NCT07247669: TrIAje Project

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).

Active, enrolled Last updated 25 November 2025

What this trial tests

trial in Chest Pain in 5,000,000 participants. Participants enrolled and being followed up; not accepting new ones.

Timeline

1 January 2025

Primary endpoint
1 December 2025

31 December 2027

Quick facts

Lead sponsor	Centro de Emergencias Sanitarias 061 Andalucía
Status	Active, enrolled
Study type	OBSERVATIONAL
Enrollment	5,000,000
Start date	1 January 2025
Primary completion	1 December 2025
Estimated completion	31 December 2027
Sites	1 location across Spain

Conditions studied

• Chest Pain — all drugs for Chest Pain →
• Stroke Acute — all drugs for Stroke Acute →
• Respiratory Failure — all drugs for Respiratory Failure →
• Cardiac Arrest (CA) — all drugs for Cardiac Arrest (CA) →

Sponsor

Centro de Emergencias Sanitarias 061 Andalucía

Who can join

Eligibility, any sex, with Chest Pain or Stroke Acute. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

No peer-reviewed publications indexed yet for this trial.