Siddhartha Mukherjee, oncologist, researcher and professor at the University of Columbia, is the author of a small essay, The Laws of Medicine: field notes from an uncertain science (2015), which I found interesting enough to recommend to clinicians who desire to think a little more about what they do. "In the medical school," says the author, "they taught me a lot of facts, but they did not prepare me to navigate the immense spaces between these facts. Right now I could write a thesis on the physiology of sight, but I feel lost when I try to understand the conniving network that makes a man, who was prescribed home oxygen, give a false address to the service providers, embarrassed (I later learned), because he lived on the street."
What is a scientific law?
The sciences have laws, statements of truths based on repetitive experimental observations which describe some attributes of the nature of universal character, such as, for example, the law of gravity. Of all the sciences, biology has the least laws and within this, medicine doesn’t have any, and that is why Mukherjee, after reading The Youngest Science, by Lewis Thomas, and reflecting on clinical work, proposes three possible laws:
First law of medicine
A strong intuition is more powerful than a weak test
From the reasoning of the author to defend the first law I highlight the following: let's say the case that the HIV test gives a 1% false positives, a more than acceptable specificity and, therefore, a doctor can end up applying to people from low risk just for the sake of being sure they are clean; but as the prevalence of the infection is (thankfully) very low, 0.05%, if the test were positive, its predictive value would be only 5%. On the other hand, if the doctor, by means of a careful interview based on the choice of risks, would limit himself to requesting the test from people with a 19% probability of being infected, then the predictive power of the test would be up to 95%.
Second law of medicine
Normal cases generate the rules, outliers the laws
Lacking scientific laws, biomedical research focuses its efforts on statistical normality, which offers rules for the majority of the population affected by a given health problem, while cases that give extreme results are rejected. The data, to better highlight normality, are filtered outliers. To explain the second law better, the author resorts to Johannes Kepler, a sixteenth-century mathematician who interpreted the laws that governs the solar system through the study, for more than a decade, of the movement of Mars, the only planet that made the damn to all the previous theories, an authentic outlier for the astronomers of the time. In this sense, David Solit, an American oncologist, focuses his scientific efforts on understanding the mechanisms that cause certain people to respond exceptionally to a new drug, when this has failed in all other participants of the clinical trial, trying to find the crux of the question from the exception.
Third law of medicine
Each research project has its own bias incorporated
Each scientist carries the bias in himself. Behind the most sophisticated algorithms, protocols and technologies is always the human factor, whether for observation, interpretation or arbitration. According to the author, the main factor that influences the omnipresence of deviations in research lies in the researchers' own expectations, the desire for success. Another element to consider is the difficulty of reproducing the circumstances of the real clinic in research environments, beginning with the interference of each intervention in the emotional state of the people. You don’t have to be too scientific to know that the pulse a nurse takes in the office is always faster than usual.
As Mukherjee says, taking decisions with perfect information is easy, but modern clinical practice requires making perfect decisions with imperfect information. Therefore, the author of the three laws believes that the next revolution in medicine won’t be marked by complex algorithms, but by the ability to make reasonable decisions in uncertain environments.