# A Method to Automate the Discharge Summary Hospital Course for Neurology Patients

arXiv: https://arxiv.org/abs/2305.06416 · HTML: https://mlsanity.com/paper/2305.06416/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 57 references that are in the map.
- First posted on arXiv 10 May 2023.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization](https://mlsanity.com/paper/2005.03754/) (2020)
- [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://mlsanity.com/paper/1907.12461/) (2019)
- [Bottom-Up Abstractive Summarization](https://mlsanity.com/paper/1808.10792/) (2018)
- [Faithful to the Original: Fact Aware Neural Abstractive Summarization](https://mlsanity.com/paper/1711.04434/) (2017)
- [Annotating and Modeling Fine-grained Factuality in Summarization](https://mlsanity.com/paper/2104.04302/) (2021)
- [Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports](https://mlsanity.com/paper/1911.02541/) (2019)
- [Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques](https://mlsanity.com/paper/2005.01795/) (2020)
- [Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization](https://mlsanity.com/paper/2108.13684/) (2021)
- [Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures](https://mlsanity.com/paper/2009.08666/) (2020)
- [Best-First Beam Search](https://mlsanity.com/paper/2007.03909/) (2020)
- [What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization](https://mlsanity.com/paper/2105.00816/) (2021)
- [Hierarchical Learning for Generation with Long Source Sequences](https://mlsanity.com/paper/2104.07545/) (2021)
- [Summarizing Patients Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models](https://mlsanity.com/paper/2208.08408/) (2022)
- [Don't Say What You Don't Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search](https://mlsanity.com/paper/2203.08436/) (2022)
- [Adapting Pretrained Text-to-Text Models for Long Text Sequences](https://mlsanity.com/paper/2209.10052/) (2022)
- [CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes](https://mlsanity.com/paper/2106.02524/) (2021)
- [Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes](https://mlsanity.com/paper/2104.13498/) (2021)
- [Extractive Summarization of EHR Discharge Notes](https://mlsanity.com/paper/1810.12085/) (2018)
- [Abstractive summarization of hospitalisation histories with transformer networks](https://mlsanity.com/paper/2204.02208/) (2022)

## Built on by (most cited first, 6 of 6)

- [Overview of the First Shared Task on Clinical Text Generation: RRG24 and "Discharge Me!"](https://mlsanity.com/paper/2409.16603/) (2024)
- [A dataset and benchmark for hospital course summarization with adapted large language models](https://mlsanity.com/paper/2403.05720/) (2024)
- [PrecLLM: A Privacy-Preserving Framework for Efficient Clinical Annotation Extraction from Unstructured EHRs using Small-Scale LLMs](https://mlsanity.com/paper/2412.02868/) (2024)
- [Towards Conditioning Clinical Text Generation for User Control](https://mlsanity.com/paper/2502.17571/) (2025)
- [LCDS: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Review](https://mlsanity.com/paper/2507.05319/) (2025)
- [CareTransition-Audit: A Benchmark to Audit Discharge Summaries for Efficient Care Transitions](https://mlsanity.com/paper/2604.05435/) (2026)

## Abstract (arXiv)

Generation of automated clinical notes have been posited as a strategy to mitigate physician burnout. In particular, an automated narrative summary of a patient's hospital stay could supplement the hospital course section of the discharge summary that inpatient physicians document in electronic health record (EHR) systems. In the current study, we developed and evaluated an automated method for summarizing the hospital course section using encoder-decoder sequence-to-sequence transformer models. We fine tuned BERT and BART models and optimized for factuality through constraining beam search, which we trained and tested using EHR data from patients admitted to the neurology unit of an academic medical center. The approach demonstrated good ROUGE scores with an R-2 of 13.76. In a blind evaluation, two board-certified physicians rated 62% of the automated summaries as meeting the standard of care, which suggests the method may be useful clinically. To our knowledge, this study is among the first to demonstrate an automated method for generating a discharge summary hospital course that approaches a quality level of what a physician would write.