Data visualization is the process of putting data in a visual context to enable people to understand it more easily. The huge collection of health data collected by health practitioners has to be analyzed in an efficient way to ensure their protection. The current tools available can only analyze this data to a limited extent. This paper discusses data visualization as an effective tool for the analysis of electronic health data. It will provide adequate information for health practitioners in making informed health care decisions, especially in monitoring the provision of health care services to patients. This paper begins by defining the task to be supported by the data visualization tool. It will also review the state of art approaches that have been used previously. It will group the visualization tools in clusters of three and analyze each cluster. It will also explain the criteria used to group them and the features of each group. Furthermore, it will select the best tool for each cluster and analyze each of the selected tools. It will also apply InfoVis Toolbox for each of the selected data visualization tools for each cluster. URL and clear references will also be provided for the tools discussed in this paper.
Keywords: data-visualization-tools, healthcare-data.
Yuk and Diamond (2014) define electronic healthcare data as the type of data that relates to health conditions, reproductive health, and death causes which is stored on a computer. When this data is stored on a computer, it is referred to as electronic health data. Data visualization tools have become more popular on electronic health data. These tools have targeted government health agencies and clinical research institutions. The possibilities that these agencies will gain a lot when they use data visualization tools in the analysis of their data are high. This paper will discuss the various information visualization tools that are used in healthcare data and propose the most appropriate tool for the stakeholders in the health industry.
Health care data visualization tools
These tools use speedometer-like icons to display relative placement of data between two extremes (Wexler, Shaffer, & Cotgreave, 2017). For example, they can display data such as the height, age, or weight of the patient. They use a familiar user interface to display their data. The tools are described below:
It is a data visualization tool suitable for both database experts and newbies. It is integrated with natural language queries making users to easily interact with data. See its user interface in appendix 1. Its major drawback is that it does not support real-time data analysis. It provides both free and paid versions. The free version can only support one user at a time and does not allow a user to store more than 1 megabyte of data. The paid version is capable of multi-user support. This tool only has the commercial version that requires a user to pay 30 USD per year. It has an advanced analytics engine that works well with natural language querying platform to make an analysis of health records easier. The artificial intelligence behind this tool guides users of various expertise to use it easily. It employs wizard screens that guide new users to focus on instantly getting analyzed health information rather than how the data is processed.
Watson analytics can connect to 32 data sources among them Twitter, PayPal, CSV, Hubspot, SugarCRM, One Drive plus many more. It can also fetch direct data without using any connection from sources like MySQL, PostgreSQL, Sybase, and Oracle among others. Users can load data by first providing their login credentials. Once logged in, they can also set various rules through authentication and authorization. It ensures security and protection of health records.
A user can also clean and shape the data before uploading it by clicking on the “Shape Before” command instead of the” Upload now” command. Data cleaning ensures that the health data to be uploaded is free from errors and is accurate. Shaping data is deleting empty records to achieve conformity that aids in the analysis of health records. After the health practitioner has uploaded all the data sets, the tool will show the data sets using icons or tables customized by the user. Refer to Appendix 1. This tool does not support streaming analytics, a common feature in systems in which data is streamed and analyzed in real-time. This is a major drawback for IBM Watson Analytics since it cannot be used for cases that require real-time data analysis. However, data can be refreshed instantly after every five seconds.
Visme is a visualization tool that transforms data into interactive presentations, charts, videos, infographics, and many more visual presentations. The tool also provides the user with the ability to modify the start-up template in case they are not satisfied with its look and feel. It also offers a wide range of templates including the ones for health. With Visme, a user can make images more interactive by adding links and buttons and then uploading them online. The software supports teamwork by allowing users to work as a team. Users also have the option to work individually. Visme is cheap and provides a wide choice of visualizations. However, it does not support data analysis hence it is only suitable in a situation in which in-depth analysis is not required.
