Let's say I wanted to identify individuals who are taking an excessive amount of the blood clotting drug warfarin relative to their peers. To do this, I'm considering building a regression model that uses patient level data such as sex, age, and health status as factors and their actual drug dosage as the response. After model training, I'd apply the model to new data to generate predicted dosage values and compare those to the actual dosage. Patients who have an actual dosage higher than the predicted dosage would then be flagged as candidates for dose reduction.
Is this a reasonable plan? Essentially I'd be relying on the residuals to identify the patients who should be taking a reduced dosage.