Derleme
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Sağlıkta Güncel Simülasyon Yaklaşımları: Bir Derleme Çalışması

Yıl 2019, , 1 - 16, 31.03.2019
https://doi.org/10.7240/jeps.444190

Öz

Son on yılda, sağlık hizmeti maliyetlerindeki çarpıcı artış,
araştırmacıları ve sağlık politikacılarını maliyetleri düşürmek için yeni
yollar bulmaya zorlamıştır. Simülasyon sağlık hizmetlerinde verimliliği
artırmak ve kaliteyi yükseltmek için kullanılan popüler ve etkili bir yöntem
haline gelmiştir. Araştırmacıların sağlık hizmetlerinde gerçekleştirilen simülasyon
çalışmalarına olan ilgileri, yayın sayısında sürekli bir artışa neden olmuştur.
Bu derleme çalışmasında, sağlık alanında gerçekleştirilen ayrık olay
simülasyonu (AOS) çalışmaları özetlenmiş ve sınıflandırılmıştır. Özellikle de
yüksek düzeyde belirsizlik ve kısıtlı kaynaklar altında çalışan ameliyathanelerde
gerçekleştirilen AOS çalışmalarına odaklanılmıştır. Derleme çalışmasının
ışığında, sağlık alanında çalışan araştırmacılar ve profesyoneller gelecek AOS
araştırma ve uygulamaları hakkında yeni önerilerde bulunabilirler. 

