Does a term exist for this concept in statistics? Suppose you have a dataset where the covariates are biomedical information for a given patient (e.g. height, weight, blood type, etc.) and the response variable is whether the patient has a certain disease. You are interested in making a classification model that will help screen patients in advance for this disease (i.e. save time and money).
Suppose you are able to create a decision tree that produces decent accuracy - but the "rules" produced by the decision tree are not very "helpful".
For example: if height between (150, 180 cm) and weight between (150, 200 lbs) then disease = true.
Suppose this rule proves to be a very accurate rule (i.e. when this condition is true, patients almost always have the disease) - but it's deemed too vague by the doctors to actually use. Instead, the doctors prefer to spend money/resources and physically screen patients.
Is there a term for this in statistics? An accurate model which is deemed too arbitrary to use?

Is this a case of overfitting? Decision trees are prone to do that. Have you got enough data to use a held out test set to validate the model? If so, the doctors might be convinced by the results on the test set.
Alternatively, it might be a case where an executive commissions a model, but when it is delivered the in-house experts whose judgement it replaces refuse to use it. It would be good to have a name for this common phenomenon!
