Neuroergonomics in Digital Operating Rooms: Applying the Two-Competitor Model of Attention to the Surgical Context

Abstract

Due to high workload and often excessive working hours, team members in operating rooms perform surgical procedures under difficult conditions and often without sufficient breaks. Despite this, the team must deal with incomplete information and unexpected distractions. This requires a suitable level of attention and the ability to balance the demands of the task with available cognitive resources. Advances in measurement technology and data analysis in neurotechnology open up new possibilities for the assessment of attention processes. Increasingly complex and demanding surgeries, especially, could benefit from the application of neuroergonomic automated assistants to minimize distraction, stress, and fatigue, and to facilitate interactions between team members. Such assistants could improve performance via monitoring of cognitive and affective states as well as the implementation of suitable interventions strategies. Understanding the impact of distractions on performance, enhancing individuals’ resilience to distractions, and potentially employing artificial assistants to mitigate their effects are critical future goals. In order to support such future developments, a standard taxonomy of attention in the operating theater is needed, as is a broader consensus regarding the nature of distraction. Ideally, such a model would serve as a basis for comparison between studies conducted in different laboratories, and in principle could also be used to bridge the gap between the laboratory and the real scenario. Here, we propose the adoption of a model of attention previously shown to be effective for modeling levels of attention in immersion and describe its application in the surgical context.