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A Discrete Event Simulation Model of an Emergency Department Service System

Year 2024, Volume: 1 Issue: 1, 52 - 56, 02.08.2024

Abstract

This paper presents a case study of a discrete event simulation model belonging to an emergency department in a regional hospital. The optimization of hospital emergency services is crucial for improving patient outcomes and operational efficiency. This study presents a discrete-event simulation model designed to enhance the performance of emergency departments by addressing key challenges such as resource scarcity, patient prioritization, and coordination between ambulances and hospitals. The model aims to reduce patient waiting times, optimize the utilization of doctors and nurses, and decrease operational costs. Simulation results indicate reduction in patient waiting times, increase in resource utilization efficiency, reduction in mortality rates for critical patients, and decrease in operational costs. These improvements highlight the potential of the proposed model to significantly enhance the quality and efficiency of emergency services. Future work will focus on refining the model and validating its effectiveness in diverse hospital settings.

References

  • Aminizadeh, S., Heidari, A., Dehghan, M., Toumaj, S., Rezaei, M., Navimipour, N. J., ... & Unal, M. (2024). Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service. Artificial Intelligence in Medicine, 149, 102779
  • Arslanhan, S. (2010). Does an increase in the examination number represent a significant increase in access?. Economic Policy Research Foundation of Turkey (EPRFT) Policy Topic. J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.
  • Doudareva, E., & Carter, M. (2022). Discrete event simulation for emergency department modelling: A systematic review of validation methods. Operations Research for Health Care, 33, 100340.
  • Ganjouhaghighi, N. (2024). Improving Emergency Department Efficiency: A Study of Physician Scheduling Strategies to Reduce Patient Wait Times.
  • Gul, M., & Guneri, A. F. (2012). A computer simulation model to reduce patient length of stay and to improve resource utilization rate in an emergency department service system. International Journal of Industrial Engineering, 19(5), 221-231
  • Horwitz, L. I., & Bradley, E. H. (2009). Percentage of US emergency department patients seen within the recommended triage time: 1997 to 2006. Archives of Internal Medicine, 169(20), 1857-1865.
  • Mousavi, S. R., Sepehri, M. M., & Najafi, S. E. (2024). Analysis of Importance-Performance of Scenarios for Improving Patient length of stay in the Operating Room. Trends in Health Informatics, 1(1), 1-8.
  • Pearce, S., Marr, E., Shannon, T., Marchand, T., & Lang, E. (2024). Overcrowding in emergency departments: an overview of reviews describing global solutions and their outcomes. Internal and Emergency Medicine, 19(2), 483-491.
  • Ponsiglione, A. M., Zaffino, P., Ricciardi, C., Di Laura, D., Spadea, M. F., De Tommasi, G., ... & Amato, F. (2024). Combining simulation models and machine learning in healthcare management: strategies and applications. Progress in Biomedical Engineering, 6(2), 022001.
  • Retezar, R., Bessman, E., Ding, R., Zeger, S. L., & McCarthy, M. L. (2011). The effect of triage diagnostic standing orders on emergency department treatment time. Annals of emergency medicine, 57(2), 89-99.
  • Soylu, S., & Tekkanat, A. (2007). Interactions amongst kernel properties and expansion volume in various popcorn genotypes. Journal of Food Engineering, 80(1), 336-341.
  • Taiwo, E. S., Zaerpour, F., Menezes, M. B., & Sun, Z. (2024). A complexity-based measure for emergency department crowding. International Journal of Operations & Production Management, 44(4), 768-789
  • Vaghani, K., Thakkar, V., Vaghasiya, S., Thaker, J., & Bhise, A. (2024, March). Implementation of Queuing Theory in Emergency Departments. In 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) (Vol. 2, pp. 1-6). IEEE.
  • van Montfort, L., Dullaert, W., & Leitner, M. (2024). Task-splitting in home healthcare routing and scheduling. arXiv preprint arXiv:2406.19288.
  • Varanasi, S., & Malathi, K. (2024). Self-improved COOT optimization-based LSTM for patient waiting time prediction. Multimedia Tools and Applications, 83(13), 39315-39333.
  • Vázquez-Serrano, J. I., Peimbert-García, R. E., & Cárdenas-Barrón, L. E. (2021). Discrete-event simulation modeling in healthcare: a comprehensive review. International journal of environmental research and public health, 18(22), 12262.
  • Zhang, X. (2018). Application of discrete event simulation in health care: a systematic review. BMC health services research, 18, 1-11.
Year 2024, Volume: 1 Issue: 1, 52 - 56, 02.08.2024

