ArticlePDF AvailableLiterature Review

Design Features of Graphs in Health Risk Communication: A Systematic Review

Authors:

Abstract and Figures

This review describes recent experimental and focus group research on graphics as a method of communication about quantitative health risks. Some of the studies discussed in this review assessed effect of graphs on quantitative reasoning, others assessed effects on behavior or behavioral intentions, and still others assessed viewers' likes and dislikes. Graphical features that improve the accuracy of quantitative reasoning appear to differ from the features most likely to alter behavior or intentions. For example, graphs that make part-to-whole relationships available visually may help people attend to the relationship between the numerator (the number of people affected by a hazard) and the denominator (the entire population at risk), whereas graphs that show only the numerator appear to inflate the perceived risk and may induce risk-averse behavior. Viewers often preferred design features such as visual simplicity and familiarity that were not associated with accurate quantitative judgments. Communicators should not assume that all graphics are more intuitive than text; many of the studies found that patients' interpretations of the graphics were dependent upon expertise or instruction. Potentially useful directions for continuing research include interactions with educational level and numeracy and successful ways to communicate uncertainty about risk.
No caption available
… 
No caption available
… 
No caption available
… 
Content may be subject to copyright.
Review Paper
Design Features of Graphs in Health Risk Communication: A
Systematic Review
JESSICA S. ANCKER, MPH, YALINI SENATHIRAJAH,RITA KUKAFKA,DRPH, MA
JUSTIN B. STARREN, MD, PHD
Abstract This review describes recent experimental and focus group research on graphics as a method of
communication about quantitative health risks. Some of the studies discussed in this review assessed effect of
graphs on quantitative reasoning, others assessed effects on behavior or behavioral intentions, and still others
assessed viewers’ likes and dislikes. Graphical features that improve the accuracy of quantitative reasoning appear
to differ from the features most likely to alter behavior or intentions. For example, graphs that make part-to-whole
relationships available visually may help people attend to the relationship between the numerator (the number of
people affected by a hazard) and the denominator (the entire population at risk), whereas graphs that show only
the numerator appear to inflate the perceived risk and may induce risk-averse behavior. Viewers often preferred
design features such as visual simplicity and familiarity that were not associated with accurate quantitative
judgments. Communicators should not assume that all graphics are more intuitive than text; many of the studies
found that patients’ interpretations of the graphics were dependent upon expertise or instruction. Potentially
useful directions for continuing research include interactions with educational level and numeracy and successful
ways to communicate uncertainty about risk.
J Am Med Inform Assoc. 2006;13:608618. DOI 10.1197/jamia.M2115.
Introduction
Quantitative risk communication is a critical component
of informatics applications that support such activities as
shared medical decision-making, informed consent,
health risk appraisal, and counseling about difficult deci-
sions pertaining to cancer or genetic screening.
1,2
Effective
risk communication can improve awareness of health
risks and promote risk-reducing behavior in support of
health promotion and disease prevention.
2,3
One of the
many challenges to risk communication with the public is
the difficulty in expressing quantitative information in an
easily comprehensible form. Universal cognitive limita-
tions cause biases in interpreting numerical probabili-
ties.
4,5
Small probabilities are particularly difficult to
interpret; under some conditions people overestimate
them, and under others they ‘round down’ to zero.
4,5
For
many consumers, these difficulties in interpreting proba-
bilities are compounded by limited numeracy skills
6,7
and
by discomfort with numerical expressions of risk.
8
Under-
standing numerical information can be even more difficult
when analytic reasoning processes are impaired by age,
stress, or other factors.
9
Graphs are an appealing alternative to numbers because
they are visually interesting and exploit rapid, automatic
visual perception skills.
10
Some aspects of graph interpre-
tation also require more effortful cognitive skills such as
interpretation and calculation, which may rely upon
learned strategies.
10,11
Nevertheless, a well designed vi-
sual display can reduce the amount of mental computa-
tion by replacing it with automatic visual perception.
12
Graphs are often used in print and electronic materials for
patient education,
13
decision support,
14
and health risk
appraisal (e.g., www.yourdiseaserisk.harvard.edu). How-
ever, the ways in which patients interpret these graphs,
and which graphs are most effective for various purposes,
are not easily determined from the research literature.
Members of the public frequently interpret graphs in
ways that are not intended by their designers.
8
As Lipkus
and Hollands pointed out in an excellent 1999 review,
15
relatively little experimental research has explored the
use of graphs for health risk communication. Many stud-
ies from fields such as psychophysics, human factors, and
marketing did not involve health risks; the studies of
health graphs were often atheoretical, and results were
sometimes inconsistent.
15
In recent years, a number of
relevant studies have appeared in the medical, psycholog-
ical, and patient counseling literature. Here, we update
Lipkus and Hollands’ article in a systematic review of
experimental or focus group research on graphs of quan-
titative health risks.
Affiliations of the authors: Department of Biomedical Informatics,
Columbia University College of Physicians and Surgeons, New
York, NY (JSA, YS, RK); Department of Sociomedical Sciences,
Mailman School of Public Health, Columbia University, New York,
NY (RK); Marshfield Clinic Research Foundation, Marshfield, WI (JBS)
Ms. Ancker is supported by Robert Wood Johnson/National Li-
brary of Medicine predoctoral fellowship 5T15-LM007079-15.
The authors thank Elke U. Weber, PhD for critical feedback.
Correspondence and reprints: Jessica S. Ancker, MPH, Department
of Biomedical Informatics, Columbia University College of Physi-
cians and Surgeons, 622 W. 168th Street, Vanderbilt Clinic 5th Floor,
New York, NY 10034; e-mail: jsa2002@columbia.edu.
Received for review: 03/30/06; accepted for publication: 06/17/06.
608 ANCKER et al., Design Features of Graphs in Health Risk Communication
Search Methods
We searched for evaluation studies of graphs describing
probabilities, frequencies, or chances of health events that
had not been covered in Lipkus and Hollands’ review. We
excluded commentaries and instructions (e.g.,
16
), studies of
pain scales, utility measures, or illustrations that communi-
cated threat or causal relationships (e.g.,
17
), and studies in
which graphics were not used as an independent variable
(e.g.,
14,18,19
). We also excluded dissertations. We searched 3
bibliographic databases (PsycInfo, MEDLINE, CINAHL)
and one portal (ACM Portal) for 1998–2005 inclusive using
topic headings chosen from the controlled terminology of
each database and additional key words.
a
We read all titles,
then read the abstract and full text of potentially relevant
articles. Upon identifying eligible articles, we used the ‘find
citing research’ and ‘find similar’ tools and searched refer-
ence lists. We also did key word searches on the websites of
selected journals (Health Psychology, Risk Analysis and Medi-
cal Decision Making). In this review, we describe 24 studies.
The searches produced: 969 unique articles on MEDLINE of
which 14 met our criteria; 245 on PsycInfo of which 3 met
our criteria and had not already been identified; and 54 on
the Medical Decision Making web site of which 2 were eligible
and had not been identified. The other searches produced no
articles that had not been previously identified; 5 articles
were identified through bibliography search. Details about
the searches are available upon request.
Outcome Measures in Graphics Studies
Synthesizing the findings of these studies is challenging
because of the variety of outcome measures used by the
researchers.
20
As others have noted,
21
risk communication
can be undertaken with different goals: (a) to increase
understanding; (b) to change risky behavior; or (c) for
cooperative conflict resolution. Also, some risk communica-
tions are designed to increase concern, while others are
designed to calm fears.
22
Researchers who pursue the first goal above—improving
understanding—use outcome measures such as the accu-
racy or consistency of quantitative reasoning or perceptions
(e.g.,
10,23–25
). Examples from this review include whether
users can estimate a proportion represented in a graphic,
25
whether they can read the number of survivors at a partic-
ular time point from a survival curve
26
and whether they
produce the same ranking of risks when expressing their
perceptions in different graphical formats.
27,28
From this
perspective, graphical elements that cause perceptions of the
risk to deviate from the probability of the outcome (includ-
ing framing, axis distortion, or relative comparisons) may be
denounced as unethical.
23,24,29–32
By contrast, researchers intending to induce behavior
change have generally evaluated risk communication tools
in terms of their effect on behaviors or intentions.
33
An
example in this review is a series of studies of users’
willingness to pay for hypothetical consumer products after
viewing graphic displays of safety risks associated with
each.
34
Health promotion specialists seeking to induce be-
havior change may exploit framing and salience effects.
35
Framing or other manipulations can be justified by their
effectiveness on desirable outcomes.
36
A third type of outcome measure used by many research-
ers
37,38,39
is users’ likes and dislikes because in real-world
settings, people may not accept or attend to graphics they
dislike. Examples of this type of research include focus
group studies in which viewers are given a choice between
different graphics.
