Based on the analysis of foreign publications, a comparative characteristic of methods and models of prediction of surgical operations duration was carried out. The importance of estimating the accuracy of prediction the duration of operations for effective planning of the use of operating rooms and high-tech equipment is shown. Analyzed statistical and regression methods to predict the duration of operations, and the use of artificial neural networks to estimate the duration of an operation. Mathematical expressions are given, allowing to estimate duration of operation as a whole, as well as data on prediction errors.
References
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24. Brown P.T. A Retrospective Analysis of Surgeon Estimated Time and Actual Operative Time to Develop an Efficient Surgical Scheduling Model. DNP, RN, CNOR. Available at: https://hsrc.himmelfarb.gwu.edu/son_dnp/2.
25. Kayis E., Wang H., Patel M., Gonzalez T., Jain S., Ramamurthi R.J., Santos C., Singhal S., Suermondt J., Sylvester K. Improving Prediction of Surgery Duration using Operational and Temporal Factors. AMIA Annu Symp Proc. 2012; 2012: 456–62.
2. 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. 2005 ; 182(12) : 612–615.
3. Brown P.T. A Retrospective Analysis of Surgeon Estimated Time and Actual Operative Time to Develop an Efficient Operating Room Scheduling System. DNP, RN, CNOR (2017). Available at: https://hsrc.himmelfarb.gwu. edu / son_dnp /2.
4. Dexter F. Application of prediction levels to OR scheduling. AORN Journal. 1996; 63(3) : 607–615. doi:10.1016/S0001–2092(06)63398-X.
5. Sorge M., Computerized O.R. scheduling: Is it an accurate predictor of surgical time? Canadian Operating Room Nursing Journal. 2001; 19(4) : 7–18.
6. Pandit J.J., Carey A. Estimating the duration of common elective operations: Implications for operating list management. Anaesthesia. 2006; 61(8) : 768–776.
7. Eijkemans M.J., 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. 2010; 112(1) : 41–49. doi:10.1097/ALN.0b013e3181c294c2).
8. Macario A., Dexter F. Estimating the Duration of a Case When the Surgeon Has Not Recently Scheduled the Procedure at The Surgical Suite. Anesth. Analg. 1999; 89 : 1241–5.
9. Zhou J., Dexter F.: Method to assist in the scheduling of add-on surgical cases: Upper prediction bounds for surgical case durations based on the lognormal distribution. Anesthesiology. 1998; 89 : 8911228–3.
10. Strum D.P., May J.H., Vargas L. G. Modeling the uncertainty of surgical procedure times: comparison of log- normal and normal models. Anesthesiology. 200 0; 92(4):1160–7.
11. Hosseini N., Sir M.Y., Jankowski C.J., Pasupathy K.S. Surgical Duration Estimation via Data Mining and Predictive Modeling: A Case Study. AMIA Annu Symp Proc. 2015 ; 640–648.
12. Misyuk N.S., Mastykin A.S., Kuznecov G.P. Korrelyacionno-regressionnyj analiz v klinicheskoj medicine. M.: Medicina. 1975. (In Russ).
13. Rebrova O. Y u. Statisticheskij analiz medicinskih dannyh. Primenenie paketa prikladnyh programm STATISTICA. M.: MediaSfera. 2003. (In Russ).
14. Edelman E.I., van Kuijk S. M.J., Hamaeker A.E.W., de Korte M.J.M., van Merode G.G., Buhre W.F. Improving the Prediction of Total surgical Procedure Time Using linear regression Modeling. Front Med (Lausanne). 2017; 4: 85. doi : 10.3389/fmed.2017.00085.
15. 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). 2010; 42(2) : 121–135.
16. Shahabik ar gar Z., Khanna S., Good N., Sattar A., Lind J., O’Dwyer , J. 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. d oi : 10.1007/978-3-319-13560-1_86.
17. Ng N. H., Gabriel R. A., McAuley J., Elkan C., Lipton Z. C. Predicting Surgery Duration with Neural Heteroscedastic Regression. Proceedings of Machine Learning for Healthcare. 2017 ; 68: 100-111.
18. Galushkin A.I. Teoriya nejronnyh setej. Kn.1: Ucheb. posobie dlya vuzov. Obshchaya red. A. I. Galushkina. M.: IPRZHR. 2000. (In Russ).
19. Hamid A., Dwivedi U.S., Singh T.N., Gopi Kishore M., Mahmood M., Singh H. et al. Artificial neural networks in predicting optimum renal stone fragmentation by extracorporeal shock wave lithotripsy: a preliminary study. BJU International. 2003; 91 : 821–824.
20. Seckiner Ilker , Seckiner Serap et al. A neural network – based algorithm for predicting stone-free status after ESWL therapy. Int Braz J. Urol. 2017; 43(6): 1110-1114. doi : 10.1590/S1677–5538.IBJU.2016.0630.
21. ShahabiKargar Z., Khanna S. Predicting Procedure Duration to Improve Scheduling of Elective Surgery. PRICAI Proceedings: Trends in Artificial Intelligence. 2014 ; 998 1009.
22. Franke S., Meixensberger J., Neumuth T. Intervention time prediction from surgical low-level tasks. Journal of Biomedical Informatics. 2013 ; 46 : 152–159.
23. Guédon A.C.P., Paalvast M., Meeuwsen F.C. It is Time to Prepare the Next patient’ Real-Time Prediction of Procedure Duration in Laparoscopic Cholecystectomies. J. Med. Syst. 2016 ; 40: 271 DOI 10.1007/ s10916 016 0631 1.
24. Brown P.T. A Retrospective Analysis of Surgeon Estimated Time and Actual Operative Time to Develop an Efficient Surgical Scheduling Model. DNP, RN, CNOR. Available at: https://hsrc.himmelfarb.gwu.edu/son_dnp/2.
25. Kayis E., Wang H., Patel M., Gonzalez T., Jain S., Ramamurthi R.J., Santos C., Singhal S., Suermondt J., Sylvester K. Improving Prediction of Surgery Duration using Operational and Temporal Factors. AMIA Annu Symp Proc. 2012; 2012: 456–62.
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