# Almost Clinical: Linguistic properties of synthetic electronic health records

arXiv: https://arxiv.org/abs/2601.01171 · HTML: https://mlsanity.com/paper/2601.01171/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 3 Jan 2026.

## Builds on (most cited first, 3 of 3)

- [Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes](https://mlsanity.com/paper/2309.00237/) (2023)
- [DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation](https://mlsanity.com/paper/2311.09581/) (2023)
- [Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining](https://mlsanity.com/paper/2105.10419/) (2021)

## Abstract (arXiv)

This study evaluates the linguistic and clinical suitability of synthetic electronic health records in mental health. First, we describe the rationale and the methodology for creating the synthetic corpus. Second, we examine expressions of agency, modality, and information flow across four clinical genres (Assessments, Correspondence, Referrals and Care plans) with the aim to understand how LLMs grammatically construct medical authority and patient agency through linguistic choices. While LLMs produce coherent, terminology-appropriate texts that approximate clinical practice, systematic divergences remain, including registerial shifts, insufficient clinical specificity, and inaccuracies in medication use and diagnostic procedures. The results show both the potential and limitations of synthetic corpora for enabling large-scale linguistic research otherwise impossible with genuine patient records.