38
Related measures include the effect on
anxiety and satisfaction with the information or with a
decision
40
and on perceived persuasiveness of the informa-
tion.
41
As we shall discuss in this review, graphical features that
improve the accuracy of quantitative reasoning appear to be
different from the features that induce behavior change, and
features that viewers like may not support either of the two
other goals.
Design Features of Risk Graphs
In our summary of the findings, we describe each type of
graphic (bar chart, pie chart, survival curve, etc.) and its data
scale (ordinal, discrete, continuous). We call attention to 3
design features that have not always been highlighted in the
original studies but that help shed light on the results: (1)
whether part-to-whole relationships can be assessed visu-
ally; (2) the graphical perception abilities exploited; and (3)
the format of numbers in the graphics.
1 Part-to-whole relationships: The ability to estimate what
proportion object A represents of a larger object B appears
to be an automatic perceptual skill that can be invoked
when a graphic displays the entire object B.
11
An example
is a stacked bar chart, a bar that extends from 0% to 100%
with a segment in a different color to indicate the propor-
tion affected by the disease. The part-to-whole relation-
ship is available visually: a segment 10 units high in a bar
100 units high represents a risk of exactly 10%. Usually, in
part-to-whole graphics, the size of the graphic element is
proportional to the quantity it depicts, so a segment 10
units high depicts a risk exactly twice as large as one 5
units high. This property is considered desirable for data
integrity.
23,29
In other cases, such as a bar chart with a y
axis truncated at 50%, the sizes of the graphic elements are
proportional to the quantities, but the part-to-whole rela-
tionship for each risk is not available visually. It is also
possible for graphics to be non-proportionate and to not
contain part-to-whole visual information (e.g., a risk scale
ranging from 1 in a million to 1 in 10). In these, the size
difference between elements is not directly related to the
difference between quantities.
2 Features that exploit basic graphical perception abilities:
The classic psychophysical research of Cleveland and
a
PsycInfo: controlled terms: decision making, graphical displays,
numbers, risk taking, visual displays, risk perception, statistics,
comprehension; key words: graph, graphical data, chart, pictogram,
numeracy; limits: human; MEDLINE: MeSH headings: decision mak-
ing, data display, risk taking, statistics, comprehension: key words:
graph, computer graphics, health promotion, risk perception, risk
communication, pictogram, numeracy; limits: evaluation studies,
clinical trial, controlled clinical trial, randomized controlled trial, or
validation studies, and human; CINAHL: controlled terms: decision
making, risk-taking behavior, graphics, computer graphics, health
promotion: key words: risk, graph, risk communication: limits: re-
search; ACM Portal: key words: graphical perception, graphical user
interface, human computer interaction, consumer health informat-
ics, interactive health communication, risk, risk communication.
Journal of the American Medical Informatics Association Volume 13 Number 6 Nov / Dec 2006 609
McGill ranked visual perception tasks by their accuracy.
10,42
Accuracy was excellent when judging positions or lengths
against a common scale (such as heights of bars of a bar
graph); good when judging angles (such as size of slices in
a pie chart) and slopes (such as slopes of a line graph); fair
when judging areas (such as circles); and poor when
judging volumes or color and gray-scale densities.
10
3 Numerical format: Performing mathematical calculations
such as converting from ratios to percentages is a learned
skill; ability to perform such tasks varies with education,
health literacy, and numeracy.
6,7,43
A probability of 6 in
100 is formally equivalent to both 6% and 0.06, but the
different formats strongly affect reasoning. For example,
with ratios, problem-solving ability and comprehension
are worse when the denominators are different than when
they are the same: it is harder to compare and calculate
with the pair of numbers “1 in 250” and “1 in 1000” than
it is with “4 in 1000” and “1 in 1000”.
5,44,45
Ratios with the
same denominator have been called “natural frequen-
cies.”
5,44,45
In a study in outpatient clinics, only 56% could
identify the larger of two risks when they were written in
the “1-in-x” format.
46
Complex-looking ratios such as
513/570 are more demanding to process than equivalent
but simpler ones (such as 9/10) or decimals (e.g., 0.90), as
shown by preference reversals with different formats.
47
A discussion of more complex graphical perception tasks,
such as integrating information from multiple sources,
would require attention to more complex theories.
12
How-
ever, most risk graphics involve relatively simple tasks such
as providing information about an individual risk, compar-
ing several risks, or judging trends in risk over time.
Research Review
Icon Arrays
An icon array portrays a risk at the discrete level of
measurement as a group of individual icons, such as dots or
stick figures. In numerical reasoning, people tend to perform
better on probability problems when the data are presented
at the discrete level rather than as percentages or propor-
tions.
44,45,48
Slovic et al review evidence that presenting
information in terms of individuals can produce mental
imagery with strong affective elements.
9
An icon display reduced the influence of vivid text anec-
dotes in a study of choices of medical treatment (Fig. 1).
49
In
this study, people were asked to imagine having angina and
being offered more successful (75% success rate) but more
arduous bypass surgery, or less successful (50% success rate)
but less arduous balloon angioplasty. They also read anec-
dotes about patients who had had the procedures. The
number of anecdotes describing success strongly affected
participants’ choices. When the proportion of successes in
the anecdotes was the same as the treatments’ success rates
(for example, when 3 of the 4 bypass stories described a
treatment success), respondents became more likely to
choose the more successful alternative (bypass). When one
anecdote described success and one a failure, most respon-
dents chose the less arduous treatment (angioplasty). The
anecdote effect was significantly smaller when respondents
saw icon displays depicting the two treatments’ success
rates.
49
The icon array showed the part-to-whole relation-
ship and the square icons were touching, so the display
might have been visually processed as areas rather than as
discrete icons.
In a focus group of women, participants preferred icon
arrays with smaller denominators because they seemed
simpler but also tended to think that graphics with larger
denominators portrayed risks as smaller.
37
The findings are
not consistent with the common ratio-bias effect, in which
risks described as ratios of small numbers are considered
smaller than numerically equivalent risks described with
large numbers (e.g., 1 in 20 is considered less likely than 10
in 200).
50
In another focus group study with low-income
women, participants preferred seeing an individualized risk
estimate depicted as a bar chart with an ordinal scale (low,
average, or high risk) rather than as an icon array or a
percentage, and rather than a bar chart showing a series of
relative risks for women in different risk categories.
39
Fuller et al. used several tasks to assess how elderly patients
interpreted discrete icon displays.
51
The patients could
match percentages to icon arrays displaying different pro-
portions (70% to 98% accuracy for different tasks). They
were less accurate when marking the graph to show prob-
abilities (either ratios with different denominators [38% to
79% accuracy] or percentages [51% to 98% accuracy]). The
authors did not assess whether the graphs were successful in
conveying the personal applicability of the risk. A short
Figure 1. Part-to-whole icon array with sequential ar-
rangement. Proportions are easy to judge in this icon array
because the part-to-whole information is available visually.
Because the square icons are arranged as a block and are
touching each other, it is possible that they are visually
processed as areas rather than as discrete units. From
Fagerlin A, Wang C, Ubel PA. Reducing the influence of
anecdotal reasoning on people’s health care decisions: Is a
picture worth a thousand statistics? Med Decis Making
2005;25:398405. Copyright 2005 by Sage Publications. Re-
printed by permission of Sage Publications, Inc.
610 ANCKER et al., Design Features of Graphs in Health Risk Communication
report by the authors
52
described similar results but with
few details.
Part-to-Whole Relationships in Icon Arrays
The importance of the part-to-whole relationship is sug-
gested by a series of studies by Stone and colleagues
34,53
to
follow up a 1997 study.
54
In these studies, undergraduates
received information about pairs of fictitious products, each
carrying a small probability of a harmful effect (e.g., tire
blowouts with tires). Participants estimated how much the
safer product would be worth. The graph showed the
number harmed but the at-risk group was provided as a
number, so the part-to-whole relationship was not available
visually (Fig. 2). People were willing to pay more (i.e., were
more risk-averse) when the number harmed was depicted as
an array of stick figures, asterisks, or faces, or as a bar graph
than when it was number. Asterisks and faces led to similar
results, suggesting that human figures had no ‘humanizing
effect.’ In one study,
34
the graphs were compared to graphs
that did portray the part-to-whole relationships visually, for
example, a bar graph showing only the number affected
compared to a stacked bar graph of those affected as a
proportion of the entire group (Fig. 3). Participants were
willing to pay more for the safer product with a graph that
did not show the part-to-whole relationship than when
given a number. However, when they saw part-to-whole
relationship in the graph, they were not willing to pay more
than when they saw numerical probabilities. Stone et al.
suggest that risk aversion for rare events is the result of
graphs that fail to show part-to-whole relationships, not of
all graphs, and label the effect a ‘foreground effect’.
