AI Tool Cuts Unexpected Deaths In Hospital By 26%, Canadian Study Finds

An anonymous reader quotes a report from CBC News: Inside a bustling unit at St. Michael’s Hospital in downtown Toronto, one of Shirley Bell’s patients was suffering from a cat bite and a fever, but otherwise appeared fine — until an alert from an AI-based early warning system showed he was sicker than he seemed. While the nursing team usually checked blood work around noon, the technology flagged incoming results several hours beforehand. That warning showed the patient’s white blood cell count was “really, really high,” recalled Bell, the clinical nurse educator for the hospital’s general medicine program. The cause turned out to be cellulitis, a bacterial skin infection. Without prompt treatment, it can lead to extensive tissue damage, amputations and even death. Bell said the patient was given antibiotics quickly to avoid those worst-case scenarios, in large part thanks to the team’s in-house AI technology, dubbed Chartwatch. “There’s lots and lots of other scenarios where patients’ conditions are flagged earlier, and the nurse is alerted earlier, and interventions are put in earlier,” she said. “It’s not replacing the nurse at the bedside; it’s actually enhancing your nursing care.”

A year-and-a-half-long study on Chartwatch, published Monday in the Canadian Medical Association Journal, found that use of the AI system led to a striking 26 percent drop in the number of unexpected deaths among hospitalized patients. The research team looked at more than 13,000 admissions to St. Michael’s general internal medicine ward — an 84-bed unit caring for some of the hospital’s most complex patients — to compare the impact of the tool among that patient population to thousands of admissions into other subspecialty units. “At the same time period in the other units in our hospital that were not using Chartwatch, we did not see a change in these unexpected deaths,” said lead author Dr. Amol Verma, a clinician-scientist at St. Michael’s, one of three Unity Health Toronto hospital network sites, and Temerty professor of AI research and education in medicine at University of Toronto. “That was a promising sign.”

The Unity Health AI team started developing Chartwatch back in 2017, based on suggestions from staff that predicting deaths or serious illness could be key areas where machine learning could make a positive difference. The technology underwent several years of rigorous development and testing before it was deployed in October 2020, Verma said. Dr. Amol Verma, a clinician-scientist at St. Michael’s Hospital who helped lead the creation and testing of CHARTwatch, stands at a computer. “Chartwatch measures about 100 inputs from [a patient’s] medical record that are currently routinely gathered in the process of delivering care,” he explained. “So a patient’s vital signs, their heart rate, their blood pressure … all of the lab test results that are done every day.” Working in the background alongside clinical teams, the tool monitors any changes in someone’s medical record “and makes a dynamic prediction every hour about whether that patient is likely to deteriorate in the future,” Verma told CBC News.