Recently, with the support of Peking University and Peking University Department of Medicine, the launching and launching conference of "Peking University Medical" health care big data sharing platform and major disease research center (hereinafter referred to as "platform and center") was held in Beijing. Tang Yonglin, Deputy Director of the Informatization and Big Data Application Office of the Planning and Information Department of the National Health and Family Planning Commission, Wang Dantong, Director of the Social Insurance Management Center of the Ministry of Human Resources and Social Security of China, Duan Liping, Deputy Director of the Peking University Medical Department, Hao Weidong, Secretary of the School of Public Health, Peking University, and More than one hundred guests from leaders and experts of domestic well-known medical institutions, industry experts in the field of health care, big data technology teams and industry attended the meeting.
Duan Liping emphasized that the development of medical science and technology is an important key foundation for building a healthy China. Integrating China’s high-quality health and medical data resources will be conducive to the establishment of an integrated and synergistic system with the characteristics of systematic integration, multi-factor collaboration, and large-scale organization adapted to the era of big data research. The research model promotes the coordinated development of "policy, industry, science, research, and research" in the field of health care to form a data ecosystem of sharing, co-construction, symbiosis, and win-win.
Tang Yonglin pointed out that data-driven research in the field of health care is becoming an important engine for achieving effective results in disease prevention and control, and will play a huge role in accelerating disease prevention and control technology breakthroughs, improving medical supply models, and restructuring health service systems. It is of great significance to interconnect and interconnect high-quality health and medical data from different sources, through safe and win-win data sharing and cooperation mechanisms, to open up the original data island, and finally to realize the opening and sharing of health and medical data. Tang Yonglin said that Peking University Medical Department is in a leading position in the field of clinical medicine, public health, pharmacy, basic research and many other medical research fields. He hopes that "Peking University Medical" health care big data sharing platform and major disease research center can be The field of health care data opening and sharing plays a leading role and promotes the development process of health care data applications. At the meeting, Zhan Siyan, director of the Department of Epidemiology and Health Statistics at the School of Public Health, Peking University, introduced China Cohort Consortium and presided over the award ceremony of the platform LOGO.
According to reports, the platform is hosted by the School of Public Health of Peking University. Through the construction of the infrastructure, the existing queue resources are standardized for information display, and a multi-layered three-dimensional cooperation strategy and sharing mechanism are established to form information management and information interaction. , Tool development and knowledge support, a multi-functional information integration platform. The platform will provide new cooperation channels and data sources for the development of public health and clinical research. At the same time, the first batch included more than ten cohorts and related research projects hosted and participated by the School of Public Health of Peking University, such as the Chinese adult cohort led by Professor Li Liming, the twin cohort, etc., covering chronic diseases, infectious diseases, maternal and child health, occupational diseases In many research fields, data sources cover all provinces, autonomous regions and municipalities directly under the Central Government.
Zhang Luxia, executive deputy director of Peking University Health and Medical Big Data Research Center, introduced the China Medical Data Sharing System (C-MEDS). The C-MEDS platform is designed to select and integrate high-quality, multi-source data in the health care field; gradually form a safe and win-win data sharing mechanism, establish in-depth cooperation with other related data platforms; establish a pool of high-quality data resources, attract high-quality researchers, and produce A data ecosystem that produces high-level academic research results; promote the resolution of key common problems in the field of health care big data. C-MEDS has integrated data from 150,000 cohort research individuals from scientific research institutions, nearly 30,000 clinical trials and post-marketing drug observation individual data from pharmaceutical companies (AstraZeneca Pharmaceutical Co., Ltd.), and will focus on specialists. Collect real-world diagnosis and treatment data of Peking University Medical School affiliated and teaching hospitals. Next, the Peking University Health and Medical Big Data Research Center will recruit and select high-quality researchers to conduct research by setting up a start-up fund.
Zhao Minghui, director of the Department of Nephrology of Peking University, introduced that under the background that kidney disease has become a global public health problem, the Department of Nephrology of Peking University and the Peking University Health and Medical Big Data Research Center are preparing to establish the Peking University Medical Department Kidney Disease Big Data Research Center. Conception.
Experts said that the "Peking University Medical" health care big data sharing platform and major disease research center welcome the participation of domestic and foreign medical institutions, technical teams and related companies to jointly promote the opening and sharing of China's health care data and promote the health care big data government The integrated construction of study and research.
Source: China In Vitro Diagnostic Network
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