34,53
These studies provide a new perspective on an older pair of
studies that used dots to depict only the denominator of the
probability; viewers were told that the risk was 1 against the
number of dots. An icon display of the risk of rare side-
effects from a vaccine increased the number of subjects who
said they would get vaccinated, presumably by focusing
their attention on the denominator.
55
However, a similar
study could not replicate the effect.
56
Figure 2. Icon array without part-to-whole information. In this pair of discrete icon arrays, the part-to-whole relationship
(those injured in relation to the entire group at risk) is not available visually. The difference between the risks is thus
emphasized. Reprinted with permission from p. 250 of Stone ER, Yates JF, Parker AM. Effects of numerical and graphical
displays on professed risk-taking behavior. J Exp Psychol: Appl. 1997;3(4):243–56 (published by the American Psychological
Association).
Figure 3. Part-to-whole bar graph. The part-to-whole relationship is available visually in this stacked bar chart; this
arrangement did not appear to emphasize the difference between the two risks. Reprinted from p. 28 of Stone ER, Sieck WR,
Bull BE, Yates JF, Parks SC, & Rush CJ. Foreground:background salience: Explaining the effects of graphical displays on risk
avoidance. Org Behav Hum Decis Proc. 2003;90(1):19–36, with permission from Elsevier.
Journal of the American Medical Informatics Association Volume 13 Number 6 Nov / Dec 2006 611
Human Figures Versus Other Icons
Although Stone et al found no differences in behavior after
viewing asterisk and face displays,
54
Schapira et al’s quali-
tative study found that women considered human figure
icons (like those in Fig. 2) to be more meaningful, easier to
understand, and easier to identify with than bar charts.
37
Women in one of the focus groups, which had a lower mean
age and educational level, perceived risk of breast cancer as
larger when it was shown as a part-to-whole human icon
display than when it was shown as a part-to-whole bar
graph; however, no quantitative results were collected.
37
Some participants said the icon display suggested popula-
tion risk, while a continuous scale suggested personal risk.
In direct contrast, Royak-Schaler et al. found that focus
groups of low-income women preferred a part-to-whole bar
chart (similar to Fig. 3) to a part-to-whole icon array;
however, in this study the bar chart had evaluative labels
(‘high risk,’ ‘low risk’, or ‘average risk’), but the icon array
did not, which may have made it difficult for viewers to
place the risk in context.
39
Random Arrangement in Icon Arrays
In the Stone, Fagerlin, and Schapira studies, the icons
depicting people affected by disease were arranged in a row
or a block, so risks could be estimated by judging block
length or area (Figs. 2 and 3).
10
The Royak-Schaler icon array
also did so, but the human figures were staggered rather
than arranged in neat rows, which could have made it
somewhat more difficult to use visual area judgment. Other
arrays show those affected scattered throughout the array
(Fig. 4). In genetic risk counseling, such a random arrange-
ment has been described as helpful in promoting under-
standing of chance.
13
With random icon arrays, area judg-
ment is not available to the viewer. It is possible that
comparisons are made through a gray-scale judgment (one
of the least accurate of the visual perception tasks
10
),
through mental summation of areas (also relatively inaccu-
rate),
11
or by counting or computing. Viewers were less
accurate at estimating proportions in random arrays than in
sequentially arranged ones.
25
In the Schapira et al. study,
women disliked the random arrangement because they
could determine the probability only by counting.
37
How-
ever, some women said it better conveyed the idea of
randomness.
Risk Tables, Ladders, and Scales
A risk table, ladder, or scale depicts a range of risks from
very low to very high as context for an individual risk. When
risks are ranked vertically in a table, a graphic is called a risk
ladder, and when they are horizontal, the graphic is called a
risk scale (Fig. 5 and 6) or visual analog scale. Because
position on a risk ladder or scale is evaluated as distance
from a baseline, Lipkus and Hollands propose that they
exploit the most efficient of the Cleveland and McGill basic
10
visual perception skills.
15
Risk ladders and scales often provide information about
other risks for comparison. A horizontal scale was used to
compare the risks from a blood transfusion (such as con-
tracting HIV) with other hazards such as the annual chance
Figure 4. Random-arrangement icon array. The individuals affected by the risk are scattered among the whole group. The
proportion affected is more difficult to judge, but some studies suggest that this arrangement better conveys the idea of chance.
Reprinted from p. 415 of Lenert LA, Cher DJ. Use of meta-analytic results to facilitate shared decision making. JAMIA
1999;6(5):412–9. Reprinted with permission from the American Medical Informatics Association.
612 ANCKER et al., Design Features of Graphs in Health Risk Communication
Figure 5. Risk scale with comparative risks. This scale was used for demonstrating risk magnitudes; how viewers interpret
the logarithmic scale is not known. Reprinted with permission from Blackwell Publishing from page 787 of Lee DH, Mehta MD.
Evaluation of a visual risk communication tool: effects on knowledge and perception of blood transfusion risk. Transfusion
2003;43:77987.
Figure 6. Magnifier risk scale. This scale was used for eliciting risk perceptions. The magnifying lens at the low end allowed
users to respond with smaller values for very low risks; a second study suggests that it reduces the magnitude of higher risks
as well. From Woloshin S, Schwartz LM, Byram S, Fischhoff B, Welch HG. A new scale for assessing perceptions of chance: A
validation study. Med Decis Making 2000;20(3):298–307. Copyright 2000 by Sage Publications. Reprinted by permission of Sage
Publications, Inc.
Journal of the American Medical Informatics Association Volume 13 Number 6 Nov / Dec 2006 613
of dying in a car accident (Fig. 5).
57
The ladder was as
effective as numbers alone in increasing knowledge and
reducing dread about rare hazards of transfusion. This scale,
the Paling Perspective Scale,
58
depicts a range of probabili-
ties on a logarithmic scale from 1 in 1 (certainty) to 1 in 1
trillion, centered on 1 in a million, described as “effective
zero.” The rationale for choosing the comparative risks was
not described; some were familiar, others unfamiliar, some
cumulative, others one-time.
More systematic explorations of risk ladder design have
been done in environmental health. When a person’s expo-
sure to an environmental hazard was explained by referring
to a location on a risk ladder, perceived risk was associated
with location on the ladder rather than numerical magni-
tude of the risk.
59
The ladders also illustrated unfamiliar
concepts with text and graphics, such as icon arrays of the
number of cigarettes needed to produce a cancer risk com-
parable to a given level of radon risk.
59
Another way to place
unfamiliar concepts in context was to place a pointer at the
level of risk at which protective actions are recommended;
this helped viewers determine that levels of risk below this
threshold are not very serious. Johnson and Slovic compared
numbers and a risk ladder for communicating uncertainty
(confidence intervals) about risk estimates.
60
The numbers
were ratios with different denominators, and the graphic
showed no part-to-whole information. When compared to
numbers alone, the ladder did not affect perceived risk; it
did decrease trust in the information but also improved
peoples’ ability to notice the full range of possible risks.
60
Survival and Mortality Curves
Survival and mortality trends are cognitively complex be-
cause they involve changes over time. In a classic study,
McNeil et al provided numeric information about a treat-
ment with high short-term mortality but good long-term
and median survival (surgery) and a treatment with good
short-term but worse long-term and median survival (radi-
ation).
61
When information was framed in terms of mortal-
ity, more people chose radiation, perhaps because the short-
term mortality appeared more salient in this frame. Framing
effects were nearly as strong with physicians as with lay-
people.
When survival or mortality data are presented graphically
(Fig. 7), changes in risk are inferred from curve slopes.
23,42
Part-to-whole relationships are available, though not very
salient, when the y axis extends to 100%. Because line graphs
portray data points as a single visual element, viewers do
not need to integrate the information themselves; line
graphs thus help experts perform complex tasks such as
assessing rates of change.
12
However, Armstrong et al.
showed that only 74% of a sample recruited from a jury pool
could interpret a survival curve well enough to determine
the number of survivors at various time points, and only
55% could calculate the difference in survival between two
time points.
62
After a training exercise, ability to interpret
survival at one time point improved but accuracy in calcu-
lating differences was unchanged. The effect of learning can
also be inferred from older studies in which choice of
treatments was strongly affected by the amount of instruc-
tion in interpreting survival curves.
63
With minimal expla-
nation, patients tended to choose the treatment with better
long-term survival; patients given extensive explanations
were more likely to take medium-range outcomes into
account.
63
Physicians were more likely to be influenced by
middle parts of the curves than were patients,
64,65
which
could also be due to education. Survival curves may reduce
a tendency to overweight immediate survival by drawing
attention to longer-term outcomes. When patients were
given a choice between treatments described with survival
percentages, 59% chose the treatment with better immediate
survival; this dropped to 34% when they viewed a pair of
survival curves.
