Trying to Predict in Real Time the Risk of Unplanned Hospital Readmissions

Trying to Predict in Real Time the Risk of Unplanned Hospital Readmissions

DOI: 10.4018/978-1-7998-1371-2.ch022
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This study aims to identify predictors for patients likely to be readmitted to a hospital within 28 days of discharge and to develop and validate a prediction model for identifying patients at a high risk of readmission. Numerous attempts have been made to build similar predictive models. However, the majority of existing models suffer from at least one of the following shortcomings: the model is not based on Australian Health Data; the model uses insurance claim data, which would not be available in a real-time clinical setting; the model does not consider socio-demographic determinants of health, which have been demonstrated to be predictive of readmission risk; or the model is limited to a particular medical condition and is thus limited in scope.
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Literature Review And Background

Recent developments in the fields of data warehousing and data science have enabled researchers to contribute to a growing body of knowledge in predictive analytics (Buhl et al., 2013). In particular, the building, training and application of predictive models to stratify patients into various risk groups based on information from administrative, insurance, clinical, and government registry sources is becoming a key focus (Chechulin et al., 2014). Such studies are aimed at first aligning complex and sensitive information across multiple sources (Blumenthal, Chernof, Fulmer, Lumpkin, & Selberg, 2016). This information is then used to identify patients in need of additional healthcare resources by means of various intervention methods (Blumenthal et al., 2016).

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