Fatigue and vigilance in medical experts detecting breast cancer

Abstract

An abundance of laboratory-based experiments has described a vigilance decrement of reducing accuracy to detect targets with time on task, but there are few real-world studies, none of which have previously controlled the environment to control for bias. We describe accuracy in clinical practice for 360 experts who examined >1 million women’s mammograms for signs of cancer, whilst controlling for potential biases. The vigilance decrement pattern was not observed. Instead, test accuracy improved over time, through a reduction in false alarms and an increase in speed, with no significant change in sensitivity. The multiple-decision model explains why experts miss targets in low prevalence settings through a change in decision threshold and search quit threshold and propose it should be adapted to explain these observed patterns of accuracy with time on task. What is typically thought of as standard and robust research findings in controlled laboratory settings may not directly apply to real-world environments and instead large, controlled studies in relevant environments are needed.

Optimising the diagnostic accuracy of First post-contrAst SubtracTed breast MRI (FAST MRI) through interpretation-training: a multicentre e-learning study, mapping the learning curve of NHS Breast Screening Programme (NHSBSP) mammogram readers using an enriched dataset

“…For example, the survival benefit achieved, through screening women with BRCA mutations (high pre-test probability), using fpMRI, is dependent on fpMRI’s high sensitivity for aggressive breast cancers and necessitates the prioritisation of sensitivity over specificity for this relatively small population of women [ 19 – 21 ]. In contrast, the specificity for mammographic mass screening that is achieved through double reading in the NHS Breast Screening Programme (NHSBSP), is 96% [ 22 ] while reported reader sensitivity is much lower (67–78%) [ 23 ]. For population-risk women, who have a low pre-test probability, specificity is arguably the most important diagnostic accuracy parameter to optimise because small changes in specificity can have a large effect on the number of false positive recalls in a population screening programme, with each recall causing harm to the woman screened and also incurring a financial and workforce cost [ 24 – 26 ].…”