A Statistical Approach for Visualizing the Quality of Multi-Hospital Data.

The age of Big Data and the associated proliferation of large data sets have necessitated the development of methods that allow for an easy interpretation of data analysis results. Such methods are usually the product of a symbiotic relationship between the helds of data visualization, infographics, and statistics. In this work we explore the interplay between data visualization and the mathematical framework used to analyze inter-hospital differences in database queries. Such differences can reflect disparities in the quality of care or more fundamental disparities in data quality. As the volume of queries is large and increasing, it is important to develop an incisive way of visualizing these differences. Specifically, we demonstrate the importance of choosing a mathematical framework that calculates the statistics necessary to visualize the results in a maximally concise and intuitive way. We derive symbolic statistical representations of inter-hospital query differences using a Bayesian probabilistic formalism to indicate statistically significant discrepancies. These statistical representations serve the need for visual representation of differences and their meaning apart from statistical expertise. The calculations were performed with a publically-available package, DQM, available at http://sourceforge.net/projects/databasequalitymanagement. [ABSTRACT FROM AUTHOR]