Suboptimal exchange of information can have tragic consequences to patient's safety and survival. To this end, the Joint Commission lists communication error among the most common attributable causes of sentinel events. The risk management literature further supports this finding, ascribing communication error as a major factor (70%) in adverse events. Despite numerous strategies to improve patient safety, which are rooted in other high reliability industries (e.g., commercial aviation and naval aviation), communication remains an adaptive challenge that has proven difficult to overcome in the sociotechnical landscape that defines healthcare. Attributing a breakdown in information exchange to simply a generic "communication error" without further specification is ineffective and a gross oversimplification of a complex phenomenon. Further dissection of the communication error using root cause analysis, a failure modes and effects analysis, or through an event reporting system is needed. Generalizing rather than categorizing is an oversimplification that clouds clear pattern recognition and thereby prevents focused interventions to improve process reliability. We propose that being more precise when describing communication error is a valid mechanism to learn from these errors. We assert that by deconstructing communication in healthcare into its elemental parts, a more effective organizational learning strategy emerges to enable more focused patient safety improvement efforts. After defining the barriers to effective communication, we then map evidence-based recovery strategies and tools specific to each barrier as a tactic to enhance the reliability and validity of information exchange within healthcare.

Objective: To review common qualitative and quantitative methods of measuring shared mental models appropriate for use in the healthcare setting. Background: Shared mental models are the overlap of individuals' set of knowledge and/or assumptions that act as the basis for understanding and decision making between individuals. Within healthcare, shared mental models facilitate effective teamwork and theorized to influence clinical decision making and performance. With the current rapid growth and expansion of healthcare teams, it is critical that we understand and correctly use shared mental model measurement methods assess optimal team performance. Unfortunately, agreement on the proper measurement of shared mental models within healthcare remains diffuse. Method: This paper presents methods appropriate to measure shared mental models within healthcare. Results: Multiple shared mental model measurement methods are discussed with regard to their utility within this setting, ease of use, and difficulties in deploying within the healthcare operational environment. For rigorous analysis of shared mental models, it is recommended that a combination of qualitative and quantitative analyses be employed. Conclusion: There are multitude of shared mental model measurement methods that can be used in the healthcare domain; although there is no perfect solution for every situation. Researchers can utilize this article to determine the best approach for their needs.

Virtual reality, augmented reality, and other forms of virtual environments have the potential to dramatically change how individuals work, learn, and interact with each other. A key objective of human factors research and practice is to determine how these environments should be designed to maximize performance efficiency, ensure health and safety, and circumvent potential human virtual environment interaction problems. This session will demonstrate some of the distinct and diverse uses of virtual reality, mixed reality, and virtual environments in an alternative format. The session will begin with each demonstrator providing a brief overview of their virtual environment and describing how it has been used to address a particular problem or research need. Following the description portion of the session, all demonstrations will be set-up around the room, and session attendees will be encouraged to directly interact with the environment and ask demonstrators questions about their research and inquire about the effectiveness of using their virtual environment for research, training, and evaluation purposes. The overall objective of this alternative session is to provoke ideas among the attendees for how virtual reality, mixed reality, and virtual environments can help address their research, training, education or business needs.

No abstract

Virtual environments and immersive technologies are growing in popularity for human factors purposes. Whether it is training in a low-risk environment or using simulated environments for testing future automated vehicles, virtual environments show promise for the future of our field. The purpose of this session is to have current human factors practitioners and researchers demonstrate their immersive technologies. This is the eighth iteration of the “Me and My VE” interactive session. Presenters in this session will provide a brief introduction of their virtual reality, augmented reality, or virtual environment work before engaging with attendees in an interactive demonstration period. During this period, the presenters will each have a multimedia display of their immersive technology as well as discuss their work and development efforts. The selected demonstrations cover issues of designing immersive interfaces, military and medical training, and using simulation to better understand complex tasks. This includes a mix of government, industry, and academic-based work. Attendees will be virtually immersed in the technologies and research presented allowing for interaction with the work being done in this field.