Realistic and interactive high-resolution 4D environments for real-time surgeon and patient interaction

Copyright © 2016 John Wiley & Sons, Ltd. Background: Remote consultations that are realistic enough to be useful medically offer considerable clinical, logistical and cost benefits. Despite advances in virtual reality and vision hardware and software, these benefits are currently often unrealised. Method: The proposed approach combines high spatial and temporal resolution 3D and 2D machine vision with virtual reality techniques, in order to develop new environments and instruments that will enable realistic remote consultations and the generation of new types of useful clinical data. Results: New types of clinical data have been generated for skin analysis and respiration measurement; and the combination of 3D with 2D data was found to offer potential for the generation of realistic virtual consultations. Conclusion: An innovative combination of high resolution machine vision data and virtual reality online methods, promises to provide advanced functionality and significant medical benefits, particularly in regions where populations are dispersed or access to clinicians is limited. Copyright © 2016 John Wiley & Sons, Ltd

Global Innovations in Measurement and Evaluation

We researched the latest developments in theory and practice in measurement and evaluation. And we found that new thinking, techniques, and technology are influencing and improving practice. This report highlights 8 developments that we think have the greatest potential to improve evaluation and programme design, and the careful collection and use of data. In it, we seek to inform and inspire—to celebrate what is possible, and encourage wider application of these ideas

What does it take to make integrated care work? A ‘cookbook’ for large-scale deployment of coordinated care and telehealth

The Advancing Care Coordination & Telehealth Deployment (ACT) Programme is the first to explore the organisational and structural processes needed to successfully implement care coordination and telehealth (CC&TH) services on a large scale. A number of insights and conclusions were identified by the ACT programme. These will prove useful and valuable in supporting the large-scale deployment of CC&TH. Targeted at populations of chronic patients and elderly people, these insights and conclusions are a useful benchmark for implementing and exchanging best practices across the EU. Examples are: Perceptions between managers, frontline staff and patients do not always match; Organisational structure does influence the views and experiences of patients: a dedicated contact person is considered both important and helpful; Successful patient adherence happens when staff are engaged; There is a willingness by patients to participate in healthcare programmes; Patients overestimate their level of knowledge and adherence behaviour; The responsibility for adherence must be shared between patients and health care providers; Awareness of the adherence concept is an important factor for adherence promotion; The ability to track the use of resources is a useful feature of a stratification strategy, however, current regional case finding tools are difficult to benchmark and evaluate; Data availability and homogeneity are the biggest challenges when evaluating the performance of the programmes

Method Usefulness for Quality Improvement in Care

Complexity in care arises from several concurrent sources, such as siloed care organisations and complex care processes handled by several medical specialities. Due to factors including the ongoing development of personalised care and increasingly older populations suffering from multi-sickness, care complexity can only be expected to increase. Simultaneously, organisational efficiency needs to be increased alongside this growing complexity. To address these challenges, there is a need to understand care complexity in order to drive care improvement.Quality improvement (QI) aims to develop health and social care. Methods are central for QI by describing the care and thereby support i) planning for future care, ii) acquisition of knowledge and understanding of the current practice, and iii) prediction of the future of care from historical data. Methods for QI generally display data in a simple, graphic way, so that they are easy for practitioners to understand; however, this strong focus on simplicity may limit the understanding of care complexity and thereby reduce the support provided for QI. As QI research with a focus on methods describing care complexity is scarce, the purpose of this thesis is to explore the usefulness of methods describing care complexity for QI in care.To fulfil this purpose, two research questions guided the analysis of the five appended papers. The first research question (What usefulness can visual methods describing care process complexity have for QI?) addresses the need to identify new methods describing care complexity where current methods are lacking. Two methods are chosen, guided by visual analytics theory: Lexis diagram and process mining. Two case studies and a literature review explore the usefulness of Lexis diagrams and process mining through visualisation of process variations at a patient and a population level, across groups and over time. The second research question (What usefulness can methods describing care organisation complexity have for QI in public procurement?) expands and explores the use of current methods describing complexity into the public care procurement context. First, the current state of QI in public care procurement is explored through an archival study, and next, a case study is conducted to explore the use of business excellence models to support QI in public care procurement. The thesis is guided by a pragmatic approach, leading to a mixed-methods approach and domain expert collaboration.This thesis makes three main contributions. First, each method’s properties are connected to a set of evaluative and organisational benefits, revealing the possibility of and need for matching methods to the local contextual conditions and needs for QI. Subsequently, a framework for this task is presented. Second, the results on the explored methods describing care complexity yield additional understanding of variations and care systems across stakeholders compared to traditional methods used for QI in each context. Methods describing care complexity may, therefore, be useful to support QI efforts. Third, when methods describe care complexity, stakeholders might be supported in driving local QI efforts, and as the new perspectives seem to challenge their mental models, they also seem to develop their understanding of QI.The findings and conclusions of this thesis primarily contribute to the QI research field but can also inform other research on Lexis diagrams, process mining, and public care procurement

