The active psyche of a patient, for instance, makes it particularly treacherous to run studies or trials that depend on memory. In 1993, a Harvard researcher named Edward Giovannucci set out to determine whether high-fat diets altered the risk of breast cancer. He identified a set of women with breast cancer and an age-matched cohort without breast cancer, then asked each group about their dietary habits over the last decade. The survey produced a pronounced signal: women with breast cancer were much more likely to have consumed diets higher in fat.
But this study had a twist: the women in Giovannucci’s study had also completed a survey of their diets nearly a decade before this study, and the data had safely been stored away in a computer. When the two surveys were compared, in women without breast cancer, the actual diet and the recalled diet were largely identical. In women with breast cancer, however, the actual diet had no excess of fat. Only the “remembered” diet was high in fat. These women had unconsciously searched their memories for a cause of their cancers and had invented a culprit: their own bad habits. What better blame than self-blame?
But don’t prospective, controlled, randomized, double-blind studies eliminate all these biases? The very existence of such a study—in which both control and experimental groups are randomly assigned, patients are treated prospectively and both doctors and patients are ignorant of the treatment—is a testament to how seriously medicine takes its own biases, and what contortions we must perform to guard against them (in few other scientific disciplines are such drastic measures used to eliminate systematic biases). The importance of such studies cannot be overemphasized. Several medical treatments thought to be deeply beneficial to patients based on strong anecdotal evidence, or decades of nonrandomized studies, were ultimately proved to be harmful based on randomized studies. These include, among other examples, the use of high-dose oxygen therapy for neonates, antiarrhythmic drugs after heart attacks, and hormone-replacement therapy in women.
Yet even that desperate experimental contortion cannot eliminate the subtlest of biases. It’s the Heisenbergian principle at work again: when patients are enrolled in a study, they are inevitably affected by that enrollment. A man’s decision to enroll in a study to measure the effect of exercise on diabetic management, say, is an active decision. It means that he participates in the medical process, follows certain instructions, or lives in particular neighborhoods with accessible health care and so forth. It might mean that he belongs to a certain race or ethnic group or a particular socioeconomic class. A randomized study might make particular conclusions about the effectiveness of a medicine—but in truth it has only judged that effectiveness in the subset of people who were randomized. The power of the experiment is critically dependent on its strong limits—and this is the very thing that makes it limited. The experiment may be perfect, but whether it is generalizable is a question.
The reverential status of randomized, controlled trials in medicine is its own source of bias. The BCG vaccine against tuberculosis was shown to have a potent protective effect in a randomized trial, but the effectiveness of the vaccine seems to decrease almost linearly as we move in latitude from the North to the South—where, incidentally, TB is the most prevalent (we still don’t understand the basis for this effect, although genetic variation is the most obvious culprit). These distortions—call them heuristic biases—are not peripheral to the practice of medicine. Virtually every day I’m asked to decide whether a particular drug will work for a patient—an African-American man, say—when the trial was run on a population of predominantly white men in Kansas. Women are notoriously underrepresented in randomized studies. In fact, female mice are notoriously underrepresented in laboratory studies. Extracting medical wisdom from a randomized study thus involves much more than blithely reading the last line of the study published in some august medical journal. It involves human perception, arbitration, and interpretation—and hence involves bias.
The advent of new medical technologies will not diminish bias. They will amplify it. More human arbitration and interpretation will be needed to make sense of studies—and thus more biases will be introduced. Big data is not the solution to the bias problem; it is merely a source of more subtle (or even bigger) biases.
Perhaps the simplest way to tackle the bias problem is to confront it head-on and incorporate it into the very definition of medicine. The romantic view of medicine, particularly popular in the nineteenth century, is of the doctor as a “disease hunter” (in 1926, Paul de Kruif’s book Microbe Hunters ignited the imagination of an entire generation). But most doctors don’t really hunt diseases these days. The greatest clinicians who I know seem to have a sixth sense for biases. They understand, almost instinctively, when prior bits of scattered knowledge apply to their patients—but, more important, when they don’t apply to their patients. They understand the importance of data and trials and randomized studies, but are thoughtful enough to resist their seductions. What doctors really hunt is bias.
