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.

The Future of Nurse Scholarship: The Ethical Challenges

2021 ◽  
Vol 35 (1) ◽  
pp. 25-27
Author(s):  
Constance L. Milton
Keyword(s):  
Public Health ◽  
Big Data ◽  
National Policy ◽  
Nurse Education ◽  
Health Priorities ◽  
Ethical Challenges ◽  
Data Movement ◽  
Ethical Implications ◽  
Policy Statements ◽  
Competency Based

The advancement of a healthcare discipline is reliant on the disciplines’ ability to produce rigorous scholarship activities and products. The healthcare disciplines, especially nursing, are facing ever-changing priorities as shortages loom and exhaustion permeates the climate. Empirical public health priorities during the pandemic have dominated professional healthcare literature and global health communications. This article shall offer ethical implications for the discipline of nursing as it seeks the advancement of scholarship. Topics include straight-thinking issues surrounding nursing and medicine national policy statements, the big data movement, and evolutionary return of competency-based nurse education.

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.

scholarly journals Beyond the Debate on Promises and Risks in Digital Health: Analysing the Psychological Function of Wearable Devices

2016 ◽  
Vol 8 (4) ◽  
pp. 26 ◽  
Author(s):  
Maria del Rio Carral ◽  
Pauline Roux ◽  
Christine Bruchez ◽  
Marie Santiago-Delefosse
Keyword(s):  
Lived Experience ◽  
Digital Health ◽  
Rapid Development ◽  
Wearable Devices ◽  
The Body ◽  
Physiological Data ◽  
Psychological Function ◽  
Biometric Data ◽  
Health Related ◽  
Definition Of

<p>In the past years, the recording and collection of physical and physiological data from the body through wearable devices has become an increasingly common health-related practice in contemporary Western societies. The rapid development of digital self-tracking technologies has given rise to the production of different scientific discourses. The analysis of 200 published articles has led to the definition of a continuum between “technophile-promises” and “technocritical-risks” representations. However, these representations include different views of corporeality and sociality. Beyond this debate, we propose an alternative theoretical framework that links corporeality and sociality. It interrogates the psychological function that wearable devices may take (or not) for subjects to which these “tools” are addressed. We argue that such psychological function must be embraced by taking into consideration of activity done by the users of these technologies, which engages meaning: It is not the device, but the user him/herself who is confronted to the interpretation of biometric data linked to his/her own body functions on the basis of concrete lived experience. Moreover, we discuss that the activity of users can only be analysed in the sociocultural context to which the associated practices relate (health, sports, play, medicalisation). The conclusion highlights the need to further study the appropriation process of new personal experimentation instruments as to better understand the potential collaborations, risks or resistances that users may develop.</p>

scholarly journals Navigating Workplace Wellness Programs in the Age of Technology and Big Data

2020 ◽  
Vol 17 (01) ◽  
Author(s):  
Hannah-Kaye Fleming
Keyword(s):  
Big Data ◽  
Wellness Programs ◽  
Wearable Devices ◽  
Workplace Wellness ◽  
Governance Mechanism ◽  
Ethical Challenges ◽  
Wearable Technologies ◽  
Ethical Concerns ◽  
Workplace Wellness Programs ◽  
Security Information

Workplace wellness programs come in a myriad of forms, each with the goal of improving employee health and productivity while reducing healthcare costs. In the age of big data, wearable devices are ubiquitously incorporated into workplace wellness programs. Wearable devices in wellness programs can be beneficial for employers, employees, and health insurers alike. Nevertheless, there is an increasingly complex risk landscape associated with wearable devices in wellness programs, raising profound legal and ethical concerns related to privacy, security, information abuse, and employee autonomy. This paper will discuss the benefits and challenges of wearable devices in workplace wellness programs. Part I will introduce the benefits of workplace wellness programs. Part II will discuss the incorporation of wearable technologies in workplace wellness programs. Part III will analyze the legal and ethical challenges associated with the use of wearable technologies in wellness programs. Finally, Part IV will propose soft law, or best practices, as the most efficacious governance mechanism for employers and employees to secure benefits and balance concerns associated with the use of wearable devices in workplace wellness programs.

