1887
Volume 2015, Issue 1
  • E-ISSN: 2223-506X

Abstract

The emergency department (ED) is a primary health care unit and one of the main entrances to the hospital system where appropriate, timely and good performance can save lives. Lack of sufficient resources, such as beds and qualified health care professionals, are major stumbling blocks to providing timely and suitable services; but resources availability and moving towards the ideal situation without attention to budget restrictions is neither practical nor achievable. In this study, simulation optimization is used to finding the best configuration in ED resources (e.g., Bed, Nurse, and GP) that affects a patient's length of stay, subject to budget constraints. Simulation is used to analyze the system and estimate target function an optimization model is then solved under different budget constraints. By considering the current budget, the new configuration of 20 inpatient beds, 3 nurses and 1 GP, with 554.4 minutes of a patient's length of stay shows 8.1% length of stay (LOS) improvement. Whilst with a maximum 35.5 budget units allocation of 20 inpatient beds, 4 nurses and 3 GPs a 9.5% decrease in LOS is proposed.

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2015-08-01
2019-12-12
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References

  1. Abo-Hamad W, Arisha A. Simulation-based framework to improve patient experience in an emergency department. European Journal of Operational Research. 2013; 224:1:154166.
    [Google Scholar]
  2. Ahmad N, Abdul Ghani N, Abdulbasah Kamil A, Mat Tahar R, Howe Teo A. Evaluating emergency department resource capacity using simulation. Modern Applied Science. 2012; 6:11:919.
    [Google Scholar]
  3. Ahmed M, Alkhamis T. Simulation optimization for an emergency department healthcare unit in Kuwait. European Journal of Operational Research. 2009; 198:3:936942.
    [Google Scholar]
  4. Asante AD, Zwi AB. Factors influencing resource allocation decisions and equity in the health system of Ghana. Public Health. 2009; 123:5:371377.
    [Google Scholar]
  5. Banks J, Carson J, Nelson B, Nicol D. Discrete-event system simulation. 4th Edition. Prentice Hall 2004.
    [Google Scholar]
  6. Bekker R, Koeleman PM. Scheduling admissions and reducing variability in bed demand. Health Care Management Science. 2011; 14:39:237249.
    [Google Scholar]
  7. Brailsford S, Vissers J. OR in healthcare: A European perspective. European Journal of Operational Research. 2011; 212:2:223234.
    [Google Scholar]
  8. Brenner S, Zeng Z, Liu Y, Wang J, Howard J. Modeling and analysis of the emergency department at University of Kentucky Chandler Hospital using simulations. Journal of Emergency Nursing. 2010; 36:4:303310.
    [Google Scholar]
  9. Cochran J, Broyles J. Developing nonlinear queueing regressions to increase emergency department patient safety: Approximating reneging with balking. Computers and Industrial Engineering. 2010; 59:3:378386.
    [Google Scholar]
  10. Faezipour M, Ferreira S. A system dynamics perspective of patient satisfaction in healthcare. Procedia Computer Science. 2013; 16::148156.
    [Google Scholar]
  11. Ferreira J, Gomes C, Yasin M. Improving patients’ satisfaction through more effective utilization of operating rooms resources: An informational-based perspective. Clinical Governance: An International Journal. 2011; 16:4:291307.
    [Google Scholar]
  12. Griffiths J, Jones M, Williams J. A simulation model of bed-occupancy in a critical care unit. Journal of Simulation. 2010; 4:1:5259.
    [Google Scholar]
  13. Holm L, Lurås H, Dahl F. Improving hospital bed utilisation through simulation and optimisation: With application to a 40% increase in patient volume in a Norwegian general hospital. International Journal of Medical Informatics. 2013; 82:2:8089.
    [Google Scholar]
  14. Holm LB, Dahl FA. Simulating the influence of a 45% increase in patient volume on the emergency department of Akershus University Hospital. In Proceedings of the 2010 Winter Simulation Conference, 2010;:24552461.
  15. Izady N, Worthington D. Setting staffing requirements for time dependent queueing networks: The case of accident and emergency departments. European Journal of Operational Research. 2012; 219:3:531540.
    [Google Scholar]
  16. Jacobson SH, Hall SN, Swisher JR. Discrete-event simulation of healthcare systems. Operations Research and Management Science. 2006; 91::211252.
    [Google Scholar]
  17. Laker LF, Froehle CM, Lindsell CJ, Ward MJ. The Flex Track: Flexible partitioning between low- and high-acuity areas of an emergency department. Annals of Emergency Medicine. 2014; 64:6:591603.
    [Google Scholar]
  18. Lim ME, Worster A, Goeree R, Tarride J-É. Simulating an emergency department: the importance of modeling the interactions between physicians and delegates in a discrete event simulation. BMC Medical Informatics and Decision Making. 2013; 13::59.
    [Google Scholar]
  19. Ma W, Gafni A, Goldman R. Correlation of the Canadian pediatric emergency triage and acuity scale to ED resource utilization. American Journal of Emergency Medicine. 2008; 26:8:893897.
    [Google Scholar]
  20. Ng C-J, Hsu K-H, Kuan J-T, Chiu T-F, Chen W-K, Lin H-J, Bullard MJ, Chen J-C. Comparison between Canadian triage and acuity scale and Taiwan triage system in emergency departments. Journal of the Formosan Medical Association. 2010; 109:11:828837.
    [Google Scholar]
  21. Reyes-Santías F, Cadarso-Suárez C, Martínez-Calvo A. Applying a simulation model in order to manage waiting lists for hospital inpatient activity in an EU region. Mathematical and Computer Modelling. 2013; 57:7–8:18401846.
    [Google Scholar]
  22. Schmidt R, Geisler S, Spreckelsen C. Decision support for hospital bed management using adaptable individual length of stay estimations and shared resources. Medical Informatics and Decision Making. 2013; 13:3:119.
    [Google Scholar]
  23. Shimada M, Tanabe A, Gunshin M, Riffenburgh RH, Tanen DA. Resource utilization in the emergency department of a tertiary care university-based hospital in Tokyo before and after the 2011 Great East Japan earthquake and tsunami. Prehospital and Disaster Medicine. 2012; 27:6:515518.
    [Google Scholar]
  24. Weng S-J, Cheng B-C, Ting Kwong S, Wang L-M,, Chang C-Y. Simulation optimization for emergency department resources allocation. In Proceedings of the 2011 Winter Simulation Conference. Phoenix, AZ, 2011;:12311238.
  25. Xu M, Wong T, Chin K. Modeling daily patient arrivals at Emergency Department and quantifying the relative importance of contributing variables using artificial neural network. Decision Support Systems. 2013; 54:3:14881498.
    [Google Scholar]
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  • Article Type: Research Article
Keyword(s): cost , emergency department , resource allocation and simulation optimization
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