Stopping Infection Outbreaks with AI and Big Data

Hospitals are where we go to get cured of infections and diseases, but sadly, sometimes tragically, and ironically, they are also places we go to get them. According to the Centers for Disease Control, “On any given day, about one in 31 hospital patients has at least one healthcare-associated infection.”

Yet, according to Dr Lee Harrison, “The current method used by hospitals to find and stop infectious disease transmission among patients is antiquated. These practices haven’t changed significantly in over a century.”

Until perhaps now. Doctors at the University of Pittsburgh and the University of Pittsburgh Medical Center have developed a new method that uses three distinct, relatively new, technologies, whole-genome sequencing surveillance, and machine learning, and electronic health records to identify undetected outbreaks and their transmission routes.

Dr Lee Harrison is a Professor at the University of Pittsburgh, where he’s the Associate Chief of Epidemiology and Education and, more to our point today, the head of its Infectious Diseases Epidemiology Research Unit. He’s the corresponding author of a new paper that describes the new methodology and he’s my guest today.

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