Medication Errors in Pediatric Emergency Departments: A Systematic Review and Recommendations for Enhancing Medication Safety
Abstract
OBJECTIVE: This systematic review aims to investigate the prevalence, preventability, and severity of medication errors in pediatric emergency departments (P-EDs). It also aims to identify common types of medication errors, implicated medications, risk factors, and evaluate the effectiveness of interventions in preventing these errors.
METHODS: A systematic review analyzed 6 primary studies with sample sizes ranging from 96 to 5000 pediatric patients in P-EDs. The review followed Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and included observational studies and randomized controlled trials involving patients aged 18 years and younger. Comprehensive searches in biomedical databases were conducted, and conflicts in record screening were resolved by a third reviewer using systematic review software.
RESULTS: Medication errors in P-EDs are prevalent, ranging from 10% to 15%, with dosing errors being the most common, accounting for 39% to 49% of reported errors. These errors primarily stem from inaccurate weight estimations or dosage miscalculations. Inadequate dosing frequency and documentation also contribute significantly to medication errors. Commonly implicated medications include acetaminophen, analgesics, corticosteroids, antibiotics, bronchodilators, and intravenous fluids. Most errors are categorized as insignificant/mild (51.7% to 94.5%) or moderate (47.5%). Risk factors associated with medication errors in P-EDs include less…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 5,000 participants or records.
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.