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. “We really need high-quality care augmentation tools that will allow providers to do more with less.” “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 healthcare 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. Leveraging vast amounts of data, TREWS provides real-time patient insights and a unique level of transparency into its reasoning, according to study co-author and Johns Hopkins internal medicine physician Albert Wu. The Targeted Real-time Early Warning System, or TREWS, scans through hospitals’ electronic health records-digital versions of patients’ medical histories-to identify clinical signs that predict sepsis, alert providers about at-risk patients, and facilitate early treatment. 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.” Advertisement 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 healthcare, 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. “How does that happen in a modern society?” his father, Ciaran Staunton, said in a recent interview with Undark.Įach year in the United States, sepsis kills over a quarter million people-more than stroke, diabetes, or lung cancer. Three days later, Rory died of sepsis after bacteria from the scrape infiltrated his blood and triggered organ failure. It was just the stomach flu, they were told. He woke up the next day with a 104° F fever, so his parents took him to the pediatrician and eventually the emergency room. Ten years ago, 12-year-old Rory Staunton dove for a ball in gym class and scraped his arm.
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