Introduction
Modern medicine offers unprecedented measurement precision; however, the author of the book Bad Influence warns against the trap of data overload. This article analyzes the gap between the technical ability to detect anomalies and their actual clinical significance.
Readers will discover why the obsessive optimization of biological parameters can lead to a phenomenon known as biomedicalization. The text explains how routine screening transforms healthy individuals into "patients-in-waiting," managing a portfolio of risks rather than functional wellness.
More Data Does Not Equal Greater Certainty
The author argues that increasing the number of tests does not always lead to a more accurate diagnosis. There is a tipping point beyond which additional testing generates more ambiguity than certainty, a result rooted in diagnostic mathematics and Bayes' theorem.
High device sensitivity does not guarantee clinical benefit if the pre-test probability is low. An example is the AMH test, which, while measuring ovarian reserve, does not provide a reliable prediction of the chances of natural conception for women without infertility issues.
The value of a test lies in the decision it informs, not in the detection itself. A test is useful only when the result changes rational medical management, rather than merely providing an illusory sense of control.
The Trap of Overdiagnosis and the Illusion of Life-Saving
The author emphasizes the distinction between a diagnostic error and overdiagnosis. The latter occurs when a real biological change is detected that would never have caused symptoms or led to the patient's death.
Screening asymptomatic individuals often leads to unnecessary treatment of pathologies that would have remained dormant. The author cites South Korea as an example, where mass thyroid screenings drastically increased the number of cancer diagnoses without simultaneously reducing mortality rates.
This leads to a cascade of care, or a diagnostic cascade. A single incidental signal triggers a series of costly and stressful procedures that ultimately do not improve health, but merely extend the time spent living under a patient label.
High Diagnostic Sensitivity Does Not Guarantee Clinical Benefit
Whole-body scans (whole-body MRI) in healthy individuals often detect so-called incidentalomas. While the technology is efficient, most of these findings are biologically inert and do not indicate disease.
The author notes that striving for "optimal" rather than "normal" results is a hallmark of wellness marketing. A result outside the statistical norm does not always signify pathology; it may simply be an expression of natural biological variability.
Modern biomedicalization shifts the focus from the ontology of disease to the ontology of risk. Health ceases to be the absence of symptoms and becomes an endless project of biomarker management, often leading to the privatization of testing profits and the socialization of anxiety costs.
Summary
Health should not be defined as static compliance with ideal metrics, but as the organism's dynamic ability to adapt. The pursuit of biological perfection can, paradoxically, strip us of our functional freedom.
In striving for the ideal of optimal parameters, are we trading the freedom of living for a slavery to our own charts? The more perfect our measurement tools become, the more we risk losing the ability to be imperfect and yet fully functional.
Frequently Asked Questions
Does performing more tests and having more accurate data always allow for a more precise diagnosis?
No, a greater amount of data does not always increase the quality of the diagnostic decision. There is a point beyond which further testing generates more ambiguity than certainty, because a test result depends on the clinical context and pre-test probability.
1. What is the difference between overdiagnosis and a misdiagnosis, and why can screening lead to unnecessary treatment?
2. Overdiagnosis differs from misdiagnosis in that the condition meets the criteria for disease but would never have caused symptoms or death. Screening can lead to unnecessary treatment because medicine cannot determine in advance whether a detected pathology will remain biologically harmless.
3. How often do whole-body scans of healthy individuals detect something concerning, and does this always indicate a disease?
4. Whole-body scans of asymptomatic individuals often detect deviations or incidental findings; however, most of these are benign. A confirmed malignancy is identified in only a small percentage of those tested (from approx. 1.1% to 1.57%), meaning that not every concerning finding indicates a disease.
5. Why do increasingly precise diagnostic tests not always translate into better health outcomes for the patient?
6. Higher resolution imaging allows for the detection of minor anomalies (so-called 'incidentalomas'), which are often biologically inert or ambiguous. This can trigger a 'cascade of care'—a series of additional costly and risky diagnostic procedures that do not improve health but only increase the patient's uncertainty.
7. Can an AMH test result serve as a reliable prediction of the chances of conceiving naturally?
8. No, the AMH test result is not a reliable predictor of the chances of natural conception. While it helps assess ovarian reserve and response to hormonal stimulation, it is a poor predictor of current reproductive potential; age remains a significantly stronger indicator of success.
