Background: To characterize the regional and national variation in prescribing patterns in the Medicare Part D program using dimensional reduction visualization methods.
Methods: Using publicly available Medicare Part D claims data, we identified and visualized regional and national provider prescribing profile variation with unsupervised clustering and t-distributed stochastic neighbor embedding (t-SNE) dimensional reduction techniques. Additionally, we examined differences between regionally representative prescribing patterns for major metropolitan areas.
Results: Distributions of prescribing volume and medication diversity were highly skewed among over 800,000 Medicare Part D providers. Medical specialties had characteristic prescribing patterns. Although the number of Medicare providers in each state was highly correlated with the number of Medicare Part D enrollees, some states were enriched for providers with > 10,000 prescription claims annually. Dimension-reduction, hierarchical clustering and t-SNE visualization of drug- or drug-class prescribing patterns revealed that providers cluster strongly based on specialty and sub-specialty, with large regional variations in prescribing patterns. Major metropolitan areas had distinct prescribing patterns that tended to group by major geographical divisions.
Conclusions: This work demonstrates that unsupervised clustering, dimension-reduction and t-SNE visualization can be used to analyze and visualize variation in provider prescribing patterns on a national level across thousands of medications, revealing substantial prescribing variation both between and within specialties, regionally, and between major metropolitan areas. These methods offer an alternative system-wide and pattern-centric view of such data for hypothesis generation, visualization, and pattern identification.
Keywords: Healthcare variation; Machine learning; Medicare; Prescribing; t-SNE.
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- U54 TR001625/National Center for Advancing Translational Sciences (US)/International
- UL1 TR000042/TR/NCATS NIH HHS/United States
- R01 AI098112/AI/NIAID NIH HHS/United States
- KL2 TR001999/TR/NCATS NIH HHS/United States
- R01 AI069351/AI/NIAID NIH HHS/United States