Healthcare is mired in a series of false starts. First it was the COVID-19 pandemic, then the vaccine roll-out, and now, the latest is monkey pox. It feels as though healthcare cannot help but get in its way, over and over.
We blame politicians. We blame policy. But in reality, we have no one to blame. These false starts are not some problem plaguing healthcare for which we have to find a solution. Rather, they are a feature.
Healthcare by nature is reactionary. We observe things over time and react to the changes iteratively. Rarely do we preemptively give medication for a disease that we have yet to diagnose. And when we do, we usually have a strong suspicion for what it could be, and we call it empirical treatment.
It is a fancy way of saying we make guesses based on what we see, what our experiences tell us. But this is a tricky game, because appearances can be deceiving, particularly in medicine. An initial presenting diagnosis may look one way, and then change, based on additional information gleaned either through clinical evaluation or additional testing.
Ultimately, it is the constellation of symptoms and signs that determine the diagnosis, which takes time to collect. But in the interval, while waiting for the information to present itself, we hedge toward what we think the diagnosis could be.
We hedge toward what we know. In medicine, this is considered good clinical practice. But nothing in medicine is entirely good or bad. And what can be good in one scenario can be quite the opposite in another. This overreliance on the familiar produces a bias, which we aptly name familiarity bias.
It is one of the most common cognitive biases affecting clinicians. We gravitate toward what we know until what we know becomes all there is to know. It is why patients with rare diseases are misdiagnosed at first. These patients go through a gauntlet of more common diagnoses with more common treatment options, until they all prove to be wrong.
This is familiarity bias manifesting over patient care. Eventually, we arrive at the right diagnosis and provide the correct treatment, but only after iteratively parsing through multiple clinical conditions. It is how healthcare works. It is the scientific method applied to clinical decision-making.
The problem comes when we apply this thinking to health policy. We forget that healthcare is not only a science, but an art as well. Or, as Sir William Osler, the progenitor of modern medicine would say, “a science of uncertainty and an art of probability”.
But we dislike uncertainty in health policy. We like quick fixes. They are politically advantageous and make for better public narratives. Just look at recent history. The first year of the COVID-19 pandemic coincided with the 2020 presidential election. Nothing health policy related was free of political implications. Every COVID projection, data point, or policy had some ramification on the pending election – and politicians distorted all things clinical to be politically favorable.
As a result, we saw a slew of narratives from various policy leaders with overt political agendas. Some decried the pandemic as a hoax, some said it was like a “bad case of the flu”, while others advocated for draconian lockdowns.
In hindsight, none of these narratives were entirely correct. Just like nothing in medicine is entirely correct or incorrect. Healthcare is a complex blend of changing information that dynamically shifts over time.
No single health policy is uniformly right all the time because such a concept does not exist in healthcare. No single narrative can ever be truly right from start to finish. More than anything else, these narratives are first impressions.
They are the initial thoughts a clinician has about a patient diagnosis before all the clinical information has materialized. But unlike clinicians who know they need more information, policy wonks touting these narratives never considered changing their tune as more information trickled in.
They sang the same song regardless of any new information or data. For them, whatever corroborates the initial belief is valid, and whatever refutes it is invalid. Through such thinking, we create selective echo chambers in which people with similar first impressions reiterate their preexisting beliefs, over and over.
But medicine is not designed to be reiterated like this. It is iterative, changing over time as we glean new information. And what is true for the individual patient remains true at the policy level. Only we like to believe it is not. Rather, those at the helm of health policy like to believe it is not.
They would rather stick with erroneous beliefs than admit they are unsure of what to do. So we have with multiple false starts based on incomplete first impressions.
