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Real-Time Prediction Algorithm Reduces Overnight Vital Checks, Promotes Sleep in Patients

A real-time prediction algorithm can help clinicians identify clinically stable patients who can be safely removed from nighttime vital sign examinations to improve sleep, according to a study published in JAMA Internal Medicine .

Insomnia is common among hospitalized patients, often due to nighttime vital sign checks. Previous research has shown that these iatrogenic interruptions can be reduced in low-risk medical inpatients. Understanding the effect of sleep-enhancing clinical decision support (CDS) requires the use of a randomized control group.

The goal of this randomized clinical trial was to determine whether an algorithm in the real-time CDS tool could help clinicians choose an appropriately low. – Patients take risks and successfully promote their sleep.

Researchers analyzed the vital signs of inpatient encounters with General Medicine Services at the University of California, San Francisco, Medical Center to develop a prediction algorithm. A logistic regression model trained on 70% of the data set was able to correctly predict most vital signs (84% of normal nighttime vital signs; 70% of abnormal vital signs). The model triggered a sleep-promoting vital CDS (SPV) alert in the electronic health records (EHR) to indicate to core team members during the day that the patient was 90% likely to have normal vital signs over the next night. The doctor can then choose to order the SPV, delay notification by an hour, or delay until the next day. They were randomly selected from November 11 to 24, 2019 with physician notification (n=966; 41% of women aged 53-15 years) and without notification (n=964).

Physicians who received the notification can request an SPV. Encounters with this arrangement nearly doubled (770 vs 430; P < .001) before a 31% decrease in encounter level stress checks per night (0.97 vs 1.41; P < .001).

Delirium (Nu-DESC score of at least 2) was similar between the intervention and usual care groups (108 vs. 123).

Individuals in the intervention arm had a greater chance of falling asleep compared to control subjects (4.95 ± 1.45 vs 4.57 ± 1.30; P < .001).

5% of patients (53 intervention groups; 49 control individuals) who completed the vacuum survey did not report significantly different noises in or around their rooms at night. Safety outcomes were similar between groups. The trial found no difference between groups in the primary outcome, incidence of delirium and secondary results indicate that a real-time prediction algorithm embedded in the EHR Clinical Decision Support Tool can help clinicians identify clinically stable patients from He can forgo routine vital sign checks, giving him a greater chance of sleeping safely.” Tests.


Najafi N, Robinson A, Bletcher MG, et al Effectiveness of an intervention based on analyzes to reduce sleep interruption In Hospitalized Patients: A Randomized Clinical Trial JAMA Int Med . Published Online Dec 28, 2021. doi: 10.1001/jamainternmed.2021.7387

Publication Real-time Prediction Algorithm Reduces nighttime vital examinations, enhances sleep in patients First appeared in Psychiatric consultant

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