Kaynakça

  • Schroedl, C. J., Corbridge, T. C., Cohen, E. R., Fakhran, S. S., Schimmel, D., McGaghie, W. C., ve Wayne, D. B. (2012). Use of simulation-based education to improve resident learning and patient care in the medical intensive care unit: a randomized trial. J Crit Care, 27(2), 217-213. doi:10.1016/j.jcrc.2011.08.006
  • Jansson, M., Kääriäinen, M. ve Kyngäs, H. (2013). Effectiveness of Simulation-Based Education in Critical Care Nurses' Continuing Education: A Systematic Review. Clinical Simulation in Nursing, 9(9), 355-60. doi:10.1016/j.ecns.2012.07.003
  • Ballangrud, R., Hall-Lord, M. L., Persenius, M., ve Hedelin, B. (2014). Intensive care nurses' perceptions of simulation-based team training for building patient safety in intensive care: a descriptive qualitative study. Intensive Crit Care Nurs, 30(4), 179-187. doi:10.1016/j.iccn.2014.03.002
  • Wenk, M., ve Popping, D. M. (2015). Simulation for anesthesia in obstetrics. Best Pract Res Clin Anaesthesiol, 29(1), 81-86. doi:10.1016/j.bpa.2015.01.003
  • Warren, J. N., Luctkar-Flude, M., Godfrey, C., & Lukewich, J. (2016). A systematic review of the effectiveness of simulation-based education on satisfaction and learning outcomes in nurse practitioner programs. Nurse Educ Today, 46, 99-108. doi:10.1016/j.nedt.2016.08.023
  • Mirza, S., & Athreya, S. (2017). Review of Simulation Training in Interventional Radiology. Acad Radiol. doi:10.1016/j.acra.2017.10.009
  • Camp, S., & Legge, T. (2018). Simulation as a Tool for Clinical Remediation: An Integrative Review. Clinical Simulation in Nursing, 16, 48-61. doi:10.1016/j.ecns.2017.11.003
  • Reed, S., Remenyte-Prescott, R., ve Rees, B. (2017). Effect of venepuncture process design on efficiency and failure rates: A simulation model study for secondary care. Int J Nurs Stud, 68, 73-82. doi:10.1016/j.ijnurstu.2016.12.010
  • Fabian, M.P., Stout, N.K., Adamkiewicz, G., Geggel, A., Ren, C., Sandel, M., Levy, J.I. (2012). The effects of indoor environmental exposure pediatric asthma: a discrete event simulation model. Environmental Health, 11. doi: 10.1186/1476-069X-11-66
  • Chemweno, P., Thijs, V., Pintelon, L., & Van Horenbeek, A. (2014). Discrete event simulation case study: Diagnostic path for stroke patients in a stroke unit. Simulation Modelling Practice and Theory, 48, 45-57. doi:10.1016/j.simpat.2014.07.006
  • Ariöz, U., ve Günel, B. (2016). Evaluation of hearing loss simulation using a speech intelligibility index. Turkish Journal of Electrical Engineering & Computer Sciences, 24, 4193-4207. doi:10.3906/elk-1411-135
  • Pan, F., Reifsnider, O., Zheng, Y., Proskorovsky, I., Li, T., He, J., ve Sorensen, S. V. (2017). Modeling Clinical Outcomes in Prostate Cancer: Application and Validation of the Discrete Event Simulation (DES) Approach. Value in Health. doi:10.1016/j.jval.2017.09.022
  • Nikakhtar, A., ve Hsiang, S. M. (2014). Incorporating the dynamics of epidemics in simulation models of healthcare systems. Simulation Modelling Practice and Theory, 43, 67-78. doi:10.1016/j.simpat.2014.01.007
  • Viana, J., Brailsford, S. C., Harindra, V., ve Harper, P. R. (2014). Combining discrete-event simulation and system dynamics in a healthcare setting: A composite model for Chlamydia infection. EJOR, 237(1), 196-206. doi:10.1016/j.ejor.2014.02.052
  • Orbann, C., Sattenspiel, L., Miller, E., ve Dimka, J. (2017). Defining epidemics in computer simulation models: How do definitions influence conclusions? Epidemics, 19, 24-32. doi:10.1016/j.epidem.2016.12.001
  • Sadatsafavi, H., Niknejad, B., Zadeh, R., ve Sadatsafavi, M. (2016). Do cost savings from reductions in nosocomial infections justify additional costs of single-bed rooms in intensive care units? A simulation case study. J Crit Care, 31(1), 194-200. doi:10.1016/j.jcrc.2015.10.010
  • Granja, C., Almada-Lobo, B., Janela, F., Seabra, J., ve Mendes, A. (2014). An optimization based on simulation approach to the patient admission scheduling problem using a linear programing algorithm. J Biomed Inform, 52, 427-437. doi:10.1016/j.jbi.2014.08.007
  • Ben-Tovim, D., Filar, J., Hakendorf, P., Qin, S., Thompson, C., ve Ward, D. (2016). Hospital Event Simulation Model: Arrivals to Discharge–Design, development and application. Simulation Modelling Practice and Theory, 68, 80-94. doi:10.1016/j.simpat.2016.07.004
  • Ahmadi-Javid, A., Jalali, Z., ve Klassen, K. J. (2017). Outpatient appointment systems in healthcare: A review of optimization studies. EJOR, 258(1), 3-34. doi:10.1016/j.ejor.2016.06.064
  • Jun, J. B., Jacobson, S. H., ve Swisher, J. R. (1999). Application of Discrete-Event Simulation in Health Care Clinics: A Survey. The Journal of the Operational Research Society, 50(2), 109-123.
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  • Azadeh, A., Baghersad, M., Farahani, M.H., ve Zarrin, M. (2015). Semi-online patient scheduling in pathology laboratories. Artificial Intelligence in Medicine, 64(3), 217-226.
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  • Masselink, I. H. J., van der Mijden, T. L. C., Litvak, N., ve Vanberkel, P. T. (2012). Preparation of chemotherapy drugs: Planning policy for reduced waiting times. Omega, 40(2), 181-187. doi:10.1016/j.omega.2011.05.003
  • Baril, C., Gascon, V., ve Cartier, S. (2014). Design and analysis of an outpatient orthopaedic clinic performance with discrete event simulation and design of experiments. Computers & Industrial Engineering, 78, 285-298. doi:10.1016/j.cie.2014.05.006
  • Peng, Y., Qu, X., ve Shi, J. (2014). A hybrid simulation and genetic algorithm approach to determine the optimal scheduling templates for open access clinics admitting walk-in patients. Computers & Industrial Engineering, 72, 282-296. doi:10.1016/j.cie.2014.03.026
  • Baril, C., Gascon, V., Miller, J., ve Côté, N. (2016). Use of a discrete-event simulation in a Kaizen event: A case study in healthcare. EJOR, 249(1), 327-339. doi:10.1016/j.ejor.2015.08.036
  • Babashov, V., Aivas, I., Begen, M. A., Cao, J. Q., Rodrigues, G., D'Souza, D., Lock, M., Zaric, G. S. (2017). Reducing Patient Waiting Times for Radiation Therapy and Improving the Treatment Planning Process: a Discrete-event Simulation Model (Radiation Treatment Planning). Clin Oncol (R Coll Radiol), 29(6), 385-391. doi:10.1016/j.clon.2017.01.039
  • Kim, B., Elstein, Y., Shiner, B., Konrad, R., Pomerantz, A. S., ve Watts, B. V. (2013). Use of discrete event simulation to improve a mental health clinic. Gen Hosp Psychiatry, 35(6), 668-670. doi:10.1016/j.genhosppsych.2013.06.004
  • Chemweno, P., Thijs, V., Pintelon, L., ve Van Horenbeek, A. (2014). Discrete event simulation case study: Diagnostic path for stroke patients in a stroke unit. Simulation Modelling Practice and Theory, 48, 45-57. doi:10.1016/j.simpat.2014.07.006
  • Devapriya, P., Stromblad, C. T., Bailey, M. D., Frazier, S., Bulger, J., Kemberling, S. T., ve Wood, K. E. (2015). StratBAM: A Discrete-Event Simulation Model to Support Strategic Hospital Bed Capacity Decisions. J Med Syst, 39(10), 130. doi:10.1007/s10916-015-0325-0
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  • Dan, Z., Xiaoli, H., Weiru, D., Li, W., ve Yue, H. (2016). Outpatient Pharmacy Optimization Using System Simulation. Procedia Computer Science, 91, 27-36. doi:10.1016/j.procs.2016.07.038
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Recent Simulation Studies in Healthcare: A Review

