Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model

Abstract

Impaired sleep for hospital patients is an all too common reality. Sleep disruptions due to unnecessary overnight vital sign monitoring are associated with delirium, cognitive impairment, weakened immunity, hypertension, increased stress, and mortality. It is also one of the most common complaints of hospital patients while imposing additional burdens on healthcare providers. Previous efforts to forgo overnight vital sign measurements and improve patient sleep used providers’ subjective stability assessment or utilized an expanded, thus harder to retrieve, set of vitals and laboratory results to predict overnight clinical risk. Here, we present a model that incorporates past values of a small set of vital signs and predicts overnight stability for any given patient-night. Using data obtained from a multi-hospital health system between 2012 and 2019, a recurrent deep neural network was trained and evaluated using ~2.3 million admissions and 26 million vital sign assessments. The algorithm is agnostic to patient location, condition, and demographics, and relies only on sequences of five vital sign measurements, a calculated Modified Early Warning Score, and patient age. We achieved an area under the receiver operating characteristic curve of 0.966 (95% confidence interval [CI] 0.956–0.967) on the retrospective testing set, and 0.971 (95% CI 0.965–0.974) on the prospective set to predict overnight patient stability. The model enables safe avoidance of overnight monitoring for ~50% of patient-nights, while only misclassifying 2 out of 10,000 patient-nights as stable. Our approach is straightforward to deploy, only requires regularly obtained vital signs, and delivers easily actionable clinical predictions for a peaceful sleep in hospitals.

The role of artificial intelligence in enhancing clinical nursing care: A scoping review

“…This greatly reduces the constant need for nurses to collect and monitor patients' vital signs. Approximately 20%–35% of a nurse's time is expended to document patients' vital signs, whereas 10% of the time was spent retrieving vital statistics (Tóth et al, 2020). With the aid of machine learning, it allows the easier collection and monitoring of vital signs, freeing up more time for nurses to perform other tasks (e.g., nurse–patient communication or other nursing tasks).…”

“…With the aid of machine learning, it allows the easier collection and monitoring of vital signs, freeing up more time for nurses to perform other tasks (e.g., nurse–patient communication or other nursing tasks). This reduces the workload nurses have in monitoring vital signs, improving patient care (Tóth et al, 2020). In addition, machine learning can also predict certain medical problems (Bauer et al, 2017; Cho et al, 2013; Hu et al, 2020; Johnson et al, 2019; Joshi et al, 2019; Ladios‐Martin et al, 2020; Lee et al, 2020; Lindberg et al, 2020; Lodhi et al, 2015; Nakatani et al, 2020; Sullivan et al, 2019; Yokota et al, 2017; Zachariah et al, 2020).…”

Patient-nurse agreement on inpatient sleep and sleep disturbing factors

“…However, these assessments are often important to improve patient safety (and prevent deterioration over the night) and are therefore seen as necessary. Recent research has shown that machine-learning algorithms could help to predict overnight in-hospital deterioration [22] . This could potentially reduce sleep disturbance from checkups in the near future.…”

Changing Hospital Care For Older Adults: The Case for Geriatric Hospitals in the United States

“…Examples include early morning blood draws, vital signs that interrupt sleep, fasting protocols before surgery and frequent unnecessary blood sugars. ( Gustafsson et al, 2019 ; Tóth et al, 2020 ; Warnock & Latifi. 2022 ).…”