Overview of the MEDIQA-OE 2025 Shared Task on Medical Order Extraction from Doctor-Patient Consultations
This addresses the documentation burden for clinicians and impacts patient care, but is incremental as it focuses on establishing a benchmark rather than a novel solution.
The paper tackled the problem of converting doctor-patient conversations into actionable medical orders for Electronic Health Records, introducing the MEDIQA-OE 2025 shared task as the first challenge in this area, with six teams participating and experimenting with various approaches including LLMs.
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.