IDEAS home Printed from
   My bibliography  Save this paper

A Model for Predicting Readmission Risk in New Zealand


  • Rhema Vaithianathan

    () (Department of Economics, University of Auckland, Auckland, New Zealand.)

  • Nan Jiang

    () (Department of Economics, Auckland University of Technology, Auckland, New Zealand.)

  • Toni Ashton

    () (School of Population Health, University of Auckland, Auckland, New Zealand.)


Predictive Risk Models which utilize routinely collected data to develop algorithms are used in England to stratify patients according to their hospital admission risk. An individual’s risk score can be used as a basis to select patients for hospital avoidance programmes. This paper presents a brief empirical analysis of New Zealand hospital data to create a prediction algorithm and illustrates how a hospital avoidance business case can be developed using the model. A sample of 134,262 patients was analyzed in a Multivariate logistic regression, various socioeconomic factors and indictors of previous admissions were used to predict the probability that a patient is readmitted to hospital within the 12 months following discharge. The key factors for readmission prediction were age, sex, diagnosis of last admission, length of stay and cost-weight of previous admission. The prognostic strength of the algorithm was good, with a randomly selected patient with a future re-admission being 71.2% more likely to receive a higher risk score than one who will not have a future admission.

Suggested Citation

  • Rhema Vaithianathan & Nan Jiang & Toni Ashton, 2012. "A Model for Predicting Readmission Risk in New Zealand," Working Papers 2012-02, Auckland University of Technology, Department of Economics.
  • Handle: RePEc:aut:wpaper:201202

    Download full text from publisher

    File URL:
    Download Restriction: no

    More about this item


    Hospital readmission; Risk prediction; Prognostic strength.;

    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • O22 - Economic Development, Innovation, Technological Change, and Growth - - Development Planning and Policy - - - Project Analysis

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:aut:wpaper:201202. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Gail Pacheco). General contact details of provider: .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.