Effective person-centred care requires recognition of the personhood not only of patients but of practitioners. This chapter explores the consequences of this recognition for major debates in medical epistemology, regarding clinical reasoning and the relationship between research and practice. For too long these debates have been dominated by false dichotomies - subjectivity versus objectivity, judgement versus evidence, reason versus emotion. Based on flawed understandings of such core concepts as “objectivity” and “engagement”, this distorted dissection of the subject-object relationship has served (...) to depersonalise practice. The costs of this depersonalisation include over-regulation and micromanagement of healthcare processes by administrators and payers at the same time that information from clinical research remains under-utilized and the personhood of patients risks being ignored. Science is a human practice, founded in a broader conception of human reasoning, ontologically dependent on human beings living and engaging with the world in social, emotional and ethical contexts. After looking at different conceptions of epistemic hierarchies and their uses in the analysis and evaluation of reasoning in a range of practice contexts, we propose a “nested hierarchy” that effectively turns upside-down the flawed evidence hierarchies that have helped to depersonalise care. T.S. Eliot’s “wisdom, knowledge, information” scheme (to which we add “data” below “information”) provides a model for a person-centred epistemic hierarchy. This crucial, person-centred inversion represents levels of awareness that characterise more or less developed thinking and judgement on the part of the particular practitioner. (shrink)

The existing philosophical views on what is the meaning of causality adequate to medicine are vastly divided. We approach this question and offer two arguments in favor of pluralism regarding concepts of causality. First, we analyze the three main types of research designs (randomized-controlled trials, observational epidemiology and laboratory research). We argue, using examples, that they allow for making causal conclusions that are best understood differently in each case (in agreement with a version of manipulationist, probabilistic and mechanistic definitions, respectively). (...) Second, we analyze clinical practice and argue that these manipulationist, probabilistic and mechanistic causal claims can be used as evidence for different therapeutic decisions. We differentiate among ‘predicting’ that does not change the relata of causal claims, (mechanistic) ‘interferences’, and ‘interventions’ in the strict sense that act on causes to change effects. The central conclusion is that causal claims agreeing with diverse concepts of causality can deliver evidence for different types of therapeutic decisions. (shrink)