Sophie Bushwick in Scientific American:
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. Academics and electronic-health-record companies have developed automated systems that send reminders to check patients for sepsis, but the sheer number of alerts can cause health care providers to ignore or turn off these notices. Researchers have been trying to use machine learning to fine-tune such programs and reduce the number of alerts they generate. Now one algorithm has proved its mettle in real hospitals, helping doctors and nurses treat sepsis cases nearly two hours earlier on average—and cutting the condition’s hospital mortality rate by 18 percent.
Sepsis, which happens when the body’s response to an infection spirals out of control, can lead to organ failure, limb loss and death. Roughly 1.7 million adults in the U.S. develop sepsis each year, and about 270,000 of them die, according to the Centers for Disease Control and Prevention. Although most cases originate outside the hospital, the condition is a major cause of patient mortality in this setting. Catching the problem as quickly as possible is crucial to preventing the worst outcomes.