Yıl 2019, , 1 - 16, 31.03.2019
https://doi.org/10.7240/jeps.444190

Öz



In recent decades, the dramatic
increase in healthcare costs has compelled resarchers and healthcare policy
makers to find out new ways to reduce costs. Simulation has become a popular and
effective method for improving efficiency and enhancing quality in healthcare operations.
The interest of researchers in the domain of simulation studies in healthcare
induces a steady increase in the number of publications. In this regard,
different discrete-event simulation (DES) studies employed in healthcare field are
summarized and classified in this review. A particular attention is on the
published studies that involve DES studies of operating rooms. The operating
room is specifically the most complicated and expensive hospital resource that
runs with high level of uncertainty and limited resources. Moreover, as a
result of this review study, deficiencies in the literature about the
improvement of the operating room scheduling processes with simulation based
optimization method have been determined. In the literature, most of the
simulation models only include demand and capacity constraints. However, they
should include more constraints such as labor constraints, preferences of
surgeons for surgical scheduling, number of available beds in intensive care unit,
number of recovery beds, etc. Moreover, there is a need in the field to develop
stochastic models, which involve uncertain surgery and recovery times. In most
of the studies, it is assumed that these periods are known precisely. It is
also determined that number of multi-objective models is quite low. In
addition, only a few simulation studies have an online scheduling approach. Thus,
these issues need to be addressed. In
light of this review, healthcare professionals and researchers can make new suggestions
and improvements regarding future research and applications of DES.