An Uncertainty Visual Analytics Framework for Functional Magnetic Resonance Imaging

Improving understanding of the human brain is one of the leading pursuits of modern scientific research. Functional magnetic resonance imaging (fMRI) is a foundational technique for advanced analysis and exploration of the human brain. The modality scans the brain in a series of temporal frames which provide an indication of the brain activity either at rest or during a task. The images can be used to study the workings of the brain, leading to the development of an understanding of healthy brain function, as well as characterising diseases such as schizophrenia and bipolar disorder. Extracting meaning from fMRI relies on an analysis pipeline which can be broadly categorised into three phases: (i) data acquisition and image processing; (ii) image analysis; and (iii) visualisation and human interpretation. The modality and analysis pipeline, however, are hampered by a range of uncertainties which can greatly impact the study of the brain function. Each phase contains a set of required and optional steps, containing inherent limitations and complex parameter selection. These aspects lead to the uncertainty that impacts the outcome of studies. Moreover, the uncertainties that arise early in the pipeline, are compounded by decisions and limitations further along in the process. While a large amount of research has been undertaken to examine the limitations and variable parameter selection, statistical approaches designed to address the uncertainty have not managed to mitigate the issues. Visual analytics, meanwhile, is a research domain which seeks to combine advanced visual interfaces with specialised interaction and automated statistical processing designed to exploit human expertise and understanding. Uncertainty visual analytics (UVA) tools, which aim to minimise and mitigate uncertainties, have been proposed for a variety of data, including astronomical, financial, weather and crime. Importantly, UVA approaches have also seen success in medical imaging and analysis. However, there are many challenges surrounding the application of UVA to each research domain. Principally, these involve understanding what the uncertainties are and the possible effects so they may be connected to visualisation and interaction approaches. With fMRI, the breadth of uncertainty arising in multiple stages along the pipeline and the compound effects, make it challenging to propose UVAs which meaningfully integrate into pipeline. In this thesis, we seek to address this challenge by proposing a unified UVA framework for fMRI. To do so, we first examine the state-of-the-art landscape of fMRI uncertainties, including the compound effects, and explore how they are currently addressed. This forms the basis of a field we term fMRI-UVA. We then present our overall framework, which is designed to meet the requirements of fMRI visual analysis, while also providing an indication and understanding of the effects of uncertainties on the data. Our framework consists of components designed for the spatial, temporal and processed imaging data. Alongside the framework, we propose two visual extensions which can be used as standalone UVA applications or be integrated into the framework. Finally, we describe a conceptual algorithmic approach which incorporates more data into an existing measure used in the fMRI analysis pipeline

Information Visualisation Practices for Improving Patient Readability of Blood Pressure, Health Data, and Health Literacy

Personal health data obtained through self-monitoring is often presented through standardised representations with little intrinsic meaning for those who may need it the most since low health literacy is associated with poor health. By failing to inform users about their health status, these representations can be dangerous, leaving patients feeling lost, confused, anxious, or even depressed. Information Visualisation can play an important role in aiding patients making sense of their health data and health status, as long as it's aligned with their needs, motivations, and goals. Following Human Centred Design practices, user research methods were applied in order to understand the context of self-monitorisation, as well as identifying which metrics differed the most from participants' mental models. Thanks to quantitative data obtained from a survey, Blood Pressure was identified as the most problematic health variable. A series of interviews allowed patients of chronic conditions to vocalize the challenges they faced in the management of their conditions. Taking into account information obtained from previous steps, multiple ways to map blood pressure data onto design elements were explored and different visualisations were designed. Finally, said visualisations were tested through guided interviews with patients with blood pressure problems. Results showed that participants prefered different visualisations for different goals, and enjoyed being able to choose freely from them; participants with lower literacy but who were deeply invested in monitoring their health found tables to be the most informative visualizations; finally, participants identified colour scales as the most intuitive method to represent health status and health risk

