The Influence of Nurses' Characteristics on Medication Administration Errors: An Integrative Review.

backgroundMedication administration errors (MAEs) are a frequent cause of morbidity and mortality in acute care settings and can result in a prolonged hospital stay. The WHO estimated that medication errors cost up to $42 billion globally per a year. Therefore, MAEs was among the most common medical errors to occur in acute care settings. Studies of medication error usually focus on system factors, thus creating a gap between what researchers know about the causes of MAEs, and what frontline nurses actually do in the clinical setting. The purpose of this review is to fill a gap in the existing literature by focusing on the relationship between nurses' characteristics and MAEs.

methodsOnline databases were accessed, including CINAHL, PsycINFO, PubMed, Scopus, and Google Scholar from 2007-2020 period. This review was guided by the methods described by Whittemore and Knafl. Studies that addressed the occurrence of medication errors based on RN demographics were included in this review. The included studies were reviewed and analyzed by the two authors.

resultsOf the 1141 publications retrieved, 19 studies met inclusion criteria. The result provided strong evidence that nurses' level of education, length of experience, and attendance at training courses, are directly associated with the occurrence of MAEs. There is weak evidence of MAEs being influenced by the age and gender of nurses. Other nurse characteristics, such as cognitive load, frustration with technology, negligence, lack of attentiveness, and nurse ethnicity, are not adequately examined across the reviewed studies necessitates further research.

conclusionFocusing on nurses' characteristics might facilitate other researchers to suggest appropriate interventions that may reduce the incidence of MAEs. Interventional studies may provide convincing evidence as to whether one variable has a causal effect on another variable, and control the influence of confounding variables to enhance the generalizability of the findings.