用AI自动生成结构化病历，减轻医生文书负担
A GEN AI Framework for Medical Note Generation
- 融合大模型与语音识别，实时处理对话生成病历
- 在ACI-BENCH数据集上提升文档准确率与生成效率
- 适合临床医生、医疗系统开发者使用
电子健康记录（EHR）带来的行政负担日益加重，压缩了医生直接诊疗时间并加剧职业倦怠。为此，我们提出MediNotes——一个先进的生成式AI框架，可从医疗对话中自动生成结构化的SOAP（主诉、客观、评估、计划）病历。该框架结合大型语言模型（LLMs）、检索增强生成（RAG）与自动语音识别（ASR），支持实时或离线音频/文本输入，生成上下文准确的病历。通过采用量化低秩适配（QLoRA）与参数高效微调（PEFT），在资源受限环境下实现高效模型优化。此外，系统提供基于查询的检索功能，便于医护人员与患者快速获取相关医疗信息。在ACI-BENCH数据集上的评估表明，MediNotes显著提升了自动化病历生成的准确性、效率与可用性，为降低医疗人员行政负担、优化临床工作流程提供了可靠解决方案。
原文摘要 · Abstract (English)
The increasing administrative burden of medical documentation, particularly through Electronic Health Records (EHR), significantly reduces the time available for direct patient care and contributes to physician burnout. To address this issue, we propose MediNotes, an advanced generative AI framework designed to automate the creation of SOAP (Subjective, Objective, Assessment, Plan) notes from medical conversations. MediNotes integrates Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Automatic Speech Recognition (ASR) to capture and process both text and voice inputs in real time or from recorded audio, generating structured and contextually accurate medical notes. The framework also incorporates advanced techniques like Quantized Low-Rank Adaptation (QLoRA) and Parameter-Efficient Fine-Tuning (PEFT) for efficient model fine-tuning in resource-constrained environments. Additionally, MediNotes offers a query-based retrieval system, allowing healthcare providers and patients to access relevant medical information quickly and accurately. Evaluations using the ACI-BENCH dataset demonstrate that MediNotes significantly improves the accuracy, efficiency, and usability of automated medical documentation, offering a robust solution to reduce the administrative burden on healthcare professionals while improving the quality of clinical workflows.
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