Treffer: Prediction of Surgery Duration Based on Machine Learning Algorithms and Its Practical Application in a General Hospital in China.
Title:
Prediction of Surgery Duration Based on Machine Learning Algorithms and Its Practical Application in a General Hospital in China.
Authors:
Wu H; School of Business, Sun Yat-sen University, Guangdong, China., Cai Y; School of Business, Sun Yat-sen University, Guangdong, China., Chen Y; The Sixth Affiliated Hospital of Sun Yat-sen University, Guangdong, China., Xia L; School of Business, Sun Yat-sen University, Guangdong, China., Yao L; The Sixth Affiliated Hospital of Sun Yat-sen University, Guangdong, China.
Source:
The International journal of health planning and management [Int J Health Plann Manage] 2026 Jan; Vol. 41 (1), pp. 121-131. Date of Electronic Publication: 2025 Nov 03.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wiley Country of Publication: England NLM ID: 8605825 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1099-1751 (Electronic) Linking ISSN: 07496753 NLM ISO Abbreviation: Int J Health Plann Manage Subsets: MEDLINE
Imprint Name(s):
Original Publication: Chichester, Sussex, England : Wiley, c1985-
MeSH Terms:
References:
State Council General Office. Opinions on Strengthening the Performance Evaluation of Tertiary Public Hospitals. Report. (2019).
A. Peltokorpi, “How Do Strategic Decisions and Operative Practices Affect Operating Room Productivity?,” Health Care Management Science 14, no. 4 (2011): 370–382, https://doi.org/10.1007/s10729‐011‐9173‐8.
M. J. C. Eijkemans, M. Van Houdenhoven, T. Nguyen, E. Boersma, E. Steyerberg, and G. Kazemier, “Predicting the Unpredictable: A New Prediction Model for Operating Room Times Using Individual Characteristics and the Surgeon's Estimate,” American Society of Anesthesiologists 112, no. 1 (2010): 41–49, https://doi.org/10.1097/aln.0b013e3181c294c2.
P. S. Stepaniak, C. Heij, G. H. H. Mannaerts, M. de Quelerij, and G. de Vries, “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,” Anesthesia & Analgesia 109, no. 4 (2009): 1232–1245, https://doi.org/10.1213/ane.0b013e3181b5de07.
S. Barnoon and H. Wolfe, “Scheduling a Multiple Operating Room System: A Simulation Approach,” Health Services Research 3, no. 4 (1968): 272–285.
W. M. Hancock, P. F. Walter, R. A. More, and N. D. Glick, “Operating Room Scheduling Data Base Analysis for Scheduling,” Journal of Medical Systems 12, no. 6 (1988): 397–409, https://doi.org/10.1007/bf00992688.
D. P. Strum, J. H. May, and L. G. Vargas, “Modeling the Uncertainty of Surgical Procedure Times: Comparison of Log‐Normal and Normal Models,” American Society of Anesthesiologists 92, no. 4 (2000): 1160–1167, https://doi.org/10.1097/00000542‐200004000‐00035.
W. E. Spangler, D. P. Strum, L. G. Vargas, and J. H. May, “Estimating Procedure Times for Surgeries by Determining Location Parameters for the Lognormal Model,” Health Care Management Science 7, no. 2 (2004): 97–104, https://doi.org/10.1023/b:hcms.0000020649.78458.98.
G. Lou, N. Geng, and Y. Lu, “Surgery Duration Fitting Based on Hypergamma Probability Distribution,” Industrial Engineering & Management 23, no. 1 (2018): 51–58, https://doi.org/10.19495/j.cnki.1007‐5429.2018.01.008.
I. H. Wright, C. Kooperberg, B. A. Bonar, and G. Bashein, “Statistical Modeling to Predict Elective Surgery Time: Comparison With a Computer Scheduling System and Surgeon‐Provided Estimates,” American Society of Anesthesiologists 85, no. 6 (1996): 1235–1245, https://doi.org/10.1097/00000542‐199612000‐00003.
Y. Li, S. Zhang, R. F. Baugh, and J. Z. Huang, “Predicting Surgical Case Durations Using Ill‐Conditioned CPT Code Matrix,” IIE Transactions 42, no. 2 (2009): 121–135, https://doi.org/10.1080/07408170903019168.
