Abstract
Objectives We present a proof-of-concept digital scribe system as an emergency department (ED) consultation call-based clinical conversation summarization pipeline to support clinical documentation and report its performance.
Methods We use four pretrained large language models to establish the digital scribe system: T5-small, T5-base, PEGASUS-PubMed, and BART-Large-CNN via zero-shot and fine-tuning approaches. Our dataset includes 100 referral conversations among ED clinicians and medical records. We report the ROUGE-1, ROUGE-2, and ROUGE-L to compare model performance. In addition, we annotated transcriptions to assess the quality of generated summaries.
Results The fine-tuned BART-Large-CNN model demonstrates greater performance in summarization tasks with the highest ROUGE scores (F1ROUGE-1 = 0.49, F1ROUGE-2 = 0.23, F1ROUGE-L = 0.35) scores. In contrast, PEGASUS-PubMed lags notably (F1ROUGE-1 = 0.28, F1ROUGE-2 = 0.11, F1ROUGE-L = 0.22). BART-Large-CNN's performance decreases by more than 50% with the zero-shot approach. Annotations show that BART-Large-CNN performs 71.4% recall in identifying key information and a 67.7% accuracy rate.
Conclusion The BART-Large-CNN model demonstrates a high level of understanding of clinical dialogue structure, indicated by its performance with and without fine-tuning. Despite some instances of high recall, there is variability in the model's performance, particularly in achieving consistent correctness, suggesting room for refinement. The model's recall ability varies across different information categories. The study provides evidence toward the potential of artificial intelligence-assisted tools in assisting clinical documentation. Future work is suggested on expanding the research scope with additional language models and hybrid approaches and comparative analysis to measure documentation burden and human factors.
Keywords
text summarization - emergency department - clinical conversation - pretrained language model - documentation burden
