The interest in new and more advanced technological solutions is paving the way for the diffusion of innovative and revolutionary applications in healthcare organizations. The application of an artificial intelligence system to medical research has the potential to move toward highly advanced e-Health. This analysis aims to explore the main areas of application of big data in healthcare, as well as the restructuring of the technological infrastructure and the integration of traditional data analytical tools and techniques with an elaborate computational technology that is able to enhance and extract useful information for decision-making. We conducted a literature review using the Scopus database over the period 2010–2020. The article selection process involved five steps: the planning and identification of studies, the evaluation of articles, the extraction of results, the summary, and the dissemination of the audit results. We included 93 documents. Our results suggest that effective and patient-centered care cannot disregard the acquisition, management, and analysis of a huge volume and variety of health data. In this way, an immediate and more effective diagnosis could be possible while maximizing healthcare resources. Deriving the benefits associated with digitization and technological innovation, however, requires the restructuring of traditional operational and strategic processes, and the acquisition of new skills.
The objective of the present work is to study the relations between the mean difference and the standard deviation with reference to the most common continuous theoretical distribution models. The continuous distribution models without shape parameters, those with only one shape parameter, and those with two shape parameters have been considered. The shape parameters encountered are inequality indexes, skewness indexes or kurtosis indexes.\ud For the models without shape parameters the perfect equal ranking of the values of the two indexes have been verified. \ud For the models with only one shape parameter it was seen that with variations in the shape parameter both indexes increase or decrease, so that the relation between them is growing. The ratio between the two indexes made it possible to determine the interval in which one index is greater than the other and the one in which it is less. \ud Analogous results emerged for the models with two shape parameters, in particular the region in which one index is greater than the other and the complementary one.\ud It was confirmed that for some models the mean difference has a wider field of definition in terms of the parameters than the standard deviation
BackgroundIn the age of digitalization and big data, personal health information is a key resource for health care and clinical research. This study aimed to analyze the determinants and describe the measurement of the willingness to disclose personal health information.MethodsThe study conducted a systematic review of articles assessing willingness to share personal health information as a primary or secondary outcome. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis protocol. English and Italian peer-reviewed research articles were included with no restrictions for publication years. Findings were narratively synthesized.ResultsThe search strategy found 1,087 papers, 89 of which passed the screening for title and abstract and the full-text assessment.ConclusionNo validated measurement tool has been developed for willingness to share personal health information. The reviewed papers measured it through surveys, interviews, and questionnaires, which were mutually incomparable. The secondary use of data was the most important determinant of willingness to share, whereas clinical and socioeconomic variables had a slight effect. The main concern discouraging data sharing was privacy, although good data anonymization and the high perceived benefits of sharing may overcome this issue.
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