Using machine learning to identify at-risk hospitalized patients
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
This study is testing a new software tool that uses patient data to help doctors spot hospitalized patients who might be at risk of getting worse, so they can provide better care and improve outcomes.
Quick facts
| Phase | Not applicable |
|---|---|
| Study type | Interventional |
| Enrollment | 30000 (estimated) |
| Ages | 18 Years and up |
| Sex | All |
| Sponsor | AgileMD, Inc. Industry-sponsored |
| Locations | 2 sites (Clearwater, Florida and 1 other locations) |
| Trial ID | NCT05893420 on ClinicalTrials.gov |
What this trial studies
This study implements a software-based clinical decision support tool called eCARTv5 within the electronic health record (EHR) systems of various hospital wards. The tool utilizes a machine learning algorithm to analyze real-time patient data, such as vital signs and lab results, to predict which patients are at increased risk for clinical deterioration, including imminent death or the need for ICU transfer. By integrating this tool into clinical workflows, healthcare teams receive standardized guidance for managing high-risk patients, with the goal of reducing ventilator use, length of hospital stays, and mortality rates among hospitalized adults.
Who should consider this trial
Good fit: Ideal candidates for this study are hospitalized adults aged 18 and older who are admitted to medical-surgical units monitored by the eCART system.
Not a fit: Patients younger than 18 years old or those not admitted to an eCART-monitored unit will not benefit from this study.
Why it matters
Potential benefit: If successful, this approach could significantly improve patient outcomes by enabling earlier interventions for those at risk of severe clinical deterioration.
How similar studies have performed: Other studies utilizing machine learning for clinical decision support have shown promise, indicating that this approach could lead to meaningful advancements in patient care.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * 18 years old * Admitted to an eCART-monitored medical-surgical unit (scoring location) Exclusion Criteria: * Younger than 18 years old * Not admitted to an eCART-monitored medical surgical unit (scoring location)
Where this trial is running
Clearwater, Florida and 1 other locations
- BayCare Health System — Clearwater, Florida, United States (Recruiting)
- University of Wisconsin Health — Madison, Wisconsin, United States (Recruiting)
Study contacts
- Study coordinator: Dana P Edelson, MD, MS
- Email: dana@agilemd.com
- Phone: 415-650-0522
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.