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.     

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.     

An epistemological shift: from evidence‐based medicine to epistemological responsibility

In decision making concerning the diagnosis and treatment of patients, doctors have a responsibility to do this to the best of their abilities. Yet we argue that the current paradigm for best medical practice – evidence‐based medicine (EBM) – does not always support this responsibility. EBM was developed to promote a more scientific approach to the practice of medicine. This includes the use of randomized controlled trials in the testing of new treatments and prophylactics and rule‐based reasoning in clinical decision making. But critics of EBM claim that such a scientific approach does not always work in the clinic. In this article, we build on this critique and argue that rule‐based reasoning and the use of general guidelines as promoted by EBM does not accommodate the complex reasoning of doctors in clinical decision making. Instead, we propose that a new medical epistemology is needed that accounts for complex reasoning styles in medical practice and at the same time maintains the quality usually associated with ‘scientific’. The medical epistemology we propose conforms to the epistemological responsibility of doctors, which involves a specific professional attitude and epistemological skills. Instead of deferring part of the professional responsibility to strict clinical guidelines, as EBM allows for, our alternative epistemology holds doctors accountable for epistemic considerations in clinical decision making towards the diagnosis and treatment plan of individual patients. One of the key intellectual challenges of doctors is the ability to bring together heterogeneous pieces of information to construct a coherent ‘picture’ of a specific patient. In the proposed epistemology, we consider this ‘picture’ as an epistemological tool that may then be employed in the diagnosis and treatment of a specific patient.     

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.     

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.     

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.     

Technology‐induced bias in the theory of evidence‐based medicine

Designing trials and studies to minimize confounding and bias is central to evidence‐based medicine (EBM). The widespread use of recent technologies such as machine learning, smartphones, and the World Wide Web to collect, analyse, and disseminate information can improve the efficiency, reliability, and availability of medical research. However, it also has the potential to introduce new sources of significant, technology‐induced evidential bias. This paper assesses the extent of the impact by reviewing some of the methods by and principles according to which evidence is collected, analysed, and disseminated in EBM, supported by specific examples. It considers the effect of personal health tracking via smartphones, the current proliferation of research data and the influence of search engine “filter bubbles”, the possibility of machine learning‐driven study design, and the implications of using machine learning to seek patterns in large quantities of data, for example from observational studies and medical record databases. It concludes that new technology may introduce profound new sources of bias that current EBM frameworks do not accommodate. It also proposes new approaches that could be incorporated in to EBM theory to mitigate the most obvious risks, and suggests where further assessment of the practical implications is needed.     

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.     

Integrating the online nursing evidence‐based information resources for evidence‐based nursing study in China

At present Chinese nurses could not get the up‐to‐date and high‐quality evidences efficiently and conveniently due to language barrier and other practical difficulties. This program built a Chinese website of integrated evidence‐based network information resources for EBN studies. Researchers hope to provide practical guidance and advice for nurses in non‐English‐speaking countries..     

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.     

Toward evidence‐based policy decisions: a case study of nursing health human resources in Ontario,Canada

This paper reflects how health services research ‘evidence’ was used to influence decisions in the province of Ontario, Canada. The process involved interaction among a variety of stakeholders and decision‐makers with researchers to reduce uncertainty and to substantiate emerging service provision issues in the province. The issues presented here focus specifically on an analysis of the nursing situation completed in 1998 for the Minister of Health’s Nursing Task Force, which examined key issues in service delivery. The issues were: restructured work environments; nurse supply and declining enrollments; labour trends and utilization of the nursing workforce; patient acuity and complexity of work environments and the influence on workload; and the paucity of reliable and valid data bases for analysis of nursing’s contribution to the health system. Ontarians can be confident that the Task Force recommendations were born from solid research‐based evidence and now the challenge becomes to monitor the implementation of these resolutions over time.