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Automated detection of wrong-drug prescribing errors.

Bruce L Lambert1, William Galanter2,3, King Lup Liu4

  • 1Department of Communication Studies and Center for Communication and Health, Northwestern University, Chicago, Illinois, USA bruce.lambert@northwestern.edu.

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|August 9, 2019
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Summary
This summary is machine-generated.

An algorithm can detect look-alike/sound-alike (LASA) medication errors in electronic health records (EHRs), though real-time detection is limited. This automated method identifies potential prescribing errors missed by other systems.

Keywords:
decision support, computerizedmedication safetypatient safetyquality improvement

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Area of Science:

  • Health Informatics
  • Clinical Pharmacy
  • Medical Error Detection

Background:

  • Look-alike/sound-alike (LASA) medications pose a significant risk for prescribing errors.
  • Electronic Health Records (EHRs) offer a platform for automated detection of medication errors.
  • Existing detection methods may not capture all LASA errors.

Purpose of the Study:

  • To assess the specificity and effectiveness of an algorithm designed to detect LASA medication prescribing errors within EHR data.
  • To evaluate the algorithm's performance in identifying potential errors based on name similarity, medication frequency, and diagnostic justification.

Main Methods:

  • Development of a LASA error detection algorithm using medication orders and diagnostic claims from an urban academic medical center's EHR.
  • Algorithm refinement incorporating medication indications and justification by diagnostic claims.
  • Stratification of triggers by name similarity and review of a sample of cases by clinicians to determine specificity and positive predictive value (PPV).

Main Results:

  • The algorithm analyzed over 488,000 orders, generating 2404 triggers (0.5% rate).
  • Clinician review of 506 cases confirmed 61 errors, yielding an overall PPV of 12.1%.
  • Algorithm specificity varied based on name similarity and drug route of administration.

Conclusions:

  • Automated detection of LASA medication errors using EHR data is feasible and can uncover previously undetected errors.
  • Real-time error detection is currently limited by the availability of accurate diagnostic information.
  • Future development should focus on improving algorithm specificity and testing in diverse health systems to reduce false positives and clinician review time.