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(Click to expand) By using bar charts arranged one right below the other, and displaying the actual data (versus the converted form used to display the data in the radar chart), we can easily see and understand that: Surgeon B has the highest cost for Total Hospital Stay and Post Acute Care, the highest Postoperative Occurrence Rate and Readmission Rate, and the lowest Patient Improvement Scores across the board.
(Which brings to mind the iconic Ricky Ricardo’s line “Lucy, you got some ‘splainin’ to do.” But I digress.) We can also quickly and easily see that although Surgeon C’s Total Hospital Stay Cost is the second highest at ,000 (it is still ,000 less then Surgeon B’s), his/her Postoperative Occurrence and Readmissions rates are by far the lowest at 4.0% and 2.0% respectively, and Patient Improvement Scores are the highest.
(Contrary to popular belief, those problems can’t be solved in the blink of an eye by FM — Freaking Magic.) Let’s look at a visualization I’ve been re-designing that illustrates this idea.
I was able to say right away, “This thing is hard to understand” — but that didn’t mean I could come up with a solution equally quickly.
A radar chart allows the viewer to compare multiple quantitative variables; some would also argue that it is most useful for viewing data outliers and displaying performance.
Here, however, are a few of the reasons why this chart simply doesn’t work: The values being displayed are different, and have therefore been rendered on the same scale so that they can be displayed together on this chart.
The notes at the bottom of the chart indicate that a score of 100 equals the lowest cost, which to my perception is completely counter-intuitive, annoying and confusing.
Not only is it hard to visually compare the lengths of the different spokes shown; it is also hard to hold the scale in our memories and accurately judge the radial distances.
I think of what a wise little spider once told her dear friend Wilbur: “Trust me, Wilbur. And yes, not so coincidentally, these are sometimes displays of data using a visualization technique called Treemaps (forest, trees, Treemaps — get it? It’s important and extraordinarily helpful to understand the genesis of a visualization technique to ensure we are using it correctly. Here’s an example from Steve Few’s book Now You See It, which displays hierarchical stock market data using Shneiderman’s Treemap technique.
We can also include additional contextual information, such as the average for the entire country, using a vertical line overlaid on the bars.
Here’s the bottom line: if we focus only on the “trees” of the different functionalities and seemingly cool visualizations that many new software applications allow us to create easily — without understanding why they were conceived, and what problems they’re designed to solve — we more often than not will miss the bigger objective of creating clear, accurate, compelling views of crucial data.
Well, when it comes to the sharing the best practices for displaying healthcare data visually and finding and telling the story buried in your data that is EXACTLY what the world needs — a blog that delivers the information and help you've just got to have, but don't have easy access to.
And as much as I love the sound of my own voice (and I do, ask anyone) I encourage you to contribute your thoughts, questions and examples (HIPAA compliant please — I don't look good in stripes). Recently my colleague and Health Data Viz Senior Consultant Janet Steeger sent me the graph below and an associated article, with the subject line “Blog Posting? (Click to expand) Incredulousness was quickly followed by inspiration.
The area of the shapes presented increases as a square of the values rather than linearly.