---
title: "Almost Clinical — paper by Sharoff et al. (2026)"
description: "This work asks whether LLM-generated records could replace them for linguistic research. 19 references."
authors: ["Serge Sharoff", "John Baker", "Dr David Francis Hunt", "Alan Simpson"]
year: 2026
venue: "Proceedings of the 1st Workshop on Linguistic Analysis for Health (HeaLing 2026)"
source: "https://aclanthology.org/2026.healing-1.10/"
doi: "10.18653/v1/2026.healing-1.10"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/sharoff-2026-almost-clinical-linguistic-properties-synthetic/"
---
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# Almost Clinical: Linguistic properties of synthetic electronic health records

**Authors:** [Serge Sharoff](https://md.research.elamur.ai/free/en/authors/serge-sharoff/), John Baker, Dr David Francis Hunt and Alan Simpson · **Year:** 2026 · **Venue:** [Proceedings of the 1st Workshop on Linguistic Analysis for Health (HeaLing 2026)](https://md.research.elamur.ai/free/en/venues/healing/) ([Workshop on Linguistic Analysis for Health 2026](https://md.research.elamur.ai/free/en/venues/healing/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.healing-1.10/) · [PDF](https://aclanthology.org/2026.healing-1.10.pdf) · [DOI 10.18653/v1/2026.healing-1.10](https://doi.org/10.18653/v1/2026.healing-1.10) · [OpenAlex](https://openalex.org/W7140142548)

**Citations:** 1 citations (OpenAlex, 2026-10-14)

## Summary
Real mental health records are hard to study at scale because privacy rules restrict access and their personal narratives make re-identification more likely. This work asks whether LLM-generated records could replace them for linguistic research. The authors built patient profiles by combining age, gender, sexuality, ethnicity, diagnosis, medication, risk factors and treatment history. They then prompted two open-weight models to write four document types: initial assessments, GP correspondence, referral/handover letters and DIALOG+-style care plans. A sample of the outputs was analysed with Systemic Functional Linguistics, covering transitivity and agency, modality and connectors. Features were detected with CorpusTool and then checked by hand. The generated texts read fluently and use suitable clinical vocabulary. However, they shift register, lack clinical detail and get medication and diagnostic procedures wrong. Llama also shows ethnic bias: it brings up drug use far more often for Afro-Caribbean patients. Because the profiles are identical except for controlled variables, such biases can be measured directly. This makes the corpus useful both for linguistic research and for auditing LLMs before clinical use. The prompts and annotated corpora are released.

## Key points
- **Models and corpus size:** Four instruction-tuned models were piloted: DeepSeek V2 16B, Llama 3.2 3B, MediPhi 4B and Mistral v0.3 7B. Experts judged Llama and Mistral closest to clinical reality. Each produced 12,960 stories per genre, built from 2×2×3×3×6×3×5×4 parameter combinations.
- **Annotated sample:** For manual linguistic annotation, 24 texts per genre per model were selected.
- **Text length:** Llama writes longer and less consistent texts, with one reaching 8,098 words. Referrals are Llama's longest genre (median 973 words) but Mistral's shortest (473).
- **Care plans:** Material clauses make up most of the care plans, accounting for 83% of clauses in Llama's output and 89% in Mistral's, against 61–67% in the other genres.
- **Ethnic bias in Llama:** Marijuana appears 25 times in White British stories versus 1,189 times across the two Afro-Caribbean variants. For Mistral the figures are 26 versus 77. Llama shows a similar gap for cocaine (259 versus 1,857).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [King's College London](https://md.research.elamur.ai/free/en/universities/king-s-college-london/) — University, London, United Kingdom · 46 works · 1,714 citations
- [University of Exeter](https://md.research.elamur.ai/free/en/universities/university-of-exeter/) — University, Exeter, United Kingdom · 13 works · 203 citations

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## Cite

Cite as: Serge Sharoff, John Baker, Dr David Francis Hunt and Alan Simpson. 2026. Almost Clinical: Linguistic properties of synthetic electronic health records. Proceedings of the 1st Workshop on Linguistic Analysis for Health (HeaLing 2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{sharoff2026almost,
  title = {{Almost Clinical: Linguistic properties of synthetic electronic health records}},
  author = {Serge Sharoff, John Baker, Dr David Francis Hunt and Alan Simpson},
  booktitle = {Proceedings of the 1st Workshop on Linguistic Analysis for Health (HeaLing 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.healing-1.10},
  url = {https://doi.org/10.18653/v1/2026.healing-1.10}
}
```

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19 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 31 October 2026*