Predisposing Factors to Medication Errors by Nurses and Prevention Strategies: A Scoping Review of Recent Literature.
Fábio CoelhoLuís FurtadoNatália MendonçaHélia SoaresHugo Miguel Santos DuarteCristina Raquel Baptista CosteiraCátia SantosJoana Pereira SousaPublished in: Nursing reports (Pavia, Italy) (2024)
Medication errors have serious consequences and high costs for the patient and the system. The treatment process and the care required for critically ill patients are complex, and these patients are more vulnerable to errors and potential consequences. A scoping review using the JBI methodology was conducted across PubMed, CINAHL, and MEDLINE databases and reported by the PRISMA-ScR guidelines to explore strategies that can mitigate medication errors by nurses. The search strategy focused on references published between January 2012 and April 2023. Sixteen studies were included, and the results were organized into thematic areas. Medication errors by nurses are in the areas of preparation, administration, and documentation; organizational, system-related, procedural, personal, and knowledge and training factors are predisposing factors for errors; educational intervention, verification and safety methods, organizational changes, and error reporting are the strategic areas to mitigate medication error. The organization of the data could be different, as it depends on the reviewers' experience. Knowledge of the factors that cause medication errors and interventions to mitigate them make it possible to outline strategies to minimize their occurrence and achieve health gains. The protocol preceding this review has been registered in the Open Science Framework and published.
Keyphrases
- adverse drug
- healthcare
- electronic health record
- patient safety
- mental health
- emergency department
- randomized controlled trial
- drug induced
- public health
- newly diagnosed
- systematic review
- end stage renal disease
- risk assessment
- big data
- physical activity
- case report
- high resolution
- quality improvement
- machine learning
- chronic kidney disease
- ejection fraction
- prognostic factors
- clinical practice
- mass spectrometry
- artificial intelligence
- social media
- combination therapy
- chronic pain
- affordable care act