A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic.

Mirpouya Mirmozaffari,Reza Yazdani,Azam Boskabadi,Seyed Mohammad Khalili,Leyla Sadat Tavassoli,Elham Shadkam

doi:10.3390/bioengineering9010007

Abstract

The COVID-19 pandemic has had a significant impact on hospitals and healthcare systems around the world. The cost of business disruption combined with lingering COVID-19 costs has placed many public hospitals on a course to insolvency. To quickly return to financial stability, hospitals should implement efficiency measure. An average technical efficiency (ATE) model made up of data envelopment analysis (DEA) and stochastic frontier analysis (SFA) for assessing efficiency in public hospitals during and after the COVID-19 pandemic is offered. The DEA method is a non-parametric method that requires no information other than the input and output quantities. SFA is a parametric method that considers stochastic noise in data and allows statistical testing of hypotheses about production structure and degree of inefficiency. The rationale for using these two competing approaches is to balance each method’s strengths, weaknesses and introduce a novel integrated approach. To show the applicability and efficacy of the proposed hybrid VRS-CRS-SFA (VCS) model, a case study is presented.

Highlights

A disaster-resilient society relies heavily on healthcare infrastructure, such as hospitals, and it is important that these facilities stay operational at all times [1,2]
The input-oriented approach dictates that a hospital can only be considered technically efficient if it can cut inputs while still delivering the expected outcomes
Compared to the other two models, the proposed VCS model has a strong correlation between its coefficient and profit-risk because for the proposed VCS model the coefficient of determination for unreplicated linear functional relationship (ULFR) (R2f ) and the coefficient of simple linear regression (R2 ) is better and more than the other two models in the first six months of the COVID-19 pandemic. These findings indicate that ULFR plays a more important role than linear regression because the coefficient of determination of ULFR (R2f ) in the three proposed models (BCC-CCR, stochastic frontier analysis (SFA), and VCS, with values 0.9965, 0.9941, 0.9998, respectively) are significantly more than (BCC-CCR, SFA, and VCS, with values 0.2283, 0.0825, and 0.2991, correspondingly)

Summary

Introduction

A disaster-resilient society relies heavily on healthcare infrastructure, such as hospitals, and it is important that these facilities stay operational at all times [1,2]. The COVID-19 pandemic serves as a strong reminder that we live in an ever-changing environment, and still built environments are vulnerable to disasters [3]. Effective responses to the pandemic have necessitated several deviations from traditional norms for health care delivery organisations. There is an opportunity for health care executives to better prepare and alter their organisations for a future of unpredictability in the middle of these difficulties. Managing and controlling healthcare costs are always categorised among the challenging tasks for governments worldwide [4]. They take care of delivering high-quality services and work efficiency simultaneously [5,6]. A plus point is that to evaluate the health sector’s effectiveness to support their healthcare units’ source utilisation, payers

Methods

Results

Conclusion