Objective The aim of the study was to evaluate a technological solution in the form of an App to implement and measure person-centredness in nursing. The focus was to enhance the knowledge transfer of a set of person-centred key performance indicators and the corresponding measurement framework used to inform improvements in the experience of care. Design The study used an evaluation approach derived from the work of the Medical Research Council to assess the feasibility of the App and establish the degree to which the App was meeting the aims set out in the development phase. Evaluation data were collected using focus groups (n = 7) and semi-structured interviews (n = 7) to capture the impact of processes experienced by participating sites. Setting The study was conducted in the UK and Australia in two organizations, across 11 participating sites. Participants 22 nurses from 11 sites in two large health care organizations were recruited on a voluntary basis. Intervention Implementing the KPIs and measurement framework via the APP through two cycles of data collection. Main Outcome Measures The main outcome was to establish feasibility in the use of the App. Results The majority of nurse/midwife participants found the App easy to use. There was broad consensus that the App was an effective method to measure the patient experience and generated clear, concise reports in real time. Conclusions The implementation of the person-centred key performance indicators using the App enhanced the generation of meaningful data to evidence patient experience across a range of different clinical settings.
Activity recognition, a key component in pervasive healthcare monitoring, relies on classification algorithms that require labeled data of individuals performing the activity of interest to train accurate models. Labeling data can be performed in a lab setting where an individual enacts the activity under controlled conditions. The ubiquity of mobile and wearable sensors allows the collection of large datasets from individuals performing activities in naturalistic conditions. Gathering accurate data labels for activity recognition is typically an expensive and time-consuming process. In this paper we present two novel approaches for semi-automated online data labeling performed by the individual executing the activity of interest. The approaches have been designed to address two of the limitations of self-annotation: (i) The burden on the user performing and annotating the activity, and (ii) the lack of accuracy due to the user labeling the data minutes or hours after the completion of an activity. The first approach is based on the recognition of subtle finger gestures performed in response to a data-labeling query. The second approach focuses on labeling activities that have an auditory manifestation and uses a classifier to have an initial estimation of the activity, and a conversational agent to ask the participant for clarification or for additional data. Both approaches are described, evaluated in controlled experiments to assess their feasibility and their advantages and limitations are discussed. Results show that while both studies have limitations, they achieve 80% to 90% precision.
Improvements and portability of technologies and smart devices have enabled a rapid growth in the amount of user generated media such as photographs and videos. Whilst various media generation and management systems exist it still remains a challenge to discover the right information, for the right purpose. This paper proposes an approach to reverse geocoding by crossreferencing multiple geospatial data sources to enable the enrichment of media and therefor enable better organisation and searching of the media to create an overall picture about places. In this paper we present a system architecture that incorporates our proposed approach to aggregate several geospatial databases to enrich geo-tagged media with human readable information, which will further enable our goal of creating an overall picture about places. Our approach enables the semantic information relating to POIs (Point Of Interest).
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