[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100624229":3},{"organization":4,"outcomesModule":7,"designInfo":26,"detailedDescription":25,"studyPopulation":25,"armGroups":36,"interventions":49,"overallOfficials":60,"centralContacts":65,"locations":71,"responsibleParty":86,"collaborators":25,"id":88,"slug":89,"hasResults":90,"nctId":91,"briefTitle":92,"officialTitle":93,"acronym":25,"eligibilityCriteria":94,"healthyVolunteers":90,"sex":95,"minAge":96,"maxAge":25,"enrollmentInfo":97,"targetDuration":25,"studyType":100,"phases":101,"briefSummary":103,"conditions":104,"keywords":25,"overallStatus":106,"whyStopped":25,"lastUpdateSubmitDate":107,"lastUpdatePostDateStruct":108,"startDateStruct":111,"completionDateStruct":113,"leadSponsor":115,"locationsCount":116},{"fullName":5,"class":6},"Hospital El Salvador","OTHER_GOV",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":25},[9],{"measure":10,"description":11,"timeFrame":12},"Diagnostic Accuracy","The proportion of consultations where the primary diagnosis recorded by the participating physician matches the \"gold standard\" reference diagnosis. The reference diagnosis is established by a panel of three independent, blinded expert evaluators reviewing the anonymized clinical notes. A diagnosis is considered \"correct\" (value = 1) if it matches the reference diagnosis within the same clinically equivalent diagnostic group; otherwise, it is considered \"incorrect\" (value = 0). The analysis will compare the proportion of correct diagnoses between the AI-enabled and AI-disabled arms.","Through study completion, ~ 12-16 weeks",[14,18,21],{"measure":15,"description":16,"timeFrame":17},"Diagnostic Concordance","The level of agreement between the physician's diagnosis and the expert reference diagnosis, measured using Cohen's Kappa coefficient. This measure evaluates the reliability of the diagnoses beyond simple percentage agreement, accounting for agreement occurring by chance.","Through study completion, ~12-16 weeks",{"measure":19,"description":20,"timeFrame":17},"Diagnostic Accuracy Stratified by Physician Experience Level","Evaluation of diagnostic accuracy (proportion of correct diagnoses) compared between subgroups of physicians with \"High Experience\" (≥1 year in the program or ≥20 consultations) versus \"Low Experience\" (\\\u003C1 year in the program or \\\u003C20 consultations).",{"measure":22,"description":23,"timeFrame":24},"Diagnostic Accuracy Stratified by Clinical System","The proportion of correct diagnoses stratified by the physiological system of the pathology: Respiratory, Digestive, Urinary, or Ophthalmic. This outcome assesses if the AI's performance or utility varies depending on the specific type of clinical condition.","Through study completion, ~12-16 weeks.",null,{"allocation":27,"interventionModel":28,"interventionModelDescription":29,"primaryPurpose":30,"observationalModel":25,"timePerspective":25,"maskingInfo":31},"RANDOMIZED","PARALLEL","Because participants are assigned to one of two arms and remain in that arm throughout the trial, this is a parallel study model.","DIAGNOSTIC",{"masking":32,"maskingDescription":25,"whoMasked":33},"DOUBLE",[34,35],"INVESTIGATOR","OUTCOMES_ASSESSOR",[37,43],{"label":38,"type":39,"description":40,"interventionNames":41},"AI Disabled (Standard Telemedicine)","EXPERIMENTAL","Physicians in this arm will conduct telemedicine consultations with the AI assistant features temporarily disabled. They will perform the standard clinical workflow without automated support for history taking or diagnostic suggestions. This arm represents the removal of the AI tool to measure its impact.",[42],"Other: Standard Telemedicine Workflow (No AI)",{"label":44,"type":45,"description":46,"interventionNames":47},"AI Enabled (AI-Assisted Telemedicine)","ACTIVE_COMPARATOR","Physicians in this arm will conduct telemedicine consultations with full access to the DoctorSV AI assistant.",[48],"Device: DoctorSV AI Assistant",[50,55],{"type":51,"name":52,"description":53,"armGroupLabels":54,"otherNames":25},"DEVICE","DoctorSV AI Assistant","An AI tool integrated into the telemedicine platform, built on Google's Gemini Large Language Models (LLMs). The system operates via two modules:\n\n(1) a clinical history assistant that supports structured documentation of patient information in real-time and (2) a pre-diagnosis tool that analyzes documented clinical data to generate