[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100659058":3},{"organization":4,"outcomesModule":7,"designInfo":44,"detailedDescription":47,"studyPopulation":48,"armGroups":49,"interventions":55,"overallOfficials":43,"centralContacts":60,"locations":66,"responsibleParty":84,"collaborators":88,"id":91,"slug":92,"hasResults":93,"nctId":94,"briefTitle":95,"officialTitle":96,"acronym":97,"eligibilityCriteria":98,"healthyVolunteers":93,"sex":99,"minAge":100,"maxAge":43,"enrollmentInfo":101,"targetDuration":43,"studyType":104,"phases":43,"briefSummary":105,"conditions":106,"keywords":110,"overallStatus":121,"whyStopped":43,"lastUpdateSubmitDate":122,"lastUpdatePostDateStruct":123,"startDateStruct":126,"completionDateStruct":128,"leadSponsor":130,"locationsCount":131},{"fullName":5,"class":6},"Massachusetts General Hospital","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":43},[9],{"measure":10,"description":11,"timeFrame":12},"Proportion of AI history summaries rated useful by the evaluating physician","After completing routine care, the treating physician rates the usefulness of the AI-generated history summary on a 5-point Likert scale (1 = not useful at all, 5 = very useful). The outcome is the proportion of summaries rated 4 or higher, reported with a 95% Wilson confidence interval.","Same day as the enrollment visit, after routine care is complete (up to approximately 1 day)",[14,17,20,23,26,29,33,36,40],{"measure":15,"description":16,"timeFrame":12},"Completeness of the AI-generated history","Percentage of items from a standardized history grid that the AI captured correctly, reported overall and by history domain.",{"measure":18,"description":19,"timeFrame":12},"Correctness of the AI-generated history","Proportion of factually incorrect or missing clinically relevant items in the AI history, per physician review.",{"measure":21,"description":22,"timeFrame":12},"Plausibility of AI differential diagnoses","Physician rating of plausibility on a 1-5 Likert scale, and concordance of the AI differential with the physician's working diagnosis.",{"measure":24,"description":25,"timeFrame":12},"Plausibility of AI workup recommendations","Physician rating of the AI's suggested diagnostic workup on a 1-5 Likert scale.",{"measure":27,"description":28,"timeFrame":12},"Identification of clinically relevant red flags","Proportion of clinically relevant red flags correctly recognized by the AI versus not recognized, per physician review.",{"measure":30,"description":31,"timeFrame":32},"Patient usability (System Usability Scale)","Patient-completed System Usability Scale, score 0-100, higher scores indicating better usability.","Same day as the enrollment visit, immediately after the AI interview or after the physician consultation (up to approximately 1 day)",{"measure":34,"description":35,"timeFrame":32},"Patient acceptance and willingness to use the tool again","Patient-reported acceptance, comprehensibility, empathy, comfort, trust, and willingness to use again, each on a 1-5 Likert scale.",{"measure":37,"description":38,"timeFrame":39},"Duration of the AI interaction","Time in minutes for the patient to complete the AI interview, recorded automatically by the system.","During the AI interview at the enrollment visit (approximately 15 minutes)",{"measure":41,"description":42,"timeFrame":12},"Physician-estimated time savings","Physician's estimate, in minutes, of the time the AI history would save in clinical workflow.",null,{"allocation":43,"interventionModel":43,"interventionModelDescription":43,"primaryPurpose":43,"observationalModel":45,"timePerspective":46,"maskingInfo":43},"COHORT","PROSPECTIVE","This is a prospective, observational study of an AI-based, structured history-taking tool used before a clinical visit. The tool uses large language models and follows a fixed interview plan with defined topic blocks, such as chief complaint, current history, past conditions, medications, allergies, specialty-specific history, and social and family history. It produces a structured history summary, a list of possible diagnoses, and suggested workup steps. In earlier benchmarking on more than 670 simulated and clinical cases, the system captured most clinically relevant history items and flagged clinically important warning signs.\n\nThe study is designed so that the tool does not influence care. Participants give written informed consent, then complete the AI interview on a study tablet using a pseudonymous study ID. Participants enter no name, birth date, or address. Participants then complete a short questionnaire on usability and acceptance. The treating physician takes a full history, examines the patient, and documents a plan without access to the AI output. Only afterward does the physician receive the AI output and complete a structured evaluation of its completeness, correctness, plausibility, and usefulness. No follow-up contact occurs after the visit.