IJE TRANSACTIONS C: Aspects Vol. 30, No. 9 (August 2017) 1290-1299    Article in Press

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R. Shafaei and A. Mozdgir
( Received: December 27, 2016 – Accepted: July 07, 2017 )

Abstract    Management of surgery units and operating room (OR) play key roles in optimizing the utilization of hospitals. On this line Case Mix Planning (CMP) is normally applied to long term planning of OR. This refers to allocating OR time to each patient’s group. In this paper a mathematical model is applied to optimize the allocation of OR time among surgical groups. In addition another technique is applied to provide an Order of Preference by Similarity to Ideal Solution (TOPSIS) considering different hospital performance measures. Furthermore, robust estimation approach is used to estimate of the models\\\' parameters using real data. The proposed model is solved using GAMS software. The results of the study performed in this paper reveal that the proposed methods results in an increase in total hospital operated “value of patients” by 21.5%. This value is defined according to hospital priority and is include moral and ethical considerations. In addition, for each resource, a sensitivity analysis of the findings to the changes is conducted. The results of the performed sensitivity analysis indicated that the value of the objective function significantly increased via reallocating OR time to surgical groups and/or enhancing the OR facilities to support more surgical specialties.


Keywords    Case mix planning problem, Mathematical programming, TOPSIS, Sensitivity analysis, Robust estimation.


چکیده    مدیریت بخش‌های جراحی و اتاق‌های عمل نقشی کلیدی در استفاده بهینه از بیمارستان‌ها دارد. در این حوزه مساله “تعیین ترکیب بیماران” معمولا در برنامه ریزی بلند مدت اتاق‌های عمل مورد بررسی قرار می‌گیرد. این مساله عبارتست از تخصیص ظرفیت اتاق‌های عمل به گروه‌های مختلف بیماران. در این مقاله از یک مدل برنامه‌ریزی ریاضی جهت تخصیص بهینه ظرفیت اتاق‌های عمل به گروه‌های مختلف جراحی استفاده شده است. بعلاوه از روش رتبه بندی TOPSIS به منظور رتبه بندی گروه‌های مختلف جراحی بر اساس شاخص‌های مختلف عملکردی بیمارستان استفاده گردیده است. هچنین رویکرد برآورد کننده‌های استوار جهت برآورد پارامترهای مساله بر اساس داده‌های واقعی مورد استفاده قرار گرفته است. سپس مدل توسعه داده شده توسط نرم افزار GAMS حل گردیده است. نتایج بدست آمده حاصل از حل مدل آشکار می‌سازد که شاخص ارزش بیماران جراحی شده نسبت به وضعیت فعلی بیمارستان 21.5% افزایش می یابد. این شاخص بر اساس الویت‌های بیمارستان تعریف گردیده است و شامل ملاحظات اخلاقی می‌باشد. بعلاوه جهت بررسی حساسیت مدل به تغییر در منابع در دسترس، به تحلیل حساسیت مدل پرداخته شده است. نتایج حاصل از تحلیل حساسیت صورت گرفته نشان می‌دهد که میزان تابع هدف مساله بطور قابل ملاحظه‌ای با افزایش قابلیت اتاق‌های عمل جهت پشتیبانی گروه‌های مختلف جراحی بهبود می‌یابد.

