Big Data and Dementia: Charting the Route Ahead for Research, Ethics, and Policy

Ienca, Marcello

doi:10.3389/fmed.2018.00013

Cited by 53 publications

(35 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reason for that stems from the fact that these technologies are designed for vulnerable older individuals with physical frailty and cognitive disability, who often lack the capacity to consent to their use. Furthermore, IATs typically collect large volumes of differently structured user data that can be processed at high velocity, rendering the IAT ecosystem a “big data” ecosystem [17, 18]. This large-scale information processing might involve sensitive data including personally identifiable data, such as a person’s medical information or behavioural video-recordings, or might enable retrospective information retrieval from de-identified datasets.…”

Section: Introductionmentioning

confidence: 99%

Ethical concerns with the use of intelligent assistive technology: findings from a qualitative study with professional stakeholders

et al. 2019

Self Cite

View full text Add to dashboard Cite

BackgroundAdvances in artificial intelligence (AI), robotics and wearable computing are creating novel technological opportunities for mitigating the global burden of population ageing and improving the quality of care for older adults with dementia and/or age-related disability. Intelligent assistive technology (IAT) is the umbrella term defining this ever-evolving spectrum of intelligent applications for the older and disabled population. However, the implementation of IATs has been observed to be sub-optimal due to a number of barriers in the translation of novel applications from the designing labs to the bedside. Furthermore, since these technologies are designed to be used by vulnerable individuals with age- and multi-morbidity-related frailty and cognitive disability, they are perceived to raise important ethical challenges, especially when they involve machine intelligence, collect sensitive data or operate in close proximity to the human body. Thus, the goal of this paper is to explore and assess the ethical issues that professional stakeholders perceive in the development and use of IATs in elderly and dementia care.MethodsWe conducted a multi-site study involving semi-structured qualitative interviews with researchers and health professionals. We analyzed the interview data using a descriptive thematic analysis to inductively explore relevant ethical challenges.ResultsOur findings indicate that professional stakeholders find issues of patient autonomy and informed consent, quality of data management, distributive justice and human contact as ethical priorities. Divergences emerged in relation to how these ethical issues are interpreted, how conflicts between different ethical principles are resolved and what solutions should be implemented to overcome current challenges.ConclusionsOur findings indicate a general agreement among professional stakeholders on the ethical promises and challenges raised by the use of IATs among older and disabled users. Yet, notable divergences persist regarding how these ethical challenges can be overcome and what strategies should be implemented for the safe and effective implementation of IATs. These findings provide technology developers with useful information about unmet ethical needs. Study results may guide policy makers with firsthand information from relevant stakeholders about possible solutions for ethically-aligned technology governance.

show abstract

Section: Introductionmentioning

confidence: 99%

Ethical concerns with the use of intelligent assistive technology: findings from a qualitative study with professional stakeholders

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Big data also enables researchers to draw health insights from data sources that are not strictly medical—data from wearable trackers, social media, and Internet searches, for example 4 . Big data research opens new prospects to accelerate health‐related research and potentially elicit breakthroughs that will benefit patients 5 …”

mentioning

confidence: 99%

Big Data, Biomedical Research, and Ethics Review: New Challenges for IRBs

Ferretti

Ienca

Hurst

et al. 2020

Ethics & Human Research

Self Cite

View full text Add to dashboard Cite

T raditionally, human subjects research in the biomedical field engages healthy and sick people as research participants in order to test certain hypotheses about health and disease according to a well-defined study design. The study designs, such as randomized controlled trials and cohort studies, are carefully reviewed by institutional review boards (IRBs)-also known as ethical review committees (ERCs) in some countries. IRBs are committed to protecting the rights and welfare of human subjects recruited to participate in biomedical or behavioral research (including social science research). 1 This approach has long been the standard for biomedical research. Recently, however, biomedical research has begun to pursue opportunities afforded by big data. Big data research relies on large-scale databases, multiplication of data sources, advanced storage capacity, and novel computational tools that allow for high-velocity data analytics. 2 In the biomedical domain, big data trends are enabled by and allow for advances in areas such as whole genome sequencing, brain imaging, mobile health, and digital phenotyping. 3 Today, a large portion of health-related research relies on big data. Big data also enables researchers to draw health insights from data sources that are not strictly medical-data from wearable trackers, social media, and Internet searches, for example. 4 Big data research opens new prospects to accelerate health-related research and potentially elicit breakthroughs that will benefit patients. 5 Big data has been observed to shift the way biomedical researchers design and carry out their studies. 6 This research departs from the traditional research model because it is largely exploratory rather than hypothesis driven. Health-related big data research is based on the acquisition of large amounts of data from multiple and often heterogeneous sources, which are subsequently combined and mined using powerful data analytics tools. This reverse-engineered approach to health-related research allows researchers to extract features and valuable insights from large datasets, without being able to anticipate exactly what the data analysis will find.

show abstract

“…It has been suggested in the literature that for certain diseases, for example dementia, there is a demand for software engineers to design and develop more applications to help patients. It would, therefore, be beneficial for researchers to provide a road map for the investigation of this area (Asghar, Cang, & Yu, ) by improving current predictive, diagnostic and preventive models, optimising resource allocation and delivering more personalised treatments to patients with specific disease trajectories (Ienca, Vayena, & Blasimme, ). In this study, we attempt to fill this gap.…”

Section: Discussionmentioning

confidence: 99%

Big data analytics in health: an overview and bibliometric study of research activity

Galetsi

Katsaliaki

2019

Health Info Libraries J

View full text Add to dashboard Cite

Objective The study presents an overview of the research activity in Big Data Analytics (BDA) in the field of health and demonstrates the existing knowledge through related examples. The objective is to inform health librarians about the nature and magnitude of the technological innovations in health information analysis tools, its influence, and where and how further material could be searched. Methods We performed a bibliometric and co‐citation analysis within a total of 804 papers published between 2000 and 2016 and retrieved from the Web of Science and Scopus databases. Using the NVivo text analysis software, we identified the stakeholders of BDA in health and innovative decision support systems in the field. Results Our findings show a tremendous increase in published papers after 2014. Most of them are relevant to neurology and medical oncology. The stakeholders are clinicians, researchers, patients, administrators, IT specialists, vendors and policymakers. New BDA tools in medicine are mostly developed for disease monitoring purposes while they utilise visualisation to identify disease patterns and statistical analysis of past data for making predictions. Conclusions Health analytics provide a unique opportunity for advancing health information research and medical decision making. It provides health information professionals with new tools in problem‐solving offering new perspectives in prognosis and diagnosis of diseases.

show abstract

Big Data and Dementia: Charting the Route Ahead for Research, Ethics, and Policy

Cited by 53 publications

References 53 publications

Ethical concerns with the use of intelligent assistive technology: findings from a qualitative study with professional stakeholders

Ethical concerns with the use of intelligent assistive technology: findings from a qualitative study with professional stakeholders

Big Data, Biomedical Research, and Ethics Review: New Challenges for IRBs

Big data analytics in health: an overview and bibliometric study of research activity

Contact Info

Product

Resources

About