differential diagnosis suggestions for the physician's consideration. The model uses contextual prompting to ensure suggestions are culturally and clinically appropriate for El Salvador.",[44],{"type":56,"name":57,"description":58,"armGroupLabels":59,"otherNames":25},"OTHER","Standard Telemedicine Workflow (No AI)","Standard primary care consultation via videocall without the assistance of artificial intelligence tools. Physicians rely solely on their own clinical judgment and manual documentation without automated summaries or diagnostic prompts.",[38],[61],{"name":62,"affiliation":63,"role":64},"Manuel Bello, MD","Hospital Nacional El Salvador","PRINCIPAL_INVESTIGATOR",[66],{"name":67,"role":68,"phone":69,"phoneExt":25,"email":70},"Principal Investigator","CONTACT","212-203-3323","stella@saglobalhealth.org",[72],{"facility":63,"status":25,"city":73,"state":25,"zip":25,"country":74,"countryCode":75,"cosmosGeoPoint":76,"geoPoint":81,"contacts":82},"San Salvador","El Salvador","SV",{"type":77,"coordinates":78},"Point",[79,80],-89.18718,13.68935,{"lat":80,"lon":79},[83],{"name":62,"role":68,"phone":84,"phoneExt":25,"email":85},"503 78885563","manuel.bello@doctorsv.gob.sv",{"type":87,"investigatorFullName":25,"investigatorTitle":25,"investigatorAffiliation":25,"oldNameTitle":25,"oldOrganization":25},"SPONSOR","100624229","ai-telemedicine-support-for-primary-care-physicians-in-el-salvador-100624229",false,"NCT07406919","AI Telemedicine Support for Primary Care Physicians in El Salvador","Pragmatic Randomized Clinical Trial of AI-Assisted Telemedicine to Improve Diagnostic Accuracy Among Primary Care Physicians in El Salvador","Inclusion Criteria:\n\n* Must be a physician employed by the DoctorSV telemedicine program\n* Must provide written informed consent to participate in the study\n* Consultations must be for acute pathologies of the digestive, respiratory, or urinary systems, or acute ophthalmic infections manageable in primary care\n* Consultation must be the first medical contact (first visit) for the current acute episode\n* The condition must correspond to specific ICD-11 codes defined in the study protocol\n\nExclusion Criteria:\n\n* Physicians who are inactive on the platform for more than 4 consecutive weeks\n* Physicians who transition to work modalities other than telemedicine or reduce their working hours to less than 20 hours per week\n* Physicians whose employment contract ends (resignation, dismissal, or contract completion) during the data collection period\n* Consultations classified by the physician as requiring immediate in-person attention\n* Consultations requiring referral to another level of care or specialty for definitive management\n* Consultations interrupted or incomplete due to connectivity or system failures\n* Consultations solely for administrative purposes (e.g., certificates, repeat prescriptions without clinical evaluation)\n* Consultations that are follow-up visits or controls for a previously evaluated episode","ALL","18 Years",{"count":98,"type":99},180,"ESTIMATED","INTERVENTIONAL",[102],"NA","The goal of this clinical trial is to learn whether access to an artificial intelligence (AI) clinical decision support assistant can improve diagnostic accuracy during real-world telemedicine consultations among primary care physicians in El Salvador.\n\nThe main questions it aims to answer are:\n\n* Does access to the AI assistant increase the proportion of correct diagnoses compared to telemedicine without AI assistance?\n* Does the effect of the AI assistant differ according to the physician's prior experience using AI in telemedicine?\n\nResearchers will compare physicians with the AI assistant enabled to physicians with the AI assistant temporarily disabled to see if access to AI improves diagnostic accuracy.\n\nParticipants (physicians) will:\n\n* Provide telemedicine consultations as part of their routine clinical duties.\n* Be randomly assigned to either have the AI assistant enabled or disabled during the study period.\n* Continue documenting clinical encounters in the electronic platform as usual.\n* Have their anonymized consultation notes reviewed by an independent expert panel to determine diagnostic accuracy.",[105],"Artificial Intelligence (AI) in Diagnosis","NOT_YET_RECRUITING","2026-02-13",{"date":109,"type":110},"2026-02-17","ACTUAL",{"date":112,"type":99},"2026-02",{"date":114,"type":99},"2026-08",{"name":5,"class":6},1]