Ignite Creation Date:
2025-12-25 @ 1:17 AM
Ignite Modification Date:
2025-12-25 @ 11:26 PM
Study NCT ID:
NCT07252193
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2025-11-26
First Post:
2025-10-02
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Generative AI-Based Simulation for Diagnostic Communication in Type 2 Diabetes (DIALOGUE-DM2)
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003924', 'term': 'Diabetes Mellitus, Type 2'}], 'ancestors': [{'id': 'D003920', 'term': 'Diabetes Mellitus'}, {'id': 'D044882', 'term': 'Glucose Metabolism Disorders'}, {'id': 'D008659', 'term': 'Metabolic Diseases'}, {'id': 'D009750', 'term': 'Nutritional and Metabolic Diseases'}, {'id': 'D004700', 'term': 'Endocrine System Diseases'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'TRIPLE', 'whoMasked': ['PARTICIPANT', 'INVESTIGATOR', 'OUTCOMES_ASSESSOR'], 'maskingDescription': 'Participant, Investigator, Outcomes Assessor'}, 'primaryPurpose': 'HEALTH_SERVICES_RESEARCH', 'interventionModel': 'PARALLEL', 'interventionModelDescription': 'Two-arm randomized, blinded, controlled trial comparing AI-based simulation training with traditional training in medical students facing type 2 diabetes disclosure scenarios.'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 120}}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2025-09-22', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-11', 'completionDateStruct': {'date': '2026-02-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-11-18', 'studyFirstSubmitDate': '2025-10-02', 'studyFirstSubmitQcDate': '2025-11-18', 'lastUpdatePostDateStruct': {'date': '2025-11-26', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-11-26', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2025-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Change in Diagnostic Communication Performance Score', 'timeFrame': 'Approximately 12 weeks (from pre-test to post-test per participant).', 'description': 'Improvement in diagnostic communication skills, measured using validated rubrics - the Kalamazoo Essential Elements Communication Checklist and the Medical Communication Rating Scale (MCRS) - applied to standardized patient scenarios. Independent blinded faculty evaluators and AI scoring will be used. Scores range from 0 to 100, with higher values indicating better diagnostic communication performance.'}], 'secondaryOutcomes': [{'measure': 'Change in Student Self-Reported Confidence in Diagnostic Communication', 'timeFrame': 'Approximately 12 weeks (from pre-test to post-test per participant).', 'description': "Change in students' self-reported confidence when disclosing a diagnosis of type 2 diabetes, measured through a structured questionnaire using a 5-point Likert scale (1 = very low confidence, 5 = very high confidence). Higher scores indicate greater self-perceived confidence in diagnostic communication."}, {'measure': 'Change in Domain-Specific Diagnostic Communication Scores (Kalamazoo Framework and Medical Communication Rating Scale)', 'timeFrame': 'Approximately 12 weeks (from pre-test to post-test per participant).', 'description': 'Improvement in specific communication domains - information delivery, empathy, risk explanation, and shared decision-making - evaluated using the Kalamazoo Essential Elements Communication Checklist and the Medical Communication Rating Scale (MCRS). Each domain is scored from 0 to 100, with higher scores indicating better performance.'}, {'measure': 'Agreement Between Human Evaluators and AI Scoring', 'timeFrame': 'Assessed at post-test, approximately 12 weeks after baseline per participant.', 'description': "Level of concordance between blinded human evaluators and AI-based scoring of diagnostic communication performance, assessed using Cohen's kappa coefficient (κ). Scores range from -1.0 to +1.0, where values closer to +1.0 indicate stronger agreement between evaluators."}, {'measure': 'Student Satisfaction With the Assigned Training Method', 'timeFrame': 'Assessed immediately after completion of the post-test, approximately 12 weeks after baseline per participant.', 'description': 'Satisfaction with the assigned training method (AI-based simulation vs. traditional training), measured using a structured 5-point Likert satisfaction survey (1 = very dissatisfied; 5 = very satisfied). Higher scores indicate greater satisfaction with the training method.