Currently submitted to: Journal of Medical Internet Research
Date Submitted: Mar 26, 2026
Open Peer Review Period: Mar 27, 2026 - May 22, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Barriers and Facilitators to Implementing Artificial Intelligence in Intensive Care Units in China: A CFIR-Based Qualitative Study from Nurse Managers’ Perspectives
ABSTRACT
Background:
Artificial intelligence (AI) has shown significant potential in ICU nursing practice, enhancing efficiency, making decisions, and patient safety. However, evidence regarding the implementation factors of AI in ICU nursing remains limited, particularly from the perspective of nursing leadership.
Objective:
This study aimed to explore perceived barriers and facilitators to implementing AI in ICUs in China from the perspectives of ICU nursing managers, guided by the Consolidated Framework for Implementation Research (CFIR).
Methods:
A qualitative study using semi-structured, face-to-face interviews was conducted with 11 ICU nursing managers from tertiary hospitals across seven geographic regions in China. Interview questions were informed by the CFIR framework. Data were audio-recorded, transcribed verbatim, and analyzed using a combined deductive–inductive approach with NVivo software. CFIR constructs were coded and rated as barriers, facilitators, or neutral factors.
Results:
A total of 20 factors were identified across five CFIR domains, including 5 barriers, 13 facilitators, and 2 neutral influencing factors. Key barriers included high implementation costs, limited adaptability and complexity of AI systems, ethical and privacy concerns, shortages of interdisciplinary talent, and communication challenges between clinical and technical teams. Major facilitators encompassed perceived relative advantages of AI, supportive national policies, leadership engagement, a positive implementation climate, readiness for implementation, and nurses’ self-efficacy.
Conclusions:
AI implementation in Chinese ICUs is a complex socio-technical process influenced by multilevel contextual, organizational, and individual factors. While nursing managers hold generally positive attitudes toward AI, addressing structural, ethical, and workforce-related challenges is essential for sustainable integration. These findings provide theory-informed insights to support context-sensitive implementation strategies for AI in critical care nursing practice. Clinical Trial: None.
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