Recent findings suggest that interactive game-like graphics might be useful in communicating probabilities. We developed a prototype for a risk communication module, focusing on eliciting users' preferences for different interactive graphics and assessing usability and user interpretations. Feedback from five focus groups was used to design the graphics. The final version displayed a matrix of square buttons; clicking on any button allowed the user to see whether the stick figure underneath was affected by the health outcome. When participants used this interaction to learn about a risk, they expressed more emotional responses, both positive and negative, than when viewing any static graphic or numerical description of a risk. Their responses included relief about small risks and concern about large risks. The groups also commented on static graphics: arranging the figures affected by disease randomly throughout a group of figures made it more difficult to judge the proportion affected but often was described as more realistic. Interactive graphics appear to have potential for expressing risk magnitude as well as the feeling of risk. This affective impact could be useful in increasing perceived threat of high risks, calming fears about low risks, or comparing risks. Quantitative studies are planned to assess the effect on perceived risks and estimated risk magnitudes.
"For example, in one study, participants presented with a treatment scenario were better calibrated in their perceptions of medication side effects when they created a bar graph of the risk instead of just viewing one . Another study found that a web-based, game-like, interactive risk graphic in which participants clicked in a matrix until they uncovered a risk event had the effect of reducing disparities in risk perceptions between more and less numerate participants . Such exercises could be seen as methods to increase patients’ active processing of risk information, which may lead to improved risk understanding. "
[Show abstract][Hide abstract] ABSTRACT: Making evidence-based decisions often requires comparison of two or more options. Research-based evidence may exist which quantifies how likely the outcomes are for each option. Understanding these numeric estimates improves patients' risk perception and leads to better informed decision making. This paper summarises current "best practices" in communication of evidence-based numeric outcomes for developers of patient decision aids (PtDAs) and other health communication tools.
An expert consensus group of fourteen researchers from North America, Europe, and Australasia identified eleven main issues in risk communication. Two experts for each issue wrote a "state of the art" summary of best evidence, drawing on the PtDA, health, psychological, and broader scientific literature. In addition, commonly used terms were defined and a set of guiding principles and key messages derived from the results.
The eleven key components of risk communication were: 1) Presenting the chance an event will occur; 2) Presenting changes in numeric outcomes; 3) Outcome estimates for test and screening decisions; 4) Numeric estimates in context and with evaluative labels; 5) Conveying uncertainty; 6) Visual formats; 7) Tailoring estimates; 8) Formats for understanding outcomes over time; 9) Narrative methods for conveying the chance of an event; 10) Important skills for understanding numerical estimates; and 11) Interactive web-based formats. Guiding principles from the evidence summaries advise that risk communication formats should reflect the task required of the user, should always define a relevant reference class (i.e., denominator) over time, should aim to use a consistent format throughout documents, should avoid "1 in x" formats and variable denominators, consider the magnitude of numbers used and the possibility of format bias, and should take into account the numeracy and graph literacy of the audience.
A substantial and rapidly expanding evidence base exists for risk communication. Developers of tools to facilitate evidence-based decision making should apply these principles to improve the quality of risk communication in practice.
BMC Medical Informatics and Decision Making 11/2013; 13 Suppl 2(Suppl 2):S7. DOI:10.1186/1472-6947-13-S2-S7 · 1.83 Impact Factor
"Other work, however, suggests that intention to undertake recommended lifestyle changes was in fact influenced by graphical formats but only in participants also receiving high-threat communications  . Graphical decision aids have also been shown to impact the emotional response  of participants and decrease their passivity in counseling sessions . "
[Show abstract][Hide abstract] ABSTRACT: Risk communication is a major challenge in productive patient-physician communication. Patient decision making responsibilities come with an implicit assumption that patients are sufficiently educated and confident in their abilities to make decisions about their care based on evidence based treatment recommendations. Attempts to improve health literacy in patients by way of graphical decision aids have met with success. Such decision aids typically have been designed for a general population and evaluated based on whether or not users of the decision aid can accurately report the data points in isolation. To classify decision aids, we present an information-centric framework for assessing the content delivered to patients. We provide examples of our framework from a literature survey and suggest ways improvements can be made by considering all dimensions of our framework.
"Affective communication can improve the perception of risk in high risk patients, and alleviate concerns in those whose risk is low . The EDUCORE method (Education and Coronary Risk Evaluation) may provide a means of facilitating such communication. "
[Show abstract][Hide abstract] ABSTRACT: High blood pressure (HBP) is a major risk factor for cardiovascular disease (CVD). European hypertension and cardiology societies as well as expert committees on CVD prevention recommend stratifying cardiovascular risk using the SCORE method, the modification of lifestyles to prevent CVD, and achieving good control over risk factors. The EDUCORE (Education and Coronary Risk Evaluation) project aims to determine whether the use of a cardiovascular risk visual learning method--the EDUCORE method--is more effective than normal clinical practice in improving the control of blood pressure within one year in patients with poorly controlled hypertension but no background of CVD;
This work describes a protocol for a clinical trial, randomised by clusters and involving 22 primary healthcare clinics, to test the effectiveness of the EDUCORE method. The number of patients required was 736, all between 40 and 65 years of age (n = 368 in the EDUCORE and control groups), all of whom had been diagnosed with HBP at least one year ago, and all of whom had poorly controlled hypertension (systolic blood pressure >or= 140 mmHg and/or diastolic >or= 90 mmHg). All personnel taking part were explained the trial and trained in its methodology. The EDUCORE method contemplates the visualisation of low risk SCORE scores using images embodying different stages of a high risk action, plus the receipt of a pamphlet explaining how to better maintain cardiac health. The main outcome variable was the control of blood pressure; secondary outcome variables included the SCORE score, therapeutic compliance, quality of life, and total cholesterol level. All outcome variables were measured at the beginning of the experimental period and again at 6 and 12 months. Information on sex, age, educational level, physical activity, body mass index, consumption of medications, change of treatment and blood analysis results was also recorded;
The EDUCORE method could provide a simple, inexpensive means of improving blood pressure control, and perhaps other health problems, in the primary healthcare setting;
The trial was registered with ClinicalTrials.gov, number NCT01155973 [http://ClinicalTrials.gov].
BMC Public Health 07/2010; 10(1):449. DOI:10.1186/1471-2458-10-449 · 2.26 Impact Factor
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