Identifying Future High-Cost Cases Through Predictive Modeling
Arlene S. Ash
Objective: To examine the ability of various models to prospectively identify a small group of individuals with predictable high future costs that may be mitigated through disease management. Data Sources: Diagnoses and medical costs for over a million members of employer-sponsored benefit plans from the Medstat MarketScan(R) Research Database (1997-1999). Study Design: A prior cost model, a diagnosis-based (diagnostic cost group [DCG]) model and a diagnosis + prior cost (combo) model were each calibrated on 1997-1998 data and applied to 1998 data to identify 0.5%-sized Results: Individual R2 values for the (full-range) prior cost, DCG, and combo models were 11, 16, and 21%, respectively; R2 values increased to 27% and 31% for DCG and combo models top coded at $US25 Conclusion: The DCG and combo models were better than the prior cost model for identifying groups rich in individuals who will be expensive the following year. Surprisingly, top groups based on top-coded models dominated their full-range model analogs, identifying more good picks and more people with manageable disease.
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