When we hear about Medicare for All, proponents of the policy are talking methods of paying to take care of our health. One of the reasons that Medicare for All is being put forth is that healthcare in the U.S. is so expensive that it’s one of the major reasons people regularly report avoiding the doctor’s office. It seems like every year, the cost of healthcare goes up, and I’m here to tell you: you’re not imagining it. Every single year, we are paying more and more to take care of ourselves, and it’s projected to get even worse over the next ten years if we don’t do something.
While putting this project together, I asked:
What are Americans more likely to be paying out of pocket for? How much of insurance is private versus public versus paid for by “third party providers”? Are any healthcare costs projected to fall instead of rising?
My audience for these visualizations consists of Americans who are concerned about the cost of healthcare. As healthcare costs rise, more people will likely have to pay attention to their expenditures, and I hope that being able to see these projections could help them prepare for an uncertain future in managing their healthcare costs, or even put them in a position to lobby Congress for regulation of the health and/or pharmaceutical industries in general.
I made a total of 5 visualizations for this project, which collectively tell a story about where our healthcare costs are going. All visualizations, due to the data, contain historical estimates from 2011-2017 and projections from 2018-2027. The first visualization is a simple shape chart, showing an estimate of how much we have spent on healthcare on a yearly basis from 2011-2017, and then the projection of what 2018-2027 will look like. We go from spending around 2 trillion in 2011 to 5 trillion in 2027, and these numbers have been deflated. The second and third visualizations are kind of paired—they both show a breakdown of where all that money comes from, but the area chart shows it without numbers while the heat map table shows it with. The area chart also performs the function of showing which paying options (insurance/non-insurance) will pay less and which will pay more, which is less easy to see in the table. However, the heat map table does the job of asking you to confront the amount of zeroes in each of the categories, which the area chart was not quite built for.
In my second set of visualizations, I broke down what the money was being spent on, first in a tree map showing out of pocket payments, and then in an infographic showing overall what we are spending on. The tree map can be filtered out by year, and uses percentages as opposed to numbers so that the viewer’s mind can more readily grasp the parts of the whole. The tooltips then show the actual amount in billions of dollars. The infographic uses the “medical id” symbol and represents amount spent/projected to be spent by size. This is particularly useful when sorting the fields, showing overall what is spent and providing a useful measure against the out of pocket visualization—Americans don’t usually pay for hospital costs out of pocket, but they account for the most spending. The second most in both visualizations is physician care, showing that our doctor’s visits are largely paid out of our own funds.
The data itself was fairly clean, and I was able to consolidate multiple datasets into two main datasets: one consolidated different sectors of healthcare (physician services, hospital care, etc) divided by the type of medical payment (insurance types versus out of pocket) and the other consolidated demographic data, population data, and breakdowns by federal and state government expenditures. I ended up using the first dataset mostly, because the categorical breakdown was narrower in scope and more interesting than the federal/state government.
Regarding design choices, I wanted to play with custom shapes and palettes for this project, so my first visualization uses money bag icons, and my second uses a custom palette. I tried to separate color choices by dashboard/story beat: green palette for the money, red palette for categorical medical breakdown. The two colors are contrasting as well, establishing a visual dichotomy. A lot of my data represents parts of a whole, which really tempted me to make a pie chart, and I almost caved, but in the end, it just felt too reductive.
In terms of limitations, I was attempting to make an infographic visualization regarding what one person on average pays for medical care in the US, but I couldn’t get the calculated field right. Additionally, I was looking into making a waffle chart, but what I had in mind was too complicated for Tableau.
For next steps, I would like to create a story section about individual breakdown—it’s a little dangerous, but I think it would punctuate the prior two sections by really showing how much money trillions of dollars breaks down to per person, and then making the point that some people don’t have medical debt, so that number is actually higher. Considering the data from 2011 says that healthcare spending per capita was around $8,000, I really feel like this section is necessary.
