Abstract:In recent years, a wide range of Business Intelligence (BI) technologies have been applied to different areas in order to support the decision-making process. BI enables the extraction of knowledge from the data stored. The healthcare industry is no exception, and so BI applications have been under investigation across multiple units of different institutions. Thus, in this article, we intend to analyze some open-source/free BI tools on the market and their applicability in the clinical sphere, taking into consideration the general characteristics of the clinical environment. For this purpose, six BI tools were selected, analyzed, and tested in a practical environment. Then, a comparison metric and a ranking were defined for the tested applications in order to choose the one that best applies to the extraction of useful knowledge and clinical data in a healthcare environment. Finally, a pervasive BI platform was developed using a real case in order to prove the tool viability.

The intersection of these two trends is what we call The Issue and it is helping businesses in every industry to become more efficient and productive. One's aim is to have an insight into the development and maintenance of comprehensive and integrated health information systems that enable sound policy and effective health system management in order to improve health and health care. Undeniably, different sorts of technologies have been developed, each with their own advantages and disadvantages, which will be sorted out by attending at the impact that Artificial Intelligence and Decision Support Systems have to everyone in the healthcare sector engaged to quality-of-care, i.e., making sure that doctors, nurses, and staff have the training and tools they need to do their jobs.

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In recent years, the increase of average waiting times in waiting lists is an issue that has been felt in health institutions. Thus, the implementation of new administrative measures to improve the management of these organizations may be required. Hereupon, the aim of this present work is to support the decision-making process in appointments and surgeries waiting lists in a hospital located in the north of Portugal, through a pervasive Business Intelligence platform that can be accessed anywhere and anytime by any device connected within the hospital's private network. By representing information that facilitate the analysis of information and knowledge extraction, the Web tool allows the identification in real-time of average waiting times outside the outlined patterns. Thereby, the developed platform permits their identification, enabling their further understanding in order to take the necessary measures. Thus, the main purpose is to enable the reduction of average waiting times through the analysis of information in order to, subsequently, ensure the satisfaction of patients.

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Over the years, information technologies and computer applications have been widespread amongst all fields, including healthcare. The main goal of these organizations is focused on providing quality health services to their patients, ensuring the provision of quality services. Therefore, decisions have to be made quickly and effectively. Thus, the increased use of information technologies in healthcare has been helping the decision-making process, improving the quality of their services. For an example, the insertion of Business Intelligence (BI) tools in healthcare environments has been recently used to improve healthcare delivery. It is based on the analysis of data in order to provide useful information. BI tools assist managers and health professionals through decision-making, since they allow the manipulation and analysis of data in order to extract knowledge. This work aims to study and analyze the time that physicians take to prescribe medical exams in Centro Hospitalar do Porto (CHP), though BI tools. The main concern is to identify the physicians who take more time than average to prescribe complementary means of diagnosis and treatment, making it possible to identify and understand the reason why it occurs. To discover these outliners, a BI platform was developed using the Pentaho Community. This platform presents means to represent information through tables and graphs that facilitate the analysis of information and the knowledge extraction. This information will be useful to represent knowledge concerning not only the prescription system (auditing it) but also its users. The platform evaluates the time prescription, by specialty and physician, which can afterwards be applied in the decision-making process. This platform enables the identification of measures to unravel the time differences that some physicians exhibit, in order to, subsequently, improve the whole process of electronic medical prescription.

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Abstract. Due to the high standards expected from diagnostic medical imaging, the analysis of information regarding waiting lists via different information systems is of utmost importance. Such analysis, on the one hand, may improve the diagnostic quality and, on the other hand, may lead to the reduction of waiting times, with the concomitant increase of the quality of services and the reduction of the inherent financial costs. Hence, the purpose of this study is to assess the waiting time in the delivery of diagnostic medical imaging services, like computed tomography and magnetic resonance imaging. Thereby, this work is focused on the development of a decision support system to assess waiting times in diagnostic medical imaging with recourse to operational data of selected attributes extracted from distinct information systems. The computational framework is built on top of a Logic Programming Case-base Reasoning approach to Knowledge Representation and Reasoning that caters for the handling of incomplete, unknown, or even self-contradictory information.