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

1. Juni 2026 aktualisiert von: Utku Gürhan

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 is 180 to obtain 150 paired completed assessments.

Studienübersicht

Status

Noch keine Rekrutierung

Bedingungen

Detaillierte Beschreibung

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.

Studientyp

Beobachtungs

Einschreibung (Geschätzt)

180

Kontakte und Standorte

Dieser Abschnitt enthält die Kontaktdaten derjenigen, die die Studie durchführen, und Informationen darüber, wo diese Studie durchgeführt wird.

Studienkontakt

Studienorte

  • Zypern
      • Kyrenia, Zypern
        • University of Kyrenia, Dr. Suat Gunsel Hospital - Orthopedic Outpatient Clinic
        • Kontakt:
        • Hauptermittler:
          • Utku Gurhan, MD
      • Nicosia, Zypern
        • Near East University Hospital - Orthopedic Outpatient Clinic

Teilnahmekriterien

Forscher suchen nach Personen, die einer bestimmten Beschreibung entsprechen, die als Auswahlkriterien bezeichnet werden. Einige Beispiele für diese Kriterien sind der allgemeine Gesundheitszustand einer Person oder frühere Behandlungen.

Zulassungskriterien

Studienberechtigtes Alter

  • Erwachsene
  • Älterer Erwachsener

Akzeptiert gesunde Freiwillige

Nein

Probenahmeverfahren

Nicht-Wahrscheinlichkeitsprobe

Studienpopulation

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.

Beschreibung

Inclusion Criteria:

  • 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

Exclusion Criteria:

  • 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

Studienplan

Dieser Abschnitt enthält Einzelheiten zum Studienplan, einschließlich des Studiendesigns und der Messung der Studieninhalte.

Wie ist die Studie aufgebaut?

Designdetails

Was misst die Studie?

Primäre Ergebnismessungen

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

Sekundäre Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
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).
Zeitfenster: Baseline (within 15 minutes pre-consultation) and immediately after the consultation (within 15 minutes of consultation exit), same-day index visit.
Anxiety is measured pre-consultation (item T0.14) and post-consultation (item T1.5) using the same 0-to-10 visual analogue scale. Within-patient change is calculated as T1.5 minus T0.14. Unit of measure: scale points (range -10 to +10). Analysis: paired t-test with Wilcoxon signed-rank as sensitivity analysis; Cohen's d effect size reported.
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).
Zeitfenster: Baseline (within 15 minutes pre-consultation, same-day index visit).
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.
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).
Zeitfenster: Baseline (T0.9, pre-consultation) and immediately after the consultation (H1, within 15 minutes of consultation exit), same-day index visit.
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.
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).
Zeitfenster: Immediately after the consultation (within 15 minutes of consultation exit), same-day index visit.
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).
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).
Zeitfenster: Immediately after the consultation (within 15 minutes of consultation exit), same-day index visit.
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.
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).
Zeitfenster: Immediately after the consultation (within 15 minutes of consultation exit), same-day index visit.
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.
Immediately after the consultation (within 15 minutes of consultation exit), same-day index visit.

Andere Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
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
Zeitfenster: Through study completion, an average of 12 months from first enrolment.
Multinomial logistic regression model: outcome = T1.4 (decreased / unchanged / increased trust, reference category = unchanged); predictors = age band (5-level), sex (3-level), education (5-level), employment status, weekly internet-use frequency. Unit of measure: adjusted odds ratios with 95% confidence intervals.
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
Zeitfenster: Through study completion, an average of 12 months from first enrolment.
Cronbach's alpha is estimated on the final analytic sample using the four trust-related Likert items listed. Unit of measure: alpha coefficient (range 0 to 1) with bootstrap 95% confidence interval.
Through study completion, an average of 12 months from first enrolment.

Mitarbeiter und Ermittler

Hier finden Sie Personen und Organisationen, die an dieser Studie beteiligt sind.

Sponsor

Utku Gürhan

Ermittler

  • Hauptermittler: Utku Gurhan, MD, University of Kyrenia

Publikationen und hilfreiche Links

Die Bereitstellung dieser Publikationen erfolgt freiwillig durch die für die Eingabe von Informationen über die Studie verantwortliche Person. Diese können sich auf alles beziehen, was mit dem Studium zu tun hat.

Allgemeine Veröffentlichungen

Studienaufzeichnungsdaten

Diese Daten verfolgen den Fortschritt der Übermittlung von Studienaufzeichnungen und zusammenfassenden Ergebnissen an ClinicalTrials.gov. Studienaufzeichnungen und gemeldete Ergebnisse werden von der National Library of Medicine (NLM) überprüft, um sicherzustellen, dass sie bestimmten Qualitätskontrollstandards entsprechen, bevor sie auf der öffentlichen Website veröffentlicht werden.

Haupttermine studieren

Studienbeginn (Geschätzt)

1. Juni 2026

Primärer Abschluss (Geschätzt)

1. November 2026

Studienabschluss (Geschätzt)

1. Januar 2027

Studienanmeldedaten

Zuerst eingereicht

18. Mai 2026

Zuerst eingereicht, das die QC-Kriterien erfüllt hat

1. Juni 2026

Zuerst gepostet (Tatsächlich)

8. Juni 2026

Studienaufzeichnungsaktualisierungen

Letztes Update gepostet (Tatsächlich)

8. Juni 2026

Letztes eingereichtes Update, das die QC-Kriterien erfüllt

1. Juni 2026

Zuletzt verifiziert

1. Juni 2026

Mehr Informationen

Begriffe im Zusammenhang mit dieser Studie

Schlüsselwörter

Andere Studien-ID-Nummern

  • ORTHO-OP-GPT-2026

Plan für individuelle Teilnehmerdaten (IPD)

Planen Sie, individuelle Teilnehmerdaten (IPD) zu teilen?

JA

Arzneimittel- und Geräteinformationen, Studienunterlagen

Studiert ein von der US-amerikanischen FDA reguliertes Arzneimittelprodukt

Nein

Studiert ein von der US-amerikanischen FDA reguliertes Geräteprodukt

Nein

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