A Model for Predicting Readmission Risk in New Zealand

Rhema Vaithianathan (), Nan Jiang () and Toni Ashton ()
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Rhema Vaithianathan: Department of Economics, University of Auckland, Auckland, New Zealand.
Toni Ashton: School of Population Health, University of Auckland, Auckland, New Zealand.

No 2012-02, Working Papers from Auckland University of Technology, Department of Economics

Abstract: 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.

Keywords: Hospital readmission; Risk prediction; Prognostic strength. (search for similar items in EconPapers)
JEL-codes: C13 I10 O22 (search for similar items in EconPapers)
Pages: 16 pages
Date: 2012-02
New Economics Papers: this item is included in nep-cmp and nep-for
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