Predicting Unplanned Hospital Readmissions using Patient Level Data

Abstract

The rate of unplanned hospital readmissions in the US is likely to face a steady rise after 2020. Hence, this issue has received considerable critical attention with the policy makers. Majority of hospitals in the US pay millions of dollars as penalty for readmitting patients within 30 days due to strict norms imposed by the Hospital Readmission Reduction Program. In this study, we develop two novel models: PURE (Predicting Unplanned Readmissions using Embeddings) and Hybrid DeepR, which uses the historical medical events of patients to predict readmissions within 30 days. Both these models are hybrid sequence models that leverage both sequential events (history of events) and static features (like gender, blood pressure) of the patients to mine patterns in the data. Our results are promising, and they benchmark previous results in predicting hospital readmissions. The contributions of this study add to existing literature on healthcare analytics