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Information and communication technologies (ICT) are being widely used in healthcare management systems. Rapid advancements in ICT in the last decade or so provide solutions to the problems in healthcare management systems. These include a wide spectrum of issues such as patient safety, dietary management, telemedicine, digital imaging, document management etc. This paper explores the emerging technologies which are being used for the improvement of the healthcare process and identify the problems and their probable solutions specifically in the context of developing countries. Telemedicine offers a way of improving the standard of healthcare especially in the developing world. The developing countries such as Pakistan, can exploit these to give better healthcare services as well health education. This paper highlights the growth of ICT sector in the developing world and explores its possible uses in health sector. These may help healthcare professionals and community health workers to carry out their work in a better way particularly in remote areas.

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Does K-Means reasonably divides the data into k groups is an important question that arises when one works on Image Segmentation? Which color space one should choose and how to ascertain that the k we determine is valid? The purpose of this study was to explore the answers to aforementioned questions. We perform K-Means on a number of 2-cluster, 3-cluster and k-cluster color images (k>3) in RGB and L*a*b* feature space. Ground truth (GT) images have been used to accomplish validation task. Silhouette analysis supports the peaks for given k-cluster image. Model accuracy in RGB space falls between 30% and 55% while in L*a*b* color space it ranges from 30% to 65%. Though few images used, but experimentation proves that K-Means significantly segment images much better in L*a*b* color space as compared to RGB feature space.