The trunk of the clinical decision tree is a patients major symptom or laboratory result, contained within a box. Arrows branch from the first box to other boxes. For example, a common symptom like sore throat would begin the algorithm, followed by a series of branches with yes or no questions about associated symptoms. Is there a fever or not? Are swollen lymph nodes associated with the sore throat? Have other family members suffered from this symptom? Similarly, a laboratory test like a throat culture for bacteria would appear farther down the trunk of the tree, with branches based on yes or no answers to the results of the culture. Ultimately, following the branches to the end should lead to the correct diagnosis and therapy.
Clinical algorithms can be useful for run-of-the-mill diagnosis and treatment distinguishing strep throat from viral pharyngitis, for example. But they quickly fall apart when a doctor needs to think outside their boxes, when symptoms are vague, or multiple and confusing, or when test results are inexact. In such cases the kinds of cases where we most need a discerning doctor algorithms discourage physicians from thinking independently and creatively. Instead of expanding a doctors thinking, they can constrain it.
Similarly, a movement is afoot to base all treatment decisions strictly on statistically proven data. This so-called evidence-based medicine is rapidly becoming the canon in many hospitals. Treatments outside the statistically proven are considered taboo until a sufficient body of data can be generated from clinical trials. Of course, every doctor should consider research studies in choosing a therapy. But todays rigid reliance on evidence-based medicine risks having the doctor choose care passively, solely by the numbers.
Statistics cannot substitute for the human being before you; statistics embody averages, not individuals. Numbers can only complement a physicians personal experience with a drug or a procedure, as well as his knowledge of whether a best therapy from a clinical trial fits a patients particular needs and values.
Each morning as rounds began, I watched the students and residents eye their algorithms and then invoke statistics from recent studies. I concluded that the next generation of doctors was being conditioned to function like a well-programmed computer that operates within a strict binary framework. After several weeks of unease about the students and residents reliance on algorithms and evidence-based therapies alone, and my equally unsettling sense that I didnt know how to broaden their perspective and show them otherwise, I asked myself a simple question: How should a doctor think?
This question, not surprisingly, spawned others: Do different doctors think differently? Are different forms of thinking more or less prevalent among the different specialties? In other words, do surgeons think differently from internists, who think differently from pediatricians? Is there one best way to think, or are there multiple, alternative styles that can reach a correct diagnosis and choose the most effective treatment? How does a doctor think when he is forced to improvise, when confronted with a problem for which there is little or no precedent? (Here algorithms are essentially irrelevant and statistical evidence is absent.) How does a doctors thinking differ during routine visits versus times of clinical crisis? Do a doctors emotions his like or dislike of a particular patient, his attitudes about the social and psychological makeup of his patients life color his thinking? Why do even the most accomplished physicians miss a key clue about a persons true diagnosis, or detour far afield from the right remedy? In sum, when and why does thinking go right or go wrong in medicine?
Copyright © 2007 by Jerome Groopman. Reprinted by permission of Houghton Mifflin Company.
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