用大模型实时转录总结医患对话，减轻印尼基层医生文书负担
Using LLM for Real-Time Transcription and Summarization of Doctor-Patient Interactions into ePuskesmas in Indonesia: A Proof-of-Concept Study
- 结合Whisper与GPT-3.5实现印尼语医患对话实时转写与摘要
- 300秒以上对话可在30秒内完成处理，临床准确性达标
- 适合资源匮乏地区推广，需关注隐私与语言文化偏见
印尼社区卫生中心（Puskesmas）因医生需手动将医患对话录入电子病历系统，导致效率低下。本文提出一种基于大语言模型（LLM）的原型框架，利用Whisper模型进行语音转写，GPT-3.5完成医学摘要，通过浏览器插件自动填充ePuskesmas表单。在控制条件下进行角色扮演实验并经医疗验证，证明该系统可在30秒内处理超过300秒的精简对话，保持临床准确度。本研究为资源受限医疗环境中的AI辅助病历书写奠定基础。但仍有隐私合规性及大规模临床评估中语言与文化偏差等问题待解决。
原文摘要 · Abstract (English)
One of the critical issues contributing to inefficiency in Puskesmas (Indonesian community health centers) is the time-consuming nature of documenting doctor-patient interactions. Doctors must conduct thorough consultations and manually transcribe detailed notes into ePuskesmas electronic health records (EHR), which creates substantial administrative burden to already overcapacitated physicians. This paper presents a proof-of-concept framework using large language models (LLMs) to automate real-time transcription and summarization of doctor-patient conversations in Bahasa Indonesia. Our system combines Whisper model for transcription with GPT-3.5 for medical summarization, implemented as a browser extension that automatically populates ePuskesmas forms. Through controlled roleplay experiments with medical validation, we demonstrate the technical feasibility of processing detailed 300+ seconds trimmed consultations in under 30 seconds while maintaining clinical accuracy. This work establishes the foundation for AI-assisted clinical documentation in resource-constrained healthcare environments. However, concerns have also been raised regarding privacy compliance and large-scale clinical evaluation addressing language and cultural biases for LLMs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。