Identifying Consistent High-cost Users in a Health Plan: Comparison of Alternative Prediction Models

Hsien Yen Chang, Cynthia M. Boyd, Bruce Leff, Klaus W. Lemke, David P. Bodycombe, Jonathan P. Weiner

Research output: Contribution to journalArticlepeer-review


Background: High-cost users in a period may not incur high-cost utilization in the next period. Consistent high-cost users (CHUs) may be better targets for cost-saving interventions. Objectives: To compare the characteristics of CHUs (patients with plan-specific top 20% medical costs in all 4 half-year periods across 2008 and 2009) and point high-cost users (PHUs) (top users in 2008 alone), and to build claims-based models to identify CHUs. Research Design: This is a retrospective cohort study. Logistic regression was used to predict being CHUs. Independent variables were derived from 2007 claims; 5 models with different sets of independent variables (prior costs, medications, diagnoses, medications and diagnoses, medications and diagnoses and prior costs) were constructed. Subjects: Three-year continuous enrollees aged from 18 to 62 years old from a large administrative database with $100 or more yearly costs (N=1,721,992). Measures: Correlation, overlap, and characteristics of top risk scorers derived from 5 CHUs models were presented. C-statistics, sensitivity, and positive predictive value were calculated. Results: CHUs were characterized by having increasing total and pharmacy costs over 2007-2009, and more baseline chronic and psychosocial conditions than PHUs. Individuals' risk scores derived from CHUs models were moderately correlated (∼0.6). The medication-only model performed better than the diagnosis-only model and the prior-cost model. Conclusions: Five models identified different individuals as potential CHUs. The recurrent medication utilization and a high prevalence of chronic and psychosocial conditions are important in differentiating CHUs from PHUs. For cost-saving interventions with long-term impacts or focusing on medication, CHUs may be better targets.

Original languageEnglish (US)
Pages (from-to)852-859
Number of pages8
JournalMedical care
Issue number9
StatePublished - Sep 1 2016


  • adjusted clinical group (ACG)
  • claims data
  • consistent high-cost users
  • predictive modeling
  • risk adjustment

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health


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