TY - GEN
T1 - Investigating the Acceptance of Large Language Model Technology on Nursing Interviews Among Nurse-Interns
AU - Huang, Yen Yu
AU - Lin, Chia Ju
AU - Chen, Ching Min
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85200692509
UR - https://www.scopus.com/pages/publications/85200692509#tab=citedBy
U2 - 10.1007/978-3-031-65881-5_8
DO - 10.1007/978-3-031-65881-5_8
M3 - Conference contribution
AN - SCOPUS:85200692509
SN - 9783031658808
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 64
EP - 72
BT - Innovative Technologies and Learning - 7th International Conference, ICITL 2024, Proceedings
A2 - Cheng, Yu-Ping
A2 - Pedaste, Margus
A2 - Bardone, Emanuele
A2 - Huang, Yueh-Min
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th International Conference of Innovative Technologies and Learning, ICITL 2024
Y2 - 14 August 2024 through 16 August 2024
ER -