There is a new, interactive visualization tool launched by GE, The Cost of Getting Sick. Below are snapshots looking at the cost of treating depression. The first image shows the costs for someone 50 years old. The second shows the data for someone who is 60 years old. I wonder what makes treatment of an older person for depression more expensive?

Treatment of depression, when patient is 50 years old

Treatment of depression, when patient is 60 years old

From the site:

To gain a deeper understanding of healthcare costs, we’ve combined the Medical Expenditure Panel Survey (MEPS) with 500K records from GE’s proprietary database. By combining MEPS with GE’s data, we gain a more complete picture of the costs associated with chronic conditions.

The Medical Expenditure Panel Survey (MEPS), from the Agency for Healthcare Research and Quality, is a set of large-scale surveys of families and individuals, their medical providers, and employers across the United States. MEPS is the most complete source of data on the cost and use of health care and health insurance coverage.

This visualization was brought to my attention by GE’s Megan Parker:

As healthcare is incredibly complex, we, at GE, have been looking for visual ways to synthesize, simplify and communicate the immense quantities of information becoming available each day. . . . To do this we partnered with Ben Fry, Director of Seed Visualization, to slice and dice some of the six million patient records in GE’s proprietary electronic medical records database.

YouTube has a nice video by Ben Fry about how he approaches visualization

The display of this tool is extremely clean and intuitive. The use of a pie chart obscures some of the data, in my opinion. A pie chart displays parts of a whole. I do not believe a complete set of conditions are listed, so it can give an inflated view of the proportion of one condition. Really, what is being shown is comparative costs of various conditions. A bar chart would have been much preferable. That said, I think it is a nice visualization built on a rich data source.

I’d love to hear your thoughts.

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