Artificial Intelligence System in Medical Regulation

Not yet recruiting

• Conditions: Emergency Medical Communication Centres; Call Management

• Registration Number: NCT04953845
• Lead Sponsor: Centre Hospitalier Universitaire de Besancon

Brief Summary

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.

Detailed Description

Not available

Study Timeline

• Status Verified: 2021-03
• Study First Submitted: 2021-06-15
• Study First Submitted QC: 2021-06-28
• Last Update Submitted: 2021-06-28
• Study First Posted: 2021-07-08
• Last Update Posted: 2021-07-08
• Study Start: 2021-09-01
• Primary Completion: 2024-09-30
• Study Completion: 2025-09-30

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 500000

Inclusion Criteria

• all patient which call the emergency medical communication centres

Exclusion Criteria

• patient opposed to the study

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Performance (sensitivity and specificity) of the artificial intelligence system in Emergency medical Communication Centres, concerning time sensitive disease	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)

Secondary Outcome Measures

Name	Time	Method
Patient transport time	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.