Risk Factors for Electronic Prescription Errors in Pediatric Intensive Care Patients.

OBJECTIVES: To assess risk factors for electronic prescription errors in a PICU. DESIGN: A database of electronic prescriptions issued by a computerized physician order entry with clinical decision support system was analyzed to identify risk factors for prescription errors. MEASUREMENTS AND MAIN RESULTS: Of 6,250 prescriptions, 101 were associated with errors (1.6%). The error rate was twice as high in patients older than 12 years than in patients children 6-12 and 0-6 years old (2.4% vs 1.3% and 1.2%, respectively, p < 0.05). Compared with patients without errors, patients with errors had a significantly higher score on the Pediatric Index of Mortality 2 (-3.7 vs -4.5; p = 0.05), longer PICU stay (6 vs 3.1 d; p < 0.0001), and higher number of prescriptions per patient (40.8 vs. 15.7; p < 0.0001). In addition, patients with errors were more likely to have a neurologic main admission diagnosis (p = 0.008) and less likely to have a cardiologic diagnosis (p = 0.03) than patients without errors. CONCLUSIONS: Our findings suggest that older patient age and greater disease severity are risk factors for electronic prescription errors.

Case Not Closed: Prescription Errors 12 Years after Computerized Physician Order Entry Implementation.

OBJECTIVE: To assess the prolonged impact of computerized physician order entry (CPOE) on medication prescription errors in pediatric intensive care patients. STUDY DESIGN: This observational study was conducted at a pediatric intensive care unit in which a CPOE (Metavision, iMDsoft, Israel) with a limited clinical decision support system was implemented between 2004 and 2007. Since then, no changes were made to the systems. We analyzed 2500 electronic prescriptions (1250 prescriptions from 2015 and 1250 prescriptions from 2016). Prescription errors were identified by a pediatric intensive care physician and classified as potential adverse drug events, medication prescription errors, or rule violations. Their prevalence was compared with the rate in 2007, reported in a previous study from the same unit. A randomly selected 10% of the prescriptions were also analyzed by the pediatric intensive care unit pharmacist, and the level of agreement was determined. RESULTS: The rate of prescription errors increased from 1.4% in 2007 to 3.2% in 2015 (P = .03). Following revision of the clinical decision support system tools, prescription errors decreased to 1% in 2016 (P < .0001). The potential adverse drug event rate dropped from 2% in 2015 to 0.7% in 2016 (P = .006), and the medication prescription error rate, from 1% to 0.2% (P = .01). The agreement between the 2 reviewers was excellent (k = 0.96). CONCLUSIONS: The rate of prescription errors may increase with time from implementation of a CPOE. Repeated surveillance of prescription errors is highly advised to plan strategies to reduce them. This approach should be considered in quality improvement of computerized information systems in general.