Potentially modifiable risk factors for 30-day unplanned hospital readmission preventive intervention—A data mining and statistical analysis

Abstract

Unplanned hospital readmissions have a high prevalence and substantial healthcare costs. Preventive intervention during hospitalization holds the potential for reducing readmission risk. However, it is challenging to develop individualized interventions during hospitalization because the causes of readmissions have not been clearly known and because patients are heterogeneous. This work aimed to identify potentially modifiable risk factors of readmission to help clinicians better plan and prioritize interventions for different patient subgroups during hospitalization. We performed the analysis of associations between the changes of potentially modifiable risk factors and the change of readmission status with association rule mining and statistical methods. Twenty-nine risk factors were identified from the association rules, and twenty-five of them were potentially modifiable. The association rules with potentially modifiable risk factors can be recommended to different patient subgroups to support the development of customized readmission preventive interventions.