Evaluation of Pediatric eCART Implementation

A Rapid Diagnostic of Risk in Hospitalized Pediatric Patients to Improve Outcomes Using Machine Learning

This is a study comparing 3 years of retrospective data (pre-implementation) to 2 years of prospective data after the implementation of a pediatric version of Electronic Cardiac Arrest Risk Triage (pediatric eCART), a clinical decision support (CDS) tool that uses electronic health records (EHR) to identify patients with high risk for life threatening outcomes. Up to 30,000 encounters with pediatric patients will be assessed. Acceptability of the pediatric eCART intervention will also be measured from pediatric nurse clinicians.

Study Overview

• Status: Recruiting
• Conditions: Sepsis; Pediatric ALL
• Intervention / Treatment: Device: Pediatric eCART

Detailed Description

Pediatric eCART draws upon readily available EHR data and rapidly quantifies disease severity, predicting the likelihood of critical illness onset. Currently, no consistently available system continuously tracks the risk of critical illness in children admitted to UW Health. While AFCH has an implementation of Pediatric Early Warning Scores (PEWS) available for risk monitoring, internal reports indicate limited usage. Therefore, AFCH/UW Health clinicians or care providers do not have a reliable mechanism to risk-stratify patients for effective clinical decision-making.

This proposal leverages the AgileMD clinical decision support engine and a machine learning analytic developed in a dataset of over 30,000 patients. Pediatric eCART was explicitly designed to draw attention to patients at increased risk of deterioration and optimize patient management, including the timing of and need for ICU-level care.

Preliminary studies indicate that pediatric eCART implementation at the University of Chicago has led to improved outcomes. Similar improvements among children admitted to UW Health will lead to decreased morbidity and mortality among the pediatric population.

Further, a significant gap in understanding of nurse acceptance of data-driven CDS tools remains. Nurses are the largest workforce of clinicians in the health system and play a primary role in the detection of clinical deterioration as the clinicians that spend the most time observing and assessing patients; however, AI-driven CDS acceptability has not been measured to assess nurse acceptance of these emerging tools. Acceptability is essential to increase sustained use and to decrease suboptimal outcomes such as alert fatigue or increased cognitive load so that these tools ultimately mediate nurse well-being. One study assessed nurse perceptions of the usefulness of a sepsis early warning system and found that less than half of nurses perceived the alerts to be helpful and only a third of nurses reported that the alerts impacted patient care. Understanding nurse acceptance will inform AgileMD's design strategies to foster uptake and use so that predictive tools may be leveraged to improve the cognitive burden of nurse clinicians. In the end, the study will evaluate pediatric eCART on two pediatric groups: (1) screened pediatric patients; (2) pediatric nurse clinician end-users.

Study Design: This is a pre- and post- interventional study of a machine learning algorithm integrated into the electronic health record as a clinical decision support tool. The "pre" participants are hospitalized children (less than 18 years old) who were admitted to UW Health between January 1, 2022, and the date of pediatric eCART implementation in 2025. Pediatric eCART scores will be retrospectively calculated for the "pre" participants by feeding a patient's labs and vital sign observation into the pediatric eCART tool. The "post" participants are hospitalized children (less than18 years old) who will be admitted to UW Health within the two years following pediatric eCART implementation (expected 2025-2027). Pediatric eCART scores will be calculated in real-time for these patients.

