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Table 5 False negative errors made by the medication discrepancy detection algorithm

From: An end-to-end hybrid algorithm for automated medication discrepancy detection

Cause of false negative errors identified by the chart review Error [%]
1. The medication was omitted by the medication entity detection algorithm 68.0%
2. The medication was matched to a wrong medication due to similar medication names (e.g. methylprednisolone and prednisolone) 9.1%
3. The prescription contains more ingredients than the medication in the clinical note or vice versa (e.g. albuterol vs. ipratropium albuterol) 6.3%
4. The medication in the clinical note was matched to a correct prescription (e.g. matching diastat to diazepam) but the prescription had a different route (e.g. oral route vs. rectal route) 6.0%
5. The medication and the prescription names co-occurred in the same RxNorm description as ingredients rather than synonyms (e.g. “glycerin” and “polyethylene glycol” co-occurred in the RxNorm description of “artificial tears”) 4.8%
6. Other reasons 5.8%