Evaluating Patient Readmission Risk: A Predictive Analytics Approach

American Journal of Engineering and Applied Sciences (2018)

12 Pages Posted: 23 Dec 2018 Last revised: 28 Dec 2018

See all articles by Avishek Choudhury

Avishek Choudhury

Stevens Institute of Technology

Christopher Greene

Binghamton University

Date Written: December 6, 2018

Abstract

With the emergence of the Hospital Readmission Reduction Program of the Center for Medicare and Medicaid Services on October 1, 2012, forecasting unplanned patient readmission risk became crucial to the healthcare domain. There are tangible works in the literature emphasizing on developing readmission risk prediction models; However, the models are not accurate enough to be deployed in an actual clinical setting. Our study considers patient readmission risk as the objective for optimization and develops a useful risk prediction model to address unplanned readmissions. Furthermore, Genetic Algorithm and Greedy Ensemble is used to optimize the developed model constraints.

Keywords: Prediction Model, Patient Readmission Risk, Healthcare Expenses, Healthcare Quality, Optimization Model

Suggested Citation

Choudhury, Avishek and Greene, Christopher, Evaluating Patient Readmission Risk: A Predictive Analytics Approach (December 6, 2018). American Journal of Engineering and Applied Sciences (2018). Available at SSRN: https://ssrn.com/abstract=3301097

Avishek Choudhury (Contact Author)

Stevens Institute of Technology ( email )

NJ
United States

Christopher Greene

Binghamton University ( email )

PO Box 6001
Binghamton, NY 13902-6000
United States

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