期刊
HEALTH INFORMATICS JOURNAL
卷 27, 期 1, 页码 -出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/1460458221995231
关键词
patient readmission; risk factors; unplanned; 30-day; data mining
This study aimed to identify potentially modifiable risk factors for readmission by analyzing the association between changes in these factors and readmission status, in order to help clinicians develop customized readmission preventive interventions for different patient subgroups.
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.
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