ArticleInternational journal of nursing sciences2025
Nursing Retrieval-Augmented Generation: Retrieval augmented generation for nursing question answering with large language models.
Article in International journal of nursing sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Evaluation Methods for Inference-Time Retrieval-Augmented and Graph Retrieval-Augmented Large Language Models in Health Care: Scoping Review.Journal of medical Internet research · 2026Article
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4 authors.
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Abstract
Objective: This study aimed to develop a Nursing Retrieval-Augmented Generation (NurRAG) system based on large language models (LLMs) and to evaluate its accuracy and clinical applicability in nursing question answering. Methods: A multidisciplinary team consisting of nursing experts, artificial intelligence researchers, and information engineers collaboratively designed the NurRAG framework following the principles of retrieval-augmented generation. The system included four functional modules: 1) construction of a nursing knowledge base through document normalization, embedding, and vector indexing; 2) nursing question filtering using a supervised classifier; 3) semantic retrieval and re-ranking for evidence selection; and 4) evidence-conditioned language model generation to produce citation-based nursing answers. The system was securely deployed on hospital intranet servers using Docker containers. Performance evaluation was conducted with 1,000 expert-verified nursing question-answer pairs. Semantic fidelity was assessed using Recall Oriented Understudy for Gisting Evaluation - Longest Common Subsequence (ROUGE-L), and clinical correctness was measured using Accuracy. Results: The NurRAG system achieved significant improvements in both semantic fidelity and answer accuracy compared with conventional large language models. For ChatGLM2-6B, ROUGE-L increased from (30.73 ± 1.48) % to (64.27 ± 0.27) %, and accuracy increased from (49.08 ± 0.92) % to (75.83 ± 0.35) %. For LLaMA2-7B, ROUGE-L increased from (28.76 ± 0.89) % to (60.33 ± 0.21) %, and accuracy increased from (43.27 ± 0.83) % to (73.29 ± 0.33) %. All differences were statistically significant ( Conclusion: The NurRAG system integrates domain-specific retrieval with LLMs generation to provide accurate, reliable, and traceable evidence-based nursing answers. The findings demonstrate the system's feasibility and potential to improve the accuracy of clinical knowledge access, support evidence-based nursing decision-making, and promote the safe application of artificial intelligence in nursing practice.
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