Use of point-of-care technologies for the management of the COVID-19 pandemic in Colombia

Abstract

Objective. To propose a health care model that integrates point-of-care technologies and artificial intelligence for the management of the COVID-19 pandemic. Methods. A theoretical model was used in which one million people accessed the mobile application CoronApp-Colombia, which collects personal data, signs, symptoms and epidemiological links compatible with COVID-19. With the information from the app artificial intelligence techniques (data science) were applied in a virtual situation room. Results. Users compatible with COVID-19 were prioritized and subjected to a rapid diagnostic test for anti-SARS-CoV-2 antibodies. Screening with the rapid diagnostic test would allow detection of sero-reactive individuals, for whom diagnostic confirmation would be carried out using molecular biology (PCR). Information from positive cases confirmed by PCR would be re-screened using artificial intelligence and spatial statistical techniques to identify geographical foci of infection. These foci could be actively searched for contacts with positive index cases and the diagnostic route would be followed again using the rapid diagnostic test and PCR. Conclusion. This model may be useful for countries in the region with weak or absent technological platforms for PCR diagnosis to maximize existing resources, estimate the epidemiological burden of COVID-19 (infection, morbidity, mortality and lethality) and implement containment, mitigation and control plans according to their needs.