Optimization of elective patient admissions during pandemics: case of COVID-19

With advent of the Covid pandemic, hospitals grappled with how to manage the sudden surge in demand while still treating those in need of service for other reasons. This study aims to provide an adaptive elective admission scheduling policy that maximizes throughput during a pandemic while maintaining the ability of a hospital to empty a specified number of beds over a short warning period (e.g. 5 days). This ability we call nimbleness. We propose a heuristic method based on two mixed-integer linear programming models (MILP) and a simulation model. The first MILP creates the initial schedule for patient admissions over the planning horizon while maximizing patient throughput. The second MILP considers the uncertainty of the emergency arrivals (including pandemic induced) and the patients’ length of stay and maximizes the number of scheduled patients admitted while ensuring the hospital’s nimbleness. A simulation model is built to create daily random arrivals and discharges. The models connect through an automated feedback loop until the heuristic approach converges on a solution. Numerical results demonstrate the ability of the approach to maintain high throughput while still responding to pandemic surges.