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BackgroundIn the prevention and control of infectious diseases, previous research on the application of big data technology has mainly focused on the early warning and early monitoring of infectious diseases. Although the application of big data technology for COVID-19 warning and monitoring remain important tasks, prevention of the disease’s rapid spread and reduction of its impact on society are currently the most pressing challenges for the application of big data technology during the COVID-19 pandemic. After the outbreak of COVID-19 in Wuhan, the Chinese government and nongovernmental organizations actively used big data technology to prevent, contain, and control the spread of COVID-19. ObjectiveThe aim of this study is to discuss the application of big data technology to prevent, contain, and control COVID-19 in China; draw lessons; and make recommendations. MethodsWe discuss the data collection methods and key data information that existed in China before the outbreak of COVID-19 and how these data contributed to the prevention and control of COVID-19. Next, we discuss China’s new data collection methods and new information assembled after the outbreak of COVID-19. Based on the data and information collected in China, we analyzed the application of big data technology from the perspectives of data sources, data application logic, data application level, and application results. In addition, we analyzed the issues, challenges, and responses encountered by China in the application of big data technology from four perspectives: data access, data use, data sharing, and data protection. Suggestions for improvements are made for data collection, data circulation, data innovation, and data security to help understand China’s response to the epidemic and to provide lessons for other countries’ prevention and control of COVID-19. ResultsIn the process of the prevention and control of COVID-19 in China, big data technology has played an important role in personal tracking, surveillance and early warning, tracking of the virus’s sources, drug screening, medical treatment, resource allocation, and production recovery. The data used included location and travel data, medical and health data, news media data, government data, online consumption data, data collected by intelligent equipment, and epidemic prevention data. We identified a number of big data problems including low efficiency of data collection, difficulty in guaranteeing data quality, low efficiency of data use, lack of timely data sharing, and data privacy protection issues. To address these problems, we suggest unified data collection standards, innovative use of data, accelerated exchange and circulation of data, and a detailed and rigorous data protection system. ConclusionsChina has used big data technology to prevent and control COVID-19 in a timely manner. To prevent and control infectious diseases, countries must collect, clean, and integrate data from a wide range of sources; use big data technology to analyze a wide range of big data; create platforms for data analyses and sharing; and address privacy issues in the collection and use of big data.

Aim. This study aims to investigate the level of organizational learning within urban Chinese Community Health Centres and reveal its potential association with primary care nurses' work performance and well‐being. Background. Globally, there is a push to establish learning healthcare systems for complex health reform challenges. Existing studies on organizational learning mainly focus on North American and European hospital settings, offering limited insights into primary care environments, particularly in developing countries. Design. Cross‐sectional study. Methods. We recruited 175 nurses from 38 community health centres in four Chinese cities (Shanghai, Shenzhen, Tianjin, and Jinan) using convenience sampling. Trained research assistants conducted face‐to‐face surveys, measuring organizational learning with the Learning Orientation Scale. Nurse‐level outcomes included self‐directed learning, quality of care, organizational commitment, and work stress. Data analysis employed multilevel linear modelling. Results. The 38 community health centres displayed a relatively high level of organizational learning, and there was a positive and significant association between organizational learning within community health centres and nurses' self‐directed learning as well as the quality of care. However, there was no significant association between organizational learning and nurses' organizational commitment or work stress. Conclusion. This study demonstrates a high‐level organizational learning capacity in urban community health centres in China. It provides a new perspective on the potential relationship between CHCs' organizational learning and primary care nurses' well‐being and work performance. Further research is needed to clarify unexpected findings and identify factors promoting organizational learning in primary care settings. Implications for Nursing Management. In China's evolving primary care system, nurses play a vital role amidst physician shortages. Policy should prioritize internal management reform alongside structural changes. This study highlights the importance of fostering organizational learning in primary care settings. Strategic interventions should promote a learning culture in CHCs, which may enhance nurses' self‐directed learning and improve the quality of care. [ABSTRACT FROM AUTHOR]