66
The order in which patients viewed
survival graphs significantly affected their preference for
short-term versus long-term survival.
67,68
More educated
patients were less likely to choose the treatment with better
short-term survival.
67
Participants answered comprehension questions more accu-
rately with survival curves or both survival and mortality
curves than with mortality curves alone; the effect was
strongest in the lowest educational group and among non-
whites.
26
In this study, participants were asked to imagine
being at high risk for colon cancer and were given a choice
between colectomy and an easier but less successful alter-
native (annual exam). They were less likely to choose
colectomy when viewing mortality curves; the effect might
Figure 7. Survival curves. Distance between survival
curves, rather than the arithmetic difference in survival,
predicts viewers’ estimates of the effectiveness of the treat-
ments. The top figure shows 15-year survival curves for a
fictitious disease; the bottom shows only the first 5 years of
data, stretched to fill the same width, which reduces the
distance between curves. Reprinted with permission from
Blackwell Publishing from page 590 of Zikmund-Fisher et al.
(2005) What’s time got to do with it? Inattention to duration
in interpretation of survival graphs. Risk Anal 2005;25(3):
589–95.
614 ANCKER et al., Design Features of Graphs in Health Risk Communication
have been due to the reduced understanding of the infor-
mation with the mortality curves.
26
In another study, people were influenced more by the
distance between curves than the numerical differences (Fig.
7).
69
If a pair of 15-year survival curves are displayed on an
x-axis of a certain length, they will diverge more than if the
first 5 years of data are stretched and displayed on an axis of
the same length.
70
This flattening effect markedly reduces
the difference between peoples’ estimates of treatment effec-
tiveness.
69
In a study of women with BRCA1/2 mutations who were
deciding on possible prophylactic measures against breast
cancer, those who received a set of personalized survival
curves were more satisfied with their decisions than those
who received a similar educational booklet without survival
curves.
40
However, the survival curves did not change their
actual decisions.
Persuasiveness and Comprehension
In one study, text descriptions of statistical data about
interactions between disease and genetics were better un-
derstood and perceived as higher quality evidence than bar
charts of the same data.
41
Poor comprehension was associ-
ated with impressions that the evidence was of poor quality
and was not persuasive. This study should be interpreted in
light of a difference between the graphs and texts that may
have made the graphs harder to understand: the graphs
displayed the “relative mortality rate,” explained as “the
actual number of deaths divided by the expected number of
deaths,” whereas the text described one group of people as
“about 20% more likely to die early deaths” than the other.
Data Scale
Patients’ descriptions of risk may differ if elicited with
discrete scales (e.g., a number affected out of 1000 people, or
icons), ordinal ones (low, medium, and high), or continuous
ones (e.g., on a scale from 0% to 100%). Women estimating
their risk of breast cancer provided different estimates when
using an icon display (a grid of 100 female figures) and a
continuous scale (a horizontal line anchored at 0% and
100%).
28
Icons elicited risk estimates that were higher and
farther from epidemiological risk as assessed by the Gail
model.
71
Woloshin et al. asked participants to rank the
likelihood of several health events, then asked them to
describe each event’s likelihood with words (an ordinal scale
ranging from “not at all likely” to “extremely likely”),
numbers (a “1-in-xchance”), a horizontal risk scale ranging
from 0 in 100 to 100 in 100, and another scale supplemented
with an image of a magnifying glass to illustrate probabili-
ties smaller than 1% (Fig. 6).
27
Rankings with the verbal scale
were the most reliable, usable, and strongly correlated with
participants’ rankings, and rankings with the “1-in-x” num-
bers had the worst performance. This result is consistent
with other studies of the reliability and usability of word
scales.
72,73
The magnifier scale had slightly lower correla-
tions and was perceived as less usable than the verbal scale
but permitted people to make lower estimates for very rare
events. A subsequent study by another research group
compared the magnifier with the standard horizontal risk
scale.
74
This work confirmed that the magnifier scale en-
abled appropriately low estimates for very rare events but
also showed that it substantially lowered risk estimates for
more common events.
74
This effect was seen when partici-
pants estimated risks of various health events without being
given numeric information about the magnitudes of those
risks. Licensed anglers were shown risk ladders describing
the hazards of eating contaminated fish in discrete numbers
or ordinal categories (“higher risk”, “moderate risk”, and
“lower risk”);
75
; 57% preferred the quantitative ladder. In
the study of willingness to pay for safer products, icon
arrays and bar charts produced similar results, suggesting
that level of measurement made no difference.
53
A qualitative study of 40 women found that simple bar
charts depicting absolute lifetime risk of various events were
preferred over line graphs, thermometer graphs, icon arrays,
and survival curves.
38
Participants wanted graphics to be
supplemented with text.
Multiple Quantitative Endpoints
Two series of experiments using quantitative reasoning
endpoints confirm the applicability of the basic graphic
perception findings to health settings.
10,76
Feldman-Stewart
et al. assessed speed and accuracy of students’ and patients’
judgments with 6 data formats: vertical part-to-whole bar
chart, horizontal part-to-whole bar chart, pair of numbers,
part-to-whole icon graphic with random arrangement, icon
graphic with the icons arranged in a block, and pie chart.
25
Participants were slowest and least accurate at judging the
larger of two quantities with the pie chart and the random-
arrangement icons. Estimates of the differences between
quantities were best with number pairs and sequentially
arranged icons. Participants performed no better with their
preferred formats.
25
Patients took longer than students.
One trial compared graphics for conveying risks to physi-
cians.
24
Physicians saw data from a fictitious clinical trial in
which one treatment had a high failure rate. Clinical trials
may be halted midcourse if results in one group are much
worse than in the other; the physicians were asked if the
data warranted halting the trial. Five formats were given
(tables of success rates, tables of failure rates, pie charts,
stacked bar charts, and icon arrays). Most noticed the high
failure rate in icon arrays and deals; fewer did with pie
charts or stacked bar graphs.
24
However, most liked the bar
graphs and disliked the icon array. These results are consis-
tent with research showing that proportions are difficult to
judge when mental summation is required,
11
although not
with a finding that pie charts were superior when mental
summation of slices was required.
76
The authors suggest
that the icons’ success was due to the framing effect of
drawing attention to the failures, which would be consistent
with the Stone et al ‘foreground effect’.
34
Another explana-
tion is that the discrete icons could be counted, but the other
graphs required area estimation; however, earlier findings
that results with bar charts were the same as results with
icon arrays
54
argues that viewers may not be using counting
as a strategy.
Discussion
The best design for a graphic depends upon the purpose of
the risk communication. Some communications are intended
to enhance quantitative understanding or promote good
arithmetic judgments, whereas others are intended to pro-
mote behavior change.
Journal of the American Medical Informatics Association Volume 13 Number 6 Nov / Dec 2006 615
For good quantitative judgments, the size of a graphic
element should be proportional to the number it portrays.
When the size diverges from the number, people are more
influenced by the size than by the number.
59,69,77
For exam-
ple, quantitative accuracy is best when numerator and
denominator of ratios are both visually salient.
34
Part-to-
whole bar charts and part-to-whole sequentially arranged
icon arrays probably invoke automatic visual area
processing
10
and proportion judgments
11
and can be used to
help viewers attend to the mathematical proportion.
34,53,54
This may help them de-emphasize the emotional content of
accompanying text.
49
With experts and lay users given some
instruction, survival curves can help draw attention to
information that is otherwise ignored, such as middle-term
outcomes.
26,62
Patients can recognize proportions fairly successfully with
part-to-whole sequential icon arrays.
51
By contrast, propor-
tions are difficult to assess in randomly arranged icon
arrays
25
and possibly also when the icons are jittered.
39
This
could account for the dislike of random-arrangement arrays
found in qualitative studies.
37,39
Thus, sequentially arranged
icon arrays may be better than random ones in any situation
that requires the viewer to estimate a proportion or compare
two proportions.
51
Additional work may be needed to
confirm the hint in some studies
13,37
that randomly arranged
icon arrays help convey the difficult concept of chance or
uncertainty.
Relatively few studies have attempted to express the even
more difficult concept of uncertainty around a probability
estimate (confidence intervals).
37
Communicating uncer-
tainty in risks should be a topic for continuing study, given
older findings that laypeople are often unfamiliar with the
concept of scientific uncertainty.
60
Graphs emphasizing the numerator of a risk ratio are more
likely to promote risk behavior changes.
34,53,54
When part-
to-whole information is not available, as in the Stone et al.
studies, icon arrays and bar charts apparently draw atten-
tion to the numerator; when this numerator depicts adverse
events, viewers make more risk-averse choices than they do
with the numbers alone.
