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Hospital readmission has become a critical metric of quality and cost ofhealthcare. Medicare anticipates that nearly $17 billion is paid out on the 20%of patients who are readmitted within 30 days of discharge. Although severalinterventions such as transition care management and discharge reengineeringhave been practiced in recent years, the effectiveness and sustainabilitydepends on how well they can identify and target patients at high risk ofrehospitalization. Based on the literature, most current risk prediction modelsfail to reach an acceptable accuracy level; none of them considers patient'shistory of readmission and impacts of patient attribute changes over time; andthey often do not discriminate between planned and unnecessary readmissions.Tackling such drawbacks, we develop a new readmission metric based onadministrative data that can identify potentially avoidable readmissions fromall other types of readmission. We further propose a tree based classificationmethod to estimate the predicted probability of readmission that can directlyincorporate patient's history of readmission and risk factors changes overtime. The proposed methods are validated with 2011-12 Veterans HealthAdministration data from inpatients hospitalized for heart failure, acutemyocardial infarction, pneumonia, or chronic obstructive pulmonary disease inthe State of Michigan. Results shows improved discrimination power compared tothe literature (c-statistics>80%) and good calibration.

A predictive analytics approach to reducing avoidable hospital readmission