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.

翻译：日益加重的医疗文档管理负担，特别是通过电子健康记录（EHR）系统，显著减少了可用于直接患者护理的时间，并加剧了医生的职业倦怠。为解决这一问题，我们提出了MediNotes，这是一个先进的生成式人工智能框架，旨在从医疗对话中自动生成SOAP（主观、客观、评估、计划）记录。MediNotes集成了大型语言模型（LLMs）、检索增强生成（RAG）和自动语音识别（ASR）技术，以实时或从录音中捕获并处理文本和语音输入，从而生成结构化和上下文准确的医疗记录。该框架还结合了量化低秩适应（QLoRA）和参数高效微调（PEFT）等先进技术，以便在资源受限的环境中进行高效的模型微调。此外，MediNotes提供了一个基于查询的检索系统，允许医疗保健提供者和患者快速、准确地访问相关的医疗信息。使用ACI-BENCH数据集进行的评估表明，MediNotes显著提高了自动化医疗文档记录的准确性、效率和可用性，为减轻医疗保健专业人员的行政负担并提升临床工作流程质量提供了一个稳健的解决方案。