Who Beats the Expert? Building Precision into Simulators for Surgical Skill Assessment

Abstract

Simulator training for image-guided surgical interventions allows tracking task performance in terms of speed and precision of task execution. Simulator tasks are more or less realistic with respect to real surgical tasks, and the lack of clear criteria for learning curves and individual skill assessment if more often than not a problem. Recent research has shown that trainees frequently focus on getting faster at the simulator task, and this strategy bias often compromises the evolution of their precision score. As a consequence, and whatever the degree of surgical realism of the simulator task, the first and most critical criterion for skill evolution should be task precision, not the time of task execution. This short opinion paper argues that individual training statistics of novices from a simulator task should therefore always be compared with the statistics of an expert surgeon from the same task. This implies that benchmark statistics from the expert are made available and an objective criterion, i.e. a parameter measure, for task precision is considered for assessing learning curves of novices.