A Rapid Diagnostic of Risk in Hospitalized Patients Using Machine Learning

Running, not enrolling · Not applicable

Conditions studied: Sepsis, Septicemia, Respiratory Failure, Hemodynamic Instability, COVID-19, Cardiac Arrest, Clinical Deterioration

In brief

In this study, the investigators will deploy a software-based clinical decision support tool (eCARTv5) into the electronic health record (EHR) workflow of multiple hospital wards. eCART's algorithm is designed to analyze real-time EHR data, such as vitals and laboratory results, to identify which patients are at increased risk for clinical deterioration. The algorithm specifically predicts imminent death or the need for intensive care unit (ICU) transfer. Within the eCART interface, clinical teams are then directed toward standardized guidance to determine next steps in care for elevated-risk patients. The investigators hypothesize that implementing such a tool will be associated with a decrease in ventilator utilization, length of stay, and mortality for high-risk hospitalized adults.

Key facts

Study ID

NCT05893420

Run by

AgileMD, Inc.

People needed

30000

Starts

2024-12-31

Expected to finish

2026-12-31

Last updated by the study team

2025-07-29

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

• 18 years old
• Admitted to an eCART-monitored medical-surgical unit (scoring location)

You may not qualify if…

• Younger than 18 years old
• Not admitted to an eCART-monitored medical surgical unit (scoring location)

Where it is running

• Yale New Haven Health System — New Haven, Connecticut, United States
• BayCare Health System — Clearwater, Florida, United States
• University of Wisconsin Health — Madison, Wisconsin, United States

Full record on ClinicalTrials.gov

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