Leveraging Eye Tracking to Prioritize Relevant Medical Record Data: Comparative Machine Learning Study

Abstract

Background Electronic medical record (EMR) systems capture large amounts of data per patient and present that data to physicians with little prioritization. Without prioritization, physicians must mentally identify and collate relevant data, an activity that can lead to cognitive overload. To mitigate cognitive overload, a Learning EMR (LEMR) system prioritizes the display of relevant medical record data. Relevant data are those that are pertinent to a context—defined as the combination of the user, clinical task, and patient case. To determine which data are relevant in a specific context, a LEMR system uses supervised machine learning models of physician information-seeking behavior. Since obtaining information-seeking behavior data via manual annotation is slow and expensive, automatic methods for capturing such data are needed. Objective The goal of the research was to propose and evaluate eye tracking as a high-throughput method to automatically acquire physician information-seeking behavior useful for training models for a LEMR system. Methods Critical care medicine physicians reviewed intensive care unit patient cases in an EMR interface developed for the study. Participants manually identified patient data that were relevant in the context of a clinical task: preparing a patient summary to present at morning rounds. We used eye tracking to capture each physician’s gaze dwell time on each data item (eg, blood glucose measurements). Manual annotations and gaze dwell times were used to define target variables for developing supervised machine learning models of physician information-seeking behavior. We compared the performance of manual selection and gaze-derived models on an independent set of patient cases. Results A total of 68 pairs of manual selection and gaze-derived machine learning models were developed from training data and evaluated on an independent evaluation data set. A paired Wilcoxon signed-rank test showed similar performance of manual selection and gaze-derived models on area under the receiver operating characteristic curve (P=.40). Conclusions We used eye tracking to automatically capture physician information-seeking behavior and used it to train models for a LEMR system. The models that were trained using eye tracking performed like models that were trained using manual annotations. These results support further development of eye tracking as a high-throughput method for training clinical decision support systems that prioritize the display of relevant medical record data.

A Simple Electronic Medical Record System Designed for Research

“…The Simple EMR System can be paired with a Tobii Eye Tracker 4C, to capture both a stream of eye-gaze coordinates and a stream of position coordinates for what is displayed onscreen. An algorithm then automatically maps eye-gaze to onscreen data elements to generate a list of what the user was viewing [18, 19, 20]. The scripts and accompanying documentation for incorporating eye-tracking with the Simple EMR System are freely available on GitHub at https://github.com/ajk77/EyeBrowserPy.…”

Evaluation of Eye-tracking for a Decision Support Application

“…Next, we applied our methods to collect training data and derived machine learning models to predict the relevance of laboratory tests, medications, vital sign measurements, ventilator settings and fluid intake and output for the task of pre-rounding in the ICU. The models that were derived using eye-tracking data performed as well as models that were derived using manual annotations made by physicians to indicate relevant patient information using the LEMR system [6].…”

“…An intriguing application of eye-tracking in EMR systems is to enable clinical decision support applications. For example, we have used eye-tracking to collect training data for machine learning models of information-seeking behavior in an EMR system to identify and highlight data in the EMR that are likely to be relevant to the user [6, 7, 8]. The availability of inexpensive eye-tracking devices makes the broad deployment of eye-tracking enabled systems feasible.…”

Use of a quantitative data report in a hypothetical decision scenario for health policymaking: a computer-assisted laboratory study

“…King et al used eye-tracking to explore clinicians’ reading behavior of electronic health records from patients receiving critical care. Eye-tracking data proved to be a potential alternative to manual selection for the purpose of training a model that learns an electronic health records system in displaying relevant information [ 17 ].…”