title: Enhancing risk stratification for use in integrated care: a cluster analysis of high-risk patients in a retrospective cohort study.
abstract: To show how segmentation can enhance risk stratification tools for integrated care, by providing insight into different care usage patterns within the high-risk population.
  
  
  
  A retrospective cohort study. A risk score was calculated for each person using a logistic regression, which was then used to select the top 5% high-risk individuals. This population was segmented based on the usage of different care settings using a k-means cluster analysis. Data from 2008 to 2011 were used to create the risk score and segments, while 2012 data were used to understand the predictive abilities of the models.
  
  
  
  Data were collected from administrative data sets covering primary and secondary care for a random sample of 300 000 English patients.
  
  
  
  The high-risk population was segmented based on their usage of 4 different care settings: emergency acute care, elective acute care, outpatient care and GP care.
  
  
  
  While the risk strata predicted care usage at a high level, within the high-risk population, usage varied significantly. 4 different groups of high-risk patients could be identified. These 4 segments had distinct usage patterns across care settings, reflecting different levels and types of care needs. The 2008-2011 usage patterns of the 4 segments were consistent with the 2012 patterns.
  
  
  
  Cluster analyses revealed that the high-risk population is not homogeneous, as there exist 4 groups of patients with different needs across the care continuum. Since the patterns were predictive of future care use, they can be used to develop integrated care programmes tailored to these different groups.
  
  
  
  Usage-based segmentation augments risk stratification by identifying patient groups with different care needs, around which integrated care programmes can be designed.
authors.affiliation: Institute of Global Health Innovation, Imperial College, St Mary's Hospital, London, UK.
authors.forename: Sabine I
authors.initials: SI
authors.lastname: Vuik
authors.affiliation: Department of Surgery, Imperial College, St Mary's Hospital, London, UK.
authors.forename: Erik
authors.initials: E
authors.lastname: Mayer
authors.affiliation: Institute of Global Health Innovation, Imperial College, St Mary's Hospital, London, UK.|Department of Surgery, Imperial College, St Mary's Hospital, London, UK.
authors.forename: Ara
authors.initials: A
authors.lastname: Darzi
mesh_terms: D016000:Cluster Analysis; D016208:Databases, Factual; D019033:Delivery of Health Care, Integrated / Q000592:standards*; D006801:Humans; D016015:Logistic Models; D005791:Patient Care / Q000145:classification*; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D012984:Software
keywords: STATISTICS & RESEARCH METHODS