ArticleBMJ open2025
Utilisation of AI-driven chatbots for perioperative health information seeking: a descriptive qualitative study of orthopaedic patients and family members.
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Online health information-seeking behavior during the preoperative period among patients with pancreatic tumors and caregivers in China: A qualitative study.Asia-Pacific journal of oncology nursing · 2026Article
- Development and Nationwide Multicentre Evaluation of Guideline-Grounded Large Language Model Chatbots to Support Patient Self-Management and Education in Rheumatology.Journal of medical systems · 2026Article
- Consumer and Patient Health Information Seeking With Generative AI Tools: Scoping Review of Facilitators and Barriers.Journal of medical Internet research · 2026Article
- Evolution of Chatbots as an Educational and Supportive Digital Intervention for Family Caregivers of People With Dementia: Protocol for a Systematic Integrative Review.JMIR research protocols · 2026Article
- AI chatbots for health information seeking among Chinese patients with precancerous ENT lesions: a descriptive qualitative study.BMJ open · 2026Article
- Chatbots as frontline educators in sexual reproductive health rights: evidence, limitations, and ethical considerations.Frontiers in digital health · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis 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.
designA descriptive qualitative design was employed.
settingThis study was conducted at a regional care centre for orthopaedics.
participantsWe 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.
resultsFour 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.
conclusionsAI-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.
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