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Influence of Annual Meetings of the American Society of Anesthesiologists and of Large National Surgical Societies on Caseloads of Major Therapeutic Procedures

  • Systems-Level Quality Improvement
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Abstract

Statistical methods to calculate the hours into which cases are scheduled, based on minimizing the inefficiency of use of anesthesia and/or operating room (OR) time, assure that anesthesiologists and OR nurses are available on each day to care for patients while infrequently working late. The method assumes that anesthesia staffing is planned based on the workload and not vice-versa. We used the American Society of Anesthesiologists (ASA) meeting to test the assumption across a large population. The observational study cohort was all 3,191,282 major therapeutic procedures performed during 2,517,842 cases at all 121 non-federal hospitals in the State of Iowa, 2007–16. The Fridays to Wednesdays of the annual ASA meetings were compared pairwise by year with those days of the other (mean = 43.0 [SE =0.3]) weeks without a federal holiday. Differences in counts of procedures (P = 0.45, 0.5% [0.7%]) and cases performed (P = 0.93, 0.1% [1.0%]) were not significant. In contrast, compared to non-meeting weeks, during the American College of Surgeons meeting, there were fewer general surgery procedures performed (P = 0.0009, −9.1% [1.9%]), fewer procedures performed of any type (P = 0.022, −2.1% [0.8]), and fewer cases of any type (P = 0.003, −2.5% [0.6%]). Similarly, during the American Academy of Orthopaedic Surgeons meeting, there were fewer joint arthroplasties performed (P < 0.0001, −27.1% [3.1%]), fewer procedures (P = 0.011, −6.3% [2.0%]), and fewer cases (P = 0.018, −5.9% [2.0%]). The results show that, in routine practice, anesthesia staffing and staff scheduling are being chosen to prevent the meeting from affecting patients’ and surgeons’ access to OR time.

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Notes

  1. Pairwise comparisons were not performed with generalized estimating equations for 3 reasons. First, anesthesia groups and surgeons provide care at multiple hospitals, and such data were not observed. Second, common physician scheduling programs would have been used among hospitals, again not observed. Third, many of the 121 hospitals are critical access hospitals with small case counts.

References

  1. Managerial epidemiology. https://en.wikipedia.org/wiki/Managerial_epidemiology. Accessed August 15, 2018.

  2. Iowa Hospital Association, Request data. https://www.ihaonline.org/Information/Inpatient-Outpatient-Database/Data-Request. Accessed August 15, 2018.

  3. Procedure classes 2015. https://www.hcup-us.ahrq.gov/toolssoftware/procedure/procedure.jsp. Accessed February 8, 2018.

  4. Beta procedure classes for ICD-10-PCS. https://www.hcup-us.ahrq.gov/toolssoftware/procedureicd10/procedure_icd10.jsp. Accessed February 8, 2018.

  5. Surgery flag software. https://www.hcup-us.ahrq.gov/toolssoftware/surgflags/surgeryflags.jsp. Accessed February 8, 2018.

  6. Masursky, D., Dexter, F., and Nussmeier, N. A., Operating room nursing directors’ influence on anesthesia group operating room productivity. Anesth. Analg. 107(6):1989–1996, 2008.

    Article  Google Scholar 

  7. Tanaka, M., Lee, J., Ikai, H., and Imanaka, Y., Development of efficiency indicators of operating room management for multi-institutional comparisons. J. Eval. Clin. Pract. 19(2):335–341, 2013.

    Article  Google Scholar 

  8. Moazzez, A., and de Virgilio, C., Role of surgical services in profitability of hospitals in California: An analysis of office of statewide health planning and development annual financial data. Am. Surg. 82(10):894–897, 2016.

    PubMed  Google Scholar 

  9. Clinical Classifications Software for services and procedures. https://www.hcup-us.ahrq.gov/toolssoftware/ccs_svcsproc/ccssvcproc.jsp. Downloaded August 28, 2017.

  10. Clinical Classifications Software (CCS) for ICD-9-CM. https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp. Downloaded August 28, 2017.

