Rahmanti Annisa Ristya, Yang Hsuan-Chia, Huang Chih-Wei, Huang Ching-Tzu, Lazuardi Lutfan, Lin Che-Wei, Li Yu-Chuan Jack
Department of Health Policy and Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia.
Department of Computer Science, Faculty of Science and Technology, Middlesex University, London, UK.
Med Educ Online. 2025 Dec;30(1):2497328. doi: 10.1080/10872981.2025.2497328. Epub 2025 May 8.
Nonverbal communication is essential in physician-patient interaction, especially in telemedicine where verbal cues may be limited. This study aimed to identify and validate key nonverbal cues for assessing physician empathy in telemedicine consultations through a Delphi method. A three-round Delphi study was conducted from June to November 2022, involving various experts, including academics, healthcare professionals, AI/telemedicine researchers, industry professionals, and patients. Experts evaluated the importance, validity, and reliability of potential nonverbal cues. Consensus was determined based on median responses and expert scoring percentages, with statistical agreement and stability assessed using Kendall's coefficient of concordance (Kendall's W) and Wilcoxon signed-rank test. Analyses were conducted using SPSS, version 23.0 with significance set at < 0.05. Of the 72 experts invited, 37 (51%) agreed to participate, with 35 completing the first round (95% completion rate). Eight significant nonverbal cues were identified in the first round, though one did not reach consensus. The second round obtained an 89% response rate (31/35), with three new cues introduced; one did not reach consensus. Round 3 achieved a 94% response rate (29/31), finalizing nine key cues: facial expression, eye contact, tone of voice, smiling, head nodding, body posture, hand gesture, distance, and environmental cues. Among these, facial expression, eye contact, and tone of voice were identified as the most crucial. Inter-expert agreement was statistically significant across all items with strong agreement on the importance (W = 0.739, < 0.001), good agreement on their validity (W = 0.689, < 0.001), and moderate agreement on their reliability (W = 0.452, < 0.001). This study highlights the importance of specific nonverbal cues in telemedicine, particularly facial expression, eye contact, and tone of voice. It provides a validated foundation for developing tools to enhance physician-patient interactions and potentially improve health outcomes in telemedicine.
非语言沟通在医患互动中至关重要，尤其是在远程医疗中，因为言语线索可能有限。本研究旨在通过德尔菲法识别和验证在远程医疗会诊中评估医生同理心的关键非语言线索。2022年6月至11月进行了三轮德尔菲研究，涉及包括学者、医疗保健专业人员、人工智能/远程医疗研究人员、行业专业人员和患者在内的各类专家。专家们评估了潜在非语言线索的重要性、有效性和可靠性。根据中位数回答和专家评分百分比确定共识，使用肯德尔和谐系数（肯德尔W）和威尔科克森符号秩检验评估统计一致性和稳定性。使用SPSS 23.0进行分析，显著性设定为<0.05。在邀请的72位专家中，37位（51%）同意参与，35位完成了第一轮（完成率95%）。第一轮识别出8个重要的非语言线索，不过有1个未达成共识。第二轮的回复率为89%（31/35），引入了3个新线索；1个未达成共识。第三轮的回复率为94%（29/31），确定了9个关键线索：面部表情、眼神交流、语调、微笑、点头、身体姿势、手势、距离和环境线索。其中，面部表情、眼神交流和语调被确定为最关键的。所有项目的专家间一致性在统计学上具有显著性，在重要性方面有强烈一致性（W = 0.739，<0.001），在有效性方面有良好一致性（W = 0.689，<0.001），在可靠性方面有中等一致性（W = 0.452，<0.001）。本研究强调了特定非语言线索在远程医疗中的重要性，尤其是面部表情、眼神交流和语调。它为开发增强医患互动并可能改善远程医疗健康结果的工具提供了经过验证的基础。
