Construction and application of national pediatric cancer surveillance platform
10.3760/cma.j.cn111325-20240529-00439
- VernacularTitle:国家儿童肿瘤监测平台构建与应用
- Author:
Xin XU
1
;
Zhe LI
;
Yuanhu LIU
;
Xiao ZHANG
;
Guoliang BAI
;
Xinping LI
;
Yingying LIU
;
Zhuoyu YANG
;
Xin NI
Author Information
1. 国家儿童医学中心 首都医科大学附属北京儿童医院信息中心，北京 100045
- Publication Type:Journal Article
- Keywords:
Pediatric cancer;
Surveillance platform;
Platform architecture;
Data collection;
Data security
- From:
Chinese Journal of Hospital Administration
2024;40(12):917-922
- CountryChina
- Language:Chinese
-
Abstract:
To provide comprehensive, scientific, and precise big data supports for national pediatric cancer prevention and control, the National Center for Pediatric Cancer Surveillance constructed the Surveillance Platform in 2019. Based on stratified and service-oriented design concepts, and a microservices architecture, the platform constructed five layers: document storage, data storage, service, application, and visualization. The platform supported three data reporting methods: automatic collection, file import, and manual entry. It ensured data quality through both rule-based and process-based quality control measures. Additionally, strict data security measures had been established in areas such as security domains, permission management, and data de-identification to ensure the safety and reliability of the monitoring data. As to November 2024, the platform had covered 1 750 surveillance sites(hospitals) and collected information about 6 million pediatric cancer cases, achieving positive results. This practice had laid a solid foundation for the smooth implementation of national pediatric cancer surveillance work and provided scientific evidences for pediatric cancer prevention and control in China. In the future, the platform′s performance needs to be continuously optimized and upgraded. It should further integrate relevant datasets in this field and actively explore and expand new application scenarios with the help of cutting-edge technologies such as big language models.
