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  1. Home
  3. Using Prompt Engineering To Optimize A Rag Pipeline For Ehr-nursing Data Standardization.
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  3. Using Prompt Engineering To Optimize A Rag Pipeline For Ehr-nursing Data Standardization.

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Using Prompt Engineering to Optimize a RAG Pipeline for EHR-Nursing Data Standardization.

Tamara G R Macieira1, Vedant Upganlawar2, Alexander Semenov3

  • 1University of Florida, College of Nursing, Gainesville FL USA.

Studies in Health Technology and Informatics
|May 23, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Standardizing nursing data using Retrieval-Augmented Generation (RAG) and large language models improved interoperability. This approach, while not fully automated, reduced workload and supports scalable semantic interoperability for nursing data.

Keywords:
Artificial IntelligenceElectronic Health RecordsNursingNursing InformaticsStandardized Nursing Terminology

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Area of Science:

  • Health Informatics
  • Natural Language Processing
  • Nursing Informatics

Background:

  • Electronic health records (EHRs) contain valuable nursing care plan data.
  • Heterogeneity in local terminologies hinders EHR data standardization and interoperability.
  • Standardized nursing data is crucial for large-scale research and clinical decision-making.

Purpose of the Study:

  • To evaluate an optimized Retrieval-Augmented Generation (RAG) pipeline for mapping local nursing care plan data to standardized terminologies.
  • To assess the effectiveness of different prompt engineering techniques within the RAG pipeline.
  • To determine the feasibility of using large language models for semantic interoperability in nursing informatics.

Main Methods:

  • Developed and optimized a RAG pipeline integrating large language models.
  • Applied the pipeline to map nursing care plan problems, goals, and interventions from two distinct health systems.
  • Compared performance across various prompt engineering strategies, including structured prompts and few-shot learning.

    • Main Results:

    • The optimized RAG pipeline demonstrated moderate accuracy (45-70%) in mapping local nursing terminologies to standardized ones.
    • Structured Prompts combined with Few-Shot Learning yielded the highest performance among the tested techniques.
    • The approach significantly reduced human workload compared to manual mapping.

    Conclusions:

    • Optimized RAG pipelines show promise for improving semantic interoperability of nursing data from EHRs.
    • While full automation is not yet achieved, this human-in-the-loop approach supports scalable data standardization.
    • Further refinement of prompt engineering and RAG models can enhance accuracy and efficiency for nursing informatics research.