Automated detection of heuristics and biases among pathologists in a computer-based system - PubMed (original) (raw)

Automated detection of heuristics and biases among pathologists in a computer-based system

Rebecca S Crowley et al. Adv Health Sci Educ Theory Pract. 2013 Aug.

Abstract

The purpose of this study is threefold: (1) to develop an automated, computer-based method to detect heuristics and biases as pathologists examine virtual slide cases, (2) to measure the frequency and distribution of heuristics and errors across three levels of training, and (3) to examine relationships of heuristics to biases, and biases to diagnostic errors. The authors conducted the study using a computer-based system to view and diagnose virtual slide cases. The software recorded participant responses throughout the diagnostic process, and automatically classified participant actions based on definitions of eight common heuristics and/or biases. The authors measured frequency of heuristic use and bias across three levels of training. Biases studied were detected at varying frequencies, with availability and search satisficing observed most frequently. There were few significant differences by level of training. For representativeness and anchoring, the heuristic was used appropriately as often or more often than it was used in biased judgment. Approximately half of the diagnostic errors were associated with one or more biases. We conclude that heuristic use and biases were observed among physicians at all levels of training using the virtual slide system, although their frequencies varied. The system can be employed to detect heuristic use and to test methods for decreasing diagnostic errors resulting from cognitive biases.

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Figures

Fig. 1

Fig. 1

Data collection system showing a virtual slide and diagnostic reasoning interface and b confidence measurement interface and case summary

Fig. 2

Fig. 2

Sample user data showing examples of several heuristics

Fig. 3

Fig. 3

Distribution of bias scores. Number of participants (by level of training) with average bias scores at 0.1 intervals. Bias scores on x axis reflect center of interval. The optimum point at zero is marked with a vertical line

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