Patient-Specific Readmission Prediction and Intervention for Health Care

Abstract

Hospital readmission is often associated with unfavorable patient outcomes and a large cost of resources. Therefore, preventing avoidable re-hospitalizations is imperative. To target this problem, one important metric that researchers and practitioners strive to reduce is the 30-day hospital readmission rate. In this paper, we introduce a general decision support system that utilizes machine learning (ML) based patientspecific prediction to guide the suggestion of patient intervention program assignment, with the objective of minimizing the readmission cost for hospitals. This work has three major contributions. First, the proposed solution is highly scalable by using PySpark. Second, we outline solution architecture components including (1) data injection (both real-time sensor reading and data at rest), processing, and analysis, and (2) ML model building, evaluation, deployment and scoring. Third, we discuss how the ML prediction results can be taken into account in a decision support system by presenting a rich visualization.