Task augmentation, automation, and hybridization in nursing: A conceptual framework for artificial intelligence-integrated care delivery.

Abstract

Background: The integration of artificial intelligence (AI) in healthcare is rapidly reshaping nursing roles, functions, and tasks. However, there is no established framework to guide understanding of how AI interacts with nursing practice.

Purpose: To propose a conceptual framework, grounded in sociotechnical systems theory and task-technology fit, that illustrates the potential of AI to augment, automate, and hybridize nursing work.

Methods: An exploratory review of literature across nursing, organizational theory, information systems, and management was conducted to identify patterns related to task characteristics, human-technology interaction, and organizational context.

Findings: The framework links AI interaction types (augmentation, automation, and hybridization) with task categories (manual, cognitive, and routine) and organizational factors. It demonstrates that routine tasks are more amenable to automation, while complex tasks are better suited to augmentation or hybridization, depending on how governance structures shape AI adoption.

Conclusion: This framework offers nurse leaders, educators, and clinicians a structured approach to anticipate AI's impact on nursing practice, align workflows, support ethical implementation, and inform curricula that prepare nurses for evolving AI-clinical care dynamics.