IDEAS home Printed from https://ideas.repec.org/a/taf/thssxx/v4y2015i1p5-11.html
   My bibliography  Save this article

A data mining approach for estimating patient demand for mental health services

Author

Listed:
  • Stephan Kudyba
  • Thad Perry

Abstract

The ability to better estimate future demand for health services is a critical element to maintaining a stable quality of care. With greater knowledge of how particular events can impact demand, health-care service providers can better allocate available resources to more effectively treat patients’ needs. The incorporation of data mining analytics can leverage available data to identify recurring patterns among relevant variables, and these patterns provide actionable information to corresponding decision markers at health-care organizations. The demand for mental health services can be subject to variation from time of year (seasonality) and economic factors. This study illustrates the effectiveness of data mining analytics in identifying seasonality and economic factors as measured by time that affect patient demand for mental health services. It incorporates a neural network analytic method that is applied to patient demand data at a U.S. medical center. The results indicate that day of week, month of year, and a yearly trend significantly impact the demand for patient services.

Suggested Citation

  • Stephan Kudyba & Thad Perry, 2015. "A data mining approach for estimating patient demand for mental health services," Health Systems, Taylor & Francis Journals, vol. 4(1), pages 5-11, March.
    • Handle: RePEc:taf:thssxx:v:4:y:2015:i:1:p:5-11
    DOI: 10.1057/hs.2014.12
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1057/hs.2014.12
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/hs.2014.12?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.

    More about this item

    Statistics

    Access and download statistics

    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:taf:thssxx:v:4:y:2015:i:1:p:5-11. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/thss .

    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.