<ul><li>Blood cultures are often over-ordered without clear justification, placing strain on healthcare resources and contributing to inappropriate antibiotic use.</li><li>A study analyzed 135,483 emergency department (ED) blood culture orders, developing machine learning (ML) models to predict the risk of bacteremia.</li><li>The ML models, which integrated structured electronic health record (EHR) data and provider notes via a large language model (LLM), demonstrated improved performance.</li><li>The ML models achieved higher specificity without compromising sensitivity, offering enhanced diagnostic stewardship beyond existing standards of care.</li></ul>

A Multi-Phase Analysis of Blood Culture Stewardship: Machine Learning Prediction, Expert Recommendation Assessment, and LLM Automation

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