Zhang Zhengbo, Xue Wanguo, Cao Desen, Li Tanshi
Department of Biomedical Engineering and Maintenance Center, Chinese PLA General Hospital, Beijing 100853, China (Zhang ZB, Cao DS); Medical Information Center, Chinese PLA General Hospital, Beijing 100853, China (Zhang ZB, Xue WG); Department of Emergency, Chinese PLA General Hospital, Beijing 100853, China (Li TS). Corresponding author: Li Tanshi, Email:
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2018 Jun;30(6):603-605. doi: 10.3760/cma.j.issn.2095-4352.2018.06.020.
A detailed, high-scale clinical data can be generated in the process of diagnosis and treatment of emergency critically ill patients. The integration and analysis and utilization of these data are of great value for improving the treatment level and efficiency and developing the data-driven clinical assistant decision support. China has large volume of health information resources, however, the construction of healthcare databases and subsequent secondary analysis has just started. With the effort of the Chinese PLA General Hospital in building an emergency database and promoting data sharing, the first emergency database was published in China and a health Datathon was organized utilizing this database, providing experience for clinical data integration, database construction, cross-disciplinary collaboration and data sharing. Referring to the development at home and abroad, this review discussed work in this area and further proposed establishing a big data cooperation for emergency medicine and building a learning healthcare system to integrate more clinical resources and form a closed loop of "clinical database construction-analysis-applications", and enhance the effectiveness of medical big data in reducing medical costs and improving healthcare delivery.
在急危重症患者的诊疗过程中能够产生详细、大规模的临床数据。这些数据的整合、分析与利用对于提高治疗水平和效率以及发展数据驱动的临床辅助决策支持具有重要价值。中国拥有海量的健康信息资源，然而，医疗数据库的建设及后续的二次分析工作才刚刚起步。在解放军总医院努力构建急诊数据库并推动数据共享的过程中，中国首个急诊数据库得以发布，并利用该数据库组织了一场健康数据马拉松，为临床数据整合、数据库建设、跨学科协作及数据共享提供了经验。参照国内外的发展情况，本综述探讨了该领域的工作，并进一步提出建立急诊医学大数据合作以及构建学习型医疗系统，以整合更多临床资源，形成“临床数据库建设—分析—应用”的闭环，提高医疗大数据在降低医疗成本和改善医疗服务方面的效能。
