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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%