A Simple Electronic Medical Record System Designed for Research

King, Andrew J; Calzoni, Luca; Tajgardoon, Mohammadamin; Cooper, Gregory F.; Clermont, Gilles; Hochheiser, Harry; Visweswaran, Shyam

doi:10.1101/2020.10.25.20219287

Cited by 2 publications

(4 citation statements)

References 16 publications

(14 reference statements)

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“…Figure 1 shows the LEMR system's user interface on an eye tracking-equipped computer monitor. The software for the LEMR user interface is available on GitHub [15]. A computer monitor displaying the Learning Electronic Medical Record user interface with an eye-tracking device mounted at the bottom edge of the monitor.…”

Section: Learning Electronic Medical Record Systemmentioning

confidence: 99%

“…This algorithm is described in our prior work [14]. The scripts for eye tracking are available on GitHub [19].…”

Section: Eye Trackingmentioning

confidence: 99%

“…The best performing combination was then used to train a model from the full training data set. The scripts for feature construction [23], imputation of missing values [24], feature selection [25], and for training and evaluating models [26] are available on GitHub.…”

Section: Model Trainingmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Learning Electronic Medical Record Systemmentioning

confidence: 99%

“…This algorithm is described in our prior work [14]. The scripts for eye tracking are available on GitHub [19].…”

Section: Eye Trackingmentioning

confidence: 99%

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

et al. 2020

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“…We developed in learning EMR (LEMR) system to investigate the feasibility of a CDS tool to predict and highlight relevant data. 5 The LEMR system relies on supervised machine learning models that predict which patient data are likely to be relevant in the context of a clinical task. 6 , 7 However, a critical barrier to building machine learning models for a LEMR system is the acquisition of training data regarding which patient data are relevant for a clinical task.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of eye tracking for a decision support application

et al. 2021

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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.

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A Simple Electronic Medical Record System Designed for Research

Cited by 2 publications

References 16 publications

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

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

Evaluation of eye tracking for a decision support application

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