Integrating Large Language Models Into Patient Health Education: Interview Study Among Nurses

Background Large language models (LLMs) have shown potential to support patient education, improve information accessibility, and assist with repetitive clinical communication tasks. However, the clinical integration of LLMs depends not only on technological capabilities but also on how these tools fit within existing health care workflows. Existing studies have largely focused on technical performance of LLMs or the experiences of physicians and patients, and little is known about how frontline nurses, who play a central role in patient education, experience LLM-supported education workflows in clinical practice settings. Objective This study aimed to explore frontline nurses’ experiences with an LLM-assisted patient education workflow, including perceived benefits, changes, challenges, and considerations for clinical practice. Methods Semistructured interviews were conducted with 16 frontline hematological nurses between December 2025 and March 2026 at the Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences to explore their experiences. Data were analyzed using an inductive codebook thematic analysis, with codes and themes iteratively reviewed and refined through researcher discussions. Results Nurses’ experiences were characterized by 3 interconnected tensions. First, nurses perceived a balance between efficiency gains and information safety concerns, as the LLM-assisted workflow supported more efficient and standardized information delivery while requiring additional oversight. Second, nurses perceived changes in their roles and responsibilities, accompanied by increased demands for professional knowledge and competencies. Third, nurses perceived potential patient benefits of LLM use particularly in facilitating access to specialized health information, while identifying barriers related to limited health literacy and limited familiarity with LLM-based tools. Conclusions From nurses’ perspectives, LLM use was perceived to support more efficient delivery of patient health information while also requiring attention to information safety, professional responsibility, and patient-related challenges. These findings highlight that nurses’ roles remain important in supporting the safe and appropriate use of LLM-assisted education. Future research and implementation efforts should explore workflow-oriented design strategies for integrating LLMs into clinical nursing practice while considering nurse oversight, support clinical workflow adaptation, and address patient needs to support safe and appropriate use.