Assessing the Relationship Between Nurse Fatigue and Medication Administration Errors in Clinical Settings

Posted: Dec 07, 2023

• Zane Warren
• Talia Simmons
• Easton Bell

Abstract

The relationship between healthcare provider fatigue and patient safety represents a critical concern in clinical environments, with medication administration errors posing significant risks to patient outcomes. Traditional research in this domain has largely depended on subjective self-reporting measures and retrospective error analysis, creating limitations in both temporal precision and causal attribution. This study introduces an innovative computational framework that transcends conventional methodologies by integrating real-time physiological monitoring with comprehensive workflow analysis to elucidate the nuanced dynamics between nurse fatigue states and medication error manifestations. Our research addresses several fundamental questions that remain inadequately explored in existing literature. How do different dimensions of fatigue—cognitive, physical, and emotional—differentially impact various categories of medication errors? To what extent do individual physiological responses to fatigue create distinct error susceptibility profiles? Can predictive modeling accurately identify high-risk medication administration scenarios before errors occur? These questions necessitate a methodological approach that captures the multidimensional nature of fatigue while accounting for the complex contextual factors inherent in clinical environments. The novelty of our approach lies in the integration of wearable biometric technology with advanced machine learning techniques to create a dynamic predictive model of medication error risk. By moving beyond the simplistic correlation between shift length and error rates that dominates current literature, we develop a sophisticated understanding of how specific physiological indicators interact with workflow patterns and individual characteristics to influence medication safety. This research represents a paradigm shift from reactive error reporting to proactive risk mitigation through computational prediction.