Ethical Artificial Intelligence in Nursing Workforce Management and Policymaking: Bridging Philosophy and Practice

Area of Science:

• Nursing Management
• Healthcare Policy
• Artificial Intelligence Ethics

Background:

• Healthcare artificial intelligence (AI) integration lacks a strong theoretical and philosophical basis for nursing workforce management and policymaking.
• This gap hinders the safe, ethical, effective, and sustainable use of AI, risking inequities and unmitigated risks.
• Existing AI risk assessments are often reactive, occurring after AI solution development.

Purpose of the Study:

• To present a philosophical discourse on Park's optimized nurse staffing (Sweet Spot) theory.
• To propose a novel theoretical framework and methodologies for ethical AI integration in nursing workforce management and policymaking.
• To provide the philosophical underpinnings for this new framework.

Main Methods:

• A rigorous philosophical discourse employing theoretical triangulation.
• Grounded in Park's Optimized Nursing Staffing (Sweet Spot) Estimation Theory.
• Synthesis of diverse philosophical perspectives including moderate realism, post-positivism, and contextualism.

Main Results:

• A novel theoretical framework for ethical AI-equipped nursing workforce management and policymaking is introduced.
• The framework integrates moderate realism, post-positivism, and contextualism.
• Practical solutions for equitable and fair AI integration are provided, balancing safety, ethics, effectiveness, and efficiency.

Conclusions:

• This paper pioneers a theoretical framework for ethically integrating AI into nursing workforce management and policy.
• The framework offers proactive guidance for AI safety and ethical risk assessment, unlike current reactive practices.
• It establishes groundwork for future empirical studies and practical implementations for improved healthcare decision-making.