scholarly journals Big tech, big data and the new world of digital health

2021 ◽  
Author(s):  
Dr. Jane Thomason
Keyword(s):  
Big Data ◽  
New World ◽  
Digital Health

scholarly journals Blockchain Disrupting Fintech and the Banking System

Proceedings ◽  
2021 ◽  
Vol 74 (1) ◽  
pp. 24
Author(s):  
Eduard Alexandru Stoica ◽  
Daria Maria Sitea
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Financial Institutions ◽  
New World ◽  
Great Part ◽  
Banking System ◽  
Daily Activities ◽  
Blockchain Technology ◽  
Almost All

Nowadays society is profoundly changed by technology, velocity and productivity. While individuals are not yet prepared for holographic connection with banks or financial institutions, other innovative technologies have been adopted. Lately, a new world has been launched, personalized and adapted to reality. It has emerged and started to govern almost all daily activities due to the five key elements that are foundations of the technology: machine to machine (M2M), internet of things (IoT), big data, machine learning and artificial intelligence (AI). Competitive innovations are now on the market, helping with the connection between investors and borrowers—notably crowdfunding and peer-to-peer lending. Blockchain technology is now enjoying great popularity. Thus, a great part of the focus of this research paper is on Elrond. The outcomes highlight the relevance of technology in digital finance.

scholarly journals Learning health systems need to bridge the ‘two cultures’ of clinical informatics and data science

2018 ◽  
Vol 25 (2) ◽  
pp. 126-131 ◽  
Author(s):  
Philip J. Scott ◽  
Rachel Dunscombe ◽  
David Evans ◽  
Mome Mukherjee ◽  
Jeremy C. Wyatt
Keyword(s):  
Big Data ◽  
System Design ◽  
Data Science ◽  
Digital Health ◽  
Evidence Base ◽  
Clinical Informatics ◽  
Two Cultures ◽  
Design And Implementation ◽  
The Two Cultures ◽  
The Uk

BackgroundUK health research policy and plans for population health management are predicated upon transformative knowledge discovery from operational ‘Big Data’. Learning health systems require not only data, but feedback loops of knowledge into changed practice. This depends on knowledge management and application, which in turn depends upon effective system design and implementation. Biomedical informatics is the interdisciplinary field at the intersection of health science, social science and information science and technology that spans this entire scope.IssuesIn the UK, the separate worlds of health data science (bioinformatics, ‘Big Data’) and effective healthcare system design and implementation (clinical informatics, ‘Digital Health’) have operated as ‘two cultures’. Much National Health Service and social care data is of very poor quality. Substantial research funding is wasted on ‘data cleansing’ or by producing very weak evidence. There is not yet a sufficiently powerful professional community or evidence base of best practice to influence the practitioner community or the digital health industry.RecommendationThe UK needs increased clinical informatics research and education capacity and capability at much greater scale and ambition to be able to meet policy expectations, address the fundamental gaps in the discipline’s evidence base and mitigate the absence of regulation. Independent evaluation of digital health interventions should be the norm, not the exception.ConclusionsPolicy makers and research funders need to acknowledge the existing gap between the ‘two cultures’ and recognise that the full social and economic benefits of digital health and data science can only be realised by accepting the interdisciplinary nature of biomedical informatics and supporting a significant expansion of clinical informatics capacity and capability.

scholarly journals Round table: AI and Big Data: Ethical Challenges and Health Opportunities

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
Keyword(s):  
Big Data ◽  
Digital Health ◽  
Rapid Development ◽  
Open Data ◽  
Wearable Devices ◽  
Good Evidence ◽  
Electronic Patient Records ◽  
Ethical Challenges ◽  
Data Movement ◽  
Recent Emergence

Abstract The recent emergence of Big Data in healthcare (including large linked data from electronic patient records (EPR) as well as streams of real-time geolocated health data collected by personal wearable devices, etc.) and the open data movement enabling sharing datasets are creating new challenges around ownership of personal data whilst at the same time opening new research opportunities and drives for commercial exploitation. A balance must be struck between an individual’s desire for privacy and their desire for good evidence to drive healthcare, which may sometimes be in conflict. With the increasing use of mobile and wearable devices, new opportunities have been created for personalized health (tailored care to the needs of an individual), crowdsourcing, participatory surveillance, and movement of individuals pledging to become “data donors” and the “quantified self” initiative (where citizens share data through mobile device-connected technologies). These initiatives created large volumes of data with considerable potential for research through open data initiatives. In this workshop we will hear from a panel of international speakers working across the digital health, Big Data ethics, computer science, public health divide on how they have addressed the challenges presented by increased use of Big Data and AI systems in healthcare with insights drawn from their own experience to illustrate the new opportunities that development of these movements has opened up. Key messages The potential of open access to healthcare data, sharing Big Data sets and rapid development of AI technology, is enormous - so as are the challenges and barriers to achieve this goal. Policymakers, scientific and business communities should work together to find novel approaches for underlying challenges of a political and legal nature associated with use of big data for health.

