Automated detection of wrong-drug prescribing errors. Issue 11 (7th August 2019)

• Record Type: Journal Article
• Authors: Lambert, Bruce L; Galanter, William; Liu, King Lup; Falck, Suzanne; Schiff, Gordon; Rash-Foanio, Christine; Schmidt, Kelly; Shrestha, Neeha; Vaida, Allen J; Gaunt, Michael J
• Is Part Of: BMJ quality & safety. Volume 28:Issue 11(2019)
• Page Start: 908
• Page End: 915
• Publication Date: 2019-08-07
• Subjects: medication safety -- decision support, computerized -- patient safety -- quality improvement; Medical care -- Quality control -- Periodicals; Health facilities -- Risk management -- Periodicals; Medical errors -- Prevention -- Periodicals; 362.106805
• DOI: 10.1136/bmjqs-2019-009420
• Languages: English
• ISSNs: 2044-5415

Abstract

Abstract : 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. We licensed a database of medication indications, refined it and merged it with the medication data. We developed an algorithm that triggered for LASA errors based on name similarity, the frequency with which a patient received a medication and whether the medication was justified by a diagnostic claim. We stratified triggers by similarity. Two clinicians reviewed a sample of charts for the presence of a true error, with disagreements resolved by a third reviewer. We computed specificity, positive predictive value (PPV) and yield. Results: The algorithm analysed 488 481 orders and generated 2404 triggers (0.5% rate). Clinicians reviewed 506 cases and confirmed the presence of 61 errors, for an overall PPV of 12.1% (95% CI 10.7% to 13.5%). It was not possible to measure sensitivity or the false-negative rate. The specificity of the algorithm varied as a function of name similarity and whether the intended and dispensed drugs shared the same route of administration. Conclusion: Automated detection of LASA medication errors is feasible and can reveal errors not currently detected by other means. Real-time error detection is not possible with the current system, the main barrier being the real-time availability of accurate diagnostic information. Further development should replicate this 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.