Précising definitions as a way to combat overdiagnosis

Exploring health and disease concepts in healthcare practice: an empirical philosophy of medicine study

In line with recent proposals for experimental philosophy and philosophy of science in practice, we propose that the philosophy of medicine could benefit from incorporating empirical research, just as bioethics has. In this paper, we therefore take first steps towards the development of an empirical philosophy of medicine, that includes investigating practical and moral dimensions. This qualitative study gives insight into the views and experiences of a group of various medical professionals and patient representatives regarding the conceptualization of health and disease concepts in practice and the possible problems that surround them. This includes clinical, epistemological, and ethical issues. We have conducted qualitative interviews with a broad range of participants (n = 17), working in various health-related disciplines, fields and organizations. From the interviews, we highlight several different practical functions of definitions of health and disease. Furthermore, we discuss 5 types of problematic situations that emerged from the interviews and analyze the underlying conceptual issues. By providing theoretical frameworks and conceptual tools, and by suggesting conceptual changes or adaptations, philosophers might be able to help solve some of these problems. This empirical-philosophical study contributes to a more pragmatic way of understanding the relevance of conceptualizing health and disease by connecting the participants' views and experiences to the theoretical debate. Going back and forth between theory and practice will likely result in a more complex but hopefully also better and more fruitful understanding of health and disease concepts.

The Disease Loophole: Index Terms and Their Role in Disease Misclassification

The definitions of disease proffered by philosophers and medical actors typically require that a state of ill health be linked to some known bodily dysfunction before it is classified as a disease. I argue that such definitions of disease are not fully implementable in current medical discourse and practice. Adhering to the definitions would require that medical actors keep close track of the current state of knowledge on the causes and mechanisms of particular illnesses. Yet, unaddressed problems in medical terminology can make this difficult to do. I show that unrecognized misuse of "heterogeneous," "biomarker," and other important health terms-which I call index terms-can misrepresent the current empirical evidence on illness pathophysiology, such that unvalidated illness constructs become mistaken for diseases. Thus, implementing common definitions of disease would require closing this "loophole" in medical discourse. I offer a simple rule that, if followed, could help do just that.

Data-driven overdiagnosis definitions: A scoping review

INTRODUCTION

Adequate methods to promptly translate digital health innovations for improved patient care are essential. Advances in Artificial Intelligence (AI) and Machine Learning (ML) have been sources of digital innovation and hold the promise to revolutionize the way we treat, manage and diagnose patients. Understanding the benefits but also the potential adverse effects of digital health innovations, particularly when these are made available or applied on healthier segments of the population is essential. One of such adverse effects is overdiagnosis.

OBJECTIVE

to comprehensively analyze quantification strategies and data-driven definitions for overdiagnosis reported in the literature.

METHODS

we conducted a scoping systematic review of manuscripts describing quantitative methods to estimate the proportion of overdiagnosed patients.

RESULTS

we identified 46 studies that met our inclusion criteria. They covered a variety of clinical conditions, primarily breast and prostate cancer. Methods to quantify overdiagnosis included both prospective and retrospective methods including randomized clinical trials, and simulations.

CONCLUSION

a variety of methods to quantify overdiagnosis have been published, producing widely diverging results. A standard method to quantify overdiagnosis is needed to allow its mitigation during the rapidly increasing development of new digital diagnostic tools.

Overtreatment: Is a solution possible?

Screening is a useful tool for identifying potential health issues; however, it can also lead to overtreatment. Consequently, patients are sometimes harmed by unnecessary treatments and there are cost implications. Overtreatment can also occur in other areas of medicine besides screening and sometimes medical interventions are used to improve performance rather than to treat disease. In this paper, a distinction is made between the perspectives of the patient and the government. For patients, autonomy is important, and they can refuse life-saving treatments, assuming they have decision-making capacity. They can also choose to be treated to avoid a very small risk or to improve their performance. For a government with limited funds, it is important to focus on outcomes and fund those screening programmes and other medical interventions that can potentially save the most lives or prevent severe disability. Governments also have the power to legislate to enable a level playing field by prohibiting medications that improve performance, but there is no general consensus about this, and regulations can only be applied to specific, well-defined activities. The problem with overtreatment results from the different interests involved: autonomy is the guiding idea for patients and outcome is the guiding measure for societies. A general solution will not be possible because of these inherent conflicting interests. However, medical research may improve the identification and predictions surrounding any anomalies detected during scans and reduce the problem in practice for specific conditions.

A New Approach to Disease, Risk, and Boundaries Based on Emergent Probability

The status of risk factors and disease remains a disputed question in the theory and practice of medicine and healthcare, and so does the related question of delineating disease boundaries. I present a framework based on Bernard Lonergan's account of emergent probability for differentiating (1) generically distinct levels of systematic function within organisms and between organisms and their environments and (2) the methods of functional, genetic, and statistical investigation. I then argue on this basis that it is possible to understand disease in terms of biological or higher intra-level dysfunction, risk factors-including genetic risk factors-in terms of statistical inter-level conditioning of a given stage or developmental sequence of systematic functioning, and the empirical boundaries of disease in terms of the limits of both functional categorization (from an epistemic standpoint) and upper-level integration of lower-level processes and events (from an ontological standpoint).

Health and disease as practical concepts: exploring function in context-specific definitions

Despite the longstanding debate on definitions of health and disease concepts, and the multitude of accounts that have been developed, no consensus has been reached. This is problematic, as the way we define health and disease has far-reaching practical consequences. In recent contributions it is proposed to view health and disease as practical- and plural concepts. Instead of searching for a general definition, it is proposed to stipulate context-specific definitions. However, it is not clear how this should be realized. In this paper, we review recent contributions to the debate, and examine the importance of context-specific definitions. In particular, we explore the usefulness of analyzing the relation between the practical function of a definition and the context it is deployed in. We demonstrate that the variety of functions that health and disease concepts need to serve makes the formulation of monistic definitions not only problematic but also undesirable. We conclude that the analysis of the practical function in relation to the context is key when formulating context-specific definitions for health and disease. At last, we discuss challenges for the pluralist stance and make recommendations for future research.

