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<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;">The rapid growth of digitalized medical records presents new opportunities for mining terra bytes of data that may provide new information & knowledge. The knowledge discovered as such could assist medical practitioners in a myriad of ways, for example in selecting the optimal diagnostic tool from among numerous possible choices. We analyzed the radiology department records of children who had undergone a CT scan procedure at Nagasaki University Hospital in the year 2004. We employed Self Organizing Maps (SOM), an unsupervised neural network based text-mining technique for the analysis. This approach led to the identification of keywords with a significance value within the narratives of the medical records that could predict & thereby lower the number of unnecessary CT requests by clinicians. This is important because, in spite of the valuable diagnostic capacity of such procedures, the overuse of medical radiation does pose significant health risks and staggering cost especially with regard to children.</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;"></span></p>

Informed use of medical tourism services depends on an up-to-date knowledge of the available services, and their costs and risks at various potential destinations. Such information can also assist in the competitive development of healthcare services. Innovation, product development, and health user relationship management by medical service providers is enhanced, as is knowledge base construction and management. This chapter shows that new forms of social media can provide valuable and previously difficult to obtain real-time knowledge on medical tourists' perceptions, concerns, and sentiment towards medical tourism destinations - both those already visited by other users and those under consideration for a possible visit. We show how analysis of comments from such social media as Twitter micro-blogs can be used to reveal potential and recent medical tourism motivations in the medical services markets in various locations.