Automated detection of wrong-drug prescribing errors.
Look-alike and sound-alike (LASA) drugs are a well-established source of medication errorsthat place patients at risk for adverse drug events. Prior research has shown that these medications can be automatically identified using diagnostic codes at the time of electronic prescribing. Using electronic health record data on medication orders and diagnostic claims data from a single academic medical center as well as data on medication indications, researchers developed an algorithm to identify LASA prescribing errors. Although the algorithm was able to identify LASA prescribing errors that may not have been found by other means, the positive predictive value was 12.1% and the false-positive rate was greater than 75%. The authors advocate for further research to improve specificity and sensitivity of this approach. A past WebM&M commentary discussed a case involving the mix-up of two medications with similar names.
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