34,53,54
A graphic that shows only
the numerators of two risks is analogous to the epidemio-
logic quantity of the relative risk. The part-to-whole graphs
depict numerator and denominator with equal salience and
are analogous to an incidence or rate measure. Providing
relative risks without absolute risks has long been known to
inflate the apparent magnitude of the risk difference, even
with educated audiences.
30,31
Bar charts,
37,25
risk ladders,
59
scales
27,78
and sequentially
arranged icons
25
have been used successfully to help view-
ers place individual risks in context of other risks or make
specific comparisons between risks. Perceptions are strongly
influenced by the design of graphics. Magnifying the low
end of a risk scale to call attention to very small probabilities
reduces the perceived size of low risks
27
as well as higher
risks.
74
If the scale of a ladder is altered so that a particular
risk is closer to the high end of the ladder, this inflates
viewers’ perception of that risk.
59
Studies that ask patients to express information graphically
produce somewhat different results from studies of compre-
hension. For example, viewers could match a numeric
proportion to an icon array with that proportion colored in,
but were relatively inaccurate when asked to mark the
proportion on a blank icon array.
27,51
When patients express
perceived risks on different types of graphic devices, they
give inconsistent results.
27,28
The types of graphic best for
demonstrating information to patients may be different from
the types best for eliciting patient perceptions.
Qualitative research is important to learn more about how
patients interpret graphs, but relying too heavily on pa-
tients’ likes and dislikes may pose a problem because they
sometimes like graphics that lead to poor quantitative
judgments. For example, viewers appear to like graphics
that are simpler, with fewer visual elements (for example,
arrays with few icons rather than arrays with many icons
37
and bar charts with verbal ordinal categories rather than
icon arrays
39
). However, it is not clear that such simple
graphics are successful at conveying complex information.
In fact, Schapira et al. found that women viewing the
simpler array appeared to have an inflated perception of the
risk it portrayed.
37
Elting et al. found that doctors performed
worst with the format they liked best, and best with the one
they strongly disliked,
24
and Feldman-Stewart found that
laypeople made similar judgments whether they used a
format they liked or one they disliked.
25
Focus groups liked
human figures in graphics
37
even though a different re-
search group found that replacing human figures with
asterisks in an icon array produced no difference in partic-
ipants’ decisions.
34
Personalized survival curves improved
satisfaction with choices but did not affect the choices.
40
Parrott et al. suggest that people process familiar graphic
forms through learned heuristics rather than through com-
prehension of the information, resulting in gaps between the
intended meaning and the meaning constructed by the
viewer
41
; such heuristics could also produce a preference for
a particular graphic form whether or not it results in good
comprehension. Parrott et al also point out that risk graphics
often reduce complex multivariate relationships between
hazards and multiple risk factors to simplistic unidimen-
sional relationships; if viewers believe the true relationship
is multivariate, they may dismiss the graph as noncredible.
41
Future research might help develop graphics that are both
acceptable and successful in promoting quantitative judg-
ments or behavioral outcomes.
Interactions with education level, literacy, numeracy, and
culture are also likely to be fruitful continuing areas of
research. Although graphs often seem to be more intuitive
than words, the literature shows that graphical literacy is
strongly affected by expertise and familiarity with specific
graphical formats. Patients may require instruction to
be able to interpret certain formats, such as survival
graphs,
40,62,63
and they want textual explanations for illus-
trations.
38,41
Instruction may also improve comprehension
of many other formats, even familiar ones such as bar charts;
speed and accuracy of judgments are worse among novices
than among experts.
25,65
Future research should also inte-
grate the literature on comprehension of different number
formats (e.g., percentages versus rates)
44,46,47
to avoid con-
founding from the use of hard-to-understand numbers in
graphs. If patients do not fully understand what they are
seeing, they may not find the information credible or per-
suasive.
41
Visual framing and order effects may be stronger
616 ANCKER et al., Design Features of Graphs in Health Risk Communication
with less educated viewers,
26,37,67
and lower educational
level may be associated with mistrust of depictions of
scientific uncertainty.
37
Parrott et al have suggested that the
historical use of statistics to support discriminatory theories
might lead African-Americans to be suspicious of statistics
regardless of how they are presented.
41
Such issues must be
explored in continuing research among culturally and edu-
cationally diverse participant groups. Better methods for
communicating risk can help patients integrate risk data into
genuinely informed decisions about health care.
References y
1. Kaplan RM. Shared medical decision making: a new tool for
preventive medicine. Am J Prevent Med. 2004;26(1):81–3.
2. U.S.D.H.H.S. U.S. Department of Health and Human Services.
Tracking Healthy People 2010. Washington, DC: U.S. Govern-
ment Printing Office; November 2000.
3. Rothman AJ, Kiviniemi MT. Treating people with information:
an analysis and review of approaches to communicating health
risk information. JNCI Monographs. 1999;25:44–51.
4. Tversky A, Kahneman D. Judgment under uncertainty: heuris-
tics and biases. Science. 1974(185):1124–30.
5. Cosmides L, Tooby J. Are humans good intuitive statisticians
after all? Rethinking some conclusions from the literature on
judgment under uncertainty. Cognition. 1996;58:1–73.
6. Schwartz L, Woloshin S, Black W, Welch H. The role of
numeracy in understanding the benefit of screening mammog-
raphy. Ann Intern Med. 1997;127(11):966–72.
7. Lipkus IM, Samsa G, Rimer BK. General performance on a
numeracy scale among highly educated samples. Med Decis
Making. 2001;21:37–44.
8. Anonymous. How the public perceives, processes, and inter-
prets risk information: Findings from focus group research with
the general public. Bethesda, MD: National Cancer Institute,
Office of Cancer Communications; 1998.
9. Slovic P, Peters E, Finucane M, MacGregor DG. Affect, risk, and
decision making. Health Psychol. 2005;24(4(Suppl.)):S35–S40.
10. Cleveland WS, McGill R. Graphical perception and graphical
methods for analyzing scientific data. Science. 1985;229:828–33.
11. Hollands J, Spence I. Judging proportion with graphs: the
summation model. Appl Cog Psychol. 1998;12:173–90.
12. Wickens CD, Carswell CM. The proximity compatibility princi-
ple: its psychological foundation and relevance to display
design. Human Factors. 1995;37(3):473–94.
13. Baty BJ, Venne VL, McDonald J, et al. BRCA1 testing: genetic
counseling protocol development and counseling issues. J Genet
Couns. 1997;6(2):223–44.
14. Lenert LA, Cher DJ. Use of meta-analytic results to facilitate
shared decision making. J Am Med Inform Assoc. 1999;6(5):
412–9.
15. Lipkus IM, Hollands JG. The visual communication of risk.
JNCI Monographs. 1999;25:14963.
16. Edwards A, Elwyn G, Mulley A. Explaining risks: turning
numerical data into meaningful pictures. Br Med J. 2002;324:
827–30.
17. Burger J, McDermott MH, Chess C, Bochenek E, Perez-Lugo M,
Pflugh KK. Evaluating risk communication about fish consump-
tion advisories: efficacy of a brochure versus a classroom lesson
in Spanish and English. Risk Analysis. 2003;23(4):791–803.
18. Schapira M, VanRuiswyk J. The effect of an illustrated pamphlet
decision-aid on the use of prostate cancer screening tests.
Journal of Family Practice. 2000;49(5):418–24.
19. Elwyn G, Edwards A, Hood K, et al. Achieving involvement:
process outcomes from a cluster randomized trial of shared
decision making skill development and use of risk communica-
tion aids in general practice. Fam Pract. 2004;21(4):337–46.
20. Civan A, Doctor JN, Wolf FM. What makes a good format?
Frameworks for evaluating the effect of graphic risk formats on
consumers’ risk-related behavior. Paper presented at: AMIA
2005 Symposium Proceedings, 2005; Washington, DC.
21. Rohrmann B. The evaluation of risk communication effective-
ness. Acta Psycholog. 1992;81:169–92.
22. Covello V, Sandman PM. Risk communication: Evolution and
Revolution. In: Wolbarst A, editor. Solutions to an Environment
in Peril. Baltimore, MD: John Hopkins University Press; 2001:
164–78.
23. Tufte ER. The Visual Display of Quantitative Information.
Second. ed. Cheshire (CT): Graphics Press; 2001.
24. Elting L, Martin C, Cantor S, Rubenstein E. Influence of data
display formats on physician investigator’s decisions to stop
clinical trials. Br Med J. 1999;318:1527–31.
25. Feldman-Stewart D, Kocovsky N, McConnell BA, Brundage
MD, Mackillop WJ. Perception of quantitative information for
treatment decisions. Med Decis Making. 2000;20:228–38.
