A theory of medical decision making and health: fuzzy trace theory

Abstract

The tenets of fuzzy trace theory are summarized with respect to their relevance to health and medical decision making. Illustrations are given for HIV prevention, cardiovascular disease, surgical risk, genetic risk, and cancer prevention and control. A core idea of fuzzy trace theory is that people rely on the gist of information, its bottom-line meaning, as opposed to verbatim details in judgment and decision making. This idea explains why precise information (e.g., about risk) is not necessarily effective in encouraging prevention behaviors or in supporting medical decision making. People can get the facts right, and still not derive the proper meaning, which is key to informed decision making. Getting the gist is not sufficient, however. Retrieval (e.g., of health-related values) and processing interference brought on by thinking about nested or overlapping classes (e.g., in ratio concepts, such as probability) are also important. Theory-based interventions that work (and why they work) are presented, ranging from specific techniques aimed at enhancing representation, retrieval, and processing to a comprehensive intervention that integrates these components.

Figure 1

Bar graphs emphasizing relative risk using (A) frequencies of disease for 2 treatments and (B) equivalent proportions of disease for the same treatments. Choosing bar graphs to display relative risk, rather than absolute risk, makes it more evident that treatment A is more effective.

Figure 2

Stacked bar graphs emphasizing absolute risk using (A) frequencies of disease for 2 treatments and total treated (B) equivalent proportions of disease for the same treatments and total treated. Choosing stacked bar graphs to display absolute risk makes it more evident that there is little absolute difference in the effectiveness of the 2 treatments.