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This study aimed to evaluate the ability of three generative artificial intelligence tools (ChatGPT, Gemini and DeepSeek) to generate clinically accurate, comprehensive, and readable nursing care plans aligned with standardised nursing taxonomies (North American Nursing Diagnosis Association International, Nursing Interventions Classification, and Nursing Outcomes Classification). The study further explored variations in tool performance across different nursing specialties.
A descriptive comparative design was used.
Ten expert-validated clinical scenarios representing five nursing specialties (Fundamentals of Nursing, Medical, Surgical, Paediatric and Psychiatric Nursing) were presented to the three artificial intelligence tools. Each tool responded to four standardised prompts based on the latest North American Nursing Diagnosis Association International, Nursing Interventions Classification and Nursing Outcomes Classification taxonomies. Outputs were assessed for quality, accuracy, completeness and readability by expert evaluators using validated scales.
All tools produced nursing care plans of moderate-to-high quality. DeepSeek demonstrated slightly higher accuracy and completeness compared with Gemini and ChatGPT. Surgical nursing scenarios yielded the highest performance, likely reflecting the more protocolised and pathway-driven nature of perioperative care. However, all outputs were incomplete and written at a college-level readability, limiting accessibility for clinical use.
Generative artificial intelligence tools can support the production of structured nursing care plans requiring expert review and adaptation, particularly in less standardised clinical domains, but their limitations in completeness and readability indicate they should be regarded only as preliminary drafts requiring expert review and adaptation.
The study examined whether generative artificial intelligence can reliably assist in creating nursing care plans. All tools performed moderately well, with DeepSeek showing slight advantages, but outputs were incomplete and difficult to read. Findings are relevant to clinical nurses, educators, healthcare managers and policymakers worldwide who are exploring artificial intelligence in nursing workflows.
This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.
This study did not include patient or public involvement in its design, conduct or reporting.
Commentary on: Hua Y, Lu H, Dai J, et al. Self-management challenges and support needs among patients with primary glaucoma: a qualitative study. BMC nursing. 2023 Nov 14;22(1):426.
Healthcare professionals should provide personalised and comprehensive support, addressing the medical, emotional and social challenges faced by patients with primary glaucoma. Further research is needed to explore the effectiveness of tailored self-management support programmes in improving the quality of life and treatment outcomes for patients with glaucoma.
Glaucoma is a chronic disease characterised by progressive visual field defects. It is the most common cause of irreversible blindness and is associated with a decrease in quality of life.
Background
Online surveys in nursing research have both advantages and disadvantages. Reaching a sample and attaining an appropriate response rate is an ongoing challenge and necessitates careful consideration when designing a nursing research study using an online survey approach.
Objective
In this study, we aimed to explore response rates and survey characteristics of studies by nurse researchers that used online methodologies to survey nurses, nursing students, and nursing faculty.
Methods
We conducted an integrative review of research studies that used online surveys for data collection published from 2011 to 2021. We examined response rates and survey characteristics such as recruitment method, use of incentives, question type, length of survey, time to complete the survey, and use of reminders.
Results
Our review included 51 studies published by nurses with target samples of nurses, nursing students, or nursing faculty. Study sample sizes ranged from 48 to 29,283, the number of respondents ranged from 29 to 3,607, and the response rates ranged from 3.4% to 98%, with an average of 42.46%. Few patterns emerged regarding recruitment or other factors to enhance response rates; only five studies used incentives.
Conclusion
Response rates to online surveys are unlikely to reach the rates seen in older mailed surveys. Researchers need to design online survey studies to be easily accessible, concise, and appealing to participants. 
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