Predicting all-cause unplanned readmission within 30 days of discharge using electronic medical record data: A multi-centre study

Abstract

OBJECTIVE: To develop a predictive model for identifying patients at high risk of all-cause unplanned readmission within 30 days after discharge, using administrative data available before discharge. MATERIALS AND METHODS: Hospital administrative data of all adult admissions in three tertiary metropolitan hospitals in Australia between July 01, 2015, and July 31, 2016, were extracted. Predictive performance of four mixed-effect multivariable logistic regression models was compared and validated using a split-sample design. Diagnostic details (Charlson Comorbidity Index CCI, components of CCI, and primary diagnosis categorised into International Classification of Diseases chapters) were added..