Skip to main content

Improving the Design and Operation of an Integrated Emergency Post via Simulation

  • Chapter
Operational Research for Emergency Planning in Healthcare: Volume 1

Part of the book series: The OR Essentials series ((ORESS))

  • 817 Accesses

Abstract

In the Netherlands, patients with an acute care demand after office hours often wrongly choose to visit the emergency department (ED), while they could have visited the general practitioners’ post (GPP). This may lead to overcrowding and increased costs. In this paper, we focus on an Integrated Emergency Post (IEP) at a Dutch hospital, where the ED and the GPP have been merged into a single point of access for patients. To find the optimal process design for this new IEP, we use computer simulation incorporating patient preferences. We define many potential interventions, and compare these by categorizing and grouping them, and sequentially withdrawing ineffective interventions, while accounting for possible interaction effects. Results show a sustainable solution for all stakeholders involved, reducing patients’ length of stay up to 17%. Based on these results, an intervention has been trialled in practice, showing a decrease in patient LOS.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Ahmed MA and Alkhamis TM (2009). Simulation optimization for an emergency department healthcare unit in Kuwait. European Journal of Operational Research 198 (3): 936–942.

    Article  Google Scholar 

  • Andradottir S (1998). A review of simulation optimization techniques. In: Proceedings of the 30th conference on Winter simulation, Washington, DC, USA, IEEE Computer Society Press, pp 151–158.

    Google Scholar 

  • AndradĂłttir S (2006). Chapter 20 An overview of simulation optimization via random search. In: Henderson SG and Nelson BL (eds). Handbooks in Operations Research and Management Science. Vol. 13, Elsevier: Amsterdam, pp 617–631.

    Google Scholar 

  • April J, Glover F, Kelly JP and Laguna M (2003). Simulation-based optimization: practical introduction to simulation optimization. In: Proceedings of the 35th conference on Winter simulation: driving innovation, New Orleans, Louisiana, Winter Simulation Conference, pp 71–78.

    Google Scholar 

  • Ashour OM and Kremer GEO (2013). A simulation analysis of the impact of FAHP-MAUT triage algorithm on the emergency department performance measures. Expert Systems with Applications 40 (1): 177–187.

    Article  Google Scholar 

  • Barton RR and Meckesheimer M (2006). Chapter 18: Metamodel-based simulation optimization. In: Henderson SG and Nelson BL (eds). Handbooks in Operations Research and Management Science. Vol. 13, Elsevier: Amsterdam, pp 535–574.

    Google Scholar 

  • Bettonvil B and Kleijnen JPC (1997). Searching for important factors in simulation models with many factors: Sequential bifurcation. European Journal of Operational Research 96 (1): 180–194.

    Article  Google Scholar 

  • Boesel J, Nelson BL and Kim S-H (2003). Using ranking and selection to “clean up” after simulation optimization. Operations Research 51 (5): 814–825.

    Article  Google Scholar 

  • Brailsford SC, Harper PR, Patel B and Pitt M (2009). An analysis of the academic literature on simulation and modelling in health care. Journal of Simulation 3 (3): 130–140.

    Article  Google Scholar 

  • Cheng RCH (1997). Searching for important factors: Sequential bifurcation under uncertainty. In: Proceedings of the 29th conference on Winter simulation, Atlanta, Georgia, USA, IEEE Computer Society, pp 275–280.

    Google Scholar 

  • Dale J, Green J, Reid F and Glucksman E (1995). Primary care in the accident and emergency department: I. Prospective identification of patients. Bmj 311 (7002): 423–426.

    Article  Google Scholar 

  • Doggen CJM, Hans EW, Snel JE, Velde DV and Verheij HJW (2010). Subsidieaanvraag: “Optimale logistiek en patienten voorkeuren in de acute zorgketen; de huisartsenpost en spoedeisende hulpin een geintegreerde spoedpost.” Research Proposal, University of Twente.

    Google Scholar 

  • Duguay C and Chetouane F (2007). Modeling and improving emergency department systems using discrete event simulation. Simulation 83 (4): 311–320.

    Article  Google Scholar 

  • Fone D et al (2003). Systematic review of the use and value of computer simulation modelling in population health and health care delivery. Journal of Public Health 25 (4): 325–335.

    Article  Google Scholar 

  • Fransman C. (2011). Patient and community preferences for out-ofhours emergency care using best-worst scaling. MSc thesis, University of Twente.

    Google Scholar 

  • Fu MC, Glover FW and April J (2005). Simulation optimization: a review, new developments, and applications. In: Proceedings of the 37th conference on Winter simulation, Orlando, Florida, Winter Simulation Conference, pp 83–95.

    Google Scholar 

  • Giesen P, Franssen E, Mokkink H, Bosch W, van Vugt A and Grol R (2006). Patients either contacting a general practice cooperative or accident and emergency department out of hours: A comparison. Emergency Medical Journal 23 (9): 731–734.

    Article  Google Scholar 

  • Grol R, Giesen P and van Uden C (2006). After-hours care in the United Kingdom, Denmark, and the Netherlands: New models. Health Affairs 25 (6): 1733–1737.

    Article  Google Scholar 

  • Gunal MM and Pidd M (2010). Discrete event simulation for performance modelling in health care: A review of the literature. Journal of Simulation 4 (1): 42–51.

