Using Eye-Tracking Technology to Capture the Visual Attention of Nurses During Interpretation of Patient Monitoring Scenarios from a Computer Simulated Bedside Monitor

Introduction:This study analysed the utility of eye tracking technology for gaining insight into the decision making processes of nurses during their interpretation of patient scenarios and vital signs.Methods:Five patient monitoring scenarios (vignette, vital signs [ECG, BP etc.] and scoring criteria) were designed and validated by critical care experts. Participants were asked to interpret these scenarios whilst ‘thinking aloud’. Visual attention was measured using infrared light- based eye-tracking technology. Each interpretation was scored out of 10. Subjects comprised of students (n=36) and qualified nurses (n=11). Scores and self-rated confidence (where 1=low, 10=high) are presented using mean±SD. Significance testing was performed using a t-test and ANOVA where appropriate (α = 0.05). Multivariate regression was performed to determine if a machine could use eye gaze features to accurately predict competency (dependent variable=score). Independent eye gaze only variables were used in the regression models if they statistically significantly (p