A comparative study of recent large language models on generating hospital discharge summaries for lung cancer patients

Abstract

Objective: Generating discharge summaries is a crucial yet time-consuming task in clinical practice, essential for conveying pertinent patient information and facilitating continuity of care. Recent advancements in large language models (LLMs) have significantly enhanced their capability in understanding and summarizing complex medical texts. This research aims to explore how LLMs can alleviate the burden of manual summarization, streamline workflow efficiencies, and support informed decision-making in healthcare settings.

Materials and methods: Clinical notes from a cohort of 1,099 lung cancer patients were utilized, with a subset of 50 patients for testing purposes, and 102 patients used for model fine-tuning. This study evaluates the performance of multiple LLMs, including GPT-3.5, GPT-4, GPT-4o, and LLaMA 3 8b, in generating discharge summaries. Evaluation metrics included token-level analysis (BLEU, ROUGE-1, ROUGE-2, ROUGE-L), semantic similarity scores, and manual evaluation of clinical relevance, factual faithfulness, and completeness. An iterative method was further tested on LLaMA 3 8b using clinical notes of varying lengths to examine the stability of its performance.

Results: The study found notable variations in summarization capabilities among LLMs. GPT-4o and fine-tuned LLaMA 3 demonstrated superior token-level evaluation metrics, while manual evaluation further revealed that GPT-4 achieved the highest scores in relevance (4.95 ± 0.22) and factual faithfulness (4.40 ± 0.50), whereas GPT-4o performed best in completeness (4.55 ± 0.69); both models showed comparable overall quality. Semantic similarity scores indicated GPT-4o and LLaMA 3 as leading models in capturing the underlying meaning and context of clinical narratives.

Conclusion: This study contributes insights into the efficacy of LLMs for generating discharge summaries, highlighting the potential of automated summarization tools to enhance documentation precision and efficiency, ultimately improving patient care and operational capability in healthcare settings.

Keywords: Continuity of care; Discharge Summary; EHR; GPT; LLaMA; Large language model; Lung cancer; Text summarization.