Approaching the Limits of Knowledge: The Influence of Priming on Error Detection in Simulated Clinical Rounds

Author: Elie Razzouk, MD

Primary Advisor: Trevor Cohen, MBChB, PhD (co-author)

Committee Members: Khalid Almoosa, MD (co-author); Vimla Patel, PhD, DSc (co-author)

Masters thesis, The University of Texas School of Biomedical Informatics at Houston.

Abstract:

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.