26. Armstrong K, Schwartz JS, Fitzgerald G, Putt M, Ubel PA. Effect
of framing as gain versus loss on understanding and hypothet-
ical treatment choices: survival and mortality curves. Med Decis
Making. 2002;22:7683.
27. Woloshin S, Schwartz LM, Byram S, Fischhoff B, Welch HG. A
new scale for assessing perceptions of chance: a validation
study. Med Decis Making. 2000;20(3):298–307.
28. Schapira MM, Davids SL, McAuliffe TL, Nattinger AB. Agree-
ment between scales in the measurement of breast cancer risk
perceptions. Risk Analysis. 2004;24(3):665–73.
29. Huff D. How to Lie With Statistics. New York: W.W. Norton;
1954 (1982, 1993).
30. Nuovo J, Melnikow J, Chang D. Reporting number needed to
treat and absolute risk reduction in randomized controlled
trials. JAMA. 2002;287:2813–4.
31. Naylor C, Chen E, B S. Measured enthusiasm: does the method
of reporting trial results alter perceptions of therapeutic effec-
tiveness? Ann Intern Med. 1992;117:916–21.
32. Skolbekken J-A. Communicating the risk reduction achieved by
cholesterol reducing drugs. Br Med J. 1998;316:19568.
33. Schneider T, P S, Apanovitch A, et al. The effects of message
framing and ethnic targeting on mammography use among
low-income women. Health Psychol. 2001;20:25666.
34. Stone ER, Sieck WR, Bull BE, Yates JF, Parks SC, Rush CJ.
Foreground:background salience: explaining the effects of
graphical displays on risk avoidance. Organ Behav Hum Decis
Process. 2003;90(1):19–36.
35. Witte K, Meyer G, Martell D. Effective health messages: a
step-by-step guide. Thousand Oaks, CA: Sage; 2001.
36. Witte K. The manipulative nature of health communication
research. Am Behav Sci. 1994;38(2):285–93.
37. Schapira MM, Nattinger AB, McHorney CA. Frequency or
probability? A qualitative study of risk communication formats
used in health care. Med Decis Making. 2001;21:45967.
38. Fortin J, Hirota L, Bond B, O’Connor A, Col N. Identifying
patient preferences for communicating risk estimates: a descrip-
tive pilot study. BMC Med Inform Decis Making. 2001;1(2).
39. Royak-Schaler R, Blocker D, Yali A, Bynoe M, Briant K, Smith S.
Breast and colorectal cancer risk communication approaches
with low-income African-American and Hispanic women: im-
plications for healthcare providers. J Nat Med Assoc. 2004;96(5):
598608.
40. Armstrong K, Weber B, Ubel PA, Peters N, Holmes J, Schwartz
JS. Individualized survival curves improve satisfaction with
cancer risk management decisions in women with BRCA1/2
mutations. J Clin Oncol. 2005;23 (36):9319–28.
41. Parrott R, Silk M, Dorgan K, Condit C, Harris T. Risk compre-
hension and judgments of statistical evidentiary appeals: when
the picture is not worth a thousand words. Human Comm Res.
2005;31(Three):423–452.
Journal of the American Medical Informatics Association Volume 13 Number 6 Nov / Dec 2006 617
42. Cleveland WS. The Elements of Graphing Data. Second. ed.
Summit, NJ: Hobart Press; 1994.
43. McCray AT. Promoting health literacy. JAMIA. 2005;12:152–63.
44. Gigerenzer G, Hoffrage U. How to improve Bayesian reasoning
without instruction: frequency formats. Psychol Rev. 1995;
102(4):684–704.
45. Hoffrage U, Lindsey S, Hertwig R, Gigerenzer G. Communicat-
ing statistical information. Science. 2000;290:2261–2.
46. Grimes DA, Snively GR. Patients’ understanding of medical
risks: implications for genetic counseling. Obstet Gynecol. 1999;
93:9104.
47. Johnson EJ, Payne JW, Bettman JR. Information displays and
preference reversals. Organ Behav Hum Decis Process. 1988;42:
1–21.
48. Brase GL, Cosmides L, Tooby J. Individuation, counting, and
statistical inference: the role of frequency and whole-object
representations in judgment under uncertainty. J Exper Psychol:
Gen. 2002;127(1):3–21.
49. Fagerlin A, Wang C, Ubel PA. Reducing the influence of
anecdotal reasoning on people’s health care decisions: is a
picture worth a thousand statistics? Med Decis Making. 2005;
25:398405.
50. Denes-Raj V, Epstein S, Cole J. The generality of the ratio-bias
phenomenon. Pers Soc Psychol Bull. 1995;21(10):1083–92.
51. Fuller R, Dudley N, Blacktop J. Risk communication and older
people—understanding of probability and risk information by
medical inpatients aged 75 years and older. Age Ageing. 2001;
30:473–6.
52. Fuller R, Dudley N, Blacktop J. How informed is consent?
Understanding of pictorial and verbal probability information
by medical inpatients. Postgrad Med. 2002;78:543–4.
53. Schirillo JA, Stone ER. The greater ability of graphical versus
numerical displays to increase risk avoidance involves a com-
mon mechanism. Risk Anal. 2005;25(3):555–66.
54. Stone ER, Yates JF, Parker AM. Effects of numerical and
graphical displays on professed risk-taking behavior. J Exp
Psychol: Appl. 1997;3(4):243–56.
55. Kaplan RM, Hammel B, Schimmel LE. Patient information
processing and decision to accept treatment. J Soc Behav Person.
1985;1:113120.
56. Weinstein ND, Sandman PM, Hallman WK. Testing a visual
display to explain small probabilities. Risk Anal. 1994;14(6):
895–6.
57. Lee DH, Mehta MD. Evaluation of a visual risk communication
tool: effects on knowledge and perception of blood transfusion
risk. Transfusion. 2003;43:77987.
58. Lee DE, Paling JE, Blajchman MA. A new tool for communicat-
ing transfusion risk information. Transfusion. 1998;38:1848.
59. Sandman P, Weinstein ND, Miller P. High risk or low: how
location on a ’risk ladder’ affects perceived risk. Risk Anal.
1994;14:35–45.
60. Johnson BB, Slovic P. Presenting uncertainty in health risk
assessment: initial studies of its effects on risk perception and
trust. Risk Anal. 1995;15(4):485–94.
61. McNeil B, Pauker S, Sox H, Tversky A. On the elicitation of
preferences for alternative therapies. New Eng J Med. 1982;306:
125962.
62. Armstrong K, Fitzgerald G, Schwartz JS, Ubel PA. Using sur-
vival curve comparisons to inform patient decision making: Can
a practice exercise improve understanding? J Gen Intern Med.
2001(16):482–5.
63. Mazur DJ, Hickam DH. The effect of physicians’ explanations
on patients’ treatment preferences: Five-year survival data. Med
Decis Making. 1994;14:255–8.
64. Mazur DJ, Hickam DH. Interpretation of graphic data by
patients in a general medicine clinic. J Gen Intern Med. 1990;5:
402–5.
65. Mazur DJ, Hickam DH. Patients’ and physicians’ interpretations
of graphic data displays. Med Decis Making. 1993;13:5963.
66. Mazur DJ, Hickam DH. Five-year survival curves: how much
data are enough for patient-physician decision making in gen-
eral surgery? Eur J Surg. 1996;162:101–4.
67. Mazur DJ, Merz JF. How the manner of presentation of data
influences older patients in determining their treatment prefer-
ences. J Am Geriatr Soc. 1993;41(3):223–8.
68. Mazur DJ, Hickam DH. Five-year survival data in surgical
decision making: what aspects of graphical data influence
patients’ preferences. Theoretical Surgery. 1994;9:7681.
69. Zikmund-Fisher BJ, Fagerlin A, Ubel PA. What’s time got to do
with it? Inattention to duration in interpretation of survival
graphs. Risk Anal. 2005;25(3):589–95.
70. Lau EW, Ng GA. Visual illusions created by survival curves and
the need to avoid potential misinterpretation. Medical Decision
Making. 2002;22:23844.
71. Gail MH, Costantino JP. Validating and improving models for
projecting the absolute risk of breast cancer. J Natl Cancer Inst.
2001;93:334–5.
72. Windschitl P, Wells G. Measuring psychological uncertainty:
verbal versus numeric methods. J Exp Psychol. 1996;2:343–64.
73. Diefenbach M, Weinstein N, O’Reilly J. Scales for assessing
perceptions of health hazard susceptibility. Health Edu Res.
1993;8:181–92.
74. Gurmankin AD, Helweg-Larsen M, Armstrong K, Kimmel SE,
Volpp KGM. Comparing the standard rating scale and the
magnifier scale for assessing risk perceptions. Med Decis Mak-
ing. 2005;25:560–70.