    Article  Google Scholar 

  • Hoot NR and Aronsky D (2008). Systematic review of emergency department crowding: Causes, effects, and solutions. Annals of Emergency Medicine 52 (2): 126–136.

    Article  Google Scholar 

  • Ivanova T, Malone L and Mollaghasemi M (1999). Comparison of a two-stage group-screening design to a standard 2k-p design for a whole-line semiconductor manufacturing simulation model. In: Proceedings of the 31st conference on Winter simulation: Simulation—a bridge to the future—Volume 1, Phoenix, Arizona, USA, ACM, pp 640–646.

    Google Scholar 

  • Jun JB, Jacobson SH and Swisher JR (1999). Application of discrete-event simulation in health care clinics: A survey. Journal of the Operational Research Society 50 (2): 109–123.

    Article  Google Scholar 

  • Jurishica CJ (2005). Emergency department simulations: medicine for building effective models. In: Proceedings of the 37th conference on Winter simulation, Orlando, Florida, Winter Simulation Conference, pp 2674–2680.

    Google Scholar 

  • Kelton WD (2000). Design of experiments: experimental design for simulation. In: Proceedings of the 32nd conference on Winter simulation, Orlando, Florida, Society for Computer Simulation International, pp 32–38.

    Google Scholar 

  • Kleijnen JPC (2008). Design of experiments: overview. In: Proceedings of the 40th Conference on Winter Simulation, Miami, Florida, Winter Simulation Conference, pp 479–488.

    Google Scholar 

  • Komashie A and Mousavi A (2005). Modeling emergency departments using discrete event simulation techniques. In: Proceedings of the 37th conference on Winter simulation, Orlando, Florida, Winter Simulation Conference, pp 2681–2685.

    Google Scholar 

  • Kool R, Homberg D and Kamphuis H (2008). Towards integration of general practitioner posts and accident and emergency departments: A case study of two integrated emergency posts in the Netherlands. BMC Health Services Research 8 (1): 225.

    Article  Google Scholar 

  • Kulu-Glasgow I, Delnoij D and de Bakker D (1998). Self-referral in a gatekeeping system: Patients’ reasons for skipping the general-practitioner. Health Policy 45 (3): 221–238.

    Article  Google Scholar 

  • Law AM (2007). Simulation Modeling and Analysis. McGraw-Hill: Boston, MA.

    Google Scholar 

  • Mackway-Jones K, Marsden J and Windle J Machester Triage Group (2006). Emergency Triage. Wiley: New York.

    Google Scholar 

  • Mauro CA (1984). On the performance of two-stage group screening experiments. Technometrics 26 (3): 255–264.

    Article  Google Scholar 

  • Mes M and Bruens M (2012). A generalized simulation model of an integrated emergency post. In: Proceedings of the Winter Simulation Conference, Berlin, Germany, Winter Simulation Conference, pp 1–11.

    Google Scholar 

  • Mielczarek B and UziaĹ‚ko-Mydlikowska J (2012). Application of computer simulation modeling in the health care sector: A survey. Simulation 88 (2): 197–216.

    Article  Google Scholar 

  • Montgomery DC (2008). Design and Analysis of Experiments. Wiley: New York.

    Google Scholar 

  • Paul SA, Reddy MC and DeFlitch CJ (2010). A systematic review of simulation studies investigating emergency department overcrowding. Simulation-Transactions of the Society for Modeling and Simulation International 86 (8–9): 559–571.

    Google Scholar 

  • Sinreich D, Jabali O and Dellaert NP (2012). Reducing emergency department waiting times by adjusting work shifts considering patient visits to multiple care providers. IIE Transactions (Institute of Industrial Engineers) 44 (3): 163–180.

    Google Scholar 

  • Sinreich D and Marmor Y (2005). Emergency department operations: The basis for developing a simulation tool. IIE Transactions (Institute of Industrial Engineers) 37 (3): 233–245.

    Google Scholar 

  • Thijssen WA, Giesen PH and Wensing M (2012). Emergency departments in the Netherlands. Emergency Medicine Journal 29 (1): 6–9.

    Article  Google Scholar 

  • Trocine L and Malone LC (2001). Experimental design and analysis: An overview of newer, advanced screening methods for the initial phase in an experimental design. In: Proceedings of the 33rd conference on Winter simulation, Arlington, Virginia, IEEE Computer Society, pp 169–178.

    Google Scholar 

  • WCED (1987). Our Common Future. Oxfourd University Press: New York.

    Google Scholar 

  • Yaesoubi R, Roberts SD and Klein RW (2010). Modification of cheng’s method: An alternative factor screening method for stochastic simulation models. In: Proceedings of the Winter Simulation Conference, Baltimore, Maryland, Winter Simulation Conference, pp 1034–1047.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Copyright information

© 2016 Operational Research Society

About this chapter

Cite this chapter

Borgman, N.J., Mes, M.R.K., Vliegen, I.M.H., Hans, E.W. (2016). Improving the Design and Operation of an Integrated Emergency Post via Simulation. In: Mustafee, N. (eds) Operational Research for Emergency Planning in Healthcare: Volume 1. The OR Essentials series. Palgrave Macmillan, London. https://doi.org/10.1057/9781137535696_8

Download citation

Publish with us

Policies and ethics