BACKGROUND: There is a need to identify effective interventions to minimize the threat posed by medication administration errors (MAEs). OBJECTIVE: Our objective was to review and critically appraise interventions designed to reduce MAEs in the hospital setting. DATA SOURCES: Ten electronic databases were searched between 1985 and November 2013. METHODS: Randomized controlled trials (RCTs) and controlled trials (CTs) reporting rates of MAEs or related adverse drug events between an intervention group and a comparator group were included. Data from each study were independently extracted and assessed for potential risk of bias by two authors. Risk ratios (RRs, with 95 % confidence intervals [CIs]) were used to examine the effect of an intervention. RESULTS: Six RCTs and seven CTs were included. Types of interventions clustered around four main themes: medication use technology (n = 4); nurse education and training (n = 3); changing practice in anesthesia (n = 2); and ward system changes (n = 4). Reductions in MAE rates were reported by five studies; these included automated drug dispensing (RR 0.72, 95 % CI 0.53-1.00), computerized physician order entry (RR 0.51, 95 % 0.40-0.66), barcode-assisted medication administration with electronic administration records (RR 0.71, 95 % CI 0.53-0.95), nursing education/training using simulation (RR 0.17, 95 % CI 0.08-0.38), and clinical pharmacist-led training (RR 0.76, 95 % CI 0.67-0.87). Increased or equivocal outcome rates were found for the remaining studies. Weaknesses in the internal or external validity were apparent for most included studies. LIMITATIONS: Theses and conference proceedings were excluded and data produced outside commercial publishing were not searched. CONCLUSIONS: There is emerging evidence of the impact of specific interventions to reduce MAEs in hospitals, which warrant further investigation using rigorous and standardized study designs. Theory-driven efforts to understand the underlying causes of MAEs may lead to more effective interventions in the future.
BACKGROUND: There is a need to identify effective interventions to minimize the threat posed by medication administration errors (MAEs). OBJECTIVE: Our objective was to review and critically appraise interventions designed to reduce MAEs in the hospital setting. DATA SOURCES: Ten electronic databases were searched between 1985 and November 2013. METHODS: Randomized controlled trials (RCTs) and controlled trials (CTs) reporting rates of MAEs or related adverse drug events between an intervention group and a comparator group were included. Data from each study were independently extracted and assessed for potential risk of bias by two authors. Risk ratios (RRs, with 95 % confidence intervals [CIs]) were used to examine the effect of an intervention. RESULTS: Six RCTs and seven CTs were included. Types of interventions clustered around four main themes: medication use technology (n = 4); nurse education and training (n = 3); changing practice in anesthesia (n = 2); and ward system changes (n = 4). Reductions in MAE rates were reported by five studies; these included automated drug dispensing (RR 0.72, 95 % CI 0.53-1.00), computerized physician order entry (RR 0.51, 95 % 0.40-0.66), barcode-assisted medication administration with electronic administration records (RR 0.71, 95 % CI 0.53-0.95), nursing education/training using simulation (RR 0.17, 95 % CI 0.08-0.38), and clinical pharmacist-led training (RR 0.76, 95 % CI 0.67-0.87). Increased or equivocal outcome rates were found for the remaining studies. Weaknesses in the internal or external validity were apparent for most included studies. LIMITATIONS: Theses and conference proceedings were excluded and data produced outside commercial publishing were not searched. CONCLUSIONS: There is emerging evidence of the impact of specific interventions to reduce MAEs in hospitals, which warrant further investigation using rigorous and standardized study designs. Theory-driven efforts to understand the underlying causes of MAEs may lead to more effective interventions in the future.
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