Overview of CHIP 2025 Shared Task 2: Discharge Medication Recommendation for Metabolic Diseases Based on Chinese Electronic Health Records

TL;DR

This paper overviews the CHIP 2025 Shared Task 2, which focused on developing advanced methods for recommending discharge medications for metabolic diseases using Chinese EHR data, highlighting dataset creation, competition results, and challenges.

Contribution

It introduces CDrugRed, a high-quality Chinese EHR dataset, and reports on a large-scale competition demonstrating the effectiveness of LLM-based systems for medication recommendation.

Findings

Top model achieved a Jaccard score of 0.5102

Large language models show promise in Chinese EHR medication recommendation

Competition attracted over 500 teams and extensive participation

Abstract

Discharge medication recommendation plays a critical role in ensuring treatment continuity, preventing readmission, and improving long-term management for patients with chronic metabolic diseases. This paper present an overview of the CHIP 2025 Shared Task 2 competition, which aimed to develop state-of-the-art approaches for automatically recommending appro-priate discharge medications using real-world Chinese EHR data. For this task, we constructed CDrugRed, a high-quality dataset consisting of 5,894 de-identified hospitalization records from 3,190 patients in China. This task is challenging due to multi-label nature of medication recommendation, het-erogeneous clinical text, and patient-specific variability in treatment plans. A total of 526 teams registered, with 167 and 95 teams submitting valid results to the Phase A and Phase B leaderboards, respectively. The top-performing team achieved the highest overall performance on the final test set, with a Jaccard score of 0.5102, F1 score of 0.6267, demonstrating the potential of advanced large language model (LLM)-based ensemble systems. These re-sults highlight both the promise and remaining challenges of applying LLMs to medication recommendation in Chinese EHRs. The post-evaluation phase remains open at https://tianchi.aliyun.com/competition/entrance/532411/.