An Efficiency-Based Multicriteria Strategic Planning Model for Ambulatory Surgery Centers
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Ambulatory surgery centers (ASCs) provide a low-cost alternative to traditional inpatient care. In addition, with health care reform imminent, it is likely that many currently uninsured people will soon acquire health care coverage, significantly increasing the demand for health services. ASCs are among the providers that can expect to see a substantial amount of this new pent-up demand and, therefore, ASCs are likely to continue their current growth into the foreseeable future. Those ASCs that plan accordingly by optimizing procedure mix and volume will benefit most from the increased demand. We propose a two-stage efficiency-based multicriteria decision model to guide an ASC in identifying its optimal procedure mix. The first stage uses Data Envelopment Analysis (DEA) to calculate the efficiency of each procedure based on the resources required to perform the procedure, the revenue it generates, and its risk of complications. The second stage uses the DEA factor efficiency scores in a bottleneck program to optimize the mix of procedures while satisfying the ASC’s resource and operational constraints. The criteria are to (1) maximize reimbursement while (2) minimizing the total number of complications. We demonstrate the approach using a data set based in part on data from an actual ASC.
KeywordsStrategic planning Ambulatory Surgery Center Multicriteria model Data Envelopment Analysis
- 1.Sultz, H. A., and Young, K. M., Health care USA understanding its organization and deliver. Jones and Bartlett Publishers: Sudbury, 2006.Google Scholar
- 7.Anderson, T., DEA WWW Bibliography, http://www.emp.pdx.edu/dea/deabib.html#Bibliography, 1996.
- 8.Emrouznejad, A., Ali Emrouznejad’s DEA HomePage. Warwick Business School: Coventry CV4 7AL, UK, 1995–2001.Google Scholar
- 13.Lewis, H., Using DEA factor efficiency scores to eliminate subjectivity in goal programming. Submitted for publication, 2009.Google Scholar
- 16.Farrell, M. J., The measurement of productive efficiency. J. R. Stat. Soc., A III:253–290, 1957.Google Scholar
- 20.Sexton, T. R., The methodology of data envelopment analysis. In: Silkman, R. H. (Ed.), Measuring Efficiency: An Assessment of Data Envelopment Analysis, New Directions for Program Evaluation No. 32 (pp. 7–29). San Francisco: Jossey-Bass, 1986.Google Scholar
- 21.Sexton, T. R., Silkman, R. H., and Hogan, A., Data envelopment analysis: critique and extensions. In: Silkman, R. H. (Ed.), Measuring Efficiency: An Assessment of Data Envelopment Analysis New Directions for Program Evaluation No. 32 (pp. 73–105). San Francisco: Jossey-Bass, 1986.Google Scholar
- 23.Tavares, G., A bibliography of data envelopment analysis (1978–2001) RUTCOR research report RRR 01–02. Rutgers University, Piscataway, 2002.Google Scholar
- 24.Anderson, D. R., Sweeney, D. J., Williams, T. A., and Martin, K., An introduction to management science: Quantitative approaches to decision making. South-Western, Mason, OH, 2008.Google Scholar
- 25.Winston, W. L., and Albright, S. C., Practical management science. South-Western, Mason, OH, 2007.Google Scholar