Patient AI Trust Dynamics Before and After Orthopedic Consultation (ORTHO-OP-GPT)

Официальное название

Longitudinal Pre-Post Patient AI Trust Dynamics in Orthopedic Outpatients: A Mixed-Methods Observational Study With Matched Physician-Patient Dyads

Обзор

Patients increasingly consult artificial intelligence (AI) chatbots such as ChatGPT for health information before clinical visits, yet the impact of an actual orthopedic consultation on patient trust in AI-derived information remains unknown. This prospective longitudinal observational study quantifies how a single orthopedic outpatient consultation modifies patient trust in AI chatbots, the concordance between AI-derived and physician-delivered information, and patient anxiety, using a paired pre-post survey design supplemented by a matched physician-side assessment. Adult patients (18 years and older) presenting to two orthopedic outpatient clinics in Cyprus complete a brief pre-consultation questionnaire (T0) capturing demographics, AI use patterns, prior AI consultation regarding the current complaint, baseline trust, expectations, and anxiety. Immediately after their consultation they complete a second questionnaire (T1) assessing concordance with physician advice, trust change, consultation facilitation, post-consultation anxiety, and future intention. The consulting physician completes a brief 30-second post-visit form capturing whether AI was discussed, the medical accuracy of AI-derived information conveyed by the patient, and the effect of the AI discussion on consultation duration. The primary outcomes are the paired within-patient change in AI trust between T0 and T1 and physician-patient concordance on AI versus physician advice. Target enrollment is 180 to obtain 150 paired completed assessments.

Подробное описание

Background and Rationale: Cross-sectional surveys have documented increasing patient use of AI chatbots for health information seeking. However, no published study has assessed how an actual physician consultation modifies patient trust in AI in a paired pre/post design, nor has any study captured the physician perspective on the same encounter in a matched dyad. Routine clinical encounters may be the primary mechanism by which patients calibrate their trust in AI-derived medical information.

Setting and Population: Two university-affiliated orthopedic outpatient clinics in North Cyprus.

Procedures:

* T0 (pre-consultation, waiting room, approximately 5 minutes): 14-item self-report questionnaire. * Consultation: usual care. * T1 (post-consultation, departure, approximately 5 minutes): 10-item self-report questionnaire. * Physician form (post-consultation, approximately 30 seconds): 5-item brief assessment. * Patient and physician forms are linked by an anonymous Participant ID.

Statistical Analysis Plan: Paired t-tests or Wilcoxon signed-rank tests for paired continuous outcomes; McNemar test or Stuart-Maxwell for paired categorical outcomes; Cohen's kappa for inter-rater agreement (AI versus physician); multinomial logistic regression for predictors of trust shift. All analyses two-sided, alpha equals 0.05. SPSS version 28.

Data Management: Anonymous CSV stored locally, encrypted, retained for 5 years per institutional policy. De-identified participant-level data available upon reasonable request after publication.

No formal pilot study is conducted. Instead, the first 20 participants will be prospectively monitored for protocol feasibility (mean completion time, drop-out rate, item-level missing data) as an embedded running pilot.

Первичные конечные точки

• Mean within-patient change in self-reported trust in artificial intelligence-derived health information, measured by a study-specific 5-point Likert item (T0.11) and a study-specific 3-level categorical change item (T1.4). [Срок оценки: Baseline (within 15 minutes pre-consultation in the orthopaedic outpatient waiting room) and immediately after the consultation (within 15 minutes of consultation exit, same-day index visit).]
• Patient-physician concordance on artificial intelligence-versus-physician medical advice agreement, measured by Cohen's kappa coefficient between a study-specific 4-category patient item (T1.2) and a study-specific 5-point physician-rated AI medical accu [Срок оценки: Immediately after the consultation (within 15 minutes of consultation exit), for both patient (T1.2) and physician (H2) forms; same-day index visit.]

Вторичные конечные точки (6)

• Mean within-patient change in self-reported anxiety, measured by an 11-point 0-to-10 visual analogue scale anchored 0 = no anxiety and 10 = worst possible anxiety (items T0.14 baseline, T1.5 post-consultation). [Срок оценки: Baseline (within 15 minutes pre-consultation) and immediately after the consultation (within 15 minutes of consultation exit), same-day index visit.]
• Percentage of enrolled patients reporting pre-consultation artificial intelligence use for the current orthopaedic complaint, measured by a study-specific single-item yes/no question (T0.9). [Срок оценки: Baseline (within 15 minutes pre-consultation, same-day index visit).]
• Percentage of pre-consultation artificial-intelligence users whose physician independently confirmed that AI was raised during the consultation, measured by a study-specific yes/no physician item (H1). [Срок оценки: Baseline (T0.9, pre-consultation) and immediately after the consultation (H1, within 15 minutes of consultation exit), same-day index visit.]
• Percentage of consultations in which the physician reported that the artificial-intelligence discussion shortened, did not change, or prolonged the encounter, measured by a study-specific 3-category physician item (H3). [Срок оценки: Immediately after the consultation (within 15 minutes of consultation exit), same-day index visit.]
• Mean patient rating of how prior artificial-intelligence use facilitated the consultation, measured by a study-specific 5-point Likert item (T1.4b: 1 = much more difficult, 5 = much easier). [Срок оценки: Immediately after the consultation (within 15 minutes of consultation exit), same-day index visit.]
• Mean patient-reported future intention to use and to recommend artificial intelligence for health information, measured by two study-specific 5-point Likert items (T1.7 future use; T1.8 recommendation to a friend). [Срок оценки: Immediately after the consultation (within 15 minutes of consultation exit), same-day index visit.]

Критерии участия

Критерии включения

• Age 18 years or older
• Presenting to an orthopedic outpatient clinic for any consultation
• Able to read and respond to a Turkish-language questionnaire
• Provides informed consent

Критерии исключения

• Inability to complete a self-report questionnaire (e.g., severe cognitive impairment, language barrier)
• Re-presentation within the same recruitment window (each patient is enrolled only once)
• Refusal of consent for either T0 or T1

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Cyprus · 2 центра

• University of Kyrenia, Dr. Suat Gunsel Hospital - Orthopedic Outpatient Clinic — Kyrenia
• Near East University Hospital - Orthopedic Outpatient Clinic — Nicosia

Публикации

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• Choudhury A, Shamszare H. Investigating the Impact of User Trust on the Adoption and Use of ChatGPT: Survey Analysis. J Med Internet Res. 2023 Jun 14;25:e47184. doi: 10.2196/47184. PMID 37314848

Идентификаторы

NCT: NCT07631585 · ORTHO-OP-GPT-2026

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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