Abstract
The accuracy of surgery schedules depends on precise estimation of surgery duration. Current approaches employed by hospitals include historical averages and surgical team estimates which are not accurate enough. The inherent complexity of surgery duration estimation contributes significantly to increased procedure cancellations and reduced utilisation of already encumbered resources. In this study we employ administrative and perioperative data from a large metropolitan hospital to investigate the performance of different machine learning approaches for improving procedure duration estimation. The predictive modelling approaches applied include linear regression (LR), multivariate adaptive regression splines (MARS), and random forests (RF). Cross validation results reveal that the random forest model outperforms other methods, reducing mean absolute percentage error by 28% when compared to current hospital estimation approaches.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Cardoen, B., Demeulemeester, E., Beliën, J.: Operating room planning and scheduling: A literature review. European Journal of Operational Research 201(3), 921–932 (2010)
Macario, A., Vitez, T.S., 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)
Pandit, J.J., Carey, A.: Estimating the duration of common elective operations: Implications for operating list management. Anaesthesia 61(8), 768–776 (2006)
Schofield, W.N., Rubin, G.L., Piza, M., Lai, Y.Y., Sindhusake, D., Fearnside, M.R., Klineberg, P.L.: Cancellation of operations on the day of intended surgery at a major Australian referral hospital. Med. J. Aust. 182(12), 612–615 (2005)
Kayis, E., Wang, H., Patel, M., Gonzalez, T., Jain, S., Ramamurthi, R., Santos, C., Singhal, S., Suermondt, J., Sylvester, K.: Improving Prediction of Surgery Duration using Operational and Temporal Factors. In: AMIA Annu. Symp. Proc., pp. 456–462 (2012)
Eijkemans, M.J.C., Van Houdenhoven, M., Nguyen, T., Boersma, E., Steyerberg, E.W., Kazemier, G.: Predicting the unpredictable: A new prediction model for operating room times using individual characteristics and the surgeon’s estimate. Anesthesiology 112(1), 41–49 (2010)
Dexter, F., Dexter, E.U., Masursky, D., Nussmeier, N.A.: Systematic review of general thoracic surgery articles to identify predictors of operating room case durations. Anesthesia and Analgesia 106(4), 1232–1241 (2008)
Wright, I.H., Kooperberg, C., Bonar, B.A., Bashein, G.: Statistical modeling to predict elective surgery time: Comparison with a computer scheduling system and surgeon-provided estimates. Anesthesiology 85(6), 1235–1245 (1996)
Zhou, J., Dexter, F., Macario, A., 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. Journal of Clinical Anesthesia 11(7), 601–605 (1999)
Combes, C., Meskens, N., Rivat, C., Vandamme, J.P.: Using a KDD process to forecast the duration of surgery. International Journal of Production Economics 112(1), 279–293 (2008)
Stepaniak, P.S., Heij, C., De Vries, G.: Modeling and prediction of surgical procedure times. Statistica Neerlandica 64(1), 1–18 (2010)
Li, Y., Zhang, S., Baugh, R.F., Huang, J.Z.: Predicting surgical case durations using ill-conditioned CPT code matrix. IIE Transactions (Institute of Industrial Engineers) 42(2), 121–135 (2010)
Dexter, F., Ledolter, J.: Bayesian prediction bounds and comparisons of operating room times even for procedures with few or no historic data. Anesthesiology 103(6), 1259–1267 (2005)
Dexter, F., Ledolter, J., Tiwari, V., Epstein, R.H.: Value of a scheduled duration quantified in terms of equivalent numbers of historical cases. Anesthesia & Analgesia 117(1), 205–210 (2013)
Devi, S.P., Rao, K.S., Sangeetha, S.S.: Prediction of surgery times and scheduling of operation theaters in optholmology department. Journal of Medical Systems 36(2), 415–430 (2012)
Gomes, C., Almada-Lobo, B., Borges, J., Soares, C.: Integrating data mining and optimization techniques on surgery scheduling. In: Zhou, S., Zhang, S., Karypis, G. (eds.) ADMA 2012. LNCS, vol. 7713, pp. 589–602. Springer, Heidelberg (2012)
Charlson, M.E., Pompei, P., Ales, K.L., MacKenzie, C.R.: A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J. Chronic Dis. 40(5), 373–383 (1987)
Palmer, P.B., O’Connell, D.G.: Regression Analysis For Prediction: Understanding the process. Cardiopulmonary Physical Therapy Journal 20(3), 23 (2009)
Heil, D.P., Freedson, P.S., Ahlquist, L.E., Price, J., Rippe, J.M.: Nonexercise regression models to estimate peak oxygen consumption, pp. 599–606. Williams & Wilkins, Baltimore (1995)
Dossey, J., Blum, W., Niss, M.: Using Mathematical Competencies to Predict Item Difficulty in PISA: A MEG Study. In: Research on PISA, pp. 23–37. Springer (2013)
Hedley, C.B., Yule, I.J.: A method for spatial prediction of daily soil water status for precise irrigation scheduling. Agricultural Water Management 96(12), 1737–1745 (2009)
Hastie, T., Tibshirani, R., Friedman, J.H.: The elements of statistical learning: Data mining, inference, and prediction. Springer, New York (2001)
Strum, D.P., May, J.H., Vargas, L.G.: Modeling the uncertainty of surgical procedure times: Comparison of log- normal and normal models. Anesthesiology 92(4), 1160–1167 (2000)
Friedman, J.H.: Multivariate Adaptive Regression Splines. Annals of Statistics 19(1), 1–141 (1991)
Jekabsons, G.: ARESLab: Adaptive Regression Splines toolbox for Matlab/Octave (2011), http://www.cs.rtu.lv/jekabsons/
Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)
Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation, DTIC Document (1985)
Liaw, A.: Breiman and Cutler’s random forests for classification and regression (2012), http://stat-www.berkeley.edu/users/breiman/RandomForests
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
ShahabiKargar, Z., Khanna, S., Good, N., Sattar, A., Lind, J., O’Dwyer, J. (2014). Predicting Procedure Duration to Improve Scheduling of Elective Surgery. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_86
Download citation
DOI: https://doi.org/10.1007/978-3-319-13560-1_86
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13559-5
Online ISBN: 978-3-319-13560-1
eBook Packages: Computer ScienceComputer Science (R0)