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Adverse drug events and medication errors: detection and classification methods.

T Morimoto1, T K Gandhi, A C Seger

  • 1Brigham and Women's Hospital, 1620 Tremont Street, Boston, MA 02120-1613, USA.

Quality & Safety in Health Care
|August 4, 2004
PubMed
Summary
This summary is machine-generated.

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This study presents a reliable method for detecting and classifying adverse drug events (ADEs) and medication errors. This approach enhances medication safety across various healthcare settings.

Area of Science:

  • Pharmacovigilance and Health Services Research
  • Clinical Quality Improvement
  • Patient Safety

Background:

  • Adverse drug events (ADEs) and medication errors significantly impact healthcare quality.
  • Effective detection and classification methods are essential for improving patient safety.
  • Current practices require robust systems for identifying and categorizing these incidents.

Purpose of the Study:

  • To investigate the incidence, type, and preventability of ADEs and medication errors.
  • To describe a feasible and reliable method for detecting and classifying these events.
  • To provide a framework for measuring and enhancing medication safety in clinical settings.

Main Methods:

  • Data collection through practice data extraction (charts, lab, prescriptions, databases), health professional solicitation, and patient surveys.

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  • Manual review or computer screening of practice data to identify potential signals.
  • Independent categorization of identified incidents by reviewers into ADEs, potential ADEs, medication errors, or exclusions, assessing preventability, severity, and other factors.

    • Main Results:

    • The described method for ADE and medication error detection and classification is feasible.
    • High inter-reviewer agreement was observed, ranging from satisfactory to excellent (kappa = 0.32-0.98).
    • The system demonstrated good reliability for classifying medication safety incidents.

    Conclusions:

    • The developed method offers a reliable and feasible approach to ADE and medication error detection and classification.
    • This systematic approach can be implemented in diverse clinical settings to monitor and improve medication safety.
    • Consistent application of this method is expected to contribute to a reduction in preventable ADEs and medication errors.