Exploring Environmental and Geographical Factors Influencing the Spread of Infectious Diseases with Interactive Maps

Publisher Copyright: © 2022 by the authors.Environmental problems due to human activities such as deforestation, urbanisation, and large scale intensive farming are some of the major factors behind the rapid spread of many infectious diseases. This in turn poses significant challenges not only in as regards providing adequate healthcare, but also in supporting healthcare workers, medical researchers, policy makers, and others involved in managing infectious diseases. These challenges include surveillance, tracking of infections, communication of public health knowledge and promotion of behavioural change. Behind these challenges lies a complex set of factors which include not only biomedical and population health determinants but also environmental, climatic, geographic, and socioeconomic variables. While there is broad agreement that these factors are best understood when considered in conjunction, aggregating and presenting diverse information sources requires effective information systems, software tools, and data visualisation. In this article, weargue that interactive maps, which couple geographical information systems and advanced information visualisation techniques, provide a suitable unifying framework for coordinating these tasks. Therefore, we examine how interactive maps can support spatial epidemiological visualisation and modelling involving distributed and dynamic data sources and incorporating temporal aspects of disease spread. Combining spatial and temporal aspects can be crucial in such applications. We discuss these issues in the context of support for disease surveillance in remote regions, utilising tools that facilitate distributed data collection and enable multidisciplinary collaboration, while also providing support for simulation and data analysis. We show that interactive maps deployed on a combination of mobile devices and large screens can provide effective means for collection, sharing, and analysis of health data.Peer reviewe

System upgrade: realising the vision for UK education

A report summarising the findings of the TEL programme in the wider context of technology-enhanced learning and offering recommendations for future strategy in the area was launched on 13th June at the House of Lords to a group of policymakers, technologists and practitioners chaired by Lord Knight. The report – a major outcome of the programme – is written by TEL director Professor Richard Noss and a team of experts in various fields of technology-enhanced learning. The report features the programme’s 12 recommendations for using technology-enhanced learning to upgrade UK education

Translating Predictive Models for Alzheimer’s Disease to Clinical Practice: User Research, Adoption Opportunities, and Conceptual Design of a Decision Support Tool

Alzheimer’s Disease (AD) is a common form of Dementia with terrible impact on patients, families, and the healthcare sector. Recent computational advances, such as predictive models, have improved AD data collection and analysis, disclosing the progression pattern of the disease. Whilst clinicians currently rely on a qualitative, experience-led approach to make decisions on patients’ care, the Event-Based Model (EBM) has shown promising results for familial and sporadic AD, making it well positioned to inform clinical decision-making. What proves to be challenging is the translation of computational implementations to clinical applications, due to lack of human factors considerations. The aim of this Ph.D. thesis is to (1) explore barriers and opportunities to the adoption of predictive models for AD in clinical practice; and (2) develop and test the design concept of a tool to enable EBM exploitation by AD clinicians. Following a user-centred design approach, I explored current clinical needs and practices, by means of field observations, interviews, and surveys. I framed the technical-clinical gap, identifying the technical features that were better suited for clinical use, and research-oriented clinicians as the best placed to initially adopt the technology. I designed and tested with clinicians a prototype, icompass, and reviewed it with the technical teams through a series of workshops. This approach fostered a thorough understanding of clinical users’ context and perceptions of the tool’s potential. Furthermore, it provided recommendations to computer scientists pushing forward the models and tool’s development, to enhance user relevance in the future. This thesis is one of the few works addressing a lack of consensus on successful adoption and integration of such innovations to the healthcare environment, from a human factors’ perspective. Future developments should improve prototype fidelity, with interleaved clinical testing, refining design, algorithm, and strategies to facilitate the tool’s integration within clinical practice