Does medicine have universal laws that doctors and patients can count on? It oftentimes seems like this is not the case. And still, there’s an apparent need for it. This 1.5 hour read is a book that even busy interns can manage to squeeze into their around-the-clock working schedule. And they really should.
When he started his career in medicine, Siddhartha Mukherjee noticed that his work wasn’t really about the loads of information he had learned in medical school. It was about using this information wisely – especially when the information he got was imperfect, incomplete, or uncertain. To his disappointment, this crucial skill was the one he learned nothing about.
“I had never expected medicine to be such a lawless, uncertain world. I wondered if the compulsive naming of parts, diseases, and chemical reactions – frenulum, otitis, glycolysis – was a mechanism invented by doctors to defend themselves against a largely unknowable sphere of knowledge.”
Mukherjee decided to find some laws to deal with the uncertainty he encountered in his everyday work. The “laws of medicine” he was able to identify during the course of his career are not restricted to the medical field. They are laws that deal with the imperfect, and therefore unsatisfactory, space where information meets uncertainty.
Law 1: A strong intuition is much more powerful than a weak test.
Most of the knowledge doctors rely on stems from the tests they order. What many seem to forget, is that statistics is an auxiliary science, not a means to determine the truth. Even if statistical testing might convey the illusion of certainty, medicine is rather about dealing with uncertainty than about finding perfect answers. Statistics is “a machine that modifies probabilities”. To rely on statistical tests alone will lead to false diagnosis, because you need prior knowledge to interpret the test – otherwise the test becomes useless: “A test can only be interpreted sanely in the context of prior probabilities.”, Mukherjee explains.
To make his point, Mukherjee mentions the old example of a man tossing coins. For twelve straight tosses the coin lands “heads”. So, what is the probability of the next coin toss being “heads” too?
“Most of the students in the class, trained in standard statistics and probability, would nod knowingly and say: 50 percent. But even a child knows the real answer: it’s the coin that is rigged. Pure statistical reasoning cannot tell you the answer to the question – but common sense does.”
Law 2: “Normals” teach us rules; “outliers” teach us laws.
When the workings of our cosmos were still unknown to us, a cosmologist named Tycho Brahe decided to take the best out of the existing cosmological models. According to Brahe’s hybrid model, the earth was at the center of the universe and the sun moved around it – but with the other planets revolving around the sun. The reason why Brahe’s model is so memorable is because it worked perfectly. The rules he invented worked for every measured orbit, with only one exemption: Mars.
Brahe’s assistant Johannes Kepler kept trying to solve the problem of Mars’ “retrograde motion”, which led Kepler to his conclusion: the orbits of all the planets were not circles, but ellipses around the sun. “It was the outlier, the aberration, the grain of sand in the eye of Tychonian cosmology.”, Mukherjee explains. That’s why he is convinced that we have to focus more on the outlier cases of medicine that are usually dismissed as “single patient anecdotes”.
“Most of our models of illness are hybrid models; past knowledge is mishmashed with present knowledge. These hybrid models produce the illusion of a systematic understanding of a disease—but the understanding is, in fact, incomplete.”
Law 3: For every perfect medical experiment, there is a perfect human bias.
Even when we get almost perfect information, we might not be able to interpret it perfectly. There’s one big obstacle to finding any truth, and that obstacle is called “human bias”. We interpret information based on false assumptions. Which results in even the most perfect information leading to answers that are just wrong.
“Every science suffers from human biases. Even as we train massive machines to collect, store, and manipulate data for us, humans are the final observers, interpreters, and arbiters of that data.”
New technologies and algorithms are often praised as the solution to all those misinterpretations based on human biases. They are expected to make medicine (and every other science for that matter) more objective. But this is a false conclusion, and a very dangerous one at that. Relying on technology for answers just obscures the fact that behind every “objective” technology there’s a human being who designed it that way.
“Big data is not the solution to the bias problem; it is merely a source of more subtle (or even bigger) biases.”
Mukherjee’s advice to doctors and patients alike, is to keep those laws of uncertainty in mind, and to find some of their own that they can work with. You can start finding your own laws by watching Mukherjee’s TED talk.
The best Viennese Coffee House to read this book in
The most important law of medicine is not to get ill in the first place. So, take good care of yourself during the cold season. Make sure to get out of the house and take a walk through the beautiful Viennese autumnal cityscape. And indulge in a nice cup of hot chocolate every once in a while. A good place to do this is Café Mocca in the 18th district, right at the S-Bahn station Gersthof. Have hot chocolate, coffee or breakfast there from 7 am to 4 pm every day of the week.
Photo credits: Verena Ehrnberger