# Validating censored labels

I have a database of $$P$$ patients' hospital encounters over a period of $$T$$ years. Patient $$p \in \{1, 2, \ldots P\}$$ visited the clinic $$N_p$$ times. If patient $$p$$ was diagnosed with condition C ($$C_p = 1$$), we record the timestamp of the diagnosis. If no diagnosis was made after the censoring time of $$T$$ years, we treat them as not having condition C ($$C_p = 0$$). The prevalence of condition C is small, on the order of 1-3%, and it is possible that some patients may not have been diagnosed, despite having the condition (Type II error). Without having a physician re-examine each $$C=0$$ patient's charts, notes, etc. (which would involve a lot of manual labor), is there any way to estimate the population type II error rate of the diagnoses? (If it simplifies things, you can assume the type I error rate is 0, but ideally, I would like this to be a free variable.)