14,074 research outputs found
The OCareCloudS project: toward organizing care through trusted cloud services
The increasing elderly population and the shift from acute to chronic illness makes it difficult to care for people in hospitals and rest homes. Moreover, elderly people, if given a choice, want to stay at home as long as possible. In this article, the methodologies to develop a cloud-based semantic system, offering valuable information and knowledge-based services, are presented. The information and services are related to the different personal living hemispheres of the patient, namely the daily care-related needs, the social needs and the daily life assistance. Ontologies are used to facilitate the integration, analysis, aggregation and efficient use of all the available data in the cloud. By using an interdisciplinary research approach, where user researchers, (ontology) engineers, researchers and domain stakeholders are at the forefront, a platform can be developed of great added value for the patients that want to grow old in their own home and for their caregivers
An exploration of how domains of quality of care relate to overall care experience
Purpose: To determine the relative influence of the different domains of healthcare quality from the Care Experience Feedback Improvement Tool and identify key predictors of healthcare quality from the patientsâ perspective. Measurement is necessary to determine whether quality of healthcare is improving. The Care Experience Feedback Improvement Tool was developed as a brief measure of patient experience. It is important to determine the relative influence of the different domains of healthcare quality to further clarify how the Care Experience Feedback Improvement Tool can be used and identify key predictors of healthcare quality from the patientsâ perspective. Methods: 802 people with a healthcare experience during the previous 12 months were telephoned to complete the Care Experience Feedback Improvement Tool questions and an additional eleven-point global rating of patient experience. To estimate the influence of different domains of healthcare quality on patient overall ratings of quality of healthcare experience, we regressed the overall rating of patient experience with each component of quality (safety, effectiveness, timely, caring, enables system navigation and person-centred). Findings: We found that all of the domains of the Care Experience Feedback Improvement Tool, influenced patient experience ratings of healthcare quality. Specifically, results show the degree of influence, the impact of demographics and how high scores for overall rating of patient experience can be predicted. Originality: Our findings suggest that all of the Care Experience Feedback Improvement Tool domains are important in terms of capturing the wholeness of the patient experience of healthcare quality to direct local quality improvement
The ethics of uncertainty for data subjects
Modern health data practices come with many practical uncertainties. In this paper, I argue that data subjectsâ trust in the institutions and organizations that control their data, and their ability to know their own moral obligations in relation to their data, are undermined by significant uncertainties regarding the what, how, and who of mass data collection and analysis. I conclude by considering how proposals for managing situations of high uncertainty might be applied to this problem. These emphasize increasing organizational flexibility, knowledge, and capacity, and reducing hazard
A Classification Model for Sensing Human Trust in Machines Using EEG and GSR
Today, intelligent machines \emph{interact and collaborate} with humans in a
way that demands a greater level of trust between human and machine. A first
step towards building intelligent machines that are capable of building and
maintaining trust with humans is the design of a sensor that will enable
machines to estimate human trust level in real-time. In this paper, two
approaches for developing classifier-based empirical trust sensor models are
presented that specifically use electroencephalography (EEG) and galvanic skin
response (GSR) measurements. Human subject data collected from 45 participants
is used for feature extraction, feature selection, classifier training, and
model validation. The first approach considers a general set of
psychophysiological features across all participants as the input variables and
trains a classifier-based model for each participant, resulting in a trust
sensor model based on the general feature set (i.e., a "general trust sensor
model"). The second approach considers a customized feature set for each
individual and trains a classifier-based model using that feature set,
resulting in improved mean accuracy but at the expense of an increase in
training time. This work represents the first use of real-time
psychophysiological measurements for the development of a human trust sensor.
Implications of the work, in the context of trust management algorithm design
for intelligent machines, are also discussed.Comment: 20 page
Regulating Mobile Mental Health Apps
Mobile medical apps (MMAs) are a fastâgrowing category of software typically installed on personal smartphones and wearable devices. A subset of MMAs are aimed at helping consumers identify mental states and/or mental illnesses. Although this is a fledgling domain, there are already enough extant mental health MMAs both to suggest a typology and to detail some of the regulatory issues they pose. As to the former, the current generation of apps includes those that facilitate selfâassessment or selfâhelp, connect patients with online support groups, connect patients with therapists, or predict mental health issues. Regulatory concerns with these apps include their quality, safety, and data protection. Unfortunately, the regulatory frameworks that apply have failed to provide coherent riskâassessment models. As a result, prudent providers will need to progress with caution when it comes to recommending apps to patients or relying on appâgenerated data to guide treatment
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