metadata
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: [email protected]
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 orderprovenance(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}
}