# Learning to Write Notes in Electronic Health Records

arXiv: https://arxiv.org/abs/1808.02622 · HTML: https://mlsanity.com/paper/1808.02622/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2018.

## Built on by (most cited first, 5 of 5)

- [What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization](https://mlsanity.com/paper/2105.00816/) (2021)
- [Modelling Temporal Document Sequences for Clinical ICD Coding](https://mlsanity.com/paper/2302.12666/) (2023)
- [Autocompletion of Chief Complaints in the Electronic Health Records using Large Language Models](https://mlsanity.com/paper/2401.06088/) (2024)
- [R2D2: Relational Text Decoding with Transformers](https://mlsanity.com/paper/2105.04645/) (2021)
- [Enhancing Clinical Note Generation with ICD-10, Clinical Ontology Knowledge Graphs, and Chain-of-Thought Prompting Using GPT-4](https://mlsanity.com/paper/2512.05256/) (2025)

## Abstract (arXiv)

Clinicians spend a significant amount of time inputting free-form textual notes into Electronic Health Records (EHR) systems. Much of this documentation work is seen as a burden, reducing time spent with patients and contributing to clinician burnout. With the aspiration of AI-assisted note-writing, we propose a new language modeling task predicting the content of notes conditioned on past data from a patient's medical record, including patient demographics, labs, medications, and past notes. We train generative models using the public, de-identified MIMIC-III dataset and compare generated notes with those in the dataset on multiple measures. We find that much of the content can be predicted, and that many common templates found in notes can be learned. We discuss how such models can be useful in supporting assistive note-writing features such as error-detection and auto-complete.