New XrootD Monitoring Implementation Garrido, Borja; Andreeva, Julia; Weitzel, Derek ...
EPJ Web of Conferences,
2024, Volume:
295
Journal Article, Conference Proceeding
Peer reviewed
Open access
Complete and reliable monitoring of the WLCG data transfers is an important condition for effective computing operations of the LHC experiments. WLCG data challenges organized in 2021 and 2022 ...highlighted the need for improvements in WLCG data traffic monitoring. In particular, it concerns the implementation of remote data access monitoring via the root protocol. It includes data access to native XRootD storage, as well as to other storage solutions. We refer to it as XRootD monitoring. This contribution describes the new implementation of the XRootD monitoring flow, the overall architecture, the deployment scenario, and the integration with the WLCG global monitoring system.
This study aimed to systematically review and summarize economic evaluations of noninvasive remote patient monitoring (RPM) for chronic diseases compared with usual care.
A systematic literature ...search identified economic evaluations of RPM for chronic diseases, compared with usual care. Searches of PubMed, Embase, CINAHL, and EconLit using keyword synonyms for RPM and economics identified articles published from up until September 2021. Title, abstract, and full-text reviews were conducted. Data extraction of study characteristics and health economic findings was performed. Article reporting quality was assessed using the Consolidated Health Economic Evaluation Reporting Standards checklist.
This review demonstrated that the cost-effectiveness of RPM was dependent on clinical context, capital investment, organizational processes, and willingness to pay in each specific setting. RPM was found to be highly cost-effective for hypertension and may be cost-effective for heart failure and chronic obstructive pulmonary disease. There were few studies that investigated RPM for diabetes or other chronic diseases. Studies were of high reporting quality, with an average Consolidated Health Economic Evaluation Reporting Standards score of 81%. Of the final 34 included studies, most were conducted from the healthcare system perspective. Eighteen studies used cost-utility analysis, 4 used cost-effectiveness analysis, 2 combined cost-utility analysis and a cost-effectiveness analysis, 1 used cost-consequence analysis, 1 used cost-benefit analysis, and 8 used cost-minimization analysis.
RPM was highly cost-effective for hypertension and may achieve greater long-term cost savings from the prevention of high-cost health events. For chronic obstructive pulmonary disease and heart failure, cost-effectiveness findings differed according to disease severity and there was limited economic evidence for diabetes interventions.
•Remote patient monitoring (RPM) can be effective in preventing escalation to acute care and has the potential to improve chronic disease management. There has also been a renewed interest in the cost-effectiveness of RPM since the onset of the coronavirus (COVID-19) pandemic. The cost and outcome evidence for RPM has been summarized for some disease-specific contexts (eg, heart failure); however, there are no existing summaries of the available health economic evidence of noninvasive RPM for chronic disease management.•This systematic review identified and summarized 34 articles that conducted economic evaluations of noninvasive RPM compared with usual care for chronic disease management. This review demonstrated that RPM can be cost-effective for chronic disease management, although the cost-effectiveness is dependent on capital investment, clinical context, willingness-to-pay thresholds, and the organizational processes involved in RPM implementation and provision.•Overarching health economic evidence of RPM is useful for decision makers that are contemplating RPM investment choices for chronic disease management. Compared with usual care, RPM was highly cost-effective for hypertension, where greater cost savings may be achieved in the long term because of prevention of high-cost health events. For chronic obstructive pulmonary disease and heart failure, cost-effectiveness findings often differed according to disease severity. There was limited evidence for the cost-effectiveness of RPM for diabetes and other chronic diseases.
This paper introduces factors that characterise the organisational application of remote monitoring technology (RMT) for servitised strategies. Factors were developed through in-depth case studies of ...four manufacturers operating in aerospace, industrial equipment, marine and transportation sector. They suggest a very complex, multi-levelled, multifaceted and circular relationship between RMT and servitisation. When unfolding this relationship we need to consider: the value proposition, nature of the product and its hierarchical position in the customer's business, RMT functionality, type and amount of data required, and a number of other factors that either complement or constrain the use of RMT. By providing the necessary lenses, the proposed factors can help in exploring this complexity. One such exploration questions our understanding of outcome-based contracting.
