Total views : 240

Estimating Emergency Department Maximum Capacity using Simulation and Data Envelopment Analysis

Affiliations

  • Department of Computational and Theoretical Sciences, Kulliyah of Science, Universiti Islam Antarabangsa Malaysia, Kuantan, Pahang, Malaysia
  • School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia
  • Department of Emergency Medicine, UKM Medical Centre, Cheras, Kuala Lumpur, Malaysia

Abstract


Recently, the number of Emergency Department visits in UKM Medical Centre has increased rapidly. This has encouraged several healthcare problems occur in the emergency department such as overcrowding. A study was conducted to estimate the maximum possible demand that able to serve by the emergency department with current number of resources. Discrete Event Simulation method was applied to model the Emergency Department system. The model was used to study the behavior of waiting time of the emergency department visits and predict the maximum demand. Finally, combination of Data Envelopment Analysis methods (BCC input-oriented and Super-efficiency-BCC) was used in order to determine the new configuration of number of resources (doctor and nurse) required to maintain their efficient services. Results assume that the Emergency Department capable to accommodate 230 patients per day with an additional doctor to Yellow Zone Treatment Area and new schedule time for Green Zone doctors.

Keywords

Data Envelopment Analysis, Discrete Event Simulation, Emergency Department Overcrowding, Healthcare Modelling.

Full Text:

 |  (PDF views: 251)

References


  • Nik Azlan NM, Ismail MS, Azizol M. Management of Emergency Department Overcrowding (EDOC) in a teaching hospital. Med and Health. 2013; 8(1):42–6.
  • Cowan RM, Trzeciak S. Critical review: Emergency Department Overcrowding and the potential impact on the critical ill. Critical Care. 2005; 9:291–5.
  • Nathan RH, Dominik A. Systematic review of Emergency Department Crowding: Causes, effects and solutions. Annals of Emergency Medicine. 2008; 52(2):126–36.
  • Baesler FF, Jahnsen HE, DaCosta M. The use or simulation and design of experiments for estimating maximum capacity in an emergency room. Winter Simulation Conference; 2003. p. 1903–6.
  • Brailsford SC, Harper PR, Patel B, et al. An analysis of the academic literature on simulation and modelling in health care. Journal of Simulation. 2009; 3:130–40.
  • Kelton WD, Sadowski RP, Zupick NB. Simulation with ARENA. 6th ed. McGraw-Hill Education; 2015.
  • Ali A, Hossein T, Mansour Z, et al. An integrated algorithm for performance optimization of neurosurgical ICUs. Expert System with Applications. 2016; 43:142–53.
  • Anderson D, Sweeney D, Williams T. An introduction to management science. Thomson South-Western; 2005.
  • Jun JB, Jacobson SH, Swisher JR. Application of discrete even simulation in health care clinics: A survey. Journal of the Operational Research Society. 1999; 50:109–23.
  • DeLia D. Emergency Department utilization and surge capacity in New Jersey. (A report to New Jersey Department of Health and Senior Services 2005). 1998- 2003. Available from: www.cshp.rutgers.edu/downloads/5020.pdf

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.