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- Title
Visualizing nationwide variation in medicare Part D prescribing patterns.
- Authors
Rosenberg, Alexander; Fucile, Christopher; White, Robert J.; Trayhan, Melissa; Farooq, Samir; Quill, Caroline M.; Nelson, Lisa A.; Weisenthal, Samuel J.; Bush, Kristen; Zand, Martin S.
- Abstract
<bold>Background: </bold>To characterize the regional and national variation in prescribing patterns in the Medicare Part D program using dimensional reduction visualization methods.<bold>Methods: </bold>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.<bold>Results: </bold>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.<bold>Conclusions: </bold>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.
- Subjects
MEDICARE; DRUG prescribing; HIERARCHICAL clustering (Cluster analysis); VISUALIZATION; MACHINE learning
- Publication
BMC Medical Informatics & Decision Making, 2018, Vol 18, Issue 1, pN.PAG
- ISSN
1472-6947
- Publication type
journal article
- DOI
10.1186/s12911-018-0670-2