75. Connelly NA, Knuth BA. Evaluating risk communication: ex-
amining target audience perceptions about four presentation
formats for fish consumption health advisory information. Risk
Anal. 1998;18(5):649–59.
76. Spence I, Lewandowsky S. Displaying proportions and percent-
ages. Appl Cog Psychol. 1991;5:61–77.
77. Beattie V, Jones MJ. The impact of graph slope on rate of change
judgments in corporate reports. Abacus. 2002;38(2):177–99.
78. Stallings S, Paling J. New tool for presenting risk in obstetrics
and gynecology. Obstet Gynecol. 2001;98(2):345–9.
618 ANCKER et al., Design Features of Graphs in Health Risk Communication
... Research on visualizations of patient data has primarily focused on visualizing electronic health records and patient-reported outcomes for use by clinicians [92] and visualizing risk for communication with patients [2,66]. Reviews of patient-facing visualizations provide formats that are useful and well understood [171], and the high-level goals of the visualizations [113]. ...
... • Formats for different components were selected depending on the data or the insight to be presented [142]. The review of existing tools from chapter 6 ( Figure 27), literature on review of different patient-facing visualization formats [2,171], and evidence on effective task-visualization mappings from prior work [17] informed the choices here. ...
... o Meal carb ratios are more than needed (1) o Meal carb ratios are lesser than needed (2) o Correction factor is more than needed (3) o Correction factor is lesser than needed (4) o Basal insulin is more than needed (5) o Basal insulin is lesser than needed (6) o Overcounted meal carbs (7) o Undercounted meal carbs (8) o Not recording carbs in the pump (9) o Not taking meal bolus (10) o Not recording BG in the pump (11) o Performing physical activity (12) o Frequent or excessive eating (13) o Taking bolus before meals (14) o Taking bolus after meals (15) o Too much insulin given by the Control IQ technology (16) o Insulin suspension by the pump (17) o Pump site not working (18) o Other (19) o Not sure (20) Q8 For the BG pattern, choose one of the following: ...
Thesis
People with chronic health conditions, such as diabetes, are now able to capture large amounts of health data every day owing to improved medical and consumer sensing technology. These data, known as patient-generated data, have immense potential to inform the care of chronic conditions, both individually by patients and collaboratively by patients and clinicians. Despite the increasing ability to capture personal health data, informatics tools provide limited support to enable routine use of data for disease management. Lack of support for making sense of different types of health data challenges informed decision-making and results in missed opportunities for improving care, leading to suboptimal control and poor health consequences. Motivated by these problems, my dissertation examines the data practices and decisional needs of patients and clinicians to design novel tools for the presentation of multidimensional health data and evaluates these tools in the context of Type 1 diabetes. It employs several qualitative methods that include interviews, observations, focus groups, diary study, think aloud sessions, and user-centered design. By examining how patients and clinicians interpret multiple streams of data from continuous glucose monitors and insulin pumps, I synthesized the episode-driven sensemaking framework, a novel framework that describes the different analytical stages through which multidimensional health data is made actionable. My work describes the four analytical stages of the episode-driven sensemaking framework that include episode detection, episode elaboration, episode classification, and episode-specific recommendation generation. I show that the episode-driven framework provides a promising basis to guide the design of tools for data-based sensemaking and decision-making as the different stages of the framework lend themselves to opportunities for combining computational and user agency in different ways. By examining existing data review platforms, I show that the exploratory nature of these tools makes them underutilized by lay users like patients, in addition to resulting in negative experiences, such as cognitive burden, misinterpretation, and misrepresentation of reality. Given the limitations of exploratory tools, the potential of the episode-driven framework in providing a basis for tool design, and the promise of data-driven narratives in communicating data to the lay users, I designed episode-driven data narratives to help patients review data from continuous glucose monitors and insulin pumps. An exploratory comparison of the episode-driven narratives with the commercially available data review platforms shows that the former improved data comprehension and patients’ ability to make decisions from data; and lowered the cognitive load of engaging with data. Additionally, in nuanced ways, episode-driven narratives enabled user agency in making decisions for self-care. Based on multiple studies to examine practices, and design and evaluate tools, I suggest that to support people in effectively leveraging multidimensional data for managing chronic conditions, tools must do the following - support effective problem-solving with data by creating a shared understanding of data between stakeholders, enable different types of assessments from data and help connect those assessments, and guide analytic focus using a scaffold (e.g., an episode-driven workflow) to organize and present evidence. One promising approach to implement these suggestions in the design of a tool is an episode-driven data narrative, an embodiment of the episode-driven sensemaking framework using narrative visualization techniques. By supporting the generation and presentation of episode-driven narratives from multidimensional data, tools can augment patients’ abilities to effectively inform self-care of chronic conditions with their data.
... The use of risk communication tools prior to surgery can be engaging and may result in the majority of patients understanding their surgery-associated risks well [15,16]. Several surgical risk stratification scores/tools have been developed to provide risk estimates with the goal of informing and improving care [17]. ...
... Although preliminary, these tools have enhanced patient risk comprehension, perceived quality of preoperative clinical conversations, and physician prognostic accuracy, and there is evidence they can decrease length of hospital stay [18]. However, a recent scoping review identified that only 7 (<1%) of 796 screened studies both described the methods used to calculate personalized risk and communicated these findings directly to the patient or health care professional or both [18], and many tools have failed to include patient-centered design principles [15,19]. Finally, risk communication tools should apply best practices when communicating information to patients, including the use of plain language and pictographs to present information visually [20]. ...
Article
Full-text available
Background: Pediatric surgery is associated with a risk of postoperative pain that can impact the family’s quality of life. Although some risk factors for postoperative pain are known, these are often not consistently communicated to families. In addition, although tools for risk communication exist in other domains, none are tailored to pediatric surgery. Objective: As part of a larger project to develop pain risk prediction tools, we aimed to design an easy-to-use tool to effectively communicate a child’s risk of postoperative pain to both clinicians and family members. Methods: With research ethics board approval, we conducted virtual focus groups (~1 hour each) comprising clinicians and family members (people with lived surgical experience and parents of children who had recently undergone surgery/medical procedures) at a tertiary pediatric hospital to understand and evaluate potential design approaches and strategies for effectively communicating and visualizing postoperative pain risk. Data were analyzed thematically to generate design requirements and to inform iterative prototype development. Results: In total, 19 participants (clinicians: n=10, 53%; family members: n=9, 47%) attended 6 focus group sessions. Participants indicated that risk was typically communicated verbally by clinicians to patients and their families, with severity indicated using a descriptive or a numerical representation or both, which would only occasionally be contextualized. Participants indicated that risk communication tools were seldom used but that families would benefit from risk information, time to reflect on the information, and follow-up with questions. In addition, 9 key design requirements and feature considerations for effective risk communication were identified: (1) present risk information clearly and with contextualization, (2) quantify the risk and contextualize it, (3) include checklists for preoperative family preparation, (4) provide risk information digitally to facilitate recall and sharing, (5) query the family’s understanding to ensure comprehension of risk, (6) present the risk score using multimodal formats, (7) use color coding that is nonthreatening and avoids limitations with color blindness, (8) present the most significant factors contributing to the risk prediction, and (9) provide risk mitigation strategies to potentially decrease the patient’s level of risk. Conclusions: Key design requirements for a pediatric postoperative pain risk visualization tool were established and guided the development of an initial prototype. Implementing a risk communication tool into clinical practice has the potential to bridge existing gaps in the accessibility, utilization, and comprehension of personalized risk information between health care professionals and family members. Future iterative codesign and clinical evaluation of this risk communication tool are needed to confirm its utility in practice.
... There is apparently no consensus in research on the question of how, or in what presentation format, MF's field strengths can 'best' 1 be communicated to laypeople (Ancker et al., 2006;Nielsen et al., 2010;Slovic et al., 2005;Timmermans et al., 2008). Unfortunately, informing the public often goes hand-in-hand with an unintended increase in the recipients' risk perception, just by talking about the topic (Böhmert et al., 2016;Böhmert et al., 2017;Claassen With regard to the topic of (electro-)MF, some studies were unable to identify any differences regarding the impact of different presentation formats on the recipients' understanding of the topic (e.g. ...
