Harmonising the Clinical Melody: Tuning Large Language Models for Hospital Course Summarisation in Clinical Coding
This addresses the problem of processing complex clinical documentation for clinical coders, though it is incremental as it applies existing fine-tuning methods to a specific clinical task.
The study tackled the challenge of summarizing hospital courses from clinical documentation for clinical coding by fine-tuning three pre-trained LLMs (Llama 3, BioMistral, Mistral Instruct v0.1) using Quantized Low Rank Adaptation on MIMIC III data, finding that domain fine-tuning significantly enhanced performance in hospital course summarization.
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.