Predicting 15-day unplanned readmissions in hospitalization departments: an application of logistic regression

Abstract

Hospital readmission is considered a key research area for improving care coordination and achieving potential savings. This is important because hospital readmissions can have negative consequences in terms of good health and recovery for patients. It is thus important to significantly reduce such readmissions. Unfortunately, there isn't a one-size-fits-all solution to preventing hospital readmissions. There are many variables outside of hospitals' direct control, such as social determinants and patient lifestyle factors, impacting readmissions. Although several studies have been undertaken to investigate 30-day readmissions, predicting revisits in shorter intervals (e.g., within 15 days after discharge) is highly needed to capture hospital-attributable returns better and develop more effective improvement plans. Hence, the aim of this paper is three-fold: i) to develop a comprehensive experimental study for identifying factors affecting 15-day readmission risk, ii) to classify patients according to the risk of 15-day readmission using logistic regression, and iii) provide general recommendations to reduce the 15-day readmission risk considering different predictors. To this end, the patients' characteristics were first described. Then, the significance of potential predictors, their interactions, and their effects were assessed. After this, a logistic regression model was derived to predict the likelihood of 15-day readmission in each patient. Finally, general recommendations were provided to reduce 15-day revisits. A real case study in Colombia was considered to validate the proposed methodology.