title: Using decision trees to explore the association between the length of stay and potentially avoidable readmissions: A retrospective cohort study.
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.
authors.affiliation: a Department of Health Management and Policy. Faculty of Medicine , Jordan University of Science and Technology , Irbid , Jordan.
authors.forename: Mohammad S
authors.initials: MS
authors.lastname: Alyahya
authors.affiliation: a Department of Health Management and Policy. Faculty of Medicine , Jordan University of Science and Technology , Irbid , Jordan.
authors.forename: Heba H
authors.initials: HH
authors.lastname: Hijazi
authors.affiliation: b Industrial Engineering , Jordan University of Science and Technology , Irbid , Jordan.
authors.forename: Hussam A
authors.initials: HA
authors.lastname: Alshraideh
authors.affiliation: c Department of Statistics, Faculty of Science , Yarmouk University , Irbid , Jordan.
authors.forename: Amjad D
authors.initials: AD
authors.lastname: Al-Nasser
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D015897:Comorbidity; D057225:Data Mining; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D003663:Decision Trees; D005260:Female; D006785:Hospitals, University; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010351:Patient Discharge; D010359:Patient Readmission / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D055815:Young Adult
keywords: Avoidable readmission; clinical decision support systems; decision trees (DTs); length of stay (LOS)