New AI tool supports best practices to prevent spread of dangerous C. diff infections

"At Emory, I look forward to continuing this line of work and exploring innovative ways AI can help improve patient care," says Shengpu Tang, who recently joined the university as assistant professor of computer science.

Decision-making forms the core of hospital patient care, involving an array of clinicians whose duties span diagnosis, treatment and resource allocation. The complexity of these interrelated decisions makes it challenging for physicians, nurses and other caretakers to connect all the dots in real time. 

Shengpu Tang, assistant professor of computer science at Emory University, is developing AI tools to identify, validate and transmit key data needed to most effectively support healthcare workers in decision-making processes. 

 

“The end goal is to improve patient care and patient outcomes,” Tang says. 

JAMA Open Network published the results of Tang’s latest collaborative project: the first AI guidance deployed in a hospital setting aimed at guiding best practices to prevent the spread of dangerous infections of Clostridioides difficile. 

Analysis by the researchers found that the new AI-guided protocol significantly reduced antibiotic prescriptions at Michigan Medicine — a factor that increases infection risk for vulnerable patients — with 10% to 15% fewer days on antimicrobials. Importantly, reducing days on antimicrobials did not increase the length of stay, readmission rate or mortality among patients. The already low incidence of Clostridioides difficle trended downwards during the study, but that reduction did not reach statistical significance.

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