0146 Model-based Predictions Of Neurobehavioral Performance Of Resident Physicians In A Randomized Order Safety Trial Evaluating Resident-physician Schedules (rosters)

Abstract

Introduction: Mathematical models of neurobehavioral performance are a key tool in work schedule assessment, allowing quantitative predictions of performance, especially in cases where empirical assessment would be impractical. Models have recently been extended to predict accurately the effects of chronic sleep restriction on vigilant performance, increasing their operational relevance. Methods: Using a validated mathematical model of neurobehavioral performance, we tested the hypothesis that resident physicians working an extended duration work roster (EDWR), including 24‐28 hours of continuous duty and up to 88 hours per week averaged over 4 weeks, would have worse predicted performance than resident physicians working a rapidly cycling work roster (RCWR) intervention designed to reduce duration of extended‐duration shifts. Model predictions of attentional failures were generated using 298 actual ROSTERS work and sleep schedules (n=148 for EDWR, n=150 for RCWR). Predicted outcomes were hours per week during work hours spent at moderate (equivalent to 16‐20 hours of continuous wakefulness) or high (equivalent to ≥20 hours) impairment. Results: Compared to RCWR, resident physicians working EDWR were predicted to spend significantly more time at moderate impairment (p<0.05), with this difference being most pronounced during the circadian night (p<0.001). On both schedules, performance was predicted to decline from weeks 1 + 2 to weeks 3 + 4 (p<0.001), but rate of predicted decline was significantly greater on EDWR (p<0.01). Predicted impairment was inversely related to sleep duration (p<0.001). Conclusion: The findings reveal the utility of a model that incorporates effects of circadian rhythms and acute and chronic sleep loss on performance to evaluate the safety profile of schedules for resident physicians and others. Model predictions identify potential approaches to improving schedules.