First clinical AI tool to let patients sleep/recover developed

Vital sign (VS) monitoring disruptions for hospitalized patients during overnight hours have been linked to cognitive impairment, hypertension, increased stress and even mortality. For the first time, a team at The Feinstein Institutes for Medical Research developed a deep-learning predictive clinical tool to identify which patients do not need to be woken up overnight – allowing them to rest, recover and discharge faster. The study's results, based on 24.3 million vital sign measurements, were published today in Nature Partner Journals Digital Medicine. A team, led by Theodoros Zanos, PhD, in close collaboration with Jamie Hirsch, MD, collected and analyzed data from multiple Northwell Health hospitals between 2012 and 2019, which consisted of 2.13 million patient visits. They used this vast body of clinical data from the patient visits – respiratory rate, heart rate, systolic blood pressure, body temperature, patient age, etc. – to develop an algorithm that predicts a hospitalized patient's overnight stability, and if they could be left uninterrupted overnight to sleep.