Health Systems

, Volume 6, Issue 1, pp 33–40 | Cite as

Demand and capacity modelling for acute services using discrete event simulation

  • Eren Demir
  • Murat M GunalEmail author
  • David Southern
Original Article


Increasing demand for services in England with limited healthcare budget has put hospitals under immense pressure. Given that almost all National Health Service (NHS) hospitals have severe capacity constraints (beds and staff shortages), a decision support tool (DST) is developed for the management of a major NHS Trust in England. Acute activities are forecasted over a 5-year period broken down by age groups for 10 specialty areas. Our statistical models have produced forecast accuracies in the region of 90%. We then developed a discrete event simulation model capturing individual patient pathways until discharge (in accident and emergency, inpatient and outpatients), where arrivals are based on the forecasted activity outputting key performance metrics over a period of time, for example, future activity, bed occupancy rates, required bed capacity, theatre utilisations for electives and non-electives, clinic utilisations and diagnostic/treatment procedures. The DST allows Trusts to compare key performance metrics for thousands of different scenarios against their existing service (baseline). The power of DST is that hospital decision makers can make better decisions using the simulation model with plausible assumptions that are supported by statistically validated data.


simulation decision support system hospital capacity hospital resources 


  1. Alexopoulos C, Goldsman D, Fontanesi J, Kopald D and Wilson JR (2008) Modeling patient arrivals in community clinics. Omega 36(1), 33–43.CrossRefGoogle Scholar
  2. Atun RA, Lebcir MR, McKee M, Habicht J and Coker RJ (2007) Impact of joined-up HIV harm reduction and multidrug resistant tuberculosis control programmes in Estonia: system dynamics simulation model. Health Policy 81(2), 207–217.CrossRefGoogle Scholar
  3. Bagust A, Place M and Posnett JW (1999) Dynamics of bed use in accommodating emergency admissions: stochastic simulation model. British Medical Journal 319(7203), 155–158.CrossRefGoogle Scholar
  4. Bennett P, Hare A and Townshend J (2005) Assessing the risk of vCJD transmission via surgery: models for uncertainty and complexity. Journal of the Operational Research Society 56(2), 202–213.CrossRefGoogle Scholar
  5. Brailsford SC, Lattimer VA, Turnaras P and Turnbull JC (2004) Emergency and on demand health care: modelling a large complex system. Journal of the Operational Research Society 55(1), 34–42.CrossRefGoogle Scholar
  6. Cote MJ (2000) Understanding patient flow. Decision Line 31(2000), 8–10.Google Scholar
  7. Dangerfield BC, Fang Y and Roberts CA (2001) Model-based scenarios for the epidemiology of HIV/AIDS: the consequences of highly active antiretroviral therapy. System Dynamics Review 17(2), 119–150.CrossRefGoogle Scholar
  8. Department of Health (2007) [WWW document] Patient Pathways: Department of Health - Health care, (accessed September 2015).
  9. Gallivan S, Utley M, Treasure T and Valencia O (2002) Booked inpatient admissions and hospital capacity: mathematical modelling study. British Medical Journal 324(7332), 280–282.CrossRefGoogle Scholar
  10. Gunal MM and Pidd M (2010) Discrete event simulation for performance modeling in healthcare: a review of the literature. Journal of Simulation 4(1), 42–51.CrossRefGoogle Scholar
  11. Gunal MM and Pidd M (2011) DGHPSIM: generic simulation of hospital performance. ACM Transactions on Modeling and Computer Simulation (TOMACS) 21(4), 1–22.CrossRefGoogle Scholar
  12. Gunal MM (2012) A guide for building hospital simulation models. Health Systems 1(1), 17–25.CrossRefGoogle Scholar
  13. Hall R, Belson D, Murali P and Dessouky M (2006) Modeling patient flow through the healthcare system. In Patient Flow: Reducing Delay in Healthcare Delivery, (Randolph WH, Ed), pp 1–44, Springer-Verlag, Los Angeles, California, USA.CrossRefGoogle Scholar
  14. Harper PR (2002) A framework for operational modelling of hospital resources. Heath Care Management Science 5(3), 165–173.CrossRefGoogle Scholar
  15. Harper PR and Shahani AK (2002) Modelling for the planning and management of bed capacities in hospitals. Journal of Operational Research Society 53(1), 11–18.CrossRefGoogle Scholar
