A Cognitive Agent Model of Burnout for Front-line Healthcare Professionals in Times of COVID-19 Pandemic

Abstract

The global pandemic (Covid-2019) has severely affected all aspects of our life and even changed the way we live and work. As the pandemic outspread, healthcare professionals need urgent interventions to control the harmful consequences. Encountering such a crisis makes them more prone to negative psychological ramifications in their work environment, making them unable to provide the proper support. Burnout is the most negative feeling increased among healthcare professionals while compacting the virus. One essential move to remedy the impacts of burnout is understanding its determinants and their causal relationships. This paper addresses the design of a computational (cognitive) agent model of burnout for healthcare professionals using a temporal-causal network model. Several determinants of burnout with their causal relationships were identified from the literature and formalised to construct the proposed cognitive agent model. In addition, different simulation experiments were implemented to obtain a clear insight into the causal relationships among burnout determinants and those experiments are exhibited similar behaviours to exiting literature. Furthermore, the developed model was evaluated using two different methods: mathematical analysis to prove its implementation was achieved right and automated logical verification to check several properties as shown in the literature to confirm that the suitable model was built. The obtained cognitive agent model could be helpful to develop a covid-19-aware analytics software agent that can monitor healthcare professionals' mental health