Preprint
Full-text available
People's acceptance level for high voltage power lines (HVPL) is generally low. Among others, they suspect potential health risks arising from magnetic fields (MF) associated with HVPL, although there are only very few, scientifically proven health risks originating from them. It is therefore crucial to inform the public about this misconception. Studies in risk communication have already tested whether certain presentation formats are perceived as easier to understand than others. The results are inconclusive. A special problem is that the mere provision of information often leads to an increase in risk perception, regardless of the content. Thus, our study focuses on recipients' risk perception rather than on the understandability of information. We ask whether the degree of narrative visualizations that a presentation format contains has an impact on citizens' risk perception of HPVL. We conducted an experimental study of 274 participants (representative of the German population) to test three different presentation formats (video, infographic, diagram). It emerged that presentation formats involving strong narrativity and visualization (e.g., video) led to lower risk perception than presentation formats involving less narrativity and visualization (e.g., diagram). Overall, our results also indicate that some people exhibit increased risk perception after reception of the stimuli. Therefore, we further investigated whether the personality traits skepticism and anxiety might affect recipients' risk perception. In the latter case, this proved to be true. Our study sheds light on how information campaigns on HVPL and MF might reduce risk perception and which individual traits might prove a hindrance.
... However, the nature of environmental data communication necessitates inclusion of numeracy elements (such as units or scales) that may not be familiar to all participants [8]. As a result, use of numbers and data displays should be guided by current research (e.g. using whole numbers, using consistent presentation of numbers, and simplifying data visualizations) [54][55][56][57]. Additionally, the language used to contextualize and describe the results and possible health risks is often technical and unfamiliar to the broader public. ...
Article
Full-text available
Background Although there is increasing interest in reporting results of environmental research efforts back to participants, evidence-based tools have not yet been applied to developed materials to ensure their accessibility in terms of literacy, numeracy, and data visualization demand. Additionally, there is not yet guidance as to how to formally assess the created materials to assure a match with the intended audience. Methods Relying on formative qualitative research with participants of an indoor air quality study in Dorchester, Massachusetts, we identified means of enhancing accessibility of indoor air quality data report-back materials for participants. Participants ( n = 20) engaged in semi-structured interviews in which they described challenges they encountered with scientific and medical materials and outlined written and verbal communication techniques that would help facilitate engagement with and accessibility of environmental health report-back materials. We coupled these insights from participants with best practice guidelines for written materials by operationalizing health literacy tools to produce accessible audience-informed data report-back materials. Results The resulting data report-back materials had a 7th -grade reading level, and between a 4th -8th grade level of overall document complexity. The numeracy skills required to engage with the material were of the lowest demand, and we incorporated best practices for risk communication and facilitating understanding and actionability of the materials. Use of a rigorous assessment tool provides evidence of accessibility and appropriateness of the material for the audience. Conclusions We outline a process for developing and evaluating environmental health data reports that are tailored to inspire risk-reduction actions, and are demonstrably accessible in terms of their literacy, numeracy, and data visualization demand. Adapting health literacy tools to create and evaluate environmental data report-back materials is a novel and evidence-based means of ensuring their accessibility.
Article
Upon shutting down operations in early 2020 due to the COVID‐19 pandemic, the movie industry assembled teams of experts to help develop guidelines for returning to operation. It resulted in a joint report, The Safe Way Forward, which was created in consultation with union members and provided the basis for negotiations with the studios. A centerpiece of the report was a set of heatmaps displaying SARS‐CoV‐2 risks for a shoot, as a function of testing rate, community infection prevalence, community transmission rate (R0), and risk measure (either expected number of cases or probability of at least one case). We develop and demonstrate a methodology for evaluating such complex displays, in terms of how well they inform potential users, in this case, workers deciding whether the risks of a shoot are acceptable. We ask whether individuals making hypothetical return‐to‐work decisions can (a) read display entries, (b) compare display entries, and (c) make inferences based on display entries. Generally speaking, respondents recruited through the Amazon MTurk platform could interpret the display information accurately and make coherent decisions, suggesting that heatmaps can communicate complex risks to lay audiences. Although these heatmaps were created for practical, rather than theoretical, purposes, these results provide partial support for theoretical accounts of visual information processing and identify challenges in applying them to complex settings.
Article
Hazard‐level forecasts constitute an important risk mitigation tool to reduce loss of economic values and human life. Avalanche forecasts represent an example of this. As for many other domains, avalanche risk is communicated using a color‐coded, categorical risk scale aimed at informing the public about past, current, and future risk. We report the results from three experiments in which we tested if an irrelevant past trend in forecasted avalanche danger affects perceptions of current and future avalanche risk. Our sample consisted of individuals from three different populations targeted by national avalanche warning services. All three experiments showed that the perception of avalanche risk is influenced by the trend, but that the effect is opposite for perceptions of current and expectations of future avalanche risk. While future avalanche risk is extrapolated in the same direction as the change from the previous day, we found that perceived current risk appears to be based on an average of past and current risk. These effects diminish when we provide participants with a scale indicating the exact level of avalanche danger. For most of our measurement instruments, however, the effects remain significant. These results imply that targeted populations may consider historic information more than was intended by the sender. As such, our results have implications for both avalanche warning services and risk communication in general.
Article
Objective To develop a usability checklist for public health dashboards. Materials and methods This study systematically evaluated all publicly available dashboards for sexually transmitted infections on state health department websites in the United States (N = 13). A set of 11 principles derived from the information visualization literature were used to identify usability problems that violate critical usability principles: spatial organization, information coding, consistency, removal of extraneous ink, recognition rather than recall, minimal action, dataset reduction, flexibility to user experience, understandability of contents, scientific integrity, and readability. Three user groups were considered for public health dashboards: public health practitioners, academic researchers, and the general public. Six reviewers with usability knowledge and diverse domain expertise examined the dashboards using a rubric based on the 11 principles. Data analysis included quantitative analysis of experts’ usability scores and qualitative synthesis of their textual comments. Results The dashboards had varying levels of complexity, and the usability scores were dependent on the dashboards’ complexity. Overall, understandability of contents, flexibility, and scientific integrity were the areas with the most major usability problems. The usability problems informed a checklist to improve performance in the 11 areas. Discussion The varying complexity of the dashboards suggests a diversity of target audiences. However, the identified usability problems suggest that dashboards’ effectiveness for different groups of users was limited. Conclusions The usability of public health data dashboards can be improved to accommodate different user groups. This checklist can guide the development of future public health dashboards to engage diverse audiences.
Article
Full-text available
Looking for patterns in data is a key part of many sectors, including those working in pharmaceuticals and healthcare. While software packages are available to automate this task and, for highly complex data, machine learning can be used, reviewing data continues to matter event for the simpler aspects of life in the sectors. This includes assessing metrics from batch record data (such as yield or a critical process parameter); microbial counts; types of deviations by category and so on. For pattern analysis, data visualization provides a quick, easy way to convey concepts universally. This article presents some approaches that can be used in order to obtain insights from data using simple visual tools. While the tools are simple, they can help with conveying an important point with clarity. Reference: Sandle, T. (2022) Digital Data #4: Looking for Data Trends and Patterns With Visualization, IVT Network, at: https://www.ivtnetwork.com/article/digital-data-4-looking-data-trends-and-patterns-visualization
Article
Misinformed beliefs are difficult to change. Refutations that target false claims typically reduce false beliefs, but tend to be only partially effective. In this study, a social norming approach was explored to test whether provision of peer norms could provide an alternative or complementary approach to refutation. Three experiments investigated whether a descriptive norm-by itself or in combination with a refutation-could reduce the endorsement of worldview-congruent claims. Experiment 1 found that using a single point estimate to communicate a norm affected belief but had less impact than a refutation. Experiment 2 used a verbally-presented distribution of four values to communicate a norm, which was largely ineffective. Experiment 3 used a graphically-presented social norm with 25 values, which was found to be as effective at reducing claim belief as a refutation, with the combination of both interventions being most impactful. These results provide a proof of concept that normative information can aid in the debunking of false or equivocal claims, and suggests that theories of misinformation processing should take social factors into account.
Article
This article discusses the genetic counseling protocols which were developed and counseling issues that have arisen in the first 2 years of evaluating a large kindred with a BRCA1 mutation. The rationale for the development of the genetic counseling protocols and specific genetic counseling visual aids are presented and discussed. The protocols and counseling aids can serve as models for other programs offering cancer susceptibility testing. The observations of study counselors about study subject concerns and responses to genetic testing at the time of the pretest and posttest counseling sessions are presented.
Article
Many decisions are based on beliefs concerning the likelihood of uncertain events such as the outcome of an election, the guilt of a defendant, or the future value of the dollar. Occasionally, beliefs concerning uncertain events are expressed in numerical form as odds or subjective probabilities. In general, the heuristics are quite useful, but sometimes they lead to severe and systematic errors. The subjective assessment of probability resembles the subjective assessment of physical quantities such as distance or size. These judgments are all based on data of limited validity, which are processed according to heuristic rules. However, the reliance on this rule leads to systematic errors in the estimation of distance. This chapter describes three heuristics that are employed in making judgments under uncertainty. The first is representativeness, which is usually employed when people are asked to judge the probability that an object or event belongs to a class or event. The second is the availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development, and the third is adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available.