System-Level AI Governance in Nursing: A Practical Framework for Safe and Scalable Adoption
Year: 2026
Type: article
Abstract: Health care organizations are rapidly adopting artificial intelligence (AI)-enabled tools, yet nurse leaders often lack a clear framework to evaluate, govern, and safely scale these technologies. This article presents a practical, system-level AI governance model grounded in risk mitigation, interdisciplinary partnership, and nursing leadership decision rights commonly enacted through nursing professional governance structures. We outline core components of an effective governance structure, including clinical usefulness, safety and reliability, fairness and inclusiveness, transparency and explainability, privacy and security, and organizational accountability. Real-world examples illustrate how nurse executives can guide responsible AI adoption to reduce cognitive load, improve documentation efficiency, and enhance patient outcomes. The framework equips nursing leaders with actionable strategies and practical workflow checklist to lead AI transformation in complex health care environments.
Source: Nursing Administration Quarterly
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7204454468