Harnessing Generative AI in Nursing Informatics: A Theoretical Critique and Policy Innovation Perspective From Japan

Area of Science:

• Nursing Informatics
• Artificial Intelligence in Healthcare
• Health Policy

Background:

• The rapid evolution of artificial intelligence (AI) is significantly impacting healthcare delivery.
• Generative AI presents opportunities to improve patient outcomes and clinical decision-making.
• Integrating AI into nursing practice necessitates addressing ethical, educational, and regulatory considerations.

Purpose of the Study:

• To provide a theoretical critique of generative AI integration in nursing informatics.
• To offer policy innovation perspectives for AI adoption in nursing.
• To examine the potential and challenges of generative AI in nursing based on a Japanese survey and established models.

Main Methods:

• Analysis of a 2023 nationwide survey conducted in Japan by the Health and Global Policy Institute.
• Comprehensive literature review of key studies and foundational models like the technology acceptance model and risk perception frameworks.
• Theoretical critique and policy innovation perspective on generative AI integration.

Main Results:

• Generative AI can potentially enhance information access and service quality in healthcare.
• Significant challenges include low digital literacy, liability concerns, data accuracy, and privacy issues.
• A multidimensional strategy is needed for effective integration, including education and ethical governance.

Conclusions:

• Generative AI holds promise for nursing informatics but requires careful management of risks.
• Successful adoption hinges on balancing innovation with robust regulatory and educational strategies.
• Enhancing digital literacy, clarifying liability, ensuring data security, and fostering collaboration are vital for safe AI integration.