Bayesian Health have released the results of three prospective multisite cohort studies which offer a comprehensive and rigorous evaluation of the efficacy of their adaptive AI approach and show patient lives saved.

The studies, which appear in Nature Medicine and npj Digital Medicine were conducted in collaboration with researchers from Johns Hopkins University.

Using data from 764,707 patient encounters (17,538 with sepsis) across five hospitals in both academic and community-based hospital settings with 2,000+ providers using the software, this research shows accurate early detection (1 in 3 cases were physician confirmed) at high sensitivity (82%) and significant lead time (5.7 hours earlier), high provider adoption (89%), and associated significant reductions in mortality, morbidity and length of stay.

Most significantly, the studies show timely use of Bayesian’s AI platform is associated with a relative reduction in mortality of 18.2%.

“There aren’t many things left in medicine that have a 30% mortality rate like sepsis,” said Neri Cohen, MD, PhD, President of The Center for Healthcare Innovation and Bayesian collaborator. “What makes it so vexing, is that it is relatively common and we still have made very little progress in recognizing it early enough to materially reduce the morbidity and mortality. To reduce mortality by nearly 20% is remarkable and translates to many lives saved.”

“While we all understand the value of leveraging AI to improve the delivery of care, achieving measurable impact has proven to be much harder than advertised,” said Suchi Saria, PhD, CEO of Bayesian Health and Director of Machine Learning, AI and Healthcare Lab at Johns Hopkins University. “These results showing high physician adoption and associated mortality and morbidity reductions are a milestone for the field of AI and are the culmination of nearly a decade of significant technological investment, deep collaboration, the development of novel techniques and rigorous evaluation.”

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