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Related Concept Videos

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The two sources for collecting information are primary and secondary. After gathering information, interpretation and validation help to complete the data. The purpose of assessment is to establish data with the initial information, to interpret data about the patient's perceived needs and health problems, and to respond to these problems identified.
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Nursing Clinical Information System (NCIS)
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Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
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Related Experiment Video

Updated: Jan 9, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Nursing Retrieval-Augmented Generation: Retrieval augmented generation for nursing question answering with large

Liping Xiong1, Qiqiao Zeng1, Weixiang Luo2

  • 1Department of Ophthalmology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, China.

International Journal of Nursing Sciences
|December 10, 2025
PubMed
Summary

The Nursing Retrieval-Augmented Generation (NurRAG) system enhances large language models (LLMs) for nursing questions, significantly improving answer accuracy and reliability. This AI tool supports evidence-based nursing practice and safer clinical decision-making.

Keywords:
Evidence-based nursingLarge language modelsNursing knowledge baseQuestion-answering systemRetrieval-augmented generation

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Area of Science:

  • Artificial Intelligence in Nursing
  • Clinical Informatics
  • Natural Language Processing

Background:

  • Large language models (LLMs) show promise for healthcare applications but require domain-specific refinement for accuracy.
  • Ensuring the reliability and clinical applicability of AI-generated nursing information is crucial for patient safety.
  • Existing LLM-based systems may struggle with the nuanced and evidence-based requirements of nursing knowledge.

Purpose of the Study:

  • To develop and evaluate a Nursing Retrieval-Augmented Generation (NurRAG) system for accurate nursing question answering.
  • To assess the clinical applicability and performance of the NurRAG system compared to conventional LLMs.
  • To enhance the generation of evidence-based and guideline-concordant nursing responses.

Main Methods:

  • A multidisciplinary team designed the NurRAG framework, incorporating a nursing knowledge base, question filtering, semantic retrieval, and evidence-conditioned generation.
  • The system utilized document normalization, embedding, vector indexing, supervised classification, and semantic re-ranking for evidence selection.
  • Performance was evaluated using 1,000 expert-verified nursing Q&A pairs, measuring semantic fidelity (ROUGE-L) and clinical accuracy.

Main Results:

  • The NurRAG system significantly improved ROUGE-L scores and accuracy for both ChatGLM2-6B and LLaMA2-7B models compared to baseline LLMs (P < 0.001).
  • Accuracy increased from 49.08% to 75.83% for ChatGLM2-6B and from 43.27% to 73.29% for LLaMA2-7B.
  • Case analysis confirmed NurRAG's effectiveness in reducing hallucinations and producing evidence-based, guideline-concordant nursing answers.

Conclusions:

  • The NurRAG system effectively integrates domain-specific retrieval with LLM generation for accurate, reliable, and traceable nursing answers.
  • The findings support the feasibility of NurRAG to enhance clinical knowledge access and evidence-based nursing decision-making.
  • This AI approach holds potential for the safe and effective application of artificial intelligence in nursing practice.