Data Envelopment Analysis in Healthcare Management: Overview of the Latest Trends

Data Envelopment Analysis in Healthcare Management: Overview of the Latest Trends

Copyright: © 2024 |Pages: 16
DOI: 10.4018/979-8-3693-0255-2.ch011
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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.
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Data Envelopment Analysis (DEA) is a non-parametric method used in operations research and economics for the estimation of production frontiers (Koengkan et al., 2022). It is used to empirically measure productive efficiency of decision-making units (DMUs), which can be businesses, organizations, or parts of organizations (Nepomuceno et al., 2023). DEA was first proposed by Charnes, Cooper, and Rhodes in 1978 (Charnes et al., 1978), and it has been widely used in many different fields such as healthcare, transportation, finance, and more. This method is particularly useful when the production process involves multiple inputs and outputs, which is difficult to handle in traditional statistical techniques. The key concept of DEA is the efficiency score, which is calculated by comparing the DMU's performance to a best practice frontier that is constructed from the data (Horta et al., 2013). The best practice frontier is essentially the envelope of the most efficient DMUs. If a DMU is on the frontier, it is considered as efficient (efficiency score equals to 1) and if it is below the frontier, it is considered as inefficient (efficiency score less than 1) (Jahed et al., 2015; Jiang et al., 2020). The further a DMU is from the frontier, the lower its efficiency score.

The modern healthcare landscape is increasingly complex and requires innovative solutions to balance efficiency, effectiveness, and quality of care. In this demanding environment, DEA has emerged as a pivotal tool, offering a unique perspective on operational efficiency and resource utilization. In healthcare management, DEA's focus on efficiency takes on profound importance. With finite resources and ever-growing demands, healthcare organizations are continually challenged to deliver high-quality care without over-utilizing resources. DEA provides an analytical framework to understand how well healthcare entities are converting inputs (such as staff hours, medical equipment, and funds) into desired outputs (like patient satisfaction, recovery rates, and overall health outcomes) (Kohl et al., 2019). The application of DEA in healthcare has led to significant insights into the workings of different healthcare settings (Tompkins et al., 2017). From assessing the performance of individual healthcare providers to understanding the efficiency of entire healthcare systems, DEA offers a nuanced and flexible approach that accommodates the multifaceted nature of medical care.

Beyond its analytical capabilities, DEA fosters a culture of continuous improvement. By benchmarking against the most efficient units, healthcare providers can learn from best practices, implement changes, and monitor progress over time. This iterative process supports data-driven decision-making and encourages healthcare organizations to strive for excellence in both efficiency and quality of care. However, it is essential to recognize that DEA is not without limitations and challenges. The next section provides a background for this study followed by the review of literature, solutions, findings, as well as recommendations for future research.

Key Terms in this Chapter

Systematic Literature Exploration: A comprehensive and structured approach to reviewing and synthesizing the existing body of literature on a specific topic. It involves identifying, selecting, and analyzing relevant studies to draw conclusions and identify research gaps.

Electronic Health Records (EHR): Digital versions of patients' medical histories, including information such as diagnoses, treatment plans, and medications. The adoption of EHR systems can impact hospital efficiency and patient care.

Benchmarking: The process of comparing one's performance metrics with those of similar organizations to identify best practices and areas for improvement.

Data Envelopment Analysis (DEA): A non-parametric method used to assess the relative efficiency of decision-making units (DMUs), such as hospitals or healthcare systems, by comparing the ratio of inputs to outputs.

Performance Metrics: Quantitative measures used to assess the performance of organizations, such as hospitals or healthcare systems. Common performance metrics in healthcare include patient satisfaction, treatment outcomes, and resource utilization.

Resource Allocation: The process of distributing resources, such as staff, budget, or equipment, among various units or departments within an organization. Effective resource allocation is crucial for optimizing efficiency and performance in healthcare management.

Input-Oriented DEA Model: 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.

Efficiency: The ability to achieve desired outcomes with the least amount of resources. In the context of healthcare, it refers to the ability of a hospital or healthcare system to deliver high-quality care with optimal resource utilization.

Output-Oriented DEA Model: A DEA model that seeks to maximize outputs while keeping inputs constant. This approach is used when the goal is to increase service levels or performance.

Variables: In the context of DEA, variables refer to the inputs and outputs used in the analysis. Inputs represent the resources used by a hospital or healthcare system, while outputs represent the outcomes or services provided.

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