Effects of Communication Media Choice on the Quality and Efficacy of Emergency Calls Assisted by a Mobile Nursing Protocol Tool:

The transition from paper to electronic-based records in the healthcare industry has posed several challenges to conventional medical practices. The introduction of technology in day-to-day medical and nursing practices deserves careful consideration. In this work, we report the results of a controlled experiment to compare nurses' consultation in emergency calls in six different conditions. We studied the effect that the type of communication media (face-to-face, telephone, videoconference) and type of nursing protocol media (paper-based, electronic-based) can have upon consultation time, mistakes made, pauses during consultation, eye contact, and efficacy of the consultation. We found that the type of communication media has an effect on consultation time; on average, fewer mistakes were made during telephone-based consultations; for eye contact, there were significantly fewer eye contacts during face-to-face than during videoconference consultations; finally, the type of communication media or protocol media did not have any effect in the efficacy of the consultation

LaCasa: Location And Context-Aware Safety Assistant

Abstract—Wandering is a common behavior among people with dementia (PwD). It is also one of the main concerns of caregivers since it can cause the person to get lost and injured. The frequency and manner in which a person wanders is highly influenced by the person’s background and contextual factors specific to the situation. In this paper we investigate some of the properties of wandering behaviours, particularly related to our ability to sense them with mobile devices. We then propose a novel decision-theoretic model that estimates the risk faced by the PwD and decides on the appropriate action to take, such as prompting the PwD or calling the caregiver. The model can be tailored to the user needs (e.g. known locations, level of cognitive decline) and takes into account uncertainty, and contextual information gathered from sensors, such as current location, noise, and proximity to the caregiver. A preliminary version of the system has been instantiated in a wandering assistance application for mobile devices running on an Android platform. I

Design of a Predictive Scheduling System to Improve Assisted Living Services for Elders

International audienceAs the number of older adults increases, and with it the demand for dedicated care, geriatric residences face a shortage of caregivers, who themselves experience work overload, stress and burden. We conducted a long-term field study in three geriatric residences to understand the work conditions of caregivers with the aim of developing technologies to assist them in their work and help them deal with their burden. From this study we obtained relevant requirements and insights of design that were used to design, implement and evaluate two prototypes for supporting caregivers' tasks (e.g. electronic recording and automatic notifications), in order to validate the feasibility of their implementation in-situ and the technical requirements. The evaluation in-situ of the prototypes was conducted for a period of four weeks. The results of the evaluation, together with the data collected from six months of use, motivated the design of a predictive schedule. Such design was iteratively improved and evaluated in participative sessions with caregivers. PRESENCE, the predictive schedule we propose, triggers real-time alerts of risky situations (e.g. falls, entering off-limits areas such as the infirmary or the kitchen) and, informs caregivers of routine tasks that need to be performed (e.g. medication administration, diaper change, etc.). Moreover, PRESENCE helps caregivers to record caring tasks (such as diaper changes or medication) and wellbeing assessments (such as the mood), which are difficult to automatize. This facilitates caregiver's shift handover, and can help to train new caregivers by suggesting routine tasks and by sending reminders and timely information about the residents. It can be seen as a tool to reduce the workload of caregivers and medical staff. Instead of trying to substitute the caregiver with an automatic caring system, as proposed by others, we propose the design of our predictive schedule system that blends caregiver's assessments and measurements from sensors. We show the feasibility of predicting caregiver's tasks and a formative evaluation with caregivers that provides preliminary evidence of its utility