Algorithms can augment human decision-making by integrating and analyzing more data, and more kinds of data, than a human can comprehend. But to realize the full potential of artificial intelligence (AI) and machine learning (ML) for patients, researchers must foster greater confidence in the accuracy, fairness, and usefulness of clinical AI algorithms. Getting there will require guardrails -- along with a commitment from AI developers to use them -- that ensure consistency and adherence to the highest standards when creating and using clinical AI tools. Such guardrails would not only improve the quality of clinical AI but would also instill confidence among patients and clinicians that all tools deployed are reliable and trustworthy. STAT, along with researchers from MIT, recently demonstrated that even "subtle shifts in data fed into popular health care algorithms -- used to warn caregivers of impending medical crises -- can cause their accuracy to plummet over time."

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.