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Generative AI', 'Simulation-Based Education', 'Medical Students', 'Diagnostic Communication Skills', 'Artificial Intelligence in Healthcare', 'DIALOGUE-DM2'], 'conditions': ['Type 2 Diabetes Mellitus', 'Medical Education', 'Diagnostic Communication', 'Artificial Intelligence Simulation']}, 'referencesModule': {'references': [{'pmid': '40863274', 'type': 'BACKGROUND', 'citation': 'Suarez-Garcia RX, Chavez-Castaneda Q, Orrico-Perez R, Valencia-Marin S, Castaneda-Ramirez AE, Quinones-Lara E, Ramos-Cortes CA, Gaytan-Gomez AM, Cortes-Rodriguez J, Jarquin-Ramirez J, Aguilar-Marchand NG, Valdes-Hernandez G, Campos-Martinez TE, Vilches-Flores A, Leon-Cabrera S, Mendez-Cruz AR, Jay-Jimenez BO, Saldivar-Ceron HI. DIALOGUE: A Generative AI-Based Pre-Post Simulation Study to Enhance Diagnostic Communication in Medical Students Through Virtual Type 2 Diabetes Scenarios. Eur J Investig Health Psychol Educ. 2025 Aug 7;15(8):152. doi: 10.3390/ejihpe15080152.'}]}, 'descriptionModule': {'briefSummary': 'This randomized controlled trial evaluates the effectiveness of a generative artificial intelligence (AI)-based simulation program in improving diagnostic communication skills among medical students. The study is conducted at the Faculty of Higher Studies Iztacala, National Autonomous University of Mexico (UNAM).\n\nA total of 120 medical students are randomized to either an intervention group using the DIALOGUE-DM2 AI simulation platform or a control group following traditional educational methods. Participants complete a pre-test, receive training according to group assignment, and then undergo a post-test evaluation.\n\nThe primary outcome is improvement in diagnostic communication skills, measured by standardized patient scenarios and validated rubrics. Secondary outcomes include self-reported confidence, communication domains, and inter-rater agreement between faculty evaluators and AI scoring.\n\nThis trial aims to provide high-quality evidence on the potential of generative AI to enhance communication training in medical education, specifically in the context of type 2 diabetes diagnosis.', 'detailedDescription': 'This study builds on a prior pilot trial (published in 2024) that demonstrated the feasibility of using generative artificial intelligence (AI) to train medical students in diagnostic communication. The current trial extends that work with a randomized, blinded, controlled design and a larger sample size.\n\nDesign:\n\nThe study is a randomized, blinded, parallel-group, controlled trial conducted at the Faculty of Higher Studies Iztacala (FES Iztacala), UNAM. A total of 120 medical students are enrolled and randomized (1:1) into either the intervention group (AI-based simulation training) or the control group (traditional training with standardized patients and faculty feedback).\n\nIntervention:\n\n* Intervention group: Students interact with the DIALOGUE-DM2 platform, which provides generative AI-driven simulated patients. They complete multiple diagnostic disclosure scenarios and receive immediate feedback on performance, based on standardized communication rubrics.\n* Control group: Students receive standard training, including lectures and supervised practice with peer role-play and faculty-guided feedback.\n\nAssessments:\n\n* Pre-test: All students complete one standardized patient scenario with faculty and AI evaluation prior to intervention.\n* Training phase: Participants complete their assigned training (AI vs. standard).\n* Post-test: Students complete a standardized diagnostic disclosure scenario. Independent faculty evaluators (blinded to group assignment) and the AI platform score performance.\n\nOutcomes:\n\n* Primary outcome: Change in diagnostic communication performance score from pre-test to post-test, measured by validated rubrics (Kalamazoo framework, MRS).\n* Secondary outcomes:\n* Student self-assessment of communication confidence.\n* Domain-specific improvements (information delivery, empathy, risk explanation, shared decision-making).\n* Agreement between human evaluators and AI scoring.\n\nEthics and Oversight:\n\nThe study has been reviewed and approved by the Research Ethics Committee of FES Iztacala, UNAM (Approval Number CE/FESI/042025/1915). Risks are minimal, as the intervention is educational and non-invasive.\n\nSignificance:\n\nThis is the first randomized controlled trial in Mexico to evaluate a generative AI-based simulation for diagnostic communication. Results will inform the integration of AI-driven training tools into medical education curricula and could contribute to scalable innovations in the training of healthcare professionals for chronic disease management, starting with type 2 diabetes.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT'], 'maximumAge': '29 Years', 'minimumAge': '18 Years', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Medical students currently enrolled in the Faculty of Medicine (Medical Surgeon Program), UNAM-FES Iztacala.\n* Age between 18 and 30 years.