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.
Highlights
Human judgments made under uncertainty have been shown to be subject to a wide array of cognitive heuristics that may produce systematic biases (Tversky and Kahneman 1974)
Many authors have suggested that use of cognitive heuristics by physicians may contribute to medical errors (Bornstein and Emler 2001; Croskerry 2003; Dawson and Arkes 1987; Elstein 1999; Graber et al 2002; Kempainen et al 2003), supported by some empirical studies of heuristic use among medical students, residents, and attending physicians (Christensen-Szalanski and Bushyhead 1981; Kassirer and Kopelman 1989; Kern and Doherty 1982; Payne and Crowley 2008; Voytovich et al 1985; Wallsten 1981)
We ‘turned off’ the educational intervention of an existing intelligent tutoring system to collect data on heuristics and biases as pathologists examine a set of virtual slides
Summary
Human judgments made under uncertainty have been shown to be subject to a wide array of cognitive heuristics (mental ‘‘short-cuts’’ or ‘‘rules of thumb’’) that may produce systematic biases (Tversky and Kahneman 1974). An alternative view is that heuristic use can be a highly effective strategy, producing ‘‘fast and frugal’’ judgments (Gigerenzer and Gaissmaier 2011) of potentially high accuracy in clinical contexts (Eva and Norman 2005). This dualistic view on the underlying nature of these shortcuts reflects the fact that they may lead to a correct diagnosis, in which case they are termed ‘‘heuristics’’, or to an incorrect one, in which case they are termed ‘‘biases’’ or ‘‘heuristic biases’’. Examples of observed biases include anchoring with insufficient adjustment (Croskerry 2002; Ellis et al 1990; Richards and Wierzbicki 1990; Tversky and Kahneman 1974), availability bias (Dawson and Arkes 1987; Poses and Anthony 1991; Tversky and Kahnamen 1980), base rate neglect (Christensen-Szalanski and Bushyhead 1981), representativeness (Dawson and Arkes 1987; Payne and Crowley 2008; Tversky and Kahnamen 1980), confirmation bias (Croskerry 2002; Nickerson 1998; Pines 2006), and satisficing (premature closure) (Simon 1956; Redelmeier and Shafir 1995)
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