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

TL;DR

The paper introduces the MEDIQA-OE 2025 shared task focused on extracting medical orders from doctor-patient conversations, aiming to improve clinical documentation and patient care through NLP techniques.

Contribution

It presents the first shared task on medical order extraction from conversations, including dataset, evaluation, and analysis of diverse approaches by multiple teams.

Findings

Multiple approaches tested, including large language models

Top-performing models achieved significant accuracy in extraction

The shared task establishes a benchmark for future research

Abstract

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.