Podrobná bibliografie
| Název: |
Perspectives on AI-Driven Nursing Science Among Nursing Professionals from China: A Qualitative Study. |
| Autoři: |
Chen, Yi, Wu, Fulei, Zhang, Wen, Xing, Weijie, Zhu, Zheng, Huang, Qingmei, Yuan, Changrong |
| Zdroj: |
Nursing Reports; Jun2025, Vol. 15 Issue 6, p218, 19p |
| Témata: |
WORK, DECISION support systems, DOCUMENTATION, HUMAN services programs, QUALITATIVE research, ENDOWMENTS, ARTIFICIAL intelligence, INTERVIEWING, UNIVERSITIES & colleges, INVESTMENTS, CONTENT analysis, NURSING education, JUDGMENT sampling, PHILOSOPHY of nursing, THEMATIC analysis, COLLEGE teacher attitudes, ATTITUDES of medical personnel, NURSING practice, RESEARCH methodology, SOFTWARE architecture, DATA analysis software, EXPERIENTIAL learning, USER interfaces |
| Geografický termín: |
CHINA |
| Abstrakt: |
Background: As artificial intelligence (AI) continues to advance in healthcare, limited research has explored how nursing professionals perceive its integration into clinical practice and education—particularly among those directly involved in AI-driven initiatives. This qualitative study aimed to investigate the perceptions, experiences, and expectations of nursing educators and clinical practitioners regarding the application of AI in nursing and to provide insights for the advancement of AI-driven nursing science. Methods: A descriptive qualitative design was employed. Between September and December 2024, semi-structured interviews were conducted with 12 nursing professionals from universities and hospitals in Shanghai, Suzhou, and Chengdu, China. Participants were selected using maximum variation sampling, and data were analyzed using content analysis. Results: Three major themes and eleven sub-themes were identified: (1) The potential of multi-perspective development of AI-driven nursing science and practice, including aiding in decision-making, assisting with writing nursing documents, helping in care practices with high exposure risks and heavy physical exertion, and supporting the development of nursing activities. (2) A multi-dimensional response to the wave of intelligent nursing research and practice: education and scientific research come first, then we fully explore the application scenarios, and then conduct deep interdisciplinary integration. (3) Obstacles for intelligent nursing research and practice: interaction factors of "human–technology–machine" for application, transformation, and promotion; financial support and continuous investment; the controversy behind the intelligent maturity level; and application risk and fault tolerance. Conclusions: Participants emphasized the importance of evidence-based, cautious, and context-sensitive application of AI technologies to ensure that intelligent nursing evolves in alignment with clinical realities. The findings suggest a need for strengthened policy, education, and resource allocation to support the sustainable integration of AI in nursing. [ABSTRACT FROM AUTHOR] |
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