Journal of Medical Systems

, Volume 35, Issue 5, pp 1029–1037 | Cite as

An Efficiency-Based Multicriteria Strategic Planning Model for Ambulatory Surgery Centers

  • Herbert F. Lewis
  • Thomas R. Sexton
  • Melissa A. Dolan
Original Paper

Abstract

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.

Keywords

Strategic planning Ambulatory Surgery Center Multicriteria model Data Envelopment Analysis 

References

  1. 1.
    Sultz, H. A., and Young, K. M., Health care USA understanding its organization and deliver. Jones and Bartlett Publishers: Sudbury, 2006.Google Scholar
  2. 2.
    Kuo, P. C., Schroeder, R. A., Mahaffey, S., and Bollinger, R. R., Optimization of operating room allocation using linear programming techniques. J. Am. Coll. Surg. 197(6):889–895, 2003.CrossRefGoogle Scholar
  3. 3.
    Mulholland, M. W., Abrahamse, P., and Bahl, V., Linear programming to optimize performance in a department of surgery. J. Am. Coll. Surg. 200(6):861–868, 2005.CrossRefGoogle Scholar
  4. 4.
    Blake, J. T., and Carter, M. W., A Goal-Programming Approach to Resource Allocation in Acute-Care Hospitals. Eur. J. Oper. Res. 140(3):541–561, 2002.CrossRefMATHMathSciNetGoogle Scholar
  5. 5.
    Blake, J. T., and Carter, M. W., Physician and hospital funding models under decreasing resources. Socio-Econ. Plann. Sci. 37:45–68, 2003.CrossRefGoogle Scholar
  6. 6.
    Ogulata, S. N., and Erol, R., A hierarchical multiple criteria mathematical programming approach for scheduling general surgery operations in large hospitals. J. Med. Syst. 27(3):259–270, 2003.CrossRefGoogle Scholar
  7. 7.
    Anderson, T., DEA WWW Bibliography, http://www.emp.pdx.edu/dea/deabib.html#Bibliography, 1996.
  8. 8.
    Emrouznejad, A., Ali Emrouznejad’s DEA HomePage. Warwick Business School: Coventry CV4 7AL, UK, 1995–2001.Google Scholar
  9. 9.
    Emrouznejad, A., Parker, B., and Tavares, G., Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA. Journal of Socio-Economics Planning Science 42(3):151–157, 2008.CrossRefGoogle Scholar
  10. 10.
    Wagner, J. M., Shimshak, D. G., and Novak, M. A., Advances in physician profiling: The use of DEA. Socio-Econ. Plann. Sci. 27:141–163, 2003.CrossRefGoogle Scholar
  11. 11.
    Dexter, F., and O’Neill, L., Data envelopment analysis to determine by how much hospitals can increase elective inpatient surgical workload for each specialty. Journal of Anesthesia and Analgesia 99:1492–1500, 2004.CrossRefGoogle Scholar
  12. 12.
    Valdmanis, V., Hospital quality, efficiency, and input slack differentials. Health Serv. Res. 43:1830–1848, 2008.CrossRefGoogle Scholar
  13. 13.
    Lewis, H., Using DEA factor efficiency scores to eliminate subjectivity in goal programming. Submitted for publication, 2009.Google Scholar
  14. 14.
    Lewis, H. F., and Sexton, T. R., Data envelopment analysis with reverse inputs and outputs. J. Prod. Anal. 21:113–132, 2004.CrossRefGoogle Scholar
  15. 15.
    Charnes, A., Cooper, W. W., and Rhodes, E., Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2:429–444, 1978.CrossRefMATHMathSciNetGoogle Scholar
  16. 16.
    Farrell, M. J., The measurement of productive efficiency. J. R. Stat. Soc., A III:253–290, 1957.Google Scholar
  17. 17.
    Charnes, A., Cooper, W. W., and Rhodes, E., Measuring the efficiency of decision making units: Short communication. Eur. J. Oper. Res. 3:339, 1979.CrossRefGoogle Scholar
  18. 18.
    Charnes, A., Cooper, W. W., and Rhodes, E., Evaluating program and managerial efficiency: An application of data envelopment analysis to Program Follow Through. Manage. Sci. 27:668–697, 1981.CrossRefGoogle Scholar
  19. 19.
    Forsund, F. R., Knox-Lovell, C. A., and Schmidt, P., A survey of frontier production functions and of their relationship to efficiency measurement. J. Econom. 13:5–25, 1980.CrossRefMATHGoogle Scholar
  20. 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. 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
  22. 22.
    Cooper, W. W., Seiford, L. M., and Tone, K., Data envelopment analysis: A comprehensive text with models, applications, references and DEA-Solver software. Kluwer Academic Publishers: Boston, 1999.MATHGoogle Scholar
  23. 23.
    Tavares, G., A bibliography of data envelopment analysis (1978–2001) RUTCOR research report RRR 01–02. Rutgers University, Piscataway, 2002.Google Scholar
  24. 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. 25.
    Winston, W. L., and Albright, S. C., Practical management science. South-Western, Mason, OH, 2007.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Herbert F. Lewis
    • 1
  • Thomas R. Sexton
    • 1
  • Melissa A. Dolan
    • 1
  1. 1.Stony Brook UniversityStony BrookUSA

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