A predictive analytics approach to reducing avoidable hospital readmission

Hospital readmission has become a critical metric of quality and cost of healthcare. Medicare anticipates that nearly $17 billion is paid out on the 20% of patients who are readmitted within 30 days of discharge. Although several interventions such as transition care management and discharge reengineering have been practiced in recent years, the effectiveness and sustainability depends on how well they can identify and target patients at high risk of rehospitalization. Based on the literature, most current risk prediction models fail to reach an acceptable accuracy level; none of them considers patient's history of readmission and impacts of patient attribute changes over time; and they often do not discriminate between planned and unnecessary readmissions. Moreover, the effect of different time intervals that defines readmissions has not been looked at before. In this study, we tackle such drawbacks by developing and validating a predictive analytics framework for avoidable readmissions. We further assert that the government endorsed 30 day time window that is used to count and report readmissions is not appropriate for chronic conditions such as chronic obstructive pulmonary disease. The proposed methods and tools are demonstrated with real world datasets from four hospitals of the Veterans Health Administration system.