AI Tools Successfully Identify Healthcare-Associated Infections: Study, US

Artificial intelligence (AI) technologies have proven to be effective in identifying cases of health care-associated infections (HAI), as highlighted in a recent study published in the American Journal of Infection Control. The study emphasized the importance of using clear and consistent language when utilizing AI tools for this purpose. Researchers found that AI can accurately detect HAIs, even in complex clinical situations, showcasing the potential for incorporating this technology into routine infection surveillance programs. HAIs pose a significant risk to hospital patients, particularly those who are critically ill or have inserted devices like central lines and catheters. While infection surveillance programs exist in many health care facilities, they require substantial resources and expertise to maintain. The study evaluated the performance of AI-powered tools in identifying central line-associated bloodstream infections (CLABSI) and catheter-associated urinary tract infections (CAUTI) and demonstrated their accuracy in detecting HAIs when provided with clear information. However, the study also highlighted the importance of human oversight to address missing or ambiguous data that could impact the AI tools’ performance. This research underscores the need for continued development of AI tools in real-world healthcare settings to enhance infection prevention efforts.