Investigating the Acceptance of Large Language Model Technology on Nursing Interviews Among Nurse-Interns

With the global healthcare landscape evolving, precision medicine has become pivotal, leveraging individual health data to tailor patient care and optimize resource use. However, the scarcity of registered nurses and the resultant increased workload pose challenges, including the risk of incomplete patient care records due to reliance on manual documentation during clinical interviews. To mitigate these issues, this research employs the ChatGPT mobile application to facilitate recording and collecting voice data, utilizing voice-to-text and LLM technologies. By conducting a Unified Theory of Acceptance and Use of Technology questionnaire survey among 25 nurse-interns, the study assesses the acceptance and impact of such technological applications in nursing practices. This study explores the integration of Large Language Models (LLMs) and Artificial Intelligence (AI) in addressing nursing shortages and enhancing precision medicine, aiming to improve the organization of nursing interview data, and provide empirical evidence for the benefits of LLM technology in nursing interviews, thereby offering recommendations for nursing education, enhancing patient care, and supporting the broader adoption of precision medicine strategies.