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Am J Manag Care. 2011 Aug 1;17(8):e324-32.

Identifying subgroups of complex patients with cluster analysis.

Author information

1
Institute for Health Research, Kaiser Permanente Colorado, Denver, USA. Sophia.r.newcomer@kp.org

Abstract

OBJECTIVE:

To illustrate the use of cluster analysis for identifying sub-populations of complex patients who may benefit from targeted care management strategies.

STUDY DESIGN:

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.

PMID:
21851140
[Indexed for MEDLINE]
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