Complications (5 page)

Many dispute this presumption. “Look, most people understand what it is to be a doctor,” a health policy expert insisted, when I visited his office not long ago. “We have to stop lying to our patients. Can people take on chances for societal benefit?” He paused and then answered his question. “Yes,” he said firmly.

It would certainly be a graceful and happy solution. We’d ask patients—honestly, openly—and then they’d say yes. Hard to imagine, though. I noticed on the expert’s desk a picture of his child, born just a few months before, and a completely unfair question popped into my mind. “So did you let the resident deliver?” I asked.

There was silence for a moment. “No,” he admitted. “We didn’t even allow residents in the room.”

One reason I doubt that we could sustain a system of medical training that depended on people saying “Yes, you can practice upon me” is that I myself have said no. One Sunday morning, when my eldest child, Walker, was eleven days old, he suddenly went into congestive heart failure from what proved to be a severe cardiac defect. His aorta was not transposed, but a long segment of it had failed to grow at all. My wife and I were beside ourselves with fear—his kidneys and liver began failing, too—but he made it to surgery, the repair was a success, and although his recovery was erratic, after two and a half weeks he was ready to come home.

We were by no means home free, however. He was born a healthy six pounds plus but now, at a month of age, weighed only five, and would need strict monitoring to insure that he gained weight. He was on two cardiac medications from which he would have to be weaned. And in the longer term, the doctors warned us, his repair would eventually prove inadequate. As Walker grew, his aorta would require either dilation with a balloon or wholesale replacement in surgery. Precisely when and how many such procedures would be necessary over the years they could not say. A pediatric cardiologist would have to follow him closely and decide.

Nearing discharge, we had not chosen who that cardiologist would be. In the hospital, Walker had been cared for by a full team of cardiologists, ranging from fellows in specialty training to attendings who had practiced for decades. The day before discharge, one of the young fellows approached me, offering his card and a suggested
appointment time to bring Walker to see him. Of those on the team, he was the one who had put in the most time caring for Walker. He was the one who saw Walker when we brought him in inexplicably short of breath, the one who made the diagnosis, who got Walker the drugs that stabilized him, who coordinated with the surgeons, and who came to see us each day to answer our questions. Moreover, I knew fellows always got their patients this way. Most families don’t know the subtle gradations among players, and after a team has saved their child’s life, they take whatever appointment they’re handed.

But I knew the differences. “I’m afraid we’re thinking of seeing Dr. Newburger,” I said. She was the hospital’s associate cardiologistin-chief, and a published expert on conditions like Walker’s. The young physician looked crestfallen. It was nothing against him, I said. She just had more experience, that was all.

“You know, there is always an attending backing me up,” he said. I shook my head.

I know this was not fair. My son had an unusual problem. The fellow needed the experience. Of all people, I, a resident, should have understood. But I was not torn about the decision. This was
my child
. Given a choice, I will always choose the best care I can for him. How can anybody be expected to do otherwise? Certainly, the future of medicine should not rely on it.

In a sense, then, the physician’s dodge is inevitable. Learning must be stolen, taken as a kind of bodily eminent domain. And it was, during Walker’s stay—on many occasions, now that I think back on it. A resident intubated him. A surgical trainee scrubbed in for his operation. The cardiology fellow put in one of his central lines. None of them asked me if they could. If offered the option to have someone more experienced, I certainly would have taken it. But that was simply how the system worked—no such choices were offered—and
so
I went along. What else could I do?

The advantage of this coldhearted machinery is not merely that it gets the learning done. If learning is necessary but causes harm, then above all it ought to apply to everyone alike. Given a choice,
people wriggle out, and those choices are not offered equally. They belong to the connected and the knowledgeable, to insiders over outsiders, to the doctor’s child but not the truck driver’s. If choice cannot go to everyone, maybe it is better when it is not allowed at all.

It is 2
P.M.
I am in the intensive care unit. A nurse tells me Mr. G’s central line has clotted off. Mr. G has been with us for more than a month now. He is in his late sixties, from South Boston, emaciated, exhausted, holding on by a thread—or a line, to be precise. He has several holes in his small bowel that surgery has failed to close, and the bilious contents leak out onto his skin through two small reddened openings in the concavity of his abdomen. His only chance is to be fed by vein and wait for these fistulae to heal. He needs a new central line.

I could do it, I suppose. I am the experienced one now. But experience brings a new role: I am expected to teach the procedure instead. “See one, do one, teach one,” the saying goes, and it is only half in jest.

There is a junior resident on the service. She has done only one or two lines before. I tell her about Mr. G. I ask her if she is free to do a new line. She misinterprets this as a question. She says she still has patients to see and a case coming up later. Could I do the line? I tell her no. She is unable to hide a grimace. She is burdened, as I was burdened, and perhaps frightened, as I was frightened.

