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Modelling patient flows and resource provision in health systems

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  • Davies, R
  • Davies, HTO

Abstract

Patient flow models may be used for planning health services for both acute and chronic patients. There are models which assume sub-groups of patients are homogenous and events occur at equally spaced intervals of time. These include Markov and semi-Markov chain models, queueing models and deterministic models of the transition of patients between states. These techniques are useful for examining patient flow in large population groups where Markov assumptions, or simple extensions of these, can be made. Discrete event simulation models allow patients to have individual attributes and to interact with resource provision but they are more time consuming to test and run. They are particularly suitable for models of systems of patient care where the constraints on resource availability are important. They may also be used on unconstrained population models with several thousands of patients. A significant development in simulation is the facility to model entities so that they can participate in more than one activity simultaneously and interrupt each other. The credibility of any model is dependent on reliable data which are not always readily available in the British Health Service.

Suggested Citation

  • Davies, R & Davies, HTO, 1994. "Modelling patient flows and resource provision in health systems," Omega, Elsevier, vol. 22(2), pages 123-131, March.
    • Handle: RePEc:eee:jomega:v:22:y:1994:i:2:p:123-131
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    Citations

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    Cited by:

    1. Sally Brailsford & Walter Gutjahr & Marion Rauner & Wolfgang Zeppelzauer, 2007. "Combined Discrete-event Simulation and Ant Colony Optimisation Approach for Selecting Optimal Screening Policies for Diabetic Retinopathy," Computational Management Science, Springer, vol. 4(1), pages 59-83, January.
    2. Diego Tlapa & Ignacio Franco-Alucano & Jorge Limon-Romero & Yolanda Baez-Lopez & Guilherme Tortorella, 2022. "Lean, Six Sigma, and Simulation: Evidence from Healthcare Interventions," Sustainability, MDPI, vol. 14(24), pages 1-25, December.
    3. K Cooper & R Davies & J Raftery & P Roderick, 2008. "Use of a coronary heart disease simulation model to evaluate the costs and effectiveness of drugs for the prevention of heart disease," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1173-1181, September.
    4. Lagergren, Marten, 1998. "What is the role and contribution of models to management and research in the health services? A view from Europe," European Journal of Operational Research, Elsevier, vol. 105(2), pages 257-266, March.
    5. K. Cooper & S. Brailsford & R. Davies & J. Raftery, 2006. "A review of health care models for coronary heart disease interventions," Health Care Management Science, Springer, vol. 9(4), pages 311-324, November.
    6. D C Lane & E Husemann, 2008. "System dynamics mapping of acute patient flows," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(2), pages 213-224, February.
    7. R B van der Meer & L A Rymaszewski & H Findlay & J Curran, 2005. "Using OR to support the development of an integrated musculo-skeletal service," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(2), pages 162-172, February.
    8. Davies, Ruth & Roderick, Paul, 1998. "Planning resources for renal services throughout UK using simulation," European Journal of Operational Research, Elsevier, vol. 105(2), pages 285-295, March.
    9. Danny Segev & Retsef Levi & Peter Dunn & Warren Sandberg, 2012. "Modeling the impact of changing patient transportation systems on peri-operative process performance in a large hospital: insights from a computer simulation study," Health Care Management Science, Springer, vol. 15(2), pages 155-169, June.
    10. Marion Rauner & Michaela Schaffhauser-Linzatti & Helmut Niessner, 2012. "Resource planning for ambulance services in mass casualty incidents: a DES-based policy model," Health Care Management Science, Springer, vol. 15(3), pages 254-269, September.
    11. K Cooper & S C Brailsford & R Davies, 2007. "Choice of modelling technique for evaluating health care interventions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 168-176, February.
    12. Yong-Hong Kuo & Omar Rado & Benedetta Lupia & Janny M. Y. Leung & Colin A. Graham, 2016. "Improving the efficiency of a hospital emergency department: a simulation study with indirectly imputed service-time distributions," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 120-147, June.
    13. M S Rauner & S C Brailsford & S Flessa, 2005. "Use of discrete-event simulation to evaluate strategies for the prevention of mother-to-child transmission of HIV in developing countries," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(2), pages 222-233, February.
    14. Almehdawe, Eman & Jewkes, Beth & He, Qi-Ming, 2016. "Analysis and optimization of an ambulance offload delay and allocation problem," Omega, Elsevier, vol. 65(C), pages 148-158.
    15. Sun-Young Kim & Sue Goldie, 2008. "Cost-Effectiveness Analyses of Vaccination Programmes," PharmacoEconomics, Springer, vol. 26(3), pages 191-215, March.
    16. Rashwan, Wael & Abo-Hamad, Waleed & Arisha, Amr, 2015. "A system dynamics view of the acute bed blockage problem in the Irish healthcare system," European Journal of Operational Research, Elsevier, vol. 247(1), pages 276-293.
    17. Brailsford, S.C. & Harper, P.R. & Sykes, J., 2012. "Incorporating human behaviour in simulation models of screening for breast cancer," European Journal of Operational Research, Elsevier, vol. 219(3), pages 491-507.

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