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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 1 of 1 Research Studies DisplayedLambert BL, Galanter W, Liu KL
Automated detection of wrong-drug prescribing errors.
Investigators assessed the specificity of an algorithm designed to detect look-alike/sound-alike (LASA) medication prescribing errors in electronic health record (EHR) data. They found that automated detection of LASA medication errors is feasible and can reveal errors not currently detected by other means. Additionally, real-time error detection is not possible with the current system. They suggested that further development should replicate their analysis in other health systems and on a larger set of medications and should decrease clinician time spent reviewing false-positive triggers by increasing specificity.
AHRQ-funded; HS021093.
Citation: Lambert BL, Galanter W, Liu KL .
Automated detection of wrong-drug prescribing errors.
BMJ Qual Saf 2019 Nov;28(11):908-15. doi: 10.1136/bmjqs-2019-009420..
Keywords: Adverse Drug Events (ADE), Adverse Events, Clinical Decision Support (CDS), Electronic Health Records (EHRs), Health Information Technology (HIT), Medical Errors, Medication, Patient Safety