Evaluation of the ACRRM National Radiology Program for Australian rural and remote medical practitioners

Introduction: In 2000, the Australian College of Rural and Remote Medicine (ACRRM) developed a national radiology quality assurance (QA) and continuing medical education (CME) program for rural and remote non-specialist Australian doctors. The program commenced on 1 January 2001. It required rural doctors to obtain 30 radiology QA/CME points over a 4 year period. At least 15-20 of these points had to be obtained by one of two mandatory options of the program, either: (1) film interpretation, report and review clinical audit activity; or (2) a radiology clinical attachment.\ud \ud Method: Doctors submitted their completed film review forms and clinical attachment logbooks to the program manager as confirmation of their educational activity to receive their professional development points. Data from film review forms and clinical attachment logbooks were de-identified and entered into two Microsoft EXCEL spreadsheets. The data were categorised and analysed in EXCEL.\ud \ud Results: From 1 January 2001 to September 2004, 823 rural and remote doctors enrolled in the ACRRM radiology program. This included 281 locums who enrolled in the short-term locum option of the program and 563 doctors who enrolled in the full program. In September 2004, 419 doctors had completed a radiology film review with a radiologist and 41 doctors completed a radiology clinical attachment in 31 different public and private radiology practices. One hundred and ninety-five doctors completed the short-term locum activity. Ninety-two different specialist radiologists participated in the program and assisted rural and remote doctors to enhance their radiology knowledge, confidence and skills. This article describes results from the two mandatory activities.\ud \ud Conclusion: The evaluation of the ACRRM radiology program after its first 3 years and 9 months shows there are a large number of rural and remote Australian doctors undertaking professional development and quality assurance activities in radiology

Reasoning over Knowledge-based Generation of Situations in Context Spaces to Reduce Food Waste

Abstract. Situation awareness is a key feature of pervasive computing and requires external knowledge to interpret data. Ontology-based reasoning approaches allow for the reuse of predefined knowledge, but do not provide the best reasoning capabilities. To overcome this problem, a hybrid model for situation awareness is developed and presented in this paper, which integrates the Situation Theory Ontology into Context Space Theory for inference. Furthermore, in an effort to rely as much as possible on open IoT messaging standards, a domain-independent framework using the O-MI/O-DF standards for sensor data acquisition is developed. This framework is applied to a smart neighborhood use case to reduce food waste at the consumption stage

Enriching a Situation Awareness Framework for IoT with Knowledge Base and Reasoning Components

Theimportanceofsystem-levelcontext-andsituationaware- ness increases with the growth of the Internet of Things (IoT). This paper proposes an integrated approach to situation awareness by providing a semantically rich situation model together with reliable situation infer- ence based on Context Spaces Theory (CST) and Situation Theory (ST). The paper discusses benefits of integrating the proposed situation aware- ness framework with knowledge base and efficient reasoning techniques taking into account uncertainty and incomplete knowledge about situa- tions. The paper discusses advantages and impact of proposed context adaptation in dynamic IoT environments. Practical issues of two-way mapping between IoT messaging standards and CST are also discussed

Using ontologies for recognition: An example

— In this paper we investigate a scenario which the fusion process (the algorithm) could be in at run time. This goal can be achieved in synthesized steps: first synthesize a formal specification of the two process and then generate code from the specifi-fusion In this paper we show the first of these steps. cation. Ontologies, Unified Modeling Language

Situation Awareness: Issues and Challenges 1 Basic Situation Awareness Concepts

To explain what I mean by “situation awareness ” I often use the example of watching the games like American football or baseball. Since I have never learned the rules and the strategies of these games, when watching them on TV, although I can clearly see where each player is and where the ball is, I still have no idea of what is going on. Clearly, in this case I cannot claim that I am aware of the situation. The term “situation awareness ” has been often interpreted in a somewhat simplistic way as merely the knowledge of all the objects in a specific area, and possibly their kinematic states. It is clear, however, that the meaning of this term implies more than that. For instance, in [1], awareness is explained as “AWARE implies vigilance in observing or alertness in drawin

On Similarity Methods in Machine Learning

This paper reviews a number of uses of similarity theory in the context of machine learning. First, it shows how similarity theory can be used to uncover the fact that some of the relevant variables are missing in a given model. Then, it shows how the same idea can give hints on which variables should be considered as relevant. This is followed by an idea of how dimensional analysis can help in finding physical laws based upon measurements of a physical phenomenon. Finally, the paper introduces the notion of “critical hypersurface ” and shows how this notion can be utilized in monitoring time-varying dynamical systems.