Book chapter · 2024
Data Envelopment Analysis in Healthcare Management: Overview of the Latest Trends
In Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency, Advances in Business Information Systems and Analytics, pp. 245–260, IGI Global · Published
Summary
Vajjhala and Eappen (2024) conducted a narrative review of a broad range of studies applying data envelopment analysis (DEA), a non-parametric method for evaluating the efficiency of decision-making units, in healthcare management. They find that DEA has been applied extensively in hospital management, nursing and outpatient services, contributing to efficiency measurement, benchmarking, resource allocation and optimization, and performance evaluation. They also identify several limitations and challenges, including input/output selection, sensitivity to outliers, inability to handle statistical noise, lack of inherent uncertainty measures, the homogeneity assumption, and the static nature of traditional DEA models.
Practical implication. The limitations and challenges the chapter identifies point to a need for further research and methodological advancements in applying data envelopment analysis (DEA) in healthcare management.
- The Problem
- Healthcare managers need robust ways to measure and compare efficiency, and the contribution and limits of data envelopment analysis (DEA) in healthcare had not been consolidated.
- The Methodology
- Narrative review chapter consolidating published applications of data envelopment analysis (DEA), a non-parametric efficiency method, in hospital management, nursing and outpatient services.
- The Core Finding
- The review shows that DEA is widely applied in hospital management, nursing and outpatient services for efficiency measurement, benchmarking, resource allocation and performance evaluation, and identifies six limitations, including input/output selection, outlier sensitivity, inability to handle statistical noise and the static nature of traditional models.
- The Citation
- Vajjhala, N. R., & Eappen, P. (2024). Data Envelopment Analysis in Healthcare Management: Overview of the Latest Trends. In Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency (pp. 245–260). IGI Global. https://doi.org/10.4018/979-8-3693-0255-2.ch011
The summary, key findings, methods, implications and relevance notes on this page are this website’s own description of the chapter, written from the published abstract and text. The official abstract and citation details are given further down.
Key findings
- Finding 1. The chapter's review finds that data envelopment analysis (DEA) has found extensive applications in healthcare sectors such as hospital management, nursing, and outpatient services.
- Finding 2. According to the review, DEA's significant contributions to healthcare management are in efficiency measurement, benchmarking, resource allocation and optimization, and performance evaluation.
- Finding 3. The chapter identifies limitations of DEA in healthcare management including the selection of inputs and outputs, sensitivity to outliers, and inability to handle statistical noise.
- Finding 4. The review also highlights DEA's lack of inherent uncertainty measures, its homogeneity assumption, and the static nature of traditional DEA models as challenges for healthcare applications.
- Finding 5. The authors conclude that these challenges underscore the need for further research and methodological advancements in applying DEA in healthcare management.
Source: Vajjhala & Eappen (2024), In Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency, Advances in Business Information Systems and Analytics, pp. 245–260, IGI Global. DOI: 10.4018/979-8-3693-0255-2.ch011
The chapter in detail
What question does this chapter answer?
How has data envelopment analysis (DEA) been applied in healthcare management, what has it contributed, and what are its limitations and challenges?
What did the chapter find, in detail?
Reviewing a broad range of studies, the chapter finds that DEA has been applied extensively in hospital management, nursing and outpatient services, contributing to efficiency measurement, benchmarking, resource allocation and optimization, and performance evaluation. It also identifies six limitations: input/output selection, sensitivity to outliers, inability to handle statistical noise, lack of inherent uncertainty measures, the homogeneity assumption, and the static nature of traditional DEA models.
Why does it matter?
The limitations and challenges identified in the chapter underscore the need for further research and methodological advancements in applying DEA in healthcare management.
