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.

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