Visme offers a wide range of unique and attractive features such as fonts, graphics, templates, and backgrounds for a presenter. It also possesses many customizations that enable the health practitioner to customize their data in a way that fosters their understanding. With the customization tool, the user can fetch their data from third-party sources and customize it in the software using available templates. The data analyzed in Visme can be downloaded in various forms or shared on social media websites. Visme has a powerful analytical engine that provides updated and accurate data to other visitors. It enables the healthcare practitioner to track the content most visited by users and the time spent on each content. This data can also be downloaded and viewed offline. Refer to appendix to for Visme dashboard.
Visme’s features are summarized below:
This tool provides data analysis together with a drag-and-drop feature that enables users to create various data visualizations with ease. It allows connectivity to multiple data sources thus allowing data to be queried instantaneously via dashboards. The information can then be shared among various health departments via the dashboards. Sisense provides interactive visual analysis of data allowing a user to explore it instantly and get the answers to their queries. Sisense provides the functionality for scorecards and dashboards. It also contains a data warehouse allowing a user to extract data from various data sources, transform the data in the format understood by the data warehouse, and then load the data into the software. The dashboards are used to project the data via charts, maps, scatter plots and many more. See Appendix 3 for a sample user interface of this tool.
Crowd accelerated business intelligence is a technology used by Sisense to provide the capability to share the analyzed data with users. Sharing of data fosters teamwork among the health practitioners. Sisense can be deployed on the cloud or on a desktop that is installed with Windows 8, Windows 10 and a web browser. Iliinsky and Steel (2011) argue that Sisense offers a wide array of advantages which include; a simple usability interface that allows a user to use it with ease, ability to customize visualizations, better performance in terms of data processing, and adequate user support via helpful customer service. Its major drawback is the smaller number of functionalities it provides compared to other data visualization tools. It also limits the user to structured query language (SQL) expressions.
This tool is highly customizable and has a variety of exciting features. However, it will require a lot of time to grasp its usage. It offers powerful data visualization capabilities together with strong artificial intelligence capabilities. It boasts of a clean clutter-free user interface. This tool works well when combined with other tools in handling data exploration. According to Knaflic (2015), a lot of third-party software is available free, which can be integrated with Qlikview together with online help. This tool enables one to make connections in the data, which minimizes wastage and improves health care delivery. It enables the health practitioner to get a clear understanding of the patient’s journey and the way it relates to operational functions. It also determines the financial and clinical effects.
QlikView has the ability to reveal data that cannot be found with query-based tools. Using its associative data-indexing engine, this tool can expose health data insights and their association with a variety of sources. Furthermore, QlikView offers collaborative analytics that fosters teamwork and allows the sharing of healthcare data among healthcare practitioners. With this tool, a user can easily build analytical applications without any programming and coding knowledge. This software contains an inference engine used to automatically find relationships in the data uploaded. It is easy to learn and can even be used with non-IT professionals like the healthcare experts to comprehend hidden trends. It offers an online web console that is used to administer user access. See Appendix 5 Qlikview’s user interface. It can be deployed on a computer running Windows or Mac operating system and installed with a web browser software.
This is a data visualization tool that resides on the cloud making it efficiently analyze data in real-time. With Klipfolio, one can easily connect to a wide variety of data sources, including offline and online data sources. Online data sources include relational databases, Twitter, Google sheets, Oracle cloud etc. Offline data sources include Microsoft Excel, JSON, extensible markup language (XML) and many more. The data sources can be used to integrate various metrics to get the visual data, transform, customize, and share it among other users.
With Klipfolio, dashboards can be discovered using multiple technological platforms such as personal computers, tablets, smart TVs, and smartphones (Eckerson, 2011). This tool is suitable for live monitoring and control of continuous data flows (Murray, 2017). Live data connection facilitates data retrieval that maintains time consistency for accuracy, reliability, and responsiveness of data.