N. Hosseini, M. Y. Sir, C. J. Jankowski, and K. S. Pasupathy, “Surgical Duration Estimation via Data Mining and Predictive Modeling: A Case Study,” AMIA Annual Symposium Proceedings 2015 (2015): 640–648, PMID: 26958199, PMCID: PMC4765628.
E. Kayis, H. Wang, M. Patel, et al., “Improving Prediction of Surgery Duration Using Operational and Temporal Factors,” AMIA Annual Symposium Proceedings 2012 (2012): 456–462.
S. Li, J. Lin, Y. Sun, et al., “Analysis of Influencing Factors on Specific Surgical Durations in a Hospital,” Chinese Hospital Management 43, no. 10 (2023): 59–63.
C. Combes, N. Meskens, C. Rivat, and J. P. Vandamme, “Using a KDD Process to Forecast the Duration of Surgery,” International Journal of Production Economics 112, no. 1 (2008): 279–293, https://doi.org/10.1016/j.ijpe.2006.12.068.
N. Master, Z. Zhou, D. Miller, D. Scheinker, N. Bambos, and P. Glynn, “Improving Predictions of Pediatric Surgical Durations With Supervised Learning,” International Journal of Data Science and Analytics 4, no. 1 (2017): 35–52, https://doi.org/10.1007/s41060‐017‐0055‐0.
N. H. Ng, R. A. Gabriel, J. McAuley, et al., “Predicting Surgery Duration With Neural Heteroscedastic Regression,” in Machine Learning for Healthcare Conference (PMLR, 2017), 100–111.
J. P. Tuwatananurak, S. Zadeh, X. Xu, et al., “Machine Learning Can Improve Estimation of Surgical Case Duration: A Pilot Study,” Journal of Medical Systems 43, no. 3 (2019): 1–7, https://doi.org/10.1007/s10916‐019‐1160‐5.
M. A. Bartek, R. C. Saxena, S. Solomon, et al., “Improving Operating Room Efficiency: Machine Learning Approach to Predict Case‐Time Duration,” Journal of the American College of Surgeons 229, no. 4 (2019): 346–354.e3, https://doi.org/10.1016/j.jamcollsurg.2019.05.029.
Y. Jiao, A. Sharma, A. Ben Abdallah, T. M. Maddox, and T. Kannampallil, “Probabilistic Forecasting of Surgical Case Duration Using Machine Learning: Model Development and Validation,” Journal of the American Medical Informatics Association 27, no. 12 (2020): 1885–1893, https://doi.org/10.1093/jamia/ocaa140.
C. T. Strömblad, R. G. Baxter‐King, A. Meisami, et al., “Effect of a Predictive Model on Planned Surgical Duration Accuracy, Patient Wait Time, and Use of Presurgical Resources: A Randomized Clinical Trial,” JAMA Surgery 156, no. 4 (2021): 315–321, https://doi.org/10.1001/jamasurg.2020.6361.
A. E. Hoerl and R. W. Kennard, “Ridge Regression: Applications to Nonorthogonal Problems,” Technometrics 12, no. 1 (1970): 69–82, https://doi.org/10.2307/1267352.
R. Tibshirani, “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society Series B 58, no. 1 (1996): 267–288, https://doi.org/10.1111/j.2517‐6161.1996.tb02080.x.
T. Chen and C. Guestrin, “Xgboost: A Scalable Tree Boosting System,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2016), 785–794.
J. H. Friedman, “Greedy Function Approximation: A Gradient Boosting Machine,” Annals of Statistics 29, no. 5 (2001): 1189–1232, https://doi.org/10.1214/aos/1013203451.
F. Pedregosa, G. Varoquaux, A. Gramfort, et al., “Scikit‐Learn: Machine Learning in Python,” Journal of Machine Learning Research 12 (2011): 2825–2830, https://dl.acm.org/doi/abs/10.5555/1953048.2078195.
J. Chu, C. H. Hsieh, Y. N. Shih, et al., “Operating Room Usage Time Estimation With Machine Learning Models,” Healthcare. 10, no. 8 (2022): 1518, https://doi.org/10.3390/healthcare10081518.