\n\nThe study is not randomized and has no control arm. The physician's independently obtained history serves as the reference standard. The first site is the Department of Gynecology at Leipzig University Hospital, with about 100 patients and 8 to 15 physicians. Analysis is descriptive, with subgroup comparisons by reason for visit. Additional sites may be added later. The study is a collaboration between Leipzig University Hospital and Massachusetts General Hospital and Harvard Medical School.","Adult women presenting for the first time, or with a new complaint, to the general gynecology outpatient clinic, the gynecology emergency clinic, or as referrals to the gynecology outpatient clinic at the first study site (Leipzig University Hospital, Germany). Patients are recruited consecutively in the clinic waiting area by trained study staff. Additional sites may be added later.",[50],{"label":51,"type":43,"description":52,"interventionNames":53},"Adult Clinic Patients","Adult patients with a first presentation or new complaint at the participating outpatient clinic who consent to complete the AI-based pre-visit history interview and questionnaire. Their physician completes routine care blinded to the AI output, then evaluates the AI-generated summary.",[54],"Other: AI-Based Structured History-Taking Chatbot",[56],{"type":6,"name":57,"description":58,"armGroupLabels":59,"otherNames":43},"AI-Based Structured History-Taking Chatbot","A large language model-based agentic chatbot that conducts a structured, pre-visit history interview on a study tablet and generates a history summary, differential diagnosis list, and workup suggestions. The output is released to the treating physician only after the physician has completed an independent history, examination, and care plan, and does not influence patient care.",[51],[61],{"name":62,"role":63,"phone":64,"phoneExt":43,"email":65},"Sonu Subudhi, MBBS, PhD","CONTACT","617-726-8051","ssubudhi@mgh.harvard.edu",[67],{"facility":68,"status":43,"city":69,"state":43,"zip":70,"country":71,"countryCode":72,"cosmosGeoPoint":73,"geoPoint":78,"contacts":79},"Universität Leipzig","Leipzig","04103","Germany","DE",{"type":74,"coordinates":75},"Point",[76,77],12.37129,51.33962,{"lat":77,"lon":76},[80],{"name":81,"role":63,"phone":82,"phoneExt":43,"email":83},"Bahriye Aktas","+49 341 97 23400","bahriye.aktas@medizin.uni-leipzig.de",{"type":85,"investigatorFullName":86,"investigatorTitle":87,"investigatorAffiliation":5,"oldNameTitle":43,"oldOrganization":43},"PRINCIPAL_INVESTIGATOR","Sonu Subudhi","Instructor",[89],{"name":90,"class":6},"University of Leipzig","100659058","augmenting-clinical-workflow-with-a-structured-chatbot-for-patient-history-collection-and-diagnostic-support-100659058",false,"NCT07860762","Augmenting Clinical Workflow With a Structured Chatbot for Patient History Collection and Diagnostic Support","Prospective Observational Evaluation of a Structured, Agentic AI Chatbot for Pre-Visit Patient History Collection and Diagnostic Support in Outpatient Clinical Workflows","CHAT-HX","Inclusion Criteria:\n\nWomen 18 years or older\n\nFirst presentation or new complaint at the gynecology outpatient clinic (self-presentation, emergency, or referral)\n\nSufficient German language skills to complete a structured patient interview independently\n\nAble to complete study participation without external support\n\nWritten informed consent\n\nExclusion Criteria:\n\nAcute emergency requiring immediate treatment (for example, impaired consciousness, hemodynamic instability, or acute bleeding requiring immediate care)\n\nLack of legal or decisional capacity to consent\n\nInsufficient language skills\n\nSevere cognitive, visual, or motor impairment preventing tablet use\n\nPrevious participation in this study\n\nRefusal or withdrawal of consent","FEMALE","18 Years",{"count":102,"type":103},100,"ESTIMATED","OBSERVATIONAL","Doctors spend much of each visit collecting the patient's medical history, and incomplete histories can delay diagnosis. This study tests an AI chatbot that interviews patients before their appointment and produces a structured summary of their history for the doctor. Adult patients at the first study site, a university gynecology outpatient clinic in Germany, answer the chatbot's questions on a tablet before the doctor visit. The doctor then takes the history and examines the patient as usual, without seeing the chatbot's output. Only after the doctor has finished planning care does the doctor review the chatbot's summary and rate its usefulness and accuracy. The chatbot does not change patient care. Patients also rate the ease of use and acceptability of the chatbot. The investigators will report how useful doctors find the summaries, how complete and correct the summaries are, and how patients experience the tool.",[107,108,109],"Medical History Taking","Artificial Intelligence in Clinical Workflow","Outpatient Clinical Encounter",[111,112,113,114,115,116,117,118,119,120],"Large language model","Agentic AI","Chatbot","Patient History Collection","Pre-visit intake","Clinical decision support","Diagnostic support","Usability","Structured Interview","Digital Health","NOT_YET_RECRUITING","2026-10-05",{"date":124,"type":125},"2026-10-07","ACTUAL",{"date":127,"type":103},"2026-10-02",{"date":129,"type":103},"2027-10-01",{"name":5,"class":6},1]