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Using decision trees to explore the association between the length of stay and potentially avoidable readmissions: A retrospective cohort study.
- Source :
-
Informatics for health & social care [Inform Health Soc Care] 2017 Dec; Vol. 42 (4), pp. 361-377. Date of Electronic Publication: 2017 Jan 13. - Publication Year :
- 2017
-
Abstract
- Background: There is a growing concern that reduction in hospital length of stay (LOS) may raise the rate of hospital readmission. This study aims to identify the rate of avoidable 30-day readmission and find out the association between LOS and readmission.<br />Methods: All consecutive patient admissions to the internal medicine services (n = 5,273) at King Abdullah University Hospital in Jordan between 1 December 2012 and 31 December 2013 were analyzed. To identify avoidable readmissions, a validated computerized algorithm called SQLape was used. The multinomial logistic regression was firstly employed. Then, detailed analysis was performed using the Decision Trees (DTs) model, one of the most widely used data mining algorithms in Clinical Decision Support Systems (CDSS).<br />Results: The potentially avoidable 30-day readmission rate was 44%, and patients with longer LOS were more likely to be readmitted avoidably. However, LOS had a significant negative effect on unavoidable readmissions.<br />Conclusions: The avoidable readmission rate is still highly unacceptable. Because LOS potentially increases the likelihood of avoidable readmission, it is still possible to achieve a shorter LOS without increasing the readmission rate. Moreover, the way the DT model classified patient subgroups of readmissions based on patient characteristics and LOS is applicable in real clinical decisions.
- Subjects :
- Adolescent
Adult
Age Factors
Aged
Aged, 80 and over
Algorithms
Child
Child, Preschool
Comorbidity
Data Mining
Female
Hospitals, University
Humans
Logistic Models
Male
Middle Aged
Patient Discharge
Retrospective Studies
Young Adult
Decision Support Systems, Clinical organization & administration
Decision Trees
Length of Stay statistics & numerical data
Patient Readmission statistics & numerical data
Subjects
Details
- Language :
- English
- ISSN :
- 1753-8165
- Volume :
- 42
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- Informatics for health & social care
- Publication Type :
- Academic Journal
- Accession number :
- 28084856
- Full Text :
- https://doi.org/10.1080/17538157.2016.1269105