Reorganizing the System of Care Surrounding Laparoscopic Surgery: A Cost-Effectiveness Analysis Using Discrete-Event Simulation

Abstract

Purpose. To determine the cost-effectiveness of a proposed reorganization of surgical and anesthesia care to balance patient volume and safety.Methods.Discrete-event simulation methods were used to compare current surgical practice with a newmodular system in which patient care is handed off between 2 anesthesiologists. Ahealth care system’s perspective, using hospital and professional costs, was chosen for the cost-effectiveness analysis. Outcomes were patient throughput, flow time, wait time, and resource use. Sensitivity analyses were performed on staffing levels, mortality rates, process times, and scheduled patient volume.Results.The new strategy was more effective (average 4.41 patients/d [median = 5] v. 4.29 [median = 4]) and had similar costs (average cost/ patient/d = $5327 v. $5289) to the current strategywith an incremental cost-effectiveness of $318/additional patient treated/d. Surgical mortality rate must be >4% or hand-off delay >15min before the new strategy is no longermore effective.Conclusion.The proposed system is more cost-effective relative to current practice over a wide range of mortality rates, hand-off times, and scheduled patient volumes.

Keywords

computer simulation discrete event simulation anesthesia cost-effectiveness

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References

1. Institute of Medicine CoHCiA . Crossing the Quality Chasm: A New Health System for the 21st Century. Washington (DC): National Academy Press; 2001.

2. Institute ofMedicine USCoQoHCiA . To Err Is Human: Building a Safer Health System. Washington (DC): National Academy Press; 2000.

3. Reason J. Human Error . Cambridge (UK): Cambridge University Press; 1990.

4. OwingsM, Kozak L. Ambulatory and inpatient procedures in the United States, 1996 National Center for Health Statistics. Vital and Health Statistics. 1998;13:139-139.

5. Banks J , ed. Handbook of Simulation. New York: John Wiley; 1998.

6. Kelton W , Sadowski R , Sadowski D. Simulation with Arena. Boston: McGraw-Hill; 1998.

7. Law A , Kelton D. Simulation Modeling and Analysis. 2nd ed. New York: McGraw-Hill; 1991.

8. Pegden C , Shannon R , Sadowski R. Introduction to Simulation Using SIMAN. 2nd ed. New York: McGraw-Hill; 1995.

9. Dexter F , Macario A , Traub R.

Which algorithm for scheduling add-on elective cases maximizes operating room utilization?

Use of bin packing algorithms and fuzzy constraints in operating roommanagement. Anesthesiology. 1999;91:1491–1500.

10.

10. Dexter F , Macario A , O’Neill L. Scheduling surgical cases into overflow block time: computer simulation of the effects of scheduling strategies on operating roomlabor costs. AnesthAnalg. 2000;90:980–988.

11.

11. Chandler C , Lane J , Ferguson P , Thompson J , Ashley S. Prospective evaluation of early versus delayed laparoscopic cholecystectomy for treatment of acute cholecystitis. Am Surg. 2000;66:896–900.

12.

12. Dexter F , Macario A , Traub R , Hopwood M , Lubarsky D. An operating roomscheduling strategy tomaximize the use of operating room block time: computer simulation of patient scheduling and survey of patients’ preferences for surgical waiting time. Anesth Analg. 1999;89:7–20.

13.

13. Dexter F , Macario A. Changing allocations of operating room time from a system based on historical utilization to one where the aim is to schedule as many surgical cases as possible. Anesth Analg. 2002;94:1272–1279.

14.

14. Dexter F , Traub R. How to schedule elective surgical cases into specific operating rooms to maximize the efficiency of use of operating room time. Anesth Analg. 2002;94:933–942.

15.

15. Bosch J , Lester J , McMahon P. Hospital costs for elective endovascular and surgical repairs of infrarenal abdominal aortic aneurysms. Radiology. 2001;220:492–497.

16.

16. HCFA Bureau of Data Management and Strategy. Medicare ProviderAnalysis and Review(MEDPAR) of Short-StayHospitals, 100% MEDPAR inpatient hospital fiscal year 1999. June 2000 update for total Medicare reimbursements and number of discharges. Available from: URL: http://www.hcfa.gov/stats/medpar/medpar.htm. Accessed 19 March 2002.

17.

17. Dexter F , Macario A , Lubarsky D. The impact on revenue of increasing patient volume at surgical suiteswith relatively high operating room utilization. Anesth Analg. 2001;92:1215–1221.

18.

18. Maslach C , Schaufli W , Leiter M. Job burnout. Annu Rev Psychol. 2001;52:397–422.

19.

19. McGuire H , Horsely J , Salter D. Measuring and managing quality of surgery: statistical vs. incidental approaches. Arch Surg. 1992;127:733–734.

20.

20. Kelly Healthcare Resources. Available from: URL: www.kellyservices.com. Accessed 30 December 2001.

21.

21. American Medical Association . AMA Socioeconomic Monitoring System 1996 Core Survey. Chicago: American Medical Association; 1996.

22.

22. Bass E , Pitt H , Lillemoe K. Cost-effectiveness of laparoscopic cholecystectomy versus open cholecystectomy. Am J Surg. 1993;165:466–471.

23.

23. Shea J , Berlin J , Bachwich D , et al. Indications for and outcomes of cholecystectomy a comparison of the pre and post laparoscopic eras. Ann Surg. 1998;227:343–350.

24.

24. Steiner C , Bass E , Talamini M , Pitt H , Steinberg E. Surgical rates of operativemortality for open and laparoscopic cholecystectomy in Maryland. N Engl J Med. 1994;330:403–408.

25.

25. Ransohoff D , Gracie W. Treatment of gallstones. Ann InternMed. 1993;119:606–619.

26.

26. Bass E , Steinberg E , Pitt H , et al. Cost-effectiveness of extracorporeal shock-wave lithotripsy versus cholecystectomy for symptomatic gallstones. Gastroenterology. 1991;101:189–199.

27.

27. McGinn F , Miles A , Uglow M , Ozmen M , Terzi C , Humby M. Randomized trial of laparoscopic cholecystectomy and minicholecystectomy. Br J Surg. 1995;82:1374–1377.