Inferring Visual Behaviour from User Interaction Data on a Medical Dashboard

Ainhoa Yera, Markel Vigo, Javier Muguerza, Olatz Arbelaitz, Iñigo Perona, Richard Keers, Darren Ashcroft, Richard Williams, Niels Peek, Caroline Jay
2018 Proceedings of the 2018 International Conference on Digital Health - DH '18  
Making medical software easy to use and actionable is challenging due to the characteristics of the data (its size and complexity) and its context of use. This results in user interfaces with a highdensity of data that do not support optimal decision-making by clinicians. Anecdotal evidence indicates that clinicians demand the right amount of information to carry out their tasks. This suggests that adaptive user interfaces could be employed in order to cater for the information needs of the
more » ... on needs of the users and tackle information overload. Yet, since these information needs may vary, it is necessary first to identify and prioritise them, before implementing adaptations to the user interface. As gaze has long been known to be an indicator of interest, eye tracking allows us to unobtrusively observe where the users are looking, but it is not practical to use in a deployed system. Here, we address the question of whether we can infer visual behaviour on a medication safety dashboard through user interaction data. Our findings suggest that, there is indeed a relationship between the use of the mouse (in terms of clickstreams and mouse hovers) and visual behaviour in terms of cognitive load. We discuss the implications of this finding for the design of adaptive medical dashboards.
doi:10.1145/3194658.3194676 dblp:conf/ehealth/YeraMAPKAWPJV18 fatcat:eng7cgkxhngatmpvj7u3aoezbi