This tool contains the following interesting features:
Kipfolio contains a large number of visualization types such as bar charts, tables, linear charts, pie charts and more. It allows users to customize their visualizations using HTML and cascading style sheets. The components are laid out on dashboards with the aid of an editor that supports what you see is what you get WYSIWYG feature (Iliinsky & Steele, 2011). A user can add formulas and equations for visualizations. See Appendix 4 for Kipfolio dashboard.
Checklist for Kipfolio
|Texture||Animate Shift of Focus||Yes|
|Shading||Focus plus Context||Yes|
|Depth Cues||Details on Demand||Yes|
|Surface||Yes||Output / Input||Yes|
|Proximity||Yes||Maximize Data-Ink ratio|
|Similarity||Yes||Maximize Data Density||Yes|
|Continuity||Yes||Minimize Lie Factor||Yes|
Klipfolio was selected as the best tool in this cluster because of its capability to reside on the cloud hence making an analysis of real-time data quite efficient. This tool also provides fast feedback and is very interactive. Furthermore, it allows easy customization of color, shape, orientation, and size. It supports user interaction via dynamic sliders, direct manipulation, and semantic zoom.
Geographical mapping tools
These tools use a visual display in the form of a location map. They use position and color to show health data for a number of selected regions. These tools are used to discuss the data associated with healthcare and the way they can be analyzed to provide efficient health services to patients. They include the following tools:
This is a lightweight data visualization tool that is integrated into websites and used to develop interactive maps. It provides multiple hooks where a user can add their own code making it a fully customizable tool. Its library can be scaled with more add-ons increasing its functionality thus offering more data integration options (Boscoe, 2013). Check Appendix 6 for Modest Maps user interface.
Heatmaps enable data visualizations to provide instant recognition. They make the visualizations intuitive using colors. This tool has the same usability features as provided by other free tools such as drag and drop. It has at least two rows for longitude and latitude. These values are included in all the data points required for the map. See Appendix 7. MyHeatMap is fully interactive since it enables users to freely zoom and display their data at any size. Viewers can also switch among various data sets in the same map more easily. Furthermore, this tool also offers a turnkey solution allowing a user to upload their data in CSV format upon which the map is instantly viewable. A user is shelved from the task of performing complex coding. They only upload the data and it becomes viewable on the map (Murray, 2017). However, the free version of this tool lacks privacy and only allows the creation of public maps that can contain a maximum of 30 data points.
Checklist for ‘MyHeatMaps’
The below checklist is a copy of Information Visualization Toolbox developed to be used in the course of Data Visualization. The toolbox is divided into three sections namely, perceptual coding, interactivity and information density. A Yes next to the technique shows that MyHeatMaps uses this technique while a blank indicates that the tool does not support this method. Under the interaction section, a Yes indicates that a user can manipulate this tool in a manner defined. Under information density, a Yes shows that the tool performs the activities listed while the absence of a Yes shows that the tool cannot perform those functions listed. Under the perceptual coding section, a Yes shows whether the human visual system feature can be used to show information in MyHeatMaps.
|Texture||Animate Shift of Focus|
|Shading||Focus plus Context|
|Depth Cues||Details on Demand||Yes|
|Surface||Output / Input||Yes|
|Proximity||Yes||Maximize Data-Ink ratio|
|Similarity||Maximize Data Density|
|Continuity||Yes||Minimize Lie Factor|
MyHeatMaps was chosen as the best tool among the Mapping visualization tools because it leads in the three criteria under the visualization toolbox. It is the best mapping software that can be used by a health practitioner to analyze health data among patients of various regions. It allows direct manipulation of data and provides immediate feedback hence it is highly interactive. It uses color under perception to differentiate between various locational data. Users can hover the mouse across various regions and locate an area that is more prone to a certain disease base on a color code. This will enable them to provide fast and efficient health services to the patients in that particular region. From the toolbox above, this tool has a higher information density and can provide details on demand. Though it lacks some features, the features it supports far more outweighs the unsupported features.
