Health Care Management Science

, Volume 9, Issue 1, pp 87–98 | Cite as

Impact of surgical sequencing on post anesthesia care unit staffing

  • Eric MarconEmail author
  • Franklin Dexter


This paper analyzes the impact of sequencing rules on the phase I post anesthesia care unit (PACU) staffing and over-utilized operating room (OR) time resulting from delays in PACU admission. The sequencing rules are applied to each surgeon's list of cases independently. Discrete event simulation shows the importance of having a sufficient number of PACU nurses. Sequencing rules have a large impact on the maximum number of patients receiving care in the PACU (i.e., peak of activity). Seven sequencing rules are tested, over a wide range of scenarios. The largest effect of sequencing was on the percentage of days with at least one delay in PACU admission. The best rules are those that smooth the flow of patients entering in the PACU (HIHD (Half Increase in OR time and Half Decrease in OR time) and MIX (MIX OR time)). We advise against using the LCF (Longest Cases First) and equivalent sequencing methods. They generate more over-utilized OR time, require more PACU nurses during the workday, and result in more days with at least one delay in PACU admission.


Operating Room and PACU: schedule Optimization Staffing Economics 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dexter F, Traub RD (2000) Sequencing cases in the operating room: Predicting whether one surgical case will last longer than another, (Anesthesia and Analgesia 90) 975–979Google Scholar
  2. 2.
    Macario A, Dexter F (1999) Estimating the duration of a case when the surgeon has not recently scheduled the procedure at the surgical suite, (Anesthesia and Analgesia 89) 1241–1245Google Scholar
  3. 3.
    May JH, Strum DP, Vargas LG (2000) Fitting the lognormal distribution to surgical procedure times, (Decision Sciences 31) 129–148Google Scholar
  4. 4.
    Zhou J, Dexter F, Macario A, Lubarsky DA (1999) Relying solely on historical surgical times to estimate accurately future surgical times is unlikely to reduce the average length of time cases finish late, (Journal of Clinical Anesthesiology 11) 601–605Google Scholar
  5. 5.
    Blake JT, Dexter F, Donald J, (2002) Operating room managers' use of integer programming for assigning block time to surgical groups: A case study, (Anesthesia and Analgesia 94) 143–148Google Scholar
  6. 6.
    Blake JT, Donald J (2002) Mount Sinai hospital uses integer programming to allocate operating room time, (Interfaces 32) 66–73CrossRefGoogle Scholar
  7. 7.
    Dexter F, Traub RD, Macario A (2003) How to release allocated operating room time to increase efficiency: Predicting which surgical service will have the most underutilized operating room time, (Anesthesia and Analgesia 96) 507–512Google Scholar
  8. 8.
    Marcon E, Kharraja S, Smolski N, Luquet B, Viale JP (2003) Determining the number of beds in postanesthesia care unit: a computer simulation flow approach, Journal of the International Anesthesia research society, (Anesthesia and Analgesia 96) 1415–1423.Google Scholar
  9. 9.
    Strum DP, Vargas LG, May JH (1999) Surgical subspeciality block utilization and capacity planning: A minimal cost analysis model, (Anesthesiology 90) 1176–1185CrossRefGoogle Scholar
  10. 10.
    Dexter F, Macario A, Traub RD, Hopwood M, Kubarsky DA (1999) An operating room scheduling strategy to maximize the use of operating room block time: Computer simulation of patient scheduling and survey of patients' preferences for surgical waiting time, (Anesthesia and Analgesia 89) 7–20Google Scholar
  11. 11.
    Dexter F, Traub RD (2002) How to schedule elective surgical cases into specific operating rooms to maximize the efficiency of use of operating room time, (Anesthesia and Analgesia. 94) 933–42Google Scholar
  12. 12.
    Lebowitz P (2003) Schedule the short procedure first to improve OR efficiency, (AORN Journal 78) 651–658Google Scholar
  13. 13.
    Dexter F, Tinker JH (1995) Analysis of strategies to decrease postanesthesia care unit costs, (Anesthesiology 82), 94–101Google Scholar
  14. 14.
    Dexter F, Macario A, Traub RD (1999) Which algorithm for scheduling add-on elective cases maximizes operating room utilization? Use of bin packing algorithms and fuzzy constraints in operating room management, (Anesthesiology 91) 1491–1500Google Scholar
