Mechanistic understanding in clinical practice: complementing evidence‐based medicine with personalized medicine

In the last century, medicine has undergone an unprecedented wave of radical changes. From the implementation of surgery up to the development of single gene‐targeted therapies, clinical decision making has become increasingly complex to handle. Today, this complexity needs to be rethought in the light of two emerging paradigms: evidence‐based medicine (EBM) and personalized medicine (P‐Med). The new availability of diverse sources of scientific evidence raises significant issues concerning how clinicians will compare, evaluate and orient their decisions in front of a rapidly growing plethora of therapies, procedures, medical technologies and drugs. In this paper, we compare the background visions behind these two paradigms, evaluating their respective relevance for present and future clinical decision making. In particular, we argue that EBM and P‐Med are driven by two diverse modes of reasoning about ‘evidence making’ in medicine. EBM is grounded on statistical notions and epidemiological data, generally gathered through systematic meta‐reviews of randomized controlled trials; P‐Med, instead, is grounded on mechanistic explanations of molecular interactions, metabolic pathways and biomarkers. While both paradigms are epistemically sound, we argue that they cannot, and should not, be hybridized into a unique model. Rather, they ought to represent two compatible, but alternative ways of informing the clinical practice. Hence, we conclude that clinicians may expect to see their responsibility increasing as they will deal with diverse, but equally compelling, ways of reasoning and deciding about which intervention will qualify as the ‘best one’ in each individual case.     

Reconciling evidence‐based medicine and patient‐centred care: defining evidence‐based inputs to patient‐centred decisions

Evidence‐based and patient‐centred health care movements have each enhanced the discussion of how health care might best be delivered, yet the two have evolved separately and, in some views, remain at odds with each other. No clear model has emerged to enable practitioners to capitalize on the advantages of each so actual practice often becomes, to varying degrees, an undefined mishmash of each. When faced with clinical uncertainty, it becomes easy for practitioners to rely on formulas for care developed explicitly by expert panels, or on the tacit ones developed from experience or habit. Either way, these tendencies towards ‘cookbook’ medicine undermine the view of patients as unique particulars, and diminish what might be considered patient‐centred care. The sequence in which evidence is applied in the care process, however, is critical for developing a model of care that is both evidence based and patient centred. This notion derives from a paradigm for knowledge delivery and patient care developed over decades by Dr. Lawrence Weed. Weed's vision enables us to view evidence‐based and person‐centred medicine as wholly complementary, using computer tools to more fully and reliably exploit the vast body of collective knowledge available to define patients’ uniqueness and identify the options to guide patients. The transparency of the approach to knowledge delivery facilitates meaningful practitioner–patient dialogue in determining the appropriate course of action. Such a model for knowledge delivery and care is essential for integrating evidence‐based and patient‐centred approaches.     

The judgements that evidence‐based medicine adopts

In “The evidence that evidence‐based medicine omits”, Brendan Clarke and colleagues argue that when establishing causal facts in medicine, evidence of mechanisms ought to be included alongside evidence of correlations. One of the reasons they provide is that correlations can be spurious and generated by unknown confounding variables. A causal mechanism can provide a plausible explanation for the correlation, and the absence of such an explanation is an indication that the correlation is not causal. Evidence‐based medicine (EBM) proponents remain sceptical about this argument, one problem being that the formulation of a mechanism requires judgements that are external to the evaluation of data and experimental designs—for instance judgements of plausibility against, or derivability from, background knowledge. Because background knowledge is always incomplete and therefore unreliable, EBM proponents maintain that the plausibility of a hypothesis should be evaluated mainly by the quality of population data that yielded it. Here, I use the example of oestrogen replacement therapy's effect on coronary heart disease, an example that is often quoted in defence of the epistemic advantage of randomized controlled trials, to show that the evaluation of the most reliable study design necessarily implies the adoption of judgements that are external to the specific evidence of correlation. The exclusion of evidence of mechanism, therefore, is not effective in bypassing paradigm‐dependent judgements, which are external to specific evidence. Because such judgements cannot be excluded by evidence evaluation, they can only be kept under scrutiny, or adopted uncritically. I propose that the latter option can hinder the maintenance of an active critical inquiry, as well as the analysis of experts' disagreement.     