Kaynakça

  • Schroedl, C. J., Corbridge, T. C., Cohen, E. R., Fakhran, S. S., Schimmel, D., McGaghie, W. C., ve Wayne, D. B. (2012). Use of simulation-based education to improve resident learning and patient care in the medical intensive care unit: a randomized trial. J Crit Care, 27(2), 217-213. doi:10.1016/j.jcrc.2011.08.006
  • Jansson, M., Kääriäinen, M. ve Kyngäs, H. (2013). Effectiveness of Simulation-Based Education in Critical Care Nurses' Continuing Education: A Systematic Review. Clinical Simulation in Nursing, 9(9), 355-60. doi:10.1016/j.ecns.2012.07.003
  • Ballangrud, R., Hall-Lord, M. L., Persenius, M., ve Hedelin, B. (2014). Intensive care nurses' perceptions of simulation-based team training for building patient safety in intensive care: a descriptive qualitative study. Intensive Crit Care Nurs, 30(4), 179-187. doi:10.1016/j.iccn.2014.03.002
  • Wenk, M., ve Popping, D. M. (2015). Simulation for anesthesia in obstetrics. Best Pract Res Clin Anaesthesiol, 29(1), 81-86. doi:10.1016/j.bpa.2015.01.003
  • Warren, J. N., Luctkar-Flude, M., Godfrey, C., & Lukewich, J. (2016). A systematic review of the effectiveness of simulation-based education on satisfaction and learning outcomes in nurse practitioner programs. Nurse Educ Today, 46, 99-108. doi:10.1016/j.nedt.2016.08.023
  • Mirza, S., & Athreya, S. (2017). Review of Simulation Training in Interventional Radiology. Acad Radiol. doi:10.1016/j.acra.2017.10.009
  • Camp, S., & Legge, T. (2018). Simulation as a Tool for Clinical Remediation: An Integrative Review. Clinical Simulation in Nursing, 16, 48-61. doi:10.1016/j.ecns.2017.11.003
  • Reed, S., Remenyte-Prescott, R., ve Rees, B. (2017). Effect of venepuncture process design on efficiency and failure rates: A simulation model study for secondary care. Int J Nurs Stud, 68, 73-82. doi:10.1016/j.ijnurstu.2016.12.010
  • Fabian, M.P., Stout, N.K., Adamkiewicz, G., Geggel, A., Ren, C., Sandel, M., Levy, J.I. (2012). The effects of indoor environmental exposure pediatric asthma: a discrete event simulation model. Environmental Health, 11. doi: 10.1186/1476-069X-11-66
  • Chemweno, P., Thijs, V., Pintelon, L., & Van Horenbeek, A. (2014). Discrete event simulation case study: Diagnostic path for stroke patients in a stroke unit. Simulation Modelling Practice and Theory, 48, 45-57. doi:10.1016/j.simpat.2014.07.006
  • Ariöz, U., ve Günel, B. (2016). Evaluation of hearing loss simulation using a speech intelligibility index. Turkish Journal of Electrical Engineering & Computer Sciences, 24, 4193-4207. doi:10.3906/elk-1411-135
  • Pan, F., Reifsnider, O., Zheng, Y., Proskorovsky, I., Li, T., He, J., ve Sorensen, S. V. (2017). Modeling Clinical Outcomes in Prostate Cancer: Application and Validation of the Discrete Event Simulation (DES) Approach. Value in Health. doi:10.1016/j.jval.2017.09.022
  • Nikakhtar, A., ve Hsiang, S. M. (2014). Incorporating the dynamics of epidemics in simulation models of healthcare systems. Simulation Modelling Practice and Theory, 43, 67-78. doi:10.1016/j.simpat.2014.01.007
  • Viana, J., Brailsford, S. C., Harindra, V., ve Harper, P. R. (2014). Combining discrete-event simulation and system dynamics in a healthcare setting: A composite model for Chlamydia infection. EJOR, 237(1), 196-206. doi:10.1016/j.ejor.2014.02.052
  • Orbann, C., Sattenspiel, L., Miller, E., ve Dimka, J. (2017). Defining epidemics in computer simulation models: How do definitions influence conclusions? Epidemics, 19, 24-32. doi:10.1016/j.epidem.2016.12.001
  • Sadatsafavi, H., Niknejad, B., Zadeh, R., ve Sadatsafavi, M. (2016). Do cost savings from reductions in nosocomial infections justify additional costs of single-bed rooms in intensive care units? A simulation case study. J Crit Care, 31(1), 194-200. doi:10.1016/j.jcrc.2015.10.010
