TY - JOUR
T1 - Identifying patient readmission subtypes from unplanned readmissions to hospitals in Hong Kong: A cluster analysis
AU - Chan, Moon Fai
AU - Wong, Kam Yuet
AU - Chang, Ka Pik Katherine
AU - Chow, Susan
AU - Chung, Loretta
AU - Lee, Wai Man
AU - Lee, Rance
PY - 2009/3/26
Y1 - 2009/3/26
N2 - It has been conjectured with regard to patient readmission patterns that there might be significant differences in patient characteristics, need factors, enabling resources, and health behavior. The aim of this study was to identify the profiles of readmitted patients in Hong Kong (n = 120) based on their predisposing characteristics, needs, health behavior, and enabling resources. All the readmitted patients were recruited to the study in three hospitals from 2003 to 2005. A cluster analysis yielded three clusters: Clusters 1, 2, and 3 constituted 27.5% (n = 33), 27.5% (n = 33), and 45.0% (n = 54) of the total sample, respectively. The study results show that community nurse services do affect the rate at which patients are admitted to hospital for a second time. The findings might help by providing important information that will enable health-care policy-makers to identify strategies to target a specific group of patients in the hope of reducing its readmission rate.
AB - It has been conjectured with regard to patient readmission patterns that there might be significant differences in patient characteristics, need factors, enabling resources, and health behavior. The aim of this study was to identify the profiles of readmitted patients in Hong Kong (n = 120) based on their predisposing characteristics, needs, health behavior, and enabling resources. All the readmitted patients were recruited to the study in three hospitals from 2003 to 2005. A cluster analysis yielded three clusters: Clusters 1, 2, and 3 constituted 27.5% (n = 33), 27.5% (n = 33), and 45.0% (n = 54) of the total sample, respectively. The study results show that community nurse services do affect the rate at which patients are admitted to hospital for a second time. The findings might help by providing important information that will enable health-care policy-makers to identify strategies to target a specific group of patients in the hope of reducing its readmission rate.
KW - Cluster analysis
KW - Unplanned readmission
UR - http://www.scopus.com/inward/record.url?scp=62749139539&partnerID=8YFLogxK
U2 - 10.1111/j.1442-2018.2009.00427.x
DO - 10.1111/j.1442-2018.2009.00427.x
M3 - Journal article
C2 - 19298307
SN - 1441-0745
VL - 11
SP - 37
EP - 44
JO - Nursing and Health Sciences
JF - Nursing and Health Sciences
IS - 1
ER -