Emotional analysis of operating room nurses in acute care hospitals in Japan: insights using ChatGPT
Abstract Aim This study aimed to explore the emotions of operating room nurses in Japan towards perioperative nursing using generative AI and human analysis, and to identify factors contributing to burnout and turnover. Methods A single-center cross-sectional study was conducted from February 2023 t...
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| Format: | Article |
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BMC
2025-01-01
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| Series: | BMC Nursing |
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| Online Access: | https://doi.org/10.1186/s12912-024-02655-9 |
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| author | Kentaro Hara Reika Tachibana Ryosuke Kumashiro Kodai Ichihara Takahiro Uemura Hiroshi Maeda Michiko Yamaguchi Takahiro Inoue |
| author_facet | Kentaro Hara Reika Tachibana Ryosuke Kumashiro Kodai Ichihara Takahiro Uemura Hiroshi Maeda Michiko Yamaguchi Takahiro Inoue |
| author_sort | Kentaro Hara |
| collection | DOAJ |
| description | Abstract Aim This study aimed to explore the emotions of operating room nurses in Japan towards perioperative nursing using generative AI and human analysis, and to identify factors contributing to burnout and turnover. Methods A single-center cross-sectional study was conducted from February 2023 to February 2024, involving semi-structured interviews with 10 operating room nurses from a national hospital in Japan. Interview transcripts were analyzed using generative AI (ChatGPT-4o) and human researchers for thematic, emotional, and subjectivity analysis. A comparison between AI and human analysis was performed, and data visualization techniques, including keyword co-occurrence networks and cluster analysis, were employed to identify patterns and relationships. Results Key themes such as patient care, surgical safety, and nursing skills were identified through thematic analysis. Emotional analysis revealed a range of tones, with AI providing an efficient overview and human researchers capturing nuanced emotional insights. High subjectivity scores indicated deeply personal reflections. Keyword co-occurrence networks and cluster analysis highlighted connections between themes and distinct emotional experiences. Conclusions Combining generative AI with human expertise offered nuanced insights into the emotions of operating room nurses. The findings emphasize the importance of emotional support, effective communication, and safety protocols in improving nurse well-being and job satisfaction. This hybrid approach can help address emotional challenges, reduce burnout, and enhance retention rates. Future research with larger and more diverse samples is needed to validate these findings and explore the broader applications of AI in healthcare. |
| format | Article |
| id | doaj-art-f4719a684270464d9999059adbecca81 |
| institution | Kabale University |
| issn | 1472-6955 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Nursing |
| spelling | doaj-art-f4719a684270464d9999059adbecca812025-01-12T12:13:47ZengBMCBMC Nursing1472-69552025-01-0124111510.1186/s12912-024-02655-9Emotional analysis of operating room nurses in acute care hospitals in Japan: insights using ChatGPTKentaro Hara0Reika Tachibana1Ryosuke Kumashiro2Kodai Ichihara3Takahiro Uemura4Hiroshi Maeda5Michiko Yamaguchi6Takahiro Inoue7Department of Operation Center and Department of Nursing, Chiba University and Hospital National Hospital Organization Nagasaki Medical Center and Nagasaki University Graduate School of Biomedical SciencesDepartment of Operation Center, National Hospital Organization Nagasaki Medical CenterDepartment of Operation Center, National Hospital Organization Nagasaki Medical CenterDepartment of Operation Center, National Hospital Organization Nagasaki Medical CenterDepartment of Operation Center, National Hospital Organization Nagasaki Medical CenterDepartment of Operation Center, Juntendo University School of Medicine Juntendo HospitalDepartment of Anesthesiology, National Hospital Organization Nagasaki Medical CenterDepartment of Healthcare Management Research Center, Chiba University HospitalAbstract Aim This study aimed to explore the emotions of operating room nurses in Japan towards perioperative nursing using generative AI and human analysis, and to identify factors contributing to burnout and turnover. Methods A single-center cross-sectional study was conducted from February 2023 to February 2024, involving semi-structured interviews with 10 operating room nurses from a national hospital in Japan. Interview transcripts were analyzed using generative AI (ChatGPT-4o) and human researchers for thematic, emotional, and subjectivity analysis. A comparison between AI and human analysis was performed, and data visualization techniques, including keyword co-occurrence networks and cluster analysis, were employed to identify patterns and relationships. Results Key themes such as patient care, surgical safety, and nursing skills were identified through thematic analysis. Emotional analysis revealed a range of tones, with AI providing an efficient overview and human researchers capturing nuanced emotional insights. High subjectivity scores indicated deeply personal reflections. Keyword co-occurrence networks and cluster analysis highlighted connections between themes and distinct emotional experiences. Conclusions Combining generative AI with human expertise offered nuanced insights into the emotions of operating room nurses. The findings emphasize the importance of emotional support, effective communication, and safety protocols in improving nurse well-being and job satisfaction. This hybrid approach can help address emotional challenges, reduce burnout, and enhance retention rates. Future research with larger and more diverse samples is needed to validate these findings and explore the broader applications of AI in healthcare.https://doi.org/10.1186/s12912-024-02655-9Operating room nursesGenerative AIEmotional analysisPerioperative nursingBurnoutJob satisfaction |
| spellingShingle | Kentaro Hara Reika Tachibana Ryosuke Kumashiro Kodai Ichihara Takahiro Uemura Hiroshi Maeda Michiko Yamaguchi Takahiro Inoue Emotional analysis of operating room nurses in acute care hospitals in Japan: insights using ChatGPT BMC Nursing Operating room nurses Generative AI Emotional analysis Perioperative nursing Burnout Job satisfaction |
| title | Emotional analysis of operating room nurses in acute care hospitals in Japan: insights using ChatGPT |
| title_full | Emotional analysis of operating room nurses in acute care hospitals in Japan: insights using ChatGPT |
| title_fullStr | Emotional analysis of operating room nurses in acute care hospitals in Japan: insights using ChatGPT |
| title_full_unstemmed | Emotional analysis of operating room nurses in acute care hospitals in Japan: insights using ChatGPT |
| title_short | Emotional analysis of operating room nurses in acute care hospitals in Japan: insights using ChatGPT |
| title_sort | emotional analysis of operating room nurses in acute care hospitals in japan insights using chatgpt |
| topic | Operating room nurses Generative AI Emotional analysis Perioperative nursing Burnout Job satisfaction |
| url | https://doi.org/10.1186/s12912-024-02655-9 |
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