ArticleJournal of biomedical informatics2015
Automated detection of medication administration errors in neonatal intensive care.
Article in Journal of biomedical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 35 citations in OpenAlex.
- Transforming the electronic health record from a documentation application to an automated diet program for personalizing neonatal nutrition and improving feeding administration safety through process improvement.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition · 2025Article
- Optical Identification of Parenteral Nutrition Solutions Exploiting Refractive Index Sensing.Sensors (Basel, Switzerland) · 2022Article
- Patient Safety Incidents Related to the Use of Parenteral Nutrition in All Patient Groups: A Systematic Scoping Review.Drug safety · 2022Article
- Parenteral nutrition: a call to action for harmonization of policies to increase patient safety.European journal of clinical nutrition · 2021Review
- Article
- The Generalizability of a Medication Administration Discrepancy Detection System: Quantitative Comparative Analysis.JMIR medical informatics · 2020Article
- Iatrogenic severe hyperglycemia due to parenteral administration of glucose in children - a case series.Italian journal of pediatrics · 2020Article
- Integrating and Evaluating the Data Quality and Utility of Smart Pump Information in Detecting Medication Administration Errors: Evaluation Study.JMIR medical informatics · 2020Article
- Data Challenges With Real-Time Safety Event Detection And Clinical Decision Support.Journal of medical Internet research · 2019Article
- Computerized Physician Order Entry in the Neonatal Intensive Care Unit: A Narrative Review.Applied clinical informatics · 2019Review
- Designing and evaluating an automated system for real-time medication administration error detection in a neonatal intensive care unit.Journal of the American Medical Informatics Association : JAMIA · 2018Observational
- Interventions to reduce medication errors in neonatal care: a systematic review.Therapeutic advances in drug safety · 2018Review
- A Pilot Study on Developing a Standardized and Sensitive School Violence Risk Assessment with Manual Annotation.The Psychiatric quarterly · 2017Article
- Computerized Clinical Decision Support: Contributions from 2015.Yearbook of medical informatics · 2016Article
- Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
objectiveTo 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.
methodsWe 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.
resultsIn 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.
conclusionsAutomated 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.