This is a group of tools used to create all types of charts such as line charts, pie charts, and bar charts. The use color to differentiate between various data values (Miller, 2017). Furthermore, users can specify percentages for the values to indicate the comparison margins. They include the following tools:
Plotly is a multi-purpose data visualization tool that offers a wide array of customizations compared to other free tools available. It also offers better user interaction. Charts built in plotly can be exported as graphics. Though this tool has a steep learning curve, it allows a basic user to feed in some data at the start. Huang and Huang (2013) explain the two striking features of this tool as the ability to create multiple charts visualizations that are helpful when making a comparison with charts. With plotly, one can also create an account and store the charts and data within the account. One can also create folders within the account and organize the data.
Plotly allows users to collaborate with each other and share visualization elements such as maps and charts. This enables them to present data much faster and ensures that every team member is assigned a task to maximize efficiency.
Below are the benefits provided by this tool:
Plotly can be installed on a desktop running Windows or Mac operating system. It requires that the desktop contain a web browser. The commercial software offers an annual subscription. See Appendix 9 for Plotly visualization sample.
Adaptive discovery offers a health practitioner a wide range of dashboards for the data the health institution creates. The tool provides various data visualization techniques such as dial, funnel, waterfall, charts, histograms and many more. These tools also come equipped with advanced intelligence that is able to analyze data based on the institution’s budget and forecast, thus relieving the user from the task of working with complex formulas and equations. Furthermore, adaptive discovery puts a lot of emphasis on reports. It provides reporting techniques such as variances, linear regressions, and trends. See Appendix 10.
This tool has a huge customer base with tens of thousands of accounts across various industries because it is simple to use. It also provides user interactivity much better than leading business intelligence solutions since it can handle high data capacity. It is suitable for handling huge and fast-changing datasets that are applied in big data operations such as machine learning and artificial intelligence operations (Sahay, 2017). This is because it is integrated with databases such as MySQL and SAP solutions. Tableau has the capability of creating efficient graphics that enables users to easily comprehend data. The software allows a user to connect, visualize, and share information in a seamless faster experience. A user does not need any coding knowledge to load data, create, and analyze charts. Murray (2013) adds that Tableau contains a server that helps users easily analyze and publish charts anywhere via a smartphone or a desktop installed with a web browser.
Tableau offers the following benefits:
See appendix 11 for Tableau user interface.
This tool intelligently displays charts according to the data queried without the need of configuring it. It provides an easy to use widget builder simplifying the task of data visualization. This tool can provide answers to difficult queries regarding patients. It can compute nutritional values for meals and convert recipe measurements. It contains a powerful knowledge database that helps in daily health activities (Shen, 2017).
This tool substitutes the code with a visual interface and can be used with anyone. It contains a chart designer that guides a user through the data visualization process. It provides the capability of making 30 charts in its free edition and exporting them as portable network graphics format. Furthermore, the tool-free edition of the tool can provide a maximum of 5000 views per month. Its major disadvantage is that it cannot allow a user to make their private charts, thus, posing a security loophole to the user.
This is a highly efficient data visualization tool from Google Inc. that is free. It allows AdWords and YouTube, which are both Google products, to make a connection to it. A user can set up Google data studio account by using their Gmail accounts. A user can start performing data visualization tasks from a template. With this tool, a user can place the data in one location since it can easily connect the data from Google Ads, Google Analytics, spreadsheets, and many more applications. A health practitioner can also explore the data using this tool because it is capable of converting the raw data into dimensions required to create simple reports without using code (Shen, 2017). With Google data studio, a user can create charts and graphs using the drag, drop feature, and share them with a single click. It provides a wide range of customizations such as colors, graphics, shapes, thus giving users an easy way to choose data from multiple data sources such as Google Ads, YouTube and many more.
