title: Automated detection of medication administration errors in neonatal intensive care.
abstract: OBJECTIVE
  To improve neonatal patient safety through automated detection of medication administration errors (MAEs) in high alert medications including narcotics, vasoactive medication, intravenous fluids, parenteral nutrition, and insulin using the electronic health record (EHR); to evaluate rates of MAEs in neonatal care; and to compare the performance of computerized algorithms to traditional incident reporting for error detection.
  
  
  METHODS
  We developed novel computerized algorithms to identify MAEs within the EHR of all neonatal patients treated in a level four neonatal intensive care unit (NICU) in 2011 and 2012. We evaluated the rates and types of MAEs identified by the automated algorithms and compared their performance to incident reporting. Performance was evaluated by physician chart review.
  
  
  RESULTS
  In the combined 2011 and 2012 NICU data sets, the automated algorithms identified MAEs at the following rates: fentanyl, 0.4% (4 errors/1005 fentanyl administration records); morphine, 0.3% (11/4009); dobutamine, 0 (0/10); and milrinone, 0.3% (5/1925). We found higher MAE rates for other vasoactive medications including: dopamine, 11.6% (5/43); epinephrine, 10.0% (289/2890); and vasopressin, 12.8% (54/421). Fluid administration error rates were similar: intravenous fluids, 3.2% (273/8567); parenteral nutrition, 3.2% (649/20124); and lipid administration, 1.3% (203/15227). We also found 13 insulin administration errors with a resulting rate of 2.9% (13/456). MAE rates were higher for medications that were adjusted frequently and fluids administered concurrently. The algorithms identified many previously unidentified errors, demonstrating significantly better sensitivity (82% vs. 5%) and precision (70% vs. 50%) than incident reporting for error recognition.
  
  
  CONCLUSIONS
  Automated detection of medication administration errors through the EHR is feasible and performs better than currently used incident reporting systems. Automated algorithms may be useful for real-time error identification and mitigation.
authors.affiliation: Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
authors.forename: Qi
authors.initials: Q
authors.lastname: Li
authors.affiliation: Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; James M. Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
authors.forename: Eric S
authors.initials: ES
authors.lastname: Kirkendall
authors.affiliation: Division of Neonatology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
authors.forename: Eric S
authors.initials: ES
authors.lastname: Hall
authors.affiliation: Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
authors.forename: Yizhao
authors.initials: Y
authors.lastname: Ni
authors.affiliation: Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
authors.forename: Todd
authors.initials: T
authors.lastname: Lingren
authors.affiliation: Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
authors.forename: Megan
authors.initials: M
authors.lastname: Kaiser
authors.affiliation: Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
authors.forename: Nataline
authors.initials: N
authors.lastname: Lingren
authors.affiliation: Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
authors.forename: Haijun
authors.initials: H
authors.lastname: Zhai
authors.affiliation: Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; James M. Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
authors.forename: Imre
authors.initials: I
authors.lastname: Solti
authors.affiliation: Division of Neonatology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States. Electronic address: Kristin.Melton@cchmc.org.
authors.forename: Kristin
authors.initials: K
authors.lastname: Melton
mesh_terms: D000701:Analgesics, Opioid / Q000627:therapeutic use*; D001331:Automation; D006801:Humans; D007231:Infant, Newborn; D007363:Intensive Care Units, Neonatal; D015931:Intensive Care, Neonatal; D050316:Medical Order Entry Systems; D008508:Medication Errors; D061214:Patient Safety; D012308:Risk Management
chemical_list: D000701:Analgesics, Opioid
keywords: Computerized algorithms; Electronic health record; Medication administration; Medication administration errors; Medication error detection; Patient safety