ArticleHealthcare informatics research2025
Development and Evaluation of a Retrieval-Augmented Generation-Based Electronic Medical Record Chatbot System.
Article in Healthcare informatics research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers 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
- Metaverse-based objective structured clinical examinations: an exploratory approach to advancing clinical competency assessment.Korean journal of medical education · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectivesThis study aimed to develop and evaluate a retrieval-augmented generation (RAG)-based chatbot system designed to optimize hospital operations. By leveraging electronic medical record (EMR) manuals, the system seeks to streamline administrative workflows and enhance healthcare delivery.
methodsThe system integrated fine-tuned multilingual embedding models (Multilingual-E5-Large and BGE-M3) for indexing and retrieving information from EMR manuals. A dataset comprising 5,931 question-document pairs was constructed through query augmentation and validated by domain experts. Fine-tuning was performed using contrastive learning to enhance semantic understanding, with performance assessed using top-k accuracy metrics. The Solar Mini Chat API was adopted for text generation, prioritizing Korean-language responses and cost efficiency.
resultsThe fine-tuned models demonstrated marked improvements in retrieval accuracy, with BGE-M3 achieving 97.6% and Multilingual-E5-Large reaching 89.7%. The chatbot achieved high performance, with query latency under 10 ms and robust retrieval precision, effectively addressing operational EMR queries. Key applications included administrative task support and billing process optimization, highlighting its potential to reduce staff workload and enhance healthcare service delivery.
conclusionsThe RAG-based chatbot system successfully addressed critical challenges in healthcare administration, improving EMR usability and operational efficiency. Future research should focus on realworld deployment and longitudinal studies to further evaluate its impact on administrative burden reduction and workflow improvement.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.