Artificial Intelligence System in Medical Regulation (SIA-REMU)

June 28, 2021 updated by: Centre Hospitalier Universitaire de Besancon

Artificial Intelligence System for the Medical Regulation of Emergencies

Population health needs are increasing. Information and communication technologies are changing. The digital shift offers new opportunities for the exploration and analysis of mass health data. It is possible to rely on these new technologies to modernize, optimize patient management at the level of emergency medical communication centres.

Our project aims to integrate the methods and tools of artificial intelligence for emergency medical communication centres. The system aims to help regulate emergency calls at CRRA 15 in France, or Centrale 144 in Switzerland, to assess the severity of calls, identify care pathways, and improve efficiency when committing resources.

The development of such a system is aimed at securing and optimising the information system and the means of telecommunication used in the emergency medical communication centres, and provide an individualized response to the patient management.

Study Overview

Status

Not yet recruiting

Conditions

Study Type

Observational

Enrollment (Anticipated)

500000

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.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

N/A

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

all patient which call the emergency medical communication centres

Description

Inclusion Criteria:

  • all patient which call the emergency medical communication centres

Exclusion Criteria:

  • patient opposed to the study

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Cohort
  • Time Perspectives: Retrospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Performance (sensitivity and specificity) of the artificial intelligence system in Emergency medical Communication Centres, concerning time sensitive disease
Time Frame: through study completion, average 3 years

Once the artificial intelligence system in place, the diagnosis suspected by this system will be compared to the diagnosis validated in the medical record of each patient included. It will then be measured the performance values, such as sensitivity, specificity, positive and negative predictive value, time of identification of the pathology type time sensitive.

These results will then be compared to the usual practice of the Emergency medical Communication Centres without the help of the software to evaluate:

  • the added value of the software for the patient,
  • the added value of the call center through the quality indicators (intake rate, quality of service, load rate, average call duration, productivity)
through study completion, average 3 years

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Patient transport time
Time Frame: through study completion, average 3 years

The aim is to identify and model the available resources that can be used in the context of pre-hospital rescue (ambulances, helicopter, SMUR).

The following elements will be taken into consideration: location of intervention, access, clinical condition of the patient, weather conditions, traffic density.

The validity of the model will be evaluated through the access time to the patient, the transport time, the lack of ambulances or SMUR, comapred to the usual practice of the Emergency medical Communication Centres without the help of the software.
through study completion, average 3 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Centre Hospitalier Universitaire de Besancon

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Anticipated)

September 1, 2021

Primary Completion (Anticipated)

September 30, 2024

Study Completion (Anticipated)

September 30, 2025

Study Registration Dates

First Submitted

June 15, 2021

First Submitted That Met QC Criteria

June 28, 2021

First Posted (Actual)

July 8, 2021

Study Record Updates

Last Update Posted (Actual)

July 8, 2021

Last Update Submitted That Met QC Criteria

June 28, 2021

Last Verified

March 1, 2021

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2021/591

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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