Digital healthcare public health
Digital healthcare public health
New digital technologies are having important impacts on the practice of public health and the organization and delivery of healthcare. Developments in information technology ensure that public health information is now available in more timely and accessible formats; data linkage has enriched public health information by making it possible to analyse multiple data sources simultaneously; and the use of smart devices and smart cards is generating even larger data resources that may be utilized for public health benefit. Computationally intensive approaches, derived from machine learning and artificial intelligence research, can be employed to develop algorithms that may efficiently automate healthcare-related tasks that previously relied on human analytical capabilities. Prediction modelling and risk stratification are being developed to promote precision public health. Increasing population coverage, with smartphones and other smart devices, makes it possible to deliver health-related interventions remotely, blurring the distinction between healthcare and public health. The availability of social media makes the exchange of knowledge and opinion more open, but this may also contribute to the propagation of false information that may be detrimental to public health. Public health needs to embrace and understand these developments in order to be at the forefront in harnessing these new technologies to improve population health and reduce inequalities. This must be accompanied by awareness of some of the ethical challenges of big-data analysis, the potential limitations of new analytical techniques, the relevance of behavioural science in understanding the human–machine interface, and the importance of critical evaluation in an era of rapid change.
Artificial intelligence, Digital intervention, Digital public health, Machine learning, Precision public health, Prediction modelling, Social media
187-200
Arnold; Oxford University Press
Gulliford, Martin
0895f866-d8d4-41f9-bef8-7adcf8590c08
Jessop, Edmund
859c961a-a137-4e88-8444-986145ec5137
Yardley, Lucy
64be42c4-511d-484d-abaa-f8813452a22e
17 September 2020
Gulliford, Martin
0895f866-d8d4-41f9-bef8-7adcf8590c08
Jessop, Edmund
859c961a-a137-4e88-8444-986145ec5137
Yardley, Lucy
64be42c4-511d-484d-abaa-f8813452a22e
Gulliford, Martin, Jessop, Edmund and Yardley, Lucy
(2020)
Digital healthcare public health.
In,
Healthcare Public Health: Improving health services through population science.
Arnold; Oxford University Press, .
(doi:10.1093/oso/9780198837206.003.0015).
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Book Section
Abstract
New digital technologies are having important impacts on the practice of public health and the organization and delivery of healthcare. Developments in information technology ensure that public health information is now available in more timely and accessible formats; data linkage has enriched public health information by making it possible to analyse multiple data sources simultaneously; and the use of smart devices and smart cards is generating even larger data resources that may be utilized for public health benefit. Computationally intensive approaches, derived from machine learning and artificial intelligence research, can be employed to develop algorithms that may efficiently automate healthcare-related tasks that previously relied on human analytical capabilities. Prediction modelling and risk stratification are being developed to promote precision public health. Increasing population coverage, with smartphones and other smart devices, makes it possible to deliver health-related interventions remotely, blurring the distinction between healthcare and public health. The availability of social media makes the exchange of knowledge and opinion more open, but this may also contribute to the propagation of false information that may be detrimental to public health. Public health needs to embrace and understand these developments in order to be at the forefront in harnessing these new technologies to improve population health and reduce inequalities. This must be accompanied by awareness of some of the ethical challenges of big-data analysis, the potential limitations of new analytical techniques, the relevance of behavioural science in understanding the human–machine interface, and the importance of critical evaluation in an era of rapid change.
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Published date: 17 September 2020
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© Oxford University Press.
Keywords:
Artificial intelligence, Digital intervention, Digital public health, Machine learning, Precision public health, Prediction modelling, Social media
Identifiers
Local EPrints ID: 508684
URI: http://eprints.soton.ac.uk/id/eprint/508684
PURE UUID: 87b1e366-108b-4ad2-9830-e039bcaa183e
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Date deposited: 29 Jan 2026 17:48
Last modified: 31 Jan 2026 03:07
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Author:
Martin Gulliford
Author:
Edmund Jessop
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