272
Views
2
CrossRef citations to date
0
Altmetric
Original Articles

Using decision trees to explore the association between the length of stay and potentially avoidable readmissions: A retrospective cohort study

, , &
Pages 361-377 | Published online: 13 Jan 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. 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). 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. 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.

Acknowledgments

We wish to thank Eng. Anas Matalkah, Manager of Information Systems Department at KAUH, for his technical support in data collection. Also, we are very grateful to Dr. Yves Eggli (MD) from the Institute of Health Economics and Management, University of Lausanne, in Switzerland, for helping us in running the SQLape algorithm and for his valuable advice.

Funding

This research project was funded by the Deanship of Research at Jordan University of Science and Technology (108/2014).

Declaration of interest

The authors report no conflicts of interest.

Additional information

Funding

This research project was funded by the Deanship of Research at Jordan University of Science and Technology (108/2014).

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 65.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 1,155.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.