News Column

The Connected Age: Big Data & Data Visualization

July 1, 2014

Skiba, Diane J

Welcome to the world of Big Data. In health care, we amass a lot of data from various sources. Francis Collins (2014), director of the National Institutes of Health, describes a mountain of data consisting of "an enormous, ever-expanding trove of digital data through DNA sequencing, biomedical imaging, and by replacing a patient's medical chart with a lifelong electronic medical record."

In the era of Connected Health, data extracted from various health care institutions, payers, research entities, and pharmaceutical industries, as well as patient-generated health data (PGHD), are being used to create the lifelong electronic health record (EHR) Collins describes. The Big Data revolution in health care offers opportunities to discover "new threads of knowledge" (Groves, Kayyali, Knott, & Van Kuiken, 2013).

To identify these threads, it is imperative that analytic tools such as data mining be used to make sense of the trove of digital data. The analysis of these mountains of data is important to support an organi- zation as a learning health system (LHS) (Olsen, Aisner, & McGinnis, 2007; Skiba, 2011). According to the National Committee on Vital Health Statistics (2011), "In a learning system, people, actions, results, and knowledge are connected in continuous feedback loops that enable improvement and change - learning - over time" (p. 9). The informatics infrastructure, health information technology tools, and analytic techniques are "making it increasingly possible for communi- ties to become dynamic learning systems that are working to improve local health" (p. 9).

As patients, families, caregivers, and consumers become more engaged in the Connected Age, it will be equally important to connect these various data sources (PGHD as well as data from EHRs) to help individuals better manage their health. And, for maximum benefit, especially for patients and providers, there is a need to display data in an accessible, useful, and usable manner.

In a blog post on the National Center for Healthcare Leadership website, Garmen (2013) spoke to the emerging competencies needed in the era of Big Data and the LHS. Along with statistics and "contextual knowledge of the healthcare organizations the data represent," there is also a need for "advanced competencies in 'storytelling' - translating statistics into practical wisdom and action, through compelling narra- tive and visualization."

Garmen's views resonate with me as an educator and lead to questions for nursing education: 1) Will nurses need to demonstrate data visualization literacy competency to provide health care now and in the future? 2) Do we as educators need to help our students gain experience in reading and interpreting these statistical translations and using visualization techniques to provide patient data for clinical decision-making?

Before tacking these questions, it is important to understand the term data visualization literacy. I have been familiar with the term visual literacy from the work of Tufte, a statistician who uses infor- mation design to display data, information, and evidence (www. A working definition of visualization liter- acy was offered at a EuroVis 2014 Workshop: "As a scholarly subject, visualization literacy is expected to encompass both cognitive aspects (e.g., nonverbal reasoning, and spatial navigation with visual repre- sentations) and pedagogical aspects (e.g., learning visual representa- tions, metaphors and languages, educational curricula, and specialized training), and to explore cognitive abilities in visualization evaluation and competency development" ( eurovis-2014-workshop-towards-visualiza/).

Now, as visual literacy merges with data literacy to form the new concept of data visualization literacy, we must ask: Is this a competency that nurses need? A review of the literature indicates that the answer is yes. Nurses need to understand and interpret the visual displays of data in their practice.

An example comes from Virginia Commonwealth University Hospital, where nurses use a clinical dashboard on one EHR computer screen as a key performance indicator to reduce medical errors and facil- itate communication within the health care team (Bakos, Zimmerman, & Moriconi, 2012). By providing an instant snapshot of their patients' real-time status on key safety measures, the dashboard facilitates clinical decision-making based on best practices and helps ensure patient safety.

The Future of Nursing Campaign for Action uses dashboards to demonstrate progress toward meeting the recommendations set forth in the 2011 Institute of Medicine Future of Nursing report. To view these dashboards, visit

My colleagues at the University of Colorado College of Nursing have published on their use of data visualization. Vincent, Hastings- Tolsma, and Effken (2010) experimented with the use of data visu- alization techniques "to determine . . . new patterns or trends that were not evident in prior analyses" of a nurse midwifery dataset that examined perineal trauma in childbirth. A more recent publication (Alfonzo, Sakraida, & Hastings-Tolsma, 2014) examined the use of a visualization technique, bibliometric mapping, to "highlight the impact of given research on a discipline and . . . the potential to fos- ter increased data comprehension." Their exemplar demonstrates "the impact of one nursing theory on research studies conducted by begin- ning nurse scientists."

So, how do clinicians present data that are understandable and use- ful for patients? A study by Arcia and colleagues (2013) provides a great example of how data visualizations will be used as we engage consumers in their health care and help them make better health decisions. This study examined how data visualizations were used to ethically return health data to low-health-literacy patient populations. The research team developed visualizations that were suitable for testing with members of the target community for both acceptability and comprehension.

