Yen-Ho Lai
Head Nurse, Department of Nursing, Hsinchu Hospital, National Taiwan University Hospital Hsin-Chu Branch, Taiwan
Ph.D. in Information Management, College of Management, National Kaohsiung University of Science and Technology, Taiwan
E-mail：t19881001@gmail.com
Wan-Yu Lee
Registered Nurse, Health Management Center, Kaohsiung Veterans General Hospital, Taiwan
Yi-Cheng Chiu
Nursing Supervisor, Department of Nursing, Hsinchu Hospital, National Taiwan University Hospital Hsin-Chu Branch, Taiwan
Yi-Ching Chou
Nursing Supervisor, Department of Nursing, Hsinchu Hospital, National Taiwan University Hospital Hsin-Chu Branch, Taiwan
Abstract
With the rapid advancement of artificial intelligence, Generative Artificial Intelligence (Gen AI) has increasingly been applied in medical education, particularly demonstrating significant potential in nursing education and clinical skills training (Buchanan et al., 2021; Topaz et al., 2025). The Central Sterile Supply Department (CSSD) is responsible for the cleaning, disinfection, and sterilization of surgical instruments. Its processes are complex and highly dependent on professional knowledge and standard operating procedures (SOPs). Inadequate training may lead to infection risks and adverse medical events (Chen et al., 2023). Therefore, improving the effectiveness of education and training has become a critical issue in healthcare quality management.
This study aims to evaluate the effectiveness of applying generative artificial intelligence in CSSD training, focusing on surgical instrument reprocessing. It examines the impact of AI-assisted training on knowledge acquisition, operational performance, and error rates. A quasi-experimental design was adopted, with participants divided into a traditional teaching group and an AI-assisted learning group. Pre- and post-tests, operational assessments, and quality indicators were used for comparison.
The results indicate that the implementation of generative AI significantly improved participants’ knowledge comprehension, operational accuracy, and learning satisfaction, while substantially reducing operational error rates. Additionally, AI provides real-time feedback and personalized learning support, which enhances learning outcomes and clinical application abilities (Kleib et al., 2024). This study suggests that healthcare institutions should incorporate generative AI into CSSD training systems to establish intelligent learning models, thereby improving healthcare quality and patient safety.
Keywords：Generative Artificial Intelligence, CSSD Training, Surgical Instrument Reprocessing, Learning Effectiveness, Healthcare Quality