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Twitter Survey: How do you feel about Healthcare?
Question: "What do you find most disappointing in your interactions with doctors or clinics/hospitals?"
Since having gained a substantial Twitter following, the thought occurred to use this new line of communication to good use by gathering feedback direct from the people.
In the corporate medicine model, feedback is solicited through patient satisfaction surveys which are computed into Press-Ganey Scores, and used to determine how a hospital may benefit or suffer as a result of their 'customer satisfaction.' No doctor is really made aware of the specific feedback a patient may provide for a given interaction.
Even if they are, and the feedback is negative, there is usually some round-about way of dismissing the patient's concerns.
They are just mad they had to wait. The nurse did so-and-so. That patient was just really difficult. Etc.
To start things off, I wanted to understand what peoples' typical or most memorable interactions with healthcare have been.
Before diving into my perspective on the failings of healthcare, I will share some replies which moved me. You will likely observe a trend yourself.
A rather coincidental Twitter handle 'Enough Already':
KirkD on critical thinking:
"They forget we are human." Ouch.
This one is just embarrassing:
As a Radiologist, this struck a chord:
One of the most common complaints:
Leaving patients in the dark:
Finally, from a doctor herself:
There are hundreds of more replies, but with some of recurring themes:
Lack of attention and consideration for the person in front of them as an individual
Lack of critical thinking
Swift & algorithmic approach to the practice of medicine
Overreliance on prescription drugs, vaccines, and interventions
Inability to discuss health outside the realm of one-size-fits-all algorithms
Ignorance of any mode of understanding health outside of Western Medicine
Ignorance of nutrition, habits, sleep, emotional stress, etc
The State of Affairs
Make no mistake, the above concerns & complaints are a strong indictment of the state of healthcare today. If we, as doctors, have any hope of earning the public's trust and respect, we need to change our approach.
The rise in popularity of private primary care services (in which patients pay out-of-pocket) are a strong signal of where the market is moving. Several of the Twitter responses said something along the lines of:
I'm tired of this model of insurance premiums, co-pays and out-of-pocket costs for 'healthcare' that provides no appreciable benefit.
I would rather pay my doctor directly.
Finally, it's important to note that all of the above are legitimate pre-2020 era complaints – but have likely worsened by magnitudes in the last 2 years.
My 2 Cents
There will likely be some overlap between my sense of what is wrong, and what patients have described above. I would certainly hope there is, otherwise it would reflect a complete detachment from the patient experience on my part.
The events of recent years has directed my attention to the machinations of our economy. One school of economic thought that I have engaged with is that of the Austrian school – a product of the works of Hayek, Mises, & Rothbart, to name a few. Saifedean Ammous has enlightened me on the topic of monetary cycles, Keynesian economics and its relationship to inflation.
An eye-opening point he has raised on several occasions – either in relation to statistics or economics – is the fallacy of using aggregate data to understand a system that relies on the decision of individual actors in specific circumstances. This criticism dovetails nicely with one of the seminal works of the Austrian school, by Ludwig Von Mises, entitled Human Action.
The Aggregate Error
Whether you want to discuss the faults of medical management algorithms, the nature of high-volume hospital centers, or the financial incentives motivating your primary-care doctor to see as many patients as he can conceivably bill for...you can see the consequences of using aggregate data to care for the individual.
No single health-body is more guilty of this error than Public Health. When confronted with the fact that they are using aggregate data to inform sweeping policies that are naive to individual needs, the public health official will use the defense:
Oh, we are concerned with population-level health. That's why we use population data.
Reminds me of this wonderful scene from The Big Short, in which Mark Baum is flabbergasted by the audacity of the foot soldiers contributing to the mortgage market crisis of 2008.
I don't get it. Why are they confessing? They're not confessing. They're bragging.
Not Just Public Health
Public Health is merely the epitome of aggregate error in the healthcare enterprise, but not the only culprit.
Management algorithms are relatively simple 'if A then B' instructions for doctors to enact when confronted with a specific complaint or scenario. I still remember my internship days. We would go check up on our patients first thing in the morning. Then, we would meet with the team and faculty physician and discuss each case.
The faculty would take any opportunity to test our 'knowledge' (aka rote memorization) of these algorithms. The trainee that is able to rattle off the algorithm is praised - and they would wear it as a badge of honor. When it was my turn, I would usually say some smart-ass remark like "yea, yea - ABC and XYZ. But, my patient..."
Albeit comically, the entire process is reminiscent of this:
Here is a sample management algorithm for obesity, updated by the American Association of Clinical Endocrinology in 2013:
As you can see from the diagram above, the AACE has done a marvelous job of reducing the complexity of obesity and related complications into a 3-step process designed for success. Evidence of their success can be found below:
Of course, the lack of efficacy of these management algorithms will be blamed on the patient's lack of willingness and motivation to get healthy. Through no fault of the doctor or medical establishment.
I could harp on this for ages, but it's probably better suited for another format (audio, perhaps?).
Weaponized Aggregate Error
It is not merely in the realm of management algorithms in which the aggregate error rears its ugly head.
In the realm of clinical research - AKA pharma-funded randomized control trials (RCT) - the aggregate error is consciously weaponized. Let's take the example of Statins, a drug used to lower cholesterol. The companies which develop cholesterol-lowering drugs have a rather challenging task before them. How can you show a health or mortality benefit of a drug which purports to reduce lifetime risk of an adverse outcome, like stroke or heart attack? How would you even be able to prove that your intervention was the one thing which ended up lowering these risks to any appreciable degree after so many years?
This is even more challenging if the reduction in risk is relatively small (e.g. a relative risk reduction of 5-10%). But, by using very large sample sizes (thousands) you can reduce the magnitude of the standard deviation enough to make your result statistically significant. This is a function of hypothesis testing in statistics, in which lack of overlap between the confidence intervals of results signify a difference which is non-random.
Once you hit 'statistical significance,' you are off to the races. With an effective marketing strategy and purchasing influence from within the industry, you are on your way to a blockbuster drug. Which is precisely the case with Statins.
It doesn't matter that cholesterol is a molecule that is vital for all life. It doesn't matter that Statins block an enzyme so critical to human life, that it can interrupt the production of thousands of molecules your cells need for normal function. It doesn't matter that statins provide barely any mortality benefit, and in many cases the harms outweigh the marginal benefits. Lastly, it doesn't matter that the foundation of statin use, the cholesterol-heart hypothesis, is a brilliant work of fraud.
What matters is:
RCT at a size that only Big Pharma can afford
Boom, you made it into the management algorithm.
The industry knows this all too well, and uses it to its advantage all the time.
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