scholarly journals The Technology of Biotechnology and Big Data in Medicine Development

2021 ◽  
pp. 58-73
Author(s):  
Eric D. Perakslis ◽  
Martin Stanley
Keyword(s):  
Clinical Trials ◽  
Big Data ◽  
Digital Health ◽  
Wearable Devices ◽  
Sequencing Data ◽  
Detailed Understanding ◽  
Regulatory Decision ◽  
Device Discovery ◽  
Medicine Development ◽  
Dirty Data

The rise of big data and digital health in medicine have been concurrent over the last two decades. Often confused, while virtually all digital health solutions, such as sensors wearable devices, and diagnostic algorithms, involve big data, not all big data in health care originates from digital health tools. Genomic sequencing data being one example of this. In this chapter, the role and importance of big data in medicines and medical device discovery and development are detailed with the specific focus of providing a detailed understanding of the product discovery, product development, clinical trials, regulatory authorization, and marketing processes. Concepts such as “dirty data,” regulatory decision-making, remote and virtualized clinical trials, and other key elements of digital health are discussed.

Digital health investment opportunities abound, but standouts deliver disruptive change

2012 ◽  
Vol 18 (1) ◽  
Author(s):  
G. Steven Burrill
Keyword(s):  
New World ◽  
Digital Health ◽  
Low Cost ◽  
Time Cost ◽  
Wireless Internet ◽  
Health Investment ◽  
Investment Opportunities ◽  
Disruptive Change ◽  
Monitoring Devices ◽  
The Way

The convergence of ubiquitous smartphones, wireless Internet, and low-cost monitoring devices, is driving the emergence of a new world of digital health. At the same time, cost pressures on healthcare are creating demand for new ways to not just improve the way patients receive information and care and the way doctors provide it, but fundamentally change the way they interact with each other. The article discusses a venture capitalist's approach to investing in the sector. 

scholarly journals Informatics for Health 2017: Advancing both science and practice

2017 ◽  
Vol 24 (1) ◽  
pp. 1 ◽  
Author(s):  
Philip J. Scott ◽  
Ronald Cornet ◽  
Colin McCowan ◽  
Niels Peek ◽  
Paolo Fraccaro ◽  
...  
Keyword(s):  
Big Data ◽  
Medical Informatics ◽  
Health Informatics ◽  
Data Science ◽  
Digital Health ◽  
Health It ◽  
Special Issue ◽  
Organizational Intervention ◽  
Policy Makers ◽  
Multi Level

Introduction: The Informatics for Health congress, 24-26 April 2017, in Manchester, UK, brought together the Medical Informatics Europe (MIE) conference and the Farr Institute International Conference. This special issue of the Journal of Innovation in Health Informatics contains 113 presentation abstracts and 149 poster abstracts from the congress.Discussion: The twin programmes of “Big Data” and “Digital Health” are not always joined up by coherent policy and investment priorities. Substantial global investment in health IT and data science has led to sound progress but highly variable outcomes. Society needs an approach that brings together the science and the practice of health informatics. The goal is multi-level Learning Health Systems that consume and intelligently act upon both patient data and organizational intervention outcomes.Conclusions: Informatics for Health demonstrated the art of the possible, seen in the breadth and depth of our contributions. We call upon policy makers, research funders and programme leaders to learn from this joined-up approach.

The Paradoxes of Big Data

2015 ◽  
Vol 743 ◽  
pp. 603-606
Author(s):  
Xin Xing Liu ◽  
Xing Wu ◽  
Shu Ji Dai
Keyword(s):  
Big Data ◽  
Large Scale ◽  
New World ◽  
The Internet ◽  
Analyze Data

The era of Big Data poses a big challenge to our way of living and thinking. Big Data refers to things which can do at a large scale but cannot be done at a smaller size. There are many paradoxes of Big Data: In this new world far more data can be analyzed, though using all the data can make the datum messy and lose some accuracy, sometimes reach better conclusions. As massive quantities of information produced by and about people and their interactions exposed on the Internet, will large scale search and analyze data help people create better services, goods and tools or it just lead to privacy incursions and invasive marketing? In this article, we offer three main provocations, based on our analysis we have constructed some models to help explain the amazing contradiction in Big Data.

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