RT Journal Article SR Electronic T1 Is bias in the eye of the beholder? A vignette study to assess recognition of cognitive biases in clinical case workups JF BMJ Quality & Safety JO BMJ Qual Saf FD BMJ Publishing Group Ltd SP 104 OP 110 DO 10.1136/bmjqs-2015-005014 VO 26 IS 2 A1 Laura Zwaan A1 Sandra Monteiro A1 Jonathan Sherbino A1 Jonathan Ilgen A1 Betty Howey A1 Geoffrey Norman YR 2017 UL http://qualitysafety.bmj.com/content/26/2/104.abstract AB Background Many authors have implicated cognitive biases as a primary cause of diagnostic error. If this is so, then physicians already familiar with common cognitive biases should consistently identify biases present in a clinical workup. The aim of this paper is to determine whether physicians agree on the presence or absence of particular biases in a clinical case workup and how case outcome knowledge affects bias identification.Methods We conducted a web survey of 37 physicians. Each participant read eight cases and listed which biases were present from a list provided. In half the cases the outcome implied a correct diagnosis; in the other half, it implied an incorrect diagnosis. We compared the number of biases identified when the outcome implied a correct or incorrect primary diagnosis. Additionally, the agreement among participants about presence or absence of specific biases was assessed.Results When the case outcome implied a correct diagnosis, an average of 1.75 cognitive biases were reported; when incorrect, 3.45 biases (F=71.3, p<0.00001). Individual biases were reported from 73% to 125% more often when an incorrect diagnosis was implied. There was no agreement on presence or absence of individual biases, with κ ranging from 0.000 to 0.044.Interpretation Individual physicians are unable to agree on the presence or absence of individual cognitive biases. Their judgements are heavily influenced by hindsight bias; when the outcome implies a diagnostic error, twice as many biases are identified. The results present challenges for current error reduction strategies based on identification of cognitive biases.