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Shimshak DG, Lenard ML, Klimberg RK. Incorporating Quality into Data Envelopment Analysis of Nursing Home Performance: A Case Study. Omega. 2009;37(3):672-685doi: 10.1016/j.omega.2008.05.004.
Shimshak, D. G., Lenard, M. L., & Klimberg, R. K. (2009). Incorporating Quality into Data Envelopment Analysis of Nursing Home Performance: A Case Study. Omega, 37(3), 672-685. https://doi.org/10.1016/j.omega.2008.05.004
Shimshak, Daniel G, et al. "Incorporating Quality into Data Envelopment Analysis of Nursing Home Performance: A Case Study." Omega vol. 37,3 (2009): 672-685. doi: https://doi.org/10.1016/j.omega.2008.05.004
Shimshak DG, Lenard ML, Klimberg RK. Incorporating Quality into Data Envelopment Analysis of Nursing Home Performance: A Case Study. Omega. 2009 Jun 01;37(3):672-685. doi: 10.1016/j.omega.2008.05.004. PMID: 20161166; PMCID: PMC2720542.
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Omega. 2009 Jun 01;37(3):672-685. doi: 10.1016/j.omega.2008.05.004.

Incorporating Quality into Data Envelopment Analysis of Nursing Home Performance: A Case Study.

Omega

Daniel G Shimshak, Melanie L Lenard, Ronald K Klimberg

Affiliations

  1. Department of Management Science and Information Systems, University of Massachusetts Boston, 100 Morrissey Blvd, Boston, MA 02125, , , [email protected].

PMID: 20161166 PMCID: PMC2720542 DOI: 10.1016/j.omega.2008.05.004

Abstract

When using data envelopment analysis (DEA) as a benchmarking technique for nursing homes, it is essential to include measures of the quality of care. We survey applications where quality has been incorporated into DEA models and consider the concerns that arise when the results show that quality measures have been effectively ignored. Three modeling techniques are identified that address these concerns. Each of these techniques requires some input from management as to the proper emphasis to be placed on the quality aspect of performance. We report the results of a case study in which we apply these techniques to a DEA model of nursing home performance. We examine in depth not only the resulting efficiency scores, but also the benchmark sets and the weights given to the input and output measures. We find that two of the techniques are effective in insuring that DEA results discriminate between high and low quality performance.

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