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BMJ Qual Saf. 2019 Nov;28(11):908-915. doi: 10.1136/bmjqs-2019-009420. Epub 2019 Aug 7.

Automated detection of wrong-drug prescribing errors.

Author information

1
Department of Communication Studies and Center for Communication and Health, Northwestern University, Chicago, Illinois, USA bruce.lambert@northwestern.edu.
2
Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA.
3
Department of Pharmacy Systems, Outcomes and Policy, University of Illinois at Chicago, Chicago, Illinois, USA.
4
Independent Consultant, Chicago, Illinois, USA.
5
Division of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.
6
Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA.
7
Department of Pharmacy Practice, University of Illinois at Chicago, Chicago, Illinois, USA.
8
Department of Communication Studies and Center for Communication and Health, Northwestern University, Chicago, Illinois, USA.
9
Institute for Safe Medication Practices, Horsham, Pennsylvania, USA.

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.

KEYWORDS:

decision support, computerized; medication safety; patient safety; quality improvement

Conflict of interest statement

Competing interests: BLL has an ownership interest in two companies that provide software and consulting services related to the detection and prevention of drug name confusions. BLL’s companies had no role in the conduct of the studies described here.

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