An integrated framework to evaluate and improve the performance of emergency departments during the COVID-19 pandemic: A mathematical programing approach

Abstract

The emergency department (ED) is one of the most critical and high-risk sections of the health system. Providing quality services at a fast pace is vital in this ward since it directly affects people’s lives. The COVID-19 pandemic has turned into a serious challenge for physicians and emergency departments (EDs). The growing number of patients who refer to EDs creates congestion, which will reduce the quality of services. Consequently, managing and operating EDs will be more urgent during this pandemic. Considering this problem, we first used data envelopment analysis (DEA) to evaluate the performance of EDs in the central provinces of Iran. Then, sensitivity analysis was used to determine the main factors affecting the efficiency of this ward. Accordingly, the high number of admitted patients, the congestion of the ward, and the long time required to report the COVID-19 test results were found to be the most influential factors. Finally, drawing on the results of sensitivity analysis, we advance a number of measures to improve these three and other related indicators. Furthermore, appropriate strategies were presented for improving health, COVID-19 management, key performance indicators, and safety indicators in accordance with the results of strengths-weaknesses-opportunity-threat (SWOT) analysis.

The Application of Data Envelopment Analysis to Emergency Departments and Management of Emergency Conditions: A Narrative Review

“…Consequently, the management and operation of EDs became even more pressing during the pandemic. Addressing this issue, Taghipour et al [ 49 ] initially applied four basic input- and output-oriented DEAs to assess the efficiency of EDs located in the provinces situated in central Iran. The main factors were door-to-doctor time, number of admitted patients, employee absence rate, percentage of complaints handled, number of patients waiting in a queue, number of test kits, time required for receiving the test results, and finally the proportion of isolation rooms.…”