Next Article in Journal
Measurement on the Complexity Entropy of Dynamic Game Models for Innovative Enterprises under Two Kinds of Government Subsidies
Next Article in Special Issue
Transfer Learning for SSVEP Electroencephalography Based Brain–Computer Interfaces Using Learn++.NSE and Mutual Information
Previous Article in Journal
Anisotropically Weighted and Nonholonomically Constrained Evolutions on Manifolds
Previous Article in Special Issue
Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Healthcare Teams Neurodynamically Reorganize When Resolving Uncertainty

Brain Research Institute, University of California, Los Angeles School of Medicine, Culver City, CA 90230, USA
IMMEX (Interactive Multi-Media Exercises), The Learning Chameleon, Inc., Culver City, CA 90230, USA
JUMP Simulation and Education Center, 1306 N Berkeley Ave, Peoria, IL 61603, USA
Author to whom correspondence should be addressed.
Academic Editor: Osvaldo Anibal Rosso
Entropy 2016, 18(12), 427;
Received: 23 September 2016 / Revised: 13 November 2016 / Accepted: 22 November 2016 / Published: 29 November 2016
(This article belongs to the Special Issue Entropy and Electroencephalography II)
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. View Full-Text
Keywords: team neurodynamics; Shannon entropy; EEG; teamwork; healthcare; uncertainty team neurodynamics; Shannon entropy; EEG; teamwork; healthcare; uncertainty
Show Figures

Figure 1

MDPI and ACS Style

Stevens, R.; Galloway, T.; Halpin, D.; Willemsen-Dunlap, A. Healthcare Teams Neurodynamically Reorganize When Resolving Uncertainty. Entropy 2016, 18, 427.

AMA Style

Stevens R, Galloway T, Halpin D, Willemsen-Dunlap A. Healthcare Teams Neurodynamically Reorganize When Resolving Uncertainty. Entropy. 2016; 18(12):427.

Chicago/Turabian Style

Stevens, Ronald, Trysha Galloway, Donald Halpin, and Ann Willemsen-Dunlap. 2016. "Healthcare Teams Neurodynamically Reorganize When Resolving Uncertainty" Entropy 18, no. 12: 427.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop