Prevalence of intravenous medication administration errors: a cross-sectional study

Tezeta Fekadu1, Mebrahtu Teweldemedhin2, Eyerusalem Esrael1

  • 1School of Pharmacy, Department of Clinical Pharmacy, College of Health Sciences, Mekelle University, Mekelle.

Abstract

Insights

Medication administration errors (MAEs) in intravenous drug delivery are common, affecting 46.1% of patients. Older adults aged 60 and above are more susceptible to these errors, with missed doses being the most frequent issue.

Area of Science:

  • Healthcare quality and patient safety
  • Clinical nursing practices
  • Pharmacovigilance

Background:

  • Intravenous medication administration errors (MAEs) can lead to preventable adverse events and clinical complications.
  • Understanding the scope and causes of MAEs is crucial for improving patient outcomes.
  • Previous research indicates a significant impact of MAEs on healthcare quality.

Purpose of the Study:

  • To determine the prevalence of medication administration errors (MAEs) in intravenous therapy.
  • To identify specific factors associated with the occurrence of MAEs.
  • To provide data for targeted interventions to reduce medication errors.

Main Methods:

  • A hospital-based cross-sectional study was conducted between March and April 2015.
  • Direct observation using a pretested tool was employed for data collection.
  • Bivariate logistic regression analysis was used to identify risk factors for MAEs, with P < 0.05 considered significant.

Main Results:

  • A total of 134 patients were included in the study.
  • The overall prevalence of MAEs was 46.1%.
  • Missed doses constituted the most frequent error (95.8%). Older age groups (60-79 and 80-101 years) were identified as significant determinants of MAEs.

Conclusions:

  • The study revealed a high prevalence of medication administration errors in intravenous medication delivery.
  • Interventions focused on enhancing nurses' knowledge and practical skills are recommended to minimize MAEs.
  • Targeting older adult populations may be necessary for effective error reduction strategies.

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