New AI Tool Cuts Patient Vital Sign Check-Ups, Lets Them Sleep, Saves Lives

The new AI tool reduced nurse workload up to 20% to 25%, letting them pivot to acutely-ill patients.
Brad Bergan

A research team collected and analyzed data from several hospitals between 2012 and 2019 to develop an AI algorithm capable of predicting a hospitalized patient's stability overnight and decide whether they should be left undisturbed from sleep or not, according to a recent study published in the journal Nature Partner Journals Digital Medicine.


AI tool lets patients sleep when they need it, saves lives

The interruption of hospitalized patients to check vital signs during overnight hours has sadly become linked to cognitive impairment, increased stress, hypertension, and even death. But a team of researchers has designed a deep-learning predictive clinical tool capable of deciding which patients should be left to sleep without interruption — providing crucial time to rest and recover, in addition to streamlining discharging schedules.

The new study stems from The Feinstein Institutes for Medical Research, and went forward under the leadership of Professor Theodoros Zanos, who worked in close collaboration with doctor Jamie Hirsch. Together they collected and analyzed data from several Northwell Health hospitals for most of the twenty-teens, and visited with data based on 24.3 million vital sign measurements from 2.13 million patients, according to a press release shared with Interesting Engineering (IE) via email.

'Let Sleeping Patients Lie' predictive model will roll-out soon

The researchers used the vast body of clinical data — which included heart rate, body temperature, systolic blood pressure, respiratory rate, and patient age — to curate an algorithm capable of predicting a hospitalized patient's overnight status. The study also considered whether each patient ought to be left uninterrupted through the night, so they can sleep.

The rollout for the clinical tool — dubbed the "Let Sleeping Patients Lie" — will soon move forward in hospitals across Northwell Health.

"Rest is a critical element to a patient's care, and it has been well-documented that disrupted sleep is a common complaint that could delay discharge and recover," said Assistant Professor Zanos of the Feinstein Institutes' Institute of Bioelectronic Medicine, in the press release shared with IE. "Our findings highlight the safety and accuracy of machine learning-based solutions to pave the way for more peaceful and safe sleep in a hospital."

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Old-fashioned visual inspection can correct misclassified patient cases

A patient is woken up every four to five hours to check their vital signs, on average. The new study shows how the new predictive model saved roughly half of patients' overnight sleep in a hospital, and at extraordinarily low risk. The new deep-learning AI model only misclassified less than two of every 10,000 patient-nights, according to the press release sent to IE.

The new tool also helped clinical teams modify the model's predictive thresholds to carry out more strict patient assessments. Additionally, to guarantee quality care, an old-fashioned visual inspection of sleeping patients while nurses are making their usual rounds should round-up the misclassified patients — a procedure already standardized for nurses nationwide.

AI tool reduces nurse workload up to 20% to 25%

Possibilities abound for implementing the "Let Sleeping Patients Lie" predictive AI model — which goes beyond patient care and might lighten the workload for nurses and hospital staff — who are infamously overburdened amid the coronavirus crisis — when it comes to employee stress and burnout.

Nurses spend roughly 20% to 35% of their time recording vital signs, and roughly 10% of their shift collecting them. The clinical tool would help nurses cut out roughly half of their overnight vital sign measurements without added risk, engendering a workload reduction of up to 20% to 25% in a single overnight shift.

AI predictive tools recognize procedural blind spots

This extra time could be reallocated to assist patients suffering from more acute illnesses.

"Dr. Zanos and his team's expertise in machine learning enabled them to invent an effective solution for improving sleep," said President and CEO of Feinstein Institutes Kevin J. Tracey, in the press release shared with IE. "Illness and hospitalization impair sleep cycles, and the promise for artificial intelligence in this domain holds significant promise."

As AI continues to advance in nearly every field and industry, its seemingly omniscient awareness of human procedure and technology easily finds blind spots — the things we don't know we don't know — presenting us with an opportunity to streamline in ways we didn't know we could. In health care, this means we save lives, and in the time of the coronavirus crisis, we can't afford to ignore the benefits of optimizing patient sleep like a triage, because it saves lives.

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