{"title":"Digital healthcare public health","authors":"M. Gulliford, E. Jessop, L. Yardley","doi":"10.1093/oso/9780198837206.003.0015","DOIUrl":null,"url":null,"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.","PeriodicalId":100513,"journal":{"name":"Evidence-based Healthcare and Public Health","volume":"113 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Evidence-based Healthcare and Public Health","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/oso/9780198837206.003.0015","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.