Tips to Improving Data Visualization in Healthcare

By Catherine Richards, PhD, MPH, SVP of Analytics & Scientific Engagement, Panalgo
Twitter: @PanalgoInsights

Data visualization in healthcare has been a topic of conversation for years, however, the industry’s interest and investments in that arena are growing as of late. The global data visualization market is projected to grow 11.6% by 2026 and reach over $10 billion, which is being driven by factors such as the growing demand for an interactive view of data for faster business decisions – a task that requires data visualization tools.

The transformation of data into actionable insights is what drives healthcare organizations toward their goals and objectives, both internally and externally in the markets they serve, especially now as these insights are used to help inform our health system’s response to the ongoing COVID-19 pandemic. Data visualization eradicates noise, detects patterns, and identifies significant values from any given dataset to generate actionable insights that enable more timely, informed, and effective decisions. However, many organizations still struggle to fully understand, act on, and share data-driven insights across relevant stakeholders, which limits their ROI on analytics investments, hinders go-to-market initiatives, and stunts the ability to function as a data-driven company.

Challenges healthcare and life sciences organizations face in visualizing data

Unifying disparate data sources has long been a challenge in healthcare analytics, and it still exists today. The sheer volume of data that’s become available in the healthcare sector, all of which is made available through different sources and in different formats and languages, makes aggregating different datasets for comprehensive visualization projects time-consuming and difficult.

Additionally, siloed data assets, repositories and analyses combined with widely inaccessible visualization systems make generation and collaboration on these projects difficult, hindering many organizations’ efforts to get more out of their data analyses.

Lastly, data visualization is rarely, if ever, emphasized in the education or training programs that data experts are often involved in. There is a lot more to data visualization that just turning an analysis into a graph – design principles such as color theory and chart type contribute to the countless aspects that make visualizations effective. There are publicly available resources out there that aid in helping data experts learn these components, however, knowing that time and bandwidth is often extremely limited for these experts, studying what contributes to the human-visual connection often falls to the wayside when having to develop critical research in this space.

While all of these challenges require different approaches and solutions to move the practice of data visualization forward for the masses, advancements in healthcare analytics platforms have helped alleviate some of these challenges to support data visualization in the immediate term – a vital component in aiding in the COVID-19 discovery process and response now and moving forward.

Strategies to create valuable and actionable visualizations

1. Create with your end user in mind. From beginning to end, the target audience of the data visualization project at hand should be taken into consideration at every step of the development process. Nine times out of ten, this audience does not and should not have comprehensive expertise or an in-depth understanding of the topic or research being presented visually, as that is the inherent value of these visualizations. If your audience can’t understand the story or message being portrayed by your visualization after spending a few minutes with it, then it isn’t providing value. Keep it simple enough that anyone who interacts with it can understand what you are trying to get across, and take the time to hone in on what that end-goal message is in the beginning to ensure it comes across in the completed visualization.

2. Design visualization dashboards that can be easily sustained and maintained over time. New data is constantly being released, and existing data is constantly being refreshed, so it’s important that a visualization based on this data is just as updatable. This upkeep often requires staff time and resources however, it can be made much easier depending on the data analytics platform being used.

Advanced analytics platforms available in the market should provide data visualization and graphical capabilities that allow organizations to effortlessly visualize and make sense of complex analyses – turning studies into digestible stories that provide deep contextual information while also ensuring that information is kept up to date. By using platforms equipped with extensive libraries of pre-built charts to choose from, and that don’t require custom coding but still support coding with ease if ever needed for certain projects, the visualization creation process is simplified so dashboards can be conveniently designed and refreshed when updates of the data are obtainable. Easy dashboard updates help ensure organizations have access to the latest insights as soon as the data is available to them. Platforms that translate data into a common data structure also enable the use of dashboards across many different types of healthcare datasets, often without numerous updates needed. This all allows faster time to insights for all data assets of interest to an organization.

The value of utilizing data visualization tactics and technology in healthcare to make sense of complex analyses is of the greatest importance right now. The healthcare and life sciences sectors become more data-driven by the day and being able to digest and act on these insights will be crucial as organizations increasingly rely on data analytics to inform how they should run their businesses. Those that invest in the resources, skillsets and solutions to attain the right set of advanced analytics paired with the right data visualization capabilities now will likely be those that see business and market success long-term as most data-driven organizations do.