Exploring how healthcare teams balance the neurodynamics of autonomous and collaborative behaviors: a proof of concept.

Team members co-regulate their activities and move together at the collective level of behavior while coordinating their actions toward shared goals. In parallel with team processes, team members need to resolve uncertainties arising from the changing task and environment. In this exploratory study we have measured the differential neurodynamics of seven two-person healthcare teams across time and brain regions during autonomous (taskwork) and collaborative (teamwork) segments of simulation training. The questions posed were: (1) whether these abstract and mostly integrated constructs could be separated neurodynamically; and, (2) what could be learned about taskwork and teamwork by trying to do so? The taskwork and teamwork frameworks used were Neurodynamic Information (NI), an electroencephalography (EEG) derived measure shown to be a neurodynamic proxy for the pauses and hesitations associated with individual uncertainty, and inter-brain EEG coherence (IBC) which is a required component of social interactions. No interdependency was observed between NI and IBC, and second-by-second dynamic comparisons suggested mutual exclusivity. These studies show that proxies for fundamental properties of teamwork and taskwork can be separated neurodynamically during team performances of ecologically valid tasks. The persistent expression of NI and IBC were not simultaneous suggesting that it may be difficult for team members to maintain inter-brain coherence while simultaneously reducing their individual uncertainties. Lastly, these separate dynamics occur over time frames of 15-30 s providing time for real-time detection and mitigation of individual and collaborative complications during training or live patient encounters.

Parsing Neurodynamic Information Streams to Estimate the Frequency, Magnitude and Duration of Team Uncertainty.

Neurodynamic organizations are information-based abstractions, expressed in bits, of the structure of long duration EEG amplitude levels. Neurodynamic information (NI, the variable of neurodynamic organization) is thought to continually accumulate as EEG amplitudes cycle through periods of persistent activation and deactivation in response to the activities and uncertainties of teamwork. Here we show that (1) Neurodynamic information levels were a better predictor of uncertainty and novice and expert behaviors than were the EEG power levels from which NI was derived. (2) Spatial and temporal parsing of team NI from experienced submarine navigation and healthcare teams showed that it was composed of discrete peaks with durations up to 20-60 s, and identified the involvement of activated delta waves when precise motor control was needed. (3) The relationship between NI and EEG power was complex varying by brain regions, EEG frequencies, and global vs. local brain interactions. The presence of an organizational system of information that parallels the amplitude of EEG rhythms is important as it provides a greatly reduced data dimension while retaining the essential system features, i.e., linkages to higher scale behaviors that span temporal and spatial scales of teamwork. In this way the combinatorial explosion of EEG rhythmic variables at micro levels become compressed into an intermediate system of information and organization which links to macro-scale team and team member behaviors. These studies provide an avenue for understanding how complex organizations arise from the dynamics of underlying micro-scale variables. The study also has practical implications for how micro-scale variables might be better represented, both conceptually and in terms of parsimony, for training machines to recognize human behaviors that span scales of teams.

Analysis of Mirrored Psychophysiological Change of Cardiac Surgery Team Members During Open Surgery.

OBJECTIVE: Mirrored psychophysiological change in cognitive workload indices may reflect shared mental models and effective healthcare team dynamics. In this exploratory analysis, we investigated the frequency of mirrored changes, defined as concurrent peaks in heart rate variability (HRV) across team members, during cardiac surgery. DESIGN: Objective cognitive workload was evaluated via HRV collected from the primary surgical team during cardiac surgery cases (N = 15). Root mean square of the successive differences (RMSSD) was calculated as the primary HRV measure. Procedures were divided into consecutive nonoverlapping 5-minute segments, and RMSSD along with deviations from RMSSD were calculated for each segment. Segments with positive deflections represent above-average cognitive workload. Positive deflections and peaks across dyads within the same segment were counted. SETTING: Data collection for this study took place in the cardiovascular operating room during live surgeries. PARTICIPANTS: Physiological data were collected and analyzed from the attending surgeon, attending anesthesiologist, and primary perfusionist involved with the recorded cases. RESULTS: Of the 641 five-minute segments analyzed, 325 (50.7%) were positive deflections above average, concurrently across at least 2 team members. Within the 325 positive deflections, 26 (8%) represented concurrent peaks in HRV across at least 2 active team members. Mirrored peaks across team members were observed most commonly during the coronary anastomoses or valve replacement phase (N = 12). CONCLUSIONS: In this pilot study, mirrored physiological responses representing peaks in cognitive workload were observed uncommonly across dyads of cardiac surgery team members (1.73 peaks/case on average). Almost half of these occurred during the most technically demanding phases of cardiac surgery, which may underpin teamwork quality. Future work should investigate interactions between technical and nontechnical performance surrounding times of mirrored peaks and expand the sample size.