Abstract

References

  • Aminizadeh, S., Heidari, A., Dehghan, M., Toumaj, S., Rezaei, M., Navimipour, N. J., ... & Unal, M. (2024). Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service. Artificial Intelligence in Medicine, 149, 102779
  • Arslanhan, S. (2010). Does an increase in the examination number represent a significant increase in access?. Economic Policy Research Foundation of Turkey (EPRFT) Policy Topic. J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.
  • Doudareva, E., & Carter, M. (2022). Discrete event simulation for emergency department modelling: A systematic review of validation methods. Operations Research for Health Care, 33, 100340.
  • Ganjouhaghighi, N. (2024). Improving Emergency Department Efficiency: A Study of Physician Scheduling Strategies to Reduce Patient Wait Times.
  • Gul, M., & Guneri, A. F. (2012). A computer simulation model to reduce patient length of stay and to improve resource utilization rate in an emergency department service system. International Journal of Industrial Engineering, 19(5), 221-231
  • Horwitz, L. I., & Bradley, E. H. (2009). Percentage of US emergency department patients seen within the recommended triage time: 1997 to 2006. Archives of Internal Medicine, 169(20), 1857-1865.
  • Mousavi, S. R., Sepehri, M. M., & Najafi, S. E. (2024). Analysis of Importance-Performance of Scenarios for Improving Patient length of stay in the Operating Room. Trends in Health Informatics, 1(1), 1-8.
  • Pearce, S., Marr, E., Shannon, T., Marchand, T., & Lang, E. (2024). Overcrowding in emergency departments: an overview of reviews describing global solutions and their outcomes. Internal and Emergency Medicine, 19(2), 483-491.
  • Ponsiglione, A. M., Zaffino, P., Ricciardi, C., Di Laura, D., Spadea, M. F., De Tommasi, G., ... & Amato, F. (2024). Combining simulation models and machine learning in healthcare management: strategies and applications. Progress in Biomedical Engineering, 6(2), 022001.
  • Retezar, R., Bessman, E., Ding, R., Zeger, S. L., & McCarthy, M. L. (2011). The effect of triage diagnostic standing orders on emergency department treatment time. Annals of emergency medicine, 57(2), 89-99.
  • Soylu, S., & Tekkanat, A. (2007). Interactions amongst kernel properties and expansion volume in various popcorn genotypes. Journal of Food Engineering, 80(1), 336-341.
  • Taiwo, E. S., Zaerpour, F., Menezes, M. B., & Sun, Z. (2024). A complexity-based measure for emergency department crowding. International Journal of Operations & Production Management, 44(4), 768-789
  • Vaghani, K., Thakkar, V., Vaghasiya, S., Thaker, J., & Bhise, A. (2024, March). Implementation of Queuing Theory in Emergency Departments. In 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) (Vol. 2, pp. 1-6). IEEE.
  • van Montfort, L., Dullaert, W., & Leitner, M. (2024). Task-splitting in home healthcare routing and scheduling. arXiv preprint arXiv:2406.19288.
  • Varanasi, S., & Malathi, K. (2024). Self-improved COOT optimization-based LSTM for patient waiting time prediction. Multimedia Tools and Applications, 83(13), 39315-39333.
  • Vázquez-Serrano, J. I., Peimbert-García, R. E., & Cárdenas-Barrón, L. E. (2021). Discrete-event simulation modeling in healthcare: a comprehensive review. International journal of environmental research and public health, 18(22), 12262.
  • Zhang, X. (2018). Application of discrete event simulation in health care: a systematic review. BMC health services research, 18, 1-11.
There are 17 citations in total.

Details

Primary Language English
Subjects Information Modelling, Management and Ontologies
Journal Section Research Article
Authors

Sümeyra Sena Koçyiğit 0009-0006-3134-5046

Güney Gürsel 0000-0002-4063-2876

Early Pub Date July 25, 2024
Publication Date August 2, 2024
Submission Date July 19, 2024
Acceptance Date July 22, 2024
Published in Issue Year 2024 Volume: 1 Issue: 1

Cite

APA Koçyiğit, S. S., & Gürsel, G. (2024). A Discrete Event Simulation Model of an Emergency Department Service System. Uygulamalı Mühendislik Ve Tarım Dergisi, 1(1), 52-56.