What is an iatrogenic effect? Is this the result of fragile systems?
An iatrogenic effect is when harm is caused as a result of a medical treatment or intervention. Sometimes, harm is caused because of the fragile medical system, and humans’ intense need to do something in the face of suffering.
Read more about the iatrogenic effect and how human intervention can cause fragility.
Interventionism and Fragility
In Chapter 21 we explore how and why human intervention so often causes more problems than it solves. In short, it’s because we’re pitting our fragile models and theories against nature’s countless years of evolution and antifragility. We also explore the idea of exponential benefit and harm—effects that rapidly outpace the size or apparent significance of the events that caused them. Medicine provides excellent examples of both points.
Chapter 22 is about how human efforts to live forever are doomed to fail—and why that’s a good thing. Without individual fragility, there can be no societal antifragility. This argument grows from the points laid down in Chapter 21 about how human interventionism does more harm than good.
Intervention Leads to Fragility
Remember the definition of iatrogenics: unintended harm from medical treatment. Iatrogenics is common due to two logical flaws.
The first flaw is the human need to do something. Even if someone has a minor injury or disease that will heal perfectly well on its own, many people—especially doctors—feel like they have to intervene. Someone with a mild fever may take aspirin to bring it down to normal, or put ice on a swollen nose to take the swelling down—even though there’s no evidence that doing so helps it to heal faster.
The second flaw is mistaking a lack of evidence for evidence. For example, smoking was once considered to have mental and physical health benefits. The harm that it caused wouldn’t become obvious for many years, and people mistook that lack of evidence for evidence that smoking isn’t harmful.
These two tendencies combine to create a situation where harmful drugs and procedures—which we don’t yet know are harmful—are given to people who would have been just fine without them.
Statisticians have estimated that reducing medical expenditures (only up to a point and only on elective treatments) would actually increase the average lifespan in wealthy countries like the U.S. by reducing iatrogenics. This is one large-scale application of the “less is more” principle.
Convexity in Medicine and the Iatrogenic Effect
This isn’t an argument that medical care should never be given, just that we should be much more discerning about when and how much we intervene. The iatrogenics of any given drug or treatment are linear—they increase or decrease consistently with how much of the treatment is given. However, the benefits of that treatment can be convex (having accelerating benefits) based on how severe the patient’s condition is.
For example, there’s a particular drug that treats high blood pressure. When a patient suffers from mild hypertension, the effectiveness rate of this drug is only 5.6%. However, when the patient’s blood pressure is in the “high” range, the effectiveness rate is 26%; in the “severe” range it climbs to 72%. However, the side effects of the drug are consistent across all of those categories.
Clearly, then, the trick is to only intervene when the benefits outweigh the risks. In the case of the heart medication described above, it doesn’t make sense to give it to someone who only has mild hypertension; the patient will get all the downsides and probably none of the benefits. However, for someone who would die or have severely reduced quality of life without medical intervention, the iatrogenics are relatively small. The person has little to lose, so there’s not much harm that can be done.
Fooled by Randomness
However, instead of this rational approach, people will try to intervene whenever there’s the slightest hint of a problem. Someone whose blood pressure is perfectly normal may, at times, be slightly above or slightly below normal just through random chance and variation. If it happens to be high when he’s at the doctor’s office, that doctor might prescribe totally unnecessary medication to lower it. In the end, this will probably cause more harm than good. This is a classic example of being fooled by randomness.
People are fooled by randomness when they mistake a single data point, like an elevated blood pressure, for a trend. Perhaps the man had too much coffee that morning, or he was nervous about something; that one reading isn’t proof of a trend of high blood pressure, but the doctor took it as such.
A larger-scale example of being fooled by randomness would be the oft-cited statistic that the average lifespan used to be just 30 years, up until the 19th century or so. The key word there is average: It’s skewed heavily by people who died young, either at birth or in early childhood.
There’s also a case of epiphenomena here: confusing correlation with causation. It’s true that medicine has advanced greatly in the last few hundred years, and it’s also true that the average lifespan is much longer than it used to be. However, that doesn’t mean that one necessarily caused the other. There are many other possible explanations, such as improved sanitation and increased law enforcement, that could also account for (or at least contribute to) people’s longer lives.
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