The increasing volume and complexity of clinical documentation in Electronic Medical Records systems pose significant challenges for clinical coders, who must mentally process and summarise vast amounts of clinical text to extract essential information needed for coding tasks. While large language models have been successfully applied to shorter summarisation tasks in recent years, the challenge of summarising a hospital course remains an open area for further research and development. In this study, we adapted three pre trained LLMs, Llama 3, BioMistral, Mistral Instruct v0.1 for the hospital course summarisation task, using Quantized Low Rank Adaptation fine tuning. We created a free text clinical dataset from MIMIC III data by concatenating various clinical notes as the input clinical text, paired with ground truth Brief Hospital Course sections extracted from the discharge summaries for model training. The fine tuned models were evaluated using BERTScore and ROUGE metrics to assess the effectiveness of clinical domain fine tuning. Additionally, we validated their practical utility using a novel hospital course summary assessment metric specifically tailored for clinical coding. Our findings indicate that fine tuning pre trained LLMs for the clinical domain can significantly enhance their performance in hospital course summarisation and suggest their potential as assistive tools for clinical coding. Future work should focus on refining data curation methods to create higher quality clinical datasets tailored for hospital course summary tasks and adapting more advanced open source LLMs comparable to proprietary models to further advance this research.

本研究针对临床编码中的医院病程总结问题，适应了三种预训练的大型语言模型，以应对临床文档日益增加的复杂性。通过量化低秩适应微调，研究表明这类模型在医院病程总结的性能上有显著提升，显示出其作为临床编码辅助工具的潜力。

协调临床旋律：为临床编码中的医院病程总结调优大型语言模型