Psycholinguistic tests predict real-world drug name confusion error rates: a cross-sectional experimental study.

Background: Wrong-drug medication errors are common. Regulators screen drug names for confusability, but screening methods lack empirical validation. Previous work showed that psycholinguistic tests on of drug names are associated with real-world error rates in chain pharmacies.

Automated detection of wrong-drug prescribing errors.

Background: To assess the specificity of an algorithm designed to detect look-alike/sound-alike (LASA) medication prescribing errors in electronic health record (EHR) data.

Setting: Urban, academic medical centre, comprising a 495-bed hospital and outpatient clinic running on the Cerner EHR. We extracted 8 years of medication orders and diagnostic claims.

Automated detection of look-alike/sound-alike medication errors.

Purpose: The development and evaluation of an algorithm for detecting potential medication errors due to look-alike/sound-alike (LASA) drug names are described.

Summary: A computer algorithm that detects potential LASA errors by analyzing medication orders and diagnostic claims data was developed. The algorithm flags a potential error when (1) a medication order is not justified by a diagnosis documented in the patient's record, (2) another medication whose orthographic similarity to the index drug exceeds a specified threshold exists, and (3) the latter drug has an indication that matches an active documented diagnosis.