\n* Able to provide informed consent.\n* Willing to participate in all study phases (pre-test, intervention, post-test).\n\nExclusion Criteria:\n\n* Prior participation in the DIALOGUE pilot study.\n* Previous formal training in diagnostic communication beyond the standard medical curriculum.\n* Incomplete availability for scheduled sessions.\n* Refusal or inability to provide informed consent.'}, 'identificationModule': {'nctId': 'NCT07252193', 'acronym': 'DIALOGUE-DM2', 'briefTitle': 'Generative AI-Based Simulation for Diagnostic Communication in Type 2 Diabetes (DIALOGUE-DM2)', 'organization': {'class': 'OTHER', 'fullName': 'Universidad Nacional Autonoma de Mexico'}, 'officialTitle': 'Generative AI Simulation for Diagnostic Communication in Type 2 Diabetes: A Randomized Controlled Trial (DIALOGUE-DM2)', 'orgStudyIdInfo': {'id': 'UNAM-DIALOGUE-DM2-2025'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'AI-Based Simulation Training (DIALOGUE-DM2)', 'description': 'Medical students assigned to this arm will receive training using the DIALOGUE-DM2 platform, which provides generative AI-driven simulated patients. Participants will engage in multiple diagnostic disclosure scenarios focused on type 2 diabetes and receive immediate feedback generated by the AI system. Feedback is aligned with validated communication frameworks (Kalamazoo, MRS). Training is conducted over several sessions prior to the post-test evaluation.', 'interventionNames': ['Behavioral: AI-Based Simulation Training (DIALOGUE-DM2)']}, {'type': 'ACTIVE_COMPARATOR', 'label': 'Traditional Training', 'description': 'Medical students assigned to this arm will receive traditional communication skills training. This includes lectures, peer role-play, and faculty-supervised feedback sessions covering diagnostic disclosure in type 2 diabetes. Participants will complete the same number of training sessions as the intervention group before the post-test evaluation.', 'interventionNames': ['Behavioral: Traditional Training']}], 'interventions': [{'name': 'AI-Based Simulation Training (DIALOGUE-DM2)', 'type': 'BEHAVIORAL', 'description': 'Medical students interact with the DIALOGUE-DM2 platform, a generative AI-based simulation system. The platform delivers virtual patient encounters focused on type 2 diabetes diagnostic disclosure. Students complete multiple simulated scenarios and receive immediate AI-generated feedback aligned with standardized communication rubrics (Kalamazoo, MRS). Training aims to enhance diagnostic communication skills prior to post-test evaluation.', 'armGroupLabels': ['AI-Based Simulation Training (DIALOGUE-DM2)']}, {'name': 'Traditional Training', 'type': 'BEHAVIORAL', 'description': 'Medical students receive traditional training in diagnostic communication. This includes lectures, peer role-play, and faculty-supervised feedback sessions covering diagnostic disclosure in type 2 diabetes. The training duration and number of sessions are matched to the intervention group.', 'armGroupLabels': ['Traditional Training']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Tlalnepantla', 'country': 'Mexico', 'facility': 'Universidad Nacional Autónoma de México, Faculty of Higher Studies Iztacala (FES Iztacala)', 'geoPoint': {'lat': 19.54005, 'lon': -99.19538}}]}, 'ipdSharingStatementModule': {'infoTypes': ['STUDY_PROTOCOL', 'SAP', 'ICF', 'ANALYTIC_CODE'], 'timeFrame': 'IPD and supporting documents (study protocol, SAP, ICF, analytic code) will be made available beginning 6 months after publication of the primary results and for a period of at least 5 years thereafter.', 'ipdSharing': 'YES', 'description': 'De-identified individual participant data (IPD) will be shared, including rubric-based performance scores from pre-test and post-test evaluations, self-reported confidence questionnaires, satisfaction survey responses, and AI versus human evaluator ratings. Demographic data (age, sex, academic year) will also be included in anonymized form. No personally identifiable information will be shared.', 'accessCriteria': 'De-identified IPD and supporting documents will be available to qualified researchers upon reasonable request. Requests must include a methodologically sound proposal and will require a data use agreement. Access will be provided through direct communication with the Principal Investigator (Dr. Héctor Iván Saldívar Cerón, UNAM-FES Iztacala).'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Universidad Nacional Autonoma de Mexico', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal Investigator, FES Iztacala, UNAM', 'investigatorFullName': 'Héctor Iván Saldívar Cerón', 'investigatorAffiliation': 'Universidad Nacional Autonoma de Mexico'}}}}