De-escalating Vital Sign Checks

NCT04046458 | Completed

• 1 other identifier: nightvitals
• Study Type: interventional
• Target: 1,436
• Locations: 1 country
• Sites: 1

Brief Summary

The overall goals for this study are: 1) to develop a predictive model to identify patients who are stable enough to forego vital sign checks overnight, 2) incorporate this predictive model into the hospital electronic health record so physicians can view its output and use it to guide their decision-making around ordering reduced vital sign checks for select patients.

Conditions

Delirium; Sleep Disturbance

Outcome Measures

Primary Outcomes (1)

• delirium

  Nursing Delirium Screening Scale (Nu-DESC score) - assessed by the nurse, can range from zero to ten, a score \> 2 has good accuracy for delirium

  average will be measured at study completion (6 months from study start date - Sep 11, 2019)

Secondary Outcomes (2)

• sleep opportunity

  average will be calculated at study completion (6 months from study start date - Sep 11, 2019)

• patient satisfaction

  average score will be measured at study completion (6 months from study start date - Sep 11, 2019)

Other Outcomes (2)

• number of code blue events

  average number will be calculated at study completion (6 months from study start date - Sep 11, 2019)

• number of rapid response calls

  average number will be calculated at study completion (6 months from study start date - Sep 11, 2019)

Study Arms (2)

EHR Alert

EXPERIMENTAL

Physician teams will observe the EHR alert as they perform their clinical duties in the EHR.

Behavioral: Nighttime Vital Sign EHR Alert

No Alert

PLACEBO COMPARATOR

Physician teams will perform their clinical duties in the EHR as usual, with no visible alert.

Other: No EHR alert

Interventions

Nighttime Vital Sign EHR Alert BEHAVIORAL

A pop-up window in the EHR will notify a physician that their patient has been judged by a predictive algorithm to be safe for reduced overnight vital sign checks.

EHR Alert

No EHR alert OTHER

No change to EHR function; no alert visible to providers

No Alert

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• All physician teams that operate under the UCSF Division of Hospital Medicine

You may not qualify if:

• N/A

Sponsors & Collaborators

• University of California, San Francisco: lead

Study Sites (1)

UCSF

San Francisco, California, 94143, United States

Location

Related Publications (1)

• Najafi N, Robinson A, Pletcher MJ, Patel S. Effectiveness of an Analytics-Based Intervention for Reducing Sleep Interruption in Hospitalized Patients: A Randomized Clinical Trial. JAMA Intern Med. 2022 Feb 1;182(2):172-177. doi: 10.1001/jamainternmed.2021.7387.

  PMID: 34962506 DERIVED

MeSH Terms

Conditions

Delirium; Parasomnias

Condition Hierarchy (Ancestors)

Confusion; Neurobehavioral Manifestations; Neurologic Manifestations; Nervous System Diseases; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Neurocognitive Disorders; Mental Disorders; Sleep Wake Disorders

Study Officials

• Mark Pletcher, MD

  Director of the UCSF Informatics and Research Innovation Program

  STUDY DIRECTOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: PREVENTION
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: The investigators' intervention, which is a notification to the physician that is seen in the EHR, is randomized at the patient level. The patients randomized to the control group do not have a notification shown to their physician while the intervention patients do.

Study Record Dates

• First Submitted: March 9, 2018
• First Posted: August 6, 2019
• Study Start: March 11, 2019
• Primary Completion: November 4, 2019
• Study Completion: November 4, 2019
• Last Updated: December 4, 2019

Record last verified: 2019-12

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)