Performance Measurement and Improvement of Healthcare Service Using Discrete Event Simulation in Bahir Dar Clinic
Subject Areas : Management of Technology and production
aregawi
yemane
1
(industrial engineering, Faculty of mechanical and industrial engineering , bahirdar University ,bahirdar , Ethiopia)
hagazi
heniey
2
(industrial engineering,mekelle university,ethiopia)
kidane Gidey
Gebrehiwet
3
(industrial engineering, mekelle university,ethiopia)
Keywords: Discrete Event Simulation, Healthcare, performance analysis, model, WIP,
Abstract :
This paper deals with the service performance analysis and improvement using discrete event simulation has been used. The simulation of the health care has been done by arena master development 14-version software. The performance measurement for this study are patients output, service rate, service efficiency and it is directly related to waiting time of patients in each service station, work in progress, resource utilization. Simulation model was building for Bahir Dar clinic and then, prepared the proposed model for the system. Based on the simulation model run result, the output of the existing healthcare service system is low due to presence of bottlenecks on the service system. Moreover, the station with the largest queue and high resource utilization are identified as a bottleneck. The bottlenecks, which have identified are reduced by using reassigning the existing resources and add new resources and merging the similar services, which has under low resource utilization (nurses). Finally, the researchers have proposed a developed model from different scenarios. Moreover, the best scenario is developed by combining scenario 2 and 3. And then, service efficiency of the healthcare has increased by 9.86 percent, the work in progress (WIP) are reduced by 3 patients from the system and the service capacity of the system is increased 34 to 40 patients per day due to the reduction of bottleneck stations.
Appah, Sam, A., & Alex. (2014). Queuing theory and the management of Waiting-time in Hospitals: The case of Anglo Gold Ashanti Hospital in Ghana. International Journal of Academic Research in Business and Social Sciences, 34-44.
Aregawi, y., Gebremedhin, G., Teklewold, M., & Misgna, H. (2020). Productivity Improvement through Line Balancing by Using Simulation Modeling (Case study Almeda Garment Factory). Journal of Optimization in Industrial Engineering, 153-165.
Aregawi, Y., Serajul, H., & Evan, s. M. (2017). Optimal layout Design by line balancing using simulation modeling. proceeding of the international conference on Industrial engineering and operation management Bogota, (pp. 228-244).
Aregawi, yemane; serajul, haque; Iván, Santelices Malfanti. (2017). Identification Using Time Study and Simulation Modeling of Apparel Industries. International Conference on Industrial Engineering and Operations Management., (pp. 321-331).
Eduardo, C., Manel, T., MaLuisa, I., Francisco, E., & Emilio, L. (2012). Simulation Optimization for Healthcare Emergency Departments. International Conference on Computational Science (pp. 1464 –1473). Procedia Computer Science.
Eshetie, b., Selam, Y., & Sisay, G. (2018). Service Performance Improvement Model: The Case of Teklehaymanot General Hospital. Journal of Optimization in Industrial Engineering, 23-33.
Globerman, S. (2013). Reducing Wait Times for Health Care: What Canada Can Learn from Theory and International Experience. Fraser Institute.
Haussmann. (1970). waiting time as an index of quality of nursing care.
Heflin, & Harrell. (1998). Healthcare simulation modeling and optimization using MedModel.
Israel, G. D. (1992). Determining sample size. University of Flordia.
Junqiao, C., David, C., Monica, D. O., & Alexandra, F. (2019). An Analysis on the Research Orientations in Healthcare Simulation Modeling. Proceedings of the Modeling and Simulation in Medicine Symposium. doi:DOI: 10.22360/springsi
Kelton, W. D. (2002). simulation with arena second edition. Mc Graw Hill.
Mielczarek, B. (2016). Review of modelling approaches for healthcare simulation. operations research and decisions, (pp. 55-72). doi:DOI: 10.5277/ord160104, 55-72
Sally, B., & Shivam, D. J. (2010). Combining system dynamics and discrete event simulation health care. Winter Simulation Conference, (pp. 2293-2301).
Samuel Fomundam, J. H. (2007). A Survey of Queuing Theory Applications in Healthcare. ISR .
Sheldon, H., Shane, N. H., & James, R. S. (2006). in Operations Research & Management. International Series in Operations Research & Management Science book series.
Sulaf, A. (2015). Simulation modelling in healthcare: Challenges and trends. international Conference on Applied Human Factors and Ergonomics, (pp. 301-307).
Topaloglu, S. (2009). A shift scheduling model for employees with different seniority levels and an application in healthcare. European Journal of Operational Research, 943-957.
Umar, I., Oche, M. O., & Umar, A. S. (2011). Patient waiting time in a tertiary health institution in Northern Nigeria. Journal of Public Health and Epidemiology, 78-82.
Vos, L., Groothus, S., & Van, M. (2007). Evaluting hospital design from an operation management perspective. Springer Science and Business Media.
Xiange, Z. (2018). Application of discrete event simulation in health care: a systematic review. Health Services Research.