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A simulation modelling approach to evaluating length of stay, occupancy, emptiness and bed blocking in a hospital geriatric department

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  • E. El‐Darzi
  • C. Vasilakis
  • T. Chaussalet
  • P.H. Millard

Abstract

The flow of patients through geriatric hospitals has been previously described in terms of acute (short‐stay), rehabilitation (medium‐stay), and long‐stay states where the bed occupancy at a census point is modelled by a mixed exponential model using BOMPS (Bed Occupancy Modelling and Planning System). In this a patient is initially admitted to acute care. The majority of the patients are discharged within a few days into their own homes or through death. The rest are converted into medium‐stay patients where they could stay for a few months and thereafter either leave the system or move on to a long‐stay compartment where they could stay until they die. The model forecasts the average length of stay as well as the average number of patients in each state. The average length of stay in the acute compartment is artificially high if some would‐be long‐term patients are kept waiting in the short‐stay compartment until beds become available in long‐stay (residential and nursing homes). In this paper we consider the problem as a queueing system to assess the effect of blockage on the flow of patients in geriatric departments. What‐if analysis is used to allow a greater understanding of bed requirements and effective utilisation of resources. Copyright Kluwer Academic Publishers 1998

Suggested Citation

  • E. El‐Darzi & C. Vasilakis & T. Chaussalet & P.H. Millard, 1998. "A simulation modelling approach to evaluating length of stay, occupancy, emptiness and bed blocking in a hospital geriatric department," Health Care Management Science, Springer, vol. 1(2), pages 143-149, October.
    • Handle: RePEc:kap:hcarem:v:1:y:1998:i:2:p:143-149
    DOI: 10.1023/A:1019054921219
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    Cited by:

    1. Moura, Ana, 2021. "Essays in health economics," Other publications TiSEM c93abd22-fa4a-42a5-b172-d, Tilburg University, School of Economics and Management.
    2. B Shaw & A H Marshall, 2007. "Modelling the flow of congestive heart failure patients through a hospital system," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 212-218, February.
    3. Moura, Ana, 2022. "Do subsidized nursing homes and home care teams reduce hospital bed-blocking? Evidence from Portugal," Journal of Health Economics, Elsevier, vol. 84(C).
    4. Amir Elalouf & Dmitry Tsadikovich & Sharon Hovav, 2021. "A simulation-based approach for improving the clinical blood sample supply chain," Health Care Management Science, Springer, vol. 24(1), pages 216-233, March.
    5. Noa Zychlinski & Avishai Mandelbaum & Petar Momčilović & Izack Cohen, 2020. "Bed Blocking in Hospitals Due to Scarce Capacity in Geriatric Institutions—Cost Minimization via Fluid Models," Manufacturing & Service Operations Management, INFORMS, vol. 22(2), pages 396-411, March.
    6. Shola Adeyemi & Thierry Chaussalet & Eren Demir, 2011. "Nonproportional random effects modelling of a neonatal unit operational patient pathways," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(4), pages 507-518, November.
    7. Noa Zychlinski & Avishai Mandelbaum & Petar Momčilović, 2018. "Time-varying tandem queues with blocking: modeling, analysis, and operational insights via fluid models with reflection," Queueing Systems: Theory and Applications, Springer, vol. 89(1), pages 15-47, June.
    8. W. Hare & A. Alimadad & H. Dodd & R. Ferguson & A. Rutherford, 2009. "A deterministic model of home and community care client counts in British Columbia," Health Care Management Science, Springer, vol. 12(1), pages 80-98, March.
    9. Andrew S. Gordon & Adele H. Marshall & Mariangela Zenga, 2018. "Predicting elderly patient length of stay in hospital and community care using a series of conditional Coxian phase-type distributions, further conditioned on a survival tree," Health Care Management Science, Springer, vol. 21(2), pages 269-280, June.
    10. Wanlu Gu & Neng Fan & Haitao Liao, 2019. "Evaluating readmission rates and discharge planning by analyzing the length-of-stay of patients," Annals of Operations Research, Springer, vol. 276(1), pages 89-108, May.
    11. Angela Testi & Elena Tanfani & Giancarlo Torre, 2007. "A three-phase approach for operating theatre schedules," Health Care Management Science, Springer, vol. 10(2), pages 163-172, June.
    12. N C Proudlove & S Black & A Fletcher, 2007. "OR and the challenge to improve the NHS: modelling for insight and improvement in in-patient flows," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 145-158, February.
    13. Saied Samiedaluie & Vedat Verter, 2019. "The impact of specialization of hospitals on patient access to care; a queuing analysis with an application to a neurological hospital," Health Care Management Science, Springer, vol. 22(4), pages 709-726, December.
    14. Jacqueline Griffin & Shuangjun Xia & Siyang Peng & Pinar Keskinocak, 2012. "Improving patient flow in an obstetric unit," Health Care Management Science, Springer, vol. 15(1), pages 1-14, March.

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