  16. Katsaliaki K and Mustafee N (2011) Application of simulation within the healthcare context. Journal of the Operational Research Society 62(8), 1431–1451.CrossRefGoogle Scholar
  17. Knight VA, Williams JE and Reynolds I (2012) Modelling patient choice in healthcare systems: development and application of a discrete event simulation with agent-based decision making. Journal of Simulation 6(2012), 92–102.CrossRefGoogle Scholar
  18. Lane DC, Monefeldt C and Rosenhead JV (2000) Looking in the wrong place for healthcare improvements: a system dynamics study of an accident and emergency department. Journal of the Operational Research Society 51(5), 518–531.CrossRefGoogle Scholar
  19. Lane DC and Oliva R (1998) The greater whole: towards a synthesis of system dynamics and soft system methodology. European Journal of Operational Research 107(1), 214–235.CrossRefGoogle Scholar
  20. Laskowski M, Demianyk BCP, Witt J, Mukhi SN, Friesen MR and McLeod RD (2011) Agent-based modeling of the spread of influenza-like illness in an emergency department: a simulation study. IEEE Transactions on Information Technology in Biomedicine 15(6), 877–889.CrossRefGoogle Scholar
  21. Macal CM and North MJ (2010) Tutorial on agent-based modelling and simulation. Journal of Simulation 4(3), 151–162.CrossRefGoogle Scholar
  22. Pitt M, Monks T, Crowe S and Vasilakis C (2016) Systems modelling and simulation in health service policy, delivery and design making. BMJ Quality and Safety 25(1), 38–45.CrossRefGoogle Scholar
  23. Reda ML, Atun RA and Coker RJ (2010) System dynamic simulation of treatment policies to address colliding epidemics of tuberculosis, drug resistant tuberculosis, and injecting drug users driven HIV in Russia. Journal of the Operational Research Society 61(8), 1238–1248.CrossRefGoogle Scholar
  24. Ritchie-Dunham JL and Mendez-Galvan JF (1999) Evaluating epidemic interventions policies with system thinking: a case study of the dengue fever in Mexico. System Dynamics Review 15(2), 119–138.CrossRefGoogle Scholar
  25. Robinson S (2004) Simulation: The Practice of Model Development and Use. John Wiley & Sons, Chichester, West Sussex, UK; Hoboken, NJ.Google Scholar
  26. Rohleder TR, Lewkonia P, Bischak DP, Duffy P and Hendijani R (2011) Using simulation modeling to improve patient flow at an outpatient orthopaedic clinic. Health Care Management Science 14(2), 135–145.CrossRefGoogle Scholar
  27. Sterman JD (2000) Business Dynamics: Systems Thinking and Modeling for a Complex World. Mc-Graw Hill, Singapore.Google Scholar
  28. Taylor K and Dangerfield BC (2005) Modelling the feedback effects of reconfiguring health services. Journal of the Operational Research Society 56(6), 659–675.CrossRefGoogle Scholar
  29. Utley M, Gallivan S, Treasure T and Valencia O (2003) Analytical methods for calculating the capacity required to operate an effective booked admissions policy for elective inpatient services. Health Care Management Science 6(2), 97–104.CrossRefGoogle Scholar
  30. Vasilakis C, El-Darzi E and Chountas P (2008) A decision support system for measuring and modelling the multi-phase nature of patient flow in hospitals. In Intelligent Techniques and Tools for Novel System Architectures (Chountas P, Petrounias I and Kacprzyk J, Eds), pp 201–217, Springer, Berlin.CrossRefGoogle Scholar
  31. Vissers JMH (1998) Patient flow-based allocation of in-patient resources: a case study. European Journal of Operational Research 105(2), 356–370.CrossRefGoogle Scholar
  32. Vissers J and Beech R (2005) Health Operations Management: Patient Flow Logistics in Health Care. Routledge Publishing, New York, NY, USA.Google Scholar
  33. Worthington D (1991) Hospital waiting list management models. Journal of the Operational Research Society 42(10), 833–843.CrossRefGoogle Scholar
  34. Zonderland ME and Boucherie RJ (2012) Queuing networks in healthcare systems. In Handbook if Healthcare System Scheduling, International Series in Operations Research and Management Science, (Randolph WH, Ed), pp 201–243, Springer-Verlag, Los Angeles, California, USA.Google Scholar

Copyright information

© The OR Society 2016

Authors and Affiliations

  1. 1.University of HertfordshireHatfieldUK
  2. 2.Turkish Naval AcademyIstanbulTurkey
  3. 3.Pathway Communications LtdKennettUK

Personalised recommendations