IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v21y2024i8p1035-d1451281.html
   My bibliography  Save this article

A New Approach for Understanding International Hospital Bed Numbers and Application to Local Area Bed Demand and Capacity Planning

Author

Listed:
  • Rodney P. Jones

    (Healthcare Analysis & Forecasting, Wantage OX12 0NE, Oxfordshire, UK)

Abstract

Three models/methods are given to understand the extreme international variation in available and occupied hospital bed numbers. These models/methods all rely on readily available data. In the first, occupied beds (rather than available beds) are used to measure the expressed demand for hospital beds. The expressed occupied bed demand for three countries was in the order Australia > England > USA. Next, the age-standardized mortality rate (ASMR) has dual functions. Less developed countries/regions have low access to healthcare, which results in high ASMR, or a negative slope between ASMR versus available/occupied beds. In the more developed countries, high ASMR can also be used to measure the ‘need’ for healthcare (including occupied beds), a positive slope among various social (wealth/lifestyle) groups, which will include Indigenous peoples. In England, a 100-unit increase in ASMR (European Standard population) leads to a 15.3–30.7 (feasible range) unit increase in occupied beds per 1000 deaths. Higher ASMR shows why the Australian states of the Northern Territory and Tasmania have an intrinsic higher bed demand. The USA has a high relative ASMR (for a developed/wealthy country) because healthcare is not universal in the widest sense. Lastly, a method for benchmarking the whole hospital’s average bed occupancy which enables them to run at optimum efficiency and safety. English hospitals operate at highly disruptive and unsafe levels of bed occupancy, manifesting as high ‘turn-away’. Turn-away implies bed unavailability for the next arriving patient. In the case of occupied beds, the slope of the relationship between occupied beds per 1000 deaths and deaths per 1000 population shows a power law function. Scatter around the trend line arising from year-to-year fluctuations in occupied beds per 1000 deaths, ASMR, deaths per 1000 population, changes in the number of persons hidden in the elective, outpatient and diagnostic waiting lists, and local area variation in births affecting maternity, neonatal, and pediatric bed demand. Additional variation will arise from differences in the level of local funding for social care, especially elderly care. The problems associated with crafting effective bed planning are illustrated using the English NHS as an example.

