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The Impact of Overestimations of Surgical Control Times Across Multiple Specialties on Medical Systems

  • Patient Facing Systems
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Abstract

Optimization of operating room (OR) resources and costs require accurate estimations of procedure times. In some institutions, surgeons are asked to predict their surgical control time (SCT), which typically constitutes the majority of the total procedure time. Here, we examine differences in predicted versus actual SCT and variations by specialty. Data included all scheduled surgical procedures at one academic institution from October 2008 – September 2014. Exclusion criteria consisted of missing values for SCT as well as estimated SCTs that fell outside of 5 standard deviations of any given procedure’s mean. Differences in estimation were calculated by subtracting estimated SCT from actual SCT and compared against a null hypothesis of 0 with a two-tailed t-test. Differences between specialties were examined using analysis of variance and Games-Howell tests. Between 2008 and 2014, 119,410 scheduled procedures were performed. After exclusion, 116,599 cases were analyzed. On average, SCT was overestimated by 12.9 min (p < 0.0001). Overestimations persisted when divided by specialty (p < 0.0001). With thoracic surgery as a control, all other specialties except for cardiac surgery had overestimations of SCT. The greatest time differences were seen in dental (37.6 min, p < 0.0001), cardiology (33.0 min, p < 0.0001), and neurosurgery (29.7 min, p < 0.0001). Overall, SCTs are overestimated at this institution across many specialties. Depending on the methodology by which a hospital chooses to allocate OR time, SCT estimations could potentially be reduced in certain specialties to allow for better allocation of OR resources.

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References

  1. Gordon, T., Paul, S., Lyles, A., and Fountain, J., Surgical unit time utilization review: Resource utilization and management implications. J. Med. Syst. 12:169–179, 1988. doi:10.1007/BF00996639.

    Article  CAS  PubMed  Google Scholar 

  2. Eijkemans, M. J. C., van Houdenhoven, M., Nguyen, T., et al., Predicting the unpredictable. Anesthesiology 112:41–49, 2010. doi:10.1097/ALN.0b013e3181c294c2.

    Article  PubMed  Google Scholar 

  3. Peltokorpi, A., How do strategic decisions and operative practices affect operating room productivity? Health Care Manag Sci 14:370–382, 2011. doi:10.1007/s10729-011-9173-8.

    Article  PubMed  Google Scholar 

  4. Ammori, B. J., Larvin, M., and McMahon, M. J., Elective laparoscopic cholecystectomy: Preoperative prediction of duration of surgery. Surg. Endosc. 15:297–300, 2001. doi:10.1007/s004640000247.

    Article  CAS  PubMed  Google Scholar 

  5. van Veen-Berkx, E., Bitter, J., Elkhuizen, S. G., et al., The influence of anesthesia-controlled time on operating room scheduling in Dutch university medical centres. Can. J. Anesth. 61:524–532, 2014. doi:10.1007/s12630-014-0134-9.

    Article  PubMed  Google Scholar 

  6. Li, Y., Zhang, S., Baugh, R. F., and Huang, J. Z., Predicting surgical case durations using ill-conditioned CPT code matrix. IIE Trans. 42:121–135, 2009. doi:10.1080/07408170903019168.

    Article  Google Scholar 

  7. Strum, D. P., Sampson, A. R., May, J. H., and Vargas, L. G., Surgeon and type of anesthesia predict variability in surgical procedure times. Anesthesiology 92:1454–1466, 2000. doi:10.1097/00132586-200106000-00009.

    Article  CAS  PubMed  Google Scholar 

  8. Xu, R., Carty, M. J., Orgill, D. P., et al., The teaming curve. Ann. Surg. 258:953–957, 2013. doi:10.1097/SLA.0b013e3182864ffe.

    Article  PubMed  Google Scholar 

  9. Kodali, B. S., Kim, K. D., Flanagan, H., et al., Variability of subspecialty-specific anesthesia-controlled times at two academic institutions. J. Med. Syst. 38:11, 2014. doi:10.1007/s10916-014-0011-7.

    Article  PubMed  Google Scholar 

  10. Strum, D. P., May, J. H., and Vargas, L. G., Modeling the uncertainty of surgical procedure times. Anesthesiology 92:1160–1167, 2000. doi:10.1097/00000542-200004000-00035.

    Article  CAS  PubMed  Google Scholar 

  11. Liang, F., Guo, Y., and Fung, R. Y. K., Simulation-based optimization for surgery scheduling in operation theatre management using response surface method. J. Med. Syst. 39:159, 2015. doi:10.1007/s10916-015-0349-5.

    Article  PubMed  Google Scholar 

  12. Hanson, K. H., Computer-assisted operating room scheduling. J. Med. Syst. 6:311–4, 1982. doi:10.1007/BF00992808.

    Article  CAS  PubMed  Google Scholar 

  13. Marchand-Maillet, F., Debes, C., Garnier, F., et al., Accuracy of patient’s turnover time prediction using RFID technology in an academic ambulatory surgery center. J. Med. Syst. 39:12, 2015. doi:10.1007/s10916-015-0192-8.

    Article  PubMed  Google Scholar 

  14. Bhatt, A. S., Carlson, G. W., and Deckers, P. J., Improving operating room turnover time: A systems based approach. J. Med. Syst. 38:148, 2014. doi:10.1007/s10916-014-0148-4.

    Article  PubMed  Google Scholar 

  15. Van Huele, C., and Vanhoucke, M., Analysis of the integration of the physician rostering problem and the surgery scheduling problem. J. Med. Syst. 38:43, 2014. doi:10.1007/s10916-014-0043-z.

