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A system dynamics view of the acute bed blockage problem in the Irish healthcare system

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  • Rashwan, Wael
  • Abo-Hamad, Waleed
  • Arisha, Amr

Abstract

Global population ageing is creating immense pressures on hospitals and other healthcare services, compromising their abilities to meet the growing demand from elderly patients. Current demand–supply gaps result in prolonged waiting times in emergency departments (EDs), and several studies have focused on improving ED performance. However, the overcrowding in EDs generally stems from delayed patient flows to inpatient wards – which are congested with inpatients waiting for beds in post-acute facilities. This problem of bed blocking in acute hospitals causes substantial cost burdens on hospitals. This study presents a system dynamics methodology to model the dynamic flow of elderly patients in the Irish healthcare system aimed at gaining a better understanding of the dynamic complexity caused by the system's various parameters. The model evaluates the stock and flow interventions that Irish healthcare executives have proposed to address the problem of delayed discharges, and ultimately reduce costs. The anticipated growth in the nation's demography is also incorporated in the model. Policy makers can also use the model to identify the potential strategic risks that might arise from the unintended consequences of new policies designed to overcome the problem of the delayed discharge of elderly patients.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:247:y:2015:i:1:p:276-293
    DOI: 10.1016/j.ejor.2015.05.043
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    References listed on IDEAS

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    2. Jaime González & Juan-Carlos Ferrer & Alejandro Cataldo & Luis Rojas, 2019. "A proactive transfer policy for critical patient flow management," Health Care Management Science, Springer, vol. 22(2), pages 287-303, June.
    3. Jaén, Sebastián, 2024. "The decrease of ED patient boarding by implementing a stock management policy in hospital admissions," Operations Research Perspectives, Elsevier, vol. 12(C).
    4. Carter, Michael W., 2018. "High-fidelity whole-system patient flow modeling to assess health care transformation policiesAuthor-Name: Esensoy, Ali Vahit," European Journal of Operational Research, Elsevier, vol. 266(1), pages 221-237.
    5. Xie, Tian & Wei, Yao-yao & Chen, Wei-fan & Huang, Hai-nan, 2020. "Parallel evolution and response decision method for public sentiment based on system dynamics," European Journal of Operational Research, Elsevier, vol. 287(3), pages 1131-1148.
    6. Armenia, Stefano & Franco, Eduardo & Iandolo, Francesca & Maielli, Giuliano & Vito, Pietro, 2024. "Zooming in and out the landscape: Artificial intelligence and system dynamics in business and management," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    7. Sebastian Rachuba & Andrew Salmon & Zhivko Zhelev & Martin Pitt, 2018. "Redesigning the diagnostic pathway for chest pain patients in emergency departments," Health Care Management Science, Springer, vol. 21(2), pages 177-191, June.
    8. Fang, Debin & Zhao, Chaoyang & Yu, Qian, 2018. "Government regulation of renewable energy generation and transmission in China’s electricity market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 775-793.
    9. Mohammad Reza Davahli & Waldemar Karwowski & Redha Taiar, 2020. "A System Dynamics Simulation Applied to Healthcare: A Systematic Review," IJERPH, MDPI, vol. 17(16), pages 1-27, August.
    10. Amin Mahmoudian-Dehkordi & Somayeh Sadat, 2017. "Sustaining critical care: using evidence-based simulation to evaluate ICU management policies," Health Care Management Science, Springer, vol. 20(4), pages 532-547, December.

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