41,966 research outputs found
MOSAIC roadmap for mobile collaborative work related to health and wellbeing.
The objective of the MOSAIC project is to accelerate innovation in Mobile Worker Support Environments. For that purpose MOSAIC develops visions and illustrative scenarios for future collaborative workspaces involving mobile and location-aware working. Analysis of the scenarios is input to the process of road mapping with the purpose of developing strategies for R&D leading to deployment of innovative mobile work technologies and applications across different domains. One of the application domains where MOSAIC is active is health and wellbeing. This paper builds on another paper submitted to this same conference, which presents and discusses health care and wellbeing specific scenarios. The aim is to present an early form of a roadmap for validation
Towards an interoperable healthcare information infrastructure - working from the bottom up
Historically, the healthcare system has not made effective use of information technology. On the face of things, it would seem to provide a natural and richly varied domain in which to target benefit from IT solutions. But history shows that it is one of the most difficult domains in which to bring them to fruition. This paper provides an overview of the changing context and information requirements of healthcare that help to explain these characteristics.First and foremost, the disciplines and professions that healthcare encompasses have immense complexity and diversity to deal with, in structuring knowledge about what medicine and healthcare are, how they function, and what differentiates good practice and good performance. The need to maintain macro-economic stability of the health service, faced with this and many other uncertainties, means that management bottom lines predominate over choices and decisions that have to be made within everyday individual patient services. Individual practice and care, the bedrock of healthcare, is, for this and other reasons, more and more subject to professional and managerial control and regulation.One characteristic of organisations shown to be good at making effective use of IT is their capacity to devolve decisions within the organisation to where they can be best made, for the purpose of meeting their customers' needs. IT should, in this context, contribute as an enabler and not as an enforcer of good information services. The information infrastructure must work effectively, both top down and bottom up, to accommodate these countervailing pressures. This issue is explored in the context of infrastructure to support electronic health records.Because of the diverse and changing requirements of the huge healthcare sector, and the need to sustain health records over many decades, standardised systems must concentrate on doing the easier things well and as simply as possible, while accommodating immense diversity of requirements and practice. The manner in which the healthcare information infrastructure can be formulated and implemented to meet useful practical goals is explored, in the context of two case studies of research in CHIME at UCL and their user communities.Healthcare has severe problems both as a provider of information and as a purchaser of information systems. This has an impact on both its customer and its supplier relationships. Healthcare needs to become a better purchaser, more aware and realistic about what technology can and cannot do and where research is needed. Industry needs a greater awareness of the complexity of the healthcare domain, and the subtle ways in which information is part of the basic contract between healthcare professionals and patients, and the trust and understanding that must exist between them. It is an ideal domain for deeper collaboration between academic institutions and industry
Big Data and the Internet of Things
Advances in sensing and computing capabilities are making it possible to
embed increasing computing power in small devices. This has enabled the sensing
devices not just to passively capture data at very high resolution but also to
take sophisticated actions in response. Combined with advances in
communication, this is resulting in an ecosystem of highly interconnected
devices referred to as the Internet of Things - IoT. In conjunction, the
advances in machine learning have allowed building models on this ever
increasing amounts of data. Consequently, devices all the way from heavy assets
such as aircraft engines to wearables such as health monitors can all now not
only generate massive amounts of data but can draw back on aggregate analytics
to "improve" their performance over time. Big data analytics has been
identified as a key enabler for the IoT. In this chapter, we discuss various
avenues of the IoT where big data analytics either is already making a
significant impact or is on the cusp of doing so. We also discuss social
implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski
(eds.) Big Data Analysis: New algorithms for a new society, Springer Series
on Studies in Big Data, to appea
Improving fairness in machine learning systems: What do industry practitioners need?
