We set out to test whether decision aids can support self-isolation. Self-isolation is a vital element of efforts to contain COVID-19. This can pave the way toward more informed and engaged patients and citizens by delivering personalized healthcare. We discuss a layered approach for the design of the proposed system leading to an enhanced, safer clinical decision-making system. Each autonomous agent is responsible for instantiating key processes, such as user authentication and authorization, smart contracts, and knowledge graph generation through data integration among the participating stakeholders in the network. We discuss how major concerns in the health industry, i.e., trust, security and scalability, can be addressed by transitioning from existing models to convergence of the three technologies – blockchain, agent-based modeling, and knowledge graph in a decentralized ecosystem. This will enable us to augment existing blockchain-based EHR Systems. In this article, we discuss a data sharing and knowledge integration framework through autonomous agents with blockchain for implementing Electronic Health Records (EHR). We finally discuss how our solution relieves some barriers of existing systems for senior users and how it might improve appointment scheduling in online environments. We evaluate our final prototype with a user-centered approach by means of a system usability scale. We settle on an interface where users can directly find and book an appointment, without complex navigation through menus and with no COVID-19 related data. We discuss the strengths and limitations of our alternatives and decide upon reviewing the existing systems to design a low-fidelity prototype of an application centered on appointment scheduling. Particularly, we focus on designing an interface that will facilitate the appointment booking process when trying to find a vaccine. We therefore present three design alternatives to improve the accessibility of mobile phone applications for senior citizens. Overall, our results demonstrate the value of pre-testing contact-tracing apps from a behavioural perspective to boost uptake, trust and participation.ĭespite the growing number of smartphone users among the elderly, few design alternatives are developed to improve the usability issues they might face. We also found minor beneficial effects of restructuring the exposure notification, but did not find any significant differences between two different types of goal-framing, other than a subtle effect on how the exposure notification is interpreted. This finding fed into the final version of the app released in July 2020. Including additional assurances regarding the privacy of users' data in the app successfully lowered participants' privacy concerns and boosted engagement. Almost one in five participants mentioned privacy concerns in relation to their likelihood of downloading the app.
The experimental manipulations focused on three broad areas: (i) the level of privacy assurance provided in the app, (ii) the goal-framing of the purpose of the app and (iii) the structuring of the exposure notification received by users if they are recorded as a close contact. They responded to an online survey while downloading and using the app on their phones in real time.
#CORONA TRACKER APP TRIAL#
Participants were randomised to receive different versions of a trial app. The study was funded by the Department of Health and run in cooperation with the app's developers, NearForm. We report a study that behaviourally pre-tested COVID Tracker, Ireland's contact-tracing app, prior to its launch with a large sample of smartphone users. Contact-tracing mobile phone apps have the potential to play a role in controlling the spread of COVID-19, but their success hinges on widespread uptake by the public.