This paper presents a recommender system for teams of medical professionals working collaborative... more This paper presents a recommender system for teams of medical professionals working collaboratively in hospital operating rooms. The system recommends relevant virtual actions, such as retrieval of information resources and initiation of communication with professionals outside the operating rooms. Recommendations are based on the current state of the ongoing operation as recognised from sensor data using machine learning techniques. The selection and non-selection of virtual actions during operations are interpreted as implicit feedback and used to update the weight matrices that guide recommendations. A pilot user study involving medical professionals indicates that the adaptation mechanism is effective and that the system provides adequate recommendations.
Prior studies show that knowledge work is characterized by highly interlinked practices, includin... more Prior studies show that knowledge work is characterized by highly interlinked practices, including task, file and window management. However, existing personal information management tools primarily focus on a limited subset of knowledge work, forcing users to perform additional manual configuration work to integrate the different tools they use. In order to understand tool usage, we review literature on how users' activities are created and evolve over time as part of knowledge worker practices. From this we derive the activity life cycle, a conceptual framework describing the different states and transitions of an activity. The life cycle is used to inform the design of Laevo, a temporal activity-centric desktop interface for personal knowledge work. Laevo allows users to structure work within dedicated workspaces, managed on a timeline. Through a centralized notification system which doubles as a to-do list, incoming interruptions can be handled. Our field study indicates how highlighting the temporal nature of activities results in lightweight scalable activity management, while making users more aware about their ongoing and planned work.
There is a growing interest in personal health technologies that sample behavioral data from a pa... more There is a growing interest in personal health technologies that sample behavioral data from a patient and visualize this data back to the patient for increased health awareness. However, a core challenge for patients is often to understand the connection between specific behaviors and health, i.e. to go beyond health awareness to disease insight. This paper presents MONARCA 2.0, which records subjective and objective data from patients suffering from bipolar disorder, processes this, and informs both the patient and clinicians on the importance of the different data items according to the patient's mood. The goal is to provide patients with a increased insight into the parameters influencing the nature of their disease. The paper describes the user-centered design and the technical implementation of the system, as well as findings from an initial field deployment.
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Papers by Jakob Bardram