Predicting Unpanned Return to Hospital for Chronic Disease Patients

Abstract

Preventing unplanned returns, including readmissions and representations to the emergency department is increasingly becoming a performance target for hospitals across the globe. Significant successes have been reported from interventions put in to place by hospitals to reduce their incidence. However, despite several risk stratification algorithms being proposed in recent years, there is limited use of these algorithms in hospital services to identify patients for enrolment into these intervention programs. This study identifies constraints limiting the practical use of such algorithms. We also develop and validate models that focus on clinically relevant patient cohorts and are thus better suited to practical deployment in hospitals, while still offering good predictive ability.