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.
Highlights
It is a fact that the federal budget of the United States is concerned by the burgeoning healthcare expenses (Shipeng Yua, 2015)
One of the main factors contributing to the healthcare cost is the avoidable patient readmission
Our study focuses on predicting patient readmission
Summary
It is a fact that the federal budget of the United States is concerned by the burgeoning healthcare expenses (Shipeng Yua, 2015). One of the main factors contributing to the healthcare cost is the avoidable patient readmission. Unplanned patient readmission has been a significant measure of care quality. The Affordable Care Act of 2010 introduced the Readmission Reduction Program which became effective on October 1, 2012. According to the School of Public Health, Veterans Administration can save $2,140 per patient by managing patients prone to readmission (Kathleen Carey, 2016). Studies have shown that 15 to 25 percent of discharged patients are readmitted in less than 30 days. According to the Agency for Healthcare Research and Quality, about 1.8 million patients were readmitted (Anika and Hines, 2014). Fierce Healthcare reported that in 2011, hospitals spend $41.3 billion to treat unplanned readmitted patients (Shinkman, 2014)
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