Data governance system of the National Clinical Research Center for Child Health in China

Abstract

BACKGROUND: Since the national big data strategy was unveiled at the fifth plenary session of the 18th CPC (Communist Party of China) Central Committee, the big data industry has been flourishing in China. Various successful industrial data governance systems have emerged with the rapid development of big data technologies and data management theories. City Brain and Enterprise Data Middle Platform are considered the best data governance systems in urban and corporate governance, respectively. However, in the health and medical sectors, issues of data operation occur frequently due to a lack of systematic data governance. These problems need to be urgently addressed, as health and medical data have been defined as national fundamental strategic resources. Clinical researchers have an increasing demand for data analysis. METHODS: Therefore, the Medical Data Governance System (MDGS) has been designed to improve data quality and provide simple and convenient data analysis tools for the National Clinical Research Center for Child Health. The MDGS consists of the Medical Data Platform (MDP) and Operation Management System (OMS). The MDP comprises acquisition layer, middle platform, and application layer that persistently elevates data quality and significantly shortens data analysis duration. Organization construction, management regulations, and technical standards are included in the OMS, which guarantees the sustainable operation of the MDGS. The MDGS was established to advance state-of-the-art and state-of-practice data governance for the health and medical sectors in China. RESULTS: With the first phase of the MDGS, the quantity and quality of research projects increase, research transformation speeds up, and the researchers' job satisfaction increased. CONCLUSIONS: Based on our preliminary achievements, it was necessary and feasible to establish the MDGS. It is important to have comprehensive requirement study, top-level design, refined planning, phase-by-phase implementation, and continual optimization.

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