Inferring Visual Behaviour from User Interaction Data on a Medical Dashboard

Ainhoa Yera, Javier Muguerza, Olatz Arbelaitz, Iñigo Perona, Richard Keers, Darren Ashcroft, Richard Williams, Niels Peek, Caroline Jay, Markel Vigo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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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 high-density 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 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.
Original languageEnglish
Title of host publication8th International Digital Health Conference
Number of pages59
Publication statusPublished - 23 Apr 2018
Event2018 International Digital Health Conference - Lyon, France
Duration: 23 Apr 201826 Apr 2018


Conference2018 International Digital Health Conference
Internet address


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