Advertisement

Evaluating the Efficiency of Hospitals’ Perioperative Services Using Dea

  • Liam O’Neill
  • Franklin Dexter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 70)

Summary

Elective surgery typically generates 40 percent or more of a hospital’s total revenue, and individual surgeons almost always have a net positive contribution margin. Perioperative services include surgical operations, preoperative care of patients, and post-operative care. This chapter presents a method to identify best practices among hospitals’ perioperative services using Data Envelopment Analysis (DEA). This analysis included 44,033 procedures performed by 3,502 surgeons at 53 non-metropolitan Pennsylvania hospitals. Eight procedures, each performed by one surgical specialty, were selected. For each hospital, DEA 1) identifies untapped markets for surgery; 2) identifies relatively high and low procedure volumes among specialties; and 3) suggests a strategy for increasing surgical volume for inefficient hospitals. Findings may be used by managers of perioperative services to aid in resource allocation decisions, such as hiring and recruitment among different surgical specialties.

Key words

Data envelopment analysis Perioperative services 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Dexter, F., J.T. Blake, D.H. Penning, and D.A. Lubarsky (2002). Calculating a potential increase in hospital margin for elective surgery by changing operating room time allocations or increasing nursing staffing to permit completion of more cases: a case study. Anesthesia and Analgesia. 94, 138–142.PubMedGoogle Scholar
  2. [2]
    Macario, A., F. Dexter, and R.D. Traub (2001). Hospital profitability per hour of operating room time can vary among surgeons. Anesthesia and Analgesia, 93, 669–675.PubMedGoogle Scholar
  3. [3]
    Mazzei, W.J. (1998). Should the director of perioperative services be a physician? ASA Newsletter, 62.Google Scholar
  4. [4]
    Erickson, G. and S. Finkler (1985). Determinants of market share for a hospital’s services. Medical Care, 23, 1003–1018.PubMedGoogle Scholar
  5. [5]
    Adams, E.K., R. Houchens, G.E. Wright, and J. Robbins (1991). Predicting hospital choice for rural Medicare beneficiaries: the role of severity of illness. Health Services Research, 26, 583–612.PubMedGoogle Scholar
  6. [6]
    Finlayson, S.R., et al. (1999). Patient Preferences for Location of Care: Implications for Regionalization. Medical Care, 37, 204–209.CrossRefPubMedGoogle Scholar
  7. [7]
    Cohen, M.A. and H.L. Lee (1985). The determinants of spatial distribution of hospital utilization in a region. Medical Care, 23, 27–38.PubMedGoogle Scholar
  8. [8]
    Chilingerian, J. (1994). Exploring why some physicians’ hospital practices are more efficient: Taking DEA inside the hospital, in Data Envelopment Analysis: Theory, Methodology, and Applications, A. Charnes, W. Cooper, A. Lewin, L. Seiford, Eds., Kluwer Academic Publishers, Boston, MA.Google Scholar
  9. [9]
    Shelver, S.R. and L. Winston (2001). Improving surgical on-time starts through common goals. AORN Journal, 74, 506–513.PubMedGoogle Scholar
  10. [10]
    Dexter, F., R.H. Epstein, and H.M. Marsh (2001). Statistical analysis of weekday operating room anesthesia group staffing at nine independently managed surgical suites. Anesthesia and Analgesia, 92, 1493–1498.PubMedGoogle Scholar
  11. [11]
    Dexter F, and R.D. Traub (2000). Statistical method for predicting when patients should be ready on the day of surgery. Anesthesiology, 93, 1107–1114.PubMedGoogle Scholar
  12. [12]
    Dexter, F., D.A. Lubarsky, B.C. Gilbert, and C. Thompson (1998). A method to compare costs of drugs and supplies among anesthesia providers: A simple statistical method to reduce variations in cost due to variations in casemix. Anesthesiology, 88, 1350–1356.PubMedGoogle Scholar
