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In laboratory medicine, mislabeled specimens (MLS) are pre-analytical errors where blood from one patient is given an ID label from a different patient. These errors are estimated to occur in between 0.03 to 17 specimens per 1000 specimens collected (1-4). It is likely that the lower estimates may be falsely low due to the difficulty in identifying these errors in clinical practice. When MLS are not detected, they can place one or both patients at risk of harm as clinical decisions are carried out based on incorrect data. MLS are estimated to cost 280,000 USD per million specimens collected, and cause 160,000 adverse medical events per year in the United States (5). Original Article

Development and characterization of neural network-based multianalyte delta checks