The need for a rationalist turn in evidence‐based medicine

When evidence‐based medicine (EBM) became established, its dominant rhetoric was empiricist, in spite of rationalist elements in its practice. Exploring some of the key statements about EBM down the years, the paper examines the tensions between empiricism and rationalism and argues for a rationalist turn in EBM to help to develop the next generation of scholarship in the field.     

Corroborating evidence‐based medicine

Proponents of evidence‐based medicine (EBM) have argued convincingly for applying this scientific method to medicine. However, the current methodological framework of the EBM movement has recently been called into question, especially in epidemiology and the philosophy of science. The debate has focused on whether the methodology of randomized controlled trials provides the best evidence available. This paper attempts to shift the focus of the debate by arguing that clinical reasoning involves a patchwork of evidential approaches and that the emphasis on evidence hierarchies of methodology fails to lend credence to the common practice of corroboration in medicine. I argue that the strength of evidence lies in the evidence itself, and not the methodology used to obtain that evidence. Ultimately, when it comes to evaluating the effectiveness of medical interventions, it is the evidence obtained from the methodology rather than the methodology that should establish the strength of the evidence.     

Values‐based medicine and modest foundationalism

Philosophically, values refer to the basic commitments that justify judgements, beliefs and practices, both at the community and personal levels. The study of these kinds of values is axiology. We suggest that all people subscribe to three foundational values – survival, security and flourishing – and that these foundational values are expressed by way of concepts, systems, principles and practices that may differ substantially from culture to culture. Values can stand on their own as foundational justifications for health care and medicine. Many ethical quandaries can be better understood, even though they may remain unsolved, by reference to the foundational values that people can agree upon. This version of values‐based health care has strong claims to prior logical status as a justification for the whole enterprise of health care, and values‐based medicine is a part of this larger domain.     

Underdetermination in evidence‐based medicine

This article explores the philosophical implications of evidence‐based medicine's (EBM's) epistemology in terms of the problem of underdetermination of theory by evidence as expounded by the Duhem–Quine thesis. EBM hierarchies of evidence privilege clinical research over basic science, exacerbating the problem of underdetermination. Because of severe underdetermination, EBM is unable to meaningfully test core medical beliefs that form the basis of our understanding of disease and therapeutics. As a result, EBM adopts an epistemic attitude that is sceptical of explanations from the basic biological sciences, and is relegated to a view of disease at a population level. EBM's epistemic attitude provides a limited research heuristic by preventing the development of a theoretical framework required for understanding disease mechanism and integrating knowledge to develop new therapies. Medical epistemology should remain pluralistic and include complementary approaches of basic science and clinical research, thus avoiding the limited epistemic attitude entailed by EBM hierarchies.     

Rethinking bias and truth in evidence‐based health care

In modern philosophy, the concept of truth has been problematized from different angles, yet in evidence‐based health care (EBHC), it continues to operate hidden and almost undisputed through the linked concept of “bias.” To prevent unwarranted relativism and make better inferences in clinical practice, clinicians may benefit from a closer analysis of existing assumptions about truth, validity, and reality. In this paper, we give a brief overview of several important theories of truth, notably the ideal limit theorem (which assumes an ultimate and absolute truth towards which scientific inquiry progresses), the dominant way truth is conceptualized in the discourse and practice of EBHC. We draw on Belgian philosopher Isabelle Stengers' work to demonstrate that bias means one thing if one assumes a world of hard facts “out there,” waiting to be collected. It means something different if one takes a critical view of the knowledge‐power complex in research trials. Bias appears to have both an unproductive aspect and a productive aspect as argued by Stengers and others: Facts are not absolute but result from an interest, or interesse: a bias towards a certain line of questioning that cannot be eliminated. The duality that Stengers' view invokes draws attention to and challenges the assumptions underlying the ideal limit theory of truth in several ways. Most importantly, it casts doubt on the ideal limit theory as it applies to the single case scenario of the clinical encounter, the cornerstone of EBHC. To the extent that the goal of EBHC is to support inferencing in the clinical encounter, then the ideal limit as the sole concept of truth appears to be conceptually insufficient. We contend that EBHC could usefully incorporate a more pluralist understanding of truth and bias and provide an example how this would work out in a clinical scenario.     

