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Enhancing clinical documentation with voice processing and large language models: a study on the LAOS system.

Yupeng Xu1, Huixun Jia1, Maolin Wang2

  • 1Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai Gene Therapy Center, Shanghai, China.

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Summary
This summary is machine-generated.

This study introduces the LLM-based Auxiliary Ophthalmic System (LAOS) to improve clinical documentation efficiency and accuracy. The system uses Large Language Models (LLMs) and audio processing to reduce clinician workload and burnout.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Electronic Health Records (EHRs) increase clinician cognitive workload, especially in ophthalmology.
  • Ophthalmologists handle 1.6x more consultations, exacerbating documentation burdens.

Purpose of the Study:

  • Introduce the LLM-based Auxiliary Ophthalmic System (LAOS).
  • Enhance clinical documentation accuracy and efficiency using LLMs and audio processing.

Main Methods:

  • LAOS integrates voice recognition, Retrieval-Augmented Generation (RAG), and Low-Rank Adaptation (LoRA).
  • System converts clinical conversations into structured documentation with dynamic knowledge retrieval.
  • Evaluated on Admission Reports, Surgery Records, and Discharge Summaries.

Main Results:

  • LAOS demonstrated significant improvements in documentation completeness, accuracy, and efficiency.
  • Quantitative metrics (BLEU, ROUGE-L, BERT Score) and physician validation confirmed effectiveness.
  • The system shows potential to alleviate physician burnout and improve healthcare delivery.

Conclusions:

  • Speech-enabled LLM systems like LAOS can optimize clinical documentation.
  • LAOS offers a promising solution to reduce cognitive load and enhance healthcare quality.
  • Further research needed to balance documentation comprehensiveness and conciseness.