Balancing Scarce Hospital Resources During a Pandemic: A Case-Study of COVID-19 at Addenbrooke’s Hospital (UK)

Abstract

COVID-19 has disrupted healthcare operations and resulted in large-scale cancellations of elective surgery. Hospitals throughout the world made life-altering resource allocation decisions and prioritised the care of COVID-19 patients. Without effective models to evaluate resource allocation strategies encompassing COVID-19 and non-COVID-19 care, hospitals face the risk of making sub-optimal local resource allocation decisions. A discrete-event-simulation model is proposed in this paper to describe COVID-19, elective surgery, and emergency surgery patient flows. COVID-19-specific patient flows and a surgical patient flow network were constructed based on data of 475 COVID-19 patients and 28,831 non-COVID-19 patients in Addenbrooke's hospital in the UK. The model enabled the evaluation of three resource allocation strategies, for two COVID-19 wave scenarios: proactive cancellation of elective surgery, reactive cancellation of elective surgery, and ring-fencing operating theatre capacity. The results suggest that a ring-fencing strategy outperforms the other strategies, regardless of the COVID-19 scenario, in terms of total direct deaths and the number of surgeries performed. However, this does come at the cost of 50% more critical care rejections. In terms of aggregate hospital performance, a reactive cancellation strategy prioritising COVID-19 is no longer favourable if more than 7.3% of elective surgeries can be considered life-saving. Additionally, the model demonstrates the impact of timely hospital preparation and staff availability, on the ability to treat patients during a pandemic. The model can aid hospitals worldwide during pandemics and disasters, to evaluate their resource allocation strategies and identify the effect of redefining the prioritisation of patients.

Capacity Planning for Effective Cohorting of Dialysis Patients during the Coronavirus Pandemic: A Case Study

“…Wang et al (2020) focus on allocating beds among different departments in a hospital in a setting with stochastic patient arrival and service times and propose an approach that combines a MIP and a simulation model to minimize the expected cost of rejection and waiting. Melman et al (2020) develop a discrete event simulation model to evaluate alternative strategies for allocating limited resources effectively in a hospital to prioritize the surgeries of COVID-19 and uninfected patients. The authors apply their model in a hospital and show that even though elective surgeries were canceled to use major hospital capacity for COVID-19 treatment, it is better to prioritize such surgeries until a threshold to minimize total deaths.…”