1. Innovation in ECG Interpretation: Using Eye Movements to Predict Accuracy
Raj Ratwani, PhD
Scientific Director, National Center for Human Factors in Healthcare
MedStar Institute for Innovation, MedStar Health
Assistant Professor of Emergency Medicine
Georgetown University School of Medicine

2. Inferring Cognition from Eye Movements
Use eye movements as an “online” indicator of cognitive process
Procedural errors (Ratwani & Trafton, 2011,2012)
Fatigue (Ji & Zhu, 2004)
Workload (Ahlstrom & Friedman-Berg, 2006)
Outputs: x,y coordinate indicating the location of the eyes
Sampling rate: 60Hz
Fixations: defined areas of focus (min 100ms)

3. Emergency Medicine and ECGs
~ 130 million annual ED visits, 22m ECGs (CDC,2010)
Error rate of 5-45% depending on the abnormality (Davidenko et al, 2014)
Goal: Improve ECG interpretation accuracy by understanding cognitive processing of ECGs
Training systems for residents and medical students
Develop real-time predictive systems to reduce errors
(Ratwani & Trafton, 2010, 2011)

4. Method Controlled study 30 trials: ECG inspection -> Interpretation
Eye movement and RT data
30 ECGs (easy, medium, hard)
No prior patient information
Interruptions (30 sec) consisting of dosage calculations
30 trials: ECG inspection -> Interpretation
6 interrupted trials (2 easy, 2 medium, 2 hard)
Participants:
11 Residents
6 Attending Physicians

5. + + Control Trials 8 Easy 8 Medium 8 Hard ECG Response
Interrupt Trials
2 Easy
2 Medium
2 Hard +
ECG
Interrupt
ECG
Response

7. Variability in Visual Processes
Resident
Avg. Fixation Count
Attending
Avg. Fixation Duration

8. Time Spent Focusing on Specific Areas-- Avg Fixation Durations
Attendings
Residents

9. Dispersion of Attention- Avg Count of Fixations
Attendings
Residents

10. Disruptive Interruptions
Longer interpretation times and increased fixation counts during interrupted trials
Control
Interrupt

11. Improving ECG Interpretation
Clear differences:
Residents search more exhaustively (more areas examined, more fixations, longer durations)
Attendings know where to look and are processing more quickly
The impact of interruptions is more pronounced with residents
Eye movement based logistic regression models to predict accurate performance (AUC - .88)
Predicted Error
Predicted Accurate
Actual Error
49.6%(62) TP
50.4%(63) FN
Actual Accurate
6% (22) FP
94%(360) TN

12. Thank You Raj.Ratwani@MedicalHFE.org Collaborators:
Zach Hettinger, MD Allan Fong, MS
This work was supported by a grant from the Charles and Mary Latham Foundation