Measuring Real-Time Team Cognition During Team Training.

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.

Advancing Our Understandings of Healthcare Team Dynamics From the Simulation Room to the Operating Room: A Neurodynamic Perspective.

The initial models of team and team member dynamics using biometric data in healthcare will likely come from simulations. But how confident are we that the simulation-derived high-resolution dynamics will reflect those of teams working with live patients? We have developed neurodynamic models of a neurosurgery team while they performed a peroneal nerve decompression surgery on a patient to approach this question. The models were constructed from EEG-derived measures that provided second-by-second estimates of the neurodynamic responses of the team and team members to task uncertainty. The anesthesiologist and two neurosurgeons developed peaks, often coordinated, of elevated neurodynamic organization during the patient preparation and surgery which were similar to those seen during simulation training, and which occurred near important episodes of the patient preparation and surgery. As the analyses moved down the neurodynamic hierarchy, and the simulation and live patient neurodynamics occurring during the intubation procedure were compared at progressively smaller time scales, differences emerged across scalp locations and EEG frequencies. The most significant was the pronounced suppression of gamma rhythms detected by the frontal scalp sensors during the live patient intubation which was absent in simulation trials of the intubation procedure. These results indicate that while profiles of the second-by-second neurodynamics of teams were similar in both the simulation and live patient environments, a deeper analysis revealed differences in the EEG frequencies and scalp locations of the signals responsible for those team dynamics. As measures of individual and team performance become more micro-scale and dynamic, and simulations become extended into virtual environments, these results argue for the need for parallel studies in live environments to validate the dynamics of cognition being observed.

Physiological synchronization and entropy as measures of team cognitive load.

The operating room (OR) is a high-risk and complex environment, where multiple specialized professionals work as a team to effectively care for patients in need of surgical interventions. Surgical tasks impose high cognitive demands on OR staff and cognitive overload may have deleterious effects on team performance and patient safety. The aim of the present study was to investigate the feasibility and describe a novel methodological approach to characterize dynamic changes in team cognitive load by measuring synchronization and entropy of heart rate variability parameters during real-life cardiac surgery. Cognitive load was measured by capturing interbeat intervals (IBI) from three team members (surgeon, anesthesiologist and perfusionist) using an unobtrusive wearable heart rate sensor and transmitted in real-time to a smartphone application. Clinical data and operating room audio/video recordings were also collected to provide behavioral and contextual information. We developed symbolic representations of the transient cognitive state of individual team members (Individual Cognitive State - ICS), and overall team (Team Cognitive State - TCS) by comparing IBI data from each team member with themselves and with others. The distribution of TCS symbols during surgery enabled us to display and analyze temporal states and dynamic changes of team cognitive load. Shannon's entropy was calculated to estimate the changing levels of team organization and to detect fluctuations resulting from a variety of cognitive demands and/or specific situations (e.g. medical error, emergency, flow disruptions). An illustrative example from a real cardiac surgery team shows how cognitive load patterns shifted rapidly after an actual near-miss medication event, leading the team to a more organized and synchronized state. The methodological approach described in this study provides a measurement technique for the assessment of team physiological synchronization, which can be applied to many other team-based environments. Future research should gather additional validity evidence to support the proposed methods for team cognitive load measurement.

Neuroergonomics: Quantitative Modeling of Individual, Shared, and Team Neurodynamic Information.

