Prediction and management of readmission risk for congestive heart failure

This position paper investigates the problem of 30-day readmission risk prediction and management for Congestive Heart Failure (CHF), which has been identified as one of the leading causes of hospitalization, especially for adults older than 65 years. The underlying solution is deeply related to using predictive analytics to compute the readmission risk score of a patient, and investigating respective risk management strategies for her by leveraging statistical analysis or sequence mining techniques. The outcome of this paper leads to developing a framework that suggests appropriate interventions to a patient during a hospital stay, at discharge, or post hospital-discharge period that potentially would reduce her readmission risk. The primary beneficiaries of this paper are the physicians and different entities involved in the pipeline of health care industry, and most importantly, the patients. This paper outlines the opportunities in applying data mining techniques in readmission risk prediction and management, and sheds deeper light on healthcare informatics.