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

2026년 6월 1일 업데이트: 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.

연구 개요

상태

아직 모집하지 않음

정황

상세 설명

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.

연구 유형

관찰

등록 (추정된)

180

연락처 및 위치

이 섹션에서는 연구를 수행하는 사람들의 연락처 정보와 이 연구가 수행되는 장소에 대한 정보를 제공합니다.

연구 연락처

연구 장소

  • 키프로스
      • Kyrenia, 키프로스
        • University of Kyrenia, Dr. Suat Gunsel Hospital - Orthopedic Outpatient Clinic
        • 연락하다:
        • 수석 연구원:
          • Utku Gurhan, MD
      • Nicosia, 키프로스
        • Near East University Hospital - Orthopedic Outpatient Clinic

참여기준

연구원은 적격성 기준이라는 특정 설명에 맞는 사람을 찾습니다. 이러한 기준의 몇 가지 예는 개인의 일반적인 건강 상태 또는 이전 치료입니다.

자격 기준

공부할 수 있는 나이

  • 성인
  • 고령자

건강한 자원 봉사자를 받아들입니다

아니

샘플링 방법

비확률 샘플

연구 인구

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.

설명

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

공부 계획

이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.

연구는 어떻게 설계됩니까?

디자인 세부사항

연구는 무엇을 측정합니까?

주요 결과 측정

결과 측정
측정값 설명
기간
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).
기간: 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
기간: 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.

2차 결과 측정

결과 측정
측정값 설명
기간
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).
기간: 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).
기간: 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).
기간: 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).
기간: 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).
기간: 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).
기간: 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.

기타 결과 측정

결과 측정
측정값 설명
기간
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
기간: 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
기간: 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.

공동 작업자 및 조사자

여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.

스폰서

Utku Gürhan

수사관

  • 수석 연구원: Utku Gurhan, MD, University of Kyrenia

간행물 및 유용한 링크

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일반 간행물

연구 기록 날짜

이 날짜는 ClinicalTrials.gov에 대한 연구 기록 및 요약 결과 제출의 진행 상황을 추적합니다. 연구 기록 및 보고된 결과는 공개 웹사이트에 게시되기 전에 특정 품질 관리 기준을 충족하는지 확인하기 위해 국립 의학 도서관(NLM)에서 검토합니다.

연구 주요 날짜

연구 시작 (추정된)

2026년 6월 1일

기본 완료 (추정된)

2026년 11월 1일

연구 완료 (추정된)

2027년 1월 1일

연구 등록 날짜

최초 제출

2026년 5월 18일

QC 기준을 충족하는 최초 제출

2026년 6월 1일

처음 게시됨 (실제)

2026년 6월 8일

연구 기록 업데이트

마지막 업데이트 게시됨 (실제)

2026년 6월 8일

QC 기준을 충족하는 마지막 업데이트 제출

2026년 6월 1일

마지막으로 확인됨

2026년 6월 1일

추가 정보

이 연구와 관련된 용어

키워드

기타 연구 ID 번호

  • ORTHO-OP-GPT-2026

개별 참가자 데이터(IPD) 계획

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예

약물 및 장치 정보, 연구 문서

미국 FDA 규제 의약품 연구

아니

미국 FDA 규제 기기 제품 연구

아니

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