The growth of digital health technology (DHT) has led to innovative technological solutions that can help improve patient care. However, the primary focus to date has been on the passive monitoring ...of patients, which poses difficulties in clinical integration and has not succeeded in optimizing care for chronic conditions. In this article, we highlight the move from a digital care model focused on the passive monitoring of medical conditions to one where holistic patient management is provided by dedicated external healthcare teams on a longitudinal basis. This underlines the shift from remote patient monitoring to remote patient care. This approach has thus far mostly been applied at small scales, limited to specific institutions and environments. Generalization to the wider population will likely require the use of centralized entities that can scale these approaches. It is crucial to note that the role of this technology will be to supplement – rather than supplant – in-person care by allowing physicians to focus on the most relevant clinical data collected on a longitudinal basis. This approach is particularly promising for individuals living in rural or underserved areas, where traditional models of care may face barriers in implementation.
The earliest studies of collective animal behaviour were inspired by and conducted in the wild. Over the past decades much of the research in this field has shifted to the laboratory, combining ...high-resolution tracking of individuals with mathematical simulations or agent-based models. Today we are beginning to see a ‘re-wilding’ of collective behaviour thanks to technological advances, providing researchers with the opportunity to quantify and model the heterogeneity that exists within the social groupings they study and within the environments in which these groups live. The perspective we present here aims to inspire and steer this research toward answering fundamental and outstanding behavioural and ecological questions, while also tackling pertinent conservation challenges.
The field of collective animal behaviour is transforming.
Continuous behavioural tracking in the wild affords an ecological perspective.
Collective behaviour can be studied in the environment in which it has evolved and is maintained.
•Remote endpoints hold great promise for decentralized clinical trials.•They may lower participants burden and enable measurements in a “real world” setting.•We review current state and future ...directions for remote endpoints in CF trials.
The COVID-19 pandemic necessitated a rapid shift in clinical research to perform virtual visits and remote endpoint assessments, providing a key opportunity to optimize the use of remote endpoints for clinical trials in cystic fibrosis. The use of remote endpoints could allow more diverse participation in clinical trials while minimizing participant burden but must be robustly evaluated to ensure adequate performance and feasibility. In response, the Cystic Fibrosis Foundation convened the Remote Endpoint Task Force (Supplemental Table 1), a multidisciplinary group of CF researchers with remote endpoint expertise and community members tasked to better understand the current and future use of remote endpoints for clinical research. Here, we describe the current use of remote endpoints in CF clinical research, address key unanswered questions regarding their use and feasibility, and discuss the next steps to determine clinical trial readiness.
The growing worldwide population has increased the need for technologies, computerised software algorithms and smart devices that can monitor and assist patients anytime and anywhere and thus enable ...them to lead independent lives. The real-time remote monitoring of patients is an important issue in telemedicine. In the provision of healthcare services, patient prioritisation poses a significant challenge because of the complex decision-making process it involves when patients are considered ‘big data’. To our knowledge, no study has highlighted the link between ‘big data’ characteristics and real-time remote healthcare monitoring in the patient prioritisation process, as well as the inherent challenges involved. Thus, we present comprehensive insights into the elements of big data characteristics according to the six ‘Vs’: volume, velocity, variety, veracity, value and variability. Each of these elements is presented and connected to a related part in the study of the connection between patient prioritisation and real-time remote healthcare monitoring systems. Then, we determine the weak points and recommend solutions as potential future work. This study makes the following contributions. (1) The link between big data characteristics and real-time remote healthcare monitoring in the patient prioritisation process is described. (2) The open issues and challenges for big data used in the patient prioritisation process are emphasised. (3) As a recommended solution, decision making using multiple criteria, such as vital signs and chief complaints, is utilised to prioritise the big data of patients with chronic diseases on the basis of the most urgent cases.
Driven by the rapid development of big data and processing power, artificial intelligence and machine learning (ML) applications are poised to expand orthopedic surgery frontiers. Lower extremity ...arthroplasty is uniquely positioned to most dramatically benefit from ML applications given its central role in alternative payment models and the value equation.
In this report, we discuss the origins and model specifics behind machine learning, consider its progression into healthcare, and present some of its most recent advances and applications in arthroplasty.
A narrative review of artificial intelligence and ML developments is summarized with specific applications to lower extremity arthroplasty, with specific lessons learned from osteoarthritis gait models, joint-specific imaging analysis, and value-based payment models.
The advancement and employment of ML provides an opportunity to provide data-driven, high performance medicine that can rapidly improve the science, economics, and delivery of lower extremity arthroplasty.
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