Communicating Numerical Risk

Abstract

In this chapter, we review evidence from the human factors literature that verbal and visual formats can help increase the understanding of numerical risk information in health care. These visual representations of risk are grounded in empirically supported theory. As background, we first review research showing that people often have difficulty understanding numerical risks and benefits in health information. In particular, we discuss how understanding the meanings of numbers results in healthier decisions. Then, we discuss the processes that determine how communication of numerical risks can enhance (or degrade) health judgments and decisions. Specifically, we examine two different approaches to risk communication: a traditional approach and fuzzy-trace theory. Applying research on the complications of understanding and communicating risks, we then highlight how different visual representations are best suited to communicating different risk messages (i.e., their gist). In particular, we review verbal and visual messages that highlight gist representations that can better communicate health information and improve informed decision making. This discussion is informed by human factors theories and methods, which involve the study of how to maximize the interaction between humans and the tools they use. Finally, we present implications and recommendations for future research on human factors in health care.

Self-Reported Health Status Predicts Other-Cause Mortality in Men with Localized Prostate Cancer: Results from the Prostate Cancer Outcomes Study

“…40 Nevertheless, the nomogram can provide men with a gist understanding of their risk for non-cancer death. 41 Older men with localized prostate cancer who self-report fair or poor health have substantial risks for other-cause deaths and are unlikely to survive long enough to benefit from aggressive treatment. These men should strongly consider watchful waiting.…”

Shared Medical Decision Making in Lung Cancer Screening

“…In medicine, one of the most significant barriers to informed decision making is the tendency to overweight small probabilities. As a result, many studies have sought to develop improved methods to improve patients’ understanding of risk (8). Notably, Fagerlin et al (9) recently outlined a series of recommendations to improve risk communication which included presenting absolute risks instead of relative risks and using icon arrays.…”

An Overview of Judgment and Decision Making Research Through the Lens of Fuzzy Trace Theory

“…More specifically, these methods reduced the tendency for medical decision makers to act like error-prone, verbatim calculators when updating disease probabilities after testing. Rather, medical professionals trained to use visual aids to convey bottom-line meaning and to ignore exact probabilities by focusing on the relations among classes (e.g., “there are many women with positive test results, but few of them have the disease”) made better probability estimates of disease than they did without the visual aids (Lloyd & Reyna, 2001, Brust-Renck, Royer, & Reyna, 2013). Because risk perception by lay people often hinges on how medical professionals convey risks, simple but effective theoretically-motivated interventions may help the general public to form appropriate gists.…”

“…Example of 2 × 2 table separating classes to reduce probability judgment fallacies (adapted from Brust-Renck, Royer, & Reyna, 2013). …”