  • Granja, C., Almada-Lobo, B., Janela, F., Seabra, J., ve Mendes, A. (2014). An optimization based on simulation approach to the patient admission scheduling problem using a linear programing algorithm. J Biomed Inform, 52, 427-437. doi:10.1016/j.jbi.2014.08.007
  • Ben-Tovim, D., Filar, J., Hakendorf, P., Qin, S., Thompson, C., ve Ward, D. (2016). Hospital Event Simulation Model: Arrivals to Discharge–Design, development and application. Simulation Modelling Practice and Theory, 68, 80-94. doi:10.1016/j.simpat.2016.07.004
  • Ahmadi-Javid, A., Jalali, Z., ve Klassen, K. J. (2017). Outpatient appointment systems in healthcare: A review of optimization studies. EJOR, 258(1), 3-34. doi:10.1016/j.ejor.2016.06.064
  • Jun, J. B., Jacobson, S. H., ve Swisher, J. R. (1999). Application of Discrete-Event Simulation in Health Care Clinics: A Survey. The Journal of the Operational Research Society, 50(2), 109-123.
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  • Hoot, N. R., LeBlanc, L. J., Jones, I., Levin, S. R., Zhou, C., Gadd, C. S., ve Aronsky, D. (2008). Forecasting emergency department crowding: a discrete event simulation. Ann Emerg Med, 52(2), 116-125. doi:10.1016/j.annemergmed.2007.12.011
  • Khurma, N., Bacioiu, G. M., ve Pasek, Z. J. (2008). Simulation-Based Verification of Lean Improvement for Emergency Room Process. Proceedings of the 40th Conference on Winter Simulation Conference, 1490-1499.
  • Zeng, Z., Ma, X., Hu, Y., Li, J., ve Bryant, D. (2012). A simulation study to improve quality of care in the emergency department of a community hospital. J Emerg Nurs, 38(4), 322-328. doi:10.1016/j.jen.2011.03.005
  • Al-Refaie, A., Fouad, R. H., Li, M.-H., ve Shurrab, M. (2014). Applying simulation and DEA to improve performance of emergency department in a Jordanian hospital. Simulation Modelling Practice and Theory, 41, 59-72. doi:10.1016/j.simpat.2013.11.010
  • Best, A. M., Dixon, C. A., Kelton, W. D., Lindsell, C. J., ve Ward, M. J. (2014). Using discrete event computer simulation to improve patient flow in a Ghanaian acute care hospital. Ann J Emerg Med, 32(8), 917-922. doi:10.1016/j.ajem.2014.05.012
  • Radhakrishnan, S., Duvvuru, A., ve Kamarthi, S. V. (2014). Investigating Discrete Event Simulation Method to Assess the Effectiveness of Wearable Health Monitoring Devices. Procedia Economics and Finance, 11, 838-856. doi:10.1016/s2212-5671(14)00248-2
  • Lin, C. H., Kao, C. Y., & Huang, C. Y. (2015). Managing emergency department overcrowding via ambulance diversion: a discrete event simulation model. J Formos Med Assoc, 114(1), 64-71. doi:10.1016/j.jfma.2012.09.007
  • Azadeh, A., Baghersad, M., Farahani, M.H., ve Zarrin, M. (2015). Semi-online patient scheduling in pathology laboratories. Artificial Intelligence in Medicine, 64(3), 217-226.
  • Ünlüyurt, T., ve Tunçer, Y. (2016). Estimating the performance of emergency medical service location models via discrete event simulation. Computers & Industrial Engineering, 102, 467-475. doi:10.1016/j.cie.2016.03.029
  • Coelli, F. C., Ferreira, R. B., Almeida, R. M., ve Pereira, W. C. (2007). Computer simulation and discrete-event models in the analysis of a mammography clinic patient flow. Comput Methods Programs Biomed, 87(3), 201-207. doi:10.1016/j.cmpb.2007.05.006
  • Kapamara, T., Sheibani, K., Petrovic, D., Haas, O. C. L., ve Reeves, C. (2014). A simulation of a radiotherapy treatment system: A case study of a local cancer center. in Proceedings of ORP3, EURO: Cadiz, Spain, 29-35.
  • J. Klassen, K., ve Yoogalingam, R. (2009). Improving Performance in Outpatieint Appointment Services with a Simulation Optimization Approach. Production and Operations Management, 18(4), 447-458. doi:10.3401/poms.1080.01021
