Big data has been reported widely to facilitate epidemic prevention and control in health care during the coronavirus disease 2019 (COVID-19) pandemic. However, there is still a lack of practical experience in applying it to hospital prevention and control. This study is devoted to the practical experience of design and implementation as well as the preliminary results of an innovative big data-driven COVID-19 risk personnel screening management system in a hospital. Our screening system integrates data sources in four dimensions, which includes Health Quick Response (QR) code, abroad travelling history, transportation close contact personnel and key surveillance personnel. Its screening targets cover all patients, care partner and staff who come to the hospital. As of November 2021, nearly 690 000 people and 5.79 million person-time had used automated COVID-19 risk screening and monitoring. A total of 10 376 person-time (0.18%) with abnormal QR code were identified, 242 person-time with abroad travelling history were identified, 925 person-time were marked based on the data of key surveillance personnel, no transportation history personnel been reported and no COVID-19 nosocomial infection occurred in the hospital. Through the application of this system, the hospital's expenditure on manpower and material resources for epidemic prevention and control has also been significantly reduced. Collectively, this study has proved to be an effective and efficient model for the use of digital health technology in response to the COVID-19 pandemic. Based on the data from multiple sources, this system has an irreplaceable role in identifying close contacts or suspicious person, and can significantly reduce the social burden caused by COVID-19, especially the human resources and economic costs of hospital prevention and control. It may provide guidance for clinical epidemic prevention and control in hospitals, as well as for future public health emergencies.

Routine coronavirus disease 2019 (COVID-19) screening found 1 asymptomatic COVID-19 patient. An emergency sampling team was organized consisting of 1200 health-care workers, and a total of 3.2228 million COVID-19 samples had been collected and detected. This study summarizes the on-site management experience in large-scale COVID-19 nucleic acid testing from various aspects: staff preparation, materials preparation, site layout, logistics support, and information system support. Suggestions are put forward for the deficiencies and parts needing improvement. Such deficiencies included some sampling sites were not properly chosen, different areas were unclearly marked off from each other, and some site moving lines were confounding; how to communicate with the street service workers who had little professional knowledge on the epidemic spread or the working principles of the workflow and site layout; and the way to resolve conflicts on site.