Visualizing the Bayesian 2-test case: The effect of tree diagrams on medical decision making.

Karin Binder,Stefan Krauss,Jörg Marienhagen,Georg Bruckmaier,Aamir Ahmad

doi:10.1371/journal.pone.0195029

Abstract

In medicine, diagnoses based on medical test results are probabilistic by nature. Unfortunately, cognitive illusions regarding the statistical meaning of test results are well documented among patients, medical students, and even physicians. There are two effective strategies that can foster insight into what is known as Bayesian reasoning situations: (1) translating the statistical information on the prevalence of a disease and the sensitivity and the false-alarm rate of a specific test for that disease from probabilities into natural frequencies, and (2) illustrating the statistical information with tree diagrams, for instance, or with other pictorial representation. So far, such strategies have only been empirically tested in combination for “1-test cases”, where one binary hypothesis (“disease” vs. “no disease”) has to be diagnosed based on one binary test result (“positive” vs. “negative”). However, in reality, often more than one medical test is conducted to derive a diagnosis. In two studies, we examined a total of 388 medical students from the University of Regensburg (Germany) with medical “2-test scenarios”. Each student had to work on two problems: diagnosing breast cancer with mammography and sonography test results, and diagnosing HIV infection with the ELISA and Western Blot tests. In Study 1 (N = 190 participants), we systematically varied the presentation of statistical information (“only textual information” vs. “only tree diagram” vs. “text and tree diagram in combination”), whereas in Study 2 (N = 198 participants), we varied the kinds of tree diagrams (“complete tree” vs. “highlighted tree” vs. “pruned tree”). All versions were implemented in probability format (including probability trees) and in natural frequency format (including frequency trees). We found that natural frequency trees, especially when the question-related branches were highlighted, improved performance, but that none of the corresponding probabilistic visualizations did.

Highlights

Physicians, medical staff, and patients frequently have difficulty understanding what medical test results really mean
In Study 2 we focus on the issue of highlighting branches or pruning tree diagrams
Students performed better when statistical information was presented in natural frequencies (36% correct inferences across context and presentation) rather than as probability versions (5% correct inferences across context and presentation)

Summary

Introduction

Physicians, medical staff, and patients frequently have difficulty understanding what medical test results really mean. This is a serious issue because patients must often make tough decisions about specific medical treatments, for example after a positive test result from a routine screening [1]. A positive HIV test result can lead to mental disorders or even suicide [4,5]. Most counselors in the studies from Prinz et al [6], Gigerenzer et al [7], and Ellis and Brase [8] operate under an illusory belief that positive test results indicate certainty. A positive HIV test result does not indicate the presence of HIV infection with absolute certainty [9]

Objectives

Methods

Results