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Feinstein Institutes AI researchers receive $3.1M NIH grant to develop hospital risk and prevention tools

Press releases may be edited for formatting or style | September 19, 2023 Artificial Intelligence Emergency Medicine Patient Monitors
MANHASSET, N.Y.--(BUSINESS WIRE)--Hospitalized adult patients whose medical conditions worsen after being admitted, requiring escalation of care, such as transfer to the intensive care unit (ICU) or intervention of a rapid response team, may benefit from monitoring by artificial intelligence (AI). Scientists at The Feinstein Institutes for Medical Research were recently awarded $3.1 million from the National Institutes of Health (NIH) to fund a new study that would harness AI and machine learning (ML) to help doctors and nurses monitor patients in busy medical and surgical wards to identify and prevent deterioration and ultimately improve patient outcomes.

The study team is led by Theodoros Zanos, PhD, associate professor at the Feinstein’s Institute of Health System Science and Institute of Bioelectronic Medicine, and includes Karina Davidson PhD, Michael Oppenheim MD, Alex Makhnevich MD, Beth Friedman RN, and others at the Feinstein Institutes and Northwell Health, New York’s largest health system. The group will develop and implement ML models with the goal of improving the monitoring of patients once they are admitted in order to identify who might undergo a rapid decline to address it sooner.

“Some patients who are admitted for one condition are not explicitly showing symptoms of other concerns, which can lead to their health deteriorating and even dying,” said Dr. Zanos. “This research will leverage vast patient data, new continuous monitoring technologies and AI to identify those often-unidentified risks and subtle early worrisome trends and enable life-saving interventions.”

Dr. Zanos and his team will turn to Northwell’s large, diverse clinical dataset using electronic health records (EHRs) from more than 2.4 million hospitalizations to generate ML predictive models. The clinical support tools will help clinicians and nurses identify in advance patients at risk of deterioration and clinical reasons to enable timely interventions. These tools also will identify those patients who are more stable. The study will collect and leverage patient data using a continuous monitoring (CM) device, the VitalConnect VitalPatch, that will be placed on patients upon admission to the hospital, to develop more accurate prediction models.

“Merging data and technology, researchers and clinicians can work together to develop new tools to improve patient outcomes,” said Kevin J. Tracey, MD, president and CEO of the Feinstein Institutes and Karches Family Distinguished Chair in Medical Research. “Dr. Zanos’ study will harness the use of artificial intelligence to better inform and deliver care.”

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