Abstract
Aim This study aimed to elucidate the emotions of operating room nurses in Japan towards perioperative nursing using generative AI and identify factors contributing to burnout and turnover.Methods This single-center cross-sectional study, conducted from February 2023 to February 2024, employed semi-structured interviews with 10 operating room nurses from a national hospital in Japan. The interviews were designed to capture detailed qualitative data about the nurses' emotional experiences. These interviews were transcribed verbatim and analyzed using thematic, sentiment, and subjectivity analysis with ChatGPT (OpenAI, San Francisco, CA). Data visualization techniques, including keyword co-occurrence networks and cluster analyses, were also employed to uncover patterns and relationships in the data.Results Thematic analysis revealed key themes related to patient care, surgical safety, and nursing skills. The sentiment analysis showed a range of emotional tones, with high subjectivity scores indicating that the nurses' reflections were deeply personal and empathetic. Keyword co-occurrence networks highlighted the interconnectedness of various themes, such as the relationship between patient care and safety protocols. Cluster analysis identified distinct groups of emotional experiences, demonstrating the diverse emotional landscape of operating room nurses.Conclusions This study demonstrated the potential of generative AI to provide nuanced insights into the emotions of operating room nurses. The findings underscore the importance of emotional support, effective communication, and robust safety protocols in enhancing nurse well-being and job satisfaction. By leveraging AI technologies, healthcare institutions can better understand and address the emotional challenges faced by nurses, potentially reducing burnout and improving retention rates. Future research with larger and more diverse samples is needed to validate these findings and explore the broader applicability of AI in healthcare settings.