Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/16941
Title: A study on investigating unplanned readmission patterns
Authors: Chan, MF
Wong, FKY 
Chang, KKP 
Chow, SKY 
Chung, LYF
Lee, RPL
Lee, WM
Keywords: Cluster analysis
Healthcare utilization
Nurses
Nursing
Unplanned readmission
Issue Date: 2008
Publisher: Wiley-Blackwell
Source: Journal of clinical nursing, 2008, v. 17, no. 16, p. 2164-2173 How to cite?
Journal: Journal of clinical nursing 
Abstract: Aim. To explain frequent hospital readmissions, this study aimed to determine whether definable subtypes exist within a cohort of subjects with chronic illness with regard to factors associated with a patient's readmission patterns and to compare whether these factors vary between subjects in groups with different profiles. Research method. A descriptive correlational survey was conducted and data were collected by using a structured questionnaire. Seventy-four readmitted subjects were recruited in three general hospitals in Hong Kong. Outcome measures. Five outcome variables were employed in the study: predisposing characteristic, need factors, health behaviour, health status or outcomes and enabling resources. Results. A cluster analysis yielded two clusters. Each cluster represented a different profile of the sample on patient use of healthcare services. Cluster A consisted of 41.9% (n = 31) and Cluster B consisted of 58.1% (n = 43) of the patients. Cluster A patients, more of whom were male, were younger, more educated, had higher activities of daily living scores and fewer of them had received community nurse services than patients of Cluster B. Cluster A patients (32.3%) had more than one readmission record within 28 days than Cluster B patients (9.3%, p = 0.017). Conclusion. Our study shows that community nurse services can reduce the rate at which they are readmitted a second time. However, such services may have a positive effect only on a group of patients whose profile is similar to the patients in Cluster B and not for patients such as those in Cluster A. A clear profile may help healthcare policy makers make appropriate strategies to target a specific group of patients to reduce their readmission rates. Relevance to clinical practice. The identification of risk for future healthcare use could enable better targeting of interventional strategies within these groups. The results of this study might provide hospital managers with a model to design specified interventions to reduce unplanned hospital readmissions for each profile group.
URI: http://hdl.handle.net/10397/16941
ISSN: 0962-1067
EISSN: 1365-2702
DOI: 10.1111/j.1365-2702.2006.01916.x
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