For doctors, most diagnoses come from the history and examination. In many non-medical pathways, it can feel like the reverse: very little diagnosis from history/exam, and a heavy reliance on tests or onward referral, sometimes with no working diagnosis at all. If someone doesnβt understand what history and examination are for, itβs easy to skip them and default to βdo testsβ or βadmit/refer so a specialist can diagnoseβ.
The problem is that tests are then interpreted without a Bayesian βa prioriβ anchor. Without a pre-test probability built from a good story, a focused exam, and an understanding of how common conditions are so youβre left with results that can mislead, over-diagnose, or generate incidental findings. And if you donβt have a detailed grasp of disease patterns, you donβt know which questions to ask or what to look for to diagnose dementia or depression or diabetes or diphtheria. You canβt form a meaningful differential if you donβt know whatβs common, whatβs dangerous, and whatβs discriminating.
So the whole process drifts into secondary referrals and scanning as the default route to certainty. In parallel, some non-medical exams donβt require a deep knowledge base, so people are expected to βlook it upβ in real time under pressure. That isnβt the same as understanding, and itβs hard to expect consistently good outcomes from it.
The final issue is that often one doesn't know what one doesn't know. This can lead to overconfidence or a very defensive position.
Finally if you want to be seeing patients and making good diagnoses there is course for this called Medicine. Exams do expect knowledge.