Assessing Frequency and Risk of Weight Entry Errors in Pediatrics

Hagedorn, Philip; Kirkendall, Eric S.; Kouril, Michal; Dexheimer, Judith W.; Courter, Joshua; Minich, Thomas; Spooner, S. Andrew

doi:10.1001/jamapediatrics.2016.3865

Cited by 4 publications

(1 citation statement)

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“… 19–21 , 28–32 Most of the alerts are implemented with a static formula based on a combination of ordered dose and patient variables (eg, weight). 39 The simple algorithms could not capture MAEs that require dynamic reconciliation between administrations, orders, and adjustments from clinician communication. By utilizing barcoding technology, BCMA systems verify details of in-hand medications (ie, correct patient, drug, dose, route, and scheduled time) with medication orders before administration.…”

Section: Background and Significancementioning

confidence: 99%

Designing and evaluating an automated system for real-time medication administration error detection in a neonatal intensive care unit

Lingren

Hall

et al. 2018

Journal of the American Medical Informatics Association

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BackgroundTimely identification of medication administration errors (MAEs) promises great benefits for mitigating medication errors and associated harm. Despite previous efforts utilizing computerized methods to monitor medication errors, sustaining effective and accurate detection of MAEs remains challenging. In this study, we developed a real-time MAE detection system and evaluated its performance prior to system integration into institutional workflows.MethodsOur prospective observational study included automated MAE detection of 10 high-risk medications and fluids for patients admitted to the neonatal intensive care unit at Cincinnati Children’s Hospital Medical Center during a 4-month period. The automated system extracted real-time medication use information from the institutional electronic health records and identified MAEs using logic-based rules and natural language processing techniques. The MAE summary was delivered via a real-time messaging platform to promote reduction of patient exposure to potential harm. System performance was validated using a physician-generated gold standard of MAE events, and results were compared with those of current practice (incident reporting and trigger tools).ResultsPhysicians identified 116 MAEs from 10 104 medication administrations during the study period. Compared to current practice, the sensitivity with automated MAE detection was improved significantly from 4.3% to 85.3% (P = .009), with a positive predictive value of 78.0%. Furthermore, the system showed potential to reduce patient exposure to harm, from 256 min to 35 min (P < .001).ConclusionsThe automated system demonstrated improved capacity for identifying MAEs while guarding against alert fatigue. It also showed promise for reducing patient exposure to potential harm following MAE events.

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Section: Background and Significancementioning

confidence: 99%