Journal of Medical Systems

, 43:32 | Cite as

A Machine Learning Approach to Predicting Case Duration for Robot-Assisted Surgery

  • Beiqun ZhaoEmail author
  • Ruth S. Waterman
  • Richard D. Urman
  • Rodney A. Gabriel
Systems-Level Quality Improvement
Part of the following topical collections:
  1. Systems-Level Quality Improvement


Robot-assisted surgery (RAS) requires a large capital investment by healthcare organizations. The cost of a robotic unit is fixed, so institutions must maximize use of each unit by utilizing all available operating room block time. One way to increase utilization is to accurately predict case durations. In this study, we sought to use machine learning to develop an accurate predictive model for RAS case duration. We analyzed a random sample of robotic cases at our institution from January 2014 to June 2017. We compared the machine learning models to the baseline model, which is the scheduled case duration (determined by previous case duration averages and surgeon adjustments). Specifically, we used: 1) multivariable linear regression, 2) ridge regression, 3) lasso regression, 4) random forest, 5) boosted regression tree, and 6) neural network. We found that all machine learning models decreased the average root-mean-squared error (RMSE) as compared to the baseline model. The average RMSE was lowest with the boosted regression tree (80.2 min, 95% CI 74.0–86.4), which was significantly lower than the baseline model (100.4 min, 95% CI 90.5–110.3). Using boosted regression tree, we can increase the number of accurately booked cases from 148 to 219 (34.9% to 51.7%, p < 0.001). This study shows that using various machine learning approaches can improve the accuracy of RAS case length predictions, which will increase utilization of this limited resource. Further work is needed to operationalize these findings.


Robot-assisted surgery Machine learning OR efficiency Health economics Prediction Case duration 



Dr. Beiqun Zhao is funded by the National Library of Medicine Training Grant: NIH grant T15LM011271.


