Guiding Attention via a Cognitive Aid During a Simulated In-Hospital Cardiac Arrest Scenario: A Salience Effort...

Area of Science:

• Human-Computer Interaction
• Cognitive Science
• Emergency Medicine

Background:

• Cognitive aids are designed to assist operators during tasks.
• While their impact on performance is often studied, effects on attention distribution remain less explored.
• Understanding attention shifts is crucial for optimizing cognitive aid design.

Purpose of the Study:

• To investigate how a cognitive aid influences operator visual attention distribution.
• To utilize the Salience Effort Expectancy Value (SEEV) model to quantify these effects.
• To analyze attention patterns in emergency physicians during simulated cardiac arrest scenarios.

Main Methods:

• Developed an Expectancy Value (EV) model to represent optimal attention distribution.
• Collected and analyzed eye-tracking data from physicians during a simulated cardiac arrest.
• Compared attention distribution with and without a cognitive aid application.

Main Results:

• The EV model effectively captured attention distribution in simulated emergencies.
• Physicians using the cognitive aid showed a better EV model fit in early stages.
• A worse EV model fit was observed in the later stages when the aid was used.

Conclusions:

• Cognitive aids demonstrably alter visual attention distribution.
• The SEEV model is effective in identifying both expected and unexpected effects of cognitive aids.
• Aids may enhance attention in critical early phases but could potentially misdirect focus in later stages.