New Delhi, Sep 16 A team of researchers, led by one of Indian origin, has developed a novel artificial intelligence (AI) based system that can help reduce the risk of unexpected deaths by identifying hospitalised patients at high risk of deteriorating health.
Rapid deterioration among hospitalised patients is the primary cause of unplanned admission to the intensive care unit (ICU).
But CHARTWatch, acted as an early warning system to improve patient health, and alert healthcare workers to reduce unexpected deaths, said the team in the paper published in CMAJ (Canadian Medical Association Journal).
"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,” said lead author Dr. Amol Verma, a clinician-scientist at St. Michael’s Hospital, Unity Health Toronto, Canada.
“Our findings suggest that AI-based early warning systems are promising for reducing unexpected deaths in hospitals," Verma said.
CHARTWatch’s efficiency was evaluated on 13,649 patients aged 55-80 years who were admitted to the general internal medicine (GIM) (about 9,626 in the pre-intervention period and 4,023 used CHARTWatch). About 8,470 admitted to subspecialty units did not use CHARTWatch.
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, said the researchers.
A care pathway was also created for high-risk patients which prompted increased monitoring by nurses, and enhanced communication between nurses and physicians. This encouraged physicians to reassess patients.
The AI system, Verma said, can be used to support nurses and doctors in providing high-quality care.
Co-author Dr. Muhammad Mamdani, director of the University of Toronto said that the study evaluates the outcomes associated with the complex deployment of the entire AI solution.
Understanding the real-world impacts of this promising technology is important, Mamdani said.
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