How to Draw the Line Between Health and Disease? Start with Suffering

How can we draw the line between health and disease? This crucial question of demarcation has immense practical implications and has troubled scholars for ages. The question will be addressed in three steps. First, I will present an important contribution by Rogers and Walker who argue forcefully that no line can be drawn between health and disease. However, a closer analysis of their argument reveals that a line-drawing problem for disease-related features does not necessarily imply a line-drawing problem for disease as such. The second step analyzes some alternative approaches to drawing the line between health and disease. While these approaches do not provide full answers to the question, they indicate that the line-drawing question should not be dismissed too hastily. The third step investigates whether the line-drawing problem can find its solution in the concept of suffering. In particular, I investigate whether returning to the origin of medicine, with the primary and ultimate goal of reducing suffering, may provide sources of demarcation between health and disease. In fact, the reason why we pay attention to particular phenomena as characteristics of disease, consider certain processes to be relevant, and specific functions are classified as dys-functions, is that they are related to suffering. Accordingly, using suffering as a criterion of demarcation between health and disease may hinder a wide range of challenges with modern medicine, such as unwarranted expansion of disease, overdiagnosis, overtreatment, and medicalization.

Overdiagnosis: one concept, three perspectives, and a model

Defining, estimating, communicating about, and dealing with overdiagnosis is challenging. One reason for this is because overdiagnosis is a complex phenomenon. In this article we try to show that the complexity can be analysed and addressed in terms of three perspectives, i.e., that of the person, the professional, and the population. Individuals are informed about overdiagnosis based on population-based estimates. These estimates depend on professionals' conceptions and models of disease and diagnostic criteria. These conceptions in turn depend on individuals' experience of suffering, and on population level outcomes from diagnostics and treatment. As the personal, professional, and populational perspectives are not easy to reconcile, we must address them explicitly and facilitate interaction. Population-based estimates of overdiagnosis must be more directly informed by personal need for information. So must disease definitions and diagnostic criteria. Only then can individuals be appropriately informed about overdiagnosis.

Back to Basics: Overdiagnosis Is About Unwarranted Diagnosis

Epidemiologic studies of overdiagnosis are challenged by unclear definitions and the absence of unified measures. This spurs great controversies. Etymologically, overdiagnosis means too much diagnosis and stems from the inability to distinguish what is important from what is not. Accordingly, in order to grasp, measure, and handle overdiagnosis, we should revive medicine's original goal and reconnect diagnosis to what matters to professionals and patients: knowledge and suffering, respectively. This will make overdiagnosis easier to define and measure, and eventually less difficult to reduce.

Treating real people: Science and humanity

Something important is happening in applied, interdisciplinary research, particularly in the field of applied health research. The vast array of papers in this edition are evidence of a broad change in thinking across an impressive range of practice and academic areas. The problems of complexity, the rise of chronic conditions, overdiagnosis, co-morbidity, and multi-morbidity are serious and challenging, but we are rising to that challenge. Key conceptions regarding science, evidence, disease, clinical judgement, and health and social care are being revised and their relationships reconsidered: Boundaries are indeed being redrawn; reasoning is being made "fit for practice." Ideas like "person-centred care" are no longer phrases with potential to be helpful in some yet-to-be-clarified way: Theorists and practitioners are working in collaboration to give them substantive import and application.

Getting personal on overdiagnosis: On defining overdiagnosis from the perspective of the individual person

How can overdiagnosis be defined, explained, and estimated on an individual level? The answers to this question are essential for persons to be able to make informed choices and give valid consents for tests. Traditional conceptions of overdiagnosis tend to depend on counterfactual thinking and prophetic abilities as you would have to know what would happen in the future if you did not test now. To avoid this, overdiagnosis can be defined in terms of the chance of diagnosing a person with a disease when this does not avoid or reduce manifest disease. To be able to relate this to 1's own life and deliberation, I argue that we need answers to specific questions such as the following: If I am tested, and the test and subsequent test results are positive, but I am not treated, what is the chance that I would not experience and suffer from manifest disease? A definition of overdiagnosis that aims at providing answers to this question is as follows: Prospectively overdiagnosis (of an individual person) is given by the estimated chance that a person having a positive test result would not experience and suffer from manifest disease if not treated or followed up in any way. Getting personal on overdiagnosis directs the attention of overdiagnosis estimates towards what matters in medicine: the experience of individual persons.

Is an indistinct picture "exactly what we need"? Objectivity, accuracy, and harm in imaging for cancer

Assumptions about the epistemic ideal of objectivity, closely related to ontological assumptions about the nature of disease as pathophysiological abnormality, lead us into oversimplified ways of thinking about medical imaging. This is illustrated by current controversies in the early detection of cancer. Improvements in the technical quality of imaging failed to address the problem of overdiagnosis in breast cancer screening and exacerbate the problem in thyroid cancer diagnosis. Drawing on Douglas and on Daston and Galison, I distinguish 3 dimensions of objectivity (accuracy, reliability, and precision) and demonstrate ways they may be at odds, as illustrated in the early detection of cancer. Guidelines for evaluating the efficacy of diagnostic imaging are insufficiently sensitive to this complexity. Improving imaging quality may raise epistemic issues, place disease definitions in question, and lead to overall harm or to changes in the distribution of harms and benefits among population subgroups. With a nod to Wittgenstein, I argue that we cannot take for granted that "an indistinct picture" is not "exactly what we need."