Thomas’s book challenged Mukherjee to contemplate a fundamental question: Is medicine really a science? The answer to this question was, and still is, not obvious. To Mukherjee, as to others, there was no question that medical knowledge and techniques were based upon the methods, concepts, and findings of sound scientific investigation. But what about medical practice? Here, the answer was much less clear, for scientifically obtained medical knowledge and rational therapeutics often said little about what to do for a particular patient in a particular circumstance. Take chemotherapy: Randomized, prospective clinical trials might suggest a bone marrow transplantation would not be advisable for a patient declining rapidly with leukemia. But what if the decline of this particular patient is directly the result of the leukemia and not a reflection of poor general health compounded by the leukemia? Cancer chemotherapy is toxic and hard on patients, and patients in poor general health can die from such treatment. However, in this case, it could make sense to treat the “frail” patient for leukemia, hoping that as the leukemia is brought under control, strength will return and chances of survival will increase. As Mukherjee writes in his latest work, The Laws of Medicine, “My medical education had taught me plenty of facts, but little about the spaces that live between facts.”
Mukherjee thus discovered what every physician learns: that medical practice is a murky world, one that depends on clinical judgment, problem-solving skills, and sound knowledge of the individual patient. Clinical decisions must often be made based on probabilities, uncertain or incomplete information, and the specifics of the clinical situation at hand, including the patient’s emotional needs and personal wishes. “I had never expected medicine to be such a lawless, uncertain world,” he explains.
Stated another way, Mukherjee learned that medical practice is laden with uncertainty. The popular idea that medical education provides students knowledge of medicine and disease behavior that then allows them to act with certainty in every situation in medical practice is a myth. In actuality, doctors regularly encounter dilemmas, and only occasionally do diseases present in the idealized form described in textbooks. In many cases, different individuals with the same disease react differently, and the physician’s task is to try to make sense of each patient complaint. “Chest pain” could result from any one of dozens of causes, not all of which are serious, and not all of which arise from the heart. Conversely, a patient with a serious heart problem might have no chest pain at all, or symptoms that mimic those of a stomach or intestinal illness. After a diagnosis is made, therapeutic decisions often pose additional challenges. In many cases, the desired treatment of one problem could exacerbate another. Or in many surgical situations, deciding whether to operate on a frail patient could be far from straight-forward.
Mukherjee wrote this brief book with the goal of helping physicians learn what to do with information — especially when the data is imperfect, incomplete, or uncertain. He argues that three “laws” govern the sound practice of medicine. These laws, he contends, can help not only medicine but any profession that confronts uncertainty and imprecisions.
The first law is that a strong intuition is more powerful than a weak test. By this, he means that common things occur commonly and uncommon things, uncommonly. Diagnostic challenges represent a probability game, where the power of a diagnostic test depends not only on the technical quality of the test but also on the likelihood of a positive or negative test prior to the test’s being obtained. Or, as medical educators have long said, “If you hear thundering hoofbeats, think horses, not zebras — unless you happen to be in Africa.” In Mukherjee’s words:
Every diagnostic challenge in medicine can be imagined as a probability game. This is how you play the game: you assign a probability that a patient’s symptoms can be explained by some pathological dysfunction — heart failure, say, or rheumatoid arthritis — and then you summon evidence to increase or decrease the probability. Every scrap of evidence — a patient’s medical history, a doctor’s instincts, findings from a physical examination, past experiences, rumors, hunches, behaviors, gossip — raises or lowers the probability. Once the probability tips over a certain point, you order a confirmatory test — and then you read the test in the context of the prior probability.
Mukherjee’s second law is that “normals” teach us rules; “outliers” teach us laws. Models of illness, he explains, are hybrid models in which “past knowledge is mishmashed with present knowledge.” However, no model explains everything. There are typically exceptions, or what Mukherjee calls “outliers.” Deeper understanding is often created when these exceptions are explored. As an example, he discusses a cancer chemotherapeutic agent that rarely seemed effective — and then shows how a breakthrough was made when a creative investigator studied the exceptional cases when the drug worked rather than the typical cases when the drug failed. That investigator, Mukherjee writes,
wanted to understand these rare responses. These ‘exceptional responders,’ he reasoned, might have some peculiar combination of factors — genes, behaviors, risk factors, environmental exposures — that had made them respond so briskly and durably. He decided to use the latest medical tools to understand their responses as deeply and comprehensively as possible. He had inverted a paradigm: rather than spending an enormous effort trying to figure out why a drug had commonly failed, as most of his colleagues might have, he would try to understand why it had occasionally succeeded. He would try to map the landscape of the valley of death — not by querying all those who had fallen into it, but by asking the one or two patients who had clambered out.
Mukherjee’s third law is that for every perfect medical experiment, there is a perfect human bias. Scientists are human, he explains, with intellectual and personal biases, just like anyone else. Even multicenter, randomized, prospective clinical trials — in themselves evidence of how seriously doctors take their own biases — are not totally free from bias. Questions remain to what degree the results of a clinical trial are generalizable. Physicians must decide whether the results are applicable or not to the patient at hand — and why or why not. Mukherjee writes: “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.”
Mukherjee writes about medical practice of the present, not of the future. The book does not explore how current transformations — genomics, proteomics, big data, and other new technologies — might one day affect medical decision-making. However, the answer is likely to be: very little. Technology has never before been able to eliminate uncertainty from medical practice, and it is highly unlikely to do so now. An individual still might have an adverse reaction to a presumably safe medicine, surprise the surgeon in the operating room with an unanticipated anatomy, develop a common disease presenting in an uncommon fashion, or have a confluence of serious problems where the best therapeutic approach is far from obvious. Sound clinical judgment will always be required, which is why Mukherjee’s discussion is on target.
Mukherjee’s laws of medicine are laws of uncertainty, imprecision, and incompleteness. They apply to medicine because the care of patients is an uncertain art. Not surprisingly, they have been noted before by many others. Medical educators have always acknowledged them, which is why Renee C. Fox defined “training for uncertainty” as the organizing principle of medical education in her classic essay of the same name. Mukherjee’s “priors” are incorporated in “Bayes’s theorem,” which has been taught to medical students for decades. His “outliers” were heralded by Charles Darwin’s admonition to “treasure your exceptions.” And his “biases” are acknowledged in the ongoing efforts at every medical school to teach students to evaluate clinical studies critically and always to ask whether the results are applicable to the patient being treated here and now.
In short, in The Laws of Medicine, Mukherjee has written nothing new. However, what he writes is important, and he does so in an elegant, engaging fashion. This is a moving, deeply humane book. It reminds us that, even in today’s highly technological, population-based era of medicine, individuals still matter — patients, with their biological and personal idiosyncrasies, and doctors, who still need to move beyond algorithms to become problem-solvers for their patients.
Kenneth M. Ludmerer is the Mabel Dorn Reeder Distinguished Professor of Medicine in the History of Medicine at Washington University School of Medicine.