IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v58y2007i4d10.1057_palgrave.jors.2602179.html
   My bibliography  Save this article

A simple graphical decision aid for the placement of elderly people in long-term care

Author

Listed:
  • H Xie

    (University of Westminster)

  • T J Chaussalet

    (University of Westminster)

  • W A Thompson

    (Cranfield University, Shrivenham, Wilts)

  • P H Millard

    (University of Westminster)

Abstract

This paper describes the construction of a graphical decision tool to aid placement decisions of a multidisciplinary review panel for admissions to long-term care in a London borough in the UK. First we construct a prediction model of placement decisions based on an applicant's attributes. Using data from the London borough, a composite model comprising syndromic decision rules followed by a two-stage hierarchical logistic regression model is proposed. The model proved to be robust in differentiating cases needing residential home care and nursing home care. Placement outcomes generated by the model are then represented graphically on a triangle plot. This approach could potentially be used as a decision support tool by managers of long-term care for continuous monitoring and assessment of the appropriateness of placements with respect to residents’ needs.

Suggested Citation

  • H Xie & T J Chaussalet & W A Thompson & P H Millard, 2007. "A simple graphical decision aid for the placement of elderly people in long-term care," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(4), pages 446-453, April.
    • Handle: RePEc:pal:jorsoc:v:58:y:2007:i:4:d:10.1057_palgrave.jors.2602179
    DOI: 10.1057/palgrave.jors.2602179
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/palgrave.jors.2602179
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/palgrave.jors.2602179?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. H. Xie & T. J. Chaussalet & P. H. Millard, 2005. "A continuous time Markov model for the length of stay of elderly people in institutional long‐term care," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 51-61, January.
    2. C Pelletier & T J Chaussalet & H Xie, 2005. "A framework for predicting gross institutional long-term care cost arising from known commitments at local authority level," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(2), pages 144-152, 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. S McClean & P Millard, 2007. "Where to treat the older patient? Can Markov models help us better understand the relationship between hospital and community care?," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 255-261, February.
    2. 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.
    3. Adam Steventon & Adam Roberts, 2015. "Estimating Lifetime Costs of Social Care: A Bayesian Approach Using Linked Administrative Datasets from Three Geographical Areas," Health Economics, John Wiley & Sons, Ltd., vol. 24(12), pages 1573-1587, December.
    4. 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.
    5. Zuoxiang, Peng & Weng, Zhichao & Nadarajah, Saralees, 2010. "Rates of convergence of extremes for mixed exponential distributions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(1), pages 92-99.
    6. Casucci, Sabrina & Lin, Li & Nikolaev, Alexander, 2018. "Modeling the impact of care transition programs on patient outcomes and 30 day hospital readmissions," Socio-Economic Planning Sciences, Elsevier, vol. 63(C), pages 70-79.
    7. Mark Fackrell, 2009. "Modelling healthcare systems with phase-type distributions," Health Care Management Science, Springer, vol. 12(1), pages 11-26, March.
    8. Bruce Jones & Sally McClean & David Stanford, 2019. "Modelling mortality and discharge of hospitalized stroke patients using a phase-type recovery model," Health Care Management Science, Springer, vol. 22(4), pages 570-588, December.
    9. 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.
    10. 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.
    11. Henry Ergas, 2009. "Providing Aged Care: The Case for Reform," Agenda - A Journal of Policy Analysis and Reform, Australian National University, College of Business and Economics, School of Economics, vol. 16(2), pages 21-44.
    12. Harris, Shannon L. & May, Jerrold H. & Vargas, Luis G., 2016. "Predictive analytics model for healthcare planning and scheduling," European Journal of Operational Research, Elsevier, vol. 253(1), pages 121-131.
    13. C Pelletier & T J Chaussalet & H Xie, 2005. "A framework for predicting gross institutional long-term care cost arising from known commitments at local authority level," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(2), pages 144-152, February.
    14. 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.
    15. Manuel L. Esquível & Gracinda R. Guerreiro & Matilde C. Oliveira & Pedro Corte Real, 2021. "Calibration of Transition Intensities for a Multistate Model: Application to Long-Term Care," Risks, MDPI, vol. 9(2), pages 1-17, February.
    16. McGrory, C.A. & Pettitt, A.N. & Faddy, M.J., 2009. "A fully Bayesian approach to inference for Coxian phase-type distributions with covariate dependent mean," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4311-4321, October.
    17. 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.
    18. Teresa Cardoso & Mónica Oliveira & Ana Barbosa-Póvoa & Stefan Nickel, 2012. "Modeling the demand for long-term care services under uncertain information," Health Care Management Science, Springer, vol. 15(4), pages 385-412, December.
    19. 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.
    20. Sujee Lee & Philip A. Bain & Albert J. Musa & Jingshan Li, 2021. "A Markov chain model for analysis of physician workflow in primary care clinics," Health Care Management Science, Springer, vol. 24(1), pages 72-91, March.

    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:pal:jorsoc:v:58:y:2007:i:4:d:10.1057_palgrave.jors.2602179. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.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.