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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.

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.

Funding: NHS Lothian, the DATALab, and the EU H2020 project Serums: Securing Medical Data in Smart Patient-Centric Healthcare Systems(grant code:826278). The Edinburgh Cancer Centre (ECC) contains NHS Lothian cancer patient data from multiple resources. However, the lack of proxy between numerous scattered resources hinders the capability to use the information collected in a useful way. ECC data is very varied and includes patient characteristics (e.g., age, weight, height, gender), information on diagnosis (e.g., stage, site, comorbidities) and treatments (e.g., surgery, chemotherapy, radiotherapy). The visualisation of a fraction of ECC data in the form of a patient timeline can aid and enhance the process of observing and identifying the overall condition of the patient, as well as understand how it may compare with cohorts of patients with similar characteristics. We have previously developed machine learning models for predicting treatment outcomes for breast cancer patient data that have undergone chemotherapy. In this paper, we describe, examine, and propose a solution to connect all these aspects and provide a bridge for several resources. This will make it easier for clinicians and other healthcare professionals to support service planning, deliver better quality of care and consequently improve service outcome within NHS Lothian. Publisher PDF

Healthcare involves complex decision making from planning to resource management. Resources in hospitals are usually allocated by experienced managers,however, due to an inherent process complexity, decisions are surrounded by uncertainties, variabilities, and constraints. Information Systems must be robust enough to provide support to stakeholders, capable of controlling and support work flows. The present work explores the required synergy when combining business processes with discrete event simulation. The objective is to estimate performance indices and address capacity management of a surgical center as a case study. Postprint

In UML 2.0 sequence diagrams have been considerably extended and are now fundamentally better structured. Interactions in sequence diagrams can be structured using so-called interaction fragments, including alt (alternative behaviour), par (parallel behaviour), neg (forbidden behaviour), assert (mandatory behaviour) and ref (reference another diagram). The operator ref in particular greatly improves the way diagrams can be decomposed. In previous work we have given a semantics to a subset of sequence diagrams using labelled event structures, a true-concurrent model that naturally captures alternative and parallel behaviour. In this paper, we expand that work to address refinement and show how to obtain a refined model by means of a powerful categorical construction over two categories of labelled event structures. The underlying motivation for this work is reasoning and verification of complex scenario-based inter-object behavioural models. We conclude the paper with a discussion on future work.

This research is funded by the EU H2020 project SERUMS (grant 826278). We thank all project partners for contributions on the overall system development and PoC system design and evaluation. 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. Publisher PDF

Funding information: This research has been partially supported by the EU Horizon 2020 Grant 826278 "Securing Medical Data in Smart Patient-Centric Healthcare Systems" (Serums) , and the Research and Innovation Foundation (Project DiversePass: COMPLEMENTARY/0916/0182). This paper proposes a user-adaptable and personalized authentication paradigm for healthcare organizations, which anticipates to seamlessly reflect patients’ episodic and autobiographical memories to graphical and textual passwords aiming to improve the security strength of user-selected passwords and provide a positive user experience. We report on a longitudinal study that spanned over three years in which three public European healthcare organizations participated in order to design and evaluate the aforementioned paradigm. Three studies were conducted (n=169) with different stakeholders: i) a verification study aiming to identify existing authentication practices of the three healthcare organizations with diverse stakeholders (n=9); ii) a patient-centric feasibility study during which users interacted with the proposed authentication system (n=68); and iii) a human guessing attack study focusing on vulnerabilities among people sharing common experiences within location-aware images used for graphical passwords (n=92). Results revealed that the suggested paradigm scored high with regards to users’ likeability, perceived security, usability and trust, but more importantly it assists the creation of more secure passwords. On the downside, the suggested paradigm introduces password guessing vulnerabilities by individuals sharing common experiences with the end-users. Findings are expected to scaffold the design of more patient-centric knowledge-based authentication mechanisms within nowadays dynamic computation realms. Postprint

Funding: This research is funded by the EU H2020 project Serums (Securing Medical Data in Smart Patient-Centric Healthcare Systems), grant code 826278. With an increasing trend in personalised healthcare provision across Europe, we need solutions to enable the secure transnational sharing of medical records, establishing granular access rights to personal patient data. Access rules can establish what should be accessible by whom for how long, and comply with collective regulatory frameworks, such as the European General Data Protection Regulation (GDPR). The challenge is to design and implement such systems integrating novel technologies like Blockchain and Data Lake to enhance security and access control. The blockchain module must deal with adequate policies and algorithms to guarantee that no data leaks occur when authorising data retrieval requests. The data lake module tackles the need for an efficient way to retrieve potential granular data from heterogeneous data sources. In this paper, we define a patient-centric authorisation approach, incorporating a structured format for composing access rules that enable secure data retrieval and automatic rules conflict checking. Postprint