Cureus · 2026
Benchmarking Drug Library Compliance and Alert Rates in Smart Infusion Pumps: A Multicenter Retrospective Study.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Souza Ivens, Mourad Mona, Burton Samantha J, Cole Jaculin, Plopper Caio, Fernandez Sofia R, Alam Aftab
Аннотация
INTRODUCTION: Smart infusion pumps with dose error-reduction software (DERS) generate alerts to prevent infusion programming errors. However, high alert volumes may contribute to alert fatigue and impact workflow efficiency. Benchmarking continuous quality improvement (CQI) data across institutions may help identify opportunities to optimize drug library (DL) performance. This study evaluated the relationship between DL compliance and alert-related metrics using a benchmarking approach.
METHODS: A retrospective multicenter analysis of smart infusion pump data was conducted across 90 institutions (99 data pulls), including 13,749,759 infusions. Key metrics included DL compliance, alert rate, override rate, and alerts resulting in reprogramming ("good catches"). Facilities with DL compliance <95% and ≥95% were compared, and Spearman correlation analyses were performed to assess associations between DL compliance and alert-related outcomes.
RESULTS: The overall DL compliance rate was 95.9%. The mean alert rate was 33.3% (±12.20), and the overall override rate was 96.4%. On average, one good catch occurred for every 34.5 DERS infusions. Benchmarking across institutions showed no statistically significant differences in alert rates, override rates, or good catch rates between facilities with DL compliance ≥95% and those with <95%. Correlation analyses demonstrated only weak associations between DL compliance and alert rate, override rate, and good catch metrics.
CONCLUSIONS: Benchmarking CQI data across institutions demonstrates that high DL compliance alone does not necessarily indicate optimized smart infusion pump performance. Incorporating alert and override metrics into CQI analyses may help reduce non-actionable alert burden and support safer, more efficient infusion workflows.
METHODS: A retrospective multicenter analysis of smart infusion pump data was conducted across 90 institutions (99 data pulls), including 13,749,759 infusions. Key metrics included DL compliance, alert rate, override rate, and alerts resulting in reprogramming ("good catches"). Facilities with DL compliance <95% and ≥95% were compared, and Spearman correlation analyses were performed to assess associations between DL compliance and alert-related outcomes.
RESULTS: The overall DL compliance rate was 95.9%. The mean alert rate was 33.3% (±12.20), and the overall override rate was 96.4%. On average, one good catch occurred for every 34.5 DERS infusions. Benchmarking across institutions showed no statistically significant differences in alert rates, override rates, or good catch rates between facilities with DL compliance ≥95% and those with <95%. Correlation analyses demonstrated only weak associations between DL compliance and alert rate, override rate, and good catch metrics.
CONCLUSIONS: Benchmarking CQI data across institutions demonstrates that high DL compliance alone does not necessarily indicate optimized smart infusion pump performance. Incorporating alert and override metrics into CQI analyses may help reduce non-actionable alert burden and support safer, more efficient infusion workflows.