The Clinical Reasoning Series - A label too far: Overdiagnosis and medicalisation with Prof. Bjørn Hofmann
Mar 24, 2022
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Philosopher of medicine Prof. Bjørn Hofmann discusses overdiagnosis and medicalization. They explore the distinction between the two concepts and the driving factors behind them. They also touch on the positive and adverse effects of giving diagnoses. The role of AI and machine learning in improving diagnostic accuracy is highlighted. The podcast delves into the connection between medicalization and overdiagnosis, as well as the ethical complexities of determining necessary medical procedures.
Medicalization and over-diagnosis are two different critiques of the medical field, with medicalization focusing on social aspects of disease and over-diagnosis emphasizing advancements in detection.
Diagnostic labels have positive effects, providing comfort and guidance, but over-reliance can lead to over-diagnosis, highlighting the need for caution, accuracy, and relevance in predictions.
Deep dives
Distinguishing Medicalization and Over-diagnosis
Medicalization and over-diagnosis are both critiques of the way medicine and healthcare have developed. Medicalization, originating from outside of medicine, is the process of defining and treating human problems as medical problems. Over-diagnosis, on the other hand, stems from within medicine and refers to the diagnosis of conditions that would not cause symptoms, disease, or death. While medicalization highlights the social aspects of disease, over-diagnosis focuses on the advancements in detecting precursors and indicators of disease. Both critiques have different origins and drivers, but they converge in their concern over the excessive use of medical tools.
The Effects of Diagnostic Labels
Diagnostic labels have both positive and adverse effects. They provide an explanation and comfort to patients, validating their experiences and guiding healthcare professionals in determining treatment options. However, over-reliance on diagnostic labels can lead to over-diagnosis, where conditions are diagnosed and treated unnecessarily, potentially causing harm to patients. The asymmetry of aversion among health professionals, where it is worse to overlook something than to overdo something, can contribute to the problem of over-diagnosis. The use of AI and machine learning holds promise in improving the precision of diagnosis, but caution is needed to avoid false alarms and ensure the accuracy and relevance of predictions to individual patients.
The Role of High- and Low-Value Care
The concept of high- and low-value care plays a significant role in medicalization and over-diagnosis. High-value care refers to interventions or treatments that lead to improved health outcomes or reduced suffering, while low-value care offers little benefit and may even cause harm. Healthcare economics and financial considerations influence the extent to which diagnostic tools are utilized. Even within healthcare systems with financial caps, low-value care can still exist. The challenge lies in finding the balance between using diagnostic tools responsibly and avoiding unnecessary or excessive interventions.
Reducing Disease Expansion and Over-Diagnosis
An important step in reducing the detrimental effects of disease expansion and over-diagnosis is recognizing different types of expansion. These include epistemic expansion driven by increased knowledge and advancements, ontological expansion where new conditions are classified as diseases, and pragmatic expansion rooted in the possibility of intervention. Conceptual expansion, ethical expansion, and aesthetic expansion also contribute to disease expansion. Professionals and individuals should critically evaluate the value and implications of expanding disease definitions and be cautious of overusing diagnostic tools. Reflecting on the ethical and societal aspects can help ensure appropriate diagnoses that prioritize patient well-being.
And for reference we speak around Bjørn’s 2016 paper titled "Medicalization and overdiagnosis: different but alike." Published in the journal Medicine, Health Care and Philosophy (see paper here and see Bjørn’s other work on the topic here)
In this episode we speak about:
Distinguishing between the concepts of medicalisation and overdiagnosis and discuss their main drivers.
How medicine, health care, and health professionals have become ever more diligent in defining, detecting, preventing, and treating disease – covering more ground than ever and how this can lead to the adverse situation of overdiagnosis.
The positive and adverse effects of giving someone diagnosis
What Bjørn terms the ‘asymmetry of aversion’ meaning that for many health professionals is worse to overlook something than to over do something which may facilitate over diagnosis.
The role of AI and machine learning to address the crudeness and imprecision in some our diagnostic labelling and processes.
High and low-value care and the role of healthcare economics on how readily we dip into the diagnostic toolkit and medical testing.
How the expansion in the concept of disease (and diagnosis) has lead to over diagnosis and medicalization
And finally we discuss what can we do to reduce the detrimental expansion of disease and subsequent over diagnosis.
So this was another wonderful conversation with Bjørn. He is able to transfer incredibly thought provoking yet fundamental questions to clinical practice and our care of people, and I have immensely grateful to him for giving up so much of his time.