Blockchain and AI in Tanzanian EHR systems: perceptions, challenges, and solutions

Abstract

Purpose This study aims to explore the integration of blockchain and artificial intelligence (AI) into Tanzania’s electronic health record (EHR) systems. It examines stakeholder perceptions, identifies key challenges and proposes strategies to facilitate successful adoption in a resource-constrained health-care setting. Design/methodology/approach A qualitative case study approach was used, involving semi-structured interviews with 25 stakeholders, including health-care practitioners, policymakers and technology experts. Thematic analysis was used to identify perceptions, challenges and potential solutions for blockchain and AI integration in Tanzanian EHR systems. Findings Stakeholders expressed strong optimism (72%) about blockchain’s potential to enhance data security and AI’s role in predictive analytics (60%). However, critical barriers emerged, including infrastructure deficits (reported by 80% of rural participants), resistance to change (72%) and regulatory ambiguities (40%). Proposed solutions emphasise phased implementation, public–private partnerships and policy reforms adapted to Tanzania’s health-care ecosystem. Originality/value This study contributes to the limited literature on blockchain and AI adoption in low-resource health-care settings. Unlike prior studies that focus on high-income countries, this research provides a contextual understanding of the technological, organisational and regulatory factors influencing adoption in Tanzania. The insights offer policymakers, health-care leaders and technology developers’ practical recommendations to drive digital health transformation.

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