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Evaluation of Effectiveness for Connected Network for EMS Comprehensive Technical-support Using Artificial Intelligence (CONNECT-AI) System by Community Intervention

NCT04829279 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 15392

Last updated 2023-07-07

No results posted yet for this study

Summary

This study aims to verify the effectiveness of the connected network for EMS comprehensive technical-support using artificial intelligence (CONNECT-AI) system through demonstration in the local community. The study was designed as a prospective non-random cross-intervention study design in two preselected communities. The subjects of the study are patients transferred to the local emergency department(ED) through an ambulance of a fire department in the selected community. If the storage and transmission of information collected by an ambulance fails or the information of the transferred patient cannot be verified in the transferred ED, it is excluded from the study. In this study, the developed CONNECT-AI system was installed in all emergency vehicles and EDs in two regional cohorts, and the effectiveness was measured by operating an intersection for the same period. The primary outcome is the transfer time spent in the pre-hospital stage, and the secondary outcome is whether the optimal transfer hospital is selected.

Conditions

  • Emergency Patient Transported by Ambulance

Interventions

OTHER

CONNECT AI system group

A. During the Intervention period, paramedics wear equipment for multi-faceted data acquisition and press the start button of the system. B. During the Intervention period, inside the ambulance, a application that implements a function that automatically evaluates the patient's severity, displays a list of optimal transfer hospitals based on this, and shares real-time information of the hospitals, is installed so that paramedics can refer to the work. C. ED's medical staff will receive through ER-kiosk pre-hospital patient information collected and analyzed through the CONNECT AI system before arrival.

Sponsors & Collaborators

  • Yonsei University

    lead OTHER

Principal Investigators

  • Hyuk-Jae Chang · Division of Cardiology, Yonsei university college of medicine

Study Design

Allocation
NON_RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Model
CROSSOVER

Eligibility

Min Age
0 Years
Max Age
100 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-05-01
Primary Completion
2021-12-31
Completion
2021-12-31

Countries

  • South Korea

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04829279 on ClinicalTrials.gov