A reader writes:
However, you are missing the point with performance measurements and evidence based medicine. Goals for performance measurement are based on statistical models aimed at GROUPS of patients, not individual patients. In all clinical trials, some patients in the treatment group get better, some in the comparison group get better, and the reverse is true, some individuals in both groups get worse as well. A treatment becomes “evidence based†when the average improvement of all patients in the treatment group do better than the average improvement of all patients in the comparison group. This tells us that the targeted treatment is more LIKELY to be successful than whatever it is being compared to. There is no delusion on the part of “experts†that all patients will do better on the target treatment.
Oh contraire! I do believe the experts are confused. They do not understand the idea of stratification. They do not understand that a treatment can help some patients, harm some patients, yet on average be helpful. I have written about this recently -Lack of stratification – a flaw in EBM and guideline developement.
True Evidence Based Medicine would include stratification and adjustment for co-morbidities. Unfortunately, insurers and quality forums substitute averages when designing performance measures. This is not a moot argument, rather it forms the crux of the argument. Too many “experts” resort to the term evidence based without understanding the true meaning of evidence. The term has become a shorthand for those championing a position based upon some data. Evidence based as a term is supposed to stop the conversation. It has become way for experts to talk down to clinicians.
Evidence has layers. We must investigate those layers prior to developing performance measures. We should not critique clinicians based upon evidence which has not had appropriate testing.
When CMS adopted the 4 hour rule for pneumonia, they called it evidence based. Unfortunately they, like to many others spoke partial truth. Investigation of the results of the 4 hour rule showed the errors of thinking that can occur with evidence interpretation. I wrote about this issue extensively – What Einstein said
I will repeat what Onora O’Neill said because it has such relevance:
Bogus numbers can be more than an expensive irrelevance. They can create perverse incentives, especially when numbers are published in league tables for the public without the complex information needed to set them in context.
We have a moral responsibility reject numeric idiocy. We must rant against wrong thinking. For us to accept bad science and bad incentives would make us complicit in poor patient care. I personally refuse to accept these bad concepts. Fortunately, I have this blog and I can type my opinions. I must. To do otherwise would demonstrate cowardice.
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{ 5 comments… read them below or add one }
Finally, some sense. I’m all for the concept of evidence-based, but to completely dismiss other ideas because they are not evidence-based, means that the fact that we are each biochemically different is not considered. To suggest that we all require the same kind of treatment for a particular condition is illogical. Evidnece-based medicine will become truly powerful when it takes into account biochemical individuality. Currently, the way I see it, evidence-based medicine isn’t much more than playing the odds for the most part.
For example, from a nutrition perspective, to suggest that the entire population will become healthy if they actually follow the Food Guide is clearly misguided. Some people, such as fast oxidizers and parasympathetic dominants, achieve their ideal body-weight, lower their markers for disease etc. if they eat a high protein high fat low carb diet, while mixed oxidizers and balanced autonomics need an even mix of the macro-nutrients to be healthy, and slow oxidizers and sympathetic dominants will actually do well on the high carb Food Guide recommendations. It is time to recognize that we are all different biochemically, and food (and drugs) affect each of us differently. It is not the treatment that is important, but rather how the treatment interacts with the metabolism of the individual which will determine whether or not it will work. (This concept works with respect to nutrition, and I would bet that using these metabolic types would work well for drugs too, although if someone eats according to their metabolic type they probably will be healthy enough not to need any drugs.)
Until medical science stops basing its research on the flawed assumption that its subjects are the same biochemically, conclusive results won’t be had. Research needs to study metabolic differences. Rather than throw out the outliers and look at the middle ground, the outliers need to be thoroughly studied so we understand better why they are the exception rather than the rule. Subjects can be placed on a continuum based on metabolic testing, and grouped with those biochemically closest to them, and then treatment protocols can be tried on each group. I’m certain that then we would have more conclusive results, and we would better be able to give appropriate treatments for the individual. Medicine needs to get away from treating disease which is usually a one-size-fits-all kind of treatment, and focus on treating the imbalances in the individual. Then, finally, we will no longer be playing the odds, but rather giving personalized medicine that is geared to the metabolism of the individual. That is when we will truly have evidence-based medicine.
Vreni: EBM accounts for biochemical individuality. It’s called aleatory uncertainty and it’s one element of error (and sensitivity/specificity). Also, beware the broken leg argument.
The Dec. 5th Wall Street Journal editorial page has an interesting take on this issue in The Science of Gore’s Nobel by Mr. Jenkins. On a personal note, I was unable to cut and paste due to the inability of the site to remain stable. Heavily edited on my part we have the following.
Moving away from the issue of global warming we find this reference: “..another Nobel, awarded in 2002 to Daniel Kahneman for work he and the late Amos Tversky did on “availability bias,†roughly the human propensity to judge the validity of a proposition by how easily it comes to mind.
Their insight has been fruitful and multiplied: “Availability cascade “ has been coined for the way a proposition can become irresistible simply the media repeating it; “informational cascade†for the tendency to replace our beliefs with the crowd’s beliefs; and “reputational cascade†for the rational incentives to do so.
Here’s exactly the problem that availability cascade pose: What if the heads being counted to certify an alleged “consensus†arrive at their position by counting heads?
It may seem strange that scientist would participate in such a phenomenon. It shouldn’t. Scientist are human; they do not wait for proof; many devote their professional lives to seeking evidence for hypotheses (especially well-funded hypotheses) they’ve chosen to believe.â€
Relevant to this discussion is that we see EBM, P4P, and other ideas being promoted by the press, and others with a financial interest, until a consensus is achieved, making any questioning of the concept unacceptable.
In the old days we simply said; if you tell someone, something often enough, they will begin to believe it. Standards and technology will not solve all of our medical ills.
Steve Lucas
Hi Alexa,
I had never heard of aleatory uncertainty until you mentioned it, and I tried to bring myself up to speed. Maybe you can explain it to me better, but it seems to be about taking into account the probability that there are differences, but I don’t see anything about defining what those differences are, and looking at why those differences might be relevant. It seems to simply be an acknowledgment that there are differences, and leaving it at that. Have I got that wrong? I wouldn’t be surprised if I do!
My point is we need to examine the differences in detail, rather than try to fit everyone into a particular model and then use statistics to account for any differences that might show up. We don’t learn much that is useful by trying to fit square and triangular pegs into round holes and then averaging across the board in my opinion. The treatment will only work for the round pegs. Admitting that there is variability in the shape of the pegs is not helpful, until the other shapes are recognized and studied on their own.
I also agree with Steve that the informational cascade is a big problem. It is considered dogma that diets high in saturated fats cause heart disease, yet heart attacks were extremely rare before 1900 when people consumed high saturated fat diets. Since that time fat consumption has plummeted, and refined vegetable oil and sugar consumption has skyrocketed. By 1950 heart disease was the leading killer of North Americans. Even today, if one looks at the current food data, saturated fat consumption is continuing to decline, and refined vegetable oils sugar consumption is continuing to rise. Makes no sense that fat and cholesterol are the problem, yet due to the informational cascade, I’ll probably be considered a heretic or a quack for suggesting that the high carbohydrate food guides at best probably won’t work and at worst, will cause type 2 diabetes and heart disease for many people. It seems that because the idea that low fat diets are healthy is what has been repeated over and over again, everyone believes it, and very few people have noticed that the evidence does not add up.
I just finished writing a paper on the theoretical applications and the models of DVR or digital video recorders. I submitted it to a program called safeassign which checks for plagiarism. I just thought the owner of this website might find that to be comical.
- Alex