Date of Award

12-31-2014

Document Type

Campus Access Thesis

Degree Name

Master of Business Administration (MBA)

Department

Business Administration

First Advisor

Davood Gomohammadi

Second Advisor

Ehsan Elahi

Third Advisor

Peng Xu Xu

Abstract

Readmission is a major source of cost for healthcare systems. Hospital-specific readmission rates are considered an indicator of hospital performance and generate public interest regarding the health care quality. We aimed to identify those patients who are likely to be readmitted to the hospital. The identified patients can then be considered by health care personnel for application of preventive alternative measures such as: providing intensive post-discharge care, managing the conditions of the most vulnerable in their home, supporting self-care, and integrating health services and information technology systems to avoid unnecessary readmissions. Neural Network, Classification and Regression model and Chi-squared Automatic Interaction Detection models were used for the readmission prediction. All models were able to perform with an overall accuracy above 80%, with the latter two models having the advantage of providing the user with the opportunity of selecting different misclassification costs. Linear regression and Generalized linear model were used to estimate the length of stay in the following year for those who are being readmitted. We employed C5.0 algorithm to search for recurring pattern in the history or demographics of patients who have been readmitted and explored if a rule of thumb can be derived to predict those at risk of future readmissions. Moreover, the key variables influencing readmission were studied based on a large data set. The most important factors contributing to readmission were determined such as age, sex, number of previous prescriptions and length of previous stays, place of service, and number of previous claims.

Comments

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