So, despite these rules-based systems occasionally reducing mortality, there's a risk of alert fatigue, where health care workers start ignoring the flood of irritating reminders. These alerts also appear on providers' computer screens as a pop-up, which forces them to stop whatever they're doing to respond. The patient almost certainly doesn't have sepsis but would nonetheless trip the alarm. "A computerized system might say, 'Hey look, fast heart rate, breathing fast.' It might throw an alert," said Cyrus Shariat, an ICU physician at Washington Hospital in California. This broadness, while helpful for catching the various ways sepsis might present itself, triggers countless false positives. One such example, known as the SIRS criteria, says a patient is at risk of sepsis if two of four clinical signs - body temperature, heart rate, breathing rate, white blood cell count - are abnormal. Given such complexity, over the past decade doctors have increasingly leaned on electronic health records to help diagnose sepsis, mostly by employing a rules-based criteria - if this, then that. "We really need high quality care augmentation tools that will allow providers to do more with less."Ĭurrently, there's no single test for sepsis, so health care providers have to piece together their diagnoses by reviewing a patient's medical history, conducting a physical exam, running tests, and relying on their own clinical impressions. "The technology exists, the data is there," she said. Electronic health records also come with many existing problems, from burying providers under administrative work to risking patient safety because of software glitches. This vision also discounts the difficulties of implementing any new medical technology: Providers might be reluctant to trust machine learning tools, and these systems might not work as well outside controlled research settings. It's an enticing vision, but one in which Saria, as CEO of the company developing TREWS, has a financial stake. With a series of machine learning projects on the horizon, both from Johns Hopkins and other groups, Saria said that using electronic records in new ways could transform health care delivery, providing physicians with an extra set of eyes and ears - and help them make better decisions. Since their introduction in the 1960s, electronic health records have reshaped how physicians document clinical information, but decades later, these systems primarily serve as "an electronic notepad," he added. Wu said that this system also offers a glimpse into a new age of medical electronization. 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." 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. 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. The system caught 82 percent of sepsis cases and reduced deaths by nearly 20 percent. 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. 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. 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. Each year in the United States, sepsis kills over a quarter million people - more than stroke, diabetes, or lung cancer.
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