Abstract
This paper presents the framework for forecasting the surgery time by taking into account the surgical environment in an ophthalmology department (experience of surgeon in years, experience of anesthetist in years, staff experience in years, type of anesthesia etc.). The estimation of surgery times is done using three techniques, such as the Adaptive Neuro Fuzzy Inference Systems (ANFIS), Artificial Neural Networks (ANN) and Multiple Linear Regression Analysis (MLRA) and the results of estimation accuracy were compared. Though the developed framework is general, it is illustrated for three ophthalmologic surgeries such as the cataract surgery, corneal transplant surgery and Oculoplastic surgery. The framework is validated by using data obtained from a local hospital. It is hypothesized that by accurately knowing the surgery times, one can schedule the operations optimally resulting in the efficient utilization of the operating rooms. This increase in the efficiency is demonstrated through computer simulations of the operating theater.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Cardoen B., Demeulemeester E., and Belien J., Operating room planning and scheduling, Report, 2008.
Chaabane S., Meskens N., Guinet A., and Laurent M., Comparison of two operating theater planning: Application in Belgian hospital, Services Systems and Service Management IEEE conference 2006.
Dexter, F., Traub, R. D., and Lebowitz, P., Scheduling a delay between different surgeons cases in the same operating room on the same day using upper prediction bounds for case durations. Health Eco. Health Sys. 92:943–956, 2001.
Guinet, A., and Chaabane, S., Operating theater planning. Int. J. Prod. Eco. 85:69–81, 2003.
Jebali, A., Alouane, A. B. H., and Ladet, P., Operating rooms scheduling. Int. J. Prod. Eco. 99:52–62, 2006.
Kraft, M. R., Desouza, K. C., and Androwich, I., Data mining in health care information sysytems: Case study of a vetrans administration spinal cord injury, IEEE Hawaii International Conference on System Sciences, 2003.
Lorraine, T., Alain, G., and Dominique, L. M., Nurse scheduling using integer linear programming and constraint programming, International Federation of Automatic Control, 2006.
Pham, D. N., and Klinkert, A., Surgical case scheduling as a generalized job shop scheduling problem. Eur. J. Oper. Res. 185:1011–1025, 2008.
Testi, A., Tanfani, E., and Torre, G., A three phase approach for operating theatre schedules. Health Care Manage. Sci. 10:167–172, 2007.
Combesa, C., Meskensb, N., Rivatc, C., and Vandamme, J. P., Using a KDD (Knowledge discovery in database) process to forecast the duration of surgery. Int. J. Product. Eco. 112:279–293, 2008.
Rolanda, B., Di Martinellya, C., Rianea, F., and Pochet, Y., Scheduling an operating theatre under human resource constraints. Comput. Ind. Eng. 58(2):212–220, 2010.
Chen, C.-K., Lin, C., Hou, T.-H., Wang, S.-H., and Lin, H.-M., A study of operating room scheduling that integrates multiple quantitative and qualitative objectives. J. Nurs. Res. 18(1):62–74, 2010.
Augusto, V., Xiea, X., and Perdomoa, V., Operating theatre scheduling with patient recovery in both operating rooms and recovery beds. Comput. Ind. Eng. 58(2):231–238, 2010.
Saha, P., Pinjani, A., Al-Shabibi, N., Madari, S., Ruston, J., and Magos, A., Why we are wasting time in the operating theatre? Int. J. Health Plann. Manage. 24(3):225–232, 2008.
Dreiseitl, S., and Ohno-Machado, L., Logistic regression and artificial neural network classification models: a methodology review. J. Biomed. Inform. 35(6):352–359, 2002.
Eken, C., Bilge, U., Kartal, M., and Eray, O., Artificial neural network, genetic algorithm, and logistic regression applications for predicting renal colic in emergency settings. Int. J. Emerg. Med. 2(2):99–105, 2009.
Jang Roger, J. S., ANFIS: Adaptive-Network-Based Fuzzy Inference system. IEEE Trans. Syst. Man Cybern. 23(3):665–685, 1993.
Acknowledgment
Our sincere thanks, to the ophthalmology department of Sri Ramachandra Medical University & Hospital, Chennai, Tamil Nadu for providing the data.
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
Rights and permissions
About this article
Cite this article
Devi, S.P., Rao, K.S. & Sangeetha, S.S. Prediction of Surgery Times and Scheduling of Operation Theaters in Optholmology Department. J Med Syst 36, 415–430 (2012). https://doi.org/10.1007/s10916-010-9486-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10916-010-9486-z