    Article  PubMed  Google Scholar 

  16. Donham, R. T., Mazzei, W. J., and Jones, R. L., Association of anesthesia clinical Directors’ procedural times glossary. Glossary of times used for scheduling and monitoring of diagnostic and therapeutic procedures. Am. J. Anesthesiol. 23:3–12, 1996.

    Google Scholar 

  17. Williams, B. A., DeRiso, B. M., Engel, L. B., et al., Benchmarking the perioperative process: II. Introducing anesthesia clinical pathways to improve processes and outcomes and to reduce nursing labor intensity in ambulatory orthopedic surgery. J. Clin. Anesth. 10:561–569, 1998. doi:10.1016/S0952-8180(98)00082-8.

    Article  CAS  PubMed  Google Scholar 

  18. Dexter, F., Coffin, S., and Tinker, J. H., Decreases in anesthesia-controlled time cannot permit one additional surgical operation to be reliably scheduled during the workday. Anesth. Analg. 81:1263–1268, 1995. doi:10.1213/00000539-199512000-00024.

    CAS  PubMed  Google Scholar 

  19. Wright, I. H., Kooperberg, C., Bonar, B. A., and Bashein, G., Statistical modeling to predict elective surgery time comparison with a computer scheduling system and surgeon-provided estimates. J Am Soc Anesthesiol 85:1235–1245, 1996.

    Article  CAS  Google Scholar 

  20. Bravo, F., Levi, R., Ferrari, L. R., and McManus, M. L., The nature and sources of variability in pediatric surgical case duration. Pediatr. Anesth. 25:999–1006, 2015. doi:10.1111/pan.12709.

    Article  Google Scholar 

  21. Dexter, F., and Macario, A., Applications of information systems to operating room scheduling. J Am Soc Anesthesiol 85:1232–1234, 1996.

    Article  CAS  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. 21:309–322, 1997. doi:10.1023/A:1022824725691.

    Article  CAS  PubMed  Google Scholar 

  23. Dexter, F., Epstein, R. H., and Marsh, H. M., A statistical analysis of weekday operating room anesthesia group staffing costs at nine independently managed surgical suites. Anesth. Analg. 92:1493–8, 2001.

    Article  CAS  PubMed  Google Scholar 

  24. Dexter, F., and Traub, R. D., How to schedule elective surgical cases into specific operating rooms to maximize the efficiency of use of operating room time. Anesth. Analg. 94:933–42, 2002. table of contents.

    Article  PubMed  Google Scholar 

  25. van Veen-Berkx, E., Elkhuizen, S. G., van Logten, S., et al., Enhancement opportunities in operating room utilization; with a statistical appendix. J. Surg. Res. 194:43–51.e2, 2015. doi:10.1016/j.jss.2014.10.044.

    Article  PubMed  Google Scholar 

  26. Dexter, F., and Tinker, J. H., Analysis of strategies to decrease postanesthesia care unit costs. J Am Soc Anesthesiol 82:94–101, 1995.

    Article  CAS  Google Scholar 

  27. Joustra, P., Meester, R., and van Ophem, H., Can statisticians beat surgeons at the planning of operations? Empir. Econ. 44:1697–1718, 2013. doi:10.1007/s00181-012-0594-0.

    Article  Google Scholar 

  28. Office of Information Services (2013) International classification of diseases, ninth revision, clinical modification (ICD-9-CM). In: Centers Dis. Control Prev. http://www.cdc.gov/nchs/icd/icd9cm.htm. Accessed 18 Oct 2015

  29. Lyons L (2013) Discovering the significance of 5 sigma. In: arXiv Prepr. arXiv1310.1284. http://arxiv.org/pdf/1310.1284v1.pdf. Accessed 18 Oct 2015

  30. Gabriel, R. A., Gimlich, R., Ehrenfeld, J. M., and Urman, R. D., Operating room metrics score card—creating a prototype for individualized feedback. J. Med. Syst. 38:144, 2014. doi:10.1007/s10916-014-0144-8.

    Article  PubMed  Google Scholar 

  31. Malapero, R. J., Gabriel, R. A., Gimlich, R., et al., An anesthesia medication cost scorecard – concepts for individualized feedback. J. Med. Syst. 2015. doi:10.1007/s10916-015-0226-2.

    PubMed  Google Scholar 

  32. Peccora, C. D., Gimlich, R., Cornell, R. P., et al., Anesthesia report card – a customizable tool for performance improvement. J. Med. Syst. 38:105, 2014. doi:10.1007/s10916-014-0105-2.

    Article  PubMed  Google Scholar 

  33. Zhou, J., Dexter, F., Macario, A., and Lubarsky, D. A., Relying solely on historical surgical times to estimate accurately future surgical times is unlikely to reduce the average length of time cases finish late. J. Clin. Anesth. 11:601–605, 1999. doi:10.1016/S0952-8180(99)00110-5.

    Article  CAS  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Boston, MA, 02115, USA

    Albert Wu, Ethan Y. Brovman & Richard D. Urman

  2. Department of Surgery, Brigham and Women’s Hospital, Boston, MA, 02115, USA

    Edward E. Whang

  3. Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA

    Jesse M. Ehrenfeld

Authors
  1. Albert Wu
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  2. Ethan Y. Brovman
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  3. Edward E. Whang
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  4. Jesse M. Ehrenfeld
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  5. Richard D. Urman
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Correspondence to Richard D. Urman.

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This article is part of the Topical Collection on Patient Facing Systems

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Wu, A., Brovman, E.Y., Whang, E.E. et al. The Impact of Overestimations of Surgical Control Times Across Multiple Specialties on Medical Systems. J Med Syst 40, 95 (2016). https://doi.org/10.1007/s10916-016-0457-x

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