De-escalating Vital Sign Checks

NCT ID: NCT04046458

Last Updated: 2019-12-04

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 1436 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2019-03-11
• Study Completion Date: 2019-11-04

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.

Detailed Description

Patients in the hospital often report poor sleep. A lack of sleep not only affects a patient's recovery from illness and their overall feeling of wellness, but it is a leading factor in the development of delirium in the hospital. One method for improving sleep in the hospital is to reduce the number of patient care related interruptions that a patient experiences. Vital sign checks at night are one example. In hospitalized patients who are clinically stable, vital sign checks that interrupt sleep are often unnecessary. However, identifying which patients can forego these checks is not a simple task. Currently, the hospital's quality improvement team asks physicians to think about this issue every day and order reduced, or "sleep promotion", vital sign checks on patients they believe could safely tolerate it. The investigators goal is to use a predictive analytics tool to reduce the cognitive burden of this task for busy physicians.

The investigators plan to develop a logistic regression model, trained on data from the electronic health record (EHR), to predict, for a given patient on a given night, whether they could safely tolerate the reduction of overnight vital sign checks. The model will use variables, such as the patient's age, the number of days they have been in the hospital, the vital signs from that day, the lab values from that day, and other clinical variables to make its prediction. The outcome is a binary variable, whether the patient will or will not have abnormal vital signs that night. The training data is retrospective therefore it contains the nighttime vitals that were observed, which the investigators will code as a binary variable and use as the outcome variable for the model to train against.

The investigators will incorporate this algorithm into an EHR alert so physicians can observe its output during their work, and use this information, complemented by their own clinical judgment, to decide about ordering reduced vital sign checks for a given patient.

The investigators will study the effect of this EHR alert on several outcomes: in-hospital delirium (measured by nurse assessment), sleep opportunity (a measurement, based on observational EHR data, of patient care related sleep interruptions), and patient satisfaction (measured by nationally-administered post-hospitalization HCAHPS surveys). Balancing measures, to ensure that reduced vital sign checks do not cause patient harm, will be rapid response calls and code blue calls.

Physician teams will be randomized to either see the EHR alert (intervention arm) or not see the EHR alert.

Conditions

Delirium; Sleep Disturbance

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: PREVENTION
• Blinding Strategy: NONE

Study Groups

EHR Alert

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

Group Type: EXPERIMENTAL

Nighttime Vital Sign EHR Alert

Intervention Type: 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.

No Alert

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

Group Type: PLACEBO_COMPARATOR

No EHR alert

Intervention Type: OTHER

No change to EHR function; no alert visible to providers

Interventions

Nighttime Vital Sign EHR Alert

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.

Intervention Type: BEHAVIORAL

No EHR alert

No change to EHR function; no alert visible to providers

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

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

Exclusion Criteria

• N/A

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

University of California, San Francisco

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Mark Pletcher, MD

Role: STUDY_DIRECTOR

Director of the UCSF Informatics and Research Innovation Program

Locations

UCSF

San Francisco, California, United States

Countries

United States

References

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.

Reference Type: DERIVED

PMID: 34962506 (View on PubMed)

Other Identifiers

nightvitals

Identifier Type: -

Identifier Source: org_study_id