....
Priors. Outliers. Biases. That all three laws of medicine involve limits and constraints on human knowledge is instructive. Lewis Thomas would not have predicted this stickiness of uncertainties and constraints; the future of medicine that Thomas had imagined was quite different. “The mechanization of scientific medicine is here to stay,” he wrote optimistically in The Youngest Science. Thomas presaged a time when all-knowing, high-precision instruments would measure and map all the functions of the human body, leaving little uncertainty and even fewer constraints or gaps in knowledge. “The new medicine works,” he wrote. “The physician has the same obligations that he carried, overworked and often despairingly, fifty years ago—but now with any number of technological maneuvers to be undertaken quickly and with precision. . . . The hospitalized patient feels, for a time, like a working part of an immense, automated apparatus. He is admitted and discharged by batteries of computers, sometimes without even learning the doctors’ names. Many patients go home speedily, in good health, cured of their diseases. . . . If I were a medical student or an intern, just getting ready to begin, I would be more worried about this aspect of my profession. I would be apprehensive that my real job, taking care of sick people, might soon be taken away, leaving me with the quite different occupation of looking after machines.”
In reality, things have panned out quite differently: despite the increasing accuracy of tests, studies, and equipment, the doctors of today have to contend with priors, outliers, and biases with even deeper and more thoughtful engagement than doctors of the past. This is not a paradox. Tests and therapies may have evolved, but so has medicine itself. In Lewis Carroll’s Through the Looking-Glass, the Red Queen tells a bewildered Alice that the queen has to keep running to stay in place—because the world keeps running in the opposite direction. Despite the sophistication of medical technologies, uncertainties have remained endemic to medicine because the projects that medicine has taken on are vastly more complex and ambitious. Thomas imagined a future in which machines took care of sick people. Now we have better machines, but we are using them to take care of sicker people.
In Philadelphia, a six-year-old girl with a lethal, therapy-resistant, relapsed leukemia recently had her immune cells harvested, genetically modified with a virus carrying a gene that kills leukemia cells, and then reinjected into her body to act as a form of “live” chemotherapy. The cells sought out and killed her cancer with exquisite efficacy, and she remains in a profound remission. At Emory, a neurosurgeon implanted a tiny electrical stimulator into the cingulate gyrus in the brain of a woman with profound depression. Seconds after the “brain pacemaker” was pulsed on, the woman described the lifting of a permanent dark fog of despair that had been recalcitrant to the highest doses of antidepressant medicines.
The Philadelphia experiment illustrates the nature of the complexities and uncertainties faced by the new medicine. Hours after the young girl with relapsed leukemia was injected with her cancer-seeking T cells, she experienced the mos
t potent form of inflammatory response. Her physiology sensed the macabre aberration of her “self” turning on itself—an immune system attacking its own body (in fact, her T cells were attacking her cancer cells)—and she spiked a fever. Her blood pressure dropped. Her kidneys began to falter, her vessels began to clot and bleed at the same time, and she lapsed into a coma. A fleet of lab tests was sent out to monitor her status, and dozens returned with abnormal values. Which of these were the outliers, and which were the abnormal factors truly contributing to her terrifying inflammatory response? Her blood counts indicated that she might be in the beginnings of a remission—but was there an inherent bias in using these parameters to judge a remission in the setting of an acute inflammatory response?
Of all the abnormal lab values—every number capitalized, bolded, and flagged in violent red—one skyrocketing factor caught the eye of her physicians. Why? Because some prior knowledge indicated that the factor, called interleukin-6, or IL-6, sat at the hub of the inflammatory response. But also because there happened to be a drug against it: by pure chance, the leader of this trial happened to have a daughter who happened to have juvenile arthritis who happened to have been treated with a medication that blocked interleukin-6. Two days after the young girl’s initial T-cell transfusion, doctors and nurses were pulling things off the shelves to see if any agent might work against the immune attack and consequent organ failure. “She was as sick as any human can possibly be,” one physician recalled. Her vital signs fluttered on a precipice. In a last-ditch maneuver, she was injected with the antiarthritis drug. As the doctors watched, bewildered, the fever reversed. The kidneys, lungs, blood, and the heart returned to normal function. By the next morning, she awoke from her coma. One year later, she remains in remission, with no sign of cancer in her bone marrow.
