Artemis Tips: How Healthcare Data Visualizations Can Be Easy And Clear

Simplicity is such an overlooked concept, but it’s a major value for benefits leaders and advisors. As the late Steve Jobs said, “It takes a lot of hard work to make something simple, to truly understand the underlying challenges and come up with elegant solutions.” 

For benefit managers at self-insured companies, simplicity is not something they've come to expect in their day-to-day tasks. They have the difficult job of communicating their healthcare data to their stakeholders in order to make financial decisions. And they have to do it without disrupting access to great care for employees and their families. To do that, they need to have clean, accurate, and reliable data that is visually appealing in order to present their ideas and make business cases to others in their organization. That’s where healthcare data visualizations are critical. 

Some data warehouse solutions don’t make it easy to create data visualizations in the product. Often, advisors, analysts, and benefits teams are pulling reports and metrics out of their data warehouse in order to create charts and graphs that tell a story. A robust data warehouse solution should allow benefits data visualization within the tool. We’ll showcase how Artemis has prioritized visualizations with our Visualize app.

Our Visualize app has two key benefits for users. It helps them:

• Create visualizations from data (charts, graphs, etc.) 
• Share visualizations with those who make decisions
  

Let’s take a look at how it works.

To create a visualization, you’ll use standard, pre-loaded measures, breakdowns and filters. 

Measures use numerical data. This can be anything from costs to number of claims to the total members in the employee population. Additionally, benefits advisors or analysts can create custom measures to support deeper analytics work.

When you have the type of measure you want in place, the next step is to add a breakdown. Breakdowns are non-numerical ways of analyzing data; think day of the week, office location, or ICD-10 diagnosis code. You can add measures and breakdowns in any order, and some analyses work with just a measure. Breakdowns help you get deeper information and see more complex answers in the data.

Here we’ll choose to add age bins to our visualization. Once you’ve chosen a measure or breakdown, the Visualize app will start to provide recommended chart types that will work best for your analysis. You’ll see a number of options that can help you map high risk members to office location, understand the changes in your spending over time, view costs by member cohorts, and more. The recommended chart types will automatically adjust based on the type of measures, breakdowns and filters you select. For example, if you haven't chosen a location-based breakdown, the map chart isn't an option.

Adding filters can also change the story you’re telling with your visualization. Here we’re searching a filter based on diagnosis code. If you’re looking to add a diagnosis, say if you wanted to look at members that had a chronic emphysema, you can search through the filter summary page and add it to your visualization. We’ve filtered here by Member Relationship, so we can see a breakdown of costs for just subscribers, or just spouses and dependents. To save you time, you can save your filter for use later. 

Benefits analysts can continue to play around with chart types to find the best visual representation of the data and make sure it’s easy to understand. When you’re ready to save it, you can capture it as an image or export it into Excel for further data analysis. You can also save visualizations in the platform itself as individual charts or in a Story. 

Let’s try a real world example. Diabetes is a hot topic for employers. According to BenefitsPro, diabetes is one of the top ten most expensive health conditions to cover. What if an employer wanted to see which office location was a hot spot for diabetes claims? Here’s how you’d do it: 

• Step 1: Add the Measure for Average by Member Paid Amount
• Step 2: Add the Office Location breakdown 
• Step 3: Filter the result by diabetes diagnosis code(s)

In the resulting map (using sample data), we can see the office with the highest diabetes claims is in Los Angeles. Using analyses like this, self-insured employers and benefits advisors could work with their wellness provider or disease management vendor to target communications where they’ll have the greatest impact. And, the benefits team can use Artemis to track the progress of these locations over time. 

We know that many benefits teams struggle to get accurate, fast answers from their healthcare and benefits data, and our Visualize app is just one tool we provide that can help. If you’d like to learn more, get in touch for a personalized demo.