Journal of evaluation in clinical practice
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Evidence-based medicine announced its entry as heralding a new paradigm in health care practices, but it has been widely criticized for lacking a coherent theoretical basis. This paper presents the first part of a three-article series examining the epistemological, practical, and ethical dimensions of strong EBM, as well as considering alternatives that promise potential solutions to chronic conceptual and practical problems. While the focus is on the details of the arguments and evidence in thoughtful debates over the last 30 years, it is worthwhile to keep in mind the overall trajectory of modern thought, because strong EBM continues discredited positivist positions, thus repeating its major assumptions and inadequacies, now transferred to the medical sphere and vocabulary. Part 1 of the series examines the development of strong EBM by clarifying and critiquing its somewhat discontinuous accounts of scientific knowledge and epistemology, evidence, the differences between statistical probability in regard to populations and understanding the health of individuals, and its claims for direct transfer of research findings to clinical settings-all of which raises more questions regarding its application to provider-patient decision making, pedagogy, and policy.
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One of the sectors challenged by the COVID-19 pandemic is medical research. COVID-19 originates from a novel coronavirus (SARS-CoV-2) and the scientific community is faced with the daunting task of creating a novel model for this pandemic or, in other words, creating novel science. This paper is the first part of a series of two papers that explore the intricate relationship between the different challenges that have hindered biomedical research and the generation of scientific knowledge during the COVID-19 pandemic. ⋯ The COVID-19 pandemic presented challenges in terms of (1) finding and prioritising relevant research questions and (2) choosing study designs that are appropriate for a time of emergency.
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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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The goals of learning health systems (LHS) and of AI in medicine overlap in many respects. Both require significant improvements in data sharing and IT infrastructure, aim to provide more personalized care for patients, and strive to break down traditional barriers between research and care. However, the defining features of LHS and AI diverge when it comes to the people involved in medicine, both patients and providers. ⋯ LHS also encourage better coordination of specialists across the health system, but AI aims to replace many specialists with technology and algorithms. This paper argues that these points of conflict may require a reconsideration of the role of humans in medical decision making. Although it is currently unclear to what extent machines will replace humans in healthcare, the parallel development of LHS and AI raises important questions about the exact role for humans within AI-enabled healthcare.
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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.