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A Literature Review on Predicting Unplanned Patient Readmissions

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Delivering Superior Health and Wellness Management with IoT and Analytics

Part of the book series: Healthcare Delivery in the Information Age ((Healthcare Delivery Inform. Age))

Abstract

Research on predicting unplanned readmissions in hospitals is becoming more popular with a larger amount of hospital data becoming available. To gain an in-depth observation of recent insights in the field, a literature review analysing contributions between the years of 2005 and 2017 is conducted. The aggregated results show the most important risk factors included in prediction models so far, evaluation metrics of both all-cause and diagnosis-specific prediction models as well as the most prominent classification methods used in this context. Furthermore, the development of research on predicting unplanned patient readmissions over time is shown, and current gaps are identified.

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Authors and Affiliations

  1. University of Erlangen-Nuremberg, Institute of Information Systems, Nuremberg, Germany

    Isabella Eigner & Andrew Cooney

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  1. Isabella Eigner
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  2. Andrew Cooney
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Correspondence to Isabella Eigner .

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Editors and Affiliations

  1. Epworth HealthCare, Richmond, VIC, Australia, Swinburne University of Technology, Hawthorn, VIC, Australia

    Nilmini Wickramasinghe

  2. Friedrich-Alexander-University, Erlangen-Nuremberg, Erlangen, Germany

    Freimut Bodendorf

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Eigner, I., Cooney, A. (2020). A Literature Review on Predicting Unplanned Patient Readmissions. In: Wickramasinghe, N., Bodendorf, F. (eds) Delivering Superior Health and Wellness Management with IoT and Analytics. Healthcare Delivery in the Information Age. Springer, Cham. https://doi.org/10.1007/978-3-030-17347-0_12

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  • DOI: https://doi.org/10.1007/978-3-030-17347-0_12

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