Barriers and Facilitators to AI Implementation in Intensive Care Units in China: Qualitative Study Among Nurse...

AI has shown significant potential in intensive care unit (ICU) nursing practice, enhancing efficiency, decision-making, and patient safety. However, evidence regarding the implementation factors of AI in ICU nursing remains limited, particularly from the perspective of nursing leadership.

This study aimed to explore the perceived barriers and facilitators to the implementation of AI in ICUs in China from the perspectives of ICU nurse managers, guided by the Consolidated Framework for Implementation Research (CFIR).

A qualitative study using semistructured, face-to-face interviews was conducted with 11 ICU nurse managers from tertiary hospitals across 7 geographic regions in China from August to October 2025. Participants were recruited through maximum variation purposive sampling and approached via WeChat (Tencent) or telephone. Interview questions were informed by the CFIR framework. Data collection and analysis were conducted iteratively until data saturation was reached. Data were audio-recorded, transcribed verbatim, and analyzed using directed content analysis guided by the CFIR.

A total of 20 factors were identified across 5 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.

Addressing the complexity of AI systems, their limited fit with clinical contexts, and the shortage of interdisciplinary expertise is critical for successful implementation. Hospital administrators and health policymakers should optimize resource allocation, strengthen AI-related training for health care professionals, and develop context-specific implementation strategies to promote the effective, appropriate, and sustainable use of AI in critical care nursing practice.