IDEAS home Printed from https://ideas.repec.org/a/ids/ijpqma/v9y2012i2p245-257.html
   My bibliography  Save this article

Using data envelopment analysis-neural network model to evaluate hospital efficiency

Author

Listed:
  • Omur Tosun

Abstract

Due to an increasing amount of public resources dedicated to healthcare systems, it is important to measure the efficiency of hospitals. Systematically analysing hospital systems is one important way to discover and improve inefficiencies. The purpose of this study is to propose a data envelopment analysis (DEA)-artificial neural network (ANN)-based model to measure and evaluate the efficiency scores of hospitals. DEA is straightforward but requires time, knowledge and more process time than ANN. By combining these two methods, it is possible to lessen the shortcomings of DEA. In the proposed model, DEA classifies each hospital as either efficient or inefficient. Input and output variables of DEA are used for the inputs, and the efficiency scores of the hospitals are defined as the outputs of the ANN system. After the system is trained, the ANN model is applied to the test data to classify each hospital into efficient or inefficient. The results are then compared with each other, and discriminant analysis (DA) is compared with ANN. Results show that a well-trained ANN performs good classification and even gives better solutions than DA. Also, ANN shows the advantage of using less CPU time and computer resources than the DEA, especially in large data sets.

Suggested Citation

  • Omur Tosun, 2012. "Using data envelopment analysis-neural network model to evaluate hospital efficiency," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 9(2), pages 245-257.
    • Handle: RePEc:ids:ijpqma:v:9:y:2012:i:2:p:245-257
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=45194
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    Citations

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


    Cited by:

    1. Zhishuo Zhang & Yao Xiao & Huayong Niu, 2022. "DEA and Machine Learning for Performance Prediction," Mathematics, MDPI, vol. 10(10), pages 1-23, May.
    2. Rubén Elvira Herranz & Pablo García Estévez & María Auxiliadora de Vicente y Oliva & Rahul Dé, 2017. "Leveraging financial management performance of the Spanish aerospace manufacturing value chain," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 18(5), pages 1005-1022, September.
    3. Olgun Kitapci & Ömür Tosun & Murat Fatih Tuna & Tarik Turk, 2017. "The Use of Artificial Neural Networks (ANN) in Forecasting Housing Prices in Ankara, Turkey," Journal of Marketing and Consumer Behaviour in Emerging Markets, University of Warsaw, Faculty of Management, vol. 1(5), pages 4-14.

    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:ids:ijpqma:v:9:y:2012:i:2:p:245-257. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=177 .

    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.