Alyahya Mohammad S, Hijazi Heba H, Alshraideh Hussam A, Al-Nasser Amjad D
a Department of Health Management and Policy. Faculty of Medicine , Jordan University of Science and Technology , Irbid , Jordan.
b Industrial Engineering , Jordan University of Science and Technology , Irbid , Jordan.
Inform Health Soc Care. 2017 Dec;42(4):361-377. doi: 10.1080/17538157.2016.1269105. Epub 2017 Jan 13.
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.
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).
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.
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.
人们越来越担心住院时间（LOS）的缩短可能会提高医院再入院率。本研究旨在确定可避免的30天再入院率，并找出住院时间与再入院之间的关联。
对2012年12月1日至2013年12月31日期间约旦阿卜杜拉国王大学医院内科服务部门连续收治的所有患者（n = 5273）进行分析。为了确定可避免的再入院情况，使用了一种经过验证的名为SQLape的计算机算法。首先采用多项逻辑回归分析。然后，使用决策树（DTs）模型进行详细分析，决策树模型是临床决策支持系统（CDSS）中使用最广泛的数据挖掘算法之一。
潜在可避免的30天再入院率为44%，住院时间较长的患者更有可能被再次收治。然而，住院时间对不可避免的再入院有显著的负面影响。
可避免的再入院率仍然高得令人无法接受。由于住院时间可能会增加可避免的再入院可能性，在不增加再入院率的情况下实现更短的住院时间仍是有可能的。此外，决策树模型根据患者特征和住院时间对再入院患者亚组进行分类的方法适用于实际临床决策。