9. Why does a test result outside the norm or below the optimal level not always indicate a disease, and what role does medical marketing play in this?
10. A result outside the norm does not always indicate a disease because it may result from physiological variability, individual bodily characteristics, or the statistical distribution of results in a healthy population. Medical marketing exploits this gap and the concept of an 'optimal' level to transform normal results into signals for intervention and the sale of wellness services.
Why does having more test results not always translate into better medical care?
Modern medicine is capable of detecting more biological details than it can unambiguously interpret, leading to an excess of data with unclear clinical significance. This can result in so-called low-value care, where cascades of unnecessary tests generate costs and stress without bringing real health benefits.
Why is the mere performance of tests and the discovery of anomalies not equivalent to an accurate medical diagnosis?
In correct clinical reasoning, a test should be an answer to a previously formulated hypothesis, rather than the starting point for building one. Simply finding an anomaly can lead to an overproduction of interpretations and the incorrect matching of symptoms to results, whereas the value of a test lies in whether it allows for a better medical decision.
Does a test result outside the statistical norm always indicate a disease?
No, a result outside the statistical norm does not always mean disease, because biological difference is not identical to a condition requiring intervention. One can have statistically atypical values that do not cause any dysfunction and are compatible with the proper functioning of the organism.
What is health in reality, and why might striving for ideal biological parameters be mistaken?
Health is the normativity of the organism, meaning its ability to adapt and create new ways of functioning under changing conditions. Striving for ideal parameters can be mistaken because a healthy organism is not a machine with constant values, but a dynamic system for which natural variability and fluctuations are signs of efficiency.
How does modern biomedicalization differ from classical medicalization, and how does it affect the perception of a healthy person?
Classical medicalization involved recognizing new experiences as diseases, whereas biomedicalization is the techno-scientific transformation of healthy organisms for the purpose of optimization. As a result, the division between healthy and sick people disappears, and the category of 'patients-in-waiting' (the pre-disease human) emerges, whose condition is defined by a continuum of risk and biomarkers.
How does the ontology of disease differ from the ontology of risk, and how does this affect the perception of responsibility for health?
The ontology of disease describes the current state of the organism ("what is happening"), whereas the ontology of risk is based on a probabilistic relationship regarding future events ("what may happen"). Shifting the focus toward risk medicine leads to a redistribution of responsibility, where problems resulting from environmental structures and social factors are treated as an individual's personal optimization projects.
How does modern medicine change the perception of a healthy person, and where does rational prophylaxis end?
Modern medicine changes the perception of a human being, turning them into a manager of a personal risk portfolio and a digital biological profile. Rational prophylaxis ends where pathological prevention begins—treating mild deviations or risk factors as full-fledged diseases despite uncertain benefits from modifying them.
How does modern technology change the relationship between a person and their own body in the context of medical surveillance?
Modern technology leads to the emergence of an "auto-clinical gaze," in which a person monitors their own organism using a smartphone and sensors, dividing themselves into an observer and an observed object. This can result in alienation from one's own body, as trust in personal experiences is replaced by faith in algorithmic data from applications.
Is the pursuit of optimal biological indicator results equivalent to being healthy?
No, the pursuit of optimal results is not equivalent to being healthy, as it can transform health from a functional category into a ranking one. An obsession with metrics can lead to limiting life's freedom and the organism's adaptive capacities, which are key signs of health.
What is health actually in an era of excess diagnostic data, and how should we define a patient?
Health can be defined as the ability to adapt and independently manage physical, emotional, and social challenges, which allows people with chronic diseases to be considered healthy. A patient should not be defined by a biomarker deviation alone, but based on a multidimensional theory of clinical significance, taking into account, among other things, suffering, biological function, and the risk of harm.
What is the difference between treating diseases and striving for biological optimization, and what consequences does this have for human freedom?
Treating diseases focuses on reducing suffering and preventing significant ailments, whereas biological optimization aims to maximize the body's parameters. This leads to a change in the understanding of freedom: humans become less free due to the necessity of permanent self-monitoring and undergoing continuous biological correction.
