AI tool cuts unexpected deaths in hospital by 26%, Canadian study finds

September 16, 2024 Canuck

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 per cent drop in the number of unexpected deaths among hospitalized patients.

“We’re glad to see that we’re saving lives,” said co-author Dr. Muhammad Mamdani, vice-president of data science and advanced analytics at Unity Health Toronto and director of the University of Toronto Temerty Faculty of Medicine Centre for AI Research and Education in Medicine.

‘A promising sign’

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. — Verma simulates the tool’s use inside the downtown Toronto health-care facility. (Evan Mitsui/CBC)

“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.

That could mean someone getting sicker, or requiring intensive care, or even being on the brink of death, giving doctors and nurses a chance to intervene.

In some cases, those interventions involve escalating someone’s level of treatment to save their life, or providing early palliative care in situations where patients can’t be rescued.

In either case, the researchers said, Chartwatch appears to complement clinicians’ own judgment and leads to better outcomes for fragile patients, helping to avoid more sudden and potentially preventable deaths.

AI on the rise in health care

Beyond its uses in medicine, artificial intelligence is getting plenty of buzz — and blowback — in recent years.

From controversy around the use of machine learning software to crank out academic essays, to concerns over AI’s capacity to create realistic audio and video content mimicking real celebrities, politicians, or average citizens, there have been plenty of reasons to be cautious about this emerging technology.

Verma himself said he’s long been wary. But in health care, he stressed, these tools have immense potential to combat the staff shortages plaguing Canada’s health-care system by supplementing traditional bedside care.

WATCH | How AI could revolutionize health care:

How AI could change the future of our health care

5 years ago

Duration 2:52

Often called the future of health care, artificial intelligence is already finding a place in Canadian hospitals. But AI is far from perfect and some worry about the costs that could come with it.

It’s still the early days for many of those efforts. Various research teams, including private companies, are exploring ways to use AI for earlier cancer detection. Some studies suggest it has potential for flagging hypertension just by listening to someone’s voice; others show it could scan brain patterns to detect signs of a concussion.

Chartwatch is notable, Verma stressed, because of its success in keeping actual patients alive.

“Very few AI technologies have actually been implemented into clinical settings yet. This is, to our knowledge, one of the first in Canada that has actually been implemented to help us care for patients every day in our hospital,” he said.

‘Real world’ look at AI’s health-care impact

The St. Michael’s-based research does have limitations. The study took place during the COVID-19 pandemic, at a time when the health-care system faced an unusual set of challenges. The urban hospital’s patient population is also distinct, the team acknowledged, given its high level of complex patients, including individuals facing homelessness, addiction and overlapping health issues.

“Our study was not a randomized controlled trial across multiple hospitals. It was within one organization, within one unit,” Verma said. “So before we say that this tool can be used widely everywhere, I think we do need to do research on its use in multiple contexts.”

Dr. John-Jose Nunez, a psychiatrist and researcher with the University of British Columbia — who wasn’t involved in the study — agreed the research needs to be replicated elsewhere to get a better sense of how well Chartwatch might work in other facilities. There also needs to be considerations around patient privacy, he added, with the use of any emerging AI technologies.

Still, he praised the study team for providing a “real-world” example of how machine learning can improve patient care.

“I really think of AI tools as becoming one more team member on the clinical care team,” he said.

CHARTwatch technology is ‘saving lives,’ said Dr. Muhammad Mamdani, vice president of data science and advanced analytics at Unity Health Toronto and director of the University of Toronto Temerty Faculty of Medicine Centre for AI Research and Education in Medicine. (Evan Mitsui/CBC)

The Unity Health team is hopeful their technology will roll out more widely in the future, within their own Toronto-based hospital network and beyond.

Much of that work is happening through GEMINI, Canada’s largest hospital data-sharing network for research and analytics, said Mamdani, Unity Health’s vice-president of data science.

More than 30 hospitals across Ontario are working together, he said, offering opportunities to test Chartwatch and other AI tools in various clinical settings and hospitals.

“It just sets the groundwork now to be able to deploy these things well beyond our four walls,” Mamdani said.

View original article here Source