  11. Beta Clinical Classifications Software (CCS) for ICD-10-CM/PCS. https://www.hcup-us.ahrq.gov/toolssoftware/ccs10/ccs10.jsp. Downloaded August 28, 2017.

  12. Dexter, F., Weih, L. S., Gustafson, R. K., Stegura, L. F., Oldenkamp, M. J., and Wachtel, R. E., Observational study of operating room times for knee and hip replacement surgery at nine US community hospitals. Health Care Manag. Sci. 9(4):325–339, 2006.

    Article  Google Scholar 

  13. Abouleish, A. E., Prough, D. S., Whitten, C. W., Zornow, M. H., Lockhart, A., Conlay, L. A., and Abate, J. J., Comparing clinical productivity of anesthesiology groups. Anesthesiology 97(3):608–615, 2002.

    Article  Google Scholar 

  14. Dexter, F., Dutton, R. P., Kordylewski, H., and Epstein, R. H., Anesthesia workload nationally during regular workdays and weekends. Anesth. Analg. 121(6):1600–1603, 2015.

    Article  Google Scholar 

  15. Dexter, F., Jarvie, C., and Epstein, R. H., At most hospitals in the state of Iowa, most surgeons’ daily lists of elective cases include only 1 or 2 cases: Individual surgeons’ percentage operating room utilization is a consistently unreliable metric. J. Clin. Anesth. 42:88–92, 2017.

    Article  Google Scholar 

  16. McIntosh, C., Dexter, F., and Epstein, R. H., The impact of service-specific staffing, case scheduling, turnovers, and first-case starts on anesthesia group and operating room productivity: Tutorial using data from an Australian hospital. Anesth. Analg. 103:1499–1516, 2006.

    Article  Google Scholar 

  17. Tessler, M. J., Kleiman, S. J., and Huberman, M. M., A “zero tolerance for overtime” increases surgical per case costs. Can. J. Anaesth. 44(10):1036–1041, 1997.

    Article  CAS  Google Scholar 

  18. Stepaniak, P. S., Heij, C., Mannaerts, G. H., de Quelerij, M., and de Vries, G., Modeling procedure and surgical times for current procedural terminology-anesthesia-surgeon combinations and evaluation in terms of case-duration prediction and operating room efficiency: A multicenter study. Anesth. Analg. 109(4):1232–1245, 2009.

    Article  Google Scholar 

  19. Gallivan, S., Utley, M., Treasure, T., and Valencia, O., Booked inpatient admissions and hospital capacity. mathematical modelling study. BMJ 24(7332):280–282, 2002.

    Article  Google Scholar 

  20. Yang, M., Fry, M. J., Raikhelkar, J., Chin, C., Anyanwu, A., Brand, J., and Scurlock, C., A model to create an efficient and equitable admission policy for patients arriving to the cardiothoracic ICU. Crit. Care Med. 41(2):414–422, 2013.

    Article  Google Scholar 

  21. Dexter, F., Ledolter, J., and Wachtel, R. E., Tactical decision making for selective expansion of operating room resources incorporating financial criteria and uncertainty in subspecialties’ future workloads. Anesth. Analg. 100(5):1425–1432, 2005.

    Article  Google Scholar 

  22. Strum, D. P., Vargas, L. G., May, J. H., and Bashein, G., Surgical suite utilization and capacity planning: A minimal cost analysis model. J. Med. Syst. 21(5):309–322, 1997.

    Article  CAS  Google Scholar 

  23. Dexter, F., Macario, A., and O’Neill, L., Scheduling surgical cases into overflow block time - computer simulation of the effects of scheduling strategies on operating room labor costs. Anesth. Analg. 90(4):980–986, 2000.

    Article  CAS  Google Scholar 

  24. Dexter, F., and 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. 94(5):1272–1279, 2002.

    Article  Google Scholar 

  25. Epstein, R. H., and Dexter, F., Statistical power analysis to estimate how many months of data are required to identify operating room staffing solutions to reduce labor costs and increase productivity. Anesth. Analg. 94(3):640–643, 2002.

