Don’t go too far in visualizing data
Paul Bradley, chief data scientist at healthcare administration software vendor ZirMed Inc., based in Louisville, Ky., makes a point of trying to prevent visualizations from overwhelming hospital officials who use reports that the company sends to its customers. ZirMed’s software as a service applications help healthcare providers process medical insurance claims; before passing along the claims to insurers, the vendor runs predictive models against them to check for possible missed billing codes covering treatments that typically would be associated with the listed medical procedures.
As part of its services offering, ZirMed delivers reports to clients with visualizations of commonly missed billing codes and other metrics. Bradley said the company’s analysts need to keep in mind that the hospital administrators reading the reports may not have the time or interest to delve into complicated graphs and charts. “We spend so much time with complex relationships in large data sets,” he said. “But the main goal of my team is to boil that down to the minimum nuggets people need to do their job.”
Inside job on big data visualization
Things are different on development of data visualizations for use by Bradley’s own team. Working with a mixed set of data from healthcare organizations and databases at the U.S. Census Bureau and the Centers for Medicare & Medicaid Services, ZirMed’s data scientists look for correlations between tens of thousands of variables to get an idea of the medical procedures that healthcare providers tend to cluster together, and the ones that providers most frequently forget to bill patients for. Then, they use the correlations to build and update the predictive models used to check claims.
So much data is involved that the only way to make sense of it is by visualizing it, Bradley said. And in this case, he added, it’s reasonable to build more complexity into the data visualizations. The members of his team are used to grappling with complex data, so detailed visualizations don’t faze them. Some of the visualization work is done in Excel, but more complicated tasks are handled in Tableau’s BI software, enabling the data scientists to take a deep dive into the available info. “My team wants to look at the patterns and trends from that data,” Bradley said. “We want to look at all the data elements that describe what happened to the patient while they were in the care of the doctor.”
Analytics tools and techniques are advancing rapidly, with Hadoop and other big data technologies helping to push them along. But a predictive model or data mining algorithm can’t change business processes on its own. To have a real effect, the findings of big data analytics applications need to be communicated in organizations, and that’s where the power of effective big data visualization efforts becomes critical.
And it isn’t rocket science, so to speak. “I talk to people who interact with predictive modeling technology every day who don’t even know it, because it’s embedded in a way that’s non-intrusive,” Bradley said, referring to things like the product recommendation engines on Amazon and other websites. He added that analysts who are visualizing big data similarly need to find “a clean way to deliver information that comes from really complex analytics.”
This article originally appeared here.