Is the case over? Far from it. Should this girl be given chemotherapy now to “consolidate” her remission—as conventional wisdom might suggest—or would the added chemotherapy kill the very cells in her immune system that are keeping her disease in check? We don’t know because there are no priors. Is her response normal, or is she an outlier? We won’t know until we can build a model of the nature of her response and try to make all the available data fit it. How will we objectively judge this therapy in a clinical trial when no other comparable therapies for relapsed, refractory leukemia exist? Can such a trial ever be randomized?
This experiment—and hundreds of similar studies at the frontiers of medicine—suggest that human decision making, and, particularly, decision making in the face of uncertain, inaccurate, and imperfect information, remains absolutely vital to the life of medicine. There is no way around it. “The [political] revolution will not be tweeted,” wrote Malcolm Gladwell. Well, the medical revolution will not be algorithmized.
....
One last thought: there is no reason to believe that there are only three laws of medicine. My own laws are personal. They stood by me throughout my internship, residency, and fellowship. They saved me from the most egregious errors of judgment; they helped me diagnose and treat the most difficult of the cases that I encountered in my practice. Every year, I begin my teaching rounds at the hospital by explaining my version of the laws to the new medical residents. Each time I see a new patient in the wards or in the clinic, I remind myself of them.
Yet if there are other laws, I suspect that they will also concern the nature of information and uncertainty at their very core. “Doctors,” Voltaire wrote, “are men who prescribe medicines of which they know little, to cure diseases of which they know less, in human beings of whom they know nothing.” The pivotal word in this scathing description is know. The discipline of medicine concerns the manipulation of knowledge under uncertainty. Abstract away the smell of rubbing alcohol and bleach; forget the adjustable beds and ward signs and the gleaming granite of hospital lobbies; erase, for a moment, the many corporeal indignities of a man in a blue cotton gown in a room or the doctor trying to heal him—and you have a discipline that is still learning to reconcile pure knowledge with real knowledge. The “youngest science” is also the most human science. It might well be the most beautiful and fragile thing that we do.
....
ACKNOWLEDGMENTS
I’d like to thank Michelle Quint for her careful editing of the manuscript and her remarkable equanimity in guiding this book to its final form. June Cohen and Chris Anderson helped shape a very shapeless idea of “laws” into this book. I owe a special debt to Sarah Sze, Nell Breyer, Sujoy Bhattacharyya, Suman Shirokar, Gerald Fischbach, Brittany Rush, and Ashok Rai for their comments and criticisms and to Bill Helman for helping me understand some of the most important ideas about uncertainty and the future of technology.
ABOUT THE AUTHOR
PHOTO: BRET HARTMAN/TED
Siddhartha Mukherjee is a cancer physician and researcher. He is the author of The Laws of Medicine and The Emperor of All Maladies: A Biography of Cancer, winner of the 2011 Pulitzer Prize in general nonfiction. Mukherjee is an assistant professor of medicine at Columbia University and a staff cancer physician at Columbia University Medical Center. A Rhodes scholar, he graduated from Stanford University, University of Oxford, and Harvard Medical School. He has published articles in Nature, Cell, The New England Journal of Medicine, and The New York Times. In 2015, Mukherjee collaborated with Ken Burns on a six-hour, three-part PBS documentary on the history and future of cancer. Mukherjee’s scientific work concerns cancer and stem cells, and his laboratory is known for the discovery of novel aspects of stem cell biology, including the isolation of stem cells that form bone and cartilage. He lives in New York with his wife and two daughters.
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Siddhartha Mukherjee’s TED Talk, available online:
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WATCH SIDDHARTHA MUKHERJEE’S TED TALK
Siddhartha Mukherjee’s TED Talk, available for free at TED.com, is the companion to The Laws of Medicine.
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The Laws of Medicine: Field Notes From an Uncertain Science Page 5
