Abstract

Nowadays, we have much more information about our health than we had in the past, and numerical information is, or should be, commonplace in the communication between doctors and patients. Indeed, evidence-based medicine is the gold standard for public health decisions, for the development of national and international guidelines, as well as for clinical practice on individual patients. In this perspective, risk communication, conveyed mainly through numerical information, need to take into account how people perceive and understand this information. Psychological research on this issue has shown that people are affected by the way information is presented when making judgments and decisions. This paper aims to illustrate some of the main issues to be considered when designing risk communication in the medical domain. By examining some examples of non-transparent risk communication, we will illustrate the effectiveness of different types of numerical information (natural frequencies, 1 in n. percentages) and discuss the concepts of absolute and relative risk, highlighting the importance of making explicit the reference class. Additionally, considering that the same treatment can be described in terms of likelihood of survival or death, and that, although the two information are complementary, people seem to be affected by the communicator's choice, we will examine the various types of frames used in the medical domain and discuss the more recent research findings. Making explicit the reference class to which probabilities refer can also help to understand the results of a clinical test. In a simplistic way, people often think that a positive test result means that a disease is present and that a negative test result means that it is not. Even when it is acknowledged that no test is 100% certain, the margin of error and the extent to which it is affected by the frequency of the disease in the population are difficult to grasp, even by experts. For instance, even with a very precise test, a positive test result is associated with a very low likelihood of having the disease (positive predictive value) when the disease is rare. Research has also shown that people with low numeracy (the ability to reason and to apply simple numerical concepts) are especially susceptible to misunderstanding of numerical information when it is referred to groups of people and that are likely to be affected by stories about single cases (see, for instance, the current debate about childhood vaccinations). Finally, we will discuss about the new frontiers of medical research, resulting in a continuous increase in the complexity of risk communication. For example, with the progress of medical genetics and the possibility to determine the presence of genetic mutations that can be linked to the risk of developing diseases, the complexity of the information to be communicated is clearly increasing. Even if people are driven by the desire to know, we need to remember that it is not always possible to act upon the information obtained (e.g., would you want to know whether you have a genetic predisposition for Alzheimer, considering that at present there is no effective treatment?) and that the decisions made by our «present Self» might not be the same of those that our «future Self» would made for its health.