  • Villamizar, J. R., Coelli, F. C., Pereira, W. C., ve Almeida, R. M. (2011). Discrete-event computer simulation methods in the optimisation of a physiotherapy clinic. Physiotherapy, 97(1), 71-77. doi:10.1016/j.physio.2010.02.009
  • Rohleder, T. R., Lewkonia, P., Bischak, D. P., Duffy, P., ve Hendijani, R. (2011). Using simulation modeling to improve patient flow at an outpatient orthopedic clinic. Health Care Manag Sci, 14(2), 135-145. doi:10.1007/s10729-010-9145-4
  • Masselink, I. H. J., van der Mijden, T. L. C., Litvak, N., ve Vanberkel, P. T. (2012). Preparation of chemotherapy drugs: Planning policy for reduced waiting times. Omega, 40(2), 181-187. doi:10.1016/j.omega.2011.05.003
  • Baril, C., Gascon, V., ve Cartier, S. (2014). Design and analysis of an outpatient orthopaedic clinic performance with discrete event simulation and design of experiments. Computers & Industrial Engineering, 78, 285-298. doi:10.1016/j.cie.2014.05.006
  • Peng, Y., Qu, X., ve Shi, J. (2014). A hybrid simulation and genetic algorithm approach to determine the optimal scheduling templates for open access clinics admitting walk-in patients. Computers & Industrial Engineering, 72, 282-296. doi:10.1016/j.cie.2014.03.026
  • Baril, C., Gascon, V., Miller, J., ve Côté, N. (2016). Use of a discrete-event simulation in a Kaizen event: A case study in healthcare. EJOR, 249(1), 327-339. doi:10.1016/j.ejor.2015.08.036
  • Babashov, V., Aivas, I., Begen, M. A., Cao, J. Q., Rodrigues, G., D'Souza, D., Lock, M., Zaric, G. S. (2017). Reducing Patient Waiting Times for Radiation Therapy and Improving the Treatment Planning Process: a Discrete-event Simulation Model (Radiation Treatment Planning). Clin Oncol (R Coll Radiol), 29(6), 385-391. doi:10.1016/j.clon.2017.01.039
  • Kim, B., Elstein, Y., Shiner, B., Konrad, R., Pomerantz, A. S., ve Watts, B. V. (2013). Use of discrete event simulation to improve a mental health clinic. Gen Hosp Psychiatry, 35(6), 668-670. doi:10.1016/j.genhosppsych.2013.06.004
  • Chemweno, P., Thijs, V., Pintelon, L., ve Van Horenbeek, A. (2014). Discrete event simulation case study: Diagnostic path for stroke patients in a stroke unit. Simulation Modelling Practice and Theory, 48, 45-57. doi:10.1016/j.simpat.2014.07.006
  • Devapriya, P., Stromblad, C. T., Bailey, M. D., Frazier, S., Bulger, J., Kemberling, S. T., ve Wood, K. E. (2015). StratBAM: A Discrete-Event Simulation Model to Support Strategic Hospital Bed Capacity Decisions. J Med Syst, 39(10), 130. doi:10.1007/s10916-015-0325-0
  • Reynolds, M., Vasilakis, C., McLeod, M., Barber, N., Mounsey, A., Newton, S., Jacklin, A., Franklin, B. D. (2011). Using discrete event simulation to design a more efficient hospital pharmacy for outpatients. Health Care Manag Sci, 14(3), 223-236. doi:10.1007/s10729-011-9151-1
  • Dan, Z., Xiaoli, H., Weiru, D., Li, W., ve Yue, H. (2016). Outpatient Pharmacy Optimization Using System Simulation. Procedia Computer Science, 91, 27-36. doi:10.1016/j.procs.2016.07.038
  • Rodrigues, F., Zaric, G. S., ve Stanford, D. A. (2017). Discrete event simulation model for planning Level 2 “step-down” bed needs using NEMS. Operations Research for Health Care. doi:10.1016/j.orhc.2017.10.001
  • Lamiri M., Dreo, J., Xie, X. (2007). Operating Room Planning with Random Surgery Times. In: Proceedings of the 3rd Annual IEEE Conference on Automation Science and Engineering, Sept 22–25. Scottsdale, AZ, USA.
  • Lamiri, M., Grimaud, F. ve Xie, X. (2009). Optimization methods for a stochastic surgery planning problem. International Journal of Production Economics, 120(2):400{410.
  • Persson, M. J ve Persson, J. A. (2010). Analysing management policies for operating room planning using simulation. Health Care Manag Sci, 13(2):182{191.
  • Niu, Q., Peng, Q., El Mekkawy, T., Tan, Y. Y., Bruant, H. ve Bernaerdt, L. (2011). Performance analysis of the operating room using simulation. Proceedings of the Canadian Engineering Education Association.
