Evaluation and Optimization of Telephone Triage Using Artificial Intelligence (AI) Models for the Detection of Demands for Time-dependent Pathology at the Emergency and Urgent Care Coordination Center (CCUE).

NCT07247669 | Active Not Recruiting DMC

• 2 other identifiers: 2025_CES_061_IA_TRIAJE_V5SICEIA-2024-002592
• Study Type: observational
• Target: 5,000,000
• Locations: 1 country
• Sites: 1

Brief Summary

Improving Telephone Triage in Emergency Calls with AI The Coordinating Centre for Urgencies and Emergencies in Andalusia (CCUE) handles thousands of calls every day. Each call needs to be assessed based on the information given over the phone to determine how serious the case is. The reasons for calling range from minor health issues to life-threatening emergencies like cardiac arrest (CPA). This project focuses on improving telephone triage for four key emergency situations that often indicate severe or life-threatening conditions: Unconsciousness / Cardiac arrest Difficulty breathing Chest pain (non-traumatic, possible heart-related issues) Stroke symptoms Our goal is to make telephone triage more accurate and efficient by using advanced Artificial Intelligence (AI) techniques, including Machine Learning (ML) and Natural Language Processing (NLP). These tools will help CCUE operators make better and faster decisions, ensuring that patients receive the right care as quickly as possible. How it will be done: The investigators will analyze anonymized historical call data from the emergency coordination system (CCR) and digital clinical records (HCDM). This includes: Structured data: Predefined fields, such as answers to standard triage questions. Unstructured data: Free-text notes and other information recorded during the call. A hybrid AI approach will be used, combining: Traditional AI methods (supervised learning and deep learning) to classify cases. Generative AI techniques (advanced language models) to extract useful insights from free-text data. Building the Best Prediction Model To find the most effective AI model, we will test different machine learning techniques, including: Decision Trees Random Forests Support Vector Machines (SVM) XGBoost Ensemble methods Neural Networks We will also analyze which questions and variables are the most important in predicting the severity of a case. Based on this, we will suggest improvements to the current triage questions to enhance accuracy. Measuring Success We will evaluate the AI model using key performance metrics, including: Accuracy (overall correctness) Sensitivity (ability to detect real emergencies) Specificity (ability to avoid false alarms) False Positive \& False Negative Rates (how often the system makes mistakes) Likelihood Ratios (how well the system distinguishes between urgent and non-urgent cases) F1-Score \& ROC Curve (overall performance indicators) Why This Matters This project will assess how effective the current telephone triage system is and develop a new AI-powered model to improve it. The goal is to help emergency operators quickly identify the most serious cases, reducing response times and improving patient outcomes. In the future, the investigators aim to integrate this improved AI model into the CCUE system to enhance emergency response across Andalusia.

Conditions

Chest Pain; Stroke Acute; Respiratory Failure; Cardiac Arrest (CA); Coma

Keywords

Emergency Medical Communication Center; Triage Telephone; Emergency Medical Services; Priority; Machine Learning; Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

• Detection of critical severity from Medical Dispatch Center

  the medical dispatch center has to Detect a Time dependent pathology (the ones that are life-threatening emergencies).Minutes from call receipt to recognition of a life-threatening emergency (time dependent pathology)

  24 hours. From the call receipt to medical team dispatch

Secondary Outcomes (3)

• Effectiveness

  one year

• Artificial Intelligence Model Design

  2 years

• Performance of Artificial Intelligence (AI) models in dispatch medical center

  2 years

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All patients who have requested telephone assistance in the last five years to the Medical Dispatch Center in Andalusia.

You may qualify if:

• Telephone calls recorded with codes A36 + A58 (unconsciousness/cardiorespiratory arrest), A16 (respiratory distress), A23 (non-traumatic chest pain) and A54 (stroke).

You may not qualify if:

• Demands with relevant information about the patient or the event incomplete or absent.

Sponsors & Collaborators

• Centro de Emergencias Sanitarias 061 Andalucía: lead
• Junta de Andalucia: collaborator
• Andaluz Health Service: collaborator

Study Sites (1)

Centro de Emergencias Sanitarias 061

Málaga, Málaga, 29590, Spain

Location

MeSH Terms

Conditions

Chest Pain; Stroke; Respiratory Insufficiency; Heart Arrest; Coma

Condition Hierarchy (Ancestors)

Pain; Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Cerebrovascular Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Vascular Diseases; Cardiovascular Diseases; Respiration Disorders; Respiratory Tract Diseases; Heart Diseases; Unconsciousness; Consciousness Disorders; Neurobehavioral Manifestations

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 30, 2025
• First Posted: November 25, 2025
• Study Start: January 1, 2025
• Primary Completion: December 1, 2025
• Study Completion (Estimated): December 31, 2027
• Last Updated: November 25, 2025

Record last verified: 2025-11

Locations

ES (1)