Tailored visualizations of health information such as patient reported outcomes (PROs) can be used to enhance health communication, particularly to patients and community members with limited health literacy . Visualizations can serve multiple communication purposes including engaging the viewer, supporting comprehension, motivating behavior change, delivering a persuasive message, and much more [2, 3]. Visualization as a form of health communication – in the context of the availability of large amounts of health data – is increasingly important for several reasons. One, the expectation that clinical encounters will involve shared decision making necessitates informational tools to support the process. Two, individuals need tools to understand and track their health data to engage in effective health self-management. Three, researchers need tools to engage with participants and the larger community around research results. Such engagement is more important now than ever as we move into the era of precision medicine which has been broadly defined by the National Institutes of Health (NIH) as an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person. The NIH Precision Medicine Initiative (PMI) has recruitment of a diverse cohort as a goal and includes plans for communicating research results as a mechanism of engagement .
In order to be individualized for the viewer with her/his own health data, the generation of tailored visualizations must be automated in order to be scalable. Compared to single-use visualizations, automated visualizations present additional, unique design challenges when the objective is a consistently effective output. Numerous tools are available for matching data types to chart types and for automating visualizations (see Table 1), but often these are general purpose – not health focused – and most require the user to possess both domain knowledge and visualization design expertise . Prominent existing health visualizations have risk communication as a focus, [6, 7]. leaving many other aspects of health communication unexplored. We perceived a lack of resources for visualizing personal health data with patients and community members as the intended audience. Therefore, the focus of our work has been on creating culturally relevant, automatically tailored visualizations of PROs specifically designed to meet the needs of community members with varying levels of health literacy. As ours and others’ efforts advance, we may ultimately be able to contribute to a bank of visualizations for PROs, particularly those from the Patient Reported Outcomes Measurement Information System (PROMIS) , or for a variety of other self-reported and patient-generated data.
|Title and URL||Description|
|Evidence based risk-communication visualizations|
|Icon Array Generator
|Icon arrays display part-to-whole relationships for communicating health risks|
|Data Viz Project
|Interactive directory of visualization formats categorized by family, function, shape, and input|
|Use filters to find the right visualization for the data and download as Excel or PowerPoint templates|
|Choosing A Good Chart
|Decision tree for chart selection|
|Graphic Cheat Sheet
|Interactive chart selection tool|
|Properties And Best Uses Of Visual Encodings
|Suggested encoding elements according to data characteristics|
|See http://selection.datavisualization.ch/ and http://dataviz.tools/ for an extensive curated selection of data visualization tools.|
Our visualization work began in the context of the Washington Heights/Inwood Informatics Infrastructure for Community-Centered Comparative Effectiveness Research (WICER) project. For WICER, numerous PROs, including some PROMIS measures, were collected from over 5,800 members of the Washington Heights/Inwood community in Northern Manhattan. In light of the varying levels of health literacy present in the community, we elected to develop information visualizations (infographics) to communicate survey data back to the respondents who so generously contributed it. Our goal was to provide the information in a culturally relevant, easily comprehensible format that respondents could use to support their health self-management efforts. Briefly, the process we employed began with initial prototyping by a multi-disciplinary working group , followed by iterative prototyping with community feedback . In order to automate the tailoring of the resulting infographic designs, we developed a system called EnTICE:3 Electronic Tailored Infographics for Community Engagement, Education, and Empowerment . The last step is formal comprehension testing, one approach to which is an experimental protocol that evaluates the extent the infographics support comprehension compared to text alone .
Having developed a start-to-finish method for designing visualization of PROs, we are now applying the method to new PROs from the New York City Hispanic dementia caregiver Research Program (NHiRP). For NHiRP, the purpose of the visualizations will be to support Hispanic caregivers’ health self-management and caregiving efforts. Revisiting the early steps of the design process has served to highlight the utility of using a systematic method to get to know the data to be visualized. We know from experience that it is not sufficient to just broadly identify the data type (e.g., categorical, continuous) and move forward. A deeper dive is necessary for a streamlined design process. After all, “if a poor choice was made in the abstraction stage, then even perfect visual encoding and algorithm design will not create a visualization system that solves the intended problem” (p. 921) . The purpose of this paper is to share our systematic method to understanding the data. It represents a synthesis of best practices and key lessons from experience. Our focus has been on PROs and other self-reported data, but the proposed method may have relevance to numerous data types, such as the various types of data collected in clinical encounters. The considerations we present may be familiar to those working in information visualization and increasingly, journalism. Our goal is to present these data considerations from a biomedical informatics perspective with a specific focus on application to health data. As such, our method may be of greatest interest to data analysts embarking on health care projects and other health care professionals who will design visualizations.
