Healthcare is in a tailspin as the rush to offer technology and services turns otherwise healthy people into concerned patients by identifying disease that is not destined to cause them harm.  As described in the many presentations at the 2018 Preventing Overdiagnosis conference, from cancers to rheumatology, to incidentalomas identified on imaging tests—the tree of overdiagnosis branches into many areas of medicine. [2-10] While usually based on well-intended efforts to identify disease at an early stage of diagnosis, it can result in harm when patients undergo treatment that ultimately will not benefit them while exposing them to all the harms associated with the treatment and management. It can also create anxiety, affect quality of life, and in some cases, cause harm to others. 
As the tree of overdiagnosis has grown, efforts have been made to trim the branches. Initiatives such as Preventing Overdiagnosis, Too Much Medicine, Slow Medicine aim to increase our understanding of how it manifests itself. Efforts such as Choosing Wisely are underway to affect policy and change patient expectations and to change well-entrenched medical practices. 
The tree of overdiagnosis is rooted in an approach to medical care that seeks to identify pathophysiology, with scant acknowledgement of the role of conditional probability in medical practice. First codified in the United States by the Flexner Report in 1910,this rationalist paradigm results in learners prioritizing a strictly pathology-based, disease-focused process for medical decision making, one that makes difficult the shedding of a linear cause-and-effect relationship.  The resulting trust in inductive logic, which is fertilized with a combination of activism, defensive medicine, and the overselling of medical care as the solution to many aspects of the human condition, leads to overdiagnosis.
Biological plausibility, though, is a necessary, but not sufficient basis for decision-making. This inductive approach needs to be tempered when findings from outcomes trials do not support its conclusions.
As any arborist knows, it is easier to train a tree early in its growth rather than attempting heavy pruning after the tree is well established. Rather than pruning one branch at a time, we need to address the roots of overdiagnosis by introducing probability-based medicine throughout medical training. At each step in the learning process, the certainty of biomechanical thinking needs to be contrasted with the uncertainty of clinical medicine, the high ground of Schön’s “technical rationality” replaced with the swampy lowlands of clinical practice.  Every carefully-explained mechanism needs to be clinically correlated with current outcomes-based research. Only then can learners embrace the concepts of overdiagnosis and overtreatment.
Many medical schools do provide some training in the basic science of clinical epidemiology, as described by David Sackett and colleagues, along with the principles of Bayes’ theorem.  Most students, however, imprinted on a pathophysiologic-based decision making model, lack reinforcement of the inherent uncertainty in the algorithms they learn.  In residency education, the pattern recognition that develops needs to be augmented with an understanding of the current evidence supporting the newly-learned scripts, as well as the awareness to know when to question one’s current knowledge and take the time to find, evaluate, and apply the best current evidence.
To create future clinicians with the flexibility needed to understand and accept such concepts as overdiagnosis, and how to minimize the harm in perpetuating it, we need to evolve early medical education so that it focuses on the hierarchy of evidence and emphasizes patient-oriented evidence using probability-based decision-making. What ought to work needs to be subordinated to what has been shown to work.
Allen F. Shaughnessy, Tufts University School of Medicine, Boston, MA USA
David C. Slawson, University of North Carolina at Chapel Hill, Atrium Health, Charlotte, NC USA
Competing interests: None declared
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