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Patient-centred blockchain artefact for ante and postnatal health records management
Dissertation   Open access

Patient-centred blockchain artefact for ante and postnatal health records management

Khulekani Sibanda
Doctor of Philosophy (PHD), University of Johannesburg
2026
Handle:
https://hdl.handle.net/10210/521440

Abstract

Antenatal and postnatal healthcare systems in many resource-constrained contexts remain dependent on fragmented, paper-based records that undermine the continuity of care. The “maternal card”, which captures information about a pregnant woman, serves as a fragile boundary object that enrols women as passive data carriers, often disrupting information flows during referrals. This thesis investigates how Non-Fungible Tokens (NFTs) and blockchain technology can address these systemic deficiencies and strengthen patient agency. The context for this study is a resource-constrained setting in Zimbabwe. A Design Science Research Methodology (DSRM), supported by a multi-method design, guided this study. To ground the research and identify gaps, this study employed two systematic literature reviews and a bibliometric analysis of Industry 4.0 technologies in maternal healthcare and NFTs in healthcare. A qualitative case study, framed through an Actor-Network Theory (ANT) lens, analysed the problem across the four moments of translation and revealed that record fragmentation, disconnected clinics, and absent patient agency are deeply organisational rather than purely technical challenges. An NFT-based artefact was designed to restore patient agency, reduce data duplication, and ensure continuity of care across disconnected clinics. The artefact reconfigures the actor-network from institution-centric to patient-centric governance via smart contracts. An empirical evaluation of the artefact, involving 41 frontline healthcare workers (nurses and midwives) and using the Task-Technology Fit (TTF) framework, revealed that in low-trust and unstable healthcare environments, such as maternal healthcare, users prioritise Security, Immutability, and Control (Technology Characteristics) as the dominant predictors of fit (β=0.674, p <0.001), whereas routine task alignment (Task Characteristics) was non-significant...
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