数字医疗公共卫生

M. Gulliford, E. Jessop, L. Yardley
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引用次数: 0

摘要

新的数字技术正在对公共卫生实践以及医疗保健的组织和提供产生重要影响。信息技术的发展确保公共卫生信息现在以更及时和易于获取的形式提供;数据联系使同时分析多个数据源成为可能,从而丰富了公共卫生信息;智能设备和智能卡的使用正在产生更大的数据资源,可用于公共卫生利益。源自机器学习和人工智能研究的计算密集型方法可以用于开发算法,这些算法可以有效地自动化以前依赖于人类分析能力的医疗保健相关任务。正在开发预测模型和风险分层,以促进精确的公共卫生。随着智能手机和其他智能设备的普及,人口覆盖率不断提高,从而可以远程提供与健康相关的干预措施,模糊了医疗保健和公共卫生之间的区别。社交媒体的可用性使知识和意见的交流更加开放,但这也可能有助于传播可能有害于公众健康的虚假信息。公共卫生需要接受和理解这些发展,以便在利用这些新技术改善人口健康和减少不平等方面走在前列。这必须伴随着对大数据分析的一些伦理挑战的认识,新分析技术的潜在局限性,行为科学在理解人机界面方面的相关性,以及在快速变化的时代批判性评估的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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