Evaluation of eye tracking for a decision support application

Abstract

Eye tracking is used widely to investigate attention and cognitive processes while performing tasks in electronic medical record (EMR) systems. We explored a novel application of eye tracking to collect training data for a machine learning-based clinical decision support tool that predicts which patient data are likely to be relevant for a clinical task. Specifically, we investigated in a laboratory setting the accuracy of eye tracking compared to manual annotation for inferring which patient data in the EMR are judged to be relevant by physicians. We evaluated several methods for processing gaze points that were recorded using a low-cost eye-tracking device. Our results show that eye tracking achieves accuracy and precision of 69% and 53%, respectively compared to manual annotation and are promising for machine learning. The methods for processing gaze points and scripts that we developed offer a first step in developing novel uses for eye tracking for clinical decision support.

Keywords: electronic medical record system; eye tracking; relevant patient data.

Figure 1.

A computer monitor displaying the LEMR interface as it appears during the familiarization and preparation tasks (see Methods section). From left to right, the system displays patient data on vital signs, ventilator settings, intake and output, medication administrations, laboratory test results, and free-text notes and reports. The eye-tracking device mounted at the bottom is used to capture gaze points during the preparation task (see Methods section).

Figure 2.

A portion of the LEMR interface as it appears during the preparation task (see Methods section) showing four laboratory test results. The horizontal light blue band indicates the normal range for the corresponding laboratory test and the vertical light orange band indicates the most recent 24-h period. The larger green circles, red circles, and purple circles denote normal, high, and low values of the corresponding laboratory values. The smaller orange circles denote the location of gaze points recorded by the eye-tracking device; these are shown for illustrative purposes only and are not visible on the interface.

Figure 3.

A portion of the LEMR interface as it appears during the annotation task (see Methods section) showing four laboratory test results with checkboxes. Physicians indicate which patient data are relevant by clicking on the corresponding checkboxes. The glucose laboratory test is surrounded by a yellow margin to indicate that its checkbox has been clicked.