Some recent literature from the Information Visualization (InfoVis) community is relevant to this topic. Huang and colleagues have contributed a taxonomy of the data, contexts, interactions, and insights that describe personal visualization and personal visual analytics . Meyer and colleagues proposed the Nested Blocks and Guidelines Model intended to guide the design and validation of visualization systems [13, 15]. From high to low, the levels are domain characterization, data/task abstraction design, encoding interaction/technique design, and algorithm design; the model specifies the nature of within-level and between-level guidelines. The work presented in this paper falls into the level of data/task abstraction design. In the process of getting to know the data, readers may appreciate how Bigelow et al., conceptualize data attributes . Data semantics relates to the meaning of the data. Data behavior describes the shape of the data and the patterns found therein. Data structure (aka data type) reflects decisions about the organization of the data. Derived data are the result of transformations and manipulation of the original data. Data structure and derived data are known collectively as data abstraction; that is, the “specific interpretation of data that supports the high-level goals of visualization” (p. 18) . The systematic method presented in this paper concerns data abstraction. Within the medical informatics literature, Kosara & Miksch reviewed methods for visualization of measurements, display of incidents, and depiction of planned actions , however, the target audience for the visualization described in their review is clinicians rather than patients and community members.
In this paper we assume that the visualization designer has already engaged in the critical step of clearly elucidating the goals and desired outcomes of visualization . With the purpose of the visualization identified, we propose getting to know the attributes of the data following our systematic method of a) determining the meaning of the variable, b) identifying the possible and likely values, and c) understanding how values are interpreted. We present two case studies to illustrate how this method affected our design decisions.
Our first step in getting to know our data in advance of visualization was gaining an understanding of precisely what had been measured. If the variable is observed, such as cans of soda consumed per week, the meaning is typically straightforward. If the variable is latent, such as depression, the specific meaning may be more nuanced. For example, depending upon how it is scored, the PHQ-9 (a measure of depression) can provide information about whether criteria have been met for a diagnosis of depression or it may inform about symptom severity irrespective of diagnosis . Different visualization opportunities may arise depending upon subtleties of meaning; in the former case the focus may be on the diagnostic threshold whereas in the latter the visual message is likely to be about intensity/degree.
Latent variables are sometimes collected using a single item such as item A in Table 2 (known as SF1) . More commonly, though, an instrument comprising multiple items is used. In either case, it is advisable to start by identifying the underlying construct.
|Item Stem||Response Options||Comments|
|A. “In general, would you say your health is…?” ||Poor, Fair, Good, Very good, Excellent||Unipolar response options; increasing health|
|B. “Compared with 10 years ago, how is [care recipient] at recognizing the faces of family and friends?” ||Much improved, A bit improved, Not much change, A bit worse, Much worse||Bipolar response options with neutral midpoint; change in signs of dementia|
|C. “I can make time for physical activity.” ||Strongly agree, Agree, Disagree, Strongly disagree||Bipolar response options with no midpoint; supports to physical activity|
|D. Systolic blood pressure||mmHg in whole numbers||General population cutpoints at 120 and 140 mmHg |
The answers to the above questions can provide fertile source material for identifying meaningful imagery for visualizations. One instrument used in NHiRP is the Kessler Psychological Distress Scale . As a non-specific measure of psychological distress, the items in the Kessler tap multiple domains including depressed mood, anxiety, worthless guilt, fatigue, and motor agitation/retardation. These domains evoke imagery both individually and collectively that can be applied to visualization. Image searches for “distress” and “psychological distress” in Google and in clip art databases, resulted in the emergence of some consistent motifs. Chief among these were a) a slumped figure, head in hands; and b) rain, clouds, and/or lightning in or around a figure’s head. This kind of image search is useful for identifying the visual metaphors and symbolic analogies associated with the shared cultural understanding of a term. Of the designs developed for WICER, the ones that employed familiar color and/or symbolic analogies (e.g., a battery to represent sleep and energy) were often the most popular and readily understood .
