IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

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

Trying to Predict in Real Time the Risk of Unplanned Hospital Readmissions
View Sample PDF
Author(s): Nilmini Wickramasinghe (Swinburne University of Technology, Australia & Epworth HealthCare, Australia)
Copyright: 2020
Pages: 12
Source title: Handbook of Research on Optimizing Healthcare Management Techniques
Source Author(s)/Editor(s): Nilmini Wickramasinghe (Swinburne University of Technology, Australia & Epworth HealthCare, Australia)
DOI: 10.4018/978-1-7998-1371-2.ch022

Purchase

View Trying to Predict in Real Time the Risk of Unplanned Hospital Readmissions on the publisher's website for pricing and purchasing information.

Abstract

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.

Related Content

. © 2024. 27 pages.
. © 2024. 10 pages.
. © 2024. 13 pages.
. © 2024. 6 pages.
. © 2024. 23 pages.
. © 2024. 14 pages.
. © 2024. 7 pages.
Body Bottom