Modeling Attention and Situation Awareness in Acute Care Workplaces

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Situation awareness – being aware of what is going on – is important for operators in safety-critical workspaces. Losing situation awareness may jeopardize safety or even result in fatalities. In the present project, we investigate situation awareness in safety-critical domains such as acute care by considering a model-based, continuous, and objective measure to measure and predict situation awareness. The SEEV model integrates the factors Salience, Effort, Expectancy, and Value to describe the allocation of overt visual attention. The attention-situation awareness (A-SA) model extends the SEEV model to produce a general situation awareness measure. Studies of the SEEV model, however, have investigated rather small workspaces, have only addressed a single operator, were mainly aimed at improving the model fit, and were conducted in simulated settings; the A-SA model has not been empirically investigated.We aim to understand the attention distribution of staff in acute care workspaces by extending the SEEV model to accommodate data from real environments better (Aim 1) and validate the extended model (Aim 2). To this end, we will extend the present model by operationalizing the salience and effort parameters to suit the demands of the large and more distracting acute care work environment, and we will consider team and individual tasks in order to capture the shared and distinct attention demands of the team situation. We will evaluate the extended model by recoding eye-tracking data and modeling the attention distribution of the team (anesthesiologist and anesthetic nurse) during real inductions of general anesthesia.We aim to implement and validate the A-SA model which extends the attention distribution in form of the SEEV model by considering various cognitive factors such as workload, operators’ experience, and cognitive biases (Aim 3). The A-SA model can make predictions about situation awareness at specific points in time based on attention distribution data and consideration of cognitive factors. For the validation, we will run a medical simulation and collect eye tracking data as well as several so-called SAGAT probes, which are considered to be an objective and valid situation awareness measure in the literature. We will evaluate how well the A-SA model can predict SAGAT scores at various points during the procedure.The project will provide insights into the general mechanisms of human attention allocation in safety-critical domains, and it will also provide insights into the construct of situation awareness and team situation awareness. From an applied perspective, these insights can be used to improve situation awareness by facilitation the designing of specific technology or use of the models for training purposes and evaluating technology in relation to situation awareness. Our ultimate aim is to contribute to the understanding of cognition of humans in socio-technical systems and contribute to patient safety.