CHARTWatch, an AI (Artificial Intelligence)-based system, developed after three years of work, has been shown to help reduce the risk of unexpected deaths by identifying hospitalized patients at high risk of health problems.

Sudden health decline in hospitalized patients is a leading cause of unplanned ICU admissions. While earlier studies have explored the use of technology to identify these patients, there is no proper evidence for its effectiveness.

According to new research published in the Canadian Medical Association Journal (CMAJ), Researchers from Unity Health Toronto, ICES, and the University of Toronto evaluated CHARTWatch, an AI-powered early warning system, used in the general internal medicine (GIM) ward at St. Michael’s Hospital (1^✔ ✔Trusted Source
Clinical evaluation of a machine learning-based early warning system for patient deterioration

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CHARTWatch Effectiveness in Reducing Hospital Deaths

The study included 13 649 patients aged 55–80 years admitted to GIM (9626 in the pre-intervention period and 4023 using CHARTWatch) and 8470 admitted to subspecialty units that did not use CHARTWatch.

During the 19-month-long intervention period, 482 patients in GIM became high-risk, compared with 1656 patients who became high-risk in the 43-month-long pre-intervention period. There were fewer nonpalliative deaths in the CHARTWatch group than in the pre-intervention group (1.6% v. 2.1%).

“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 Dr. Amol Verma, a clinician-scientist at St. Michael’s Hospital, Unity Health Toronto, and Temerty professor of AI research and education in medicine, University of Toronto, Toronto, Ontario. “Our findings suggest that AI-based early warning systems are promising for reducing unexpected deaths in hospitals.”

Regular communications helped reduce deaths as CHARTWatch engaged clinicians with real-time alerts, twice-daily emails to nursing teams, and daily emails to the palliative care team. The team also created a care pathway for high-risk patients with increased monitoring by nurses, enhanced communication between nurses and physicians, and prompts to encourage physicians to reassess patients.

Advancements in AI for Patient Care with CHARTWatch

“Ultimately, this study shows how AI systems can support nurses and doctors in providing high-quality care,” says Dr. Verma.

The authors hope that AI solutions like CHARTWatch can improve patient health and avoid premature deaths.

“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 coauthor 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.

“We hope other institutions can learn from and improve upon Unity Health Toronto’s experiences to benefit the patients they serve. Unity Health Toronto is a collaborative leader already helping to spread our AI tools via innovative partnerships with more to come.”

A second article published in Canadian Medical Association Journal (CMAJ) provides a snapshot of what physicians should know if they are thinking of using AI scribes in clinical practice, including the importance of obtaining patient consent, reviewing AI-generated notes for errors, and ensuring the software complies with local privacy legislation (2^✔ ✔Trusted Source
Artificial intelligence scribes in primary care

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Reference:

1. Clinical evaluation of a machine learning–based early warning system for patient deterioration – (https:www.cmaj.ca/content/196/30/E1027)

Source-Eurekalert