Over two years, a machine-learning program warned thousands of health care providers about patients at high risk of sepsis, allowing them to begin treatments nearly two hours sooner
Hospital patients are at risk of a number of life-threatening complications, especially sepsis—a condition that can kill within hours and contributes to one out of three in-hospital deaths in the U.S. Overworked doctors and nurses often have little time to spend with each patient, and this problem can go unnoticed until it is too late.
But in a busy hospital, prompt sepsis diagnosis can be difficult. Under the current standard of care, Saria explains, a health care provider should take notice when a patient displays any two out of four sepsis warning signs, including fever and confusion. Some existing warning systems alert physicians when this happens—but many patients display at least two of the four criteria during a typical hospital stay, Saria says, adding that this can give warning programs a high false-positive rate.
“I think that this model for machine learning may prove as vital to sepsis care as the EKG [electrocardiogram] machine has proved in diagnosing a heart attack,” Molander says. “It is going to allow the clinician to go from the computer..., trying to analyze 15 years’ worth of information, to go back to the bedside and reassess the patient more rapidly—which is where we need to be.”
As an emergency room physician, Molander was impressed by the fact that the AI does not make sepsis decisions on behalf of health care providers. Instead it flags a patient’s electronic health record so that when doctors or nurses check the record, they see a note that the patient is at risk of sepsis, as well as a list of reasons why.
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