Abstract
Medical physiology is generally studied as a static and complicated system where dynamic processes are captured as flow charts. From this perspective, when “X” increases, “Y” decreases, captured as feedback loops and cascades, or, after “A” happens, then “B” takes place. Such a static and sequential view on human physiology lends itself well to the biomarker approach to diagnosis, therapy, and maintenance of health. Here, “abnormal” levels of biomarkers are indicators of disease to specific physiological systems and health would be defined by the absence of aberrant biomarker levels. However, the human body is a complex system where “the whole is greater than the sum of its parts”. The human body has processes that occur simultaneously and fluctuate consistently over time. Complexity affords human physiology the capacity for compensatory adaptive patterns and creates a distinction between illness and disease, where one does not necessitate the other. This paper will discuss the concepts of complexity and degeneracy, providing a theoretical framework of the relationship between the whole and parts. The primacy of complex, interacting dynamic physiological patterns will be presented in contrast to the biomarker approach. It presents a challenge to the idea that the absence of visible, “meaningful” structural or functional disruption alone cannot be used as an indicator of health. The absence of disease is not the absence of illness, and the presence of disease is not a reflection of illness. Clinically relevant outcomes from the complex dynamics perspective will be provided, where alternative views of the detection and management of disease and illness will be presented. The critical issue is the emphasis on the importance of rediscovering the pathway to health that goes beyond the normalization of single physiological biomarkers.
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Hong, S.L., Hain, S.J. (2016). Complicated vs. Complex, Disease vs. Illness: Rethinking Diagnosis, Therapy, and Restoring Health. In: Sturmberg, J. (eds) The Value of Systems and Complexity Sciences for Healthcare. Springer, Cham. https://doi.org/10.1007/978-3-319-26221-5_3
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DOI: https://doi.org/10.1007/978-3-319-26221-5_3
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