  • Chow, V. S., Puterman, M. L., Salehirad, N., Huang, W. ve Atkins, D. (2011). Reducing surgical ward congestion through improved surgical scheduling and uncapacitated simulation. Production and Operations Management, 20(3):418{430.
  • Gul, S., Denton, B. T., Fowler, J. W. ve Huschka. T. (2011). Bi-criteria scheduling of surgical services for an outpatient procedure center. Production and Operations Management, 20(3):406{417.
  • Batun, S., Denton, B. T., Huschka, T. R. ve Schaefer, A. J. (2011). Operating room pooling and parallel surgery processing under uncertainty. INFORMS Journal on Computing, 23(2):220{237.
  • Saremi, A., Jula, P., El Mekkawy, T. ve Wang, G. G. (2013). Appointment scheduling of outpatient surgical services in a multistage operating room department. International Journal of Production Economics, 141(2):646{658.
  • Aringhieri, R., Landa, P., Soriano, P., Tànfani, E. ve Testi, A. (2015). A two level metaheuristic for the operating room scheduling and assignment problem. Computers & Operations Research, 54:21{34.
  • Saadouli, H., Jerbi, B., Dammak, A., Masmoudi, L. ve Bouaziz, A. (2015). A stochastic optimization and simulation approach for scheduling operating rooms and recovery beds in an orthopedic surgery department. Computers & Industrial Engineering, 80:72{79.
  • Astaraky, D. ve Patrick, J. (2015). A simulation based approximate dynamic programming approach to multi-class, multi-resource surgical scheduling. EJOR, 245(1):309{319.
  • Duma, D. ve Aringhieri, R. (2015). An online optimization approach for the Real Time Management of operating rooms. Operations Research for Health Care, 7, 40-51.
  • Landa, P., Aringhieri, R., Soriano, P., Tànfani, E. ve Testi, A. (2016). A hybrid optimization algorithm for surgeries scheduling. Operations Research for Health Care, 8:103{114.
  • Yahia, Z., Eltawil, A. B. ve Harraz, N. A. (2016). The operating room case-mix problem under uncertainty and nurses capacity constraints. Health Care Manag Sci , 19 (4):383{394.
  • Samudra, M., Demeulemeester, E., Cardoen, B., Vansteenkiste, N. ve Rademakers, F. E. (2017). Due time driven surgery scheduling. Health Care Manag Sci, 20 (3):326{352.
  • Banditori, C., Cappanera, P. ve Visintin, F. (2013). A combined optimization {simulation approach to the master surgical scheduling problem. IMA Journal of Management Mathematics, 24(2):155{187.
  • Xiang, W. (2017). A multi-objective aco for operating room scheduling optimization. Natural Computing, 16(4):607{617.
  • van Oostrum, J. M., Van Houdenhoven, M., Vrielink, M. MJ., Klein, J., Hans, E. W., Klimek, M., Wullink, G., Steyerberg, E. W. ve Kazemier, G. (2008). A simulation model for determining the optimal size of emergency teams on call in the operating room at night. Anesthesia & Analgesia, 107(5):1655{1662, 2008.
  • Arnaout, J. ve Kulbashian, S. (2008). Maximizing the utilization of operating rooms with stochastic times using simulation. In Proceedings of the 40th conference on winter simulation, pages 1617{1623. Winter Simulation Conference.
  • Beliën, J., Demeulemeester, E. ve Cardoen, B. (2009). A decision support system for cyclic master surgery scheduling with multiple objectives. Journal of Scheduling, 12(2): 147.
  • MHallah, R. ve Al-Roomi, A. (2014). The planning and scheduling of operating rooms: A simulation approach. Computers & Industrial Engineering, 78:235-248.
  • Molina-Pariente, J. M., Fernandez-Viagas, V. ve Framinan, J. M. (2015). Integrated operating room planning and scheduling problem with assistant surgeon dependent surgery durations. Computers & Industrial Engineering, 82:8{20.
Toplam 78 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Derleme
Yazarlar

