Utilisation of AI-driven chatbots for perioperative health information seeking: a descriptive qualitative study of orthopaedic patients and family members
Tao Chen, Qiang Li, Deng Zhao, Wenjing Zhang, Yong Chen, Jin‐Dong Yang, Chun Pu, Qiang Fu
Abstract
OBJECTIVE: This study aimed to explore orthopaedic patients' and families' experiences with artificial intelligence (AI)-driven chatbots for perioperative health information, focusing on usability, effectiveness and perceptions. DESIGN: A descriptive qualitative design was employed. SETTING: This study was conducted at a regional care centre for orthopaedics. PARTICIPANTS: We recruited 13 participants (patients undergoing orthopaedic surgeries and family members) through purposive sampling. Face-to-face semistructured interviews were conducted to capture participants' experiences and insights. Data collection was concluded when data saturation was achieved. All interviews were audio recorded and transcribed verbatim within 24 hours. Transcripts were verified and analysed using the Colaizzi's data analysis method. RESULTS: Four themes emerged from interviews, including: (1) preference of AI chatbots over search engines; (2) improved accessibility and quality of information; (3) preference of AI over human interactions and (4) importance of effective prompting. CONCLUSIONS: AI-driven chatbots offer a promising adjunct to perioperative patient education by delivering immediate, tailored guidance that overcomes the limitations of conventional search engines and busy clinical settings. Study participants valued chatbots' efficient, context-sensitive retrieval, professional-level advice and non-judgmental interactions, which fostered trust and reduced anxiety. Effective prompting emerged as a key user skill, directly shaping response relevance and accuracy. Chatbot-generated health information should be regularly reviewed for accuracy. Structured tutorials may be offered for user capacity building.