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AMIA Jt Summits Transl Sci Proc. 2017 Jul 26;2017:512-521. eCollection 2017.

Eye-tracking for clinical decision support: A method to capture automatically what physicians are viewing in the EMR.

Author information

1
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
2
Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.
3
Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA.

Abstract

Eye-tracking is a valuable research tool that is used in laboratory and limited field environments. We take steps toward developing methods that enable widespread adoption of eye-tracking and its real-time application in clinical decision support. Eye-tracking will enhance awareness and enable intelligent views, more precise alerts, and other forms of decision support in the Electronic Medical Record (EMR). We evaluated a low-cost eye-tracking device and found the device's accuracy to be non-inferior to a more expensive device. We also developed and evaluated an automatic method for mapping eye-tracking data to interface elements in the EMR (e.g., a displayed laboratory test value). Mapping was 88% accurate across the six participants in our experiment. Finally, we piloted the use of the low-cost device and the automatic mapping method to label training data for a Learning EMR (LEMR) which is a system that highlights the EMR elements a physician is predicted to use.

PMID:
28815151
PMCID:
PMC5543363

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