| Résumé | Among the widely available information technologies for remote health care, emergency care, and more specifically self-care in urgent situations (through automated, protocol-based intervention), has not been considered to date. Urgent care is particularly difficult due to the individual transient and dynamic patient context; the design-for-all approach currently being implemented does not consider the varying manifestation of physical states and cognitive disabilities. Therefore, the objective was to design and integrate into a remote health care platform, an intelligent user interface scheme that adapts in real time to the clinically urgent patient condition. The current study presents a mobile implementation, targeting diabetic hypoglycemia treatment. Continuous physiological data from medical-grade wearable sensors, and results from customized cognitive exercises evaluating the corresponding common emergency symptoms are used to define the patient state. This state feeds the adaptation rules and determines the most appropriate interaction interface delivering the intervention instructions. The designed adaptations prioritize the incorporation of multimodal interfaces (including voice and facial recognition and interactions), with the goal of augmenting patient-technology information exchange, thus providing more efficient and effective treatment delivery. This application is being validated with clinical partners in endocrinology. |
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