Felix G. Rebitschek

A Simple Tool for Communicating the Benefits and Harms of Health Interventions A Guide for Creating a Fact Box

One of the major hurdles to promoting informed decision making in health is the continued use of poor risk presentation formats. This article offers a guide to develop a Fact Box, a simple decision tool to present data about the benefits and harms of treatments that has been demonstrated to improve understanding of health risks, an important part of risk literacy. The article offers guidance about how to determine the evidence basis for a health topic, select outcomes to report, extract and present numbers or outcomes, and design the layout. The guide also addresses potential challenges for summarizing evidence and provides alternatives for addressing issues related to missing, insufficient, imprecise, or conflicting evidence and for dealing with issues related to statistical and clinical significance. The guide concludes with details on how to document the development of the Fact Box for the purpose of transparency and reproducibility. Fact Boxes are an efficient tool to promote risk literacy and should be available in every physician’s office.

Memory activation of multiple hypotheses in sequential diagnostic reasoning

In sequential diagnostic reasoning, observed evidence activates hypotheses about possible causes in memory. These memory activations have been previously examined with a probe reaction task for problems with a single correct diagnosis. We applied this process tracing method to ambiguous problems with multiple compatible hypotheses. When participants reasoned about the causes of ambiguous symptom sequences, they were prompted to respond to probes representing hypotheses. The response time to a probe was shorter if the current support for the respective hypothesis was stronger indicating that the processing of compatible hypotheses can be traced. For sequences with two equally supported hypotheses, the initial hypothesis was more often chosen as the final diagnosis (a primacy effect). Probe reaction times suggest that the initial hypothesis has been activated more strongly already early, when it was finally chosen as the diagnosis. Nevertheless, substantial variance in response times limits the tasks applicability for process tracing.

The diversity effect in diagnostic reasoning.

Diagnostic reasoning draws on knowledge about effects and their potential causes. The causal-diversity effect in diagnostic reasoning normatively depends on the distribution of effects in causal structures, and thus, a psychological diversity effect could indicate whether causally structured knowledge is used in evaluating the probability of a diagnosis, if the effect were to covary with manipulations of causal structures. In four experiments, participants dealt with a quasi-medical scenario presenting symptom sets (effects) that consistently suggested a specified diagnosis (cause). The probability that the diagnosis was correct had to be rated for two opposed symptom sets that differed with regard to the symptoms’ positions (proximal or diverse) in the causal structure that was initially acquired. The causal structure linking the diagnosis to the symptoms and the base rate of the diagnosis were manipulated to explore whether the diagnosis was rated as more probable for diverse than for proximal symptoms when alternative causations were more plausible (e.g., because of a lower base rate of the diagnosis in question). The results replicated the causal diversity effect in diagnostic reasoning across these conditions, but no consistent effects of structure and base rate variations were observed. Diversity effects computed in causal Bayesian networks are presented, illustrating the consequences of the structure manipulations and corroborating that a diversity effect across the different experimental manipulations is normatively justified. The observed diversity effects presumably resulted from shortcut reasoning about the possibilities of alternative causation.