PURPOSE: To investigate the errors made by junior doctors (first year after primary medical qualification) in simulated acute care settings, using (and, for some purposes, amplifying) a previously published generic error-modeling system (GEMS). Possible error types were skill-based slips and lapses, rule-based mistakes, knowledge-based mistakes, and violations. METHOD: In August 2010, 38 junior doctors participated in high-fidelity simulated acute care scenarios in NHS Lothian, Scotland. Each video-recorded scenario was immediately followed by an audio-recorded debrief that encouraged articulation of underlying cognitive processes. Two researchers used evidence from the scenario, debrief, and field notes to determine which errors were attributable to a single underlying cause. In such cases, the errors were coded by template analysis into the GEMS framework. Errors for which a single cause could be identified but which did not fit the framework were coded inductively. RESULTS: A total of 243 errors were identified, with sufficient evidence available to identify a single cause in 190. Skill-based slips and lapses, rule-based mistakes, and knowledge-based mistakes were all clearly identified within the data. Two error types not originally included in the GEMS framework were identified: compound errors and submission errors. CONCLUSIONS: Amplification of GEMS provides a valid framework for categorization of the errors made by junior doctors in simulated acute care contexts. In addition, the amplified framework may be transferable to other, team-based contexts. An improved understanding of the knowledge and skills that are most vulnerable to each specific type of error will allow tailored educational strategies to be developed.
PURPOSE: To investigate the errors made by junior doctors (first year after primary medical qualification) in simulated acute care settings, using (and, for some purposes, amplifying) a previously published generic error-modeling system (GEMS). Possible error types were skill-based slips and lapses, rule-based mistakes, knowledge-based mistakes, and violations. METHOD: In August 2010, 38 junior doctors participated in high-fidelity simulated acute care scenarios in NHS Lothian, Scotland. Each video-recorded scenario was immediately followed by an audio-recorded debrief that encouraged articulation of underlying cognitive processes. Two researchers used evidence from the scenario, debrief, and field notes to determine which errors were attributable to a single underlying cause. In such cases, the errors were coded by template analysis into the GEMS framework. Errors for which a single cause could be identified but which did not fit the framework were coded inductively. RESULTS: A total of 243 errors were identified, with sufficient evidence available to identify a single cause in 190. Skill-based slips and lapses, rule-based mistakes, and knowledge-based mistakes were all clearly identified within the data. Two error types not originally included in the GEMS framework were identified: compound errors and submission errors. CONCLUSIONS: Amplification of GEMS provides a valid framework for categorization of the errors made by junior doctors in simulated acute care contexts. In addition, the amplified framework may be transferable to other, team-based contexts. An improved understanding of the knowledge and skills that are most vulnerable to each specific type of error will allow tailored educational strategies to be developed.