Efficient Fine-Tuning of Large Language Models for Automated Medical Documentation

TL;DR

This paper presents MediGen, a fine-tuned large language model based on LLaMA3-8B, designed to automate medical report generation from dialogues, aiming to reduce administrative burden and improve healthcare efficiency.

Contribution

It introduces a novel fine-tuning approach for LLaMA3-8B to generate accurate medical reports, demonstrating high performance metrics in clinical text summarization.

Findings

Achieved a ROUGE score of 58% in medical report generation.

Attained a BERTScore-F1 of 72%, indicating high relevance.

Potential to reduce physicians' administrative workload.

Abstract

Scientific research indicates that for every hour spent in direct patient care, physicians spend nearly two additional hours on administrative tasks, particularly on electronic health records (EHRs) and desk work. This excessive administrative burden not only reduces the time available for patient care but also contributes to physician burnout and inefficiencies in healthcare delivery. To address these challenges, this study introduces MediGen, a fine-tuned large language model (LLM) designed to automate the generation of medical reports from medical dialogues. By leveraging state-of-the-art methodologies for fine-tuning open-source pretrained models, including LLaMA3-8B, MediGen achieves high accuracy in transcribing and summarizing clinical interactions. The fine-tuned LLaMA3-8B model demonstrated promising results, achieving a ROUGE score of 58% and a BERTScore-F1 of 72%, indicating its effectiveness in generating accurate and clinically relevant medical reports. These findings suggest that MediGen has the potential to significantly reduce the administrative workload on physicians, improving both healthcare efficiency and physician well-being.