Skip to main content

Advertisement

SpringerLink
Log in

Benchmarking of Anesthesia and Surgical Control Times by Current Procedural Terminology (CPT®) Codes

  • Implementation Science & Operations Management
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

A Correction to this article was published on 11 August 2022

This article has been updated

Abstract

Over half of hospital revenue results from perioperative patient care, thus emphasizing the importance of efficient resource utilization within a hospital’s suite of operating rooms (ORs). Predicting surgical case duration, including Anesthesia-controlled time (ACT) and Surgical-controlled time (SCT) has been significantly detailed throughout the literature as a means to help manage and predict OR scheduling. However, this information has previously been divided by surgical specialty, and only limited benchmarking data regarding ACT and SCT exists. We hypothesized that advancing the granularity of the ACT and SCT from surgical specialty to specific Current Procedural Terminology (CPT®) codes will produce data that is more accurate, less variable, and therefore more useful for OR schedule modeling and management. This single center study was conducted using times from surgeries performed at the University of Colorado Hospital (UCH) between September 2018 – September 2019. Individual cases were categorized by surgical specialty based on the specialty of the primary attending surgeon and CPT codes were compiled from billing data. Times were calculated as defined by the American Association of Clinical Directors. I2 values were calculated to assess heterogeneity of mean ACT and SCT times while Levene’s test was utilized to assess heterogeneity of ACT and SCT variances. Statistical analyses for both ACT and SCT were calculated using JMP Statistical Discovery Software from SAS (Cary, NC) and R v3.6.3 (Vienna, Austria). All surgical cases (n = 87,537) performed at UCH from September 2018 to September 2019 were evaluated and 30,091 cases were included in the final analysis. All surgical subspecialties, with the exception of Podiatry, showed significant variability in ACT and SCT values between CPT codes within each surgical specialty. Furthermore, the variances of ACT and SCT values were also highly variable between CPT codes within each surgical specialty. Finally, benchmarking values of mean ACT and SCT with corresponding standard deviations are provided. Because each mean ACT and SCT value varies significantly between different CPT codes within a surgical specialty, using this granularity of data will likely enable improved accuracy in surgical schedule modeling compared to using mean ACT and SCT values for each surgical specialty as a whole. Furthermore, because there was significant variability of ACT and SCT variances between CPT codes, incorporating variance into surgical schedule modeling may also improve accuracy. Future investigations should include real-time simulations, logistical modeling, and labor utilization analyses as well as validation of benchmarking times in private practice settings.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Change history

References

  1. Macario A, Vitez TS, Dunn B, McDonald T. Where are the costs in perioperative care? Analysis of hospital costs and charges for inpatient surgical care. Anesthesiology. 83(6):1138-1144, 1995.

    Article  CAS  Google Scholar 

  2. Roberts RR, Frutos PW, Ciavarella GG, et al. Distribution of variable vs fixed costs of hospital care. JAMA. 281(7):6, 1999.

    Article  Google Scholar 

  3. McIntosh C, Dexter F, Epstein RH. The impact of service-specific staffing, case scheduling, turnovers, and first-case starts on anesthesia group and operating room productivity: a tutorial using data from an Australian hospital. Anesth. Analg. 103(6):1499-1516, 2006.

    Article  Google Scholar 

  4. Strum DP, Vargas LG, May JH. Surgical subspecialty block utilization and capacity planning: a minimal cost analysis model. Anesthesiology. 90(4):1176-1185, 1999.

    Article  CAS  Google Scholar 

  5. Dexter F, Coffin S, Tinker JH. Decreases in Anesthesia-Controlled Time Cannot Permit One Additional Surgical Operation to Be Reliably Scheduled during the Workday. Anesth. Analg. 81(6):1263-1268, 1995.

    CAS  PubMed  Google Scholar 

  6. Abouleish AE, Dexter F, Whitten CW, Zavaleta JR, Prough DS. Quantifying net staffing costs due to longer-than-average surgical case duration. Anesthesiology. 100(2):403-412, 2004.

    Article  Google Scholar 

  7. Wu ZF, Jian GS, Lee MS, et al. An analysis of anesthesia-controlled operating room time after propofol-based total intravenous anesthesia compared with desflurane anesthesia in ophthalmic surgery: a retrospective study. Anesth. Analg. 119(6):1393-1406, 2014.

