Knowledge about a patient can impact the diagnostic process that a physician goes through, and therefore can impact the outcome of that diagnostic process.
For example, a patient presenting to the emergency department without any classic coronary risk factors might get a truncated cardiac workup (no repeat troponins, or no follow-up testing) and therefore ACS might be under-diagnosed in these patients. On the other hand, a patient with multiple coronary risk factors might get a very extensive workup, even with atypical chest pain. Resulting research could therefore be biased toward the traditional risk factors, and alternative risk factors could be hidden.
This is a type of selection bias.
This post is part of a series of posts on bias in medical research. You can find the whole bias catalogue here.
Sackett DL. Bias in analytic research. Journal of chronic diseases. 1979; 32(1-2):51-63. PMID: 447779