Complications by Atul Gawande Read Free Book Online

an EKG is that in a heart attack a portion of the muscle dies, causing the electrical impulses to change course when they travel around the dead tissue. As a result, the waves on the printout change, too. Sometimes those changes are obvious; more often they are subtle—or, in medical argot, “nonspecific.”
    To medical students, EKGs seem unmanageably complex at first. Typically, an EKG uses twelve leads, and each one produces a different-looking tracing on the printout. Yet students are taught to discern in these tracings a dozen or more features, each of which is given an alphabetical label: for instance, there’s the downstroke at the start of a beat (the Q wave), the upstroke at the peak of heart contraction (the R wave), the subsequent downstroke (the S wave), and the rounded wave right after the beat (the T wave). Sometimes small changes here and there add up to a heart attack; sometimes they don’t. When I was a medical student, I first learned to decode the EKG as if it were a complex calculation. My classmates and I would carry laminated cards in our white-lab-coat pockets with a list of arcane instructions: calculate the heart rate and the axis of electrical flow, check for a rhythm disturbance, then check for an ST-segment elevation greater than one millimeter in leads V1 to V4, or for poor R-wave progression (signifying one type of heart attack), and so on.
    With practice, it gets easier to manage all this information, just as putting a line in gets easier. The learning curve operates in matters of diagnosis no less than technique. An experienced cardiologist can sometimes make out a heart attack at a glance, the way a child can recognize his mother across a room. But at bottom the test remains stubbornly opaque. Studies have shown that between 2 and 8 percent of patients with heart attacks who are seen in emergency rooms are mistakenly discharged, and a quarter of these people die or suffer a complete cardiac arrest. Even if such patients aren’t mistakenly sent home, crucial treatment may be delayed when an EKG is misread. Human judgment, even expert human judgment, falls well short of certainty. The rationale for trying to teach a computer to read an EKG, therefore, is fairly compelling. If the result should prove to beeven a slight improvement on human performance, thousands of lives could be saved each year.
    The first suggestion that a computer could do better came in 1990, in an influential article published by William Baxt, then an emergency physician at the University of California at San Diego. Baxt described how an “artificial neural network”—a kind of computer architecture—could make sophisticated clinical decisions. Such expert systems learn from experience much as humans do: by incorporating feedback from each success and each failure to improve their guesswork. In a later study, Baxt showed that a computer could handily outperform a group of doctors in diagnosing heart attacks among patients with chest pain. But two-thirds of the physicians in his study were inexperienced residents, whom you’d expect to have difficulties with EKGs. Could a computer outperform an experienced specialist?
    This question was what the Swedish study was trying to answer. The study was led by Lars Edenbrandt, a medical colleague of Ohlin’s and an expert in artificial intelligence. Edenbrandt spent five years perfecting his system, first in Scotland and then in Sweden. He fed his computer EKGs from more than ten thousand patients, telling it which ones represented heart attacks and which ones did not, until the machine grew expert at reading even the most equivocal of EKGs. Then he approached Ohlin, one of the top cardiologists in Sweden and a man who ordinarily read as many as ten thousand EKGs a year. Edenbrandt selected two thousand two hundred and forty EKGs from the hospital files to test both of them on, of which exactly half, eleven hundred and twenty, were confirmed to show heart attacks. With