• Study Type: Interventional
• Enrollment (Estimated): 30000
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Anoop Mayampurath, PhD; Phone Number: 608-261-1028; Email: mayampurath@wisc.edu

Study Locations

• United States
  • Wisconsin
    • Madison, Wisconsin, United States, 53792
      • Recruiting
      • American Family Children's Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria (pediatric patients):

• All pediatric patients scored on pediatric eCART (or eligible for scoring on either algorithm in the pre-implementation period) will be screened for study eligibility.
• Patients eligible for pediatric eCART scoring include pediatric (<18 years of age) patients
• Inpatient locations

Exclusion Criteria (pediatric patients):

• Patients who are ineligible for pediatric eCART scoring
• Neonates and birth encounters will be excluded from the pediatric eCART study

Inclusion Criteria (nurse clinicians):

• UW Health nurses who interact with eCART during patient care

Exclusion Criteria (nurse clinician):

• UW Health nurses no longer employed at UW Health

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Health Services Research
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Pediatric eCART	Device: Pediatric eCART; Integration of the pediatric version of electronic Cardiac Arrest Risk Triage as a clinical decision support tool within Epic for use by clinicians; Other Names: electronic Cardiac Arrest Risk Triage

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
In Hospital Mortality		assessed through hospital stay (typically up to 5 days on average, but may be over 60 days)
Intensive Care Unit (ICU) free days	Defined as the number of days patients were both alive and discharged from the ICU out of the first 28 days of hospitalization. Because death is biased toward fewer ICU days and is a competing outcome, patients who die prior to day 28 are assigned with 0 ICU-free days.	up to 28 days

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Median 30-day Ventilator-Free Days		assessed through hospital stay (typically up to 5 days on average, but may be over 60 days)
Summary of Critical Events	Summary of critical events, including mechanical ventilation, vasoactive administration, or death, reported by participant count.	assessed through hospital stay (typically up to 5 days on average, but may be over 60 days)
Total Hospital Length of Stay (LOS)	Reported in days	assessed through hospital stay (typically up to 5 days on average, but may be over 60 days)
Number of ICU transfers		assessed through hospital stay (typically up to 5 days on average, but may be over 60 days)
Usability of Pediatric eCART: System Usability Scale (SUS) score	SUS is a 10-item survey scored on a 5 point response scale from strongly disagree to strongly agree. Scores are transformed to a scale from 0-100 where higher scores are indicative of better system usability.	Surveys automatically sent to nurses within a week of eCART interface, responses collected up to 1 month
Acceptability of Pediatric eCART: Perceived Usefulness Scale	The Perceived Usefulness scale uses a 7-point response scale from 0 (extremely unlikely) to 6 (extremely likely) for a total possible range of scores from 0 to 36, higher scores indicate higher perceived usefulness.	Surveys automatically sent to nurses within a week of eCART interface, responses collected up to 1 month

Collaborators and Investigators

• Sponsor: University of Wisconsin, Madison
• Collaborators: AgileMD, Inc.
• Investigators: Principal Investigator: Anoop Mayampurath, PhD, UW School of Medicine and Public Health

Study record dates

Study Major Dates

• Study Start (Actual): December 1, 2025
• Primary Completion (Estimated): December 1, 2027
• Study Completion (Estimated): December 1, 2027

Study Registration Dates

• First Submitted: January 7, 2025
• First Submitted That Met QC Criteria: January 7, 2025
• First Posted (Actual): January 13, 2025

Study Record Updates

• Last Update Posted (Actual): December 17, 2025
• Last Update Submitted That Met QC Criteria: December 15, 2025
• Last Verified: December 1, 2025

More Information

Terms related to this study

• Keywords: machine learning; artificial intelligence; triage; clinical decision support; electronic medical records
• Additional Relevant MeSH Terms: Pathologic Processes; Neoplasms; Immune System Diseases; Infections; Neoplasms by Histologic Type; Systemic Inflammatory Response Syndrome; Inflammation; Hematologic Diseases; Lymphatic Diseases; Lymphoproliferative Disorders; Immunoproliferative Disorders; Leukemia, Lymphoid; Leukemia; Pathological Conditions, Signs and Symptoms; Hemic and Lymphatic Diseases; Sepsis; Precursor Cell Lymphoblastic Leukemia-Lymphoma
• Other Study ID Numbers: 2024-1835; A531200 (Other Identifier: UW Madison); Protocol Version 3/6/25 (Other Identifier: UW Madison)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: No