Methods and data
| Research question | What are the applications, contributions, limitations and challenges of DEA in healthcare management? |
|---|---|
| Design | Review chapter consolidating findings from a broad range of studies |
| Methods | Data envelopment analysis (DEA), a non-parametric method for evaluating the efficiency of decision-making units |
| Main result | DEA contributes to efficiency measurement, benchmarking, resource allocation and optimization, and performance evaluation in hospital management, nursing and outpatient services |
| Implication | Further research and methodological advancements are needed to address DEA's limitations in healthcare management |
| Citation | Vajjhala & Eappen (2024) · DOI 10.4018/979-8-3693-0255-2.ch011 |
Definitions and key terms
- Data envelopment analysis (DEA)
- A non-parametric method used for evaluating the efficiency of decision-making units. — as used in Vajjhala & Eappen (2024)
- Decision-making unit (DMU)
- An entity, such as a hospital or clinic, whose conversion of inputs into outputs is assessed for relative efficiency in DEA. — as used in Vajjhala & Eappen (2024)
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.
Abstract as published in Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency.
Practical implications
- Researchers applying data envelopment analysis (DEA) in healthcare management should direct further research and methodological advancements at the limitations the chapter identifies, such as the selection of inputs and outputs and sensitivity to outliers.
- Healthcare managers can apply data envelopment analysis (DEA) to efficiency measurement, benchmarking, resource allocation and optimization, and performance evaluation, as the reviewed studies have done in hospital management, nursing and outpatient services.
- Healthcare managers using results from data envelopment analysis (DEA) should bear in mind its lack of inherent uncertainty measures, its homogeneity assumption and the static nature of traditional DEA models, which the review highlights as challenges.
Drawn from Vajjhala & Eappen (2024), In Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency, Advances in Business Information Systems and Analytics, pp. 245–260, IGI Global.
When this research may be relevant
This chapter may be relevant to literature searches on data envelopment analysis in healthcare, hospital efficiency measurement and benchmarking, resource allocation in health services, and the methodological limitations of DEA such as input/output selection, outlier sensitivity and homogeneity assumptions.
Research topics addressed: data envelopment analysis; DEA; healthcare management; efficiency measurement; benchmarking; resource allocation; performance evaluation; hospital management; nursing; outpatient services; non-parametric methods; literature review
How to cite
- APA 7
- Vajjhala, N. R., & Eappen, P. (2024). Data Envelopment Analysis in Healthcare Management: Overview of the Latest Trends. In Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency (pp. 245–260). IGI Global. https://doi.org/10.4018/979-8-3693-0255-2.ch011
- MLA 9
- Vajjhala, Narasimha Rao, and Philip Eappen. “Data Envelopment Analysis in Healthcare Management: Overview of the Latest Trends.” Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency, IGI Global, 2024, pp. 245–60. https://doi.org/10.4018/979-8-3693-0255-2.ch011.
- IEEE
- N. R. Vajjhala and P. Eappen, “Data Envelopment Analysis in Healthcare Management: Overview of the Latest Trends,” in Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency. IGI Global, 2024, pp. 245–260, doi: 10.4018/979-8-3693-0255-2.ch011.
BibTeX
@incollection{vajjhala2024dataenvelopment,
title = {Data Envelopment Analysis in Healthcare Management: Overview of the Latest Trends},
author = {Vajjhala, Narasimha Rao and Eappen, Philip},
booktitle = {Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency},
series = {Advances in Business Information Systems and Analytics},
pages = {245--260},
year = {2024},
publisher = {IGI Global},
doi = {10.4018/979-8-3693-0255-2.ch011},
url = {https://doi.org/10.4018/979-8-3693-0255-2.ch011}
}RIS
TY - CHAP TI - Data Envelopment Analysis in Healthcare Management: Overview of the Latest Trends AU - Vajjhala, Narasimha Rao AU - Eappen, Philip T2 - Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency T3 - Advances in Business Information Systems and Analytics PY - 2024 DA - 2024/01/16 SP - 245 EP - 260 PB - IGI Global SN - 2327-3275 (print); 2327-3283 (electronic) DO - 10.4018/979-8-3693-0255-2.ch011 UR - https://www.narasimharao.net/research/data-envelopment-analysis-healthcare-management-review/ LA - en ER -
Links and files
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