Literature Insights / Data Visualization Exploration
Distribution of evidence over time
Goal of Visualization
Help the user understand the breadth of literature available on a specific topic at a glance so that they can see trends in the evidence over time and dive deeper into the papers with high expert scores or new findings reported.
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Dot Histogram A
Pros: Easy to see trend of data over time, No y-axis means no clustered data
Cons: No additional data about the publications
Dot Histogram B
Pros: Y-axis provides additional information about quality
Cons: Not as easy to see trend at a glance, Clustered data
User Feedback
We heard from oncologists that understanding the quality of evidence is an important piece of data when evaluating which papers to read. This meant that Dot Histogram B’s ability to provide that additional layer of data on the Y-axis made it more valuable.
Relevant treatments by volume and quality
Goal of Visualization
Help the user understand which drugs have been studied for their target cohort and the quality of the published articles where these drugs were studied to inform their evaluation of different treatment options.
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Bubble Chart
PROS: Quick summary of relevant publications
CONS: Difficult to display treatment names, Question value of combining quality of papers into an average, Relies on color to distinguish between the quality categories
Stacked Bar Graph
PROS: EASIER TO COMPARE QUANTITY OF PUBLICATIONS, LABEL CATEGORIES OF QUALITY, EASIER TO DISPLAY TREATMENT NAMES
CONS: RELIES ON COLOR TO DISTINGUISH BETWEEN QUALITY CATEGORIES
User Feedback
We heard from oncologists that the additional layer of quality average or quality breakdown per treatment was not something that would help them make decisions. Instead, they expressed interest in a chart that visualized how the treatments compared (see next section).
Relevant treatment outcome comparison
Goal of Visualization
Help the user understand which drugs have been studied for their target cohort and how they compared against each other so that they can confirm or build their treatment hypotheses using the available data.
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Arc Diagram
PROS: EASY TO SCAN FOR TREATMENT NAMES
CONS: LIST VIEW MAY IMPLY HIERARCHY, TAKES UP A LOT OF VERTICAL SCREEN REAL ESTATE
Chord Diagram
PROS: ORGANIZE TREATMENTS BY DRUG CLASS, A LOT OF DATA IN SMALL SPACE, ADDITIONAL LAYER OF DATA (# OF PATIENTS STUDIED)
CONS: NOT EASY TO SCAN FOR TREATMENTS
Network Diagram w/ Arcs
PROS: EASY TO VIEW CLUSTERS OF TREATMENTS WHICH HAVE BEEN COMPARED OR NOT COMPARED, LABEL BY DRUG CLASS
CONS: less common DATA VISUALIZATION
User Feedback
We heard from oncologists that comparing outcomes was a more valuable way to summarize relevant treatments. By providing a snapshot of not only what drugs had been compared, but also which drugs had yet to be compared, we were providing a quick insight into the literature’s sentiment. One piece of feedback that applied to all explorations was that it might be valuable to add another layer of data to the outcome “arcs” – what outcome was being studied? Overall survival, progression-free survival, toxicity, response?
Overall, user feedback indicated the network diagram with arcs seemed to be heading in the best direction. Because the network-style visualization forms clusters it is a bit easier to see which drugs have been compared/not compared.
Unfortunately, this visualization was not able to be included in the product beta due to implementation complexity and the accelerated project timeline leading up to the ASCO conference. Moving forward, the development of this visualization will be re-sized to be introduced into the Literature Insights experience.