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Improving access to health information and territorial resources can be a way to help strengthen health-related quality of life perception in disadvantaged communities. We argue that, in countries facing diverse economic and social disparities and challenges, technology can be leveraged to improve communities' access to evidence-based, up-to-date, comprehensive, and culturally appropriate supportive territorial resources, thereby improving individuals' quality of life, especially for those living with one or more chronic diseases. This paper discusses the benefits of introducing and designing a user-friendly, relevant, and effective web-based technology to broaden patients' access to healthcare resources and tailored health information, supporting community-based quality of life transformation.

Advances in computer communication technology have enabled the rapid growth of e-health services for delivering healthcare, such as facilitating online consent and data sharing between patients and health professionals. Developing a patient-centric healthcare system is challenging because by necessity, it should be secure, reliable, and resilient to cyber threats, whilst remaining user-friendly. Key to any development aiming for a refined proof-of-concept (PoC) system is the pursuit of comprehensive public system testing and evaluation. This paper focuses on the methodology and results obtained from the participatory approach adopted by the EU H2020 project Serums to evaluate and demonstrate the effectiveness of a smart healthcare system based on emergent technologies like blockchain, data lake, and multi-factor authentication. We discuss the challenges faced by remote PoC system evaluations with end-users as a consequence of the Covid-19 pandemic.

To facilitate personalised healthcare provision across Europe, we envision solutions that enable the secure integration and sharing of medical health records. These solutions should address privacy concerns, such as granular access control to personal data, establishing what should be accessible when and by whom, whilst complying with collective regulatory frameworks such as the European General Data Protection Regulation (GDPR) and adhering to international standards on how to manage information security. The proposed healthcare system design integrates technologies such as blockchain and scalable data lakes with adequate system routines to guarantee the secure access of confidential data. In this paper, we present the essential architectural components for the secure integration of medical records in a blockchain-based platform. We present a patient-centric data retrieval approach which incorporates a structured format to compose access rules.

The potential of healthcare systems worldwide is expanding as new medical devices and data sources are regularly presented to healthcare providers which could be used to personalise, improve and revise treatments further. However, there is presently a large gap between the data collected, the systems that store the data, and any ability to perform big data analytics to combinations of such data. This paper suggests a novel approach to integrate data from multiple sources and formats, by providing a uniform structure to the data in a healthcare data lake with multiple zones reflecting how refined the data is: from raw to curated when ready to be consumed or used for analysis. The integration further requires solutions that can be proven to be secure, such as patient-centric data sharing agreements (smart contracts) on a blockchain, and novel privacy-preserving methods for extracting metadata from data sources, originally derived from partially-structured or from completely unstructured data. Work presented here is being developed as part of an EU project with the ultimate aim to develop solutions for integrating healthcare data for enhanced citizen-centred care and analytics across Europe.

Managing the use of multiple medicines, also known as polypharmacy, is a challenge for physicians, pharmacists and patients alike, and is a particular concern for patients with multiple chronic conditions (aka multimorbidity). Patients with multimorbidity are often required to take a considerable number of medications for their different ongoing conditions, and managing/revising these medications effectively is a challenge. There is a need to periodically rearrange drugs taking into account patient's preferences and avoiding adverse drug reactions. We present an incremental, constraint solver based framework for a clinical decision support system that makes it possible to check drug prescriptions using information from multiple sources, including a constraint database and patient records. We illustrate how it can be used to manage clinical conditions while reducing polypharmacy problems and undesired side effects in a patient-centric approach.