Caisa Elizabeth Royer

Communicating Numerical Risk Human Factors That Aid Understanding in Health Care

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.

The Gist of Juries: Testing a Model of Damage Award Decision Making

Despite the importance of damage awards, juries are often at sea about the amounts that should be awarded, with widely differing awards for cases that seem comparable. We tested a new model of damage award decision making by systematically varying the size, context, and meaningfulness of numerical comparisons or anchors. As a result, we were able to elicit large differences in award amounts that replicated for 2 different cases. Although even arbitrary dollar amounts (unrelated to the cases) influenced the size of award judgments, the most consistent effects of numerical anchors were achieved when the amounts were meaningful in the sense that they conveyed the gist of numbers as small or large. Consistent with the model, the ordinal gist of the severity of plaintiffs damages and defendants liability predicted damage awards, controlling for other factors such as motivation for the award-judgment task and perceived economic damages. Contrary to traditional dual-process approaches, numeracy and cognitive style (e.g., need for cognition and cognitive reflection) were not significant predictors of these numerical judgments, but they were associated with lower levels of variability once the gist of the judgments was taken into account. Implications for theory and policy are discussed.