A. Peltokorpi, “How Do Strategic Decisions and Operative Practices Affect Operating Room Productivity?,” Health Care Management Science 14, no. 4 (2011): 370–382, https://doi.org/10.1007/s10729‐011‐9173‐8.
M. J. C. Eijkemans, M. Van Houdenhoven, T. Nguyen, E. Boersma, E. Steyerberg, and G. Kazemier, “Predicting the Unpredictable: A New Prediction Model for Operating Room Times Using Individual Characteristics and the Surgeon's Estimate,” American Society of Anesthesiologists 112, no. 1 (2010): 41–49, https://doi.org/10.1097/aln.0b013e3181c294c2.
P. S. Stepaniak, C. Heij, G. H. H. Mannaerts, M. de Quelerij, and G. de Vries, “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,” Anesthesia & Analgesia 109, no. 4 (2009): 1232–1245, https://doi.org/10.1213/ane.0b013e3181b5de07.
S. Barnoon and H. Wolfe, “Scheduling a Multiple Operating Room System: A Simulation Approach,” Health Services Research 3, no. 4 (1968): 272–285.
W. M. Hancock, P. F. Walter, R. A. More, and N. D. Glick, “Operating Room Scheduling Data Base Analysis for Scheduling,” Journal of Medical Systems 12, no. 6 (1988): 397–409, https://doi.org/10.1007/bf00992688.
D. P. Strum, J. H. May, and L. G. Vargas, “Modeling the Uncertainty of Surgical Procedure Times: Comparison of Log‐Normal and Normal Models,” American Society of Anesthesiologists 92, no. 4 (2000): 1160–1167, https://doi.org/10.1097/00000542‐200004000‐00035.
W. E. Spangler, D. P. Strum, L. G. Vargas, and J. H. May, “Estimating Procedure Times for Surgeries by Determining Location Parameters for the Lognormal Model,” Health Care Management Science 7, no. 2 (2004): 97–104, https://doi.org/10.1023/b:hcms.0000020649.78458.98.
G. Lou, N. Geng, and Y. Lu, “Surgery Duration Fitting Based on Hypergamma Probability Distribution,” Industrial Engineering & Management 23, no. 1 (2018): 51–58, https://doi.org/10.19495/j.cnki.1007‐5429.2018.01.008.
I. H. Wright, C. Kooperberg, B. A. Bonar, and G. Bashein, “Statistical Modeling to Predict Elective Surgery Time: Comparison With a Computer Scheduling System and Surgeon‐Provided Estimates,” American Society of Anesthesiologists 85, no. 6 (1996): 1235–1245, https://doi.org/10.1097/00000542‐199612000‐00003.
Y. Li, S. Zhang, R. F. Baugh, and J. Z. Huang, “Predicting Surgical Case Durations Using Ill‐Conditioned CPT Code Matrix,” IIE Transactions 42, no. 2 (2009): 121–135, https://doi.org/10.1080/07408170903019168.
N. Hosseini, M. Y. Sir, C. J. Jankowski, and K. S. Pasupathy, “Surgical Duration Estimation via Data Mining and Predictive Modeling: A Case Study,” AMIA Annual Symposium Proceedings 2015 (2015): 640–648, PMID: 26958199, PMCID: PMC4765628.
E. Kayis, H. Wang, M. Patel, et al., “Improving Prediction of Surgery Duration Using Operational and Temporal Factors,” AMIA Annual Symposium Proceedings 2012 (2012): 456–462.
S. Li, J. Lin, Y. Sun, et al., “Analysis of Influencing Factors on Specific Surgical Durations in a Hospital,” Chinese Hospital Management 43, no. 10 (2023): 59–63.
C. Combes, N. Meskens, C. Rivat, and J. P. Vandamme, “Using a KDD Process to Forecast the Duration of Surgery,” International Journal of Production Economics 112, no. 1 (2008): 279–293, https://doi.org/10.1016/j.ijpe.2006.12.068.
N. Master, Z. Zhou, D. Miller, D. Scheinker, N. Bambos, and P. Glynn, “Improving Predictions of Pediatric Surgical Durations With Supervised Learning,” International Journal of Data Science and Analytics 4, no. 1 (2017): 35–52, https://doi.org/10.1007/s41060‐017‐0055‐0.
