Predicting all-cause unplanned readmission within 30 days of discharge using electronic medical record data: a multi-center study.

Abstract

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. 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 were compared and validated using a split-sample design. Diagnostic details (Charlson Comorbidity Index CCI, components of CCI, and primary diagnosis categorized into International Classification of Diseases chapters) were added gradually in the clinically simplified model with socio-demographic, index admission, and prior hospital utilization variables. Of the total 99470 patients admitted, 5796 (5.8%) were re-admitted through emergency department of three hospitals within 30 days after discharge. The clinically simplified model was as discriminative (C-statistic 0.694, 95% CI [0.681-0.706]) as other models and showed excellent calibration. Models with diagnostic details did not exhibit any substantial improvement in predicting 30-days unplanned readmission. We propose a 10-item predictive model to flag high-risk patients in a diverse population before discharge using readily available hospital administrative data which can easily be integrated into the hospital information system.