Silk is a data visualization tool that converts spreadsheet files into a wide range of visualizations such as charts and graphs. Silk’s data is not private hence not suitable for storage of confidential data.
Fusion Charts is composed of four distinct products with each offering different types of charts. The products include:
FusionCharts offers a wide range of benefits some of which include the following:
See Appendix 16 for a snapshot of Fusioncharts layout.
The tool enables users to develop interactive charts on their sites. It is the easiest and most flexible charting tool in the market, as it is used by 72 of the top 100 companies in the world. It contains sophisticated navigation options such as date pickers, scrolling options, and navigator series. Highcharts lets one create interactive maps to display health information regarding a patient. It is suitable for standalone use or in dashboards. This tool also offers a cloud feature where a user is able to create interactive graphics and upload them on social media. Highcharts focus on cross-browser support, thus, any user can view and run their visualizations irrespective of the browser they are using. Additionally, Highcharts support data sharing between various team members and prospective stakeholders. It is simple to configure this tool since no programming expertise is required.
Highcharts offers a number of benefits such as the ones discussed below:
See Appendix 17 for a sample Highcharts’ user interface.
The visualization tool is becoming more popular among companies that analyze data using charts. It has a simple user interface making it simple to create charts, maps, and upload data in CSV format. It is easy to learn about the usage of this tool. It allows one to create interesting charts from the healthcare data. It contains three basic steps in the creation of visualizations. These steps include:
With datawrapper, one can create charts quickly without requiring any coding skills. Furthermore, it enables the creation of interactive charts allowing readers to hover over maps and charts to view underlying values and understand the chart easily. With Datawrapper, a user can easily create flexible charts and export them as images or in form of PDFs, maps or live charts. The open-source tool enables users to create appealing and interactive chart types.
This tool offers the following benefits:
See Appendix 18 for a screenshot of this tool.
The data visualization tool is simple to use. In its free version, it offers over 35 charts with plenty of room to customize them. The charts created are highly interactive and can be embedded easily. There is also support for the creation of visualization maps. With the free version, one can create reports and infographics. One can create stunning infographics that increase visitor interactivity on one’s site. Furthermore, one can create interactive marketing reports that stand out. The charts developed by Infogram are of high quality since they support data import from various sources that can be customized and shared. Infogram contains a rich set of visualizations such as over 500 maps, a huge collection of templates and 35+ interactive charts. It also supports drag-and-drop feature and the ability to import and export data. The charts can be exported in form of images and PDF. Furthermore, Infogram offers reliable ways to create responsive and attractive infographics that look appealing across multiple devices. The analyzed data can easily be published online due to the presence of highly responsive infographics that integrate with other devices seamlessly.
The free and open-source data visualization tool used to build a network and graphical visualizations. It is capable of providing the following functionalities:
No programming knowledge is required to use this tool. It provides high performance since it uses a built-in rendering engine. Furthermore, it provides support for native file formats such as NET, GML, and GraphML. One can also customize its layout, data sources, metrics, rendering presets and manipulation tools using inbuilt plugins.
Checklist for Tableau
|Texture||Animate Shift of Focus|
|Shading||Focus plus Context|
|Depth Cues||Yes||Details on Demand||Yes|
|Surface||Output / Input||Yes|
|Proximity||Yes||Maximize Data-Ink ratio|
|Similarity||Yes||Maximize Data Density||Yes|
|Continuity||Minimize Lie Factor|
Tableau was selected as the best charting tool for analysis of health data due to its higher interactivity with the health practitioner compared with the other charting software. From the visualization toolbox above, one can clearly see that it provides direct customization for health data. It provides high data density that can also be customized. This means that this tool can handle a high capacity of health data. Furthermore, it handles data on demand hence appropriate for dealing with large and fast-changing data sets.
