用住院期间的结构化数据自动生成病程记录，减轻医生负担。
Toward Relieving Clinician Burden by Automatically Generating Progress Notes using Interim Hospital Data
- 基于电子病历中的结构化数据生成病程记录，构建新框架与数据集
- 最佳模型在BERTScore上达80.53，能准确利用76.9%的结构化数据
- 适合医疗AI、临床自动化方向的研究者参考
病程记录的定期书写是导致医务人员负担过重的主要原因之一。电子病历中丰富的结构化图表信息虽然加重了负担，但也为自动撰写病程记录提供了可能。本文提出一项任务：利用电子病历中的结构化或表格数据自动生成病程记录。为此，我们提出一种新框架并构建了一个大型数据集ChartPNG，包含1616名患者、共7089个标注样本（每对包含一份病程记录和对应的中期结构化数据）。我们在该数据集上使用通用及生物医学领域的大语言模型建立基线。通过自动评估（最佳模型BERTScore F1达80.53，MEDCON得分为19.61）和人工分析（模型对相关结构化数据的利用准确率达76.9%），识别出当前任务的挑战与未来研究机遇。
原文摘要 · Abstract (English)
Regular documentation of progress notes is one of the main contributors to clinician burden. The abundance of structured chart information in medical records further exacerbates the burden, however, it also presents an opportunity to automate the generation of progress notes. In this paper, we propose a task to automate progress note generation using structured or tabular information present in electronic health records. To this end, we present a novel framework and a large dataset, ChartPNG, for the task which contains $7089$ annotation instances (each having a pair of progress notes and interim structured chart data) across $1616$ patients. We establish baselines on the dataset using large language models from general and biomedical domains. We perform both automated (where the best performing Biomistral model achieved a BERTScore F1 of $80.53$ and MEDCON score of $19.61$) and manual (where we found that the model was able to leverage relevant structured data with $76.9\%$ accuracy) analyses to identify the challenges with the proposed task and opportunities for future research.
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