Project Overview
Use data visualization to reveal insights, serve as a call for action to improve patient safety in the medical community
Coverys was releasing the “Trends in Claims Data” whitepaper, a review of 10-year medical malpractice claims. The goal was to reveal insights by analyzing data from different angles and call for action to improve patient safety.
Current Problem of the Data Report
Data analysis is the foundation of the report, the original charts they created were very flat. They wanted the data visualization to dictate the layout of the report, and facilitate a more dynamic look and feel. The initial request was to visualize the data in a more creative and interesting way.
My Role
Communicating directly with the client to lead and execute the data charts design
Providing layout guidance to the client-side visual designers
Tool I Used
Adobe Illustrator
Who is Coverys
Coverys is a company that provides medical professional liability insurance services. With the information they have, they provide insights into the healthcare delivery challenges that the medical community is facing.
Link to the Report
Design Challenge
Creating effective visualization to communicate findings
After reviewing the data, the original charts, and the draft of the report provided by the client, I noticed that like other data visualization projects, the challenge is to communicate the findings with all the data and get the message across to target audiences. That requires determining the data visualization strategy and create effective visualization with each chart instead of purely showing data.
Data and original charts provided by the client
Determine Strategy & Basic Visualization
Identify target audience groups
The target audience group of this report is the medical community - clinicians, risk managers, healthcare executives. Understanding it helps me define the point of view for better communication.
Understand context - PDF whitepaper: less interaction, more details
The final result is a whitepaper, a static media compared to the web.
The audiences have less control over how to interact with and interpret the data. It gives the designer more control and responsibility for how to present the data. At the same time, it requires a high level of detail because the design has to speak for itself, unlike a live presentation.
An example of process (Events & Indemnity Paid graph)
Understand data and use basic visual models to validate
Coverys provided a draft report and summarized data, which reveals certain patterns but isn’t strong enough to deliver valuable information to the audiences.
For each grouped dataset, I wireframed 5-6 possible visual models to test the data behaviors, then chose 1-2 that best suited the hypothesis.
eg. in the left image, with the data provided, I can show how the Percentage of Indemnity changes over 2010-2019(the two line charts), or both the trend and the composition of injury severity within each year(the stacked bar chart). If I take the average data, it shows the average composition of Injury severity(donut chart). But only the last chart gives a comparison of Events Rate with Indemnity Paid, which is important in the context of the report.
Choose most suited models but also provide varieties
Working closely with Coverys stakeholders helped me verify my understanding and hypothesis. It also helped them to dig deeper into what they want each chart to communicate. To make it more dynamic, I provided a variety of options for similar data structures for review.
An example of 3 visual explorations for the same chart (Surgical Specialties Claim Rate)
Create Effective Visualization
To help the audience focus on the meaning of each chart, I took measures to minimize visual elements to eliminate clutter for every chart.
Stylize and simplify
• Adjusted and cleaned up axis and scales
• Removed additional borders, gridlines
• Grouped elements together
Emphasize and highlight
• Readjusted colors, added highlight for clear visual hierarchy
• Added details & explanation
• Added footnote
BEFORE
The two charts mixed together; Not consistent; Mixed messages
AFTER - Emphasis message of trend
Adjusted axises for consistency; Used the same style; Added focus point.
BEFORE - No visual hierarchy
AFTER - highlighted CANCER for a breakdown
Refine and Deliver
After a final review of the production-ready designs, I double-checked all the data, generated guidelines of layout for the report. We had a brief meeting with the graphic designers of the report to explain the structure of the design file and made sure of a smooth handover of the deliverables
An example of charts color and layout
Thanks for reading