OBJECTIVE: The aim of this study was to use the same quantitative measure and scale to directly compare the neurodynamic information/organizations of individual team members with those of the team. BACKGROUND: Team processes are difficult to separate from those of individual team members due to the lack of quantitative measures that can be applied to both process sets. METHOD: Second-by-second symbolic representations were created of each team member's electroencephalographic power, and quantitative estimates of their neurodynamic organizations were calculated from the Shannon entropy of the symbolic data streams. The information in the neurodynamic data streams of health care ( n = 24), submarine navigation ( n = 12), and high school problem-solving ( n = 13) dyads was separated into the information of each team member, the information shared by team members, and the overall team information. RESULTS: Most of the team information was the sum of each individual's neurodynamic information. The remaining team information was shared among the team members. This shared information averaged ~15% of the individual information, with momentary levels of 1% to 80%. CONCLUSION: Continuous quantitative estimates can be made from the shared, individual, and team neurodynamic information about the contributions of different team members to the overall neurodynamic organization of a team and the neurodynamic interdependencies among the team members. APPLICATION: Information models provide a generalizable quantitative method for separating a team's neurodynamic organization into that of individual team members and that shared among team members.

Alternative Markers of Performance in Simulation: Where We Are and Where We Need To Go.

This article on alternative markers of performance in simulation is the product of a session held during the 2017 Academic Emergency Medicine Consensus Conference "Catalyzing System Change Through Health Care Simulation: Systems, Competency, and Outcomes." There is a dearth of research on the use of performance markers other than checklists, holistic ratings, and behaviorally anchored rating scales in the simulation environment. Through literature review, group discussion, and consultation with experts prior to the conference, the working group defined five topics for discussion: 1) establishing a working definition for alternative markers of performance, 2) defining goals for using alternative performance markers, 3) implications for measurement when using alternative markers, identifying practical concerns related to the use of alternative performance markers, and 5) identifying potential for alternative markers of performance to validate simulation scenarios. Five research propositions also emerged and are summarized.

Are Neurodynamic Organizations A Fundamental Property of Teamwork?

When performing a task it is important for teams to optimize their strategies and actions to maximize value and avoid the cost of surprise. The decisions teams make sometimes have unintended consequences and they must then reorganize their thinking, roles and/or configuration into corrective structures more appropriate for the situation. In this study we ask: What are the neurodynamic properties of these reorganizations and how do they relate to the moment-by-moment, and longer, performance-outcomes of teams?. We describe an information-organization approach for detecting and quantitating the fluctuating neurodynamic organizations in teams. Neurodynamic organization is the propensity of team members to enter into prolonged (minutes) metastable neurodynamic relationships as they encounter and resolve disturbances to their normal rhythms. Team neurodynamic organizations were detected and modeled by transforming the physical units of each team member's EEG power levels into Shannon entropy-derived information units about the team's organization and synchronization. Entropy is a measure of the variability or uncertainty of information in a data stream. This physical unit to information unit transformation bridges micro level social coordination events with macro level expert observations of team behavior allowing multimodal comparisons across the neural, cognitive and behavioral time scales of teamwork. The measures included the entropy of each team member's data stream, the overall team entropy and the mutual information between dyad pairs of the team. Mutual information can be thought of as periods related to team member synchrony. Comparisons between individual entropy and mutual information levels for the dyad combinations of three-person teams provided quantitative estimates of the proportion of a person's neurodynamic organizations that represented periods of synchrony with other team members, which in aggregate provided measures of the overall degree of neurodynamic interactions of the team. We propose that increased neurodynamic organization occurs when a team's operating rhythm can no longer support the complexity of the task and the team needs to expend energy to re-organize into structures that better minimize the "surprise" in the environment. Consistent with this hypothesis, the frequency and magnitude of neurodynamic organizations were less in experienced military and healthcare teams than they were in more junior teams. Similar dynamical properties of neurodynamic organization were observed in models of the EEG data streams of military, healthcare and high school science teams suggesting that neurodynamic organization may be a common property of teamwork. The innovation of this study is the potential it raises for developing globally applicable quantitative models of team dynamics that will allow comparisons to be made across teams, tasks and training protocols.

Tracing Neurodynamic Information Flows during Teamwork.

