Clinically Relevant Data Visualization

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  • Data visualization is complex with little formal training focused on use in a clinical environment available
  • To add value, data presentation must be actionable and at least maintain overall workload, ideally decreasing workload
  • There are no published studies on daily use of data visualization in clinical practice


The practice of data visualization seeks to communicate meaningful information through graphical depiction. Successful visualizations apply a hierarchical interpretation of data to bridge gaps in understanding.[1] Creating easily understood, meaningful graphics within biomedical disciplines, there is little evidence of formal training programs or methodologies to improve data visualization skills.[2] Clinical use of data visualization does already exist. Pediatric growth charts and labor curves are two examples of current use. At a glance, these tools convey important information with a clinical impact.

Data visualization literacy is defined as "the ability to confidently use a given data visualization to translate questions specified in the data domain into visual queries in the visual domain, as well as interpreting visual patterns in the visual domain as properties in the data domain, the ability and skill to read and interpret visually represented data in and to extract information from data visualizations, and the ability to make meaning from and interpret patterns, trends, and correlations in visual representations of data."[3] Systematic evaluation of visualization literacy enables the development of competencies and curricula designed to improve data communication.

As a next step, Cabanski, Gilbert, and Mosesova describe five principles in constructing visualizations.[4]

  • Focus on the message
  • Displays must be fit-for-purpose
  • Simplify
  • Every graph should stand on its own
  • Avoid deception

Though not clinical, some university libraries do hold "Data Visualization Clinics," which are opportunities to develop and critique visualizations while learning the principles of construction.[5]

Philosophical Impact

Increasing computerization of all facets of life brings increased data. Smart watches provide data on location, steps, heart rate and rhythm, sleep patterns. Digital imagery provides petabytes of detail of our planet from subterranean to satellite mapping. Beyond the mechanisms of creating and interpreting data visualizations, understanding the broad implications carried by big data is an important discussion point. Rodenbeck discusses his experiences through three communications principles which help define the larger theoretical framework around data usage.[6]

  • Principle 1: Public conversations about science are never just about the truth. It’s wise to plan for this, and not shrink from it
  • Principle 2: Data visualization can invite more questions than it answers
  • Principle 3: Data visualization communication is never context-free. There’s no neutral or correct way to do this work

As we develop more methods of measuring, recording, and storing data, contextualizing efforts to communicate will continue to develop.

Bridging Text into Graphics

Most journal articles present information solely through text, due in part to the difficulty in creating accurate visual representations.[7] In several randomized controlled trials, Buljan, et. al. demonstrated no difference in knowledge acquisition between text and image modalities, noting the increased readability and user-preference of graphics.[8] Creating visualizations which update clinical knowledge is a subset of data visualization with clear clinical relevance. Given the large volume of published studies, identifying relevant articles can be challenging. The American Academy of Family Physicians (AAFP) created a framework to identify and present studies impacting clinical care. Patient Oriented Evidence that Matters (POEMs) meet this requirement. The AAFP defines POEMs as "summaries of research [...] relevant to physicians and their patients and meeting three criteria:

  • Address a question that primary care physicians face in day-to-day practice;
  • Measure outcomes important to physicians and patients, including symptoms, morbidity, quality of life, and mortality;
  • Have the potential to change the way physicians practice.[9]

Recognizing the limited time for reading, evaluating, and applying even this summarized data, visualization of the information can help clinicians maintain evidence-based practice.

Integration into Workflow

Target audiences for data visualization are wide-ranging and include individuals in private institutions, academicians, and the general public. Applying the colloquialism, "a picture is worth a thousand words," visual representations can communicate complex ideas quickly. Clinically engaged professionals usually have too many requirements for the time allotted. The addition of another source to access and evaluate is only worthwhile when it decreases the amount of total work. Simply adding another system without allowing for the change management process stymies successful implementation.[10]

Use cases include:

  • Improving workflow

Node-link analysis aids in defining process relationships. The linked image displays a node-link diagram describing the process of developing the plan of care for admission of patients into home care.[11]

  • Patient communication

Disease specific information can be impactfully communicated. AIDSVu is an interactive map providing visualizations at the national, state, and local levels, such as this one. The potential impact of data visualization on public health is tremendous.[12] At an individual level, graphical depiction of weight trends is useful in monitoring fluid balance in heart failure. Growth charts are a visualization commonly used in providing well-child care and evaluating childhood failure-to-thrive.

  • Practice management

Referral patterns, Patient demographic distribution, and HEDIS trends can aid in understanding care requirements and aid in resource allocation.


This brief concatenation of resources, theory, and usability models is hopefully a jumping off point for increasing the utility of data visualization within the clinical community. As with all change processes, forward movement requires a significant amount of work to demonstrate realized potential and build a growing coalition of users and consumers.


The Data Visualization Catalogue

Data visualization:Wikipedia


  1. Frické M. The knowledge pyramid: a critique of the DIKW hierarchy. Journal of Information Science. 2008;35(2):131-42. doi:10.1177/0165551508094050.
  2. Elgendi M. Scientists need data visualization training. Nat Biotechnol. 2017;35(10):990-1. doi:10.1038/nbt.3986.
  3. Borner K, Bueckle A, Ginda M. Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments. Proc Natl Acad Sci U S A. 2019;116(6):1857-64. doi:10.1073/pnas.1807180116.
  4. Cabanski C, Gilbert H, Mosesova S. Can Graphics Tell Lies? A Tutorial on How To Visualize Your Data. Clin Transl Sci. 2018;11(4):371-7. doi:10.1111/cts.12554.
  5. LaPolla FWZ, Rubin D. The "Data Visualization Clinic": a library-led critique workshop for data visualization. J Med Libr Assoc. 2018;106(4):477-82. doi:10.5195/jmla.2018.333.
  6. Rodenbeck E. Communications Principles for Inviting Inquiry and Exploration Through Science and Data Visualization. Integr Comp Biol. 2018;58(6):1247-54. doi:10.1093/icb/icy105.
  7. Buljan I, Malicki M, Wager E, Puljak L, Hren D, Kellie F, et al. Response to letter to the editor by Mc Sween-Cadieux et al. (2017). J Clin Epidemiol. 2018;100:133-4. doi:10.1016/j.jclinepi.2018.02.021.
  8. Buljan I, Malicki M, Wager E, Puljak L, Hren D, Kellie F, et al. No difference in knowledge obtained from infographic or plain language summary of a Cochrane systematic review: three randomized controlled trials. J Clin Epidemiol. 2018;97:86-94. doi:10.1016/j.jclinepi.2017.12.003.>
  9. POEMs Department Collection. American Family Physician; 2019 Web page. Available from: Accessed 18 Oct 2019.
  10. Ulaganathan S, Fatah T, Schmidt T, Nohr C. Utilization of a Novel Patient Monitoring Dashboard in Emergency Departments. Stud Health Technol Inform. 2019;262:260-3. doi:10.3233/SHTI190068.
  11. Sockolow PS, Yang Y, Bass EJ, Bowles KH, Holmberg A, Sheryl P. Data Visualization of Home Care Admission Nurses' Decision-Making. AMIA Annu Symp Proc. 2017;2017:1597-606.
  12. Valdiserri RO, Sullivan PS. Data Visualization Promotes Sound Public Health Practice: The AIDSvu Example. AIDS Educ Prev. 2018;30(1):26-34. doi:10.1521/aeap.2018.30.1.26.

Submitted by Barrett Campbell