Military medicine
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Recent advances in the learning sciences offer remarkable potential to improve medical education and maximize the benefits of emerging medical technologies. This article describes 2 major innovation areas in the learning sciences that apply to simulation and other aspects of medical learning: Perceptual learning (PL) and adaptive learning technologies. PL technology offers, for the first time, systematic, computer-based methods for teaching pattern recognition, structural intuition, transfer, and fluency. ⋯ The author describes the Adaptive Response-Time-based Sequencing (ARTS) system, which uses each learner's accuracy and speed in interactive learning to guide spacing, sequencing, and mastery. In recent efforts, these new technologies have been applied in medical learning contexts, including adaptive learning modules for initial medical diagnosis and perceptual/adaptive learning modules (PALMs) in dermatology, histology, and radiology. Results of all these efforts indicate the remarkable potential of perceptual and adaptive learning technologies, individually and in combination, to improve learning in a variety of medical domains.
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Use of simulation-based assessments and training has become increasingly widespread in medicine. It is recognized that simulations can yield a wealth of real-time information about the trainee or examinee's performance, from which inferences about proficiency can potentially be drawn. However, for the inferences to be useful, psychometric evaluation should be conducted and validity evidence amassed. ⋯ In this article, it is argued that modern psychometric models that are dynamic, multidimensional, and based on latent variables may be useful for evaluating medical simulations. It is also argued that modern computational methods based on Bayesian statistics may provide the technical foundation. Several examples are given and issues for further research are discussed.
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The development of more effective medical simulators requires a collaborative team effort where three kinds of expertise are carefully coordinated: (1) exceptional medical expertise focused on providing complete and accurate information about the medical challenges (i.e., critical skills and knowledge) to be simulated; (2) instructional expertise focused on the design of simulation-based training and assessment methods that produce maximum learning and transfer to patient care; and (3) software development expertise that permits the efficient design and development of the software required to capture expertise, present it in an engaging way, and assess student interactions with the simulator. In this discussion, we describe a method of capturing more complete and accurate medical information for simulators and combine it with new instructional design strategies that emphasize the learning of complex knowledge. Finally, we describe three different types of software support (Development/Authoring, Run Time, and Post Run Time) required at different stages in the development of medical simulations and the instructional design elements of the software required at each stage. We describe the contributions expected of each kind of software and the different instructional control authoring support required.