The age old description of how science is (supposed to be) working to generate theories, refute them, induce statements from data and use the Popper/Pearson model to work on hypotheses does not apply to the non-scientific field of medicine. Since the seventies people have wondered how good doctors think and how to bring young ones to become good doctors. We discuss the various approaches to patients and their pathophysiology, to clinical data and thereby involve definitions of pattern matching, heuristics, induction, abduction and deduction as well as Bayes statistics to develop an understanding why working in medicine becomes more complicated and less efficient during the first years – what is known as the U-shaped learning curve of becoming a medical professional.
This is all but theoretical, but of course, you have to read the classic texts if you want to understand the details. Here is a short review on the main part of subject of today’s talk.