title: Identifying subgroups of complex patients with cluster analysis.
abstract: OBJECTIVE
  To illustrate the use of cluster analysis for identifying sub-populations of complex patients who may benefit from targeted care management strategies.
  
  
  METHODS
  Retrospective cohort analysis.
  
  
  METHODS
  We identified a cohort of adult members of an integrated health maintenance organization who had 2 or more of 17 common chronic medical conditions and were categorized in the top 20% of total cost of care for 2 consecutive years (n = 15,480). We used agglomerative hierarchical clustering methods to identify clinically relevant subgroups based on groupings of coexisting conditions. Ward's minimum variance algorithm provided the most parsimonious solution.
  
  
  RESULTS
  Ward's algorithm identified 10 clinically relevant clusters grouped around single or multiple "anchoring conditions." The clusters revealed distinct groups of patients including: coexisting chronic pain and mental illness, obesity and mental illness, frail elderly, cancer, specific surgical procedures, cardiac disease, chronic lung disease, gastrointestinal bleeding, diabetes, and renal disease. These conditions co-occurred with multiple other chronic conditions. Mental health diagnoses were prevalent (range 28% to 100%) in all clusters.
  
  
  CONCLUSIONS
  Data mining procedures such as cluster analysis can be used to identify discrete groups of patients with specific combinations of comorbid conditions. These clusters suggest the need for a range of care management strategies. Although several of our clusters lend themselves to existing care and disease management protocols, care management for other subgroups is less well-defined. Cluster analysis methods can be leveraged to develop targeted care management interventions designed to improve health outcomes.
authors.affiliation: Institute for Health Research, Kaiser Permanente Colorado, Denver, USA. Sophia.r.newcomer@kp.org
authors.forename: Sophia R
authors.initials: SR
authors.lastname: Newcomer
authors.forename: John F
authors.initials: JF
authors.lastname: Steiner
authors.forename: Elizabeth A
authors.initials: EA
authors.lastname: Bayliss
mesh_terms: D000368:Aged; D000465:Algorithms; D002908:Chronic Disease / Q000145:classification* / Q000628:therapy; D016000:Cluster Analysis; D015331:Cohort Studies; D057225:Data Mining; D019033:Delivery of Health Care, Integrated; D005260:Female; D006801:Humans; D008297:Male; D010361:Patients / Q000145:classification*; D012189:Retrospective Studies