[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100611983":3},{"organization":4,"armGroups":7,"interventions":7,"overallOfficials":7,"centralContacts":8,"locations":14,"responsibleParty":38,"collaborators":40,"id":46,"slug":47,"hasResults":48,"nctId":49,"briefTitle":50,"officialTitle":51,"acronym":52,"eligibilityCriteria":53,"healthyVolunteers":48,"sex":54,"minAge":7,"maxAge":7,"enrollmentInfo":55,"targetDuration":7,"studyType":58,"phases":7,"briefSummary":59,"conditions":60,"keywords":66,"overallStatus":17,"whyStopped":7,"lastUpdateSubmitDate":73,"lastUpdatePostDateStruct":74,"startDateStruct":77,"completionDateStruct":79,"leadSponsor":81,"locationsCount":82},{"fullName":5,"class":6},"Centro de Emergencias Sanitarias 061 Andalucía","OTHER",null,[9],{"name":10,"role":11,"phone":12,"phoneExt":7,"email":13},"María José MJ Dr. Luque Hernández, MD PhD","CONTACT","0034 617563352","mariajose.luque.sspa@juntadeandalucia.es",[15],{"facility":16,"status":17,"city":18,"state":18,"zip":19,"country":20,"countryCode":21,"cosmosGeoPoint":22,"geoPoint":27,"contacts":28},"Centro de Emergencias Sanitarias 061","RECRUITING","Málaga","29590","Spain","ES",{"type":23,"coordinates":24},"Point",[25,26],-4.42034,36.72016,{"lat":26,"lon":25},[29,32,35],{"name":30,"role":11,"phone":31,"phoneExt":7,"email":13},"María José MJ Luque-Hernández, MD PhD","+34 617563352",{"name":33,"role":34,"phone":7,"phoneExt":7,"email":7},"María José Luque Hernández, Doctor of Medicine, PhD","PRINCIPAL_INVESTIGATOR",{"name":36,"role":37,"phone":7,"phoneExt":7,"email":7},"CRISTINA LÓPEZ LÓPEZ, Doctor of Medicine","SUB_INVESTIGATOR",{"type":39,"investigatorFullName":7,"investigatorTitle":7,"investigatorAffiliation":7,"oldNameTitle":7,"oldOrganization":7},"SPONSOR",[41,44],{"name":42,"class":43},"Junta de Andalucia","OTHER_GOV",{"name":45,"class":43},"Andaluz Health Service","100611983","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-100611983",false,"NCT07247669","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).","Proyecto \"trIAje\": evaluación y optimización Del Triaje telefónico Mediante Modelos de Inteligencia Artificial (IA) Para la detección de Demandas Por patología Tiempo-dependiente en el Centro Coordinador de Urgencias y Emergencias (CCUE).","TrIAje Project","Inclusion Criteria:\n\nTelephone calls recorded with codes A36 + A58 (unconsciousness\u002Fcardiorespiratory arrest), A16 (respiratory distress), A23 (non-traumatic chest pain) and A54 (stroke).\n\nExclusion Criteria:\n\n* Demands with relevant information about the patient or the event incomplete or absent.","ALL",{"count":56,"type":57},5000000,"ESTIMATED","OBSERVATIONAL","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).\n\nThis project focuses on improving telephone triage for four key emergency situations that often indicate severe or life-threatening conditions:\n\nUnconsciousness \u002F 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.\n\nHow it will be done:\n\nThe investigators will analyze anonymized historical call data from the emergency coordination system (CCR) and digital clinical records (HCDM). This includes:\n\nStructured data: Predefined fields, such as answers to standard triage questions.\n\nUnstructured data: Free-text notes and other information recorded during the call.\n\nA hybrid AI approach will be used, combining:\n\nTraditional AI methods (supervised learning and deep learning) to classify cases.\n\nGenerative AI techniques (advanced language models) to extract useful insights from free-text data.\n\nBuilding the Best Prediction Model\n\nTo find the most effective AI model, we will test different machine learning techniques, including:\n\nDecision 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.\n\nMeasuring Success\n\nWe will evaluate the AI model using key performance metrics, including:\n\nAccuracy (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.",[61,62,63,64,65],"Chest Pain","Stroke Acute","Respiratory Failure","Cardiac Arrest (CA)","Coma",[67,68,69,70,71,72],"Emergency Medical Communication Center","Triage Telephone","Emergency Medical Services","Priority","Machine Learning","Artificial Intelligence","2026-05-12",{"date":75,"type":76},"2026-05-13","ACTUAL",{"date":78,"type":76},"2025-03-01",{"date":80,"type":57},"2027-12-31",{"name":5,"class":6},1]