Our next step toward understanding the data was to identify the values that are possible for a given variable. Getting a sense of the range of typical values also was useful. Different considerations come into play for categorical/ordinal and continuous variables.
Variables that are categorical or ordinal typically have relatively few possible values and as such, are easier to plan for and visualize in a predictable way. To understand these variables, we considered:
Some considerations we found useful for understanding continuous variables included:
Note that some variables that appear continuous are functionally categorical. Short-form PROMIS measures administered outside of a computer adaptive testing environment are one such example because scoring tables are used to convert a discrete number of raw summed scores into T-scores. An 8-item short form with raw scores ranging from 8 to 40 yields 33 possible T-score values; the 4-item instrument yields only 17. Planning for and testing visualizations with only a few dozen possible T-score values is simpler than if the T-scores were actually continuous .
Our final step in getting to know the data was understanding how values are interpreted. Considerations for value interpretation included:
Getting to know the data was an important early step in our visualization design process for three main reasons. One, the data attributes imposed the limitations within which the designs could occur. It is tempting to assume or wish for particular data attributes. In fact, research on designers’ processes revealed that they tended to begin designing prior to fully understanding the data and that the “designers’ inferences about data behavior were often inaccurate.”. Getting to know the data well can help avoid this pitfall. Two, the precise variable meaning and the data attributes sometimes suggested design opportunities or sparked new ideas. Three, understanding the full range of values the design must accommodate prepared for inclusion of all relevant use cases and paved the way for visualization automation. The biggest design and automation challenges usually were related to handling inconvenient outlier and non-missing zero values. Figures 1a–b and 2 are presented here as case studies to illustrate these three reasons and to demonstrate how some of the considerations outlined above influenced design decisions.
Based on community feedback, we selected bar graphs as the most effective means of presenting data about physical activity (see Figure 1a and b) . Given a very straightforward topic, an accurate title was all we needed to communicate the meaning of the variable. Salient data attributes for this variable were that mean values for the comparison group (i.e. “other women your age”) would fall below the recommended minimum, as would the value for the majority of the index respondents (i.e. “You”). Therefore, showing the recommended minimum as a dotted line that (in most cases) would hover above both bars, communicated the sense of a goal to be reached. The zero and outlier values were the elements that presented a challenge. In the case of non-missing zero values, we saw that the complete absence of a bar would be potentially confusing, even if the space were labeled with a zero. Our solution was to use a very shallow bar to indicate where the bar would be located, had the value been greater than zero (Figure 1a). The presence of a large outlier value (as in Figure 1b) has the effect of distorting the relative proportions of the elements within the image and the value of the recommended minimum is no longer shown on the y-axis. Although the effect of the outlier value is less than optimal from an aesthetic perspective, we opted not to attempt accommodative changes to the design for two reasons. One, the proportion of respondents possessing high outlier values was very small. Two, the primary purpose of the visualization (i.e. gist comprehension of one’s activity as compared to that of peers and the recommendation) was not compromised. It is clear that the index respondent’s physical activity far exceeds both the recommended amount and that of their peers; the precise amount of difference is not important. We could have attempted solutions such as increasing the length of the y-axis in proportion to the rest of the image so as to have less crowding around the minimum recommended value. However, the added complexity of programming for this solution was not warranted given the reasons described.
Different considerations came into play when designing an infographic to communicate BMI (see Figure 2). The BMI metric, kg/m2, is not an intuitive one for the general public and as such, the infographic had to support not only identification of the value in comparison to reference ranges, but also interpretation of the metric. Our research on existing BMI and body weight visualizations revealed that body silhouettes were sometimes employed to convey meaning. For that reason we elected to combine body silhouettes that evoke the meaning of the BMI categories with a number line that demarcates the reference ranges. Unlike the physical activity bar graph for which gist comprehension is sufficient, the BMI infographic had to support verbatim comprehension because one of the cutpoints is between whole values (18.5) and because personal change along the continuum is often seen in very small increments (tenths). As mentioned before, the cutpoints are not equidistant, which is a drawback aesthetically. Given the purpose of the visualization, we determined that it was more important to privilege the equality of intervals along the number line over making the BMI categories visually symmetrical. The selection of the range of values to be shown on the number line was driven by consideration of a) the values that would allow for presentation of the body silhouettes that is as visually balanced as possible, b) placement of the most common values in the center of the image, and c) adequate spacing between values for ease of reading. The drawback to the range of values selected (15–35) is that a substantial proportion of respondents have values above the range. Our solution in those cases was to place the indicator box off the number line but overlapping with the end of the arrow to suggest its placement, were the number line to be extended. We used colors (green, orange, red) to encode the value judgments associated with the categories.
