# Overview of the MEDIQA-OE 2025 Shared Task on Medical Order Extraction from Doctor-Patient Consultations

arXiv: https://arxiv.org/abs/2510.26974 · HTML: https://mlsanity.com/paper/2510.26974/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation](https://mlsanity.com/paper/2306.02022/) (2023)
- [Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction](https://mlsanity.com/paper/2310.06239/) (2023)
- [Does Biomedical Training Lead to Better Medical Performance?](https://mlsanity.com/paper/2404.04067/) (2024)
- [Empowering Healthcare Practitioners with Language Models: Structuring Speech Transcripts in Two Real-World Clinical Applications](https://mlsanity.com/paper/2507.05517/) (2025)
- [Efficient extraction of medication information from clinical notes: an evaluation in two languages](https://mlsanity.com/paper/2502.03257/) (2025)
- [Assessing Large Language Models for Structured Medical Order Extraction](https://mlsanity.com/paper/2510.10475/) (2025)

## Built on by (most cited first, 2 of 2)

- [Three Generations of Healthcare IT: From the Digital Record to the Computable Care Process](https://mlsanity.com/paper/2608.08806/) (2026)
- [Clinical Intent Extraction: A FHIR-Aligned Representation and the CIRCA Benchmark](https://mlsanity.com/paper/2609.29479/) (2026)

## Abstract (arXiv)

Clinical documentation increasingly uses automatic speech recognition and summarization, yet converting conversations into actionable medical orders for Electronic Health Records remains unexplored. A solution to this problem can significantly reduce the documentation burden of clinicians and directly impact downstream patient care. We introduce the MEDIQA-OE 2025 shared task, the first challenge on extracting medical orders from doctor-patient conversations. Six teams participated in the shared task and experimented with a broad range of approaches, and both closed- and open-weight large language models (LLMs). In this paper, we describe the MEDIQA-OE task, dataset, final leaderboard ranking, and participants' solutions.