scholarly journals BIMBINGAN TEKNIS PERAMALAN JUMLAH KUNJUNGAN PASIEN DENGAN TABLEAU

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Mieke Nurmalasari ◽  
Witri Zuama Qomarania ◽  
Nauri Anggita Temesvari ◽  
Tria Saras Pertiwi
Keyword(s):  
Big Data ◽  
Information Management ◽  
Data Analytics ◽  
Digital Health ◽  
Rapid Development ◽  
Health Data ◽  
Patient Visit ◽  
Professional Health ◽  
Number Of Visits ◽  
Technical Guidance

ABSTRAK.  Peramalan jumlah kunjungan pasien berguna untuk membantu manajemen dalam membuat kebijakan dan perencanaan yang efektif dan efisien. Pesatnya perkembangan teknologi menjadikan data kesehatan digital sebagai salah satu sumber big data. Perlu dilakukan peningkatan pengetahuan pada mahasiswa dan tenaga Perekan Medis dan Manajemen Informasi Kesehatan dalam menganalisis data kunjungan pasien. Metode yang digunakan dalam kegiatan ini adalah pelatihan atau bimbingan teknis yang bersifat teoritis dan praktis. Hasil dari pelatihan ini adalah peningkatan pengetahuan peserta dalam menganalisis data peramalan kunjungan pasien menggunakan software statistik A Tableau. Kata kunci: kunjungan pasien; peramalan; analisis data; public tableau ABSTRACT. Forecasting number of visits is useful to help management to make effective and efficient policies and plans. The rapid development of technology makes digital health data as a one of big data sources. It is necessary to increase the knowledge of student and Professional Health Information Management in analyzing the patient visit data. The method used in this activity is a training or technical guidance which is namely theoretical and practical. The result of this training is an increase in participants' knowledge in analyzing the forecasting of patient visit data using a statistical software Tableau. Keywords: patient visit; forecasting; data analytics; public tableau

scholarly journals Understanding the evolution of open government data research: towards open data sustainability and smartness

2021 ◽  
pp. 002085232110099
Author(s):  
Yingying Gao ◽  
Marijn Janssen ◽  
Congcong Zhang
Keyword(s):  
Artificial Intelligence ◽  
Smart Cities ◽  
Rapid Development ◽  
Academic Research ◽  
Open Data ◽  
Open Government ◽  
Data Movement ◽  
Data Practices ◽  
Open Government Data ◽  
Government Data

The past decade has witnessed a rapid development of open government data practices and academic research. However, there is no systematic survey of existing research to understand the evolution of open government data. Such research can facilitate knowledge transfer within and across domains, and foster learning for countries in the early stages of open government data development. This study quantitively extracted the evolution trajectory of open government data based on the main path analysis method and then analysed the underlying motivations. The results show that open government data research went through four main phases and that the open government data movement has spread towards developing countries and smart cities. Different challenges and issues faced by the researchers in each phase drove the evolution of open government data research. Finally, we discuss future directions of open government data research based on our findings and recent development. There is a tendency to create sustainable open government data and smartness by employing artificial intelligence and creating data marketplaces. Points for practitioners Open government data efforts have evolved over the years into a global phenomenon. Countries have learned from each other and more and more efforts are focused on innovating with open government data by stimulating co-creation and using other incentives. The way that data are opened should focus on achieving goals like innovation, participation, transparency and accountability. There is a tendency to create sustainable open government data and smartness by employing artificial intelligence and creating data marketplaces.

Navigating the Ethics of Big Data in Public Health

2019 ◽  
pp. 353-367
Author(s):  
Effy Vayena ◽  
Lawrence Madoff
Keyword(s):  
Public Health ◽  
Big Data ◽  
Credit Card ◽  
Health Sector ◽  
Public Health Research ◽  
Ethical Challenges ◽  
The Public ◽  
Health Community ◽  
Digital Disease Detection ◽  
The Public Sphere