The goal of this study was to evaluate different neurodynamic representations for their ability to describe the interactions of team members with each other and with the changing task. Electroencephalography (EEG) data streams were collected from six crew members of a submarine piloting and navigation team while they performed a required training simulation. A representation of neurodynamic organization was first generated by creating symbols every second that showed the EEG power levels of each crew member. The second-by-second expression of these symbols continuously varied with the changing task, and the magnitude, duration and frequency of these variations could be quantitated using a moving window of Shannon entropy over the symbol stream. These changes in neurodynamic organization (i.e. entropy) were seen in the alpha, beta and gamma EEG frequency bands. A representation of team members' synchrony was created by measuring the mutual information in the EEG power levels for fourteen dyad combinations. Mutual information was present in the gamma EEG band, and elevated levels were distributed throughout the task. These discrete periods of synchrony were poorly correlated at zero lag with either changes in the team's neurodynamic organization, or speech patterns.

Cross-Level Effects Between Neurophysiology and Communication During Team Training.

OBJECTIVE: We investigated cross-level effects, which are concurrent changes across neural and cognitive-behavioral levels of analysis as teams interact, between neurophysiology and team communication variables under variations in team training. BACKGROUND: When people work together as a team, they develop neural, cognitive, and behavioral patterns that they would not develop individually. It is currently unknown whether these patterns are associated with each other in the form of cross-level effects. METHOD: Team-level neurophysiology and latent semantic analysis communication data were collected from submarine teams in a training simulation. We analyzed whether (a) both neural and communication variables change together in response to changes in training segments (briefing, scenario, or debriefing), (b) neural and communication variables mutually discriminate teams of different experience levels, and (c) peak cross-correlations between neural and communication variables identify how the levels are linked. RESULTS: Changes in training segment led to changes in both neural and communication variables, neural and communication variables mutually discriminated between teams of different experience levels, and peak cross-correlations indicated that changes in communication precede changes in neural patterns in more experienced teams. CONCLUSION: Cross-level effects suggest that teamwork is not reducible to a fundamental level of analysis and that training effects are spread out across neural and cognitive-behavioral levels of analysis. Cross-level effects are important to consider for theories of team performance and practical aspects of team training. APPLICATION: Cross-level effects suggest that measurements could be taken at one level (e.g., neural) to assess team experience (or skill) on another level (e.g., cognitive-behavioral).

Modeling the neurodynamic organizations and interactions of teams.

Across-brain neurodynamic organizations arise when teams perform coordinated tasks. We describe a symbolic electroencephalographic (EEG) approach that identifies when team neurodynamic organizations occur and demonstrate its utility with scientific problem solving and submarine navigation tasks. Each second, neurodynamic symbols (NS) were created showing the 1-40 Hz EEG power spectral densities for each team member. These data streams contained a performance history of the team's across-brain neurodynamic organizations. The degree of neurodynamic organization was calculated each second from a moving window average of the Shannon entropy over the task. Decreased NS entropy (i.e., greater neurodynamic organization) was prominent in the ~16 Hz EEG bins during problem solving, while during submarine navigation, the maximum NS entropy decreases were ~10 Hz and were associated with establishing the ship's location. Decreased NS entropy also occurred in the 20-40 Hz bins of both teams and was associated with uncertainty or stress. The highest mutual information levels, calculated from the EEG values of team dyads, were associated with decreased NS entropy, suggesting a link between these two measures. These studies show entropy and mutual information mapping of symbolic EEG data streams from teams can be useful for identifying organized across-brain team activation patterns.

Toward a quantitative description of the neurodynamic organizations of teams.

The goal was to develop quantitative models of the neurodynamic organizations of teams that could be used for comparing performance within and across teams and sessions. A symbolic modeling system was developed, where raw electroencephalography (EEG) signals from dyads were first transformed into second-by-second estimates of the cognitive Workload or Engagement of each person and transformed again into symbols representing the aggregated levels of the team. The resulting neurodynamic symbol streams had a persistent structure and contained segments of differential symbol expression. The quantitative Shannon entropy changes during these periods were related to speech, performance, and team responses to task changes. The dyads in an unscripted map navigation task (Human Communication Research Centre (HCRC) Map Task (MT)) developed fluctuating dynamics for Workload and Engagement, as they established their teamwork rhythms, and these were disrupted by external changes to the task. The entropy fluctuations during these disruptions differed in frequency, magnitude, and duration, and were associated with qualitative and quantitative changes in team organization and performance. These results indicate that neurodynamic models may be reliable, sensitive, and valid indicators of the changing neurodynamics of teams around which standardized quantitative models can begin to be developed.

The organizational neurodynamics of teams.

