---
license: cdla-permissive-2.0
task_categories:
- text-classification
- token-classification
language:
- en
tags:
- clinical
- doctor-patient
- dialog
size_categories:
- n<1K
---

# Dataset Card: SIMORD (Simulated Medical Order Extraction Dataset)

## 1. Dataset Summary

- **Name**: SIMORD  
- **Full name / acronym**: SIMulated ORDer Extraction  
- **Purpose / use case**:  
  SIMORD is intended to support research in extracting structured medical orders (e.g. medication orders, lab orders) from doctor-patient consultation transcripts. It complements the SYNUR dataset by focusing on the downstream task of converting spoken clinical dialogue into structured orders. :contentReference[oaicite:0]{index=0}  
- **Version**: As released with the paper (2025)  
- **License / usage terms**: CDLA-2.0-permissive  
- **Contact / Maintainer**: jcorbeil@microsoft.com  

## 4. Data Fields / Format

- **Input fields**:  
  - `transcript`: string, the doctor-patient consultation transcript (with disfluencies, interruptions, etc.)  
  - `schema`: metadata of the target order schema (possible order types, attributes)  

- **Output / label fields**:  
  - A JSON (or list) of **order objects**  
  - Each order object includes at least:  
    * `order_type` (e.g. “medication”, “lab”)  
    * `description` (string) — the order text (e.g. “lasix 40 milligrams a day”)  
    * `reason` (string) — the clinical reason or indication for the order  
    * `provenance` (e.g. list of token indices or spans) — mapping back to parts of the transcript  

- **Annotation format constraints**: Outputs must conform to a parsable JSON format consistent with the schema defined in each example.  

## Citation

    @article{corbeil2025empowering,
      title={Empowering Healthcare Practitioners with Language Models: Structuring Speech Transcripts in Two Real-World Clinical Applications},
      author={Corbeil, Jean-Philippe and Abacha, Asma Ben and Michalopoulos, George and Swazinna, Phillip and Del-Agua, Miguel and Tremblay, Jerome and Daniel, Akila Jeeson and Bader, Cari and Cho, Yu-Cheng and Krishnan, Pooja and others},
      journal={arXiv preprint arXiv:2507.05517},
      year={2025}
    }