    Article  CAS  Google Scholar 

  8. Chan WH, Lee MS, Lin C, et al. Comparison of Anesthesia-Controlled Operating Room Time between Propofol-Based Total Intravenous Anesthesia and Desflurance Anesthesia in Open Colorectal Surgery: A Retrospective Study. PLoS One. 11(10):e0165407, 2016.

    Article  Google Scholar 

  9. Lu CH, Wu ZF, Lin BF, et al. Faster extubation time with more stable hemodynamics during extubation and shorter total surgical suite time after propofol-based total intravenous anesthesia compared with desflurane anesthesia in lengthy lumbar spine surgery. J. Neurosurg. Spine. 24(2):268-274, 2016.

    Article  Google Scholar 

  10. Lai HC, Chan SM, Lu CH, Wong CS, Cherng CH, Wu ZF. Planning for operating room efficiency and faster anesthesia wake-up time in open major upper abdominal surgery. Medicine (Baltimore). 96(7):e6148, 2017.

    Article  CAS  Google Scholar 

  11. Liu TC, Lai HC, Lu CH, et al. Analysis of anesthesia-controlled operating room time after propofol-based total intravenous anesthesia compared with desflurane anesthesia in functional endoscopic sinus surgery. Medicine (Baltimore). 97(5):e9805, 2018.

    Article  Google Scholar 

  12. Dexter F. Regional anesthesia does not significantly change surgical time versus general anesthesia - a meta-analysis of randomized studies. Reg. Anesth. Pain Med. 23(5):439-443, 1998.

    CAS  PubMed  Google Scholar 

  13. Williams BA, Kentor ML, Williams JP, et al. Process analysis in outpatient knee surgery: effects of regional and general anesthesia on anesthesia-controlled time. Anesthesiology. 93(2):529-538, 2000.

    Article  CAS  Google Scholar 

  14. Liu SS, Strodtbeck WM, Richman JM, Wu CL. A comparison of regional versus general anesthesia for ambulatory anesthesia: a meta-analysis of randomized controlled trials. Anesth Analg. 101(6), 2005.

  15. Mariano ER, Chu LF, Peinado CR, Mazzei WJ. Anesthesia-controlled time and turnover time for ambulatory upper extremity surgery performed with regional versus general anesthesia. J. Clin. Anesth. 21(4):253-257, 2009.

    Article  Google Scholar 

  16. Head SJ, Seib R, Osborn JA, Schwarz SKW. A “swing room” model based on regional anesthesia reduces turnover time and increases case throughput. Can J Anaesth. 58(8):725-732, 2011.

    Article  Google Scholar 

  17. Eappen S, Flanagan H, Bhattacharyya N. Introduction of anesthesia resident trainees to the operating room does not lead to changes in anesthesia-controlled times for efficiency measures. Anesthesiology. 101(4):1210-1214, 2004.

    Article  Google Scholar 

  18. Urman RD, Sarin P, Mitani A, Philip B, Eappen S. Presence of anesthesia resident trainees in day surgery unit has mixed effects on operating room efficiency measures. Ochsner J. 12(1):25-29, 2012.

    PubMed  PubMed Central  Google Scholar 

  19. Van Veen-Berkx E, Bitter J, Elkhuizen SG, et al. The influence of anesthesia-controlled time on operating room scheduling in Dutch university medical centres. Can. J. Anaesth. 61(6):524-532, 2014.

    Article  Google Scholar 

  20. Van Veen-Berkx E, Van Dijk MV, Cornelisse DC, Kazemier G, Mokken FC. Scheduling Anesthesia Time Reduces Case Cancellations and Improves Operating Room Workflow in a University Hospital Setting. J. Am. Coll. Surg. 223(2):343-351, 2016.

    Article  Google Scholar 

  21. Luo L, Yao DD, Huang X, et al. Sequence-dependent anesthesia-controlled times: a retrospective study in an ophthalmology department of a single-site hospital. Anesth. Analg. 119(1):151-162, 2014.

    Article  CAS  Google Scholar 

  22. Wu A, Huang CC, Weaver MJ, Urman RD. Use of Historical Surgical Times to Predict Duration of Primary Total Knee Arthroplasty. J. Arthroplasty. 31(12):2768-2772, 2016.

    Article  Google Scholar 

  23. Wu A, Brovman EY, Whang EE, Ehrenfeld JM, Urman RD. The Impact of Overestimations of Surgical Control Times Across Multiple Specialties on Medical Systems. J. Med. Syst. 40(4):95, 2016.

