Who Beats the Expert? Building Precision into Simulators for Surgical Skill Assessment
Journal Title: Biomedical Journal of Scientific & Technical Research (BJSTR) - Year 2019, Vol 13, Issue 4
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.Objective performance metrics [1-12] form an essential part of surgical simulator systems for optimal independent training. Putting such metrics to the test on users with different levels of expertise appears mandatory for perfecting existing systems. The exploitation of individual performance metrics to establish learning curves that a user can see and understand and that will help him/her elaborate task strategies for measurable skill improvement is the most important aspect of an effective training system [6-12]. Metric-based skill assessment ensures that training sessions are more than simulated clinical procedures, and that trainees are provided with insight about how they are doing in a task, and how they could improve their current scores. Not all simulator tasks are based on surgically realistic physical task models, but at the earliest stages of training surgical task realism is probably not what matters most [13-20]. Whatever the degree of realism of the simulator task, metric based skill assessment gets rid of subjectivity in evaluating skill evolution, and there is no ambiguity about the progress of training.Moreover, some work has shown that benchmarking individual levels of proficiency against the performance levels of experts on a validated, metric-based simulation system has well-established intrinsic face validity [1,2,10]. It therefore appears the better approach compared with benchmarking on abstract performance concepts or on the basis of expert consensus. Building expert performance in terms of benchmark metrics into simulator training programs would provide an almost ideal basis for automatic skill assessment and ensure that desired levels of skill are defined on the grounds of realistic criteria. Such are, in principle, available in the proficiency levels of individuals who are highly experienced at performing clinical procedures with the highest level of precision [1,11], which is probably the strongest argument for building expert performance data into any simulator system for a direct comparison with novice data at any moment of the training procedure.
Authors and Affiliations
Birgitta Dresp Langley
Keywords
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- EP ID EP594740
- DOI 10.26717/BJSTR.2019.13.002429
- Views 164
- Downloads 0
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