IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v51y2017i3d10.1007_s11135-016-0329-y.html
   My bibliography  Save this article

Artificial neural networks and their potentialities in analyzing budget health data: an application for Italy of what-if theory

Author

Listed:
  • Paolo Massimo Buscema

    (SEMEION Research Centre of Sciences of Communication
    University of Colorado)

  • Guido Maurelli

    (SEMEION Research Centre of Sciences of Communication)

  • Francesco Saverio Mennini

    (University of Rome Tor Vergata)

  • Lara Gitto

    (University of Rome Tor Vergata)

  • Simone Russo

    (University of Rome Tor Vergata
    University of Rome La Sapienza)

  • Matteo Ruggeri

    (Università Cattolica del Sacro Cuore)

  • Silvia Coretti

    (Università Cattolica del Sacro Cuore)

  • Americo Cicchetti

    (Università Cattolica del Sacro Cuore)

Abstract

Since 1992 the Italian local health units (LHU) gained financial independence and became responsible to provide and deliver health care at the local level. Management and financial accounting represent the tool utilized to monitor their net income and the working capital every year. From 2001 on, LHU budget data have being summarized by means of the “income statement”. The income statement is considered the most relevant form for the monitoring of healthcare expenditures. A big amount of data have been collected after that obligation of publishing the income statement. The application of new methods for a better understanding of relationships among variables would be worthwhile. The development of artificial neural networks (ANNs) can represent a useful tool to analyze the relationships among these variables. The purpose of this paper is showing the potentialities of ANNs and especially of artificial neural networks what-if theory (AWIT) model when applied to health budgetary data. This innovative methodology has been employed, in the present paper, to analyze data from five Italian Regions, carrying out some comparison among them. In short, using one dataset that is defined as being the ideal standard containing the relationships necessary to measure desired outcomes, another dataset can be compared to determine its degree of closeness. We can determine the degree of closeness of the second or treated dataset with the original standard. This is the key concept of the method called AWIT. The descriptive analysis carried out outlines the areas of waste LHU and suggests to develop strategies to contrast an inefficient use of resources.

Suggested Citation

  • Paolo Massimo Buscema & Guido Maurelli & Francesco Saverio Mennini & Lara Gitto & Simone Russo & Matteo Ruggeri & Silvia Coretti & Americo Cicchetti, 2017. "Artificial neural networks and their potentialities in analyzing budget health data: an application for Italy of what-if theory," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1261-1276, May.
    • Handle: RePEc:spr:qualqt:v:51:y:2017:i:3:d:10.1007_s11135-016-0329-y
    DOI: 10.1007/s11135-016-0329-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11135-016-0329-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11135-016-0329-y?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. Mennini, Francesco Saverio & Gitto, Lara & Russo, Simone & Cicchetti, Americo & Ruggeri, Matteo & Coretti, Silvia & Maurelli, Guido & Buscema, Paolo Massimo, 2017. "Does regional belonging explain the similarities in the expenditure determinants of Italian healthcare deliveries?," Economic Analysis and Policy, Elsevier, vol. 55(C), pages 47-56.
    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.

      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:spr:qualqt:v:51:y:2017:i:3:d:10.1007_s11135-016-0329-y. 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.springer.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.