Systems modeling for reducing medication errors

Medication errors significantly impact patient health and quality of care. In this study, systems modeling and simulation are used to analyze the medication workflow in a pediatric setting. The resulting simulation system allows for derivation and validation of effective intervention strategies for error mitigation. The analysis focused on 'High Alert' medications, which are more likely to produce serious patient harm when errors occur. Our study shows that strategic process interventions (e.g., independent intervention check points) can significantly reduce error occurrence (by as much as 50.25%) and the cost savings could be significant. We analyze the cost-savings versus intervention effectiveness versus the cost of the extra resource needed to perform the intervention, and identify the break-even point. The estimated errors from our model remain consistently close to the reported error statistics (within 5%). This work offers a system-decision support framework for analysis of medication workflow and helps in understanding error propagation mechanisms and process interdependencies. The framework allows users to derive intervention strategies and evaluate their overall mitigation effectiveness.