Performance of a large language model for identifying central line-Associated bloodstream infections (CLABSI) using real clinical notes

Abstract

We evaluated one of the first secure large language models approved for protected health information, for identifying central line-Associated bloodstream infections (CLABSIs) using real clinical notes. Despite no pretraining, the model demonstrated rapid assessment and high sensitivity for CLABSI identification. Performance would improve with access to more patient data.