A fuzzy approach to support decision-making in the triage process for suspected COVID-19 patients in Brazil

Triage is a fundamental process in hospitals and emergency care units, as it allows for the classification and prioritization of patient care based on the severity of their clinical conditions. In Brazil, the triage of suspected COVID-19 cases is performed using a specific protocol, which involves manual steps, requiring the completion of four different forms, by four health care professionals. Aiming to investigate the possibility of improving the triage processes in Brazil, this article proposes the use of computational techniques for decision-making based on fuzzy inference systems. We argue that fuzzy set theory is appropriate to the problem because it allows the use of natural language to express the patient's symptoms, making it easier for health care professionals. After modelling the problem in a fuzzy system we applied a pilot test. The model includes symptoms that health professionals currently use to analyse COVID-19 cases. The results suggest that the model presents convergence with the sample data, highlighting its potential application in supporting triage for the classification of the severity of COVID-19 cases. Among the benefits of the proposed model, we emphasize contributions as the reduction of the time and number of professionals required for triage as well as the reduction of exposure of health care professionals and other patients suspected of carrying the virus. In this context, this research provides an opportunity to obtain social contributions regarding the services in public hospitals improvement.