Visualizing nationwide variation in Medicare Part D prescribing patterns
| Autor: | Samir A. Farooq, Martin S. Zand, Lisa A. Nelson, Samuel J. Weisenthal, Christopher F. Fucile, Alexander F. Rosenberg, Melissa Trayhan, Kristen Bush, Caroline M. Quill, Robert J. White |
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| Rok vydání: | 2017 |
| Předmět: |
Medicare Part D
Specialty Health Informatics 030204 cardiovascular system & hematology Medicare lcsh:Computer applications to medicine. Medical informatics Disease cluster Drug Prescriptions Health informatics 03 medical and health sciences 0302 clinical medicine Data visualization Machine learning Cluster Analysis Humans Healthcare variation 030212 general & internal medicine Practice Patterns Physicians' Medical prescription business.industry Data Visualization Health Policy Metropolitan area United States t-SNE Computer Science Applications Hierarchical clustering 3. Good health Prescribing Geography lcsh:R858-859.7 business Cartography Research Article |
| Zdroj: | BMC Medical Informatics and Decision Making, Vol 18, Iss 1, Pp 1-19 (2018) BMC Medical Informatics and Decision Making |
| DOI: | 10.7287/peerj.preprints.3470v1 |
| Popis: | 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. Electronic supplementary material The online version of this article (10.1186/s12911-018-0670-2) contains supplementary material, which is available to authorized users. |
| Databáze: | OpenAIRE |
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