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 . — Timothy Wiemken , PhD , MPH

CAUTI . The descriptions included information such as the patient ’ s age , symptoms , date of admission , and dates that central lines or catheters were inserted and removed . AI responses were compared to expert answers to determine accuracy .

Wiemken and Carrico ( 2024 ) report that both AI models accurately identified CLABSI and CAUTI in all scenarios when given clear prompts and add that challenges appeared with ambiguous prompts , causing occasional inaccuracies in repeated tests .

As the researchers note , “ The study demonstrates AI ’ s potential in accurately identifying HAIs like CLABSI and CAUTI . Clear , specific prompts are crucial for reliable AI responses , highlighting the need for human oversight in AI-assisted HAI surveillance .”

The study illustrates the potential for incorporating AI technology as a cost-effective component of routine infection surveillance programs , which require extensive resources , training , and expertise to maintain . In resource-constrained settings , a cost-effective alternative could help to enhance surveillance programs and allow for better protection of high-risk patients , the researchers say .

For all six cases , both AI tools accurately identified the HAI when given clear prompts . Importantly , the researchers found that missing or ambiguous information in the descriptions could prevent the AI tools from producing accurate results . For example , one description did not include the date a catheter was inserted ; without that detail the AI tool could not give a correct response . Abbreviations , lack of specificity , use of special characters , and dates reported in numeric format instead of with the month spelled out 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 ,” says Wiemken . “ With the rapid evolution of the role of AI in medicine , our proofof-concept study validates the need for continued development of AI tools with real-world patient data to support infection preventionists .”

Both AI tools were used with retrieval augmented generation , an approach that improves the quality of prompting through a knowledge repository that gives the AI tool additional context . In this case , the repository included material from CDC ’ s National Healthcare Safety Network , a tracking system for HAIs . The ChatGPT Plus tool developed for this study , HAI Assist , is available

The Infection Prevention Exchange

By MD-Medical Data Quality & Safety Advisors , LLC

The IPEX Bundle

• Data

• Process Improvement

• Technology • Artificial Intelligence

Patient & Staff Safety

Patient Satisfaction Scores improved LeapFrog Scores improved

Financial & Operational Efficiencies 50-75 % Reductions in cost of infection

HEALTHCARE-ASSOCIATED INFECTIONS

Contact Tracing

50-75 % Reductions in infections

Inquiries : Medicaldatamanagement @ gmail . com