Context Predicting hospital readmission risk is of great interest to identify which patients
would benefit most from care transition interventions, as well as to risk-adjust readmission …

New financial penalties for institutions with high readmission rates have intensified efforts to
reduce rehospitalization. Several interventions that involve multiple components (eg, patient …

Hospital readmission measures have been touted not only as a quality measure but also as
a means to bend the healthcare cost curve. The Affordable Care Act (ACA) established the …

Heart failure is the leading cause of hospitalization among adults 65 years of age in the
United States. Annually, 1 million patients are hospitalized with a primary diagnosis of heart …

Importance Because effective interventions to reduce hospital readmissions are often
expensive to implement, a score to predict potentially avoidable readmissions may help …

Importance Readmission penalties have catalyzed efforts to improve care transitions, but
few programs have incorporated viewpoints of patients and health care professionals to …

Aims This study aimed to review the performance of machine learning (ML) methods
compared with conventional statistical models (CSMs) for predicting readmission and …

Reducing hospital readmission rates has captured the imagination of US policymakers
because readmissions are common and costly and their rates vary—and at least in theory, a …

Objective To update previous systematic review of predictive models for 28-day or 30-day
unplanned hospital readmissions. Design Systematic review. Setting/data source CINAHL …

Introduction Hospital readmission rates are increasingly used for both quality improvement
and cost control. However, the validity of readmission rates as a measure of quality of …