عنوان

An Analytics Driven Decision Support System to Investigate the Risk Of Non_Index Hospital Readmission

Number

TLpq2354751740

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

An Analytics Driven Decision Support System to Investigate the Risk Of Non_Index Hospital Readmission

General Material Designation

[Thesis]

First Statement of Responsibility

Yang, Yujing

Subsequent Statement of Responsibility

Noor E Alam, Muhammad

Name of Publisher, Distributor, etc.

Northeastern University

Date of Publication, Distribution, etc.

2019

Specific Material Designation and Extent of Item

29

Dissertation or thesis details and type of degree

M.S.

Body granting the degree

Northeastern University

Text preceding or following the note

2019

Text of Note

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.

Applied mathematics

Biomedical engineering

Health care management

Industrial engineering

Noor E Alam, Muhammad

Yang, Yujing

Electronic name

p

[Thesis]

276903

a

Y