Ignite Creation Date:
2026-03-26 @ 3:15 PM
Ignite Modification Date:
2026-03-30 @ 8:59 PM
Study NCT ID:
NCT07393568
Status:
NOT_YET_RECRUITING
Last Update Posted:
2026-02-06
First Post:
2025-12-17
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
The Influence of Patient Use of Artificial Intelligence on Doctor-Patient Interaction and Clinical Outcomes in Endometriosis Consultations
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D004715', 'term': 'Endometriosis'}], 'ancestors': [{'id': 'D005831', 'term': 'Genital Diseases, Female'}, {'id': 'D052776', 'term': 'Female Urogenital Diseases'}, {'id': 'D005261', 'term': 'Female Urogenital Diseases and Pregnancy Complications'}, {'id': 'D000091642', 'term': 'Urogenital Diseases'}, {'id': 'D000091662', 'term': 'Genital Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'CASE_CONTROL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 94}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2026-02-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-01', 'completionDateStruct': {'date': '2027-01-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-01-31', 'studyFirstSubmitDate': '2025-12-17', 'studyFirstSubmitQcDate': '2026-01-31', 'lastUpdatePostDateStruct': {'date': '2026-02-06', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-02-06', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2027-01-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Patient Satisfaction and Treatment Plan Adherence After Consultation', 'timeFrame': 'Immediately after the consultation (same day)', 'description': "Overall patient satisfaction will be measured immediately after the endometriosis consultation using a structured post-visit questionnaire and reported as a score on a 0-5 scale, where 0 indicates not satisfied at all and 5 indicates very satisfied. Intention to adhere to the physician's treatment plan will be measured immediately after the consultation as a binary outcome (yes/no) based on the patient's response to the post-visit questionnaire item asking whether she intends to follow the doctor's treatment recommendations."}], 'secondaryOutcomes': [{'measure': 'Physician-Perceived Interaction Quality and Consultation Characteristics', 'timeFrame': 'During and immediately after the consultation (same day)', 'description': "Physician-perceived patient trust, compliance, and engagement will be measured immediately after the consultation using physician-completed ratings on a 0-4 ordinal scale. Physician-perceived patient prior knowledge regarding endometriosis will be recorded using a structured post-visit physician assessment.\n\nPatient-reported concordance between information obtained from artificial intelligence tools and the physician's treatment recommendations will be measured immediately after the consultation as a binary response (yes/no) using the post-visit questionnaire. Patient-reported receipt of new or additional information from the physician beyond AI-provided content will also be measured as a binary response (yes/no). Patient perception of the necessity of an in-person visit after AI use will be recorded as a binary response (yes/no).\n\nPain experienced during the physical examination will be measured during the consultation using a 0-10 visual analog scale (VAS). Duration of the clinical"}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Endometriosis']}, 'descriptionModule': {'briefSummary': 'Generative artificial intelligence (AI), including large language models such as ChatGPT, Gemini, and Copilot, is increasingly used by patients to obtain medical information and prepare for clinical encounters. Although these tools often provide guideline-consistent information, their responses may be incomplete, inaccurate, or lack personalization, potentially influencing patient expectations and clinical interactions. The impact of patient AI use on satisfaction, adherence, and physician-patient communication remains poorly understood.\n\nThis prospective comparative study will evaluate the effects of patient AI use prior to gynecologic consultations for endometriosis. Women attending a specialized endometriosis clinic will be categorized as AI users or non-users based on their preparation for the visit. Patient-reported outcomes, including satisfaction, expectations, adherence to physician recommendations, and pain during physical examination, will be assessed using validated questionnaires and visual analogue scales. Physicians, blinded to AI use, will independently assess patient engagement, trust, and compliance. Visit duration will also be recorded.\n\nThe primary objective is to determine whether AI use affects patient satisfaction and adherence to treatment recommendations. Secondary objectives include evaluating physician-perceived interaction quality and concordance between AI-generated guidance and physician recommendations. Findings from this study will provide critical evidence on how AI influences patient behavior and clinical care in endometriosis, informing best practices for integrating AI-informed patients into routine clinical encounters.', 'detailedDescription': "Generative artificial intelligence (AI), including large language models, is increasingly used by patients to obtain medical information prior to clinical encounters. These tools provide rapid access to health-related content and may influence patient knowledge, expectations, communication style, and decision-making during physician consultations. The effect of patient use of AI tools on the doctor-patient interaction and short-term clinical outcomes in the context of endometriosis care has not been systematically evaluated.