Automating Clinical Note Summarization Using LLM

Abstract

The increasing volume of electronic health records (EHR) and the growing complexity of clinical documentation have highlighted the need for efficient and reliable summarization tools. This thesis explores the application of large language models (LLMs) in automating clinical note summarization, with the goal of reducing the cognitive and administrative burden on healthcare professionals while maintaining clinical accuracy and relevance. Leveraging state-of-the-art LLM architectures such as T5 and FLAN-T5, this research focuses on extracting key information from clinical notes, including patient diagnoses, treatment plans, and medical histories, and generating concise, structured summaries suitable for clinical workflows. The study evaluates the performance of fine-tuned LLMs on datasets such as MIMIC-III, MeQSum, and ProbSum using quantitative metrics like ROUGE and BERTScore, achieving a ROUGE F1 score of 0.95 and demonstrating high efficiency with a runtime of 2.35 seconds per note. Qualitative analysis confirms the generated summaries' clinical relevance, with outputs aligned to standard sections like diagnoses, imaging results, and treatments. Despite these strengths, challenges such as occasional hallucinated information, omitted secondary details, and inconsistent formatting are identified. Results from physician feedback underscore the practicality of LLMs in improving healthcare documentation, with models like LLaMA-Clinic achieving over 90% acceptance in a blinded review. Additionally, cost analysis reveals a 3.75-fold reduction in inference costs compared to proprietary alternatives, emphasizing the feasibility of open-source solutions. This research highlights the potential of LLMs to enhance clinical workflows, reduce diagnostic errors, and improve patient care. Future directions include expanding datasets for better generalizability, addressing hallucination issues, and ensuring seamless integration into healthcare systems through ethical and clinician-centered approaches. The findings reinforce the role of AI in advancing healthcare, promoting accessibility, and addressing global challenges in medical documentation and decision-making.