Augmenting Community Nursing Practice With Generative AI: A Formative Study of Diagnostic Synergies Using Simulation-Based Clinical Cases

Objective: To compare the diagnostic accuracy and clinical decision-making of experienced community nurses versus state-of-the-art generative AI (GenAI) systems for simulated patient case scenarios. Methods: In the months of 5 to 6/2024, 114 community Israeli nurses completed a questionnaire including 4 medical case studies. Responses were also collected from 3 GenAI models (ChatGPT-4, Claude 3.0, and Gemini 1.5), analyzed both without word limits and with a 10-word constraint. Responses were scored on accuracy, speed, and comprehensiveness. Results: Nurses scored higher on average compared to the shortened GenAI responses. GenAI responses were faster but more verbose, and contained unnecessary information. Gemini (full version) and Claude (full version) achieved the highest accuracy among the GenAI models. Conclusions: While GenAI shows potential to support aspects of nursing practice, human clinicians currently exhibit advantages in holistic clinical reasoning abilities, a skill requiring experience, contextual knowledge, and ability to bring concise and practical responses. Further research is needed before GenAI can adequately substitute nursing expertise.