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

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Abstract

This paper describes how system dynamics was used as a central part of a whole-system review of emergency and on-demand healthcare in Nottingham, England. Based on interviews with 30 key individuals across health and social care, a ‘conceptual map’ of the system was developed, showing potential patient pathways through the system. This was used to construct a stock-flow model, populated with current activity data, in order to simulate patient flows and to identify system bottle-necks. Without intervention, assuming current trends continue, Nottingham hospitals are unlikely to reach elective admission targets or achieve the government target of 82% bed occupancy. Admissions from general practice had the greatest influence on occupancy rates. Preventing a small number of emergency admissions in elderly patients showed a substantial effect, reducing bed occupancy by 1% per annum over 5 years. Modelling indicated a range of undesirable outcomes associated with continued growth in demand for emergency care, but also considerable potential to intervene to alleviate these problems, in particular by increasing the care options available in the community.

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References

  1. http://www.nottinghm-ha.trent.nhs.uk, downloaded Oct 21, 2002.

  2. Audit Commission (1996). By Accident or Design: Improving A&E Services in England and Wales. HMSO: London.

    Google Scholar 

  3. Audit Commission (2001). Accident and Emergency Acute Hospital Portfolio: Review of National Findings. Audit Commission: London.

    Google Scholar 

  4. Lattimer VA et al (2004). Reviewing emergency care systems I: insights from system dynamics modelling. Emerg Med J, In press.

    Google Scholar 

  5. Gerard K et al (2004). Reviewing emergency care systems II: measuring patient preferences using a discrete choice experiment. Emerg Med J, In press.

    Google Scholar 

  6. Brailsford SC and Hilton NA (2001). A comparison of discrete event simula-tion and system dynamics for modelling healthcare systems. In: Riley J (ed) Proceedings from ORAHS 2000, Glasgow, Scotland, pp 18–39.

    Google Scholar 

  7. Forrester JW (1961). Industrial Dynamics. MIT Press: Cambridge, MA.

    Google Scholar 

  8. Forrester JW (1960). The impact of feedback control concepts on the man-agement sciences. In: Collected Papers of J.W. Forrester (1975 collection), Wright-Allen Press: Cambridge, MA, pp 45–60.

    Google Scholar 

  9. Jun JB, Jacobson SH and Swisher JR (1999). Application of discrete-event simulation in health care clinics: a survey. J Opl Res Soc 50: 109–123.

    Article  Google Scholar 

  10. Dangerfield BC and Roberts CA (1990). Modelling the epidemiological consequences of HIV infection and AIDS: a contribution from Operational Research. J Opl Res Soc 41: 273–289.

    Article  Google Scholar 

  11. Townshend JRP and Turner HS (2000). Analysing the effect of Chlamydia screening. J Opl Res Soc 51: 812–824.

    Article  Google Scholar 

  12. Wolstenholme EF (1993). A case study in community care using systems thinking. J Opl Res Soc 44: 925–934.

    Article  Google Scholar 

  13. 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. J Opl Res Soc 51: 518–531.

    Article  Google Scholar 

  14. STELLA, High Performance Systems, 145 Lyme Road, Hanover, NH.

    Google Scholar 

  15. Department of Health (2002). Hospital Episode Statistics. Department of Health: London.

    Google Scholar 

  16. Lane DC (2000). You just don’t understand me: modes of failure and success in the discourse between system dynamics and discrete event simulation, Working paper no. LSEOR 00.34, London School of Economics.

    Google Scholar 

  17. Pidd M (1998). Computer Simulation in Management Science, 4th edn. Wiley: Chichester.

    Google Scholar 

  18. Department of Health (2001). Reforming Emergency Care, Downloadable from www.doh.gov.uk/capacityplanning/reformfirststeps.htm.

    Google Scholar 

  19. Bagust A et al (1999). Dynamics of bed use in accommodating emergency admissions: stochastic simulation model. BMJ 319: 155–159.

    Article  Google Scholar 

  20. Cooke MW, Wilson S and Pearson S (2002). The effect of a separate stream for minor injuries on accident and emergency department waiting times. Emerg Med J 19: 28–30.

    Article  Google Scholar 

  21. Simul8, www.Simul8.com.

  22. Shrimpling M (2002). Redesigning triage to reduce waiting times. Emerg Nurse 10: 34–37.

    Article  Google Scholar 

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© 2016 Operational Research Society

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Brailsford, S.C., Lattimer, V.A., Tarnaras, P., Turnbull, J.C. (2016). Emergency and On-Demand Healthcare: Modeling a Large Complex System. In: Mustafee, N. (eds) Operational Research for Emergency Planning in Healthcare: Volume 2. The OR Essentials series. Palgrave Macmillan, London. https://doi.org/10.1007/978-1-137-57328-5_2

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