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A new study by Toronto researchers evaluates the use of , an artificial intelligence early warning system developed at Unity Health Toronto that monitors hospitalized 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, , shows a 26 per cent 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 �ؿ�ζSM’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.”

One of the primary sources of unplanned admission to the ICU is the unexpected deterioration of hospitalized patients, which prompted the research team to develop this AI tool and study its effectiveness. 

“This important study evaluates the outcomes associated with the complex deployment of the entire AI solution, which is critical to understanding the real-world impacts of this promising technology,” says study co-author Muhammad Mamdani, vice president of data science and advanced analytics at Unity Health and director of U of T’s Temerty Centre for Artificial Intelligence Research and Education in Medicine.

“We hope other institutions can learn from and improve upon Unity Health Toronto’s experiences to benefit the patients they serve.” 

The study analyzed data from 13,649 patients aged 55 to 80 years old admitted to the general internal medicine unit (9,626 in the pre-intervention period and 4,023 using CHARTWatch) and 8,470 admitted to subspeciality units that did not use CHARTWatch.

Helping to prioritize patient needs

“The CHARTWatch project started at Unity Health when we asked patients, clinicians, hospital leaders, what would you want to use artificial intelligence for? If you could predict one thing that AI would tell you, what should that be?” says Verma, who is also the Temerty Professor of AI Research and Education in Medicine.

“And one of the leading priorities of everyone was to be able to predict in advance which patients might become so sick in hospital that they need ICU or might die.” 

During the 19-month-long intervention period, 482 patients in the general internal medicine became high-risk, compared with 1,656 patients who became high risk in the 43-month-long pre-intervention period. There were also fewer non-palliative deaths in the CHARTWatch group than in the pre-intervention group (1.6 per cent versus 2.1 per cent). 

“If I were a patient, I would be so relieved to know that we have this kind of system,” said co-author Yuna Lee, division head and general internist at St. Michael’s and professor in the department of medicine at U of T.

“So, when the patient gets a high-risk alert, they’re going to be assessed by senior staff right away. Also, they’re going to have quite close monitoring by nursing staff, so they check on them every one to two hours.”

Expanding the impact of CHARTWatch

CHARTWatch inputs more than 100 aspects of a patient’s medical history and current health status that are routinely stored in the hospital’s electronic medical record. It analyzes the interactions between these inputs and how they change over time. With that information, it’s able to categorize each patient by their risk for deterioration and send an alert to prioritize treatment.

The study is one of the first to evaluate how CHARTWatch can benefit hospital patients in Canada and shows the potential real-world impact of AI on the health-care sector. Damian Jankowicz, Unity Health’s vice-president and chief information and AI officer, says he hopes AI tools such as CHARTWatch will continue to have a profound impact on patients. 

“Hopefully with reduced administrative burden on our providers, they will have more time to spend with our patients and really focus on the patient needs,” he says. 

“I hope that AI will be able to distill the incredible amounts of information that’s coming at our clinicians into important components and really bring their clinical judgment to the forefront.”

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