1 research outputs found
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