A Novel Hybrid Optimization Model for Technical Efficiency Assessment in Public Hospitals: A Comparative Study During and Post-COVID-19 Pandemic

Medical facilities and healthcare systems all throughout the globe have felt the effects of the COVID-19 epidemic. Public hospitals are facing financial ruin as a result of the cost of COVID-19 and other disruptions to their businesses. Hospitalsshould institute efficiencymeasuresto swiftly regain financial stability. For the purpose of evaluating the efficacy of public hospitals both during and after the COVID-19 epidemic, we present an average technical efficiency (ATE) model that combines data envelope analysis (DEA) with stochastic frontier analysis (SFA). Aside from the input and output quantities, the DEA approach does not require any further information because it is non-parametric. Statistical hypothesis testing about production structure and degree of inefficiency is made possible by SFA, a parametric approach that takes stochastic noise in data into account. The goal of combining these two seemingly incompatible methods is to create a new integrated strategy that takes into account the advantages and disadvantages of both. The suggested Hybrid DEA and SFA model is demonstrated through a case study to demonstrate its usefulness and effectiveness.