A DEA model that seeks to minimize inputs while maintaining a given level of outputs. This approach is used when the focus is on reducing resource usage.
Published in Chapter:
Data Envelopment Analysis in Healthcare Management: Overview of the Latest Trends
Copyright: © 2024
|Pages: 16
DOI: 10.4018/979-8-3693-0255-2.ch011
Abstract
This chapter explores the applications, contributions, limitations, and challenges of data envelopment analysis (DEA) in healthcare management. DEA, a non-parametric method used for evaluating the efficiency of decision-making units, has found extensive applications in healthcare sectors such as hospital management, nursing, and outpatient services. The review consolidates findings from a broad range of studies, highlighting DEA's significant contributions to efficiency measurement, benchmarking, resource allocation and optimization, and performance evaluation. However, despite DEA's robust applications, the chapter also identifies several limitations and challenges, including the selection of inputs and outputs, sensitivity to outliers, inability to handle statistical noise, lack of inherent uncertainty measures, homogeneity assumption, and the static nature of traditional DEA models. These challenges underscore the need for further research and methodological advancements in applying DEA in healthcare management.