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

Med Care. 2016 Sep;54(9):852-9. doi: 10.1097/MLR.0000000000000566.

Abstract

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.

Publication types

  • Comparative Study

MeSH terms

  • Adolescent
  • Adult
  • Chronic Disease / economics*
  • Databases, Factual
  • Female
  • Health Care Costs / statistics & numerical data*
  • Humans
  • Insurance, Health / economics
  • Insurance, Health / statistics & numerical data*
  • Logistic Models
  • Male
  • Mental Disorders / economics*
  • Middle Aged
  • Models, Statistical*
  • Patient Acceptance of Health Care / statistics & numerical data
  • Retrospective Studies
  • Young Adult