She begins to focus when I make her talk through the steps—a kind of dry run, I figure. She hits nearly all the steps, but crucially forgets about checking the labs and about Mr. G’s nasty allergy to heparin, which is in the flush for the line. I make sure she registers this, then tell her to get set up and page me.

I am still adjusting to this role. It is painful enough taking responsibility for one’s own failures. Being handmaiden to another’s is something else entirely. It occurs to me that I could have broken open a kit and had her do an actual dry run. Then again, maybe I can’t. The kits must be a couple of hundred dollars each. I’ll have to find out for next time.

Half an hour later, I get the page. The patient is draped. The resident is in her gown and gloves. She tells me she has saline to flush the line with and that his labs are fine.

“Have you got the towel roll?” I ask.

She forgot the towel roll. I roll up a towel and slip it beneath Mr. G’s back. I look into his face and ask him if he’s all right. He nods. I see no fear. After all he’s been through, there is only resignation.

The junior resident picks out a spot for the stick. The patient is so hauntingly thin. I see every rib and fear she will puncture his lung. She injects the numbing medication. Then she puts the big needle in, and the angle looks all wrong. I motion for her to reposition. This only makes her more uncertain. She pushes in deeper and I know she does not have it. She draws back on the syringe: no blood. She takes out the needle and tries again. And again, the angle looks wrong. This time Mr. G feels the jab and jerks up in pain. I hold his arm. She gives him more numbing medication. It is all I can do not to take over. But she cannot learn without doing, I tell myself. I decide to let her have one more try.

O
ne summer day in 1996, Hans Ohlin, the fifty-year-old chief of coronary care at the University of Lund Hospital in Sweden, sat down in his office with a stack of two thousand two hundred and forty electrocardiograms. Each test result consisted of a series of wavy lines, running from left to right on a letter-size page of graph paper. Ohlin read them alone in his office so that he would not be disturbed. He scanned them swiftly but carefully, one at a time, separating them into two piles according to whether or not he thought that the patient was having a heart attack at the time the electrocardiogram (EKG) was recorded. To avoid fatigue and inattention, he did his work over the course of a week, sorting through the EKGs in shifts no longer than two hours, and taking long breaks. He wanted no careless errors; the stakes were too high. This was the medical world’s version of the Deep Blue chess match, and Ohlin was cardiology’s Gary Kasparov. He was going head to head with a computer.

The EKG is one of the most common of diagnostic tests, performed more than fifty million times a year in the United States alone. Electrodes are placed on the skin to pick up the low-voltage electrical impulses that, with each beat, travel through the heart muscle, and those impulses are reflected in the waves on an EKG
printout. The theory behind 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 be
even 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 little fanfare, the results were published in the fall of 1997. Ohlin correctly picked up six hundred and twenty. The computer picked up seven hundred and thirty-eight. Machine beat man by 20 percent.

Western medicine is dominated by a single imperative—the quest for machinelike perfection in the delivery of care. From the first day of medical training, it is clear that errors are unacceptable.
Taking time to bond with patients is fine, but every X ray must be tracked down and every drug dose must be exactly right. No allergy or previous medical problem can be forgotten, no diagnosis missed. In the operating room, no movement, no time, no drop of blood can be wasted.

The keys to this kind of perfection are routinization and repetition: survival rates after heart surgery, vascular surgery, and other operations are directly related to the number of procedures the surgeon has performed. Twenty-five years ago, general surgeons performed hysterectomies, removed lung cancers, and bypassed hardened leg arteries. Today, each condition has its specialists, who perform one narrow set of procedures over and over again. When I’m in the operating room, the highest praise I can receive from my fellow surgeons is “You’re a machine, Gawande.” And the use of “machine” is more than casual: human beings, under some circumstances, really can act like machines.

Consider a relatively simple surgical procedure, a hernia repair, which I learned to do as a first-year surgical resident. A hernia is a weakening of the abdominal wall, usually in the groin, that allows the abdomen’s contents to bulge through. In most hospitals, fixing it—pushing the bulge back in and repairing the abdominal wall—takes about ninety minutes and might cost upward of four thousand dollars. In anywhere from 10 to 15 percent of the cases, the operation eventually fails and the hernia returns. There is, however, a small medical center outside Toronto, known as the Shouldice Hospital, where none of these statistics apply. At Shouldice, hernia operations often take from thirty to forty-five minutes. Their recurrence rate is an astonishing 1 percent. And the cost of an operation is about half of what it is elsewhere. There’s probably no better place in the world to get a hernia repaired.