IDEAS home Printed from https://ideas.repec.org/a/wut/journl/v3y2020p5-19id1484.html
   My bibliography  Save this article

Simulation modelling for predicting hospital admissions and bed utilisation

Author

Listed:
  • Maria Hajłasz
  • Bożena Mielczarek

Abstract

We focus on ensuring the financial requirements of a person that has life insurance and needs money because of suffering from a terminal illness that requires costly diagnosis and treatment. On the secondary market of life insurance (the viatical market), companies offer purchase of rights to benefits after the death of the insured. The paper aims to analyse the problem of optimising the life settlement for the insured. We determine the amount of the nominal value of the benefit which the insured intends to sell in such a way so that the number of benefits and premiums maximise the average amount of funds available to the insured. We use various approaches of the insured to risk to find an optimal solution, which also allows taking into consideration the different kinds of human behaviour in risky conditions. The obtained theoretical results are illustrated with examples demonstrating the possibility of their application in practice.

Suggested Citation

  • Maria Hajłasz & Bożena Mielczarek, 2020. "Simulation modelling for predicting hospital admissions and bed utilisation," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 30(3), pages 5-27.
    • Handle: RePEc:wut:journl:v:3:y:2020:p:5-19:id:1484
    DOI: 10.37190/ord200201
    as

    Download full text from publisher

    File URL: https://ord.pwr.edu.pl/assets/papers_archive/1484%20-%20published.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.37190/ord200201?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Andersen, Anders Reenberg & Nielsen, Bo Friis & Reinhardt, Line Blander, 2017. "Optimization of hospital ward resources with patient relocation using Markov chain modeling," European Journal of Operational Research, Elsevier, vol. 260(3), pages 1152-1163.
    2. Robert Saltzman & Theresa Roeder & Judith Lambton & Lila Param & Brian Frost & Roxanne Fernandes, 2017. "The Impact of a Discharge Holding Area on the Throughput of a Pediatric Unit," Service Science, INFORMS, vol. 9(2), pages 121-135, June.
    3. Elizabeth A. Crawford & Pratik J. Parikh & Nan Kong & Charuhas V. Thakar, 2014. "Analyzing Discharge Strategies during Acute Care," Medical Decision Making, , vol. 34(2), pages 231-241, February.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Topuz, Kazim & Urban, Timothy L. & Yildirim, Mehmet B., 2024. "A Markovian score model for evaluating provider performance for continuity of care—An explainable analytics approach," European Journal of Operational Research, Elsevier, vol. 317(2), pages 341-351.
    3. Philippe Cohard, 2018. "Modeling and Markov chains," Post-Print hal-02091773, HAL.
    4. Barbato, Michele & Ceselli, Alberto & Premoli, Marco, 2023. "On the impact of resource relocation in facing health emergencies," European Journal of Operational Research, Elsevier, vol. 308(1), pages 422-435.
    5. Robert Saltzman & Theresa Roeder & Judith Lambton & Lila Param & Brian Frost & Roxanne Fernandes, 2017. "The Impact of a Discharge Holding Area on the Throughput of a Pediatric Unit," Service Science, INFORMS, vol. 9(2), pages 121-135, June.
    6. Wu, Xiaodan & Li, Juan & Chu, Chao-Hsien, 2019. "Modeling multi-stage healthcare systems with service interactions under blocking for bed allocation," European Journal of Operational Research, Elsevier, vol. 278(3), pages 927-941.
    7. Samuel Davis & Nasser Fard, 2020. "Theoretical bounds and approximation of the probability mass function of future hospital bed demand," Health Care Management Science, Springer, vol. 23(1), pages 20-33, March.
    8. Aswani, Anil & Kaminsky, Philip & Mintz, Yonatan & Flowers, Elena & Fukuoka, Yoshimi, 2019. "Behavioral modeling in weight loss interventions," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1058-1072.

    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:wut:journl:v:3:y:2020:p:5-19:id:1484. 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: Adam Kasperski (email available below). General contact details of provider: https://edirc.repec.org/data/iopwrpl.html .

    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.