Error loading player: No playable sources found

P08

A Comparative Study of Traditional Nursing Documentation and Generative AI-Based Nursing Documentation


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.

Speaker

Speaker Image for Dongkyun Lee
Dongkyun Lee, PhDc, MBA, RN

Related Products

Thumbnail for Synthesizing Current Evidence with GenAI: A Proof-of-Concept Project
Synthesizing Current Evidence with GenAI: A Proof-of-Concept Project
Purpose: Utilize generative artificial intelligence to develop a proof-of-concept tool to help with evidence-based information seeking and synthesis…
Thumbnail for In-Brief: Communication to Stop Workplace Violence/Change Management
In-Brief: Communication to Stop Workplace Violence/Change Management
Multiple speakers highlight approaches to managing issues related to data and behavioral health in the practice environment…
Thumbnail for Critical Communication: Leveraging AI for Urgency Classification in Nursing Secure Chats
Critical Communication: Leveraging AI for Urgency Classification in Nursing Secure Chats
Background: Registered nurses are the core communicators of patient status within inpatient settings, channeling and receiving critical information across complex care teams…
Thumbnail for AI's Potential to Reshape Ambulatory Care: A Focus on Nurses and Care Coordinators
AI's Potential to Reshape Ambulatory Care: A Focus on Nurses and Care Coordinators
Purpose: This study aimed to establish perceived workload and ambulatory care registered nurse (RN) and medical assistant (MA) emotions about electronic health record (EHR) patient portal messages and provide insight on end user archetypes for leadership to tailor change management prior to widespr…
Privacy Policy Update: We value your privacy and want you to understand how your information is being used. To make sure you have current and accurate information about this sites privacy practices please visit the privacy center by clicking here.