Compellingness: assessing the practical relevance of clinical research results

While the critical appraisal of clinical research has generally focused on aspects of study design and statistical analysis, other features of clinical studies appear to be more important to clinicians for assessing the value of a particular study in the care of particular patients. These features relate to the quality of the knowledge derived from the research, the value of the results to the care of individual patients, and the responsibility of clinicians to be stewards of limited resources. Twelve features of clinical research studies that affect how compelling individual clinicians find the results are proposed and examined here. By better understanding what makes clinical research compelling enough to alter or reinforce clinical practice, clinical researchers can design future studies to better serve the needs of clinicians and patients.     

Evidence‐based medicine as science

Evidence‐based medicine has claimed to be science on a number of occasions, but it is not clear that this status is deserved. Within the philosophy of science, four main theories about the nature of science are historically recognized: inductivism, falsificationism, Kuhnian paradigms, and research programmes. If evidence‐based medicine is science, knowledge claims should be derived using a process that corresponds to one of these theories. This paper analyses whether this is the case. In the first section, different theories about the nature of science are introduced. In the second section, the claim that evidence‐based medicine is science is reinterpreted as the claim that knowledge claims derived from randomized controlled trails and meta‐analyses are science. In the third section, the knowledge claims valued within evidence‐based medicine are considered from the perspective of inductivism, falsificationism, Kuhnian paradigms, and research programmes. In the final section, possible counter arguments are considered. It is argued that the knowledge claims valued by evidence‐based medicine are not justified using inductivism, falsificationism, Kuhnian paradigms, or research programmes. If these are the main criteria for evaluating if something is science or not, evidence‐based medicine does not meet these criteria.     

How evidence‐based medicine is failing due to biased trials and selective publication

Evidence‐based medicine (EBM) was announced in the early 1990s as a ‘new paradigm’ for improving patient care. Yet there is currently little evidence that EBM has achieved its aim. Since its introduction, health care costs have increased while there remains a lack of high‐quality evidence suggesting EBM has resulted in substantial population‐level health gains. In this paper we suggest that EBM's potential for improving patients' health care has been thwarted by bias in the choice of hypotheses tested, manipulation of study design and selective publication. Evidence for these flaws is clearest in industry‐funded studies. We argue EBM's indiscriminate acceptance of industry‐generated ‘evidence’ is akin to letting politicians count their own votes. Given that most intervention studies are industry funded, this is a serious problem for the overall evidence base. Clinical decisions based on such evidence are likely to be misinformed, with patients given less effective, harmful or more expensive treatments. More investment in independent research is urgently required. Independent bodies, informed democratically, need to set research priorities. We also propose that evidence rating schemes are formally modified so research with conflict of interest bias is explicitly downgraded in value.     

Many‐models medicine: diversity as the best medicine

Many medicines have been proposed to cure various ills of biomedicine including evidence‐based medicine, evolutionary medicine, narrative medicine, and complexity medicine, among others. To the extent that all models are idealizations or abstractions, all of these model medicines are imperfect in some respects. In the absence of a single unified model, if indeed unification is possible or even desirable, and despite the relative advantages of one model or another, in practice many models and methods are necessary in medicine. In this article, I consider the value of such diversity in models and methods. I briefly describe several models. Then I discuss simulations of agents who use diverse models. Advocates of models such as those discussed here typically claim that we should use their preferred model because it is the best. Evidence‐based medicine, for instance, has been promoted as the single best model of medicine while other models have been cast as lesser models or in opposition to it and each other. But isolated models and methods may never be as good as groups of models and methods. Debates about various individual models may result in better outcomes, but explicitly choosing to use many models is likely to produce even better outcomes.     

Evidence‐based medicine: A cornerstone for clinical care but not for quality improvement

Quality improvement (QI) as a clinical improvement science has been criticized for failing to deliver broad patient outcome improvement and for being a top‐down regulatory and compliance construct. These critics have argued that the focus of QI should be on increasing adherence to clinical practice guidelines (CPGs) and, as a result, should be consolidated into research structures with the science of evidence‐based medicine (EBM) at the helm. We argue that EBM often overestimates the role of knowledge as the root cause of quality problems and focuses almost exclusively on the effectiveness of care while often neglecting the domains of safety, efficiency, patient‐centredness, and equity. Successfully addressing quality problems requires a much broader, systems‐based view of health‐care delivery. Although essential to clinical decision‐making and practice, EBM cannot act as the cornerstone of health system improvement.