Japan Association for Medical Informatics

[AP1-E2-2-03] Nurses ’Search of Patients’ Electronic Medical Records and Their Understanding of Patient: A Study Using Eye Tracking and Follow-up Interviews

*Eiko Nakanishi1, Miki Takami2, Kyoko Ishigaki2 (1. College of Nursing Art and Science, University of Hyogo, Japan, 2. Graduate School of Applied Informatics, University of Hyogo, Japan)

Information-seeking Behavior, Electronic Medical Records, Nursing Informatics, Eye-tracking

Japanese nurses spend a lot of time collecting information from EMRs. The present study aimed to determine the kinds of information in a patient’s EMR that nurses search for before their shift, the amount of time needed to find such information and, ultimately, the kinds of information nurses are learning and retaining related to a patient’s care. Using a hospital’s EMR system development environment, we created medical records according to hospital rules for three hypothetical patients being treated for pneumonia. The Tobii AB Eye Tracker 4C was used to track the eye. The point of gaze and the movement of the line of sight was displayed on the screen. Each participant’s line of sight was recorded while they routinely searched for information through the EMRs. Every second of the moving image data was analyzed. After completing their searches, participants were interviewed for 30 minutes to confirm what information they were able to collect and the accuracy of the tracking. A total of five nurses participated in the study. The nurses in this study ultimately found the information they needed from three hypothetical patient EMRs to perform their jobs by viewing 89 screens in around 15 minutes. Most nurses could understand (1) activities of daily living performance of patients, (2) daily care tasks. However, they had difficulty remembering (3) symptoms, care-related points to monitor carefully, (4) clinical predictions. The results suggested that EMRs tended to impede an integrated consideration of a patient’s care that included factors such as clinical predictions.