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Demand and capacity modelling for acute services using discrete event simulation

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Health Systems

Abstract

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.

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References

  • Alexopoulos C, Goldsman D, Fontanesi J, Kopald D and Wilson JR (2008) Modeling patient arrivals in community clinics. Omega 36(1), 33–43.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Cote MJ (2000) Understanding patient flow. Decision Line 31(2000), 8–10.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Department of Health (2007) [WWW document] Patient Pathways: Department of Health - Health care, http://webarchive.nationalarchives.gov.uk/+ www.dh.gov.uk/en/Healthcare/Primarycare/Treatmentcentres/DH_4097263 (accessed September 2015).

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Gunal MM and Pidd M (2011) DGHPSIM: generic simulation of hospital performance. ACM Transactions on Modeling and Computer Simulation (TOMACS) 21(4), 1–22.

    Article  Google Scholar 

  • Gunal MM (2012) A guide for building hospital simulation models. Health Systems 1(1), 17–25.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • Harper PR (2002) A framework for operational modelling of hospital resources. Heath Care Management Science 5(3), 165–173.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Katsaliaki K and Mustafee N (2011) Application of simulation within the healthcare context. Journal of the Operational Research Society 62(8), 1431–1451.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Macal CM and North MJ (2010) Tutorial on agent-based modelling and simulation. Journal of Simulation 4(3), 151–162.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Robinson S (2004) Simulation: The Practice of Model Development and Use. John Wiley & Sons, Chichester, West Sussex, UK; Hoboken, NJ.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Sterman JD (2000) Business Dynamics: Systems Thinking and Modeling for a Complex World. Mc-Graw Hill, Singapore.

    Google Scholar 

  • Taylor K and Dangerfield BC (2005) Modelling the feedback effects of reconfiguring health services. Journal of the Operational Research Society 56(6), 659–675.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • Vissers JMH (1998) Patient flow-based allocation of in-patient resources: a case study. European Journal of Operational Research 105(2), 356–370.

    Article  Google Scholar 

  • Vissers J and Beech R (2005) Health Operations Management: Patient Flow Logistics in Health Care. Routledge Publishing, New York, NY, USA.

    Google Scholar 

  • Worthington D (1991) Hospital waiting list management models. Journal of the Operational Research Society 42(10), 833–843.

    Article  Google Scholar 

  • 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 

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Correspondence to Murat M Gunal.

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Demir, E., Gunal, M. & Southern, D. Demand and capacity modelling for acute services using discrete event simulation. Health Syst 6, 33–40 (2017). https://doi.org/10.1057/hs.2016.1

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