Background
The digital evolution of the pharmaceutical industry has made it easier to track various aspects of a patient’s journey and represent complex data with interactive and understandable flow diagrams. Rather than simply plotting possible pathways, these intelligent diagrams and graphs enable healthcare practitioners and researchers to explore and analyze hidden relationships and receive instant visual feedback to real-time changes.
In this case study, we will explore two easily-generated visual reports for treatment switch patterns: Interactive Tree Diagram & Sankey Diagram. Both of these reports empower users to explore more possibilities of visual reports by utilizing existing features in SAS.
Example of a Disease Treatment Pathway
It may be easy to follow how a single patient switches from one treatment to another, and how they move on to the next. But when millions of patient records are compounded together, the transition sequence from one drug to the next turns into a technologically-complex event. While it is still possible to depict this information in a cross-frequency dashboard, listing all possible treatment pathways and associated flows, it will require some expert skills and knowledge to interpret the results.
Figure 1: Complicated disease treatment dashboard
In Figure 1 above, it is evident that when the number of possible drug switches increases, the number of potential treatment pathway explodes exponentially. It becomes excessively hard to read, and extremely cumbersome to make comparison and inference. The underlying trend of the pattern could be well camouflaged in thousands of different possibilities, and may not be easily identifiable. To confront such obscurity, we will outline two simple, yet elegant interactive diagrams with dynamic flow analysis to showcase the power of interactive graphics.
Tree Diagram
A tree diagram may be the simplest way to visualize a series of probability events. Such diagrams can be conveniently enhanced to expose more hidden relationships than just plotting the possible pathways. In Figure 2*, the size of each circle/node is directly proportional to the number of patients following the treatment path. It provides an explicit visual reference to compare the distribution of different treatment pathways. Furthermore, additional information can be triggered to display in floating boxes adding more detailed insights to the report.
*Figure 2 links to an interactive Tree Diagram – click to try it out
Sankey Diagram
Sankey diagrams were originally invented to illustrate the flow of steam in a steam engine. It has since found a variety of applications in detailing the flow of any quantifiable matter.
Sankey diagrams inherently carry a very strong visual cue of the flow quantity over different stages or time periods. In Figure 3**, researchers can draw immediate conclusions of the embedding drug switch pattern in a large group, all thanks to the insights of this interactive Sankey diagram.
** Figure 3 links to an interactive Sankey Diagram – click to try it out
At KMK Consulting, we believe a picture is worth more than a thousand words. A well-suited data visualization will not only make it easier to articulate the multitude of information you have at hand, but also give a vantage point to healthcare practitioners and researchers when it comes to deciphering the hidden insights within.
Both of these reports utilize the popular open source D3.js JavaScript library and can be readily implemented on any web-enabled devices or platforms.
This case study has been adapted from the report Graphic Visualization for Treatment Switch Pattern, co-written by Huanxue Zhou, HEOR Principal at KMK. Read the full report here.
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