A Nurse-Centered Socio-Technical Readiness Framework for Artificial Intelligence in Clinical Practice
DOI:
https://doi.org/10.65655/OpenChristianPress.2026.117Keywords:
nursing, Artificial intelligence, AI readiness, digital health, clinical decision support, socio-technical systems, nursing ethics, technology acceptance, patient safety, AI governanceAbstract
Background: Artificial intelligence (AI) is moving from experimental promise into the everyday infrastructure of clinical work through predictive analytics, clinical decision support, remote monitoring, documentation automation, robotics, and increasingly generative AI. Nurses are central to this transition because they translate data-rich systems into bedside decisions, relational care, safety surveillance, patient advocacy, and ethical accountability. Yet the literature shows that nurses’ AI preparedness remains uneven, with readiness shaped not only by literacy and training but also by workflow fit, explainability, professional autonomy, equity, and governance. Aim: This article develops a nurse-centered socio-technical framework for AI readiness in clinical practice and clarifies why preparing nurses for AI requires more than digital skills acquisition. Design: Critical integrative review and conceptual framework development. Evidence base: The synthesis integrates recent empirical nursing studies, systematic and qualitative reviews, nursing position statements, health AI reporting standards, and global AI governance guidance. Priority was given to peer-reviewed sources published from 2018 to 2026, supplemented by foundational theories of technology acceptance, socio-technical systems, nursing ethics, and human factors. Synthesis: Six interdependent readiness domains were identified: technical, educational, ethical/legal, professional, organizational, and equity/governance readiness. These domains explain why AI adoption may fail even when systems are technically sophisticated and why nurse participation in design, implementation, oversight, and evaluation is indispensable. Conclusion: AI readiness in nursing is best understood as a socio-technical capability distributed across persons, technologies, institutions, regulations, and moral relationships. AI should augment, not displace, the nurse’s clinical judgment, relational presence, and professional responsibility. Impact: The proposed framework offers nurse educators, clinical leaders, health system executives, AI developers, regulators, and researchers a practical structure for designing, evaluating, and governing AI-enabled nursing practice without weakening the human core of care.
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Copyright (c) 2026 Placide Mutabazi, Sixbert SANGWA (Author)
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