Evaluation of eye tracking for a decision support application

The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review

Machine learning (ML) has revolutionized medical image-based diagnostics. In this review, we cover a rapidly emerging field that can be potentially significantly impacted by ML - eye tracking in medical imaging. The review investigates the clinical, algorithmic, and hardware properties of the existing studies. In particular, it evaluates 1) the type of eye-tracking equipment used and how the equipment aligns with study aims; 2) the software required to record and process eye-tracking data, which often requires user interface development, and controller command and voice recording; 3) the ML methodology utilized depending on the anatomy of interest, gaze data representation, and target clinical application. The review concludes with a summary of recommendations for future studies, and confirms that the inclusion of gaze data broadens the ML applicability in Radiology from computer-aided diagnosis (CAD) to gaze-based image annotation, physicians' error detection, fatigue recognition, and other areas of potentially high research and clinical impact.

Utilizing eye tracking to assess electronic health record use by pharmacists in the intensive care unit

DISCLAIMER

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PURPOSE

A study was conducted using high-fidelity electronic health record (EHR)-based simulations with incorporated eye tracking to understand the workflow of critical care pharmacists within the EHR, with specific attention to the data elements most frequently viewed.

METHODS

Eight critical care pharmacists were given 25 minutes to review 3 simulated intensive care unit (ICU) charts deployed in the simulation instance of the EHR. Using monitor-based eye trackers, time spent reviewing screens, clinical information accessed, and screens used to access specific information were reviewed and quantified to look for trends.

RESULTS

Overall, pharmacists viewed 25.5 total and 15.1 unique EHR screens per case. The majority of time was spent looking at screens focused on medications, followed by screens displaying notes, laboratory values, and vital signs. With regard to medication data, the vast majority of screen visitations were to view information on opioids/sedatives and antibiotics. With regard to laboratory values, the majority of views were focused on basic chemistry and hematology data. While there was significant variance between pharmacists, individual navigation patterns remained constant across cases.

CONCLUSION

The study results suggest that in addition to medication information, laboratory data and clinical notes are key focuses of ICU pharmacist review of patient records and that navigation to multiple screens is required in order to view these data with the EHR. New pharmacy-specific EHR interfaces should consolidate these elements within a primary interface.