Our objective was to apply ideas from complexity theory to derive expanded neurodynamic models of Submarine Piloting and Navigation showing how teams cognitively organize around task changes. The cognitive metric highlighted was an electroencephalography-derived measure of engagement (termed neurophysiologic synchronies of engagement) that was modeled into collective team variables showing the engagement of each of six team members as well as that of the team as a whole. We modeled the cognitive organization of teams using the information content of the neurophysiologic data streams derived from calculations of their Shannon entropy. We show that the periods of team cognitive reorganization (a) occurred as a natural product of teamwork particularly around periods of stress, (b) appeared structured around episodes of communication, (c) occurred following deliberate external perturbation to team function, and (d) were less frequent in experienced navigation teams. These periods of reorganization were lengthy, lasting up to 10 minutes. As the overall entropy levels of the neurophysiologic data stream are significantly higher for expert teams, this measure may be a useful candidate for modeling teamwork and its development over prolonged periods of training.

Cognitive neurophysiologic synchronies: what can they contribute to the study of teamwork?

OBJECTIVE: Cognitive neurophysiologic synchronies (NS) are low-level data streams derived from electroencephalography (EEG) measurements that can be collected and analyzed in near real time and in realistic settings. The objective of this study was to relate the expression of NS for engagement to the frequency of conversation between team members during Submarine Piloting and Navigation (SPAN) simulations. BACKGROUND: If the expression of different NS patterns is sensitive to changes in the behavior of teams, they may be a useful tool for studying team cognition. METHOD: EEG-derived measures of engagement (EEG-E) from SPAN team members were normalized and pattern classified by self-organizing artificial neural networks and hidden Markov models. The temporal expression of these patterns was mapped onto team events and related to the frequency of team members' speech. Standardized models were created with pooled data from multiple teams to facilitate comparisons across teams and levels of expertise and to provide a framework for rapid monitoring of team performance. RESULTS: The NS expression for engagement shifted across task segments and internal and external task changes.These changes occurred within seconds and were affected more by changes in the task than by the person speaking.Shannon entropy measures of the NS data stream showed decreases associated with periods when the team was stressed and speaker entropy was high. CONCLUSION: These studies indicate that expression of neurophysiologic indicators measured by EEG may complement rather than duplicate communication metrics as measures of team cognition. APPLICATION: Neurophysiologic approaches may facilitate the rapid determination of the cognitive status of a team and support the development of novel adaptive approaches to optimize team function.

Neurotechnology to accelerate learning: during marksmanship training.

This article explores the psychophysiological metrics during expert and novice performances in marksmanship, combat deadly force judgment and decision making (DFJDM), and interactions of teams. Electroencephalography (EEG) and electrocardiography (ECG) are used to characterize the psychophysiological profiles within all categories. Closed-loop biofeedback was administered to accelerate learning during marksmanship training in which the results show a difference in groups that received feedback compared with the control. During known distance marksmanship and DFJDM scenarios, experts show superior ability to control physiology to meet the demands of the task. Expertise in teaming scenarios is characterized by higher levels of cohesiveness than those seen in novices.

Exploring Alternative Models of Complex Patient Management with Artificial Neural Networks.

This study applied an unsupervised neural network modeling process to test data of the National Board of Medical Examiners (NBME) Computer-based Clinical Scenarios (CCS) to identify new performance categories and validate this process as a scoring technique. The classifications resulting from this neural network modeling were consistent with the NBME model in that highly rated NMBE performances (ratings of 7 or 8) were clustered together on the neural network output grid. Very low performance ratings appeared to share few common features and were accordingly classified at isolated nodes. This clustering was reproducible across three separately trained networks with greater than 80% agreement in two of the three networks trained. However, the neural network also contained performance clusters where disparate NBME-based ratings ranged from 1 (worst) to 8 (best). Here, agreement between networks was less than 60%. Through visualization of the search strategies (search path mapping), this neural network clustering was found to be sensitive to quantitative and qualitative test selections such as excessive usage of irrelevant tests reflecting broader behavioral classification in some instances. A disparity between NBME ratings and an independent human rating system was detected by the neural network model since disagreement among raters was also reflected by a lack of neural network performance clustering. Agreement between rating systems, however, was correlated with neural network clustering for 92% of the highly rated performances.