    Article  Google Scholar 

  26. Pandit, J. J., and Dexter, F., Lack of sensitivity of staffing for 8-hour sessions to standard deviation in daily actual hours of operating room time used for surgeons with long queues. Anesth. Analg. 108(6):1910–1915, 2009.

    Article  Google Scholar 

  27. Batun, S., Denton, B. T., Huschka, T. R., and Schaefer, A. J., Operating room pooling and parallel surgery processing under uncertainty. INFORMS J. Comput. 23:230–237, 2011.

    Article  Google Scholar 

  28. He, B., Dexter, F., Macario, A., and Zenios, S., The timing of staffing decisions in hospital operating rooms: Incorporating workload heterogeneity into the newsvendor problem. Manuf. Serv. Oper. Manag. 14(1):99–114, 2012.

    Article  Google Scholar 

  29. Dexter, F., Shi, P., and Epstein, R. H., Descriptive study of case scheduling and cancellations within 1 week of the day of surgery. Anesth. Analg. 115(5):1188–1195, 2012.

    Article  Google Scholar 

  30. Hosseini, N., and Taaffe, K. M., Allocating operating room block time using historical caseload variability. Health Care Manag. Sci. 18(4):419–430, 2015.

    Article  Google Scholar 

  31. Shi, P., Dexter, F., and Epstein, R. H., Comparing policies for case scheduling within one day of surgery by Markov chain models. Anesth. Analg. 122(2):526–538, 2016.

    Article  Google Scholar 

  32. Dexter, F., Wachtel, R. E., and Epstein, R. H., Decreasing the hours that anesthesiologist and nurse anesthetists work late by making decisions to reduce the hours of over-utilized operating room time. Anesth. Analg. 122(3):831–842, 2016.

    Article  CAS  Google Scholar 

  33. Lucas, C. E., Dombi, G. W., Crilly, R. J., Ledgerwood, A. M., Yu, P., and Vlahos, A., Neurosurgical trauma call: Use of a mathematical simulation program to define manpower needs. J. Trauma 42(5):818–824, 1997.

    Article  CAS  Google Scholar 

  34. Tucker, J. B., Barone, J. E., Cecere, J., Blabey, R. G., and Rha, C. K., Using queueing theory to determine operating room staffing needs. J. Trauma 46(1):71–77, 1999.

    Article  CAS  Google Scholar 

  35. Dexter, F., Macario, A., and Traub, R. D., Statistical method using operating room information system data to determine anesthetist weekend call requirements. AANA J. 68(1):21–26, 2000.

    CAS  PubMed  Google Scholar 

  36. Dexter, F., and O'Neill, L., Weekend operating room on-call staffing requirements: Schedule each staff member in-house or on standby from home? AORN J. 74:666–671, 2001.

    Article  Google Scholar 

  37. Dexter, F., and Epstein, R. H., Optimizing second shift OR staffing. AORN J. 77(4):825–830, 2003.

    Article  Google Scholar 

  38. Dexter, F., and Epstein, R. H., Holiday and weekend operating room on-call staffing requirements. Anesth. Analg. 103(6):1494–1498, 2006.

    Article  Google Scholar 

  39. van Oostrum, J. M., Van Houdenhoven, M., Vrielink, M. M., Klein, J., Hans, E. W., Klimek, M., Wullink, G., Steyerberg, E. W., and Kazemier, G., A simulation model for determining the optimal size of emergency teams on call in the operating room at night. Anesth. Analg. 107(5):1655–1662, 2008.

    Article  Google Scholar 

  40. Dexter, F., Epstein, R. H., Elgart, R. L., and Ledolter, J., Forecasting and perception of average and latest hours worked by on-call anesthesiologists. Anesth. Analg. 109(4):1246–1252, 2009.

    Article  Google Scholar 

  41. Mueller, R. N., Dexter, F., Truong, V. A., and Wachtel, R. E., Case sequencing of diagnostic imaging studies performed under general anesthesia or monitored anesthesia care during nights and weekends. A A Case Rep. 5(9):162–166, 2015.

