There’s a moment that a lot of people navigating a frightening diagnosis eventually arrive at: the growing sense that something about the system doesn’t add up. A promising approach that never seems to get studied at scale. A recommended treatment that happens to be expensive while a cheaper alternative gets dismissed. A guideline written by a committee with financial ties to the industry it’s regulating. None of these observations are imagined. They’re often accurate. The question is what to make of them.
Understanding Incentives Instead of Looking for Villains
The most emotionally satisfying explanation for these patterns is also usually the least accurate one: someone is hiding something on purpose. That explanation has the shape of a story — a hidden truth, a guilty party, a moment of revelation — and stories like that are compelling precisely because they resolve the discomfort of not knowing. But a more useful question exists, and it doesn’t require anyone to be secretly malicious: what does the system these people are operating inside actually reward?
An incentive is anything that makes a particular behavior more likely — not just money, but career advancement, professional standing, social approval, and the simple avoidance of criticism. Asking what a system rewards explains a great deal of behavior without requiring that the people inside it be either dishonest or foolish. It applies just as much to a wellness influencer optimizing for audience growth as it does to a pharmaceutical company optimizing for profitable indications, or a hospital optimizing for the metrics it’s evaluated on.
Good People, Bad Outcomes
A useful real-world case study here is the Replication Crisis — the well-documented, ongoing effort across multiple scientific fields to identify how many published findings fail to hold up when other researchers try to reproduce them. This isn’t evidence that science is broken. It’s evidence of exactly the opposite: a field noticing a systemic problem in its own incentive structure — where publishing positive, novel results advances careers far more than publishing null results or straightforward replications — and building new norms, registries, and standards to correct it.
That’s not a story about bad actors. It’s a story about ordinary, well-intentioned people responding rationally to what their field rewarded, and a field mature enough to catch the pattern and correct course. The correction itself is the reassuring part.
When a Measure Becomes a Target
There’s a well-documented pattern in economics that applies far beyond its original context. Goodhart’s Law, named for British economist Charles Goodhart, holds that once a measure becomes a target, it stops being a reliable measure — because people naturally start optimizing for the number itself rather than for what the number was originally meant to track. A related idea, Campbell’s Law, from social scientist Donald T. Campbell, makes a similar point: the more a quantitative indicator gets used to make real decisions, the more it becomes vulnerable to being gamed.
None of this requires anyone to be acting in bad faith. It’s what happens, predictably, when a system optimizes for what’s easy to measure rather than what’s actually best for the person in front of it.
Healthcare has its own versions of this. A hospital evaluated heavily on readmission rates has a real incentive to reduce readmissions — which sounds purely good, and often is, but can also quietly discourage admitting a genuinely borderline patient who needs the care. A length-of-stay metric designed to track efficiency can, taken too far, start shaping discharge timing more than the patient’s actual readiness.
Where This Leaves You
None of this is a case for assuming the worst about the people and institutions involved in your care. It’s the same evenhanded posture applied in the other direction: you don’t need everything to be reduced to a hidden conspiracy to justify healthy scrutiny, and you don’t need to trust every recommendation completely just because assuming bad faith feels unfair. Understanding incentives is a middle path between blind trust and blanket cynicism — one that lets you evaluate a claim or a recommendation without needing to first decide whether the person making it is a hero or a villain.
That’s a more demanding way to think, and also a more accurate one. I write more about applying this kind of evaluation to specific claims and recommendations in “Navigating Cancer Between Hope and Hype.”
Read the Full Story
This post touches one chapter. The full framework is in “Navigating Cancer Between Hope and Hype.”