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Related Experiment Video

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Automated detection of look-alike/sound-alike medication errors.

Christine Rash-Foanio1, William Galanter1, Michelle Bryson2

  • 1University of Illinois at Chicago, Chicago, IL.

American Journal of Health-System Pharmacy : AJHP : Official Journal of the American Society of Health-System Pharmacists
|March 25, 2017
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Summary

A new algorithm identifies potential medication errors from look-alike/sound-alike (LASA) drug names by analyzing orders and diagnoses. This system flagged errors like cycloserine instead of cyclosporine, improving patient safety.

Keywords:
Systematized Nomenclature of Medicinecycloserinecyclosporineelectronic prescribingmedication errorspatient harm

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Area of Science:

  • Pharmacovigilance
  • Health Informatics
  • Clinical Pharmacy

Background:

  • Look-alike/sound-alike (LASA) drug names are a significant source of medication errors.
  • Existing methods for detecting LASA errors are often manual and time-consuming.
  • The need for automated systems to identify and prevent LASA-related medication errors is critical.

Purpose of the Study:

  • To develop and evaluate a computer algorithm for detecting potential medication errors caused by LASA drug names.
  • To leverage medication orders and diagnostic claims data for automated LASA error detection.
  • To identify specific LASA drug pairs contributing to medication errors within a healthcare system.

Main Methods:

  • Developed a computer algorithm analyzing medication orders and diagnostic claims.
  • Algorithm flagged errors when an order lacked diagnostic justification, a similar drug existed, and that drug's indication matched a diagnosis.
  • Reviewed medication orders and diagnostic claims data at a large health system.

Main Results:

  • Identified potential LASA errors, including instances of cycloserine being ordered instead of the intended cyclosporine.
  • A 7-year review revealed 11 out of 16 cycloserine orders were erroneous.
  • An alert for cycloserine/cyclosporine LASA errors was implemented in the electronic order-entry system.

Conclusions:

  • Automated detection and confirmation of LASA errors using medication orders, diagnostic claims, and indications are feasible.
  • This approach can retrospectively identify problematic LASA drug pairs and assess error rates.
  • The techniques can be adapted for real-time error prevention through indication alerts.