01 September 2012


Anyone who has been exposed to MBTI training, or anyone familiar with learning theory or learning styles, understands that individuals intake and process new information in a variety of ways.  Some of us learn best by hearing or reading a description, others by seeing a demonstration, and others by hands-on practice of the concept.

I discovered recently that groups of people in different professions may problem-solve in ways particular to that profession.  That's the position taken by Dan Buckland, editor of the website medGadget.  He suggests in How Physicians, Engineers, and Scientists Approach Problems Differently that each group comes equipped with a particular set of skills, training, and assumptions.  If this is the case (and I have no particular reason to doubt it), then we as consumers can better understand the strengths and weaknesses of each approach, making us more effective in our interactions.  Here is the author's breakdown ~

  • The Physician.  MDs are trained in medical school to think about differentials and categories.  A patient's presenting signs and symptoms are processed, then historical information is used to determine the most common diagnosis associated with that data set.  More complicated tests are given based on the most common and most dangerous diagnoses, and then treatment is often based on the outcomes of those tests.  This is a categorical approach to problem solving.  The MD tries to determine what category the patient belongs in, and then treatment is based on the assigned category.  This is a very efficient system when a patient has a problem that has been encountered before, and [there is] a pre-existing data set that the patient can be matched to.  Often, a complete picture isn't even needed since this problem-solving approach is based on probabilities.  However, when the patient has something not seen before, this is a very inefficient way of treating the problem, as the MD moves to less and less common solutions.  Programmers would call this searching a known set, which is often the fastest way to find a solution if the solution is in the set, but it is the slowest if the solution is not, as all possibilities have to be excluded before determining that the answer isn't there.
  • The Scientist.  In contrast to the MD, the Scientist is trained to look at a problem in the abstract and use testable hypotheses to isolate all the component parts of a problem and solve them (individually, if possible) in a logical way.  Breaking down the problem into its component parts can determine the independent root causes.  Then, using those root causes, the Scientist can arrive at a solution to the overall problem.  Solving problems in this way is more resource- and time-intensive than the Physician method, but if the right hypothesis is posed, this system can handle a broader range of problems and generate new data that are applicable to other problems.  Programmers would call this a global search, which is often the least efficient way to find a solution, but the solution found would have a higher chance of being the optimal solution because it ideally takes into account the most information.
  • The Engineer.  One way to think of the Engineer's method is as a hybrid of the Scientist's and Physician's methods.  The Scientist starts with a new hypothesis for each problem, and the Physician starts with a set of solutions that can be applied.  The Engineer is trained to take a known solution and then use that as a starting point to hypothesize a solution that applies to the problem.  Thus, the Engineer's approach is also a combination of the advantages and disadvantages of the above methods.  Like the Scientist, the Engineer tries to break down the problem, but doesn't break it down all the way.  Since the Engineer isn't looking for a root cause, the problem is only simplified enough to get a solution that works with the least amount of change from the current paradigm.  Going back to our programming analogy, this is a local search ~ again, a hybrid of the two above examples.
Buckland then presents three problems, and describes "how the three archetypes would approach their solutions.  These are not random problems.  Each one is meant to show that none of the problem solving types is inherently better than the others, [and] that they are each better suited to different situations."  You can find his problem set, and the solutions, here.

I found it useful to realize that an entire profession might have a philosophy for problem-solving that is distinct from other professions.  The knowledge explains something I'd previously only intuited ~ how my perception of how to understand a set of medical symptoms might differ from the perception of my physician.  I've worked in all three fields, but my primary training is as a scientist.  So it is also useful to conceptualize the strengths and limitations of that thought process.  

Lastly, wouldn't it be an interesting exercise to have a physician, a scientist, and an engineer in the same room working on the same problem?  It could be tricky.  It would take individuals with a high tolerance for other styles of learning and problem-solving.  But with practice, I'm betting the results could be spectacular.  As the saying goes, if your only tool is a hammer, you're likely to see every problem as a nail.

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