“Big data,” which encompasses massive amounts of information from both within the health sector (such as electronic health records) and outside the health sector (social media, search queries, cell phone metadata, credit card expenditures), is increasingly envisioned as a rich source to inform public health research and practice. This chapter examines the enormous range of sources, the highly varied nature of these data, and the differing motivations for their collection, which together challenge the public health community in ethically mining and exploiting big data. Ethical challenges revolve around the blurring of three previously clearer boundaries: between personal health data and nonhealth data; between the private and the public sphere in the online world; and, finally, between the powers and responsibilities of state and nonstate actors in relation to big data. Considerations include the implications for privacy, control and sharing of data, fair distribution of benefits and burdens, civic empowerment, accountability, and digital disease detection.

scholarly journals Understanding policing demand and deployment through the lens of the city and with the application of big data

Urban Studies ◽  
2021 ◽  
pp. 004209802098100
Author(s):  
Mark Ellison ◽  
Jon Bannister ◽  
Won Do Lee ◽  
Muhammad Salman Haleem
Keyword(s):  
Big Data ◽  
Urban Environment ◽  
Urban Areas ◽  
Multilevel Model ◽  
Open Data ◽  
Front Line ◽  
Criminological Research ◽  
The Social ◽  
Generalised Method Of Moments ◽  
The City

The effective, efficient and equitable policing of urban areas rests on an appreciation of the qualities and scale of, as well as the factors shaping, demand. It also requires an appreciation of the factors shaping the resources deployed in their address. To this end, this article probes the extent to which policing demand (crime, anti-social behaviour, public safety and welfare) and deployment (front-line resource) are similarly conditioned by the social and physical urban environment, and by incident complexity. The prospect of exploring policing demand, deployment and their interplay is opened through the utilisation of big data and artificial intelligence and their integration with administrative and open data sources in a generalised method of moments (GMM) multilevel model. The research finds that policing demand and deployment hold varying and time-sensitive association with features of the urban environment. Moreover, we find that the complexities embedded in policing demands serve to shape both the cumulative and marginal resources expended in their address. Beyond their substantive policy relevance, these findings serve to open new avenues for urban criminological research centred on the consideration of the interplay between policing demand and deployment.

scholarly journals The academic viewpoint on Big data and patient data ownership (as seen in the scientific literature)

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
I Mircheva ◽  
M Mirchev
Keyword(s):  
Public Health ◽  
Big Data ◽  
Medical Research ◽  
Information And Communication Technologies ◽  
Patient Information ◽  
Scientific Literature ◽  
Medical Data ◽  
Patient Data ◽  
Academic Community ◽  
Ethical Challenges

Abstract Background Ownership of patient information in the context of Big Data is a relatively new problem, apparently not yet fully understood. There are not enough publications on the subject. Since the topic is interdisciplinary, incorporating legal, ethical, medical and aspects of information and communication technologies, a slightly more sophisticated analysis of the issue is needed. Aim To determine how the medical academic community perceives the issue of ownership of patient information in the context of Big Data. Methods Literature search for full text publications, indexed in PubMed, Springer, ScienceDirect and Scopus identified only 27 appropriate articles authored by academicians and corresponding to three focus areas: problem (ownership); area (healthcare); context (Big Data). Three major aspects were studied: scientific area of publications, aspects and academicians' perception of ownership in the context of Big Data. Results Publications are in the period 2014 - 2019, 37% published in health and medical informatics journals, 30% in medicine and public health, 19% in law and ethics; 78% authored by American and British academicians, highly cited. The majority (63%) are in the area of scientific research - clinical studies, access and use of patient data for medical research, secondary use of medical data, ethical challenges to Big data in healthcare. The majority (70%) of the publications discuss ownership in ethical and legal aspects and 67% see ownership as a challenge mostly to medical research, access control, ethics, politics and business. Conclusions Ownership of medical data is seen first and foremost as a challenge. Addressing this challenge requires the combined efforts of politicians, lawyers, ethicists, computer and medical professionals, as well as academicians, sharing these efforts, experiences and suggestions. However, this issue is neglected in the scientific literature. Publishing may help in open debates and adequate policy solutions. Key messages Ownership of patient information in the context of Big Data is a problem that should not be marginalized but needs a comprehensive attitude, consideration and combined efforts from all stakeholders. Overcoming the challenge of ownership may help in improving healthcare services, medical and public health research and the health of the population as a whole.

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