Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of “big data” for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans.

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Abstract-In this paper we propose a novel scheme for efficient content-based medical image retrieval, formalized according to the PANDA (PAtterns for Next generation Database systems) framework. The proposed scheme involves low-level feature extraction from image regions followed by clustering of the feature space to form higher-level patterns. The components of each pattern include a cluster representation and a measure that quantifies the quality of the image content representation achieved by the pattern. The similarity between two patterns is estimated as a function of the similarity between both the structure and the measure components of the patterns. Experiments were performed on a reference set of radiographic images, using standard wavelet domain image features. The results show that the proposed scheme can be more efficient than the common ground schemes for medical image retrieval, as it does not involve exhaustive, nearest neighbor searching over the whole set of the available feature vectors. Keeping the patterns in a unified form facilitates further processing and analysis by mining or visualization algorithms.

During the recent economic crisis, Greece implemented a comprehensive reform in the health care system. The 2010 health reform occurred under the constraints imposed by the memorandum of understanding that the Greek Government signed with its EU/International Monetary Fund creditors to control its deficit. The objective of the study is to examine the impact of the reform on the efficiency and productivity of public hospitals in Greece. We use the Malmquist productivity index to comparatively examine the potential changes before and after the reform years. We compare productivity, efficiency, and technological changes using panel data of 111 public acute hospitals operating in Greece throughout the recession period of 2009 to 2012. Bootstrapping methods are applied to allow for uncertainty owing to sampling error and for statistical inference for the Malmquist productivity index and its decompositions. The analysis indicates that the productivity has been increased following the policy changes. It appears that the expected benefits from the reform in general have been achieved, at least in the short-term. This result is examined in the light of management and operations activities, which are related with the reform process. Therefore, at a second stage, the Malmquist index is regressed on variables that may potentially be statistically associated with productivity growth.

This National e-Health System appears to be effective in the management of overweight and obesity in childhood and adolescence.