The influence of viewing time on visual diagnostic accuracy: Less is more Journal Articles uri icon

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abstract

  • AbstractBackgroundUnderstanding the factors that contribute to diagnostic errors is critical if we are to correct or prevent them. Some scholars influenced by the default interventionist dual‐process theory of cognition (dual‐process theory) emphasise a narrow focus on individual clinician's faulty reasoning as a significant contributor. In this paper, we examine the validity of claims that dual process theory is a key to error reduction.MethodsWe examined the relationship between a clinical experience (staff and resident physicians) and viewing time on accuracy for categorising chest X‐rays (CXRs) and electrocardiograms (ECGs). In two studies, participants categorised images as normal or abnormal, presented at viewing times of 175, 250, 500 and 1000 ms, to encourage System 1 processing. Study 2 extended viewing times to 1, 5, 10 and 20 s to allow time for System 2 processing and a diagnosis. Descriptives and repeated measures analysis of variance were used to analyse the proportion of true and false positive rates (TP and FP) as well as correct diagnoses.ResultsIn Study 1, physicians were able to detect abnormal CXRs (0.78) and ECGs (0.67) with relatively high accuracy. The effect of experience was found for ECGs only, as staff physicians (0.71, 95% CI = 0.66–0.75) had higher ECG TP than resident physicians (0.63, 95% CI = 0.58–0.68) in Study 1, and staff had lower ECG FP (0.10, 95% CI = 0.03–0.18) than resident physicians (0.27, 95% CI = 0.20–0.33) in Study 2. In other comparisons, experience was equivocal for ECG FPs and CXR TPs and FPs. In Study 2, overall diagnostic accuracy was similar for both ECGs and CXRs, (0.74). There were small interactions between experience and time for TP in ECGs and FP in CXRs, which are discussed further in the discussion and offer insights into the relationship between processing and experience.ConclusionOverall, our findings raise concerns about the practical application of models that link processing type to diagnostic error, or to specific diagnostic error reduction strategies.

publication date

  • July 2024