Effectiveness of an Analytics-Based Intervention for Reducing Sleep Interruption in Hospitalized Patients: A Randomized Clinical Trial

Abstract

Importance: Sleep has major consequences for physical and emotional well-being. Hospitalized patients experience frequent iatrogenic sleep interruptions and there is evidence that such interruptions can be safely reduced.

Objective: To determine whether a clinical decision support tool, powered by real-time patient data and a trained prediction algorithm, can help physicians identify clinically stable patients and safely discontinue their overnight vital sign checks.

Design, setting, and participants: A randomized clinical trial, with inpatient encounters randomized 1:1 to intervention vs usual care, was conducted from March 11 to November 24, 2019. Participants included physicians serving on the primary team of 1699 patients on the general medical service (not in the intensive care unit) of a tertiary care academic medical center.

Interventions: A clinical decision support notification informed the physician if the patient had a high likelihood of nighttime vital signs within the reference ranges based on a logistic regression model that used real-time patient data as input. The notification provided the physician an opportunity to discontinue measure of nighttime vital signs, dismiss the notification for 1 hour, or dismiss the notification for that day.

Main outcomes and measures: The primary outcome was delirium, as determined by bedside nurse assessment of Nursing Delirium Screening Scale scores, a standardized delirium screening tool (delirium diagnosed with score ≥2). Secondary outcomes included mean nighttime vital sign checks. Potential harms included intensive care unit transfers and code blue alarms. All analyses were conducted on the basis of intention-to-treat.

Results: A total of 1930 inpatient encounters in 1699 patients (intervention encounters: 566 of 966 [59%] men; mean [SD] age, 53 [15] years) were randomized. In the intervention vs control arm, there was a significant decrease in the mean (SD) number of nighttime vital sign checks (0.97 [0.95] vs 1.41 [0.86]; P < .001) with no increase in intensive care unit transfers (49 [5%] vs 47 [5%]; P = .92) or code blue alarms (2 [0.2%] vs 9 [0.9%]; P = .07). The incidence of delirium was not significantly reduced (108 [11%] vs 123 [13%]; P = .32).

Conclusions and relevance: While this randomized clinical trial found no difference between groups in the primary outcome, delirium incidence, the secondary findings indicate that a real-time prediction algorithm embedded within a clinical decision support tool in the electronic health record can help physicians identify clinically stable patients who can forgo routine vital sign checks, safely giving them greater opportunity to sleep. Other aspects of hospital care that depend on clinical stability, such as level of care or cardiac monitoring, may be amenable to a similar intervention.

Trial registration: ClinicalTrials.gov Identifier: NCT04046458.

Figure.. Consolidated Standards of Reporting Trials Flow Diagram

Illustrated data are for patient encounters shown for 851 intervention and 848 control patients.