Prospective issues for error detection

From the literature on error detection, the authors select several concepts relating error detection mechanisms and prospective memory features. They emphasize the central role of intention in the classification of the errors into slips/lapses/mistakes, in the error handling process and in the usual distinction between action-based and outcome-based detection. Intention is again a core concept in their investigation of prospective memory theory, where they point out the contribution of intention retrievals, intention persistence and output monitoring in the individual's possibilities for detecting their errors. The involvement of the frontal lobes in prospective memory and in error detection is also analysed. From the chronology of a prospective memory task, the authors finally suggest a model for error detection also accounting for neural mechanisms highlighted by studies on error-related brain activity.Error detection has not received much attention from the scientists since human error has been shown as the main cause of accident in complex systems. However, reducing the consequences of error depends largely on error detection. The goal of this paper is to synthesize the existing scientific knowledge on error detection, mostly based on studies conducted in laboratory or self reporting and to complete it through the analysis of a corpus of cases collected in a complex system: anaesthesia, in order to better describe how this knowledge can be used to improve our understanding of error detection modes. We used an anaesthesia accident reporting system we developed and organized at two Belgium University Hospitals to collect information about the error detection patterns. Results show that detection of errors principally occurred through standard check. We found significant relationships between the type of error, the type of error detection pattern, and the training level of the anaesthetist who committed the error

Erreur humaine et automatisation des cockpits

SIGLEAvailable at INIST (FR), Document Supply Service, under shelf-number : RP 13736 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc