Welcome back to “Left on Longwood”. It is good to be back after several months out on the job search. We’ll have to revisit some memorable aspects of looking for a job as a clinician-educator soon. I’m very grateful to have had several great options and accepted a position that I’m very excited about.
So, I was recently asked to come up with “three big ideas in science that everyone should understand”. Challenge accepted!
1) Meaning in biomedical science is made by interpreting statistical and sensory data.
2) Diagnosis is a pattern-recognition process that seeks to fit a person’s symptoms and findings to a matrix of known diseases.
3) A unique in the universe, one-time only, experiment is performed every time a person takes a medication.
1) How do we come to “know” something in biomedical science? Whether the challenge is to understand the function of a protein in regulating a particular cellular phenomenon or in deciding what treatment a person with a certain set of symptoms and tests results should receive, biomedical scientists combine probabilistic assessments of quantitative data with descriptive assessments of visual or tactile data to come up with a best guess. Quantitative data and sensory inputs are very different types of information. Quantitative data require a set of technical skills to interpret. In the case of numerical results to lab tests that are scalar variables, what difference from a “normal range” of values is significant? Sure, some appreciation of basic statistics will be helpful. More important will be experience at looking at these numbers and to consider them in the context of the question being asked. For example, a blood test result could have a very different interpretation depending on who the blood was drawn from. Sensory data, how something looks or feels, must be described before meaning can be extracted. The appearance of cells under a slide can give an experienced observer hints about the physiological stresses at play. Although biomedical data, quantitative or visual, is ostensibly objective, “knowledge”, or at least a meaningful interpretation, comes from a combination of quantitative and descriptive thinking.
2) How is the definition of a disease created? Imagine a large dart board. A large group of people experiencing symptoms of illness each take a turn throwing a dart at the board. We’ll say that the unique position of the dart on the board represents each individual’s particular set of symptoms. (In effect, we have figured out a way to map each person’s range of symptoms and the accompanying medical testing data onto two-dimensional space.) If we stand back from the board after all of the darts have been thrown, at first glance we would be impressed by clusters. Most of the darts would be clustered together in different parts of the board. If we looked more closely, we would see some darts conspicuously stuck into open parts of the board, apart from and in between the clusters.
We could then take the clusters one at a time, learn a little bit about the symptoms of the people whose darts landed there and review their testing results. Based on what we found, we could propose a name to describe that particular pattern of symptoms, findings, abnormal labs, etc. Eureka! A new disease would be described (certainly not “discovered”). What about the people whose darts did not land near a cluster? These people could have a very rare disease; we might need many, many more people to take a turn throwing a dart in order to have other darts fall nearby and form a cluster. Alternatively, these people might not be sick at all. Their symptoms could in fact be within the spectrum of normal, healthy experience. In that case, we should not have handed them a dart in the first place.
The diagnostician seeks to gather information about a patient’s symptoms and obtain objective data (all the while resisting the temptation to include extraneous details and send unnecessary tests). Does the information fit a known pattern? Physicians are familiar with the well-grouped clusters on the dart board – common diagnoses. In this way, diagnosis is a pattern-recognition process that maps symptoms and findings on to a large matrix of previously characterized diseases. Outliers to known patterns, the lone darts, could represent “noise” or a chance to add important additional information to medical knowledge.
3) My pharmacology professor, Nobelist Al Gilman, opened up a series of lectures to my second year medical school class with this assertion. Mostly, this was greeted with yawns and eye-rolling. But to a certain type of curious and susceptible student, this was actually quite a profound statement. I must have been hooked because I went on to do my PhD in his department.
The number of variables that can affect an individual’s response to a medication is endless. What time did they take the medication? Did they swallow it with orange juice or water? To what extent was their disease active that day? Did a swamped pharmacist accidentally dispense the wrong medication? The variation between us and between the me of an hour ago and the me of this instant ensures that the experiment is never repeated under exactly the same conditions. Getting the right medication to the right patient at the right time is a quest that requires excellence from every part of the biomedical enterprise from the public health officer who recognizes an at-risk population to a quality improvement scientist who figures out how to decrease the rate of error in a system to the basic scientist who makes an observation that against-all-odds informs the development of a new drug.