A GEN AI Framework for Medical Note Generation

The growing administrative demands of medical documentation, particularly through Electronic Health Records (EHR), have substantially reduced the time available for direct patient care and exacerbated physician burnout. To mitigate this challenge, we introduce MediNotes, an advanced generative AI framework designed to automate the creation of SOAP (Subjective, Objective, Assessment, Plan) notes from medical conversations. MediNotes leverages Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Automatic Speech Recognition (ASR) to process both textual and voice inputs in real time or from recorded audio, generating structured, contextually accurate medical notes. The framework employs cutting-edge techniques such as Quantized Low-Rank Adaptation (QLoRA) and Parameter-Efficient Fine-Tuning (PEFT) to optimize model performance in resource-limited settings. Furthermore, MediNotes features a query-based retrieval system, enabling healthcare providers and patients to efficiently access relevant medical information. Evaluation on the ACI-BENCH dataset demonstrates that MediNotes enhances the accuracy, efficiency, and usability of automated medical documentation, presenting a robust solution to alleviate administrative burdens on healthcare professionals while improving the quality of clinical workflows.