  13. [13]
    Abouleish, A.E., D.S. Prough, C.W. Whitten, M.H. Zornow, A. Lockhart, L.A. Conlay, and J.J. Abate (2002). Comparing clinical productivity of anesthesiology groups. Anesthesiology, 97, 608–615.CrossRefPubMedGoogle Scholar
  14. [14]
    Chilingerian, J. and D. Sherman (1990). Managing physician efficiency and effectiveness in providing hospital services. Health Services Management Research, 3, 3–15.PubMedGoogle Scholar
  15. [15]
    Sherman, H.D. (1984). Hospital efficiency measurement and evaluation: Empirical test of a new technique. Medical Care, 22, 922–938.PubMedGoogle Scholar
  16. [16]
    Hollingsworth, B., P. Dawson, and N. Maniadakis (1999). Efficiency measurement of health care: A review of non-parametric methods and applications. Health Care Management Science, 2, 161–172.CrossRefPubMedGoogle Scholar
  17. [17]
    Ozcan, Y.A. (1993). Sensitivity analysis of hospital efficiency under alternative output/input and peer groups: A Review. International Journal of Knowledge and Public Policy, 1, 1–31.Google Scholar
  18. [18]
    Morey, R.C., Y.A. Ozcan, D.L. Retzlaff-Roberts, and D. Fine (1995). Estimating the hospital-wide cost differentials warranted for teaching hospitals: An alternative to regression approaches. Medical Care, 33, 531–552.PubMedGoogle Scholar
  19. [19]
    Ozcan, Y.A., R. Luke, and C. Haksever (1992). Ownership and organizational performance. A comparison of technical efficiency across hospital types. Medical Care, 30, 781–794.PubMedGoogle Scholar
  20. [20]
    Sexton, T., A. Leiken, A. Nolan, A. Hogan, and R. Silkman (1989). Evaluating managerial efficiency of veterans administration medical centers using data envelopment analysis. Medical Care, 17, 1175–1188.Google Scholar
  21. [21]
    Chattopadhyah, S. and C.S. Ray (1996). Technical, scale, and size efficiency in nursing home care: A non-parametric analysis of Connecticut homes. Health Economics, 5, 363–373.Google Scholar
  22. [22]
    Siddharthan, K., M. Ahern, and R. Rosenman (2000). Data envelopment analysis to determine the efficiencies of health maintenance organizations. Health Care Management Science, 3, 23–29.CrossRefPubMedGoogle Scholar
  23. [23]
    Charnes, A., W.W. Cooper, and E. Rhodes (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.CrossRefMathSciNetGoogle Scholar
  24. [24]
    Charnes, A., W. Cooper, A. Lewin, and L. Seiford (1994). Data Envelopment Analysis: Theory, Methodology, and Applications, Kluwer Academic Publishers, Boston, MA.Google Scholar
  25. [25]
    Pauly, M.V.(1980). Doctors and Their Workshops: Economic Models of Physician Behavior. University of Chicago Press, Chicago, IL.Google Scholar
  26. [26]
    Harris, J., H. Ozgen, and Y. Ozcan (2000) Do mergers enhance the performance of hospital efficiency? Journal of the Operational Research Society, 51, 801–811.CrossRefGoogle Scholar
  27. [27]
    Andersen, P. and N.C. Petersen (1993). A procedure for ranking efficient units in Data Envelopment Analysis. Management Science, 39, 1261–1264.Google Scholar
  28. [28]
    O’Neill, L. (1998). Multifactor efficiency in Data Envelopment Analysis with an application to urban hospitals. Health Care Management Science, 1, 19–27.PubMedGoogle Scholar
  29. [29]
    Xue, M. and P. Harker (2002) Note: Ranking DMUs with infeasible super-efficiency DEA. Management Science, 48, 705–710.CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • Liam O’Neill
    • 1
  • Franklin Dexter
    • 2
  1. 1.Department of Policy Analysis and ManagementCornell UniversityIthaca
  2. 2.Department of AnesthesiaUniversity of IowaIowa City

Personalised recommendations