Study Data from University of Oxford Provide New Insights into Personalized Medicine (Covid-19 What Have We Learned? the Rise of Social Machines and Connected Devices In Pandemic Management Following the Concepts of Predictive, Preventive and …)

2020 AUG 26 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Current study results on Drugs and Therapies - Personalized Medicine have been published. According to news reporting originating in Oxford, United Kingdom, by NewsRx journalists, research stated, “Review, compare and critically assess digital technology responses to the COVID-19 pandemic around the world. The specific point of interest in this research is on predictive, preventive and personalized interoperable digital healthcare solutions.”

Financial supporters for this research include UK EPSRC [PETRAS project: RETCON], UK EPSRC [PETRAS project: CRatE], Cisco Research Centre.

The news reporters obtained a quote from the research from the University of Oxford, “This point is supported by failures from the past, where the separate design of digital health solutions has led to lack of interoperability. Hence, this review paper investigates the integration of predictive, preventive and personalized interoperable digital healthcare systems. The second point of interest is the use of new mass surveillance technologies to feed personal data from health professionals to governments, without any comprehensive studies that determine if such new technologies and data policies would address the pandemic crisis. This is a review paper. Two approaches were used: A comprehensive bibliographic review with R statistical methods of the COVID-19 pandemic in PubMed literature and Web of Science Core Collection, supported with Google Scholar search. In addition, a case study review of emerging new approaches in different regions, using medical literature, academic literature, news articles and other reliable data sources. Most countries’ digital responses involve big data analytics, integration of national health insurance databases, tracing travel history from individual’s location databases, code scanning and individual’s online reporting. Public responses of mistrust about privacy data misuse differ across countries, depending on the chosen public communication strategy. We propose predictive, preventive and personalized solutions for pandemic management, based on social machines and connected devices. Solutions The proposed predictive, preventive and personalized solutions are based on the integration of IoT data, wearable device data, mobile apps data and individual data inputs from registered users, operating as a social machine with strong security and privacy protocols. We present solutions that would enable much greater speed in future responses. These solutions are enabled by the social aspect of human-computer interactions (social machines) and the increased connectivity of humans and devices (Internet of Things). Inadequate data for risk assessment on speed and urgency of COVID-19, combined with increased globalization of human society, led to the rapid spread of COVID-19. Despite an abundance of digital methods that could be used in slowing or stopping COVID-19 and future pandemics, the world remains unprepared, and lessons have not been learned from previous cases of pandemics.”

According to the news reporters, the research concluded: “We present a summary of predictive, preventive and personalized digital methods that could be deployed fast to help with the COVID-19 and future pandemics.”

For more information on this research see: Covid-19 What Have We Learned? the Rise of Social Machines and Connected Devices In Pandemic Management Following the Concepts of Predictive, Preventive and Personalized Medicine. The EPMA Journal, 2020;():. The EPMA Journal can be contacted at: Springer International Publishing Ag, Gewerbestrasse 11, Cham, Ch-6330, Switzerland.

Our news correspondents report that additional information may be obtained by contacting Petar Radanliev, University of Oxford, Dept. of Engineering Sciences, Oxford, United Kingdom. Additional authors for this research include David De Roure, Rob Walton, Max Van Kleek, Rafael Mantilla Montalvo, Omar Santos, La’Treall Maddox and Stacy Cannady.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1007/s13167-020-00218-x. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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