This study was funded by the National Library of Medicine Training Grant (T15LM011271).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Cima, R. R., Brown, M. J., Hebl, J. R., Moore, R., Rogers, J. C., Kollengode, A., Amstutz, G. J., Weisbrod, C. A., Narr, B. J., and Deschamps, C., Use of lean and six sigma methodology to improve operating room efficiency in a high-volume tertiary-care academic medical center. J. Am. Coll. Surg. 213:83–92, 2011. Scholar
  2. 2.
    Macario, A., What does one minute of operating room time cost? J. Clin. Anesth. 22:233–236, 2010. Scholar
  3. 3.
    Eijkemans, M. J., van Houdenhoven, M., Nguyen, T., Boersma, E., Steyerberg, E. W., and Kazemier, G., Predicting the unpredictable. Anesthesiology 112:41–49, 2010. Scholar
  4. 4.
    Intuitive Surgical (2016) Intuitive Surgical, Inc. 2016 Annual ReportGoogle Scholar
  5. 5.
    Higgins, R. M., Frelich, M. J., Bosler, M. E., and Gould, J. C., Cost analysis of robotic versus laparoscopic general surgery procedures. Surg. Endosc. 31:185–192, 2017. Scholar
  6. 6.
    Barbash, G. I., and Glied, S. A., New technology and health care costs - the case of robot-assisted surgery. N. Engl. J. Med. 363:701–704, 2010.CrossRefGoogle Scholar
  7. 7.
    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. Scholar
  8. 8.
    Wright, I. H., Kooperberg, C., Bonar, B. A., and Bashein, G., Statistical modeling to predict elective surgery time. Anesthesiology 85:1235–1245, 1996. CrossRefPubMedGoogle Scholar
  9. 9.
    Pandit, J. J., and Carey, A., Estimating the duration of common elective operations: implications for operating list management. Anaesthesia 61:768–776, 2006. Scholar
  10. 10.
    Kougias, P., Tiwari, V., and Berger, D. H., Use of simulation to assess a statistically driven surgical scheduling system. J. Surg. Res. 201:306–312, 2016. Scholar
  11. 11.
    Wu, A., Weaver, M. J., Heng, M. M., and Urman, R. D., Predictive model of surgical time for revision total hip arthroplasty. J. Arthroplast. 32:2214–2218, 2017. Scholar
  12. 12.
    Okike, K., O’Toole, R. V., Pollak, A. N., Bishop, J. A., McAndrew, C. M., Mehta, S., Cross, W. W., Garrigues, G. E., Harris, M. B., and Lebrun, C. T., Survey finds few orthopedic surgeons know the costs of the devices they implant. Health Aff. 33:103–109, 2014. Scholar
  13. 13.
    Kayış, E., Wang, H., Patel, M., Gonzalez, T., Jain, S., Ramamurthi, R. J., Santos, C., Singhal, S., Suermondt, J., and Sylvester, K., Improving prediction of surgery duration using operational and temporal factors. AMIA Ann. Symp. Proc.:456–462, 2012.Google Scholar
  14. 14.
    Smith, C. D., Spackman, T., Brommer, K., Stewart, M. W., Vizzini, M., Frye, J., and Rupp, W. C., Re-engineering the operating room using variability methodology to improve health care value. J. Am. Coll. Surg. 216:559–570, 2013. Scholar
  15. 15.
    Tagge, E. P., Thirumoorthi, A. S., Lenart, J., Garberoglio, C., and Mitchell, K. W., Improving operating room efficiency in academic children’s hospital using lean six sigma methodology. J. Pediatr. Surg. 52:1040–1044, 2017. Scholar
  16. 16.
    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. Scholar
  17. 17.
    Dexter, F., and Epstein, R. H., Operating room efficiency and scheduling. Curr. Opin. Anaesthesiol. 18:195–198, 2005. Scholar
  18. 18.
    Stepaniak, P. S., Heij, C., Mannaerts, G. H. 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. 109:1232–1245, 2009. Scholar
  19. 19.
    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. 89:7–20, 1999. Scholar
  20. 20.
    Tiwari, V., Dexter, F., Rothman, B. S., Ehrenfeld, J. M., and Epstein, R. H., Explanation for the near-constant mean time remaining in surgical cases exceeding their estimated duration, necessary for appropriate display on electronic white boards. Anesth. Analg. 117:487–493, 2013. Scholar
  21. 21.
    Dexter, F., Ledolter, J., Tiwari, V., and Epstein, R. H., Value of a scheduled duration quantified in terms of equivalent numbers of historical cases. Anesth. Analg. 117:205–210, 2013. Scholar
  22. 22.
    Bejnordi, B. E., Veta, M., Van Diest, P. J., Van Ginneken, B., Karssemeijer, N., Litjens, G., Van Der Laak, J. A. W. M., Hermsen, M., Manson, Q. F., Balkenhol, M., Geessink, O., Stathonikos, N., Van Dijk, M. C. R. F., Bult, P., Beca, F., Beck, A. H., Wang, D., Khosla, A., Gargeya, R., Irshad, H., Zhong, A., Dou, Q., Li, Q., Chen, H., Lin, H. J., Heng, P. A., Haß, C., Bruni, E., Wong, Q., Halici, U., Öner, M. Ü., Cetin-Atalay, R., Berseth, M., Khvatkov, V., Vylegzhanin, A., Kraus, O., Shaban, M., Rajpoot, N., Awan, R., Sirinukunwattana, K., Qaiser, T., Tsang, Y. W., Tellez, D., Annuscheit, J., Hufnagl, P., Valkonen, M., Kartasalo, K., Latonen, L., Ruusuvuori, P., Liimatainen, K., Albarqouni, S., Mungal, B., George, A., Demirci, S., Navab, N., Watanabe, S., Seno, S., Takenaka, Y., Matsuda, H., Phoulady, H. A., Kovalev, V., Kalinovsky, A., Liauchuk, V., Bueno, G., Fernandez-Carrobles, M. M., Serrano, I., Deniz, O., Racoceanu, D., and Venâncio, R., Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318:2199–2210, 2017. Scholar
  23. 23.
    Upstill-Goddard, R., Eccles, D., Fliege, J., and Collins, A., Machine learning approaches for the discovery of gene-gene interactions in disease data. Brief. Bioinform. 14:251–260, 2013. Scholar
  24. 24.
    Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., and Sánchez, C. I., A survey on deep learning in medical image analysis. Med. Image Anal. 42:60–88, 2017. Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of SurgeryUniversity of California, San DiegoLa JollaUSA
  2. 2.Department of Medicine, Division of Biomedical InformaticsUniversity of California, San DiegoLa JollaUSA
  3. 3.Department of AnesthesiologyUniversity of California, San DiegoLa JollaUSA
  4. 4.Department of Anesthesiology, Perioperative and Pain MedicineBrigham and Women’s HospitalBostonUSA

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