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Even with the adoption of modern technology within the medical system, the spread of deadly pathogens remains a silent, yet deadly killer. Indeed, e-health, and in particular m-health is at the forefront of the computing portion of modern medical care. It follows the ubiquity of mobile computing devices that has become synonymous with modern life. Although the focus of m-health is patient based, this paper attempts to provide a focus on the healthcare service provider in an attempt to reduce the fatalities due to pathogen transmissions in care facilities. This paper proposes, and describes the creation and evaluation of an AI based context-aware mobile learning system designed to provide real-time training and support for medical cleaning staff. This learning system aims to bridge the gap between context-aware learning systems and m-health. As such, the system provides context information to the learner about the various threats and best ways to deal with possible pathogens in a long-term care scenario. The evaluation, field tested within the adult long-term care system demonstrates the benefit and validity of the system in both training and ongoing usage within the medical system.

Health-care education based upon technology enabled mannequins (high-fidelity simulators) is a costly investment for colleges and universities. However, a hybrid model using wearable technology integrated with human actors (standardized patients) may present a cost-effective alternative to high fidelity simulation training scenarios. A systematic literature review of papers published from 1960 to 2019 illustrates that hybrid simulation can be as effective as high fidelity simulators in certain training scenarios while at the same time providing a superior training context to enhance learners patient to care-giver interactions and to better immerse the trainee in the feelings and emotion of the scenario.