One major theme that emerged from our experience was the need for a systematic method to explore data attributes. For us, using such a method has made a potentially complicated and lengthy process easily manageable and more efficient, even when working with multiple variables. Our design process during NHiRP was dramatically shorter than it was for WICER. Some of the efficiency gains can be attributed to the numerous other lessons learned unrelated to understanding the attributes of the data. However, having implemented our method, we prepared far fewer initial prototypes overall and required fewer refinement iterations, thus reducing time and cognitive load. Also, we did not experience the frustration of having to discard prototypes because of mismatch with the data attributes as we had during WICER. We anticipate that incorporating the data attributes into a Style Guide (a tool to communicate design specifications to the programmer ) will reduce the communication burden and workload when it comes time to program EnTICE  to tailor the infographics. Any research methods designed to measure the aforementioned benefits could be used to quantify the value of our method.
The second major theme has been that data attributes are like two sides of the same coin; the limitations of the data can narrow design options but can also prompt creative solutions and innovative design opportunities. The process of understanding the precise meaning of a variable (e.g., meeting a diagnostic threshold for depression vs. intensity of symptoms) is probably where design opportunities are most likely to present themselves. However, even awareness of the specific values and cutpoints associated with a variable can inform decisions about symmetry and visual balance, or about the number of colors that will be required in a palette (see Figure 3a and b). Existing tools such as the Data Viz Project (http://www.datavizproject.com) and Visualizing Health (http://www.vizhealth.org/) have filtering capabilities to aid in the selection of an appropriate graphical format. Given a deeper understanding of how data attributes influence selection of optimal format, palette, symmetry, etc., tools such as these could be refined. For example, filtering for a format appropriate for an item with bipolar response options and a neutral midpoint (Table 2, Item B) could return a symmetrical format with an odd number of categories, whereas a similar item without a neutral midpoint (Table 2, Item C) would return a format with an even number of categories.
One limitation to our method is that it assumes access to summary statistics, which not everyone may have. In such instances, most steps of the method may be completed but users will be limited in their ability to identify the values most likely to occur in the data, and in some cases, will not be able to specify minimum and/or maximum values. Another limitation is that we have applied our method to a few dozen variables, at most. Other variable types may necessitate asking additional questions to the ones presented here. For instance, further innovation may be needed for adequate exploration of datasets that employ repeated measures or in which multivariate relationships are important. We invite teams engaged in similar work to share their own discoveries with respect to the process of exploring data attributes, particularly when the data are complex.
In this paper we have presented a systematic method to understanding the attributes of patient reported outcomes data in preparation for automated visualization. The value of the method is that it contributes to an efficient design process, uncovers design opportunities, and alerts the designer to design challenges. A streamlined visualization development process is important given the increasing utility of visualization as a health communication tool. This is increasingly significant as a component of patient engagement whether in the care context as part of efforts to integrate PROs into electronic health records or to promote self-management or in the research context as part of research results reporting and sustaining longitudinal cohorts.
Washington Heights/Inwood Informatics Infrastructure for Community-Centered Comparative Effectiveness Research (WICER) and WICER 4 U were funded by the Agency for Healthcare Research and Quality (1R01HS019853 and 1R01HS022961, respectively) and WICER also was funded by New York State Department of Economic Development NYSTAR (C090157). Dr. Arcia’s work on the New York City Hispanic dementia caregiver Research Program (NHiRP) was funded by the National Institute of Nursing Research (1R01 NR014430-03S1). Dr. Woollen’s contributions were supported by the National Institute for Nursing Research (T32 NR007969).
The authors have no competing interests to declare.
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