Research Article Open Access

Evaluating Patient Readmission Risk: A Predictive Analytics Approach

Avishek Choudhury1 and Dr. Christopher M. Greene2
  • 1 Syracuse University, United States
  • 2 Binghamton University, United States
American Journal of Engineering and Applied Sciences
Volume 11 No. 4, 2018, 1320-1331

DOI: https://doi.org/10.3844/ajeassp.2018.1320.1331

Submitted On: 31 October 2018 Published On: 7 December 2018

How to Cite: Choudhury, A. & Greene, D. C. M. (2018). Evaluating Patient Readmission Risk: A Predictive Analytics Approach. American Journal of Engineering and Applied Sciences, 11(4), 1320-1331. https://doi.org/10.3844/ajeassp.2018.1320.1331

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.

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Keywords

  • Prediction Model
  • Patient Readmission Risk
  • Healthcare Expenses
  • Healthcare Quality
  • Optimization Model