Modelling and Minimising the Impacts of Infection Control Routines on Nurse Workload in Acute Care Under Varying COVID‑19 Outbreak Scenarios
COVID‑19 is taking a significant toll on front-line healthcare providers- especially nurses with over 1700 infected and 6 deaths to date. It is no surprise that nurses are questioning the safety of current SARS-CoV-2 infection control routines. These routines also pose extra work in a system where nurses are already working to capacity. If nurses are overworked, then fatigue develops and errors start to occur. Anticipating the demands and required extra personnel for an unknown number of incoming coronavirus patients is difficult. This research team will tackle this problem in two ways. First, we will work with nurses and professionals to refine their infection control routines so as to minimise the workload while simultaneously creating highly reliable safety routines. Secondly, we will develop an approach to creating computer simulations of two emergency departments that allow nurse workload and care delivery times to be precisely quantified. By modelling the care delivery process we are able to see the impact of varying severities of coronavirus outbreaks on the nursing team and, ultimately, how this extra workload affects their ability to deliver the care required to all patients in the unit. This project is a collaboration between researchers at Ryerson University and personnel at the University Health Network. The team will work collaboratively to engage front line personnel in developing the simulation model and co-designing improved infection control routines. The computer models, of two emergency departments with front-line responsibility for coronavirus patient treatment, can be readily adapted to other similar units across Canada. These models provide next-generation decision making support for managers who have to anticipate the unknown impacts of coronavirus, and be prepared to deliver the highest quality of care in ways that are safe for both patients and nurses.
