Overview of the MEDIQA-OE 2025 Shared Task on Medical Order Extraction from Doctor-Patient Consultations
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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.
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.
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10 of 25 references.
References · 10
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- 2025Efficient extraction of medication information from clinical notes: an evaluation in two languagescited by 2
- 2025Assessing Large Language Models for Structured Medical Order Extractioncited by 2
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Citing papers per year: 2026: 2.
2 papers.