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.
You can find more evidence based medicine resources here.
Sackett DL. Bias in analytic research. Journal of chronic diseases. 1979; 32(1-2):51-63. PMID: 447779
Morgenstern, J. Diagnostic suspicion bias, First10EM, April 10, 2018. Available at: