README.md

---
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.
- **Version**: As released with the EMNLP industry track paper (2025)  
- **License / usage terms**: CDLA-2.0-permissive  
- **Contact / Maintainer**: jcorbeil@microsoft.com  

## Building the dataset

### Method 1: HF datasets

1. Make sure you have `datasets==3.6.0` or less, otherwise builder is not supported in recent versions.
2. Git clone and install requirements from `https://github.com/jpcorb20/mediqa-oe`
3. Add `mediqa-oe` to python path `PYTHONPATH=$PYTHONPATH:/mypath/to/mediqa_oe` (UNIX).
4. Run `load_dataset("microsoft/SIMORD", trust_remote_code=True)`, which will merge transcripts from ACI-Bench and Primock57 repos into the annotation files.

### Method 2: GitHub script

Follow the steps in `https://github.com/jpcorb20/mediqa-oe` to merge transcripts from ACI-Bench and Primock57 into the annotation files provided in the repo.

## 4. Data Fields / Format

**Input fields**:  
  - **transcript** (dict of list): the doctor-patient consultation transcript as dict of three lists using those keys:
    - `turn_id` (int): index of that turn.
    - `speaker` (str): speaker of that turn *DOCTOR* or *PATIENT*.
    - `transcript` (str): line of that turn.

**Output fields**:  
  - A JSON (or list) of **expected orders**  
  - 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  

## Splits

- `train`: examples for in-context learning or fine-tuning.
- `test1`: test set used for the EMNLP 2025 industry track paper. Also, previously named `dev` set for MEDIQA-OE shared task of ClinicalNLP 2025.
- `test2`: test set for MEDIQA-OE shared task of ClinicalNLP 2025.

## Citation

If you use this dataset, please cite:

    @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}
    }