\n\nThis prospective comparative study is designed to examine differences in patient-reported and physician-reported outcomes between patients who used AI tools to prepare for an endometriosis-related consultation and those who did not. The study is conducted in a gynecology outpatient clinic specializing in endometriosis care. All eligible adult women attending the clinic during the study period are invited to participate. Following informed consent, participants are categorized into two groups based on self-reported use of AI tools for preparation prior to the clinic visit.\n\nData collection is performed using structured questionnaires administered before and after the clinical consultation. The pre-visit questionnaire captures baseline information regarding prior use of artificial intelligence tools, including use for medical information and preparation for the current visit. Baseline demographic and clinical characteristics are also collected. The post-visit questionnaire captures patient-reported satisfaction with the consultation, intention to adhere to the physician's treatment recommendations, perceived concordance between AI-provided information and physician guidance, perceived added value of the physician beyond AI, and perceived necessity of the in-person visit.\n\nPhysicians conducting the consultations are blinded to patient AI usage status and complete a structured assessment immediately after each visit. Physician-reported measures include perceived patient trust, compliance, engagement, and prior knowledge, as well as the duration of the consultation. Pain experienced during the physical examination is recorded using a visual analog scale.\n\nNo discussion of questionnaire responses occurs between physicians and participants during the visit. All physicians involved in data collection hold valid Good Clinical Practice certification and are listed as investigators or sub-investigators. Data are collected anonymously using coded identifiers and stored securely in accordance with institutional data protection policies.\n\nComparisons are performed between AI users and non-users to evaluate differences in patient satisfaction, intention to adhere to treatment recommendations, physician-perceived interaction quality, pain during examination, and visit duration. This study aims to provide structured evidence regarding the influence of patient use of artificial intelligence on the clinical encounter in endometriosis care and to inform future integration of AI-informed patients into routine clinical practice."}, 'eligibilityModule': {'sex': 'FEMALE', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'genderBased': True, 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'The study population will consist of adult women (aged ≥18 years) attending a specialized gynecology outpatient clinic for evaluation or management of endometriosis-related complaints. Eligible participants must be able to provide informed consent and complete study questionnaires independently.\n\nParticipants will include both new and returning patients with suspected or confirmed endometriosis. Individuals with cognitive impairment or psychiatric conditions that significantly interfere with communication or the ability to provide informed consent will be excluded.\n\nFollowing enrollment, participants will be categorized based on self-reported use of generative artificial intelligence (AI) tools to prepare for their clinical visit. Demographic and baseline clinical characteristics-including age, ethnicity, body mass index, smoking status, medical comorbidities, reproductive history, and prior surgical history-will be collected to characterize the study population and allow for compariso', 'genderDescription': 'endometriosis is only womens disease', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Women aged ≥18.\n* Attending clinic for endometriosis-related complaints.\n* Able to give informed consent.\n\nExclusion Criteria:\n\n* Cognitive impairment or psychiatric conditions that affect communication or the ability to provide informed consent'}, 'identificationModule': {'nctId': 'NCT07393568', 'briefTitle': 'The Influence of Patient Use of Artificial Intelligence on Doctor-Patient Interaction and Clinical Outcomes in Endometriosis Consultations', 'organization': {'class': 'OTHER', 'fullName': 'Hadassah Medical Organization'}, 'officialTitle': 'The Influence of Patient Use of Artificial Intelligence on Doctor-Patient Interaction and Clinical Outcomes in Endometriosis Consultations', 'orgStudyIdInfo': {'id': '0423-25-HMO'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'using Chat gpt before outpatient clinic visit', 'description': 'using Chat gpt before outpatient clinic visit', 'interventionNames': ['Other: using Chat gpt before outpatient clinic visit']}, {'label': 'Chat -gpt non users'}], 'interventions': [{'name': 'using Chat gpt before outpatient clinic visit', 'type': 'OTHER', 'description': "This study involves a behavioral, non-randomized observational intervention based on patients' self-directed use of generative artificial intelligence (AI) tools prior to their clinical visit.\n\nThe intervention group consists of patients who report using AI-based large language models (e.g., ChatGPT or similar tools) to prepare for their endometriosis-related consultation. AI use may include seeking information about symptoms, diagnosis, treatment options, prognosis, or formulating questions for the physician. No specific AI platform, prompts, or duration of use is mandated, and AI engagement occurs independently and outside the clinical setting.\n\nThe control group includes patients who report no use of AI tools in preparation for the visit.\n\nNo AI tools are introduced, recommended, or used during the clinical encounter by study personnel. Physicians are blinded to patient AI use status and conduct consultations according to standard clinical practice. Aside from questionnaire administ", 'armGroupLabels': ['using Chat gpt before outpatient clinic visit']}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Hadassah Medical Organization', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}