Ignite Creation Date:
2025-12-24 @ 11:07 PM
Ignite Modification Date:
2025-12-30 @ 6:45 PM
Study NCT ID:
NCT07022769
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-06-26
First Post:
2025-06-07
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Testing an AI Large Language Model Tool for Cognitive Debiasing in Musculoskeletal Care
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D009140', 'term': 'Musculoskeletal Diseases'}]}}, 'documentSection': {'largeDocumentModule': {'largeDocs': [{'date': '2025-06-06', 'size': 645797, 'label': 'Study Protocol and Statistical Analysis Plan', 'hasIcf': False, 'hasSap': True, 'filename': 'Prot_SAP_000.pdf', 'typeAbbrev': 'Prot_SAP', 'uploadDate': '2025-06-07T00:16', 'hasProtocol': True}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'SUPPORTIVE_CARE', 'interventionModel': 'PARALLEL', 'interventionModelDescription': 'Treatment arm will be determined at time of presentation by a validated random number generator (random.org). Patients will be randomized to receive either an LLM-facilitated structured debiasing checklist (1) or LLM-facilitated diagnostic feedback (0) prior to consultation with the clinician.'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 150}}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-06-23', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-06', 'completionDateStruct': {'date': '2025-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-06-21', 'studyFirstSubmitDate': '2025-06-07', 'studyFirstSubmitQcDate': '2025-06-07', 'lastUpdatePostDateStruct': {'date': '2025-06-26', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-06-15', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Trust and Experience with the Clinician Scale (TRECS-7)', 'timeFrame': 'Measured once, immediately following consultation with the musculoskeletal specialist', 'description': "The Trust and Experience with the Clinician Scale (TRECS-7) is a validated 7-item scale that measures patients' trust in and experience with their clinician during a medical consultation. Designed to minimize ceiling effects, it enables more sensitive detection of variation in patient experience across different clinical interactions (Brinkman et al.). Each of 7 statements is scored from 0-4 (strongly disagree, disagree, neutral, agree, strongly agree), resulting in a total score between 0 and 28. Higher scores indicate greater perceived trust in the clinician.\n\nSource: Brinkman N, Looman R, Jayakumar P, Ring D, Choi S. Is It Possible to Develop a Patient-reported Experience Measure With Lower Ceiling Effect? Clin Orthop Relat Res. 2025 Apr 1;483(4):693-703."}], 'secondaryOutcomes': [{'measure': 'Subjective Experience Using the LLM', 'timeFrame': 'Measured once, immediately following consultation with the musculoskeletal specialist', 'description': "Subjective experience will be assessed using three custom items rated on a 0-100 scale, capturing participants' perceptions of the AI interaction. These items evaluate whether the computer provided an accurate summary, promoted a healthy mindset, and increased confidence in self-management, respectively. Higher scores will indicate more positive experience while lower scores will indicate more negative experience ratings."}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Musculoskeletal disorders', 'Orthopaedic care', 'Non-traumatic pain', 'Shared Decision-Making', 'Health Communication', 'Behavioural Intervention', 'Randomized Controlled Trial', 'Patient Experience', 'Cognitive Bias', 'Illness Perceptions', 'Artificial intelligence', 'Large Language Models', 'Patient-physician relationship'], 'conditions': ['Any Chronic, Non-traumatic Orthopedic Condition']}, 'descriptionModule': {'briefSummary': 'The goal of this clinical trial is to find out whether using an artificial intelligence (AI) tool called a Large Language Model (LLM) can help patients think more clearly about their symptoms and improve their trust and experience during a visit to a musculoskeletal specialist.\n\nThe study will answer two main questions:\n\n1. Does an LLM-guided checklist that encourages patients to reflect on their beliefs about their symptoms improve their trust in the clinician (measured using the TRECS-7 scale)?\n2. Does the checklist improve how patients feel about their consultation overall?\n\nParticipants will be randomly assigned to one of two groups:\n\n* One group will receive an LLM-guided checklist that helps them think more flexibly about their condition.\n* The other group will receive an LLM-generated likely diagnosis and brief explanation of their symptoms.\n\nIn both groups, the information from the AI tool will be shared with both the patient and the clinician before the consultation.\n\nPatients in the debiasing (intervention) group will:\n\n* Complete a short set of questions with help from a researcher\n* Receive a simple summary from the AI that reflects their beliefs and gently challenges any unhelpful thinking\n* Attend their regular specialist appointment\n* Complete a short survey afterwards capturing their thoughts, experience and basic demographics\n\nPatients in the diagnosis-only (control) group will:\n\n* Describe their symptoms to the AI LLM\n* Receive a likely diagnosis and short explanation based on this description\n* Attend their regular specialist appointment\n* Complete a short survey afterwards capturing their thoughts, experience and basic demographics', 'detailedDescription': "A patient's experience of physical discomfort and incapability is closely tied to how they interpret bodily sensations. The human mind is a meaning-making system that rapidly forms stories and assumptions about internal experiences. When individuals experience musculoskeletal pain or dysfunction, their initial interpretations often fall into broad cognitive categories: (1) harm that requires rest and protection; (2) threat to valued roles and activities; or (3) the belief that symptom elimination is the sole path to recovery. These automatic, unconscious interpretations can be adaptive in acute or dangerous situations, but they may also lead to biased or inaccurate symptom appraisals. When misaligned with the underlying pathology, such heuristics can exacerbate emotional distress, delay accurate diagnosis, and drive unnecessary investigations or treatments. The challenge, therefore, lies in supporting patients to reframe these beliefs and engage with their symptoms more adaptively.