Literature DB >> 27326548

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

Hsien-Yen Chang1, Cynthia M Boyd, Bruce Leff, Klaus W Lemke, David P Bodycombe, Jonathan P Weiner.   

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.

Entities:  

Mesh:

Year:  2016        PMID: 27326548     DOI: 10.1097/MLR.0000000000000566

Source DB:  PubMed          Journal:  Med Care        ISSN: 0025-7079            Impact factor:   2.983


  12 in total

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