通用数据模型在医药领域的应用进展与思考

doi:10.19803/j.1672-8629.20260471

摘要/Abstract

摘要： 目的梳理医药领域主流通用数据模型（CDM）的类型及其应用进展,分析其在公共卫生与流行病学监测、药物研发与临床试验、医疗服务质量与绩效管理中的价值。方法介绍10种主流CDM的核心特征、技术架构与适用场景,结合典型研究案例,分析CDM在医药领域的应用现状和当前面临的主要挑战与发展方向。结果 CDM通过统一数据结构与术语体系,有效支撑多源异构医疗数据的语义互操作,在跨国真实世界研究、流行病学监测及突发公共卫生事件响应中展现出较高的应用价值。基于CDM分布式分析框架可在保护患者隐私前提下实现大规模数据协同利用,显著降低多中心研究门槛。然而,中文临床数据非结构化程度高、分布式分析效率偏低、国际数据标准异构以及国内配套融合体系欠完善等问题,仍制约其在国内的规模化落地。结论 CDM可打破医疗数据孤岛,为高质量真实世界证据生成提供核心技术支撑。建议未来结合大语言模型、轻量级联邦学习等技术提升自动化映射与分布式分析能力,推动多标准互操作与行业生态建设,加快其在医药科研与管理中的深度应用。

关键词: 通用数据模型, 公共卫生, 药物研发, 医疗大数据, 数据标准化, 真实世界证据, 多中心研究

Abstract: Objective To summarize the types and new applications of mainstream Common Data Models (CDMs) in the medical field, analyze their applicability in public health and epidemiological surveillance, drug research and clinical trials, and quality and performance management of health care. Methods The leading characteristics, technical architecture, and applications of ten mainstream CDMs were introduced. In combination with typical cases of research at home and abroad, the current applications of CDMs in the medical field, main challenges and developments were analyzed. Results CDMs could effectively support semantic interoperability of multi-source heterogeneous medical data by unifying the data structure and terminology system, so they were highly applicable to cross-border and real-world research, epidemiological surveillance, and response to public health emergencies. The distributed analysis framework based on CDMs could ensure large-scale and collaborative use of data while protecting patients' privacy, which significantly reduced the threshold for multi-center research. However, such issues as the strongly unstructured clinical data in Chinese, low efficiency of distributed analysis, heterogeneity of international data standards, and the imperfect domestic integration system still restrict their large-scale implementation in China. Conclusion CDMs can break down medical data silos and provide core technical support for generating high-quality real-world evidence. It is recommended that such technologies as large language models and lightweight federated learning be combined to enhance automated mapping and distributed analysis, promote multi-standard interoperability and construction of the industrial ecosystem, and accelerate their extensive applications in medical research and management.

Key words: Common Data Models (CDMs), Public Health, Drug Development, Medical Big Data, Data Standardization, Real-World Evidence, Multi-Center Study

中图分类号:

R951
R994.11

齐悦, 王凡, 郭晓晶, 叶小飞. 通用数据模型在医药领域的应用进展与思考[J]. 中国药物警戒, 2026, 23(9): 968-973.

Qi Yue, Wang Fan, Guo Xiaojing, Ye Xiaofei. Progress in applications of common data models in the medical field[J]. Chinese Journal of Pharmacovigilance, 2026, 23(9): 968-973.

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https://zgywjj.magtechjournal.com/CN/Y2026/V23/I9/968

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