Approaching the limits of knowledge: the influence of priming on error detection in simulated clinical rounds.

Errors are inevitable in all clinical settings, posing substantial risk to patients. Studies have shown detection and correction are essential to error management. This paper documents the use of Opensimulator, a virtual world development platform, to create a virtual Intensive Care Unit where error recovery can be studied in a controlled, yet realistic environment. Subjects participated in rounds presented by computer-generated characters. Errors were embedded in these presentations, and subjects were evaluated for their ability to detect them. Eight subjects were asked to evaluate two cases and answer related knowledge-based questions under two conditions: primed (forewarned of the presence of errors) and un-primed. Subjects frequently failed to detect errors despite having the prerequisite knowledge. Priming significantly improved detection, suggesting a role for interventions that aim to shift clinicians' error detection toward the limits of their knowledge. Such interventions may provide means to decrease adverse events resulting from human error.

Distributed cognition artifacts on clinical research data collection forms.

Medical record abstraction, a primary mode of data collection in secondary data use, is associated with high error rates. Cognitive factors have not been studied as a possible explanation for medical record abstraction errors. We employed the theory of distributed representation and representational analysis to systematically evaluate cognitive demands in medical record abstraction and the extent of external cognitive support employed in a sample of clinical research data collection forms.We show that the cognitive load required for abstraction in 61% of the sampled data elements was high, exceedingly so in 9%. Further, the data collection forms did not support external cognition for the most complex data elements. High working memory demands are a possible explanation for the association of data errors with data elements requiring abstractor interpretation, comparison, mapping or calculation. The representational analysis used here can be used to identify data elements with high cognitive demands.