Abstract:A surge in the workload of the hospital for testing potential patients and an increased risk of cross-infection among patients due to emergent epidemic, poses a critical social public safety problem to solve. In reference to the Protocol for Prevention and Control of emergent epidemic issued by the National Health Commission of China, through literature review, expert consultation and subjective weighting (involving experts from the Departments of Infection Control, Respiratory Medicine, Critical Care, and the Center for Disease Control), and human-computer interactive design, user test and usability evaluation methods, the content of online affected risk self-check application, available options, risk weighting and threshold, and human-computer interactions of high usability have been investigated. The online emergent epidemic affected risk self-check application take the age of patients, engagement in high-risk occupations, history of contact and exposure in epidemic areas, medical history, and suggested symptoms into considerations. Based on the objective inputs by the patients and risk score calculation, the application gives reliable suggestions to self-monitor at home, self-isolate, or seek immediate medical attention. It is easy to use, understand and promote online to patients of different cultural background. Following small-scale internal testing, the application was launched and promoted to the general public by the official WeChat account of Xiangya Hospital, the Department of Respiratory Medicine, Fever Clinic, and Social Media Platforms between February 5 and 9, 2020. The number of visits exceeded 200,000 in just a few days and reached a daily maximum of 90,000. The online emergent epidemic affected risk self-check application alleviates the social panic, resolves the shortage of manual screening, and reduces the risk of cross-infection among patients by providing a straightforward, easy-to-understand scientific protocol. It provides references for future new epidemics prevention and control through online classification and offline triage of patients of suspected symptoms.