A comprehensive description of the competencies required for the performance of an ultrasound-guided axillary brachial plexus blockade

We addressed four research questions, each relating to the training and assessment of the competencies associated with the performance of ultrasound-guided axillary brachial plexus blockade (USgABPB). These were: (i) What are the most important determinants of learning of USgABPB? (ii) What is USgABPB? What are the errors most likely to occur when trainees learn to perform this procedure? (iii) How should end-user input be applied to the development of a novel USgABPB simulator? (iv) Does structured simulation based training influence novice learning of the procedure positively? We demonstrated that the most important determinants of learning USgABPB are: (a) Access to a formal structured training programme. (b) Frequent exposure to clinical learning opportunity in an appropriate setting (c) A clinical learning opporunity requires an appropriate patient, trainee and teacher being present at the same time, in an appropriate environment. We carried out a comprehensive description of the procedure. We performed a formal task analysis of USgABPB, identifying (i) 256 specific tasks associated with the safe and effective performance of the procedure, and (ii) the 20 most critical errors likely to occur in this setting. We described a methodology for this and collected data based on detailed, sequential evaluation of prototypes by trainees in anaesthesia. We carried out a pilot randomised control trial assessing the effectiveness of a USgABPB simulator during its development. Our data did not enable us to draw a reliable conclusion to this question; the trail did provide important new learning (as a pilot) to inform future investigation of this question. We believe that the ultimate goal of designing effective simulation-based training and assessment of ultrasound-guided regional anaesthesia is closer to realisation as a result of this work. It remains to be proven if this approach will have a positive impact on procedural performance, and more importantly improve patient outcomes.

Delivering mobile cloud services to the user: description, discovery, and consumption

The mobile cloud computing paradigm can offer relevant and useful services to the users of smart mobile devices. Such public services already exist on the web and in cloud deployments, by implementing common web service standards. However, these services are described by mark-up languages, such as XML, that cannot be comprehended by non-specialists. Furthermore, the lack of common interfaces for related services makes discovery and consumption difficult for both users and software. The problem of service description, discovery, and consumption for the mobile cloud must be addressed to allow users to benefit from these services on mobile devices. This paper introduces our work on a mobile cloud service discovery solution, which is utilised by our mobile cloud middleware, Context Aware Mobile Cloud Services (CAMCS). The aim of our approach is to remove complex mark-up languages from the description and discovery process. By means of the Cloud Personal Assistant (CPA) assigned to each user of CAMCS, relevant mobile cloud services can be discovered and consumed easily by the end user from the mobile device. We present the discovery process, the architecture of our own service registry, and service description structure. CAMCS allows services to be used from the mobile device through a user's CPA, by means of user defined tasks. We present the task model of the CPA enabled by our solution, including automatic tasks, which can perform work for the user without an explicit request.

SDC: Scalable description coding for adaptive streaming media

Video compression techniques enable adaptive media streaming over heterogeneous links to end-devices. Scalable Video Coding (SVC) and Multiple Description Coding (MDC) represent well-known techniques for video compression with distinct characteristics in terms of bandwidth efficiency and resiliency to packet loss. In this paper, we present Scalable Description Coding (SDC), a technique to compromise the tradeoff between bandwidth efficiency and error resiliency without sacrificing user-perceived quality. Additionally, we propose a scheme that combines network coding and SDC to further improve the error resiliency. SDC yields upwards of 25% bandwidth savings over MDC. Additionally, our scheme features higher quality for longer durations even at high packet loss rates.