A Rapid Diagnostic of Risk in Hospitalized Patients Using Machine Learning

Description

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.

Conditions

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

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Study Overview

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Study Details

Study overview

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.

A Rapid Diagnostic of Risk in Hospitalized Patients With COVID-19, Sepsis, and Other High-Risk Conditions to Improve Outcomes and Critical Resource Allocation Using Machine Learning

A Rapid Diagnostic of Risk in Hospitalized Patients Using Machine Learning

Condition
Sepsis
Intervention / Treatment

-

Contacts and Locations

Clearwater

BayCare Health System, Clearwater, Florida, United States, 33759

Madison

University of Wisconsin Health, Madison, Wisconsin, United States, 53792

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

For general information about clinical research, read Learn About Studies.

Eligibility Criteria

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

Ages Eligible for Study

18 Years to

Sexes Eligible for Study

ALL

Accepts Healthy Volunteers

No

Collaborators and Investigators

AgileMD, Inc.,

Dana P Edelson, MD, MS, STUDY_CHAIR, AgileMD, Inc.

Study Record Dates

2025-06-30