Coping up with the Information Overload in the Medical Profession

The recent technological advancement has proved to be tremendously helpful for medical consultants. However, this advancement has also generated an enormous volume and variety of data, with a high velocity causing an information load for the medical consultants. Information overload can be defined as a difficulty a person can have in comprehending issue and making judgments that are caused by the presence of too much information. Information overload occurs when the amount of input to a system surpasses its processing capability. Decision-makers have a limited cognitive processing ability. Consequently, when information overload happens, it is possible that a decline in decision quality will take place. Decision-makers, such as medical consultants, have fairly limited cognitive processing capacity. Consequently, when information overload occurs, it is likely that a reduction in decision quality will occur. The aim of this study is to assess the impact of information overload on medical consultants’ life, its causes, and potential ways to deal with it. We performed a literature review to find the effects of information overload on medical consultants. Twelve research papers were considered for thematic analysis using NVivo 10 tool. These papers revealed four themes: 1) traditional methods of data collection;2) modern ways of data collection;3) consequences of modern ways of data collection;and 4) the need for handling information overload. This study suggests the development of a Continuing Professional Development course that explains how to deal with information overload, and availing the same through E-Learning mode might be one immediate solution.

Identifying Semantic in High-Dimensional Web Data Using Latent Semantic Manifold

Latent Semantic Analysis involves natural language processing techniques for analyzing relationships between a set of documents and the terms they contain, by producing a set of concepts (related to the documents and terms) called semantic topics. These semantic topics assist search engine users by providing leads to the more relevant document. We develope a novel algorithm called Latent Semantic Manifold (LSM) that can identify the semantic topics in the high-dimensional web data. The LSM algorithm is established upon the concepts of topology and probability. Asearch tool is also developed using the LSM algorithm. This search tool is deployed for two years at two sites in Taiwan: 1) Taipei Medical University Library, Taipei, and 2) Biomedical Engineering Laboratory, Institute of Biomedical Engineering, National Taiwan University, Taipei. We evaluate the effectiveness and efficiency of the LSM algorithm by comparing with other contemporary algorithms. The results show that the LSM algorithm outperforms compared with others. This algorithm can be used to enhance the functionality of currently available search engines.