Summary of Toward Relieving Clinician Burden by Automatically Generating Progress Notes Using Interim Hospital Data, By Sarvesh Soni et al.
Toward Relieving Clinician Burden by Automatically Generating Progress Notes using Interim Hospital Data
by Sarvesh Soni, Dina Demner-Fushman
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel framework and dataset, ChartPNG, to automate progress note generation using structured information in electronic health records. The dataset contains 7089 annotation instances across 1616 patients, making it suitable for training large language models. Baselines are established on the dataset using general and biomedical domain models. Automated analysis shows that the best-performing model achieves a BERTScore F1 of 80.53 and MEDCON score of 19.61. Manual analysis reveals that the model can leverage relevant structured data with 76.9% accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to automate progress note generation in medical records using structured information. It proposes a new framework and dataset called ChartPNG, which contains over 7,000 annotation instances from more than 1,600 patients. The researchers use large language models to establish baselines on the data. The results show that the best model can generate progress notes with an accuracy of around 80%. This research has implications for reducing clinician burden and improving patient care. |
