Personalizing simulation-based medical education: the case for novel learning management systems

Abstract

Simulation-based medical education (SBME) is often delivered as one-size-fits-all, with no clear guidelines for personalization to achieve optimal performance. This essay is intended to introduce a novel approach, facilitated by a home-grown learning management system (LMS), designed to streamline simulation program evaluation and curricular improvement by aligning learning objectives, scenarios, assessment metrics and data collection, as well as integrate standardized sets of multimodal data (self-report, observational and neurophysiological). Results from a pilot feasibility study are presented. Standardization is important to future LMS applications and could promote development of machine learning-based approaches to predict knowledge and skill acquisition, maintenance and decay, for personalizing SBME across healthcare professionals.