Predicting ICU Transfers and Other Unforeseen Events (PICTURE)-Pediatric

Last updated: March 3, 2026
Sponsor: University of Michigan
Overall Status: Active - Recruiting

Phase

N/A

Condition

N/A

Treatment

Control Arm - Scores and alerts hidden

PICTURE-Pediatric scores and alerts

Clinical Study ID

NCT07304050
HUM00274862
  • Ages 30-25
  • All Genders

Study Summary

The purpose of this study is to evaluate the effectiveness and user satisfaction of the study teams early warning system, called PICTURE, which utilizes artificial intelligence (AI) techniques and algorithms to identify patient deterioration on pediatric units within Mott Children's Hospital.

In this pilot study the patient care team will review the PICTURE information and alerts. Morning rounds will be partially informed by the PICTURE scores and the scores will be included in the hand off notes for the patients with a red score.

The primary purpose of this study is to test the hypothesis that the combination of the PICTURE-Pediatric model, the proposed workflow and the proposed interface results in at least 80% compliance.

No participants will be consented as the Institutional review board has approved a waiver of consent for THE clinicians and the patients information being reviewed.

The enrollment numbers will include only the clinicians.

Eligibility Criteria

Inclusion

Inclusion Criteria - Patients:

  • Age ≥30 days and ≤25 years

  • Be on the general care wards of Mott Children's Hospital or boarding in the Emergency Department (ED), but admitted to a pediatric floor medical team. These will be enforced by these criteria:

  1. Patient class: not emergency b. Patient level of care: not Intensive Care unit (ICU) c. Patient locations (per protocol)

Exclusion Criteria - Patients:

  • Patients that do not meet the inclusion criteria

Study Design

Total Participants: 375
Treatment Group(s): 2
Primary Treatment: Control Arm - Scores and alerts hidden
Phase:
Study Start date:
February 09, 2026
Estimated Completion Date:
January 31, 2027

Study Description

PICTURE Pediatric evaluates large amounts of patient data, labs, monitored data, and vital signs (all variables that are measured as part of routine clinical care and that are stored in the electronic health record in real-time); it does this every 15-minutes and delivers a deterioration risk index score that has been shown to outperform Pediatric Early Warning Score (PEWS), and other similar scoring systems in identifying patients at risk for clinical deterioration. Clinical implementation of PICTURE Pediatric could identify patients at high risk of deterioration early enough to intervene and decrease the need for transfer to the Pediatric Intensive Care unit (PICU), and it could also reduce morbidity and hospital length of stay. This in turn could also improve bed availability for patients awaiting admission in the emergency department and improve efficient transfer out of the PICU, all of which improves care and reduces cost for the patients, their families, and the hospital system.

This protocol also does not mandate or require clinical decisions based on alerts generated (e.g. there is no mandate to transfer to the Intensive Care unit, perform an intervention, or change therapy/clinical course based on PICTURE alerts); rather, the clinical team will decide whether additional investigation, therapy, intervention, or transfer is required after alerts are generated based on staff's clinical experience, knowledge and with consultation with an attending physician when applicable. In case of device failure, there would be no deterioration risk scores generated and the clinical teams would simply continue to follow the current workflow.

Connect with a study center

  • University of Michigan

    Ann Arbor, Michigan 48109
    United States

    Active - Recruiting

  • University of Michigan

    Ann Arbor 4984247, Michigan 5001836 48109
    United States

    Site Not Available

Map preview placeholder

Not the study for you?

Let us help you find the best match. Sign up as a volunteer and receive email notifications when clinical trials are posted in the medical category of interest to you.