line-Associated Bloodstream Infections (CLABSIs) remain a persistent and costly challenge in U.S. hospitals, contributing to increased mortality, prolonged hospital stays, and elevated healthcare costs. In 2022 alone, Providence St. Joseph Health (PSJH) recorded 275 CLABSIs across 430,000 central line days. Despite the implementation of best-practice prevention bundles, these infections continue to occur, prompting the exploration of machine learning (ML) as a tool to predict and mitigate CLABSI risk. While prior studies have demonstrated the predictive potential of ML models-with area…