ABSTRACT
The rapid advancement of clinical research has resulted into numerous therapeutic options currently available for most of the diseases. During the patient therapeutic journey, many health-related decisions are necessary requiring patients to choose between the potential health benefits of an intervention, versus the countervailing risk of serious adverse health outcomes. Studies focusing exactly on those patient preferences aim to elicit preferences with the common objective to generate information that facilitates comparing the importance of attributes of interest. The world has experienced a dramatic change in patient's preferences during the pandemic. Despite the importance of patient preference studies in healthcare decision making, there is a lack of effective storage and accessibility of relevant data for wider use. In this paper the authors present the design of a platform to systematically collect, curate, annotate, index, synthesize and make available pertinent information of patient preference studies so that they can be further exploited by decision support tools. © 2021 IEEE.
ABSTRACT
During the burst of the coronavirus pandemic, in early-midst 2020, public health authorities worldwide considered appropriate identification, isolation and contact tracing as the most appropriate strategy for infection containment. This work presents an outbreak response tool, designed for public health authorities to effectively track suspect, probable and confirmed incidence cases in a pandemic by means of a mobile app used by citizens to provide immediate feedback. It is developed based on an already existing personal health record app, which has been extended to properly accommodate specific needs that emerged during the crisis. The aim is to better support human tracers and should not be confused with proximity tracking apps. It respects safety and security regulations, while at the same time it conforms to international standards and widely accepted medical protocols. Issues relevant to privacy concerns, and interoperability with available patient registries and data analytics tools are also examined to better support public healthcare delivery and contain the spread of the infection.
ABSTRACT
We protect the community. We protect ourselves. We decongest the health system. We stay safe in COVID-19. One of the many responses to the global call against the world pandemic of COVID-19 resulted in "Safe in COVID-19", an electronic platform developed by the Institute of Computer Science of the Foundation for Research and Technology - Hellas (FORTH-ICS), which is intended for tracing suspect, probable and confirmed incidence cases.
ABSTRACT
The ongoing coronavirus pandemic, is affecting the lives of millions of people, while changing our society by establishing new norms for social life, business, and traveling. The digital health domain has already tried to respond to the pandemic challenges, by the rapid development and release of mobile apps aiming to “flatten the curve” of the increasing number of COVID-19 cases. In this paper, starting from our own developed app to support citizens staying “Safe in COVID-19”, we present our vision on how semantics and data management could highly contribute, along with personal health apps, to the secondary usage of available data in order to support effective disease management, prediction and increase the collective knowledge on the disease. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).