Suggested Citation

  • Rodney P. Jones, 2024. "A New Approach for Understanding International Hospital Bed Numbers and Application to Local Area Bed Demand and Capacity Planning," IJERPH, MDPI, vol. 21(8), pages 1-30, August.
    • Handle: RePEc:gam:jijerp:v:21:y:2024:i:8:p:1035-:d:1451281
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/21/8/1035/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/21/8/1035/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jo Røislien & Signe Søvik & Torsten Eken, 2018. "Seasonality in trauma admissions – Are daylight and weather variables better predictors than general cyclic effects?," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-15, February.
    2. A. Bruin & R. Bekker & L. Zanten & G. Koole, 2010. "Dimensioning hospital wards using the Erlang loss model," Annals of Operations Research, Springer, vol. 178(1), pages 23-43, July.
    3. Litvak, Nelly & van Rijsbergen, Marleen & Boucherie, Richard J. & van Houdenhoven, Mark, 2008. "Managing the overflow of intensive care patients," European Journal of Operational Research, Elsevier, vol. 185(3), pages 998-1010, March.
    4. Rodney P. Jones, 2023. "Addressing the Knowledge Deficit in Hospital Bed Planning and Defining an Optimum Region for the Number of Different Types of Hospital Beds in an Effective Health Care System," IJERPH, MDPI, vol. 20(24), pages 1-36, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rodney P. Jones, 2025. "Capacity Planning (Capital, Staff and Costs) of Inpatient Maternity Services: Pitfalls for the Unwary," IJERPH, MDPI, vol. 22(1), pages 1-47, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Samantha L. Zimmerman & Alexander R. Rutherford & Alexa Waall & Monica Norena & Peter Dodek, 2023. "A queuing model for ventilator capacity management during the COVID-19 pandemic," Health Care Management Science, Springer, vol. 26(2), pages 200-216, June.
    2. Stef Baas & Sander Dijkstra & Aleida Braaksma & Plom Rooij & Fieke J. Snijders & Lars Tiemessen & Richard J. Boucherie, 2021. "Real-time forecasting of COVID-19 bed occupancy in wards and Intensive Care Units," Health Care Management Science, Springer, vol. 24(2), pages 402-419, June.
    3. Yuta Kanai & Hideaki Takagi, 2021. "Markov chain analysis for the neonatal inpatient flow in a hospital," Health Care Management Science, Springer, vol. 24(1), pages 92-116, March.
    4. Fermín Mallor & Cristina Azcárate & Julio Barado, 2016. "Control problems and management policies in health systems: application to intensive care units," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 62-89, June.
    5. Hui Zhang & Thomas J. Best & Anton Chivu & David O. Meltzer, 2020. "Simulation-based optimization to improve hospital patient assignment to physicians and clinical units," Health Care Management Science, Springer, vol. 23(1), pages 117-141, March.
    6. John Bowers, 2013. "Balancing operating theatre and bed capacity in a cardiothoracic centre," Health Care Management Science, Springer, vol. 16(3), pages 236-244, September.
    7. Hideaki Takagi & Yuta Kanai & Kazuo Misue, 2017. "Queueing network model for obstetric patient flow in a hospital," Health Care Management Science, Springer, vol. 20(3), pages 433-451, September.
    8. Michael Samudra & Carla Van Riet & Erik Demeulemeester & Brecht Cardoen & Nancy Vansteenkiste & Frank E. Rademakers, 2016. "Scheduling operating rooms: achievements, challenges and pitfalls," Journal of Scheduling, Springer, vol. 19(5), pages 493-525, October.
    9. Aleksandra Marcikic Horvat & Branislav Dudic & Boris Radovanov & Boban Melovic & Otilija Sedlak & Monika Davidekova, 2020. "Binary Programming Model for Rostering Ambulance Crew-Relevance for the Management and Business," Mathematics, MDPI, vol. 9(1), pages 1-13, December.
    10. Burdett, Robert L & Corry, Paul & Yarlagadda, Prasad & Cook, David & Birgan, Sean, 2024. "Multicriteria optimization techniques for understanding the case mix landscape of a hospital," European Journal of Operational Research, Elsevier, vol. 319(1), pages 263-291.
    11. Arne Schulz & Malte Fliedner, 2023. "Minimizing the expected waiting time of emergency jobs," Journal of Scheduling, Springer, vol. 26(2), pages 147-167, April.
    12. Julian Schiele & Thomas Koperna & Jens O. Brunner, 2021. "Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(1), pages 65-88, February.
    13. Dellaert, Nico & Jeunet, Jully & Mincsovics, Gergely, 2011. "Budget allocation for permanent and contingent capacity under stochastic demand," International Journal of Production Economics, Elsevier, vol. 131(1), pages 128-138, May.
    14. Daniel F. Silva & Bo Zhang & Hayriye Ayhan, 2018. "Admission control strategies for tandem Markovian loss systems," Queueing Systems: Theory and Applications, Springer, vol. 90(1), pages 35-63, October.
    15. Willoughby, Keith A. & Chan, Benjamin T.B. & Marques, Shauna, 2016. "Using simulation to test ideas for improving speech language pathology services," European Journal of Operational Research, Elsevier, vol. 252(2), pages 657-664.
    16. Song-Hee Kim & Ward Whitt, 2014. "Are Call Center and Hospital Arrivals Well Modeled by Nonhomogeneous Poisson Processes?," Manufacturing & Service Operations Management, INFORMS, vol. 16(3), pages 464-480, July.
    17. Fermín Mallor & Cristina Azcárate & Julio Barado, 2015. "Optimal control of ICU patient discharge: from theory to implementation," Health Care Management Science, Springer, vol. 18(3), pages 234-250, September.
    18. Yin-Chi Chan & Eric W. M. Wong & Gavin Joynt & Paul Lai & Moshe Zukerman, 2018. "Overflow models for the admission of intensive care patients," Health Care Management Science, Springer, vol. 21(4), pages 554-572, December.
    19. Rodney P. Jones, 2025. "Capacity Planning (Capital, Staff and Costs) of Inpatient Maternity Services: Pitfalls for the Unwary," IJERPH, MDPI, vol. 22(1), pages 1-47, January.
    20. Jie Bai & Andreas Fügener & Jochen Gönsch & Jens O. Brunner & Manfred Blobner, 2021. "Managing admission and discharge processes in intensive care units," Health Care Management Science, Springer, vol. 24(4), pages 666-685, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:21:y:2024:i:8:p:1035-:d:1451281. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.