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Related Concept Videos

What is Variation?01:14

What is Variation?

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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
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Conservative Site-specific Recombination and Phase Variation02:53

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Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
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Variation01:19

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An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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The coefficient of variation measures the dispersion of the data points or distribution around the mean. Using the coefficient of variation, we can compare two data series with drastically different means or different units of measurement. The coefficient of variation for a sample and a population is expressed as a percentage of the ratio of standard deviation to the mean.
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Visualizing nationwide variation in medicare Part D prescribing patterns.

Alexander Rosenberg1,2, Christopher Fucile1,2, Robert J White1,3

  • 1Rochester Center for Health Informatics at the University of Rochester Medical Center, 265 Crittenden Blvd - 1.207, Rochester, 14642, NY, USA.

BMC Medical Informatics and Decision Making
|November 21, 2018
PubMed
Summary
This summary is machine-generated.

Medicare Part D prescribing patterns show significant regional and national variation. Unsupervised clustering and t-distributed stochastic neighbor embedding (t-SNE) visualization reveal distinct provider profiles based on specialty and location.

Keywords:
Healthcare variationMachine learningMedicarePrescribingt-SNE

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Area of Science:

  • Health Services Research
  • Data Science in Healthcare
  • Pharmacoeconomics

Background:

  • The Medicare Part D program covers prescription drugs for eligible seniors and individuals with disabilities.
  • Understanding variations in prescribing patterns is crucial for healthcare quality and cost management.
  • Previous analyses often lacked comprehensive visualization of national prescribing behavior.

Purpose of the Study:

  • To characterize regional and national variations in physician prescribing patterns within the Medicare Part D program.
  • To apply dimensional reduction visualization methods for analyzing complex prescribing data.
  • To identify distinct prescribing profiles among healthcare providers.

Main Methods:

  • Utilized publicly available Medicare Part D claims data from over 800,000 providers.
  • Employed unsupervised clustering and t-distributed stochastic neighbor embedding (t-SNE) for dimensional reduction and visualization.
  • Examined prescribing patterns at national, state, and major metropolitan area levels.

Main Results:

  • Prescribing volume and medication diversity showed skewed distributions among providers.
  • Providers strongly clustered by medical specialty and sub-specialty.
  • Significant regional variations in prescribing patterns were observed, with metropolitan areas grouping geographically.

Conclusions:

  • Unsupervised clustering and t-SNE visualization effectively reveal substantial prescribing variations across specialties, regions, and metropolitan areas.
  • These methods provide a system-wide, pattern-centric view for hypothesis generation and data visualization.
  • The findings highlight the utility of advanced analytical techniques for understanding complex healthcare prescribing behavior.