Military Medicine, Volume 183, Issue suppl_1, 1 March 2018, Pages 47–54 (April 2018).
Medical providers must master a large number of complicated tasks to deliver quality care and minimize unwanted clinical outcomes. In order to optimally train these tasks, medical training systems would benefit from models of skill that enable objective assessment of proficiency and define important declarative knowledge, cognitive states, and decision-making rules that are necessary for effective learning and performance. This article describes the Methodology for Annotated Skill Trees (MAST), a skill-modeling framework that facilitates the creation of descriptive and rule-based content that supports skill acquisition. This framework is used to generate models of trauma assessment skills from two existing curricula: Advanced Trauma Life Support (ATLS) and the Trauma Nurse Core Course (TNCC). Key differences between these curricula’s teaching methods for the same procedure and skill are highlighted through the use of the model framework. The framework comparison provides insight into the underlying teaching approach and highlights the fact that some skills are not represented in medical education materials.
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