DATA ENVELOPMENT ANALYSIS AND MODELS IN HEALTHCARE SERVICES: A REVIEW

Abstract

Data Envelopment Analysis (DEA) is a widely used method for measuring efficiency and performance in healthcare services. In this article, we will examine the role of DEA and the models used in the healthcare sector. DEA is a method used to measure the efficiency of units (such as hospitals, clinics) with multiple inputs and outputs. Essentially, data envelopment analysis conducts a performance analysis that shows how units utilize their existing resources and how they can optimize these resources. Units such as hospitals, clinics, and healthcare organizations must deliver more services with limited resources. DEA helps these organizations to use their resources most efficiently. DEA is used to increase operational efficiency, reduce costs, and improve service quality in healthcare services. There are various models available for data envelopment analysis. The most common ones include Data Envelopment Analysis (DEA) and Stochastic Data Envelopment Analysis (SDEA). While DEA is used for measuring efficiency that can be either constant or variable in scale, SDEA considers uncertainties and random effects. There are various advantages and disadvantages. Facilitates efficient use of resources, brings a data-driven approach to the decision-making process and objectively evaluates performance in healthcare services. Disadvantages are data deficiencies or poor quality may pose challenges in some cases and the complexity of the model may complicate the application and interpretation process. Data envelopment analysis in healthcare services is a powerful tool for measuring and improving the efficiency of organizations. However, proper use of data and careful application of the model are essential.