Research on demand, implementation pathways and effectiveness of medical big data platform

Background With the development of medical informatization, medical big data platforms have become an important foundation for clinical research and a key breakthrough point for resource re analysis and utilization. However, the multi-source heterogeneity of medical data, the diversity of data standards, and the high requirements of patient privacy protection increase the difficulty of data acquisition and application.

Objective To analyze the requirements for establishing a medical big data platform, develop a self-service, full-process data governance platform and tools, and construct a multi-center medical big data platform.

Methods By reviewing the application needs of hospital data and adopting a modular and component-based design approach, the platform architecture was designed. The universally applicable components and management tools that were relatively independent of specific application systems were extracted and utilized to build a multi-center, multi-source heterogeneous medical big data platform.

Results The electronic medical record data of the outpatient, emergency and inpatient departments of the Chinese PLA General Hospital had been aggregated and processed. A full-process, visual data governance tool was developed. The defined event schema graph covered 29 ontology categories, 128 concepts, 1 009 relationships and 3 022 attributes, including clinical evidence-based medical knowledge, clinical diagnosis and treatment, Internet of Things, medical imaging and other data. The consistency and traceability of data governance reached 99.99%, and the knowledge accuracy rate was over 95%. A one-stop data intelligent retrieval and scientific research analysis system and a specialized disease database intelligent analysis system covering the entire scientific research process and different research types have been constructed.

Conclusion The platform not only provides a data retrieval and analysis system for clinical researchers, but also provides data management and platform operation and maintenance tools for data engineers, which improves the scalability and flexibility of the platform.