Aggregation of Outputs and Inputs for DEA Analysis of Hospital Efficiency: Economics, Operations Research and Data Science Perspectives

Bao Hoang Nguyen and Valentin Zelenyuk
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Bao Hoang Nguyen: University of Queensland

A chapter in Data-Enabled Analytics, 2021, pp 123-158 from Springer

Abstract: Abstract Data envelopment analysis (DEA) has been widely recognised as a powerful tool for performance analysis over the last four decades. The application of DEA in empirical works, however, has become more challenging, especially in the modern era of big data, due to the so-called ‘curse of dimensionality’. Dimension reduction has been recently considered as a useful technique to deal with the ‘curse of dimensionality’ in the context of DEA with large dimensions for inputs and outputs. In this study, we investigate the two most popular dimension reduction approaches: PCA-based aggregation and price-based aggregation for hospital efficiency analysis. Using data on public hospitals in Queensland, Australia, we find that the choice of price systems (with small variation in prices) does not significantly affect the DEA estimates under the price-based aggregation approach. Moreover, the estimated efficiency scores from DEA models are also robust with respect to the two different aggregation approaches.

Keywords: Hospital efficiency; Big wide data; DEA; PCA-based aggregation; Price-based aggregation (search for similar items in EconPapers)
Date: 2021
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Working Paper: PAggregation of Outputs and Inputs for DEA Analysis of Hospital Efficiency: Economics, Operations Research and Data Science Perspectives (2020)
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DOI: 10.1007/978-3-030-75162-3_5

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