Each year in the United States, sepsis kills over a quarter million people — more than stroke, diabetes, or lung cancer. One reason for all this carnage is that sepsis isn’t well understood, and if not detected in time, it’s essentially a death sentence. Consequently, much research has focused on catching sepsis early, but the disease’s complexity has plagued existing clinical support systems — electronic tools that use pop-up alerts to improve patient care — with low accuracy and high rates of false alarm.
That may soon change. Back in July, Johns Hopkins researchers published a trio of studies in Nature Medicine and npj Digital Medicine, showcasing an early warning system that uses artificial intelligence. The system caught 82 percent of sepsis cases and reduced deaths by nearly 20 percent. While AI — in this case, machine learning — has long promised to improve health care, most studies demonstrating its benefits have been conducted on historical datasets. Sources told Undark that, to the best of their knowledge, when used on patients in real-time, no AI algorithm has shown success at scale. Suchi Saria, director of the Machine Learning and Health Care Lab at Johns Hopkins University and senior author of the studies, said the novelty of this research is how “AI is implemented at the bedside, used by thousands of providers, and where we’re seeing lives saved.”
