A Model for Predicting Readmission Risk in New Zealand
AbstractPredictive 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.
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Bibliographic InfoPaper provided by Auckland University of Technology, Department of Economics in its series Working Papers with number 2012-02.
Length: 16 pages
Date of creation: Feb 2012
Date of revision:
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Hospital readmission; Risk prediction; Prognostic strength.;
Find related papers by 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, Technological Change, and Growth - - Development Planning and Policy - - - Project Analysis
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-03-21 (All new papers)
- NEP-CMP-2012-03-21 (Computational Economics)
- NEP-FOR-2012-03-21 (Forecasting)
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