Melis Almula Karadayı 0000-0002-6959-9168

Yakup Görkem Gökmen 0000-0003-0722-2629

Lamia Gülnur Kasap 0000-0002-1030-3051

Hakan Tozan 0000-0002-0479-6937

Yayımlanma Tarihi 31 Mart 2019
Yayımlandığı Sayı Yıl 2019

Kaynak Göster

APA Karadayı, M. A., Gökmen, Y. G., Kasap, L. G., Tozan, H. (2019). Sağlıkta Güncel Simülasyon Yaklaşımları: Bir Derleme Çalışması. International Journal of Advances in Engineering and Pure Sciences, 31(1), 1-16. https://doi.org/10.7240/jeps.444190
AMA Karadayı MA, Gökmen YG, Kasap LG, Tozan H. Sağlıkta Güncel Simülasyon Yaklaşımları: Bir Derleme Çalışması. JEPS. Mart 2019;31(1):1-16. doi:10.7240/jeps.444190
Chicago Karadayı, Melis Almula, Yakup Görkem Gökmen, Lamia Gülnur Kasap, ve Hakan Tozan. “Sağlıkta Güncel Simülasyon Yaklaşımları: Bir Derleme Çalışması”. International Journal of Advances in Engineering and Pure Sciences 31, sy. 1 (Mart 2019): 1-16. https://doi.org/10.7240/jeps.444190.
EndNote Karadayı MA, Gökmen YG, Kasap LG, Tozan H (01 Mart 2019) Sağlıkta Güncel Simülasyon Yaklaşımları: Bir Derleme Çalışması. International Journal of Advances in Engineering and Pure Sciences 31 1 1–16.
IEEE M. A. Karadayı, Y. G. Gökmen, L. G. Kasap, ve H. Tozan, “Sağlıkta Güncel Simülasyon Yaklaşımları: Bir Derleme Çalışması”, JEPS, c. 31, sy. 1, ss. 1–16, 2019, doi: 10.7240/jeps.444190.
ISNAD Karadayı, Melis Almula vd. “Sağlıkta Güncel Simülasyon Yaklaşımları: Bir Derleme Çalışması”. International Journal of Advances in Engineering and Pure Sciences 31/1 (Mart 2019), 1-16. https://doi.org/10.7240/jeps.444190.
JAMA Karadayı MA, Gökmen YG, Kasap LG, Tozan H. Sağlıkta Güncel Simülasyon Yaklaşımları: Bir Derleme Çalışması. JEPS. 2019;31:1–16.
MLA Karadayı, Melis Almula vd. “Sağlıkta Güncel Simülasyon Yaklaşımları: Bir Derleme Çalışması”. International Journal of Advances in Engineering and Pure Sciences, c. 31, sy. 1, 2019, ss. 1-16, doi:10.7240/jeps.444190.
Vancouver Karadayı MA, Gökmen YG, Kasap LG, Tozan H. Sağlıkta Güncel Simülasyon Yaklaşımları: Bir Derleme Çalışması. JEPS. 2019;31(1):1-16.