Demonstration of a Method for Real-time Detection of Anomalies in Team Communication

Abstract

Real-time analysis of team communication data to detect anomalies and/or perturbations in the team environment is an ideal method to improve on teams’ interactions and responses to potential crises. In this paper, we demonstrate a method to detect anomalies through observing communication patterns of neurosurgery teams. We simulated the real-time process by analyzing previously collected communication data to assess the effectiveness of a nonlinear prediction model to detect anomalies. We compared predicted values of communication determinism (a measure of how organized communication patterns are) to previous values in each team’s time series. These deviations formed a separate root mean square error (RMSE) time series, and we examined the magnitudes of the RMSE time series at the points of known perturbations. Additionally, we examined the effect of window size on perturbation detection. We found that our nonlinear prediction model accurately detected the perturbations and shows promise for future real-time analysis.