Journal of evaluation in clinical practice
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Parts 1 and 2 in this series of three articles have shown that and how strong evidence-based medicine has neither a coherent theoretical foundation nor creditable application to clinical practice. Because of its core commitment to the discredited positivist tradition it holds both a false concept of scientific knowledge and misunderstandings concerning clinical decision-making. Strong EBM continues attempts to use flawed adjustments to recover from the unsalvageable base view. ⋯ While most of papers 1, 2, and 3 are written in the classical mode of contrasting the theoretical-logical and empirical evidence offered by contending positions bearing on the decision making and judgement in clinical practice, a shift occurs when considerations move beyond what is possible for clinical practitioners to accomplish. A different, discontinuous level of power operates in the trans-personal realm of instrumental policy, insurance, and hospital management practices. In this social-economic-political-ethical realm what happens in clinical practice today increasingly becomes a matter of what is "done unto" clinical practitioners, of what hampers their professional action and thus care of individual patients and clients.
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According to an influential taxonomy of varieties of uncertainty in health care, existential uncertainty is a key aspect of uncertainty for patients. Although the term "existential uncertainty" appears across a number of disciplines in the research literature, its use is diffuse and inconsistent. To date there has not been a systematic attempt to define it. The aim of this study is to generate a theoretically-informed conceptualisation of existential uncertainty within the context of an established taxonomy. ⋯ Humans rely on identity, worldview, and a sense of meaning in life as ways of managing the ineradicable uncertainty of our being-in-the-world, and these can be challenged by a serious diagnosis. It is important that medical professionals acknowledge issues around existential uncertainty as well as issues around scientific uncertainty, and recognise when patients might be struggling with these. Further research is required to identify ways of measuring existential uncertainty and to develop appropriate interventions, but it is hoped that this conceptualisation provides a useful first step towards that goal.
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Transdisciplinary research and generalist practice both face the task of integrating and discerning the value of knowledge across disciplinary and sectoral knowledge cultures. Transdisciplinarity and generalism also both offer philosophical and practical insights into the epistemology, ontology, axiology, and logic of seeing the 'whole'. Although generalism is a skill that can be used in many settings from industry to education, the focus of this paper is the literature of the primary care setting (i.e., general practice or family medicine). Generalist philosophy and practice in the family medicine setting highly values whole person care that uses integrative and interpretive wisdom to include both biomedical and biographical forms of knowledge. Generalist researchers are often caught between reductionist (positivist) biomedical measures and social science (post-positivist) constructivist theories of knowing. Neither of these approaches, even when juxtaposed in mixed-methods research, approximate the complexity of the generalist clinical encounter. A theoretically robust research methodology is needed that acknowledges the complexity of interpreting these ways of knowing in research and clinical practice. ⋯ The concurrence between these approaches to knowing is offered here as Transdisciplinary Generalism - a coherent epistemology for both primary care researchers and generalist clinicians to understand, enact, and research their own sophisticated craft of managing diverse forms of knowledge.
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Artificial intelligence and big data are more and more used in medicine, either in prevention, diagnosis or treatment, and are clearly modifying the way medicine is thought and practiced. Some authors argue that the use of artificial intelligence techniques to analyze big data would even constitute a scientific revolution, in medicine as much as in other scientific disciplines. Moreover, artificial intelligence techniques, coupled with mobile health technologies, could furnish a personalized medicine, adapted to the individuality of each patient. In this paper we argue that this conception is largely a myth: what health professionals and patients need is not more data, but data that are critically appraised, especially to avoid bias. ⋯ The large amount of data thus appears rather as a problem than a solution. What contemporary medicine needs is not more data or more algorithms, but a critical appraisal of the data and of the analysis of the data. Considering the history of epidemiology, we propose three research priorities concerning the use of artificial intelligence and big data in medicine.