Development and Evaluation of a Retrieval-Augmented Generation Chatbot for Orthopedic and Trauma Surgery Patient Education: Mixed-Methods Study
Abstract
Abstract Background Large language models are increasingly applied in health care for documentation, patient education, and clinical decision support. However, their factual reliability can be compromised by hallucinations and a lack of source traceability. Retrieval-augmented generation (RAG) enhances response accuracy by combining generative models with document retrieval mechanisms. While promising in medical contexts, RAG-based systems remain underexplored in orthopedic and trauma surgery patient education, particularly in non-English settings. Objective This study aimed to develop and evaluate a RAG-based chatbot that provides German-language, evidence-based information on common orthopedic conditions. We assessed the system’s performance in terms of response accuracy, contextual precision, and alignment with retrieved sources. In addition, we examined user satisfaction, usability, and perceived trustworthiness. Methods The chatbot integrated OpenAI’s GPT language model with a Qdrant vector database for semantic search. Its corpus consisted of 899 curated German-language documents, including national orthopedic guidelines and patient education content from the Orthinform platform of the German Society of Orthopedics and Trauma Surgery. After preprocessing, the data were segmented into 18,197 retrievable chunks. Evaluation occurred in two phases: (1) human validation by 30 participants (orthopedic specialists, medical students, and nonmedical users), who rated 12…
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