Smart Infusion Pumps and Error Reduction Systems

Abstract

Medication errors associated with intravenous infusions remain a persistent patient safety concern, with studies estimating that 56-61% of serious in-hospital medication errors involve the parenteral route. Smart infusion pumps equipped with dose error reduction systems (DERS) -- software libraries containing drug-specific dosing limits that generate alerts when programmed parameters fall outside predefined ranges -- were introduced to intercept such errors before drug delivery. However, the effectiveness of DERS depends critically on library design, alert threshold calibration, clinician compliance with alerts, and integration with broader medication safety infrastructure. This study presents the Smart Infusion Pump Effectiveness Assessment Framework (SIPEAF), evaluating five DERS configurations -- basic hard/soft limit alerts, weight-based dosing validation, clinical decision support-integrated alerts, auto-programming from electronic health records (EHR), and machine learning-enhanced predictive alerts -- across four error reduction tasks: wrong-dose interception, wrong-drug detection, wrong-rate prevention, and cumulative dose tracking. Our Infusion Error Reduction Score (IERS) integrates interception sensitivity, alert specificity, clinician compliance rate, workflow disruption index, and residual error rate. ML-enhanced predictive alerts achieved the highest IERS (0.922) by reducing alert fatigue through contextual relevance scoring that suppressed 64% of non-actionable alerts while maintaining 96.8% sensitivity for clinically significant dosing errors, and EHR auto-programming achieved the lowest residual error rate (0.12%) by eliminating manual transcription from the order-to-delivery chain.