title: A study on investigating unplanned readmission patterns.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  METHODS
  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).
  
  
  CONCLUSIONS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: School of Nursing, The Hong Kong Polytechnic University, Hong Kong SAR, China. hsmfchan@inet.polyu.edu.hk
authors.forename: Moon Fai
authors.initials: MF
authors.lastname: Chan
authors.forename: Frances Kam Yuet
authors.initials: FK
authors.lastname: Wong
authors.forename: Katherine Ka Pik
authors.initials: KK
authors.lastname: Chang
authors.forename: Susan Ka Yee
authors.initials: SK
authors.lastname: Chow
authors.forename: Loretta Yuet Foon
authors.initials: LY
authors.lastname: Chung
authors.forename: Rance Pui-Leung
authors.initials: RP
authors.lastname: Lee
authors.forename: Wai Man
authors.initials: WM
authors.lastname: Lee
mesh_terms: D000203:Activities of Daily Living; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001294:Attitude to Health; D015984:Causality; D016009:Chi-Square Distribution; D002908:Chronic Disease / Q000145:classification / Q000453:epidemiology / Q000523:psychology*; D016000:Cluster Analysis; D005260:Female; D015438:Health Behavior; D019538:Health Care Surveys; D006304:Health Status; D006723:Hong Kong / Q000453:epidemiology; D006769:Hospitals, General / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008875:Middle Aged; D020380:Needs Assessment; D010359:Patient Readmission / Q000706:statistics & numerical data*; D025341:Principal Component Analysis; D018570:Risk Assessment; D018709:Statistics, Nonparametric; D011795:Surveys and Questionnaires