An Intelligent Medical Isolation Observation Management System Based on the Internet of Things

Abstract

Abstract Background Since COVID-19 (coronavirus disease 2019) was discovered in December 2019, it has spread worldwide. Early isolation and medical observation management of cases and their close contacts are the key to controlling the spread of the epidemic. However, traditional medical observation requires medical staff to measure body temperature and other vital signs face to face and record them manually. There is a general shortage of human and personal protective equipment and a high risk of occupational exposure, which seriously threaten the safety of medical staff. Methods We designed an intelligent crowd isolation medical observation management system framework based on the Internet of Things using wireless telemetry and big data cloud platform remote management technology. Through a smart wearable device with built-in sensors, vital sign data and geographical locations of medical observation subjects are collected and automatically uploaded to the big data monitoring platform on demand. According to the comprehensive analysis of the set threshold parameters, abnormal subjects are screened out, and activity tracking and health status monitoring for medical observation and management objectives are performed through monitoring and early warning management and post-event data traceability. In the trial of this system, the subjects wore the wristwatches designed in this study and real-time monitoring was conducted throughout the whole process. Additionally, for comparison, the traditional method was also used for these people. Medical staff came to measure their temperature twice a day. The subjects were 1,128 returned overseas Chinese from Europe. Results Compared with the traditional vital sign detection method, the system designed in this study has the advantages of a fast response, low error, stability, and good endurance. It can monitor the temperature, pulse, blood pressure, and heart rate of the monitored subject in real time. The system designed in this study and the traditional vital sign detection method were both used to monitor 1,128 close contacts with COVID-19. There were six cases of abnormal body temperature that were missed by traditional manual temperature measurement in the morning and evening, and these six cases (0.53%) were sent to the hospital for further diagnosis. The abnormal body temperature of these six cases was not found in time when the medical staff came to check the temperature on a twice-a-day basis. The system designed in this study, however, can detect the abnormal body temperature of all these six people. The sensitivity and specificity of our system were both 100%. Conclusion The system designed in this study can monitor the body temperature, blood oxygen, blood pressure, heart rate, and geographical location of the monitoring subject in real time. It can be extended to COVID-19 medical observation isolation points, shelter hospitals, infectious disease wards, and nursing homes.