\n\nCognitive debiasing strategies have emerged as a promising approach to address this concern. These strategies aim to slow down automatic thinking, challenge entrenched assumptions, and promote more flexible, reflective, and value-aligned reasoning. By encouraging a more nuanced understanding of bodily signals, cognitive debiasing may improve the quality of clinical decisions and overall patient experience-offering advantages over traditional educational or informational tools.\n\nRecent advances in Artificial Intelligence (AI), particularly the rise of Large Language Models (LLMs), have opened new possibilities for enhancing cognitive debiasing interventions. LLMs such as ChatGPT can analyze and synthesize patient-reported symptoms and beliefs to generate supportive, plain-language summaries of their thinking. This process may help patients recognize their own interpretive patterns and consider alternative, less distressing explanations for their symptoms. In parallel, LLMs can assist clinicians by flagging potentially unhelpful or distorted beliefs prior to a consultation, allowing for more tailored and empathic communication.\n\nThis trial tests whether a structured, LLM-facilitated debiasing intervention can better support accurate symptom appraisal and enhance the clinical encounter, compared to LLM-generated diagnosis alone. This work builds on the recognition that there is wide variation in musculoskeletal care experience and decision-making, with existing tools such as decision aids and question prompt lists often falling short in challenging rigid or unhelpful thinking patterns."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': "Inclusion Criteria:\n\n* Adults (18+)\n* New or return patient seeking musculoskeletal specialty care at an Orthopedic outpatient clinic\n* Total combined score on the 6 feelings and thoughts items of \\> 10\\* (Appendix 3 of study protocol)\n* English-speaking\n* Pre-visit diagnosis of chronic, non-traumatic musculoskeletal condition (including, but not limited to: osteoarthritis, carpal tunnel syndrome, trigger digit, Dupuytren's, De Quervain's, lateral epicondylitis)\n\nExclusion Criteria:\n\n* Any impairment preventing completion of surveys on a tablet"}, 'identificationModule': {'nctId': 'NCT07022769', 'briefTitle': 'Testing an AI Large Language Model Tool for Cognitive Debiasing in Musculoskeletal Care', 'organization': {'class': 'OTHER', 'fullName': 'University of Texas at Austin'}, 'officialTitle': 'Comparison of a Large Language Model (LLM)-Facilitated Cognitive Debiasing Strategy Versus LLM-Generated Diagnostic Feedback Alone in Musculoskeletal Specialty Care: A Randomized Controlled Trial', 'orgStudyIdInfo': {'id': 'STUDY00004831'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'LLM-Facilitated Cognitive Debiasing Aid', 'description': 'The intervention is a four-part, tablet-based cognitive debiasing aid that uses a Large Language Model (LLM) to help patients reflect on and re-evaluate their beliefs about their symptoms prior to a musculoskeletal specialty care visit. Patient responses are summarized by the LLM in supportive language to promote flexible thinking, and a separate LLM-generated summary of potential unhelpful beliefs is shared with the clinician to guide empathic, individualized communication.', 'interventionNames': ['Behavioral: LLM-facilitated cognitive debiasing aid']}, {'type': 'NO_INTERVENTION', 'label': 'Usual Care', 'description': 'In the control arm, patients use a tablet-based tool to describe their presenting musculoskeletal symptom, which is transcribed and input into a Large Language Model (LLM). The LLM generates a likely diagnosis with a brief neutral description, which is shared with both the patient and the clinician before the consultation. This approach offers diagnostic feedback without engaging in cognitive debiasing or reflection.'}], 'interventions': [{'name': 'LLM-facilitated cognitive debiasing aid', 'type': 'BEHAVIORAL', 'description': 'As part of the intervention, patients first respond to a series of questions about their beliefs regarding their symptoms (e.g., "What\'s usually behind these symptoms?"), with responses transcribed verbatim via tablet. These responses are input into a Large Language Model (LLM), which generates a brief, supportive summary of the patient\'s beliefs, shared back with the patient to encourage self-awareness and reflection. Patients are then invited to consider prompts such as, "What emotions or circumstances might be influencing your thinking?" with their reflections again transcribed. The LLM analyzes these reflections to identify potential signs of emotional distress or maladaptive beliefs, and this output is again provided to the patient. The LLM summary of identified maladaptive beliefs is then also shown to the clinician ahead of the consultation to support more tailored, empathetic communication.', 'armGroupLabels': ['LLM-Facilitated Cognitive Debiasing Aid']}]}, 'contactsLocationsModule': {'locations': [{'zip': '78712', 'city': 'Austin', 'state': 'Texas', 'country': 'United States', 'contacts': [{'name': 'David Ring, MD, PhD', 'role': 'CONTACT', 'email': 'david.ring@austin.utexas.edu', 'phone': '512-495-5555', 'phoneExt': '+1'}, {'name': 'Emily H Jaarsma, MD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Dell Medical School, University of Texas at Austin', 'geoPoint': {'lat': 30.26715, 'lon': -97.74306}}], 'centralContacts': [{'name': 'Emily H Jaarsma, MD', 'role': 'CONTACT', 'email': 'emily.jaarsma@austin.utexas.edu', 'phone': '7472879601', 'phoneExt': '+1'}], 'overallOfficials': [{'name': 'David Ring, MD, PhD', 'role': 'STUDY_DIRECTOR', 'affiliation': 'Dell Medical School, University of Texas at Austin, TX, United States'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University of Texas at Austin', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Associate Dean for Comprehensive Care Professor and Associate Chair for Faculty Academic Affairs Department of Surgery and Perioperative Care, Courtesy Professor of Psychiatry and Behavioral Sciences', 'investigatorFullName': 'David Ring', 'investigatorAffiliation': 'University of Texas at Austin'}}}}