Why is the use of data visualization in health care significant to educators? As noted in Educause's 7 Things you should know about data visualization II (2009), it is significant for educators to present valuable data and information in an engaging format that is easily understandable and "bring data to life and draw it into the reach of non-experts." It is also important for researchers to present complex statistical findings to a larger audience.

What are we as educators doing to prepare our students to inter- pret these data? Think about it. We tend to focus on the narrative when teaching about clinical documentation, and we have a heavy emphasis on writing narratives for many class assignments. But are your students capable of interpreting data presented in visual displays? Do we give them opportunities to present their knowledge through visualizations? How much exposure to visual representations of data and information is available in your curriculum?

To help you explore the newly emerging area of visualization literacy, some resources are provided in Figure 1. Do not feel limited in your choice of visualization techniques. Rather, take a look at the Periodic Table of Visualization Methods in Figure 2 (www.visual-, which allows you to view data visualization from various perspectives: information, concept, strategy, metaphor, and compound visualization methods. The chart has a mouse-over feature that shows you an example of each element. See Lengler and Eppler (2007) to learn how the periodic table was developed. As always, contact me at to share how you are exploring visualization literacy in your classes.

Figure 1: Examples of Data Visualization

See nursing infographics and data visualizations together at

For examples of data visualization from census date, see

For examples of census data infographics, see

For a review of major features of a web application that allows you to create a visualization from a Medicare dataset, see

These three videos are from the Healthy Communities Data Summit in 2013:

* A Better Pie Chart and Beyond: The Evolution of Data Visualization at

* Consumer Access to Health Care Data at

* Harnessing Data to Maximize Outcomes at

Ted-Ed Lessons Worth Sharing:

* Hans Rosling's Best Stats You Have Ever Seen at stats-you-ve-ever-seen)

* David McCandless' Beauty of Visualization at


Alfonzo, P. M., Sakraida, T. J., & Hastings-Tolsma, M. (2014). Bibliometrics: Visualizing the impact of nursing research. Online Journal of Nursing Informatics (OJNI), 18(1). Retrieved from

Arcia, A., Bales, M. E., Brown, W., Co, M. C., Jr., Gilmore, M., Lee, Y. J., . . . Bak ken, S. (2013). Method for the development of data visualizations for community members w ith varying levels of health literacy. A MI A Annual Symposium Proceedings. Retrieved from w w pmc/articles/PMC3900122/?report=classic

Bakos, K. K., Zimmerman, D., & Moriconi, D. (2012, June 23). Implementing the clinical dashboard at VCUHS. Paper presented at 11th International Congress of Nursing Informatics, Montreal, Quebec, Canada. Retrieved from

Collins, F. (2014, May 6). Mining the Big Data Mountain [NIH Director's Blog]. Retrieved from mining-the-big-data-mountain/

Educause. (2009, August). 7 Things you should know about data visualization II. Educause Learning Initiative. Retrieved from w w /ir/ library/pdf/ELI7052.pdf

Garmen, A. (2013, June 21). "Big data" and the Learning Healthcare System: Implications for leadership development [Blog of the National Center for Healthcare Leadership]. Retrieved from http://nchlblog. org/2013/06/21/big-data-and-the-learning-healthcare-system-implica- tions-for-leadership-development/

Groves, P., Kayyali, B., Knott, D., & Van Kuiken, S. (2013, January). The "Big Data" revolution in healthcare: Accelerating value and innovation. New York, NY: McKinsey & Company. Retrieved from q8booqr

Lengler, R., & Eppler, M. J. (2007). Towards a Periodic Table of Visualization Methods for Management. Retrieved from odic_table/periodic_table.pdf

National Committee on Vital Health Statistics. (2011). The community as a learning health system: Using local data to improve local health. Retrieved from w w

Olsen, L., Aisner, D., & McGinnis, J. M. (Eds.). (20 07). The learning health- care system: Worksho p summar y (IOM Roundtable on Evidence-Based Medicine). Washington, DC: National Academies Press.

Skiba, D. (2011). Informatics a nd the learning hea lthcare system. Nursing Education Perspectives, 32(5), 334-336. doi:10.5480/1536-5026-32.5.334

Vincent, D., Hastings-Tolsma, M., & Effken, J. (2010). Data visualization and large nursing datasets. Online Journal of Nursing Informatics (OJNI), 14(2). Retrieved from

Diane j. skiba, editor

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Source: Nursing Education Perspectives

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