    Article  Google Scholar 

  42. Dexter, F., Jarvie, C., and Epstein, R. H., Lack of generalizability of observational studies’ findings for turnover time reduction and growth in surgery based on the state of Iowa, where from one year to the next, most growth was attributable to surgeons performing only a few cases per week. J. Clin. Anesth. 44:107–113, 2018.

    Article  Google Scholar 

  43. Dexter, F., Macario, A., Traub, R. D., Hopwood, M., and Lubarsky, D. A., 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. Anesth. Analg. 89(1):7–20, 1999.

    CAS  PubMed  Google Scholar 

  44. Logvinov, I. I., Dexter, F., Dexter, E. U., and Brull, S. J., Patient survey of referral from one surgeon to another to reduce maximum waiting time for elective surgery and hours of over-utilized operating room time. Anesth. Analg. 126(4):1249–1256, 2018.

    Article  Google Scholar 

  45. Marcon, E., and Dexter, F., An observational study of surgeons’ sequencing of cases and its impact on postanesthesia care unit and holding area staffing requirements at hospitals. Anesth. Analg. 105(1):119–126, 2007.

    Article  Google Scholar 

  46. Dexter, F., Epstein, R. H., Ledolter, J., and Wanderer, J. P., Interchangeability of counts of cases and hours of cases for quantifying a hospital’s change in workload among four-week periods of 1 year. J. Clin. Anesth. 49:118–125, 2018.

    Article  Google Scholar 

  47. Dexter, F., Masursky, D., Ledolter, J., Wachtel, R. E., and Smallman, B., Monitoring changes in individual surgeon's workloads using anesthesia data. Can. J. Anesth. 59(6):571–577, 2012.

    Article  Google Scholar 

  48. Masursky, D., Dexter, F., O’Leary, C. E., Applegeet, C., and Nussmeier, N. A., Long-term forecasting of anesthesia workload in operating rooms from changes in a hospital’s local population can be inaccurate. Anesth. Analg. 106(4):1223–1231, 2008.

    Article  Google Scholar 

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Acknowledgments

Craig Jarvie, MMR, Senior Planning Analyst at University of Iowa Health Care, manages the Iowa Hospital Association data at the hospital and prepared the data fields from it.

Funding

Support was provided solely from departmental sources.

Author information

Authors and Affiliations

  1. Department of Anesthesia, University of Iowa, 200 Hawkins Drive, 6 JCP, Iowa City, IA, 52242, USA

    Franklin Dexter

  2. Department of Anesthesiology, Perioperative Medicine & Pain Management, University of Miami, Coral Gables, FL, USA

    Richard H. Epstein

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Contributions

Franklin Dexter helped design the study, obtain the data, analyze the data, and write the manuscript.

Richard H. Epstein helped design the study and write the manuscript.

Corresponding author

Correspondence to Franklin Dexter.

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Conflict of interest

The Division of Management Consulting of the University of Iowa’s Department of Anesthesia performs the calculations described in the Discussion for hospitals and anesthesia groups. Franklin Dexter receives no funds personally other than his salary and allowable expense reimbursements from the University of Iowa, and has tenure with no incentive program. He and his family have no financial holdings in any company related to his work, other than indirectly through mutual funds for retirement. Income from the Division’s consulting work is used to fund Division research. Richard H. Epstein is president of Medical Data Applications, Ltd., which receives license fees for software used for some of the consulting activities of the Division of Management Consulting described in the Discussion.

Ethical approval

The University of Iowa Institutional Review Board determined on April 11, 2018 that this research does not meet the regulatory definition of human subjects research.

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This article is part of the Topical Collection on Systems-Level Quality Improvement

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Dexter, F., Epstein, R.H. Influence of Annual Meetings of the American Society of Anesthesiologists and of Large National Surgical Societies on Caseloads of Major Therapeutic Procedures. J Med Syst 42, 259 (2018). https://doi.org/10.1007/s10916-018-1114-3

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