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Health Datapalooza highlights different approaches to visualizing data

Health Data Consortium launched an obesity data challenge in the U.S. to encourage the development of visualization tools.

One of the big themes from Health Datapalooza this week was the push to develop better ways to visualize data to make data streams more manageable, contextualized and make it easier to develop insights.

Health Data Consortium launched an obesity data challenge in the U.S. and England. The U.S. competition will collaborate with deBeaumont Foundation to attract visualization tools that help healthcare professionals and health official work with patients to reduce obesity.  The NHS will preside over the challenge in England.

In an interview with Chris Boone, the CEO of the Health Data Consortium, he said the data visualization challenge was intended to make data more actionable. “People are beyond the idea of liberating data for the sake of it. It’s about how do we get smarter with our decision making.”

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He added that it’s also about making data more contextualized — not just in the name of building a better API.

The deadline for submissions is July 31.

One startup that has interesting approach to data visualization is Rowdmap. It actually uses a children’s artist to visualize its data, according to co-founder and CSO Joshua Rosenthal, to avoid appearing generic and because it gives the company better utilization tools.

He referenced an article in the New Yorker by Atul Gawande, which cited a 2010 Institute of Medicine report finding that waste accounted for 30 percent of healthcare spending, or $750 billion a year.

The Louisville, Kentucky-based business developed what it calls a Risk-Readiness Platform to match physicians and physician groups to “optimal payment arrangements” based on their practice patterns as a group, characteristics of their populations and geography. It is intended to help health plans and providers identify and manage risk-ready physicians and to eliminate fraud and abuse.

It combines government and health data sets to create proxy metrics and automated analysis. Its data includes government health data such as Medicare Advantage bid data or Exchange Enrollment, academic health data –including the Dartmouth Atlas of Healthcare, census data and

Rosenthal noted that CMS has said it will move half of Medicare fee-for-service payments to pay-for-value by 2018 through different risk-sharing models.