Guiding Attention via a Cognitive Aid During a Simulated In-Hospital Cardiac Arrest Scenario: A Salience Effort Expectancy Value Model Analysis

Objective To investigate the effect of a cognitive aid on the visual attention distribution of the operator using the Salience Effort Expectancy Value (SEEV) model. Background Cognitive aids aim to support an operator during the execution of a task. The effect of cognitive aids on performance is frequently evaluated but whether a cognitive aid improved, for example, attention distribution has not been considered. Method We built the Expectancy Value (EV) model version which can be considered to indicate optimal attention distribution for a given event. We analyzed the eye tracking data of emergency physicians while using a cognitive aid application versus no application during a simulated in-hospital cardiac arrest scenario. Results The EV model could fit the attention distribution in such a simulated emergency situation. Partially supporting our hypothesis, the cognitive aid application group showed a significantly better EV model fit than the no application group in the first phases of the event, but a worse fit in the last phase. Conclusion We demonstrated that a cognitive aid affected attention distribution and that the SEEV model provides the means of capturing these effects. We suggest that the aid supported and improved visual attention distribution in the stressful first phases of a cardiopulmonary resuscitation but may have focused attention on objects that are relevant for lower priority goals in the last phase. Application The SEEV model can provide insights into expected and unexpected effects of cognitive aids on visual attention distribution and may help to design better artifacts.

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NT-SEEV: A model of attention capture and noticing on the Flight Deck

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/154193120905301202 ◽

2009 ◽

Vol 53 (12) ◽

pp. 769-773 ◽

Cited By ~ 8

Author(s):

Christopher Wickens ◽

Jason McCarley ◽

Kelly Steelman-Allen

Keyword(s):

Large Scale ◽

Dynamic Environment ◽

Attention Capture ◽

Data Sets ◽

Expectancy Value ◽

Attentional Set ◽

Flight Mode ◽

Effort Expectancy ◽

Value Model ◽

Flight Deck

N-SEEV is a model that predicts the noticeability of events that occur in the context of routine task-driven scanning across large scale visual environments. The model is an extension of the SEEV (salience, effort, expectancy, value) model, incorporating the influence of attentional set and allowing the possibility of a dynamic environment. The model was validated against two empirical data sets. In a study of pilot scanning across a high fidelity automated 747 cockpit, the SEEV component of the model predicted the distribution of attention with correlations of 0.85 and 0.88. In a lower fidelity study of pilot noticing of the onset of critical cockpit events (flight mode annunciators) the model predicted differences in noticing time and accuracy with correlations (across conditions) above 0.95. Other properties of the model are described.

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All Hands on Deck: Sustaining Improved Hand Hygiene Compliance in the Operating Room

Ergonomics in Design The Quarterly of Human Factors Applications ◽

10.1177/1064804621995097 ◽

2021 ◽

pp. 106480462199509

Author(s):

Orysia Bezpalko ◽

Siddarth Ponnala ◽

James C. Won

Keyword(s):

Hand Hygiene ◽

Operating Room ◽

Hand Hygiene Compliance ◽

Complex Environments ◽

Expectancy Value ◽

Hands On ◽

Effort Expectancy ◽

Systemic Factors ◽

Hygiene Compliance ◽

Value Model

Hand hygiene is an essential component of infection prevention in the health care setting. Despite diligent efforts, clinicians can be susceptible to hand hygiene misses in fast-paced, complex environments such as the operating room due to systemic factors such as the physical environment, workflow, and sporadic interactions with other personnel. Through the use of human factors and resilience engineering concepts, work-as-done were studied to identify barriers to hand hygiene compliance in the operating rooms of a pediatric hospital in an urban area. The saliency, effort, expectancy, value model was applied to design a multifaceted intervention that resulted in a sustained 95% hand hygiene compliance.

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The Effect of Effort on the Allocation of Visual Attention During the Induction of General Anesthesia: A SEEV Model Analysis

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181320641081 ◽

2020 ◽

Vol 64 (1) ◽

pp. 359-363

Author(s):

Tobias Grundgeiger ◽

Katharina Beckh ◽

Oliver Happel

Keyword(s):

General Anesthesia ◽

Explanatory Power ◽

Future Research ◽

Data Set ◽

Expectancy Value ◽

Effort Expectancy ◽

Model Predictions ◽

Ev Model ◽

Methodological Considerations

Anesthesiologists work in complex work environments where optimal scanning of information is critical for patient safety. The Salience, Effort, Expectancy, Value (SEEV) model can be used to model attention distributions of individuals. We used an existing data set of eye tracking data of anesthesiologists inducing general anesthesia to (1) develop a method for considering the effort parameter in the model in such an environment and (2) investigate the explanatory power of an EEV model compared to an EV model. To operationalize effort, we created a 3D model using Unreal Engine 4. We used Markov Chain Monte Carlo simulations to obtain EV and EEV model predictions. The inclusion of effort did not yield an advantage over the model which did not include effort. We discuss methodological considerations for future research and suggest to simultaneously consider salience and effort to be able to assess the role of effort more accurately.

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