Toward Relieving Clinician Burden by Automatically Generating Progress Notes using Interim Hospital Data

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.