Study Explores AI’s Role in Infection Prevention

According to a new study published in the American Journal of Infection Control (AJIC), artificial intelligence (AI) technologies can accurately identify cases of healthcare-associated infections (HAI)—even in complex clinical scenarios.

In the study, titled Assisting the Infection Preventionist: Use of Artificial Intelligence for Healthcare-Associated Infection Surveillance, researchers at Saint Louis University and the University of Louisville School of Medicine evaluated the performance of two AI-powered tools for accurate identification of HAIs. One tool was built using OpenAI’s ChatGPT Plus, and the other was developed using an open-source large language model known as Mixtral 8x7B.

The tools were tested on two types of HAIs: central line-associated bloodstream infection (CLABSI) and catheter-associated urinary tract infection (CAUTI). Descriptions of six fictional patient scenarios with varying levels of complexity were presented to the AI tools; the tools were then asked whether the descriptions represented a CLABSI or a CAUTI. The descriptions included information such as the patient’s age, symptoms, date of admission, and dates when central lines or catheters were inserted or removed. The AI responses were then compared to human experts’ answers to determine accuracy.

In all six cases, both AI tools accurately identified the HAI when given clear prompts. The researchers also found that missing or ambiguous information in the descriptions could prevent the AI tools from producing accurate answers. Abbreviations, lack of specificity, use of special characters, and dates reported in numeric format instead of spelling out the month all led to inconsistent responses.

“Our results are the first to demonstrate the power of AI-assisted HAI surveillance in the healthcare setting, but they also underscore the need for human oversight of this technology,” said Timothy L. Wiemken, PhD, MPH, Saint Louis University associate professor in the division of infectious diseases, allergy, and immunology and lead author of the study. “With the rapid evolution of the role of AI in medicine, our proof-of-concept study validates the need for continued development of AI tools with real-world patient data to support infection preventionists.”

Tania Bubb, PhD, APIC president, added, “HAI surveillance is a critical responsibility for infection preventionists, and our community needs every possible tool to help us ensure the safety of our patients. This study suggests that AI-powered tools may offer a cost-effective means of improving our surveillance programs by assisting infection preventionists in day-to-day work functions.”