This monthly blog highlights and discuss emerging trends and challenges related to healthcare data and its ever changing life cycle.
By Melanie Meyer, PhD, MHA, RHIT, CCS, CPHQ
The scale of healthcare data has grown tremendously in recent years, increasing the need to present that data in ways that are more understandable and insightful. Data visualization, including storytelling, is an essential tool for doing this.
What is data visualization?
Data visualization places data in a visual context to help people better understand the data’s significance.
The process of visualization synthesizes large volumes of data to get at the essence of that data and convey key insights.
Data storytelling goes further—linking data, visuals, and narrative, for example, in an infographic or storyboard. Adding the narrative to the data visualization helps highlight what is important in the visualization and how and why data changes over time. Data stories can help frame a discussion or provide a path to further analysis. Often, data stories are told through a series of linked visualizations.
Why is data visualization important?
Visuals or pictures convey meaning more easily than thousands or even millions of data points; storytelling focuses the message. Some examples of data visualization are here and here and here. Visualization also allows analysts and end users to recognize patterns and relationships in large volumes of data that may not be easily seen in the raw data or reports. This may help identify emerging trends, for example, to allow an organization to address quality or safety issues before they become bigger problems. The goal is to provide actionable insights that help drive change.
As access to data grows, data analysts and other users of healthcare data will require new skills and approaches for working with that data. A recent Forbes article found that the need for data storytellers will increase as analytic self-service capabilities grow and more people generate insights.
How is data visualization used?
Many organizations produce data visualizations in the areas of healthcare delivery, patient-facing applications, population health, public health, or global health. Some examples include:
- The Institute for Health Metrics and Evaluation, a population health research center at UW Medicine, regularly features data visualization on its site regarding topics such as the social determinants of health and obesity.
- Visualizing Health is a project of the Robert Wood Johnson Foundation and the University of Michigan Center for Health Communications Research that provides visualizations that communicate healthcare risk information.
- The Center for Disease Control’s National Center for Health Statistics offers a data visualization gallery based on the data the organization collects.
- The Agency for Healthcare Research and Quality (AHRQ) offers a data visualization site that highlights findings from the Agency’s Medical Expenditure Panel Survey, the Healthcare Cost and Utilization Project, and other AHRQ data sources.
Healthcare delivery organizations also create data visualizations via projects such as dashboards for quality initiatives. Some examples of dashboard best practices can be found here. A recent case study highlights the dashboard development process for facility-level data visualizations using electronic health record data. The visualizations focused on two areas: sepsis patient outcomes and 30-day readmissions. The goal was to provide meaningful summaries while highlighting critical performance metrics that were easy to understand by various end users across the organization. Utilization of the dashboards resulted in significant time savings and more readily available information.
Whether your role is data analyst or another area of health information management, understanding and using data visualization with storytelling will become more important in the future as more roles have access to and require use of a wider range of healthcare data.
Melanie Meyer is performance improvement leader at EVOSCALE Health.