Purpose and Impact of data visualization in the Healthcare industry
There are many applications of data analytics in the healthcare industry: physician and ambulatory care centers, hospitals and health systems, managed care plans and HMOs, genomic studies, and Accountable care organizations (Cyranoski, 2015; eInfochips, n.d.). Therefore, visualizing health data could help tell stories through analyzing relevant data such that data-driven decisions and actions could be made (California HealthCare Foundation [CHCF], 2014; eInfochips, n.d.). Cleardata (n.d.) suggested that presentation of data for data visualization should consist of the following best practices: the use of relevant data, begin with understanding what should be communicated then design towards that, make visualizations easy for the consumer, ensure HIPAA-compliance when showing data, and create visualizations that can lead in making data-driven decisions and action. Therefore, before selecting the right visualization tool, a presentation approach must be considered, which takes into account: personal level of expertise, visualization methods, and interactivity of the visualization (CHCF, 2014).
It is not enough to analyze the relevant data for data-driven decisions but also selecting relevant visualizations of that data to enable those data-driven decision (eInfochips, n.d.). There are many types of ways to visualize the data to highlight key facts through style and succinctly: tables and rankings, bar charts, line graphs, pie charts, stacked bar charts, tree maps, choropleth maps, cartograms, pinpoint maps, or proportional symbol maps (CHCF, 2014). The above visualization plots, charts, maps and graphs could be part of an animated, static, and Interactive Visualizations and would it be a standalone image, dashboards, scorecards, or infographics (CHCF, 2014; eInfochips, n.d.).
The CHCF (2014) recommended that data visualization tools that everyone could use would be: Google Charts & Maps, Tableau Public, Mapbox, Infogram, Many Eyes, iCharts, and Datawrapper. CHCF (2014) also recommended some data visualization tools for developers such as High Charts, TileMill, D3.js, FLOT, Fusion Charts, OpenLayers, and JSMap. Whereas eInfochips (n.d.) suggested visualization tools like Tableau, R, and Spotfire. Many eyes have been shut down by IBM and have been replaced by Watson Analytics (Machlis, 2011).
Summary on three data visualization tools that are used in health care (Machlis, 2011):
| Tool Name | Description | Advantages | Disadvantages | Skill Level Required | Runs on |
| R | A statistical analysis tool that can not only do simple arithmetic and regression analysis, but it can also do complex data preprocessing, data mining, machine learning, and static data visualizations. | Library of code is supported by the community, which is subject matter experts. | Runs as a command line program, therefore there is a need to install a Graphical User Interface. | Linux, Mac OS X, Unix, & Windows | Advance Beginner |
| Tableau or
Tableau public (Free version) |
A tool mostly used for interactive visualization that can do all the visualizations mentioned in the post, through dragging and dropping variables. | A drag-and-drop interface allows for quick work to do data analysis that would take the time to manually code. | Data stored in Tableau Public is stored on the web for free for others to use, which may make data privacy hard to control. Otherwise, the full software is over $1K for a single user. There is limited customization in its interface, but can be done through code. | Windows and Mac OS X | Beginner to Intermediate |
| Google Chart Tools | A self-contained application for storing data on the cloud and visualizing it anywhere, through the use of JavaScript visualization libraries. | Integration with other Google products like Google Spreadsheets and is heavily documented JavaScript library. | Requires coding to make the visualizations, and you don’t have access to the JavaScript codes and have to rely on continuous Google support. | Any device with a web browser. | Advanced to Expert
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Resources
- Clear Data. (n.d.). Healthcare Data Visualization: A clear picture of actionable insights. Retrieved from https://www.cleardata.com/knowledge-hub/healthcare-data-visualization-a-clear-picture-of-actionable-insights/
- California HealthCare Foundation. (2014). Worth a thousand words: How to display health data. Retrieved from http://www.chcf.org/~/media/MEDIA%20LIBRARY%20Files/PDF/PDF%20W/PDF%20WorthThousandWordsDataViz.pdf
- Cyranoski, D. (2015). Exclusive: Genomics Pioneer Jun Wang on his new AI venture. Nature. Retrieved from http://www.nature.com/news/exclusive-genomics-pioneer-jun-wang-on-his-new-ai-venture-1.18091
- (n.d.). Revolutionizing the healthcare industry with big data, analytics and visualizations. Retrieved from https://www.einfochips.com/whitepaper/Revolutionizing-the-Healthcare-Industry-with-Big-Data-Analytics-and-Visualization.pdf
- Machlis, S. (2011) 22 free tools for data visualization and analysis. Computer world. Retrieved from http://www.computerworld.com/article/2507728/enterprise-applications/enterprise-applications-22-free-tools-for-data-visualization-and-analysis.html
Dr. Skylar (Sky) Hernandez 