Surviving the Medical Meltdown

Surviving the Medical Meltdown by Lee Hieb Read Free Book Online

Book: Surviving the Medical Meltdown by Lee Hieb Read Free Book Online
Authors: Lee Hieb
might come forward with supportive data. Once again, physicians are afraid to advance real knowledge if it does not conform to the accepted norm. And you, the patient/customer, are given yesterday’s medical information.
    Adding insult to injury is the creeping odium of consensus in science – the notion that truth is discovered by majority vote among investigators, not by careful application of testing and scientific method. As Michael Crichton – a physician as well as an author – said in 2003, in a speech at Cal Tech:
Let’s be clear: the work of science has nothing whatever to do with consensus. Consensus is the business of politics. Science, on the contrary, requires only one investigator who happens to be right, which means that he or she has results that are verifiable by reference to the real world. In science consensus is irrelevant. What are relevant are reproducible results. The greatest scientists in history are great precisely because they broke with the consensus. There is no such thing as consensus science. If it’s consensus, it isn’t science. If it’s science, it isn’t consensus. Period. 1
    “Best practice,” is the new idea for improving medicine through standardization. It is essentially consensus applied to medicine. University clinicians decide on the best way to treat something; then this is written down in an algorithmic form and disseminated to all doctors. For example, we are told that it is best practice to give antibiotics within forty-five minutes of the surgical incision. So all over the country, hospitals attempt to comply by giving the antibiotic within forty-five minutes of the start of all operations. Predictably, what was first sold as a suggestion now is becoming law, reinforced by government and insurance third-party payers: fail to follow best practice and we will fail to pay you. So now, Medicare penalizes hospitals if the antibiotic is given forty-six minutes before cut time, instead of the maximum of forty-five.
    Unfortunately such clinical dogma ignores the fact that peopleare individuals with individualized problems. While the algorithmic approach may apply 90 percent of the time and may be a useful learning tool or reference point, the good physician needs to be able to vary treatment when his patient’s problem varies from the norm. In orthopaedics, for example, we are told to “anticoagulate all hip fracture patients” (give blood thinners) because, statistically, patients with hip fractures are at risk of dangerous blood clotting in their legs. But if the patient’s fracture is fixed in a minimally invasive way within hours of the trauma and the patient mobilized the same day, does he or she really need Lovenox or Coumadin, with its attendant risks? Do we thin our blood with chemicals every time we go to sleep? Of course not, and to treat these patients with blood thinner increases their risks for bleeding and hemorrhagic stroke while, at the same time, not really making a difference in their risk of clinically important blood clots. In other words, it adds risk without benefit – a classic formula for bad medical care. Uniformity of thought leads to mediocrity of science and inappropriateness of care.
    Evidence-based medicine (EBM) – the latest government/university brainchild – only makes this problem worse. It sounds good. Evidence. What’s not to like? But EBM is an upside-down approach to medical progress. In the past, clinicians faced with a new or unusual medical problem were allowed to think. They were able to offer treatment they thought might be effective as long as the treatment would “first do no harm.” Patient safety always came first. They based their treatment decisions not only on the literature but also on their understanding of basic science, their clinical experience, and their judgment. With EBM, on the other hand, we doctors are prohibited from offering treatment unless we can show, preferably with “high-powered” long-term

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