  15. 15.
    Zhou J, Dexter F (1998) Method to assist in the scheduling of add-on surgical cases–upper prediction bounds for surgical case duration based on the log-normal distribution, (Anesthesiology 89) 1228–1232CrossRefGoogle Scholar
  16. 16.
    Dexter F, Epstein RH, De Matta R, Marcon E (2005) Strategies to reduce delays in admission into a postanesthesia care unit from operating rooms. (Journal of PeriAnesthesia Nursing 20(2)) 92–102.Google Scholar
  17. 17.
    Macario A, Glenn D, Dexter F (1999) What can the postanesthesia care unit manager do to decrease costs in the postanesthesia care unit?, (Journal of Perianesthia Nursing 14(5)) 284–293Google Scholar
  18. 18.
    Dexter F, Epstein RH, Penning DH (2001) Statistical analysis of post-anesthesia care unit staffing at a surgical suite with frequent delays in admission from the operating room - a case study. (Anesthesia & Analgesia 92) 947–949Google Scholar
  19. 19.
    Epstein RH, Dexter F, Traub RD (2002) Statistical power analysis to estimate how many months of data are required to identify post anesthesia care unit staffing to minimize delays in admission from operating rooms, (Journal of PeriAnesthesia Nursing 17(2)) 84–88CrossRefGoogle Scholar
  20. 20.
    Hsu VN, De Matta R, lee CY (2003) Scheduling patients in an ambulatory surgical center. (Naval Research Logistics 50) 218–238CrossRefGoogle Scholar
  21. 21.
    Banks J (2000) Introduction to simulation, (Proceedings of the 2000 Winter Simulation Conference, JA Joines, RR Barton, K. Kang, and PA Fishwick, edsGoogle Scholar
  22. 22.
    Magerlein JM, Martin JB (1978) Surgical demand scheduling: A review, (Health Serv Res 13) 418–433Google Scholar
  23. 23.
    Dexter F, Penning DH, Traub RD (2001) Statistical analysis by Monte-Carlo simulation of the impact of administrative and medical delays in discharge from the postanesthesia care unit on total patient care hours, (Anesthesia & Analgesia 92) 1222–1225Google Scholar
  24. 24.
    Spangler WE, Strum DP, Vargas LG, May JM (2004) Estimating procedure times for surgeries by determining location parameters for the lognormal model. (Health Care Management Science 7) 97–104CrossRefGoogle Scholar
  25. 25.
    Strum DP, May JH, Sampson AR, Vargas LG, Spangler WE (2003) Estimating times of surgeries with two component procedures: comparison of the lognormal and normal models, (Anesthesiology 98) 232–240CrossRefGoogle Scholar
  26. 26.
    Kennedy MH (1992) Bin-packing, knapsack, and change-constrained approaches to operating room scheduling, (Troy, New York: Rensselaer Polytechnic Institute, Department of Decision Sciences and Engineering Systems) 83–86, 90, 99Google Scholar
  27. 27.
    Goldman J, Knappenberger HA, Shearon WT (1970) A study of variability of surgical estimates, (Hospital Management 110) 46–46DGoogle Scholar
  28. 28.
    Marcon E, Kharraja S, Smolski NN, Luquet B, Viale JP (2003) Determining the number of beds in postanesthesia care unit: a computer simulation flow approach, Journal of the International Anesthesia research society, (Anesthesia & Analgesia 96) 1415–1423Google Scholar
  29. 29.
    Dexter F, Macario A, (2002) 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, (Anesthesia and Analgesia 94) 1272–1279Google Scholar
  30. 30.
    Lebowitz P (2003) Why can't my procedures start on time?, (AORN Journal. 77(3)) 594–597Google Scholar
  31. 31.
    Johnson SM (1954) Optimal two and three stage production schedule with set-up times included, (Naval Research Logistics Quarterly, 1) 61–68Google Scholar
  32. 32.
    Jackson JR (1956) An extension of Johnson's results on job lot scheduling, (Naval Research Logistics Quarterly, 3) 201–203Google Scholar
  33. 33.
    Dexter F, Epstein RD, Traub RD, Xiao Y (2004) Making management decisions on the day of surgery based on operating room efficiency and patient waiting times. (Anesthesiology 101) 1444–1453Google Scholar

Copyright information

© Springer Science + Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Laboratory of Signal and Manufacturing Systems Analysis, Department Manufacturing System Management and MaintenanceJean Monnet University of Saint EtienneFrance
  2. 2.LASPI – IUT de RoanneCity, RoanneCedex - France
  3. 3.Division of Management Consulting, Departments of Anesthesia and Health Management and PolicyUniversity of IowaIowa City

Personalised recommendations