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

NCT07631585 | Not Yet Recruiting

• 1 other identifier: ORTHO-OP-GPT-2026
• Study Type: observational
• Target: 180
• Locations: 1 country
• Sites: 2

Brief Summary

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.

Conditions

Orthopedics; Patient Health Information Seeking Behavior; Trust; Health Literacy

Keywords

ChatGPT; large language model; artificial intelligence chatbot; patient education; health information seeking; trust; physician-patient communication; orthopedics; cross-sectional; pre-post

Outcome Measures

Primary Outcomes (2)

• 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).

  Trust in AI-derived health information is assessed pre-consultation by a study-specific single-item 5-point Likert scale (item T0.11: "How much do you trust the AI's answer?"; anchors 1 = not at all, 5 = completely), administered only to patients who reported pre-consultation AI use (item T0.9 = Yes). Post-consultation, trust change is reassessed by a study-specific 3-level categorical item (item T1.4: increased trust / unchanged / decreased trust). For paired analysis, the post-consultation score is derived by mapping T1.4 categories to integer shifts (+1 / 0 / -1, with floor 1 and ceiling 5) relative to T0.11. Unit of measure: Likert score points on a 1-5 scale (continuous derived score) and proportion of patients per 3-level category. Primary analysis: paired Wilcoxon signed-rank test on the derived continuous score; sensitivity analysis: McNemar test on the 3-level categorical change.

  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

  Concordance is assessed by Cohen's kappa coefficient comparing patient-reported AI-physician concordance (item T1.2: fully concordant / partially concordant / discordant / physician did not address; dichotomized to concordant vs. non-concordant) and physician-reported AI medical accuracy (item H2: 5-point Likert anchored 1 = entirely incorrect to 5 = entirely correct; dichotomized at ≥ 3 as concordant). Unit of measure: kappa coefficient (range -1 to +1) with 95% confidence interval, and percentage of dyads classified as concordant on each instrument.

  Immediately after the consultation (within 15 minutes of consultation exit), for both patient (T1.2) and physician (H2) forms; same-day index visit.

Secondary Outcomes (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).

  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).

  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).

  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).

  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).

  Immediately after the consultation (within 15 minutes of consultation exit), same-day index visit.

Other Outcomes (2)

• Exploratory association between categorical post-consultation trust change and demographic predictors, estimated by multinomial logistic regression with the study-specific 3-level trust change item (T1.4) as the outcome and age band, sex, education level

  Through study completion, an average of 12 months from first enrolment.

• Internal consistency of a four-item artificial-intelligence trust subscale, measured by Cronbach's alpha across items T0.11 (baseline trust), T1.4 (post-consultation trust change, linearly recoded), T1.7 (future-use intention), and T1.8 (recommendation

  Through study completion, an average of 12 months from first enrolment.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Consecutive adult patients (18 years and older) presenting to the participating orthopedic outpatient clinics during the recruitment window who consent to participate. No specific orthopedic diagnosis is required.

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Utku Gürhan: lead

Study Sites (2)

University of Kyrenia, Dr. Suat Gunsel Hospital - Orthopedic Outpatient Clinic

Kyrenia, Cyprus

Location

Near East University Hospital - Orthopedic Outpatient Clinic

Nicosia, Cyprus

Location

Related Publications (9)

• Gultekin O, Hirschmann MT, Arikan HI, Kilinc BE, Yilmaz B, Abul S, Inoue J, Kayaalp ME. Evaluating deepresearch and deepthink in total knee arthroplasty patient education: ChatGPT-4o excels in comprehensiveness, Deepseek R1 leads in clarity and readability of orthopedic information. Jt Dis Relat Surg. 2026 May 1;37(2):470-476. doi: 10.52312/jdrs.2026.2645. Epub 2026 Mar 17.

  PMID: 41906842 BACKGROUND

• Gultekin O, Inoue J, Yilmaz B, Cerci MH, Kilinc BE, Yilmaz H, Prill R, Kayaalp ME. Evaluating DeepResearch and DeepThink in anterior cruciate ligament surgery patient education: ChatGPT-4o excels in comprehensiveness, DeepSeek R1 leads in clarity and readability of orthopaedic information. Knee Surg Sports Traumatol Arthrosc. 2025 Aug;33(8):3025-3031. doi: 10.1002/ksa.12711. Epub 2025 Jun 1.

  PMID: 40450565 BACKGROUND

• Kahan R, Shen C, Wellborn P, Lauder A, Berchuck S, Javeed H, Pean C, Federer A. Artificial Intelligence in Triaging Patient Questions: An Evaluation of a Large Language Model for Distal Radius Fractures. J Am Acad Orthop Surg. 2026 Jan 1;34(1):e106-e115. doi: 10.5435/JAAOS-D-25-00456. Epub 2025 Aug 27.

  PMID: 40896839 BACKGROUND

• Schepman A, Rodway P. Initial validation of the general attitudes towards Artificial Intelligence Scale. Comput Hum Behav Rep. 2020 Jan-Jul;1:100014. doi: 10.1016/j.chbr.2020.100014. Epub 2020 May 18.

  PMID: 34235291 BACKGROUND

• Yildirim TO, Karaman M. Development and psychometric evaluation of the artificial intelligence attitude scale for nurses. BMC Nurs. 2025 Apr 22;24(1):441. doi: 10.1186/s12912-025-03098-6.

  PMID: 40264200 BACKGROUND

• Norman CD, Skinner HA. eHEALS: The eHealth Literacy Scale. J Med Internet Res. 2006 Nov 14;8(4):e27. doi: 10.2196/jmir.8.4.e27.

  PMID: 17213046 BACKGROUND

• Bafna Sherma N. Factors influencing patients' engagement with ChatGPT for accessing health-related information. Crit Public Health. 2024.

  BACKGROUND

• 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 BACKGROUND

• Christy M, Morris MT, Goldfarb CA, Dy CJ. Appropriateness and Reliability of an Online Artificial Intelligence Platform's Responses to Common Questions Regarding Distal Radius Fractures. J Hand Surg Am. 2024 Feb;49(2):91-98. doi: 10.1016/j.jhsa.2023.10.019. Epub 2023 Dec 8.

  PMID: 38069953 BACKGROUND

Study Officials

• Utku Gurhan, MD

  University of Kyrenia

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Utku Gurhan, MD

CONTACT

+90 539 112 6898; utkugrhn@gmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Assistant Professor of Orthopaedics and Traumatology

Study Record Dates

• First Submitted: May 18, 2026
• First Posted: June 8, 2026
• Study Start: June 1, 2026
• Primary Completion (Estimated): November 1, 2026
• Study Completion (Estimated): January 1, 2027
• Last Updated: June 8, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will share

Locations

CY (2)