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Integrated Analysis of Healthcare Efficiency: A Systematic Review
Victor John M Cantor1, Kim Leng Poh2
1Industrial Systems Engineering and Management, National University of Singapore, Singapore, Singapore. victorjohn.cantor@u.nus.edu.
Journal of Medical Systems
|November 24, 2017
Summary
Data Envelopment Analysis (DEA) offers healthcare efficiency insights but has limitations. Integrating DEA with other statistical methods, like regression, provides a more comprehensive evaluation of healthcare system performance.
Area of Science:
- Health Services Research
- Operations Research
- Econometrics
Background:
- Data Envelopment Analysis (DEA) is a key tool for healthcare efficiency assessment.
- Theoretical and methodological limitations of DEA can hinder a complete understanding of healthcare efficiency.
- Integrating DEA with other statistical methods is a common strategy to overcome these limitations.
Purpose of the Study:
- To review 57 studies applying DEA in the healthcare industry.
- To illustrate the integrated analysis of healthcare efficiency using DEA.
- To identify common methodologies for enhancing DEA in healthcare settings.
Main Methods:
- Review of 57 empirical studies utilizing DEA in healthcare.
- Analysis of integrated approaches combining DEA with other statistical techniques.
- Focus on input-oriented radial DEA models with constant or variable returns to scale.
Main Results:
- Regression models and statistical tests are frequently integrated with DEA.
- Capacity-related inputs and activity-related outputs are predominantly used in DEA healthcare models.
- The review highlights the prevalence of specific DEA model assumptions (CRS/VRS).
Conclusions:
- Integrated DEA approaches offer a more robust evaluation of healthcare efficiency.
- Methodological advancements are crucial for overcoming DEA's limitations in healthcare.
- Standardized application of DEA models in healthcare efficiency studies is evident.
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