The 2025 OHDSI APAC Symposium welcomed a fully-engaged group of over 150 registrants during its annual event, held Dec. 6-7 in Shanghai, China. The symposium offered valuable insight into the current landscape of healthcare data and research in China, and it created an important platform to further expand awareness and adoption of OHDSI across the APAC region.  

The program featured an introductory OMOP CDM tutorial with strong hands-on participation, alongside updates on the progress of the 2025 APAC Studies launched in March. Through the showcase of OMOP CDM transformation efforts and LLM-based, multimodal AI approaches for generating real-world evidence, the symposium emerging innovations in data standardization and analytics.  

Overall, the event strengthened global collaboration and in-person connections, while building momentum to advance APAC-led studies into 2026. 

2025 OHDSI APAC Symposium Agenda 

Day 1 – Tutorial

Morning Session

• Introduction of OHDSI/OMOP
• OMOP CDM and Vocabulary
• OMOP Conversion Process
• ETL Exercises

Afternoon Session

• OHDSI Analyses: Building Cohorts & Hands-on
• CohortDiagnostics and Population-Level Estimation
• Interpreting Results

Day 2 – Conference

Session 1 – From Global to Regional Impact: OHDSI across APAC & Africa

• Opening Speech (University Vice President)
• Keynote Speech: From Data to Impact: OHDSI’s Role in RWE (Mui Van Zandt)
• APAC Regional Chapter Updates (Korea, India, Japan, Singapore, Malaysia, Vietnam, Australia)

Session 2 – From Research to Reflection: 2025 APAC Studies and Lessons Learned

• Database: from FPG to Medical Burden (Jiaqi Liu, Fudan)
• GLP-1 vs SGLT-2 in T2D (Observational Study) (Sun Feng, Peking)
• Disease Trajectory of T2D with Transformer Models (Shilu (Margaret) Liu, USTC)
• Standards of Observational Research (Seng Chan You, JACC)
• 12:10 – 12:30 Panel Discussion

Session 3 – From Regional Insights to Local Challenges: Real-World Evidence and OHDSI/OMOP in China

• Collaborator Showcase: Lightning Talks
• The Huatuo LLM & AI for EBM (Guangjun Yu)
• AI-driven Biomedical Multimodal Data Analysis (Huiying Liang)
• RWE: From Observational to Analytical Studies (Le Gao)
• Large Model powered OMOP CDM Mapping (Mengchun Gong)
• AI-assisted Prescription Review System (Bo Jin)
• Transforming Prostate Cancer Outcomes Data to OMOP (German Zhu, Bayer & Meng Yin)