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Defining ethical standards for the application of digital tools to population health research

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Defining ethical standards for the application of digital tools to population health research. / Samuel, G.; Derrick, G.

In: Bulletin of the World Health Organization, Vol. 98, No. 4, 17.01.2020, p. 239-244.

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Samuel, G. ; Derrick, G. / Defining ethical standards for the application of digital tools to population health research. In: Bulletin of the World Health Organization. 2020 ; Vol. 98, No. 4. pp. 239-244.

Bibtex

@article{81542d788d7648b38694cac96a02ff30,
title = "Defining ethical standards for the application of digital tools to population health research",
abstract = "There is growing interest in population health research, which uses methods based on artificial intelligence. Such research draws on a range of clinical and non-clinical data to make predictions about health risks, such as identifying epidemics and monitoring disease spread. Much of this research uses data from social media in the public domain or anonymous secondary health data and is therefore exempt from ethics committee scrutiny. While the ethical use and regulation of digital-based research has been discussed, little attention has been given to the ethics governance of such research in higher education institutions in the field of population health. Such governance is essential to how scholars make ethical decisions and provides assurance to the public that researchers are acting ethically. We propose a process of ethics governance for population health research in higher education institutions. The approach takes the form of review after the research has been completed, with particular focus on the role artificial intelligence algorithms play in augmenting decision-making. The first layer of review could be national, open-science repositories for open-source algorithms and affiliated data or information which are developed during research. The second layer would be a sector-specific validation of the research processes and algorithms by a committee of academics and stakeholders with a wide range of expertise across disciplines. The committee could be created as an off-shoot of an already functioning national oversight body or health technology assessment organization. We use case studies of good practice to explore how this process might operate. ",
keywords = "artificial intelligence, assessment method, decision making, disease spread, education, epidemic, ethics, higher education, public health, social media, stakeholder, standard (reference), algorithm, Article, digital forensics, human, influenza, medical ethics, medical research, phenotype, population health, seasonal variation, telecommunication",
author = "G. Samuel and G. Derrick",
year = "2020",
month = jan,
day = "17",
doi = "10.2471/BLT.19.237370",
language = "English",
volume = "98",
pages = "239--244",
journal = "Bulletin of the World Health Organization",
issn = "0042-9686",
publisher = "World Health Organization",
number = "4",

}

RIS

TY - JOUR

T1 - Defining ethical standards for the application of digital tools to population health research

AU - Samuel, G.

AU - Derrick, G.

PY - 2020/1/17

Y1 - 2020/1/17

N2 - There is growing interest in population health research, which uses methods based on artificial intelligence. Such research draws on a range of clinical and non-clinical data to make predictions about health risks, such as identifying epidemics and monitoring disease spread. Much of this research uses data from social media in the public domain or anonymous secondary health data and is therefore exempt from ethics committee scrutiny. While the ethical use and regulation of digital-based research has been discussed, little attention has been given to the ethics governance of such research in higher education institutions in the field of population health. Such governance is essential to how scholars make ethical decisions and provides assurance to the public that researchers are acting ethically. We propose a process of ethics governance for population health research in higher education institutions. The approach takes the form of review after the research has been completed, with particular focus on the role artificial intelligence algorithms play in augmenting decision-making. The first layer of review could be national, open-science repositories for open-source algorithms and affiliated data or information which are developed during research. The second layer would be a sector-specific validation of the research processes and algorithms by a committee of academics and stakeholders with a wide range of expertise across disciplines. The committee could be created as an off-shoot of an already functioning national oversight body or health technology assessment organization. We use case studies of good practice to explore how this process might operate.

AB - There is growing interest in population health research, which uses methods based on artificial intelligence. Such research draws on a range of clinical and non-clinical data to make predictions about health risks, such as identifying epidemics and monitoring disease spread. Much of this research uses data from social media in the public domain or anonymous secondary health data and is therefore exempt from ethics committee scrutiny. While the ethical use and regulation of digital-based research has been discussed, little attention has been given to the ethics governance of such research in higher education institutions in the field of population health. Such governance is essential to how scholars make ethical decisions and provides assurance to the public that researchers are acting ethically. We propose a process of ethics governance for population health research in higher education institutions. The approach takes the form of review after the research has been completed, with particular focus on the role artificial intelligence algorithms play in augmenting decision-making. The first layer of review could be national, open-science repositories for open-source algorithms and affiliated data or information which are developed during research. The second layer would be a sector-specific validation of the research processes and algorithms by a committee of academics and stakeholders with a wide range of expertise across disciplines. The committee could be created as an off-shoot of an already functioning national oversight body or health technology assessment organization. We use case studies of good practice to explore how this process might operate.

KW - artificial intelligence

KW - assessment method

KW - decision making

KW - disease spread

KW - education

KW - epidemic

KW - ethics

KW - higher education

KW - public health

KW - social media

KW - stakeholder

KW - standard (reference)

KW - algorithm

KW - Article

KW - digital forensics

KW - human

KW - influenza

KW - medical ethics

KW - medical research

KW - phenotype

KW - population health

KW - seasonal variation

KW - telecommunication

U2 - 10.2471/BLT.19.237370

DO - 10.2471/BLT.19.237370

M3 - Journal article

VL - 98

SP - 239

EP - 244

JO - Bulletin of the World Health Organization

JF - Bulletin of the World Health Organization

SN - 0042-9686

IS - 4

ER -