The Relationship Between Nurse Staffing Mix and the Incidence of Medication Administration Errors

Posted: Jan 19, 2023

• Skylar Mendoza
• Landon Becker
• Bianca Rhodes

Abstract

This research presents a novel computational framework for analyzing the complex relationship between nurse staffing composition and medication administration errors in acute care settings. Unlike traditional healthcare studies that rely on linear regression models, we introduce a multi-agent simulation system that models nurse workflows, cognitive load, and environmental factors as dynamic, interacting systems. Our approach integrates principles from complex systems theory, cognitive science, and operations research to capture the non-linear relationships between staffing variables and error rates. The simulation incorporates realistic nurse behavior patterns, medication administration protocols, and ward environmental conditions across 1,000 simulated shifts. Our findings reveal several counterintuitive relationships: first, that optimal staffing mixes are highly dependent on unit-specific workflow patterns rather than universal ratios; second, that certain combinations of experienced and novice nurses can paradoxically increase error rates despite higher overall experience levels; and third, that temporal factors such as shift transitions and medication administration timing create critical windows where staffing mix has disproportionate effects on error probability. The model demonstrates that traditional nurse-to-patient ratios fail to account for the complex interplay between experience distribution, workflow synchronization, and cognitive resource allocation. This research contributes both methodologically through the application of complex systems modeling to healthcare staffing problems and substantively through the identification of previously unrecognized patterns in medication safety. Our computational approach provides hospital administrators with a sophisticated tool for optimizing staffing decisions that goes beyond simple ratio-based recommendations.