Abstract
When physicians are asked to determine the positive predictive value from the a priori probability of a disease and the sensitivity and false positive rate of a medical test (Bayesian reasoning), it often comes to misjudgments with serious consequences. In daily clinical practice, however, it is not only important that doctors receive a tool with which they can correctly judge—the speed of these judgments is also a crucial factor. In this study, we analyzed accuracy and efficiency in medical Bayesian inferences. In an empirical study we varied information format (probabilities vs. natural frequencies) and visualization (text only vs. tree only) for four contexts. 111 medical students participated in this study by working on four Bayesian tasks with common medical problems. The correctness of their answers was coded and the time spent on task was recorded. The median time for a correct Bayesian inference is fastest in the version with a frequency tree (2:55 min) compared to the version with a probability tree (5:47 min) or to the text only versions based on natural frequencies (4:13 min) or probabilities (9:59 min).The score diagnostic efficiency (calculated by: median time divided by percentage of correct inferences) is best in the version with a frequency tree (4:53 min). Frequency trees allow more accurate and faster judgments. Improving correctness and efficiency in Bayesian tasks might help to decrease overdiagnosis in daily clinical practice, which on the one hand cause cost and on the other hand might endanger patients’ safety.
Highlights
Importance of Bayesian reasoning for medical students and physiciansIn daily clinical practice, physicians are often confronted with so-called Bayesian reasoning situations: For example, when they have to explain test results of a mammogram, it is important for the patient to know what exactly these results mean, that means how likely it is that a positive result indicates a sickness
Physicians are often confronted with so-called Bayesian reasoning situations: For example, when they have to explain test results of a mammogram, it is important for the patient to know what exactly these results mean, that means how likely it is that a positive result indicates a sickness
If a woman who participates in a routine screening has breast cancer, the probability is 80% that she will have a positive mammogram (sensitivity P(M + |B))
Summary
Physicians are often confronted with so-called Bayesian reasoning situations: For example, when they have to explain test results of a mammogram, it is important for the patient to know what exactly these results mean, that means how likely it is that a positive result indicates a sickness. If a woman who participates in a routine screening has breast cancer, the probability is 80% that she will have a positive mammogram (sensitivity P(M + |B)). If a woman who participates in a routine screening does not have breast cancer, the probability is 9.6% that she will have a false-positive mammogram (false-alarm rate P(M + |¬B)). What is the probability that a woman who participates in a routine screening and has a positive mammogram has breast cancer?
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