Datasets:

microsoft
/

SIMORD

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.
Version: As released with the EMNLP industry track paper (2025)
License / usage terms: CDLA-2.0-permissive
Contact / Maintainer: [email protected]

Building the dataset

Method 1: HF datasets

Make sure you have datasets==3.6.0 or less, otherwise builder is not supported in recent versions.
Git clone and install requirements from https://github.com/jpcorb20/mediqa-oe
Add mediqa-oe to python path PYTHONPATH=$PYTHONPATH:/mypath/to/mediqa_oe (UNIX).
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}
}