The potential for machine learning (ML) systems to amplify social inequities
and unfairness is receiving increasing popular and academic attention. A surge
of recent work has focused on the development of algorithmic tools to assess
and mitigate such unfairness. If these tools are to have a positive impact on
industry practice, however, it is crucial that their design be informed by an
understanding of real-world needs. Through 35 semi-structured interviews and an
anonymous survey of 267 ML practitioners, we conduct the first systematic
investigation of commercial product teams' challenges and needs for support in
developing fairer ML systems. We identify areas of alignment and disconnect
between the challenges faced by industry practitioners and solutions proposed
in the fair ML research literature. Based on these findings, we highlight
directions for future ML and HCI research that will better address industry
practitioners' needs.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in
Computing Systems (CHI 2019
Business models for sustained ehealth implementation: lessons from two continents
There is general consensus that Computers and Information Technology have the potential to enhance health systems applications, and many good examples of such applications exist all over the world. Unfortunately, with respect to eHealth and telemedicine, there is much disillusionment and scepticism. This paper describes two models that were developed separately, but had the same purpose, namely to facilitate a holistic approach to the development and implementation of eHealth solutions. The roadmap of the Centre for eHealth Research (CeHRes roadmap) was developed in the Netherlands, and the Telemedicine Maturity Model (TMMM) was developed in South Africa. The purpose of this paper is to analyse the commonalities and differences of these approaches, and to explore how they can be used to complement each other. The first part of this paper comprises of a comparison of these models in terms of origin, research domain and design principles. Case comparisons are then presented to illustrate how these models complement one another
Respiratory Therapy Staff Retention: A Systems Thinking Approach
Respiratory therapists are an integral part of the healthcare workforce in the United States. The healthcare workforce shortage accelerated by the COVID global pandemic has impacted respiratory therapy departments across the nation, with organizations struggling to hire and retain staff. The prevailing approaches in combating this is linear in nature and does not consider the complexity of the system. The purpose of this dissertation is to address retention issues in respiratory therapy departments from a systems thinking approach and to develop a prototype utilizing systems thinking methodologies and tools
Living Innovation Laboratory Model Design and Implementation
Living Innovation Laboratory (LIL) is an open and recyclable way for
multidisciplinary researchers to remote control resources and co-develop user
centered projects. In the past few years, there were several papers about LIL
published and trying to discuss and define the model and architecture of LIL.
People all acknowledge about the three characteristics of LIL: user centered,
co-creation, and context aware, which make it distinguished from test platform
and other innovation approaches. Its existing model consists of five phases:
initialization, preparation, formation, development, and evaluation.
Goal Net is a goal-oriented methodology to formularize a progress. In this
thesis, Goal Net is adopted to subtract a detailed and systemic methodology for
LIL. LIL Goal Net Model breaks the five phases of LIL into more detailed steps.
Big data, crowd sourcing, crowd funding and crowd testing take place in
suitable steps to realize UUI, MCC and PCA throughout the innovation process in
LIL 2.0. It would become a guideline for any company or organization to develop
a project in the form of an LIL 2.0 project.
To prove the feasibility of LIL Goal Net Model, it was applied to two real
cases. One project is a Kinect game and the other one is an Internet product.
They were both transformed to LIL 2.0 successfully, based on LIL goal net based
methodology. The two projects were evaluated by phenomenography, which was a
qualitative research method to study human experiences and their relations in
hope of finding the better way to improve human experiences. Through
phenomenographic study, the positive evaluation results showed that the new
generation of LIL had more advantages in terms of effectiveness and efficiency.Comment: This is a book draf
Embracing complexity: theory, cases and the future of bioethics
This paper reflects on the relationship between theory and practice in bioethics, by using various concepts drawn from debates on innovation in healthcare research—in particular debates around how best to connect up blue skies ‘basic’
research with practical innovations that can improve human lives. It argues that it is a mistake to assume that the most difficult and important questions in bioethics are
the most abstract ones, and also a mistake to assume that getting clear about abstract cases will automatically be of much help in getting clear about more complex cases.
It replaces this implicitly linear model with a more complex one that draws on the idea of translational research in healthcare. On the translational model, there is a continuum of cases from the most simple and abstract (thought experiments) to the most concrete and complex (real world cases). Insights need to travel in both
directions along this continuum—from the more abstract to the more concrete and from the more concrete to the more abstract. The paper maps out some difficulties in
moving from simpler to more complex cases, and in doing so makes recommendations about the future of bioethics
End-to-End Privacy for Open Big Data Markets
The idea of an open data market envisions the creation of a data trading
model to facilitate exchange of data between different parties in the Internet
of Things (IoT) domain. The data collected by IoT products and solutions are
expected to be traded in these markets. Data owners will collect data using IoT
products and solutions. Data consumers who are interested will negotiate with
the data owners to get access to such data. Data captured by IoT products will
allow data consumers to further understand the preferences and behaviours of
data owners and to generate additional business value using different
techniques ranging from waste reduction to personalized service offerings. In
open data markets, data consumers will be able to give back part of the
additional value generated to the data owners. However, privacy becomes a
significant issue when data that can be used to derive extremely personal
information is being traded. This paper discusses why privacy matters in the
IoT domain in general and especially in open data markets and surveys existing
privacy-preserving strategies and design techniques that can be used to
facilitate end to end privacy for open data markets. We also highlight some of
the major research challenges that need to be address in order to make the
vision of open data markets a reality through ensuring the privacy of
stakeholders.Comment: Accepted to be published in IEEE Cloud Computing Magazine: Special
Issue Cloud Computing and the La
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