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. 2019 Sep;8(1):110-114.
doi: 10.1177/2327857919081025. Epub 2019 Sep 15.

Can Eye Tracking be Used to Predict Performance Improvements in Simulated Medical Training? A Case Study in Central Venous Catheterization

Hong-En Chen  1 , Rucha R Bhide  1 , David F Pepley  1 , Cheyenne C Sonntag  2 , Jason Z Moore  1 , David C Han  2 , Scarlett R Miller  1
Affiliations

Can Eye Tracking be Used to Predict Performance Improvements in Simulated Medical Training? A Case Study in Central Venous Catheterization

Hong-En Chen et al. Proc Int Symp Hum Factors Ergon Healthc. 2019 Sep.

Abstract

Manikins have traditionally been used to train ultrasound-guided Central Venous Catheterization (CVC), but are static in nature and require an expert observer to provide feedback. As a result, virtual simulation and personalized learning has been increasingly adopted in medical education to efficiently provide quantitative feedback. The Dynamic Haptic Robotic Trainer (DHRT) trains surgical residents in CVC needle insertions by simulating various patient profiles and presenting personalized feedback on objective performance. However, no studies have examined the learning gains of the personalized learning feedback or the relation of feedback to what the user is focusing on during the training. Thus, this study was developed to determine the effectiveness of the current personalized learning interface through a long-term investigation with 7 surgical residents. The eye tracking analysis showed that residents spent significantly more time fixated on percent aspiration throughout the study; the more time participants spent looking at the Number of Insertions, Percent Aspiration and the Angle of Insertion on the DHRT GUI, the better they performed on subsequent trials on the DHRT system.

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Figures

Figure 1:
Figure 1:
(Left) Personalized learning interface with (1) Overall Grade, (2) Case Difficulty, (3) Number of Insertions, (4) Angle of insertion, (5) Distance to the Center of the Vein, (6) Percent of time spent aspirating. (Right) Sample heat map of participant viewing their feedback on one trial, where red represents a high concentration of fixation.
Figure 2:
Figure 2:
(Left) Eye-tracking the DHRT system setup and (Right) Gaze plots for with a two sample fixation points. The size of the fixation circle grows as the length of time spent fixating in that area increases.
Figure 3:
Figure 3:
Regression analysis for changes in # insertions between trials. The time spent fixating on the # insertions and aspiration % in the GUI significantly contributed to the model.
Figure 4:
Figure 4:
Regression for changes in the angle of insertion between trials. The time spent fixating on the aspiration percent in the GUI significantly contributed to the model.
See this image and copyright information in PMC

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