Visualizing nationwide variation in Medicare Part D prescribing patterns
- Subject Areas
- Bioinformatics, Health Policy, Translational Medicine, Data Science
- Medicare, Health Informatics, Prescribing Patterns, Data Visualization, Dimensional Reduction, t-Stochastic Network Embedding, Practice Variation, Hierarchical Clustering
- © 2017 Rosenberg et al.
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. Visualizing nationwide variation in Medicare Part D prescribing patterns. PeerJ Preprints 5:e3470v1 https://doi.org/10.7287/peerj.preprints.3470v1
Objective: To characterize the regional and national variation in prescribing patterns in the Medicare Part D program using machine learning and dimensional reduction visualization methods.
Methods: Using publicly available Medicare Part D claims data, we identified 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, and 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. Hierarchical clustering and t-SNE dimension-reduction 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: There is substantial prescribing variation among providers in Medicare Part D both between and within specialties. Large regional variations in prescribing patterns, particularly among major metropolitan areas, were also seen. Unsupervised clustering and t-SNE dimension-reduction are an effective means to examine variation in provider prescribing patterns, including substantial regional and medical specialty variation.
This manuscript was submitted for peer review to BMC Medical Informatics and Decision Making on September 3, 2017.