Patients are 20% less likely to die of sepsis because a new AI system catches symptoms hours earlier than traditional methods, new research shows.

The system scours medical records and clinical notes to identify patients at risk of life-threatening complications. The work, which could significantly cut patient mortality from one of the top causes of hospital deaths worldwide, is published in Nature Medicine and Nature Digital Medicine.

“It is the first instance where AI is implemented at the bedside, used by thousands of providers, and where we’re seeing lives saved,” says Suchi Saria, founding research director of the Malone Center for Engineering in Healthcare at Johns Hopkins University, and lead author of the studies, which evaluated more than a half million patients over two years.

“This is an extraordinary leap that will save thousands of sepsis patients annually. And the approach is now being applied to improve outcomes in other important problem areas beyond sepsis.”

Sepsis occurs when an infection triggers a chain reaction throughout the body. Inflammation can lead to blood clots and leaking blood vessels, and ultimately can cause organ damage or organ failure. About 1.7 million adults develop sepsis every year in the United States and more than 250,000 of them die.

Sepsis is easy to miss because symptoms such as fever and confusion are common in other conditions, Saria says. The faster it’s caught, the better a patient’s chances for survival.

“One of the most effective ways of improving outcomes is early detection and giving the right treatments in a timely way, but historically this has been a difficult challenge due to lack of systems for accurate early identification,” says Saria, who directs the Machine Learning and Healthcare Lab at Johns Hopkins.

Read the full article about AI and sepsis by Laura Cech at Futurity.