Comparison of Nursing Diagnoses and Interventions Based on the Omaha System Between AI-Based ChatGPT and an Expert Nurse: A Case-Based Mixed-Method Comparative Study
Öz
Aim: This case-based mixed-method comparative study was planned to compare nursing care plans developed using the Omaha Classification System by ChatGPT and by a faculty member.
Material and Method: A case-based mixed-method comparative research design was used. A theoretical case scenario based on a real public health issue was presented to both ChatGPT and an experienced faculty member. Each was asked to identify nursing diagnoses and propose intervention plans using the Omaha Classification System. Data were collected using a structured Omaha Evaluation Form and analyzed through both quantitative and qualitative content comparison methods.
Results: ChatGPT identified 12 nursing diagnoses, while the faculty member identified 10. ChatGPT demonstrated a broader diagnostic range, balanced use of intervention categories, and measurable target formulation. The faculty member, however, selected more context-specific diagnoses, tailored interventions to individual characteristics, and emphasized socioeconomic determinants. ChatGPT occasionally produced generalized responses lacking contextual relevance.
Conclusions: ChatGPT was found to have high potential for providing systematic, multidimensional, and rapid suggestions in the nursing care process. However, expert human judgment remains essential for contextual interpretation, prioritization, and consideration of individual variations. Artificial intelligence can be an effective support tool in nursing education and decision-making processes; however, it should not be regarded as the final decision-maker in clinical practice.
Anahtar Kelimeler
Artificial ıntelligence, Nursing, Generative artificial ıntelligence, Nursing diagnosis, Clinical reasoning, OMAHA
Etik Beyan
The authors affirm that the research was conducted in accordance with ethical standards and that necessary approvals (e.g., IRB) were obtained where applicable.
Teşekkür
The authors would like to express their sincere appreciation to the field experts who provided valuable insights and critical feedback throughout the evaluation process. Their contributions were instrumental in assessing the conceptual consistency, accuracy, and scope of the artificial intelligence–generated nursing diagnoses. Although the study did not involve direct participation of human subjects, all evaluations were conducted in accordance with ethical standards governing scientific research.
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