Predicting physician gaze in clinical settings using optical flow and positioning

Electronic health record systems used in clinical settings to facilitate informed decision making, affects the dynamics between the physician and the patient during clinical interactions. The interaction between the patient and the physician can impact patient satisfaction, and overall health outcomes. Gaze during patient-doctor interactions was found to impact patient-physician relationship and is an important measure of attention towards humans and technology. This study aims to automatically label physician gaze for video interactions which is typically measured using extensive human coding. In this study, physicians' gaze is predicted at any time during the recorded video interaction using optical flow and body positioning coordinates as image features. Findings show that physician gaze could be predicted with an accuracy of over 83%. Our approach highlights the potential for the model to be an annotation tool which reduces the extensive human labor of annotating the videos for physician's gaze. These interactions can further be connected to patient ratings to better understand patient outcomes.