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Visualizing nationwide variation in medicare Part D prescribing patterns.

Alexander RosenbergChristopher FucileRobert J WhiteMelissa TrayhanSamir FarooqCaroline M QuillLisa A NelsonSamuel J WeisenthalKristen BushMartin S Zand
Published in: BMC medical informatics and decision making (2018)
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.
Keyphrases
  • primary care
  • adverse drug
  • machine learning
  • electronic health record
  • single cell
  • quality improvement
  • big data
  • healthcare
  • rna seq
  • cross sectional
  • living cells
  • artificial intelligence
  • bioinformatics analysis