Big data and health

Snyder, M; Zhou, Wenyu

doi:10.1016/s2589-7500(19)30109-8

Cited by 30 publications

(18 citation statements)

References 10 publications

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“…Traditional in-person clinical trials serve as the cornerstone of modern healthcare advancement. While a pivotal source of evidence generation for advancing clinical knowledge, in-person trials are also costly and time-consuming, typically running for [3][4][5] years from conception to completion, at a cost of millions of dollars per study. These timelines have often meant that promising treatments take years to get to market, which can create unnecessary delays in advancing clinical practice.…”

Section: Introductionmentioning

confidence: 99%

“…These timelines have often meant that promising treatments take years to get to market, which can create unnecessary delays in advancing clinical practice. Additionally, clinical research suffers from several other challenges 1 , 2 including 1) recruiting sufficiently large and diverse cohorts quickly, and 2) tracking day-to-day fluctuations in disease severity that often go undetected in episodic in-clinic evaluations 3,4 . Scientists have recently turned to digital technology 5,6 to address these challenges, hoping to collect real-world evidence 7 from large and diverse populations to track long-term health outcomes and variations in disease trajectories at a fraction of the cost of traditional research 8 .…”

Section: Introductionmentioning

confidence: 99%

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Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants

et al. 2020

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Digital technologies such as smartphones are transforming the way scientists conduct biomedical research using real-world data. Several remotely-conducted studies have recruited thousands of participants over a span of a few months. Unfortunately, these studies are hampered by substantial participant attrition, calling into question the representativeness of the collected data including generalizability of findings from these studies. We report the challenges in retention and recruitment in eight remote digital health studies comprising over 100,000 participants who participated for more than 850,000 days, completing close to 3.5 million remote health evaluations. Survival modeling surfaced several factors significantly associated(P < 1e-16) with increase in median retention time i) Clinician referral(increase of 40 days), ii) Effect of compensation (22 days), iii) Clinical conditions of interest to the study (7 days) and iv)Older adults(4 days). Additionally, four distinct patterns of daily app usage behavior that were also associated(P < 1e-10) with participant demographics were identified. Most studies were not able to recruit a representative sample, either demographically or regionally. Combined together these findings can help inform recruitment and retention strategies to enable equitable participation of populations in future digital health research.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants

et al. 2020

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“…Digital epidemiology is a novel field of science that has been growing rapidly in the past few years. It has been fueled by the increasing availability of data and computing power, as well as by breakthroughs in data analytics [12,13]. The digital health revolution has transformed the collection and analysis of electronic health records, as well as physiological and behavioral measurements at the individual level.…”

Section: The Digital Citizen Science Policy Solutionmentioning

confidence: 99%

“…Apart from obtaining informed consent, citizen scientists should be provided an option to dropout and delete their own data (ie, embedding of open source features as much as possible). However, to truly conduct ethical surveillance, data co-ownership is essential, where citizen scientists are able to participate in data visualization, analysis, and knowledge translation [9,12,13]. Ethical surveillance is paramount as COVID-19 tracking escalates across the world because the elimination of ethical data collection now can potentially open doors for invasive surveillance in the future [23].…”

Section: Data Privacy and Securitymentioning

confidence: 99%

A Global Digital Citizen Science Policy to Tackle Pandemics Like COVID-19

Katapally¹

2020

J Med Internet Res

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The coronavirus disease (COVID-19) pandemic is an extremely complex existential threat that requires cohesive societal effort to address health system inefficiencies. When our society has faced existential crises in the past, we have banded together by using the technology at hand to overcome them. The COVID-19 pandemic is one such threat that requires not only a cohesive effort, but also enormous trust to follow public health guidelines, maintain social distance, and share necessities. However, are democratic societies with civil liberties capable of doing this? Mobile technology has immense potential for addressing pandemics like COVID-19, as it gives us access to big data in terms of volume, velocity, veracity, and variety. These data are particularly relevant to understand and mitigate the spread of pandemics such as COVID-19. In order for such intensive and potentially intrusive data collection measures to succeed, we need a cohesive societal effort with full buy-in from citizens and their representatives. This article outlines an evidence-based global digital citizen science policy that provides the theoretical and methodological foundation for ethically sourcing big data from citizens to tackle pandemics such as COVID-19.

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“…Many experts and initiatives around the world consider big data availability as a key driver to transform health and education sectors in the next decades (e.g. [9][10][11][12][13] ), with far-reaching consequences to how knowledge is produced and shared.…”

Section: Introductionmentioning

confidence: 99%

An active human role is essential in big data-led decisions and data-intensive science

Seghier

2021

F1000Res

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Big data is transforming many sectors, with far-reaching consequences to how decisions are made and how knowledge is produced and shared. In the current move toward more data-led decisions and data-intensive science, we aim here to examine three issues that are changing the way data are read and used. First, there is a shift toward paradigms that involve a large amount of data. In such paradigms, the creation of complex data-led models becomes tractable and appealing to generate predictions and explanations. This necessitates for instance a rethinking of Occam's razor principle in the context of knowledge discovery. Second, there is a growing erosion of the human role in decision making and knowledge discovery processes. Human users’ involvement is decreasing at an alarming rate, with no say on how to read, process, and summarize data. This makes legal responsibility and accountability hard to define. Third, thanks to its increasing popularity, big data is gaining a seductive allure, where volume and complexity of big data can de facto confer more persuasion and significance to knowledge or decisions that result from big-data-based processes. These issues call for an active human role by creating opportunities to incorporate, in the most unbiased way, human expertise and prior knowledge in decision making and knowledge production. This also requires putting in place robust monitoring and appraisal mechanisms to ensure that relevant data is answering the right questions. As the proliferation of data continues to grow, we need to rethink the way we interact with data to serve human needs.

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Big data and health

Cited by 30 publications

References 10 publications

Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants

Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants

A Global Digital Citizen Science Policy to Tackle Pandemics Like COVID-19

An active human role is essential in big data-led decisions and data-intensive science

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