Clinical evaluation of a machine learning–based early warning system for patient deterioration (16 September 2024)

Summary

A new study by Toronto researchers evaluates the use of CHARTWatch, an artificial intelligence (AI) early warning system developed at Unity Health Toronto that monitors hospitalised patients in real-time, identifies those at high risk of unexpected death or transfer to an intensive care unit, and alerts doctors and nurses to intervene early.

The study, published in the Canadian Medical Association Journal, shows a 26% reduction in unanticipated mortality after the tool was implemented in the general internal medicine ward of Unity Health Toronto’s St. Michael’s Hospital.

“As AI tools are increasingly being used in medicine, it is important that they are evaluated carefully to ensure that they are safe and effective,” says lead author Amol Verma, general internist at Unity Health and professor in the department of medicine in the University of Toronto’s Temerty Faculty of Medicine who led the development and implementation of CHARTWatch.

“Our findings suggest that AI-based early warning systems are promising for reducing unexpected deaths in hospitals.”