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Related Experiment Video

Updated: Jun 7, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Voice-Based Structured Nursing Documentation Using Automatic Speech Recognition and Large Language Models:

Meng-Han Su¹, Wei-Chun Wang¹, Yi-Min Hsu²

¹Artificial Intelligence and Robotics Innovation Center, China Medical University Hospital, China Medical University, No. 2, Yude Rd, North Dist, Taichung, 404327, Taiwan, 886 0422052121 ext 12584.

|June 5, 2026

Summary

This summary is machine-generated.

This study introduces an integrated automatic speech recognition (ASR) and large language model (LLM) system to streamline nursing documentation. The system significantly reduces manual data entry, improving efficiency and record completeness for clinical nurses.

Keywords:

automatic speech recognition code-switching documentation large language model nursing nursing records

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Area of Science:

Clinical Informatics
Natural Language Processing
Healthcare Technology

Background:

Manual nursing documentation in Hospital Information Systems (HISs) is time-consuming and error-prone.
Previous speech recognition faced challenges with code-switching, medical terms, and noisy environments.
Advances in customized ASR and LLMs enable feasible speech-based, structured nursing documentation.

Purpose of the Study:

To develop and evaluate an integrated ASR and LLM system for transforming spoken nursing input into structured DART notes.
To assess the system's accuracy, usability, and clinical feasibility within HIS workflows.

Main Methods:

Fine-tuned Whisper ASR model using a code-switching nursing speech corpus.
Employed LLMs for schema-constrained DART record generation from ASR transcripts.
Evaluated ASR accuracy (mixed error rate), DART classification (F1-scores), hallucination rates, and nurse feedback.

Main Results:

Reduced ASR mixed error rate from 44.79% to 6.67%.
Achieved a macroaveraged F1-score of 0.82 for DART generation, meeting noninferiority to human transcripts.
Observed a 2.51% hallucination rate and a significant increase in documented notes; 75.8% of nurses reported reduced workload and improved completeness.

Conclusions:

The integrated ASR and LLM system is feasible and performs well, demonstrating strong acceptance among clinical nurses.
The system effectively reduces the manual documentation burden and enhances record completeness.
Supports the value of ASR and LLM-assisted workflows for optimizing nursing documentation.