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      The Potential of Big Data Research in HealthCare for Medical Doctors’ Learning


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          The main goal of this article is to identify the main dimensions of a model proposal for increasing the potential of big data research in Healthcare for medical doctors’ (MDs’) learning, which appears as a major issue in continuous medical education and learning. The paper employs a systematic literature review of main scientific databases (PubMed and Google Scholar), using the VOSviewer software tool, which enables the visualization of scientific landscapes. The analysis includes a co-authorship data analysis as well as the co-occurrence of terms and keywords. The results lead to the construction of the learning model proposed, which includes four health big data key areas for MDs’ learning: 1) data transformation is related to the learning that occurs through medical systems; 2) health intelligence includes the learning regarding health innovation based on predictions and forecasting processes; 3) data leveraging regards the learning about patient information; and 4) the learning process is related to clinical decision-making, focused on disease diagnosis and methods to improve treatments. Practical models gathered from the scientific databases can boost the learning process and revolutionise the medical industry, as they store the most recent knowledge and innovative research.

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          Most cited references35

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          Response to COVID-19 in Taiwan: Big Data Analytics, New Technology, and Proactive Testing

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            Patient adherence to treatment: three decades of research. A comprehensive review.

            Low compliance to prescribed medical interventions is an ever present and complex problem, especially for patients with a chronic illness. With increasing numbers of medications shown to do more good than harm when taken as prescibed, low compliance is a major problem in health care. Relevant studies were retrieved through comprehensive searches of different database systems to enable a thorough assessment of the major issues in compliance to prescribed medical interventions. The term compliance is the main term used in this review because the majority of papers reviewed used this term. Three decades have passed since the first workshop on compliance research. It is timely to pause and to reflect on the accumulated knowledge. The enormous amount of quantitative research undertaken is of variable methodological quality, with no gold standard for the measurement of compliance and it is often not clear which type of non-compliance is being studied. Many authors do not even feel the need to define adherence. Often absent in the research on compliance is the patient, although the concordance model points at the importance of the patient's agreement and harmony in the doctor-patient relationship. The backbone of the concordance model is the patient as a decision maker and a cornerstone is professional empathy. Recently, some qualitative research has identified important issues such as the quality of the doctor-patient relationship and patient health beliefs in this context. Because non-compliance remains a major health problem, more high quality studies are needed to assess these aspects and systematic reviews/meta-analyses are required to study the effects of compliance in enhancing the effects of interventions.
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              Artificial intelligence in medicine.

              Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology-up to and including today's "omics". AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application.

                Author and article information

                J Med Syst
                J Med Syst
                Journal of Medical Systems
                Springer US (New York )
                7 January 2021
                : 45
                : 1
                [1 ]GRID grid.7311.4, ISNI 0000000123236065, INESC TEC, GOVCOPP, Department of Economics, Management, Industrial Engineering and Tourism, , University of Aveiro, ; Aveiro, Portugal
                [2 ]GRID grid.481585.5, ISNI 0000 0004 0392 8189, Bavarian Nordic A/S, ; Hellerup, Denmark
                [3 ]GRID grid.45349.3f, ISNI 0000 0001 2220 8863, ISCTE Instituto Universitário de Lisboa, ; Lisbon, Portugal
                [4 ]GRID grid.36511.30, ISNI 0000 0004 0420 4262, Lincoln International Business School, , University of Lincoln, ; Lincoln, UK
                [5 ]University of Technology and Applied Sciences, Salalah CAS, Salalah, Oman
                [6 ]GRID grid.411170.2, ISNI 0000 0004 0412 4537, Faculty of Tourism & Hotels, , Fayoum University, ; Fayoum, Egypt
                © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                Education & Training
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                © Springer Science+Business Media, LLC, part of Springer Nature 2021

                Public health

                big data, systematic literature review, mds, learning, vosviewer analysis


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