ObjectivesInformation and communication technology (ICT) tools are increasingly important for clinical care, research, data management, international collaborations, and dissemination. Many technologies would be particularly useful for healthcare workers in resource-limited settings; however, these individuals are the least likely to utilize ICT tools, in part because they lack knowledge and skills necessary to use them. Our program aimed to train researchers in low-resource settings on using ICT tools.MethodsWe conducted a tiered, blended learning program for researchers in Kenya on three areas of ICT: geographic information systems, data management, and communication tools. Tiers included didactic online courses for 100-300 students for each topic, skills workshops for 30 students, and mentored projects for 10. Concurrently, a training of trainers course comprised of an online course and a skills workshop to ensure sustainable ongoing training.ResultsCourse ratings were high, particularly when participants engaged in hands-on skill building activities. Teaching that incorporated local examples was most valuable. Discussion boards were sometimes distracting, depending on multiple factors. Mentored projects were most useful when there were clear expectations, pre-existing projects or data, and clear timelines.DiscussionTraining in the use of ICT tools is essential to improve their use among researchers in low-income settings. However, very few training courses have been described. Our students demonstrated acquisition of new skills and felt these skills to be valuable in their workplaces.ConclusionsFurther and ongoing training in ICT skills should be considered in other low-resource settings, and could use our program as a foundational model.
The increasing use of Electronic Health Records (EHRs) in healthcare delivery settings has led to increase availability of electronic clinical data. They generate a lot of patients’ clinical data each day, requiring physicians to review them to find clinically relevant information of different patients during care episodes. The availability of electronically collected healthcare data has created the need of computational tools to analyze them. One of the types of data which doctors have access to is clinical notes that resides in electronic health records. These notes are useful as they provide comprehensive information about patients’ health histories with many practical uses. For example, doctors always review these notes during care episodes to appraise themselves about the health history of a patient. These reviews are currently manual where a doctor reads a patient’s chart while looking for specific clinical information. Without the proper support, this manual process leads to information overload and increases physician cognitive workload. Current electronic health records (EHRs) do not provide support to help physicians reduce cognitive workload when completing clinical tasks. This is especially true for long clinical documents which require quick review at the point of care. The growing amount of clinical documentation available in EHRs has arose the need of tools that support synthesize of information in EHRs. The use of visual analytics to explore healthcare data is one such research direction to address this problem. However, existing visualization techniques are mainly based on structured electronic health record and rarely support therapeutic activities. Therefore, visualization of unstructured clinical records to support clinical practice is required. In this paper we propose a unique approach for graphically representing and visualizing the semantic structure of a clinical text document to aid doctors in reviewing electronic clinical notes. A user evaluation demonstrates that the proposed method for visualizing and navigating a document’s semantic structure facilitates a user’s document information exploration.
On our everyday operations there is need to engage agents to perform some duties on our behalf, hence they are gaining acceptance as a technology and are being used. Most of the networked offices, networked homes, cyber cafes, learning institutions and other arenas where computers are interconnected on a Wi-Fi network, have peer-to-peer networks. In Wi-Fi peer-to-peer networks, it is difficult to identify the network details of all the network devices connected such as the IP addresses, Mac addresses and computer names of all computers connected on the Wi-Fi peer to peer network at one go; which we hereby refer to as fundamental network details. This is mainly possible in a client-server based architecture where the server monitors all the computers on the network. From the above gap, we developed a mobile agent that could be run in any computer on the Wi-Fi peer-to-peer network and it lists these fundamental details of all the computers connected to the Wi-Fi peer-to-peer network. In developing this mobile agent, we used the MaSE agent methodology. The mobile agent was coded, implemented and tested. The agent was then subjected to various controls which it overcame and managed to return the desired fundamental details with over 80% accuracy. They had the capacity to classify every computer on the network as either intruder or non intruder based on the list of authorized computers supplied by the user. The agent suffered major limitation which included: -the agent took longer time to learn and return the results, as well as it could not communicate to the intruders or shut them down. In future, the agent could therefore be improved to reduce its processing time, communicate with the intruding computers, and shut down the intruding computers or deny them network access. General TermsMultiagent systems, computer networking
Game theory is not only useful in understanding the performance of human and autonomous game players, but it is also widely employed in solving resource allocation problems in distributed decision-making systems. Reinforcement learning is a promising technique that can be used by agents to learn and adapt their strategies in such systems. We have enhanced the carrier sense multiple access with collision avoidance mechanism used in random access networks by using concepts from the two fields so that nodes using different strategies can adapt to the current state of the wireless environment. Simulation results show that the enhanced mechanism outperforms the existing mechanism in terms of throughput, dropped packets and fairness. This is especially noticeable as the network size increases. However the existing mechanism performs better in terms of delay which can be attributed to increased processing.
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