Objective A method for detecting real-time changes in team cognition in the form of significant communication reorganizations is described. We demonstrate the method in the context of scenario-based simulation training. Background We present the dynamical view that individual- and team-level aspects of team cognition are temporally intertwined in a team’s real-time response to challenging events. We suggest that this real-time response represents a fundamental team cognitive skill regarding the rapidity and appropriateness of the response, and methods and metrics are needed to track this skill. Method Communication data from medical teams (Study 1) and submarine crews (Study 2) were analyzed for significant communication reorganization in response to training events. Mutual information between team members informed post hoc filtering to identify which team members contributed to reorganization. Results Significant communication reorganizations corresponding to challenging training events were detected for all teams. Less experienced teams tended to show delayed and sometimes ineffective responses that more experienced teams did not. Mutual information and post hoc filtering identified the individual-level inputs driving reorganization and potential mechanisms (e.g., leadership emergence, role restructuring) underlying reorganization. Conclusion The ability of teams to rapidly and effectively reorganize coordination patterns as the situation demands is a team cognitive skill that can be measured and tracked. Application Potential applications include team monitoring and assessment that would allow for visualization of a team’s real-time response and provide individualized feedback based on team member’s contributions to the team response.

Abstract:Research on the microscale neural dynamics of social interactions has yet to be translated into improvements in the assembly, training and evaluation of teams. This is partially due to the scale of neural involvements in team activities, spanning the millisecond oscillations in individual brains to the minutes/hours performance behaviors of the team. We have used intermediate neurodynamic representations to show that healthcare teams enter persistent (50-100 s) neurodynamic states when they encounter and resolve uncertainty while managing simulated patients. Each of the second symbols was developed situating the electroencephalogram (EEG) power of each team member in the contexts of those of other team members and the task. These representations were acquired from EEG headsets with 19 recording electrodes for each of the 1-40 Hz frequencies. Estimates of the information in each symbol stream were calculated from a 60 s moving window of Shannon entropy that was updated each second, providing a quantitative neurodynamic history of the team's performance. Neurodynamic organizations fluctuated with the task demands with increased organization (i.e., lower entropy) occurring when the team needed to resolve uncertainty. These results show that intermediate neurodynamic representations can provide a quantitative bridge between the micro and macro scales of teamwork.

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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.

Objective: To determine whether a dynamical analysis of neural and communication data streams provide fine-grained insights into healthcare team debriefings. Background: Debriefing plays a key role in experiential learning activities such as healthcare simulation because it bolsters the transfer of experience into learning through a process of reflection. There have been few studies examining the neural and communication dynamics of teams as team members are supported by trained facilitators in making better sense of their performance. Method: Electroencephalographic (EEG)–derived brain waves and speech were recorded from experienced and medical student healthcare teams during post-simulation debriefings. Quantitative estimates of the neurodynamic organizations of individual team members and the team were modeled from the EEG data streams at different scalp locations and at frequencies from 1-40 Hz. In parallel the dynamics of speech turn taking were quantified by recurrence frequency analysis. Results: Neurodynamic organizations were preferentially detected from sensors over the parietal lobes with activities present in the alpha, beta and gamma frequency bands. Rhythmic structures emerged as correlations between speech, discussion blocks and team & team member neurodynamic organizations. Conclusion: Organizational representations help reveal the neurodynamic, communication, and cognitive structures of debriefing. Application: The quantitative neurodynamic and communication measures will allow direct comparisons of debriefing structures across teams and debriefing protocols.

This paper describes how meaning can be extracted from large-scale dynamical data to make inferences about teamwork that are useful in both the theoretical and practical sense. The dynamics of an anesthesiology team are viewed from the perspectives of: 1) changes in the team’s neurodynamic organizations with large and small changes in the task; 2) how team member’s neurodynamics contribute to team neurodynamics; 3) the relationships between task events, heart-rate and neural dynamic organizations; 4) the linkages between speech flow, team and team member neurodynamics and topic discussions during Debriefing; and, 5) the micro-scale neural dynamics reflecting the involvement of the parietal lobes and gamma frequencies. These examples show how different sources of team data can contribute to multi-modal understandings of individual and teams dynamics that span micro and macro scales of teamwork.

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Healthcare systems have increased patients' exposure to their own health materials to enhance patients' health levels, but this has been impeded by patients' lack of understanding of their health material. We address potential barriers to their comprehension by developing a context-aware text simplification system for health material. Given the scarcity of annotated parallel corpora in healthcare domains, we design our system to be independent of a parallel corpus, complementing the availability of data-driven neural methods when such corpora are available. Our system compensates for the lack of direct supervision using a biomedical lexical database: Unified Medical Language System (UMLS). Compared to a competitive prior approach that uses a tool for identifying biomedical concepts and a consumer-directed vocabulary list, we empirically show the enhanced accuracy of our system due to improved handling of ambiguous terms. We also show the enhanced accuracy of our system over directly-supervised neural methods in this low-resource setting. Finally, we show the direct impact of our system on laypeople's comprehension of health material via a human subjects' study (n = 160).