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

TL;DR

This paper introduces a new task and dataset for automatically generating hospital progress notes from structured electronic health record data, aiming to reduce clinician documentation burden.

Contribution

It presents a novel framework and the large ChartPNG dataset for progress note generation from structured data, along with baseline evaluations using large language models.

Findings

BiMistral achieved a BERTScore F1 of 80.53

Model leveraged relevant data with 76.9% accuracy

Identified challenges and future opportunities in automated note generation

Abstract

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.