Longitudinal Pre-Post Patient AI Trust Dynamics in Orthopedic Outpatients: A Mixed-Methods Observational Study With Matched Physician-Patient Dyads
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 350
- 试验地点
- 4
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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
排除标准
- •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
结局指标
主要结局
未指定
次要结局
- Mean within-patient change in consultation-related anxiety, measured by a study-specific six-item instrument (four-item anxiety subscale, range 4-20).(Baseline (within 15 minutes pre-consultation) and immediately after the consultation (within 15 minutes of consultation exit), same-day index visit.)
研究者
Utku Gürhan
Assistant Professor of Orthopaedics and Traumatology
University of Kyrenia