Quantifying Cognitive Workload in ICU Nursing: A Computational Modeling Approach to ECMO Circuit Priming

Abstract Despite the increasing use of extracorporeal membrane oxygenation (ECMO) and the critical responsibilities of ICU nurses during circuit priming, their cognitive workload in this high-stakes task remains poorly understood. This study aimed to conduct a quantitative assessment of cognitive workload among ICU nurses during ECMO circuit priming. A QN-MHP simulation model was developed to quantitatively assess cognitive workload during ECMO circuit priming. And, thirty Chinese ICU nurses performed an ECMO priming task, with their subjective cognitive workload quantitatively assessed via NASA-TLX questionnaires for permit direct comparison against model predictions. Simulation results from the QN-MHP model revealed that among the 18 subtasks of the ECMO circuit priming, 88.9% were classified as high cognitive workload tasks. The model-predicted task reaction times demonstrated no significant difference compared with experimentally observed data (U=5,p=0.673>0.05). Model simulations identified predominant activation in the cognitive (>20%) and motor control (>60%) subnetworks, exceeding all other subsystems (p<0.001). Significant differences were identified across workload dimensions: predicted values for mental workload, performance load, and effort load were significantly higher than those for physical load and frustration level (p<0.001). Further regression analysis confirmed a significant correlation between model-predicted cognitive workload values and NASA-TLX scale measurements (R²>0.730,p<0.001). The results demonstrate that the QN-MHP model provides a validated framework for quantifying nurses’ cognitive workload during ECMO priming, potentially improving ICU emergency care and patient safety.