| Introduction: Proper management of health systems requires the use of a suitable decision making logic. Simulation is a good tool for accurate and evidence-based decision making. The main objective of this study was to developing a simulation model to minimize the waiting time of patients in the cardiac subspecality clinic of Kowsar Hospital.Methods: This is a cross-sectional study. The statistical population consisted of 576 patients, referring to Kowsar cardiac clinic in the morning and afternoon shifts. Data collection was conducted according to a designed timetable form. Scenarios were defined to receive the best answer. These scenarios were as follows: scenario A: decrease of the average of the service time; scenario B: increase of the mean time between the two entries; and scenario C: decrease of the service time and increase of the time between two entries. ARENA software was used to simulate and review the scenarios and General Algebraic Modeling System (GAMS) softwarewas used to obtain a definite answer.Results: Simulation results showed that in scenario A the mean time spent in the system was 85.55 minutes in the morning and 77.05 minutes in the evening shifts. In scenario B, the average time spent in the system was 65.95 minutes in the morning and 79.63 in the evening shifts. In scenario C, the mean time spent in the system was 73.90 minutes in the morning and 61.17 minutes in the evening shifts. The result of the final model showed that the average time spent in the system was 97.33 min in the morning and 86.85 min in the evening.Conclusion: According to the results obtained from the use of the definitive and simulated models, it was found that the simulation model, due to its probability, faces a percentage of error. Comparison of the definitive and simulated models revealed that the best scenario to the definitive answer was scenario B (increasing the mean time between the two logs).Keywords: Clinic, Waiting time, Queuing theory, Simulation |
- Masoumpour S, Rahimi S, Kharazmi E, Kavousi Z, MOSALAH NH, Abedi Z. Assessing waiting time in emergency department of Shahid Faghihi hospital, Shiraz and presenting appropriate strategies using quality function deployment (QFD) method, 2011-2012. Hakim Research Journal. 2013;16(2):159-68.
- McDougall GH, Levesque T. Customer satisfaction with services: putting perceived value into the equation. Journal of services marketing. 2000;14(5):392-410. doi: 10.1108/08876040010340937 .
- Zare Mehrjardi Y, Hoboubati M, Safaee Nik F. Improvement of waiting time for patients referring to emergency room using discrete event simulation. SSU_Journals. 2011;19(3):302-12.
- Ibrahim IM, Liong C-Y, Bakar SA, Ahmad N, Najmuddin AF, editors. A proposed simulation optimization model framework for emergency
- department problems in public hospital. AIP Conference Proceedings; 2015: AIP Publishing. p. 1-9. doi: 10.1063/1.4937030.
- Jun J, Jacobson SH, Swisher JR. Applicationcreteevent simulation in health care clinics: A survey. J Oper Res Soc. 1999;50(2):109-23. doi:
- 2307/3010560 .
- Weng S-J, Cheng B-C, Kwong ST, Wang L-M, Chang C-Y, editors. Simulation optimization for emergency department resources allocation. Proceedings of the Winter Simulation Conference; 2011: Winter Simulation Conference.
- Babashov V, Aivas I, Begen M, Cao J, Rodrigues G, DâSouza D, et al. Reducing Patient Waiting Times for Radiation Therapy and Improving the Treatment Planning Process: a Discreteevent Simulation Model (Radiation Treatment Planning). Clin Oncol. 2017;29(6):385-91. doi:
- 1016/j.clon.2017.01.039 .
- Ibrahim IM, Liong C-Y, Bakar SA, Ahmad N, Najmuddin AF, editors. Minimizing patient waiting time in emergency department of public hospital using simulation optimization approach. AIP Conference Proceedings; 2017: AIP Publishing.
- Almomani I, AlSarheed A. Enhancing outpatient clinics management software by reducing patientsâ waiting time. Journal of infection and
- public health. 2016;9(6):734-43. doi: 10.1016/j. jiph.2016.09.005.
- Riff M-C, Cares JP, Neveu B. RASON: A new approach to the scheduling radiotherapy problem that considers the current waiting times. Expert Systems with Applications. 2016;64:287-95. doi: 10.1016/j.eswa.2016.07.045 .
- Kozlowski D, Worthington D. Use of queue modelling in the analysis of elective patient treatment governed by a maximum waiting time policy. European Journal of Operational Research. 2015;244(1):331-8. doi: 10.1016/j.ejor.2015.01.024 .
- MORADI H, RAZAVI M. The simulation model for paraclinical services in Hafez hospital in Shiraz, Iran, and evaluation of scenarios to reduce waiting time. 2016;13(1):8-11.
- Bahadori M, Mohammadnejhad SM, Ravangard R, Teymourzadeh E. Using queuing theory and simulation model to optimize hospital
- pharmacy performance. Iranian Red Crescent medical journal. 2014;16(3):e16807. doi: 10.5812/ ircmj.16807.
- McVay BC. Waiting time analysis of patient flow in an outpatient OB/GYN clinic-A simulation approach: University of Central Arkansas; 2014.
- Hu X. Optimization analysis of patient waiting time and resource utilization in an OB/GYN clinic-A simulation approach: University of
- Central Arkansas; 2013.
- Pianykh OS, Rosenthal DI. Can We Predict Patient Wait Time? Journal of the American College of Radiology : JACR. 2015;12(10):1058-66. doi: 10.1016/j.jacr.2015.04.010.
- Lade IP, Sakhare V, Shelke M, Sawaitul P. Reduction of Waiting Time by Using Simulation & Queuing Analysi. International Journal on
- Recent and Innovation Trends in Computing and Communication. 2015;3(2):55-9.
- Vass H, Szabo ZK. Application of queuing model to patient flow in emergency department. Case study. Procedia Economics and Finance. 2015;32:479-87. doi: 10.1016/s2212-5671(15)01421-5.
- Duguay C, Chetouane F. Modeling and improving emergency department systems using discrete event simulation. Simulation. 2007;83(4):311-20. doi: 10.1177/0037549707083111.
- Salimifard K, Hosseinee S, Moradi M. Improving emergency department processes using computer simulation. Journal of Health Administration (JHA). 2014;17(55).
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