Key Takeaways & Executive Findings
- •• Standardized data models are crucial for enabling interoperability and secondary use of clinical data, but adoption in China faces significant barriers. • Key challenges include data heterogeneity, privacy concerns, and the lack of standardized vocabularies across institutions. • Emerging technologies such as FHIR and artificial intelligence offer promising solutions to facilitate data standardization and integration. • A collaborative ecosystem involving researchers, healthcare providers, and policymakers is essential to overcome these challenges and unlock the full potential of clinical data.
Abstract
Standardized data models are essential for enabling interoperability and secondary use of clinical data in research. This paper reviews the current landscape of data models for clinical research, focusing on the challenges and opportunities in China. We analyze the adoption of common data models such as OMOP CDM and PCORnet, and discuss the barriers to implementation, including data heterogeneity, privacy concerns, and lack of standardized vocabularies. We also highlight the potential of emerging technologies like FHIR and AI to facilitate data standardization. Our findings suggest that a collaborative approach involving stakeholders, investment in infrastructure, and policy support are critical for advancing data-driven clinical research in China.
1. Introduction
Clinical research increasingly relies on the secondary use of electronic health records (EHRs) to generate real-world evidence. However, the heterogeneity of data across institutions and the lack of standardized formats hinder efficient data sharing and analysis. Standardized data models (SDMs) provide a common structure and vocabulary, enabling interoperability and facilitating multi-center studies. In China, the adoption of SDMs is still in its infancy, with many institutions using proprietary formats. This paper reviews the current state of SDMs for clinical research, identifies the challenges specific to the Chinese context, and discusses opportunities for future development.
We begin by outlining the key SDMs used internationally, such as the Observational Medical Outcomes Partnership (OMOP) Common Data Model and the Patient-Centered Outcomes Research Institute (PCORnet) model. We then examine the barriers to their adoption in China, including technical, regulatory, and cultural factors. Finally, we propose strategies to promote the use of SDMs, emphasizing the role of collaboration, infrastructure investment, and policy support. Our goal is to provide a comprehensive overview that can guide stakeholders in advancing data-driven clinical research in China.
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Y. Zhang, L. Wang, H. Li, J. Chen (2026). Standardized Data Models for Clinical Research: Challenges and Opportunities in China. Chinese Journal of New Drugs. https://doi.org/10.1007/s10916-025-02123-4
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Frequently Asked Questions
What are standardized data models in clinical research?
Standardized data models are common structures and vocabularies for organizing clinical data, enabling interoperability and secondary use across different systems and institutions.
Why are standardized data models important?
They facilitate data sharing, multi-center studies, and real-world evidence generation by ensuring data consistency and comparability.
What are the main challenges to adopting standardized data models in China?
Challenges include data heterogeneity, privacy concerns, lack of standardized vocabularies, and limited infrastructure and expertise.
How can emerging technologies help in data standardization?
Technologies like FHIR and artificial intelligence can automate mapping, improve data quality, and enable semantic interoperability.
What is the future outlook for standardized data models in China?
With increasing investment and policy support, China is likely to see wider adoption of SDMs, enabling more efficient and impactful clinical research.
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