    Article  Google Scholar 

  24. Overdyk FJ, Harvey SC, Fishman RL, Shippey F. Successful Strategies for Improving Operating Room Efficiency at Academic Institutions. Anesth. Analg. 86:896-906, 1998.

    CAS  PubMed  Google Scholar 

  25. Kodali BS, Kim KD, Flanagan H, Ehrenfeld JM, Urman RD. Variability of subspecialty-specific anesthesia-controlled times at two academic institutions. J. Med. Syst. 38(2):11, 2014.

    Article  Google Scholar 

  26. Cruz PL, Prudente ES, Lapitan MC. Benchmarking Anesthesia-Controlled Times at a Tertiary General Hospital in the Philippines. Acta Medica Philippina. 49(4):62-68, 2015.

    Article  Google Scholar 

  27. Dotson P. CPT Codes: What Are They, Why Are They Necessary, and How Are They Developed? Adv. Wound Care. 2(10):583-587, 2013.

    Article  Google Scholar 

  28. U.S. Centers for Medicare and Medicad Services: HCPCS - General Information. www.cms.gov/Medicare/Coding/MedHCPCSGenInfo/index.html. Published 2021. Accessed May 3rd, 2021.

  29. AAPC. What Is Medical Coding? https://www.aapc.com/medical-coding/medical-coding.aspx. Published 2021. Accessed May 3rd, 2021.

  30. Diez DM, Barr CD, Cetinkaya-Rundel M. OpenIntro Statistics. OpenIntro; 2012.

  31. Soh KW, Walker C, O’Sullivan M, Wallace J. An Evaluation of the Hybrid Model for Predicting Surgery Duration. J. Med. Syst. 44(2):42, 2020.

    Article  CAS  Google Scholar 

  32. Van Eijk RPA, Van Veen-Berkx E, Kazemier G, Eijkemans MJC. Effect of Individual Surgeons and Anesthesiologists on Operating Room Time. Anesth. Analg. 123(2):445-451, 2016.

    Article  Google Scholar 

  33. Glance LG, Dutton RP, Feng C, Li Y, Lustik SJ, Dick AW. Variability in case durations for common surgical procedures. Anesth. Analg. 126(6):8, 2018.

    Article  Google Scholar 

  34. Vinden C, Malthaner R, McGee J, et al. Teaching surgery takes time: the impact of surgical education on time in the operating room. Can. J. Surg. 59(2):87-92, 2016.

    Article  Google Scholar 

Download references

Funding

Support was provided solely from institutional and/or departmental sources.

Author information

Authors and Affiliations

  1. Department of Anesthesiology, University of Colorado School of Medicine, Aurora, CO, USA

    Colby G. Simmons, Adeel A. Faruki, Rachel M. Kacmar, Vesna Jevtovic-Todorovic & Nathaen S. Weitzel

  2. US Anesthesia Partners of Colorado, Denver, CO, USA

    Nicholas J. Alvey

  3. Center for Innovative Design and Analysis, Department of Biostatistics and Informatics, University of Colorado-Anschutz Medical Campus, Colorado School of Public Health, Aurora, CO, USA

    Alexander M. Kaizer & Kayla Williamson

Authors
  1. Colby G. Simmons
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nicholas J. Alvey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexander M. Kaizer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kayla Williamson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Adeel A. Faruki
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rachel M. Kacmar
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Vesna Jevtovic-Todorovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Nathaen S. Weitzel
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Simmons – The author helped design the study, collect data, analyze data, write and edit the final manuscript. Alvey – The author helped design the study, collect data, analyze data, write and edit the final manuscript. Kaizer – The author helped analyze data, write and edit the final manuscript. Williamson – The author helped analyze data, write and edit the final manuscript. Faruki – The author helped write and edit the final manuscript. Kacmar – The author helped write and edit the final manuscript. Todorovic – The author helped edit the final manuscript. Weitzel – The author helped design the study, collect data, analyze data, write and edit the final manuscript.

Corresponding author

Correspondence to Colby G. Simmons.

Ethics declarations

Conflicts of Interest

The authors declare no competing interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Implementation Science & Operations Management

The original online version of this article was revised: The author surname "Jevtovic-Todorovic" was misspelled in the original publication of this article.

Supplementary Information

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Simmons, C.G., Alvey, N.J., Kaizer, A.M. et al. Benchmarking of Anesthesia and Surgical Control Times by Current Procedural Terminology (CPT®) Codes. J Med Syst 46, 19 (2022). https://doi.org/10.1007/s10916-022-01798-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-022-01798-z

Keywords

Search

Navigation