Abstract: Objective To construct a high-risk specimen traceability management platform based on 5G Internet of Things mode, and to explore its application value in the detection and management of COVID-19-like specimens. Methods A total of 500 samples from November 2020 to April 2020 were included in the control group, and 500 samples from May 2021 to October 2021 were included in the observation group. Traditional tracking management mode and Internet of Things tracking management mode were adopted respectively. Among them, the observation group built a 5G traceability management platform for multidimensional information collection of specimens, personnel, equipment, and environment around the six links of specimen collection, packaging, transport, testing, preservation, and abandonment, and carried out data management and browsing query through the LIS system. Results The traceability rates of collection, packaging, transport, testing, preservation, and abandonment of specimens in the observation group were higher than those in the control group, and the differences were statistically significant （P<0.05）; The report time of the observation group was shorter than that of the control group. The rates of overtemperature and RNA degradation in the observation group were lower than those in the control group, and the differences between the two groups were statistically significant （P<0.05）; the proportion of missed detection was lower than that of the control group, and there was no significant difference （P>0.05））. Conclusion A traceability management system for high-risk COVID-19-like specimens has been established based on 5G Internet of Things mode, which has improved the timeliness of data collection for detection of high-risk COVID-19-like specimens, optimized the whole process of detection of the full life cycle of specimens, and reduced the biosecurity risks of specimen detection. It has important application value in improving hospital management level and diagnosis and treatment ability.

Key words: Internet of things, Specimen collection, Nucleic acid testing, Traceability management, Biosafety