Designing and evaluating an automated system for real-time medication administration error detection in a neonatal intensive care unit. (10th January 2018)

• Record Type: Journal Article
• Authors: Ni, Yizhao; Lingren, Todd; Hall, Eric S; Leonard, Matthew; Melton, Kristin; Kirkendall, Eric S
• Is Part Of: Journal of the American Medical Informatics Association. Volume 25:Number 5(2018)
• Page Start: 555
• Page End: 563
• Publication Date: 2018-01-10
• Subjects: automated error detection -- real-time error detection -- medication administration error -- harm mitigation; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocx156
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Background: Timely 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. Methods: Our 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). Results: Physicians 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). Conclusions: The 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.