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

**Authors:** Narasimha Rao Vajjhala (University of New York Tirana, Albania) — ORCID 0000-0002-8260-2392; Philip Eappen (Cape Breton University, Canada) — ORCID 0000-0002-8120-8449
**Type:** Book chapter
**Source:** In Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency, Advances in Business Information Systems and Analytics, pp. 245–260, IGI Global
**Published:** 2024-01-16
**DOI:** https://doi.org/10.4018/979-8-3693-0255-2.ch011
**Canonical page:** https://www.narasimharao.net/research/data-envelopment-analysis-healthcare-management-review/
**Indexing:** Scopus

## Summary

**Question.** How has data envelopment analysis (DEA) been applied in healthcare management, what has it contributed, and what are its limitations and challenges?

**Finding.** 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 it matters.** The limitations and challenges identified in the chapter underscore the need for further research and methodological advancements in applying DEA in healthcare management.

## Key findings

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.
2. According to the review, DEA's significant contributions to healthcare management are in efficiency measurement, benchmarking, resource allocation and optimization, and performance evaluation.
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.
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.
5. The authors conclude that these challenges underscore the need for further research and methodological advancements in applying DEA in healthcare management.

## Study at a glance

| Item | Detail |
|---|---|
| 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 |

## 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.

## Key terms

- **Data envelopment analysis (DEA):** A non-parametric method used for evaluating the efficiency of decision-making units.
- **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.

## How to cite

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

```bibtex
@incollection{vajjhala2024data,
  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}
}
```