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.
None
Demands with relevant information about the patient or the event incomplete or absent.
All patients who have requested telephone assistance in the last five years to the Medical Dispatch Center in Andalusia.
Cohort
Retrospective
Not listed
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)
Effectiveness
Effectiveness of the current telephone triage system for these demands in the CCUE of Andalusia, using clinically relevant performance metrics, especially under-triage and over-triage (including accuracy, sensitivity, specificity, positive and negative likelihood ratios, false positive and false negative rates, area under the ROC curve and F1-score).
Artificial Intelligence Model Design
Engineers have to develop and validate an Artificial Intelligence (AI) model that integrates classical AI and generative AI techniques to improve the accuracy and efficiency of telephone triage of users requesting assistance for unconsciousness/cardiorespiratory arrest (registered in the system as types of demands A36 and A58 respectively), respiratory distress (A16), non-traumatic chest pain (A23) and stroke (A54) in the Emergency and Urgency Coordination Centres (CCUE) of Andalusia.
Performance of Artificial Intelligence (AI) models in dispatch medical center
Engineers have to performance different AI algorithms in predicting the severity of telephone calls for unconsciousness/cardiopulmonary arrest, respiratory distress, non-traumatic chest pain, and stroke, using structured and unstructured data from the CCR and HCDM databases.
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Centro de Emergencias Sanitarias 061 Andalucía
Lead sponsor
Junta de Andalucia
Collaborator
Andaluz Health Service
Collaborator
