Nursing documentation is a critical component of healthcare delivery, serving as a comprehensive record of patient care, assessments, interventions, and outcomes. The substantial time investment required for documentation, accounting for nearly one-third of nurses' working hours, underscores the need for more efficient and effective documentation practices. This significant time allocation can potentially detract from direct patient care activities, making it imperative to explore innovative solutions that can streamline the documentation process without compromising the quality and accuracy of the information recorded. The advent of generative artificial intelligence (AI) presents a promising avenue for revolutionizing nursing documentation. By comparing traditional documentation methods with AI-assisted approaches, this study aims to identify potential benefits and challenges associated with integrating advanced technology into nursing workflows.

40 nurses with a minimum of six months of clinical experience participated in the study. During the pre-assessment phase, participants documented a specific nursing scenario utilizing conventional electronic nursing records. In the post-assessment phase, the participants employed the Smart ENR AI system, a generative AI-based nursing documentation tool developed by the research team. The system, constructed on OpenAI's ChatGPT 4.0 API, was adapted to adhere to domestic nursing standards and support formats, including NANDA, SOAPIE, Focus DAR, and narrative records. The documentation was evaluated for accuracy, comprehensiveness, usability, ease of use, and fluency.

Participants possessed an average of 64 months of clinical experience. The completion of traditional documentation required an average of 467.18 ± 314.77 seconds, whereas the utilization of generative AI reduced this duration to 182.68 ± 99.71 seconds—a reduction of approximately 40%. The evaluation of AI-generated documentation yielded the following scores (on a 5-point scale): accuracy (3.62 ± 1.29), comprehensiveness (4.13 ± 1.07), usability (3.50 ± 0.93), ease of use (4.80 ± 0.61), and fluency (4.50 ± 0.88).

The aforementioned findings indicate that generative artificial intelligence (AI) possesses the potential to substantially reduce nurses' workload and enhance documentation efficiency. Continued refinement of AI models based on diverse nursing scenarios is imperative to further improve accuracy, thereby ensuring that AI-based systems can be readily implemented in clinical practice with minimal manual modifications by nursing professionals.

This investigation elucidates the potential of generative AI in nursing practice through a direct comparison of documentation produced by experienced nurses with AI-generated records. It is anticipated that generative AI will facilitate nurses in improving both the efficiency and accuracy of nursing documentation in future clinical settings.

Dongkyun Lee PhDc, MBA, RN

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