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

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 automatic ally recommend ing appropriate discharge medications using real - world Chinese EHR data . For this task, w e 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, heterogeneous 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 leader-boards, respect ively. 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 system s .