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

An integrated approach for evaluating and improving the performance of hospital ICUs based on ergonomic and work-motivational factors

The intensive care unit (ICU) holds significant importance in hospitals. Primarily concerned with monitoring and providing care to critically ill patients, the ICU has proven effective in reducing mortality rates and minimizing complications of diseases, thanks to the highly complex and specific measures taken within this department. Considering the unique contributions made by the staff in this unit, its performance assessment can help improve patient care and satisfaction. This study presents a framework that utilizes ergonomic and work-motivational factors (WMFs) to assess the performance of various ICUs. Upon the identification of these indicators, a standard questionnaire is developed to collect the required data. The mean efficiency score of the units is then determined using the data envelopment analysis (DEA). The model is validated using the principal component analysis (PCA). Ultimately, the SWOT (strengths, weaknesses, opportunities, and threats) matrix is employed to formulate an appropriate strategy and offer improvement measures to the managerial team to enhance their ICU performance. The proposed framework can be applied to evaluate the performance of other healthcare departments. Among the studied ICU centers, including general ICU, isolation ICU catering to individuals with infectious diseases, cardiac care unit (CCU), and neonatal ICU (NICU). NICU and general ICU have the best and worst performance in terms of macro- and micro-ergonomic and motivational indicators, which are on average 0.826% more elevated and 0.659% lower, respectively. According to the performed sensitivity analysis, the ICUs in question demonstrate the most appropriate and inappropriate performance about the indicators of "knowledge, situation assessment, and situation analysis" and "work stress", respectively.

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

The healthcare industry is one application for data envelopment analysis (DEA) that can have significant benefits for standardizing health service delivery. This narrative review focuses on the application of DEA in emergency departments (EDs) and the management of emergency conditions such as acute ischemic stroke and acute myocardial infarction (AMI). This includes benchmarking the proportion of patients that receive treatment for these emergency conditions. The most frequent primary areas of study motivating work in DEA, EDs and management of emergency conditions including acute management of stroke are sorted into five distinct clusters in this study: (1) using basic DEA models for efficiency analysis in EDs, i.e., applying variable return to scale (VRS), or constant return to scale (CRS) to ED operations; (2) combining advanced and basic DEA approaches in EDs, i.e., applying super-efficiency with basic DEA or advanced DEA approaches such as additive model (ADD) and slack-based measurement (SBM) to clarify the dynamic aspects of ED efficiency throughout the duration of a first-aid program for AMI or heart attack; (3) applying DEA time series models in EDs like the early use of thrombolysis and percutaneous coronary intervention (PCI) in AMI treatment, and endovascular thrombectomy (EVT) in acute ischemic stroke treatment, i.e., using window analysis and Malmquist productivity index (MPI) to benchmark the performance of EDs over time; (4) integrating other approaches with DEA in EDs, i.e., combining simulations, machine learning (ML), multi-criteria decision analysis (MCDM) by DEA to reduce patient waiting times, and futile transfers; and (5) applying various DEA models for the management of acute ischemic stroke, i.e., using DEA to increase the number of eligible acute ischemic stroke patients receiving EVT and other medical ischemic stroke treatment in the form of thrombolysis (alteplase and now Tenecteplase). We thoroughly assess the methodological basis of the papers, offering detailed explanations regarding the applied models, selected inputs and outputs, and all relevant methodologies. In conclusion, we explore several ways to enhance DEA's status, transforming it from a mere technical application into a strong methodology that can be utilized by healthcare managers and decision-makers.