Patient Health Information Seeking BehaviorORTHO-OP-GPTNCT07631585
Patient AI Trust Dynamics Before and After Orthopedic Consultation (ORTHO-OP-GPT)
An observational study of Patient Health Information Seeking Behavior and Trust, run by Utku Gürhan at 2 sites in Cyprus.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Patient Health Information Seeking Behavior, Trust, Health Literacy
- How many people
- 350 participants
- Where
- 2 sitesCyprus
- Study dates
- 1 Jun 2026to 31 Jul 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
2 sites in 1 country.
Cyprus2 sites
University of Kyrenia, Dr. Suat Gunsel Hospital - Orthopedic Outpatient Clinic
Kyrenia
Near East University Hospital - Orthopedic Outpatient Clinic
Nicosia
About this study
Official title: Longitudinal Pre-Post Patient AI Trust Dynamics in Orthopedic Outpatients: A Mixed-Methods Observational Study With Matched Physician-Patient Dyads
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 was 180 to obtain 150 paired completed assessments; 350 participants were enrolled.
Read the detailed description
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.
- Keywords
- ChatGPT, large language model, artificial intelligence chatbot, patient education, health information seeking, trust, physician-patient communication, orthopedics, cross-sectional, pre-post
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 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 consultation-related anxiety, measured by a study-specific six-item instrument (four-item anxiety subscale, range 4-20).
Anxiety is measured immediately before and immediately after the consultation using a study-specific six-item instrument modelled on the Amsterdam Preoperative Anxiety and Information Scale, with the referent moved from an impending procedure to the outpatient consultation. The anxiety subscale is the sum of four items (range 4-20). Protocol change: the 0-10 visual analogue scale originally registered for this outcome (items T0.14 and T1.5) was replaced by this instrument before data collection began and was never administered. Unit of measure: scale points. Analysis: paired t-test, with analysis of covariance for between-group comparison.
Measured: 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).
Proportion of enrolled patients responding "Yes" to item T0.9 ("Before today's appointment, did you ask an AI chatbot a question about this health concern?"). Unit of measure: percentage of participants, reported with exact (Clopper-Pearson) 95% confidence interval.
Measured: 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).
Among patients responding "Yes" to T0.9, the proportion in whom the treating physician independently reported "Yes" to item H1 ("Did the patient raise AI during this consultation?"). Unit of measure: percentage of patients with exact 95% confidence interval.
Measured: 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).
Among consultations in which the patient raised AI (H1 = Yes), the physician's categorical rating of effect on consultation duration (H3: "shortened" / "no change" / "prolonged"). Unit of measure: percentage of consultations per category (descriptive).
Measured: 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).
Among patients with T0.9 = Yes, patient-reported facilitation by prior AI use (item T1.4b). Unit of measure: Likert score points (mean with standard deviation), and percentage of participants endorsing scores ≥ 4.
Measured: 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).
Future-use intention (item T1.7: 1 = definitely will not, 5 = definitely will) and recommendation intention (item T1.8: 1 = definitely will not, 5 = definitely will). Unit of measure: Likert score points (mean with standard deviation), and percentage of participants endorsing scores ≥ 4 on each item.
Measured: Immediately after the consultation (within 15 minutes of consultation exit), same-day index visit.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Jun 2026
- Study start1 Jun 2026
- Primary completion31 Jul 2026
- Study completion31 Jul 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (9)Papers the sponsor has linked to this study.
- Other trials for Patient Health Information Seeking BehaviorStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 350 participants (actual)
Sponsor and investigators
- Lead sponsor
- Utku Gürhan
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Utku Gürhan, University of Kyrenia
- Investigators
- Utku Gurhan, MD, Principal investigator, University of Kyrenia
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07631585
- Sponsor's protocol ID
- ORTHO-OP-GPT-2026
- Acronym
- ORTHO-OP-GPT
- Status last verified
- Aug 2026
- First submitted
- 18 May 2026
- Last updated
- 31 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes
Publications
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.
PubMed 41906842Gultekin 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.
PubMed 40450565Kahan 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.
PubMed 40896839Schepman 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.
PubMed 34235291Yildirim 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.
PubMed 40264200Norman 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.
PubMed 17213046Bafna Sherma N. Factors influencing patients' engagement with ChatGPT for accessing health-related information. Crit Public Health. 2024.
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.
PubMed 37314848Christy 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.
PubMed 38069953
Study at a glance
From the registry record, last updated by the sponsor on 31 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Longitudinal Pre-Post Patient AI Trust Dynamics in Orthopedic Outpatients: A Mixed-Methods Observational Study With Matched Physician-Patient Dyads
- ClinicalTrials.gov ID
- NCT07631585
- Acronym / sponsor ID
- ORTHO-OP-GPT / ORTHO-OP-GPT-2026
- Keywords
- ChatGPT, large language model, artificial intelligence chatbot, patient education, health information seeking, trust, physician-patient communication, orthopedics
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 350 participants (actual)
- Lead sponsor
- Utku GürhanUniversity, hospital or other
- Study start
- 1 Jun 2026 (actual)
- Primary completion
- 31 Jul 2026 (actual)
- Study completion
- 31 Jul 2026 (actual)
- First posted
- 8 Jun 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 was 180 to obtain 150 paired completed assessments; 350 participants were enrolled.
Detailed description
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (3 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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).
Time frame: 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).
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.
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
Time frame: Immediately after the consultation (within 15 minutes of consultation exit), for both patient (T1.2) and physician (H2) forms; same-day index visit.
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.
6 secondary outcomes
Mean within-patient change in consultation-related anxiety, measured by a study-specific six-item instrument (four-item anxiety subscale, range 4-20).
Time frame: 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).
Time frame: 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).
Time frame: 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).
Time frame: 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).
Time frame: 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).
Time frame: Immediately after the consultation (within 15 minutes of consultation exit), same-day index visit.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 31 Aug 2026.
- Background: PubMed 41906842Gultekin 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.
- Background: PubMed 40450565Gultekin 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.
- Background: PubMed 40896839Kahan 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.
- Background: PubMed 34235291Schepman 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.
- Background: PubMed 40264200Yildirim 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.
- Background: PubMed 17213046Norman 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.
- BackgroundBafna Sherma N. Factors influencing patients' engagement with ChatGPT for accessing health-related information. Crit Public Health. 2024.
- Background: PubMed 37314848Choudhury 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.
- Background: PubMed 38069953Christy 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.
- Individual participant dataSharing planned: Yes
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Utku Gurhan, MD principal investigator
University of Kyrenia
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Patient Health Information Seeking Behavior and Trust, run by Utku Gürhan at 2 sites in Cyprus.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 31 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Cyprus. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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). (time frame: 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).). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07631585, which also lists 9 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 31 Aug 2026; AzTrials last refreshed its copy on 26 Oct 2026.