From the tools above, the best tools that support healthcare data are those with the ability to provide high interactivity for health practitioners. They should enable users to easily customize their data in a form that is visually suitable for them to understand and analyze the data effectively. It is through the correct analysis of the electronic health data that will help them make informed decisions regarding the treatment of various diseases. The tools should also be capable of pulling data from multiple data sources both from sources such as Oracle cloud and offline sources such as MS Excel. The best tools also provide the ability to upload healthcare data in CSV format for data manipulation.
The huge number of data visualization tools demands their categorization into various groups in terms of their features and functionalities is required. The tools have been classified into three major clusters namely charting software, mapping software, and dashboard software.
Charting visualization tools display their data in form of charts such as bar charts, line charts, and bar charts. Mapping tools provide data in form of geographical maps while dashboard tools display their data and manipulations in the form of speedometer-like graphics. Both tools use color to differentiate between the various types of data values.
The best tool among each cluster is selected based on how good it performs as shown on the data visualization toolbox. A good visualization tool should be easier to learn and relieve the user from the heavy task of coding. It should provide documentation and application programming interface help to enable the health practitioner to start creating data visualizations quickly.
Furthermore, the data visualization tools vary in terms of cost. The ones provided free can experience certain limitations search as privacy, security, and a number of charts, maps, or dashboards created. A better visualization tool should be free, open-source, and provide the health practitioner with a wide range of functionalities.
Even though there exist powerful data visualization tools for healthcare data, as seen from the previous discussions, there lacks software that can produce 100% of the elements in the visualization toolbox. Elements such as connectedness, shading, and surface are completely blank in all the existing visualization tools. This opens an avenue for future research and development of an all-purpose visualization tool capable of closing this existing gap.
Huang, M. L., & Huang, W. (2013). Innovative approaches to data visualization and visual analytics. Hershey, PA: IGI Global.
Miller, J. D. (2017). Big Data Visualization. Birmingham: Packt Publishing.
Murray, D. (2013). Tableau your data!: Fast and easy visual analysis with tableau software. Indianapolis, Indiana: Wiley.
Boscoe, F. P. (2013). Geographic health data: Fundamental techniques for analysis. Wallingford, Oxfordshire: CAB International.
Yuk, M., & Diamond, S. (2014). Data visualization for dummies: [learn to develop meaningful illustrations of large volumes of data, create eye-catching visualizations, make your data stand out in a crowd in full color!]. Hoboken, New Jersey: Wiley.
Knaflic, C. N. (2015). Storytelling with Data: A data visualization guide for business professionals. Hoboken, N.J.: Wiley.
Shen, B. (2017). Healthcare and big data management. Singapore: Springer Nature.
Iliinsky, N. P. N., & Steele, J. (2011). Designing data visualizations. Beijing: O’Reilly.
Murray, S. (2017). Interactive data visualization for the web: An introduction to designing with D3. Beijing: O’Reilly Media.
Eckerson, W. W. (2011). Performance dashboards: Measuring, monitoring, and managing your business. Hoboken, N.J: Wiley.
Wexler, S., Shaffer, J., & Cotgreave, A. (2017). The big book of dashboards: Visualizing your data using real-world business scenarios. Hoboken, N.J: John Wiley & Sons, Inc.
Appendix 1: IBM Watson Analytics user interface
Appendix 2: Visme dashboard
Appendix 3: Sisense dashboard
Appendix 4: Kipfolio dashboard
Appendix 5: Qlikview dashboard
Appendix 6: Modest Maps user interface
Appendix 7: MyHeatMap visualization user interface
Appendix 8: Leaflet
Appendix 9: Plotly visualization UI
Appendix 10: Adaptive discovery
Appendix 11: Tableau
Appendix 12: WolframAlpha
Appendix 13: Chartblocks
Appendix 14: Google Data Studio
Appendix 15: Silk
Appendix 16: FusionCharts
Appendix 17: HighCharts
Appendix 18: Datawrapper
Appendix 19: Infogram
Appendix 20: Gephi
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