References    [1] Hof, S., Fügener, A., Schoenfelder, J., & Brunner, J. O. (2015). Case mix planning in hospitals: a review and future agenda. Health care management science, 1-14. [2] Yahia, Z., Eltawil, A. B., & Harraz, N. A. (2015). The operating room case-mix problem under uncertainty and nurses capacity constraints. Health care management science, 1-12. [3] Healthcare Financial Management Association. (2003). Achieving operating room efficiency through process integration. Healthcare financial management: journal of the Healthcare Financial Management Association,57(3), suppl-1. [4] Thirapan, K. (2013). A hospital admission planning model to improve operating room resource utilization. [5] Hans, E. W., Van Houdenhoven, M., & Hulshof, P. J. (2012). A framework for healthcare planning and control. In Handbook of healthcare system scheduling (pp. 303-320). Springer US. [6] Güler, M. G., & Yilmaz Güler, E. (2013). A goal programming model for scheduling residents in an anesthesia and reanimation department. Expert Systems with Applications, 40(6), 2117-2126. [7] Patterson Patterson P (1996) What makes a well-oiled scheduling system.OR Manager 12(9):19–23 [8] Vacanti, C., Segal, S., Sikka, P., & Urman, R. (Eds.). (2011). Essential clinical anesthesia. Cambridge University Press. [9] Roth, A. V., & DIERDONCK, R. (1995). Hospital resource planning: concepts, feasibility, and framework. Production and operations management4(1), 2-29. [10] Vissers, J. M., Bertrand, J. W. M., & De Vries, G. (2001). A framework for production control in health care organizations. Production Planning & Control12(6), 591-604. [11] Abdelrasol, Z. Y., Harraz, N., & Eltawil, A. (2013). A proposed solution framework for the operating room scheduling problems. In Proceedings of the world congress on engineering and computer science (Vol. 2, pp. 23-25). [12] Guerriero, F., & Guido, R. (2011). Operational research in the management of the operating theatre: a survey. Health care management science14(1), 89-114. [13] Cardoen, B., Demeulemeester, E., & Beliën, J. (2010). Operating room planning and scheduling: A literature review. European Journal of Operational Research201(3), 921-932. [14] Robbins, W. A., & Tuntiwongpiboom, N. (1989). Linear programming a useful tool in case-mix management. Healthcare financial management: journal of the Healthcare Financial Management Association43(6), 114-116. [15] Milsum, J. H., Turban, E., & Vertinsky, I. (1973). Hospital admission systems: their evaluation and management. Management Science19(6), 646-666. [16] Ma, G., & Demeulemeester, E. (2013). A multilevel integrative approach to hospital case mix and capacity planning. Computers & Operations Research,40(9), 2198-2207. [17] Testi, A., Tanfani, E., & Torre, G. (2007). A three-phase approach for operating theatre schedules. Health Care Management Science10(2), 163-172. [18] Ma, G., Beliën, J., Demeulemeester, E., & Wang, L. (2009, July). Solving the strategic case mix problem optimally by using branch-and-price algorithms. In Conf. proc, ORAHS. [19] Hobbs, T. (1963). Linear programming as applied to the admissions of elective surgery patients at presbyterian-university hospital. Department of Industrial Engineering, University of Pittsburgh (mimeographed).  [20] Meyer, G. C., Taylor, J. W., & Damewood, E. Z. (1992). Using the Operations Management Modeling Technique of Linear Programming to Determine Optimal Case Mix of Three Cardiac Services. Journal of Cardiopulmonary Rehabilitation and Prevention12(6), 407-412. [21] Rifai, A. K., & Pecenka, J. O. (1990). An application of goal programming in healthcare planning. International Journal of Operations & Production Management10(3), 28-37. [22] Blake, J. T., & Carter, M. W. (2002). A goal programming approach to strategic resource allocation in acute care hospitals. European Journal of Operational Research140(3), 541-561. [23] Mulholland, M. W., Abrahamse, P., & Bahl, V. (2005). Linear programming to optimize performance in a department of surgery. Journal of the American College of Surgeons200(6), 861-868. [24] Baligh, H. H., & Laughhunn, D. J. (1969). An economic and linear model of the hospital. Health services research4(4), 293. [25] Rauner, M. S., Schneider, G., & Heidenberger, K. (2005). Reimbursement systems and regional inpatient allocation: a non-linear optimisation model.IMA Journal of Management Mathematics16(3), 217-237. [26] Mandic, K., Delibasic, B., Knezevic, S., & Benkovic, S. (2014). Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods. Economic Modelling43, 30-37. [27] Hwang, C.L.; Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. New York: Springer-Verlag [28] Moshiri, B., Asharif, M. R., & HoseinNezhad, R. (2002). A new approach to self-localization for mobile robots using sensor data fusion. International Journal of Engineering-Transactions B15, 145-156. [29] Sogandi, F., & Amiri, A. (2014). Estimating the time of a step change in Gamma regression profiles using MLE approach. International Journal of Engineering-Transactions B: Applications28(2), 224-231 [30] Vakilian, F., Amiri, A., & Sogandi, F. (2015). ISOTONIC CHANGE POINT ESTIMATION IN THE AR (1) AUTOCORRELATED SIMPLE LINEAR PROFILES. International Journal of Engineering-Transactions A: Basics28(7), 1059. [31] Ruckstuhl, A. F., & Welsh, A. H. (2001). Robust fitting of the binomial model. Annals of statistics, 1117-1136.

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