Validation of design artefacts for blockchain-enabled precision healthcare as a service.

Healthcare systems around the globe are currently experiencing a rapid wave of digital disruption. Current research in applying emerging technologies such as Big Data (BD), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Augmented Reality (AR), Virtual Reality (VR), Digital Twin (DT), Wearable Sensor (WS), Blockchain (BC) and Smart Contracts (SC) in contact tracing, tracking, drug discovery, care support and delivery, vaccine distribution, management, and delivery. These disruptive innovations have made it feasible for the healthcare industry to provide personalised digital health solutions and services to the people and ensure sustainability in healthcare. Precision Healthcare (PHC) is a new inclusion in digital healthcare that can support personalised needs. It focuses on supporting and providing precise healthcare delivery. Despite such potential, recent studies show that PHC is ineffectual due to the lower patient adoption in the system. Anecdotal evidence shows that people are refraining from adopting PHC due to distrust. This thesis presents a BC-enabled PHC ecosystem that addresses ongoing issues and challenges regarding low opt-in. The designed ecosystem also incorporates emerging information technologies that are potential to address the need for user-centricity, data privacy and security, accountability, transparency, interoperability, and scalability for a sustainable PHC ecosystem. The research adopts Soft System Methodology (SSM) to construct and validate the design artefact and sub-artefacts of the proposed PHC ecosystem that addresses the low opt-in problem. Following a comprehensive view of the scholarly literature, which resulted in a draft set of design principles and rules, eighteen design refinement interviews were conducted to develop the artefact and sub-artefacts for design specifications. The artefact and sub-artefacts were validated through a design validation workshop, where the designed ecosystem was presented to a Delphi panel of twenty-two health industry actors. The key research finding was that there is a need for data-driven, secure, transparent, scalable, individualised healthcare services to achieve sustainability in healthcare. It includes explainable AI, data standards for biosensor devices, affordable BC solutions for storage, privacy and security policy, interoperability, and usercentricity, which prompts further research and industry application. The proposed ecosystem is potentially effective in growing trust, influencing patients in active engagement with real-world implementation, and contributing to sustainability in healthcare

Soft systems modelling of design artefacts for blockchain-enabled precision healthcare as a service

Precision Healthcare (PHC) is a disruptive innovation in digital health that can support mass customisation. However, despite the potential, recent studies show that PHC is ineffectual due to the lower patient adoption into the system. This paper presents a Blockchain-enabled PHC ecosystem that addresses ongoing issues and challenges regarding low opt-in rates. Soft Systems Methodology was adopted to create and validate UML design artefacts. Research findings report that there is a need for data-driven, secure, transparent, scalable, individualised and precise medicine for the sustainability of healthcare and suggests further research and industry application of explainable AI, data standards for biosensor devices, affordable Blockchain solutions for storage, privacy and security policy, interoperability, and user-centricity

A systematic review of emerging information technologies for sustainable data-centric health-care

© 2021 Elsevier B.V. Background: Of the Sustainable Development Goals (SDGs), the third presents the opportunity for a predictive universal digital healthcare ecosystem, capable of informing early warning, assisting in risk reduction and guiding management of national and global health risks. However, in reality, the existing technology infrastructure of digital healthcare systems is insufficient, failing to satisfy current and future data needs. Objective: This paper systematically reviews emerging information technologies for data modelling and analytics that have potential to achieve Data-Centric Health-Care (DCHC) for the envisioned objective of sustainable healthcare. The goal of this review is to: 1) identify emerging information technologies with potential for data modelling and analytics, and 2) explore recent research of these technologies in DCHC. Findings: A total of 1619 relevant papers have been identified and analysed in this review. Of these, 69 were probed deeply. Our analysis found that the extant research focused on elder care, rehabilitation, chronic diseases, and healthcare service delivery. Use-cases of the emerging information technologies included providing assistance, monitoring, self-care and self-management, diagnosis, risk prediction, well-being awareness, personalized healthcare, and qualitative and/or quantitative service enhancement. Limitations identified in the studies included vendor hardware specificity, issues with user interface and usability, inadequate features, interoperability, scalability, and compatibility, unjustifiable costs and insufficient evaluation in terms of validation. Conclusion: Achievement of a predictive universal digital healthcare ecosystem in the current context is a challenge. State-of-the-art technologies demand user centric design, data privacy and protection measures, transparency, interoperability, scalability, and compatibility to achieve the SDG objective of sustainable healthcare by 2030