Abstract Background As the U.S. Armed Forces shift from counterterrorism operations to peer conflict, preparing Critical Care Air Transport (CCAT) teams for high-volume patient movement is imperative. Quantifying baseline CCAT task load and its impact on team performance is essential for guiding future research aimed at mitigating task saturation during the complex transport missions anticipated in peer conflict scenarios. Materials and Methods Individual provider task load and team performance were evaluated during a high-fidelity simulation conducted as part of the CCAT-Advanced Course. The 55-minute scenario involved the simultaneous care of 2 critically ill patients and was designed to impose a mild-to-moderate task load for mechanical ventilation management and a moderate-to-high task load for medication infusion management. Immediately following the simulation, participants completed the unweighted National Aeronautics and Space Administration Task Load Index (NASA-TLX). Team non-technical performance was assessed using the Team Emergency Assessment Measure (TEAM) instrument. Rather than aggregating NASA-TLX and TEAM subscale scores through arithmetic means (e.g., sums or averages), we applied multilevel Bayesian modeling. Specifically, we implemented a multivariate cumulative logit model—appropriate for ordinal Likert-scale data—that jointly modeled NASA-TLX and TEAM subscale responses. The model accounted for provider type as a predictor of task load (NASA-TLX) and nested observations within teams. Results Data from 44 CCAT teams were analyzed. Across the cohort, the NASA-TLX dimensions with the highest estimated scores were Effort (13.4, IQR = 8.12, 17.14), Mental Demand (13.4, IQR = 8.72, 17.1), and Temporal Demand (11.6, IQR = 6.71, 15.2), while Frustration (7.94), Performance (5.63), and Physical Demand (5.29) were lower. This pattern—elevated cognitive and temporal demands with lower physical and emotional strain—was consistent across provider types. Among physicians (MDs), there was a significant negative correlation between individual task load (total NASA-TLX score) and team performance (total TEAM score), with Spearman’s rank correlation coefficient ρ = −0.23 (95% credible interval = −0.36 to −0.09). In contrast, correlations for respiratory therapists (RTs) and registered nurses (RNs) were not statistically reliable, with ρ = −0.07 (95% CI = −0.21 to 0.07) and ρ = −0.02 (95% CI = −0.16 to 0.13), respectively. For MDs, higher levels of Frustration, Mental Demand, Temporal Demand, and Effort, along with lower self-rated Performance, were significantly associated with lower TEAM scores. No comparable associations were observed for RNs or RTs. Finally, all MD-RN NASA-TLX dimension pairs were significantly correlated, ρ between 0.31 and 0.44; and essentially no correlations were found between the RN-RT or MD-RT comparisons. Conclusions We observed a negative association between physicians’ self-rated Frustration, Mental Demand, Temporal Demand, Performance, and Effort and the total TEAM score—a pattern not observed among RNs or RTs. Physicians were the only provider type to both recognize team underperformance and attribute it to perceived shortcomings in their own performance. Although all MD and RN task load dimensions were correlated—likely reflecting task sharing between the 2 roles—only physicians’ self-reported task load measures were associated with their team’s non-technical skills performance.
Strilka et al. (Thu,) studied this question.