N. H. Ng, R. A. Gabriel, J. McAuley, et al., “Predicting Surgery Duration With Neural Heteroscedastic Regression,” in Machine Learning for Healthcare Conference (PMLR, 2017), 100–111.
J. P. Tuwatananurak, S. Zadeh, X. Xu, et al., “Machine Learning Can Improve Estimation of Surgical Case Duration: A Pilot Study,” Journal of Medical Systems 43, no. 3 (2019): 1–7, https://doi.org/10.1007/s10916‐019‐1160‐5.
M. A. Bartek, R. C. Saxena, S. Solomon, et al., “Improving Operating Room Efficiency: Machine Learning Approach to Predict Case‐Time Duration,” Journal of the American College of Surgeons 229, no. 4 (2019): 346–354.e3, https://doi.org/10.1016/j.jamcollsurg.2019.05.029.
Y. Jiao, A. Sharma, A. Ben Abdallah, T. M. Maddox, and T. Kannampallil, “Probabilistic Forecasting of Surgical Case Duration Using Machine Learning: Model Development and Validation,” Journal of the American Medical Informatics Association 27, no. 12 (2020): 1885–1893, https://doi.org/10.1093/jamia/ocaa140.
C. T. Strömblad, R. G. Baxter‐King, A. Meisami, et al., “Effect of a Predictive Model on Planned Surgical Duration Accuracy, Patient Wait Time, and Use of Presurgical Resources: A Randomized Clinical Trial,” JAMA Surgery 156, no. 4 (2021): 315–321, https://doi.org/10.1001/jamasurg.2020.6361.
A. E. Hoerl and R. W. Kennard, “Ridge Regression: Applications to Nonorthogonal Problems,” Technometrics 12, no. 1 (1970): 69–82, https://doi.org/10.2307/1267352.
R. Tibshirani, “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society Series B 58, no. 1 (1996): 267–288, https://doi.org/10.1111/j.2517‐6161.1996.tb02080.x.
T. Chen and C. Guestrin, “Xgboost: A Scalable Tree Boosting System,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2016), 785–794.
J. H. Friedman, “Greedy Function Approximation: A Gradient Boosting Machine,” Annals of Statistics 29, no. 5 (2001): 1189–1232, https://doi.org/10.1214/aos/1013203451.
F. Pedregosa, G. Varoquaux, A. Gramfort, et al., “Scikit‐Learn: Machine Learning in Python,” Journal of Machine Learning Research 12 (2011): 2825–2830, https://dl.acm.org/doi/abs/10.5555/1953048.2078195.
J. Chu, C. H. Hsieh, Y. N. Shih, et al., “Operating Room Usage Time Estimation With Machine Learning Models,” Healthcare. 10, no. 8 (2022): 1518, https://doi.org/10.3390/healthcare10081518.
Grant Information:
72301303 National Natural Science Foundation of China; 72342006 National Natural Science Foundation of China; 72371253 National Natural Science Foundation of China; 2023B03J1277 Guangzhou Municipal Science and Technology Project
Contributed Indexing:
Keywords: healthcare operations management; machine learning; surgery duration prediction; surgical scheduling
Entry Date(s):
Date Created: 20251103 Date Completed: 20260112 Latest Revision: 20260112
Update Code:
20260112
DOI:
10.1002/hpm.70032
PMID:
41182686
Database:
MEDLINE
Weitere Informationen
This study aims to optimise operating room scheduling and improve hospital operational efficiency by predicting surgery durations using machine learning algorithms. Traditional methods often face challenges in accuracy, while machine learning models offer superior predictive performance. Using real-world operating room data from a large general hospital in China, the study compares various machine learning algorithms and selects the XGBoost model as the most effective predictive framework. Three types of models were developed: an all-inclusive model, a department-specific model, and a doctor-specific model. The department-specific model demonstrated the highest accuracy, outperforming the others. The results were applied to the hospital's surgical centre scheduling process, significantly enhancing operating room resource utilisation. The study highlights the importance of data preprocessing and feature selection in improving prediction accuracy. Overall, machine learning-based surgery duration prediction can effectively address challenges in surgical scheduling and provide data-driven support for hospital operational management.
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