A GEN AI Framework for Medical Note Generation

TL;DR

MediNotes is a comprehensive AI framework that automates medical note generation from conversations, integrating speech recognition, large language models, and retrieval techniques to improve clinical documentation efficiency and accuracy.

Contribution

The paper introduces MediNotes, a novel AI system combining LLMs, RAG, and ASR with efficient fine-tuning for real-time medical note creation from audio and text inputs.

Findings

Significantly improves medical note accuracy and efficiency.

Reduces administrative burden for healthcare providers.

Enhances clinical workflow quality.

Abstract

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.