Moving our Understanding of Team Dynamics from the Simulation Room to the Operating Room

摘要

Healthcare organizations rely on simulations of complex processes to provide the training required for individuals and teams to evolve their skills and maintain high levels of competence in medical domains. Inherent in this process is the belief, generally founded on macro-scale measures such as observations and workplace-based assessments, that simulations provide the degree of psychological fidelity needed to accomplish this goal. A paradigm shift is underway toward a more dynamic perspective of teamwork to include psycho-physiological measures which will shape the creation of new forms of simulations, performance measures, and practices. Initially it is expected that these dynamic understandings will be derived from simulation studies. However, it is currently unknown at the neural / physiologic/ cognitive level how well simulation training elicits the types of dynamic thinking that is actually used by operating room teams during live-patient surgery, i.e. the ecological validity of simulation environments is unknown for dynamic neural and physiologic measures of team performance. This panel will describe efforts to address this question. Among the questions the panel will consider are: 1.To what extent do neurodynamic behaviors seen during simulations diverge from those in the operating room? 2.What are the implications for improving patient safety when communication, cognitive, and neurodynamic analysis become real-time? 3.Can biometric and communication measures better inform root cause analyses and best practices during live-patient encounters? The topics discussed anticipate the time when dynamic biometric data can contribute to our understanding of how to rapidly determine a team's functional status, and how to use this information to optimize outcomes and training. The rapid, dynamic and task neutral measures will make the lessons learned in healthcare applicable to other complex group and team environments. They will also provide a foundation for incorporating these models into machines to support the training and performance of teams.