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" An Analytics Driven Decision Support System to Investigate the Risk Of Non_Index Hospital Readmission "
Yang, Yujing
Noor E Alam, Muhammad
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Latin Dissertation
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| Language of Document
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English
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| Record Number
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1105980
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| Doc. No
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TLpq2354751740
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| Main Entry
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Noor E Alam, Muhammad
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Yang, Yujing
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| Title & Author
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An Analytics Driven Decision Support System to Investigate the Risk Of Non_Index Hospital Readmission\ Yang, YujingNoor E Alam, Muhammad
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| College
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Northeastern University
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| Date
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2019
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| student score
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2019
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| Degree
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M.S.
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| Page No
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29
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| Abstract
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Improving the quality of healthcare during hospitalization and after discharging can be realized by identification of 30-day unplanned hospital readmission risk. Prior research suggests that a significant proportion of preventable hospital readmission is attributed to non-index hospital readmission. In particular, followed by the implementation of Hospital Readmission Reduction Program (HRRP), non-index hospital readmission has increased although index hospital readmission has shown a decreasing trend. The existing models in prior researches might not capture the underlying association of predictors with non-index readmission and may lack the reliability and practicability when predicting non-index hospital readmission. Therefore, there exists a critical need to proactively predict non-index hospital readmission in an effort to recommend custom designed post-discharge protocols for patients at risk of experiencing readmission to a non-index hospital. To address this challenge, this study introduces a framework to examine the risk of non-index hospital readmission. Leveraging the state of California hospital discharge datasets, this study uses and compares the predictive models of four machine learning algorithms: logistic regression, random forest, decision tree, and gradient boosting, to predict the likelihood of non-index hospital readmission. AUC and recall scores are used to compare model performance. Results show that the logistic regression model outperforms the other tree-based algorithms, in terms of AUC and recall score. The prominent features shown from the results support previous research findings. This study has the potential to be implemented as a decision support system in clinical setting to help identify the risk of non-index hospital readmission, and thus to recommend effective interventions in order to improve healthcare quality.
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| Subject
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Applied mathematics
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Biomedical engineering
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Health care management
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Industrial engineering
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