Groopman’s position, when his various arguments are gathered and assembled, becomes untenable. He admits doctors suffer from innumerable biases that diminish the accuracy of diagnosis, reducing many diagnoses to idiosyncratic responses fueled by mood, whether the patient is liked or disliked, advertisements recently seen, etc. Thus Groopman agrees with decision scientists’ diagnosis of doctor decision making; but then he goes on to wantonly dismiss what many of the very same researchers claim is the best (and perhaps only) remedy, the way to “debias” diagnosis: evidence-based medicine and the use of decision aids. In place of statistics what does Groopman suggest doctors rely on? Clinical intuition of course, the very source of the cognitive biases he pays lip service to throughout his book.
...One study famously showed that a successful predictive instrument for acute ischemic heart disease (which reduced the false positive rate from 71% to 0) was, after its use in randomized trials, all but discarded by doctors (only 2.8% of the sample continued to use it).5 It is no secret many doctors despise evidence-based medicine. It is impersonal “cookbook medicine.” It is “dehumanizing,” treating people like statistics. Patients do not like it either. They think less of doctors’ abilities who rely on such aids.6
The problem is that it is usually in patients’ best interest to be treated like a “statistic.” Doctors cannot outperform mechanical diagnoses because their own diagnoses are inconsistent. An algorithm guarantees the same input results in the same output, and whether one likes this or not, this maximizes accuracy. If the exact same information results in variable and individual output, error will increase. However, the psychological baggage associated with the use of statistics in medicine (doctors’ pride and patients’ insistence on “certainty”) makes this a difficult issue to overcome.
It is better to be certainly wrong than statistically right. That is, doctors and patients feel better if the doctor makes a diagnosis based on intuition about the specific case rather than based on some statistical model.
In hindsight, it often appears that one could have made the right decision using better intuition. That is why an anecdotal approach to looking at medical decision-making is biased in favor of intuition and against statistical models. That same sort of bias is at work in the cries for more regulation of financial markets in wake of the subprime mortgage problems. In hindsight, you think that a regulator could have prevented the problem. But that is a biased perception.