AI-Augmented Handoffs and Shift Notes in Saudi Hospitals: Exploring the Role of Generative Artificial Intelligence in Reducing Nursing Docume Ntation Fatigue
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Abstract
Nursing documentation remains one of the most time-consuming responsibilities in modern healthcare settings. Although electronic health records have improved accessibility and continuity of care, they have also increased administrative workload for nurses, particularly during shift handoffs and end-of-shift reporting. In many hospitals, nurses spend a significant portion of their working hours documenting patient information instead of engaging in direct patient care. This growing documentation burden has been linked to stress, fatigue, reduced job satisfaction, and professional burnout.
Recent advances in generative artificial intelligence (AI) have introduced new possibilities for automating clinical documentation processes. AI-powered voice-to-text systems such as Microsoft Dragon Copilot and Heidi Health AI are capable of converting spoken clinical conversations into structured nursing notes and patient handoff summaries. These technologies may reduce repetitive administrative tasks while improving communication efficiency and workflow organization.
This review paper examines the potential role of generative AI in supporting nursing handoffs and shift documentation within Saudi Arabian hospitals, particularly in institutions such as King Fahad University Hospital and King Fahad Military Medical Complex. The paper critically discusses current challenges associated with nursing documentation, the importance of effective clinical handoffs, emerging AI-assisted documentation systems, and the opportunities and risks associated with AI implementation in healthcare environments. Additionally, the paper proposes a mixed-methods pre/post-intervention study aimed at evaluating the impact of generative AI on nursing documentation fatigue, workflow efficiency, and nurse satisfaction.
The review concludes that AI-assisted documentation systems may significantly improve nursing workflow and reduce administrative burden when implemented responsibly. However, concerns related to data privacy, accuracy, ethical accountability, and staff acceptance remain important considerations for future research and healthcare policy development.