A novel immersive anatomy education system (anat_hub): Redefining blended learning for the musculoskeletal system

Immersive technologies are redefining ways of interacting with 3D objects and their environments. Moreover, efforts in blended learning have presented several advantages of incorporating educational technology into the learning space. The advances in educational technology have in turn helped to widen the choice of different pedagogies for improving learner engagement and levels of understanding. However, there is limited research in anatomy education that has considered the use and adoption of immersive technologies for the musculoskeletal system, despite its immense advantage. This research presents a practical immersive anatomy education system (coined Anat_Hub) developed using the agile scrum and participatory design method at a selected tertiary institution in Cape Town, South Africa, which promotes learner engagement through an asynchronous technological means using augmented reality (AR). The aim of the study was to develop an immersive AR mobile application that will assist learners and educators in studying and teaching the names, attachments, and actions of muscles of the human musculoskeletal system (upper and lower limbs). The Anat_Hub application offers a wide range of useful features for promoting active and self-regulated learning, such as 3D and AR modes, glossary, and quiz features

Visual Analytics for Performing Complex Tasks with Electronic Health Records

Electronic health record systems (EHRs) facilitate the storage, retrieval, and sharing of patient health data; however, the availability of data does not directly translate to support for tasks that healthcare providers encounter every day. In recent years, healthcare providers employ a large volume of clinical data stored in EHRs to perform various complex data-intensive tasks. The overwhelming volume of clinical data stored in EHRs and a lack of support for the execution of EHR-driven tasks are, but a few problems healthcare providers face while working with EHR-based systems. Thus, there is a demand for computational systems that can facilitate the performance of complex tasks that involve the use and working with the vast amount of data stored in EHRs. Visual analytics (VA) offers great promise in handling such information overload challenges by integrating advanced analytics techniques with interactive visualizations. The user-controlled environment that VA systems provide allows healthcare providers to guide the analytics techniques on analyzing and managing EHR data through interactive visualizations. The goal of this research is to demonstrate how VA systems can be designed systematically to support the performance of complex EHR-driven tasks. In light of this, we present an activity and task analysis framework to analyze EHR-driven tasks in the context of interactive visualization systems. We also conduct a systematic literature review of EHR-based VA systems and identify the primary dimensions of the VA design space to evaluate these systems and identify the gaps. Two novel EHR-based VA systems (SUNRISE and VERONICA) are then designed to bridge the gaps. SUNRISE incorporates frequent itemset mining, extreme gradient boosting, and interactive visualizations to allow users to interactively explore the relationships between laboratory test results and a disease outcome. The other proposed system, VERONICA, uses a representative set of supervised machine learning techniques to find the group of features with the strongest predictive power and make the analytic results accessible through an interactive visual interface. We demonstrate the usefulness of these systems through a usage scenario with acute kidney injury using large provincial healthcare databases from Ontario, Canada, stored at ICES