Proposed EthMed Platform: An AI-Enabled, Blockchain-Governed and Digital-Twin-Ready Health Platform for Ethnic Minority Communities in Remote and Hard-To-Reach Areas

Abstract

Objectives: Access to quality healthcare remains limited for ethnic minority populations in remote and mountainous areas because of geographical barriers, shortages of healthcare resources, fragmented medical information, limited connectivity, and linguistic or cultural barriers. The objective of this study was to design and justify EthMed, an integrated digital health platform that combines community screening, interoperable electronic health records (EHRs), telemedicine, artificial intelligence (AI), permissioned blockchain governance and a staged digital-health-twin pathway for hard-to-reach populations.
Methods: A design-science research approach was used to transform requirements from the target setting into a modular platform artefact and an evaluation framework. The methodology comprised requirement elicitation, requirement-to-design traceability, architecture design, technology selection, prototype demonstration, technical and clinical-workflow evaluation, and staged validation. The proposed pilot includes at least 3,000 participants; this number is treated as an initial development and validation cohort rather than as a universal sample size for every disease model. AI development uses prespecified targets, leakage-controlled data partitioning, internal validation, calibration, subgroup fairness assessment, and external/prospective validation before clinical use. The blockchain layer is limited to consent, provenance and audit functions, while clinical data remain off-chain. The digital-twin roadmap begins with risk twins and requires longitudinal state updating, uncertainty estimation and validation before physiology-twin functions are introduced.
Results: The resulting six-layer architecture integrates community access, interoperability, secure storage, AI analytics, telemedicine/referral coordination and governance. Priority pathways include hypertension, diabetes, cardiovascular and stroke risk, chronic respiratory and kidney disease, malnutrition and anaemia, maternal-child health, and selected musculoskeletal and prolonged-fatigue pathways. The design establishes explicit technical, clinical, user, fairness and governance evaluation criteria and a traceable pathway from community requirements to platform functions.
Conclusion: EthMed provides a research and implementation framework for equitable digital health in remote ethnic minority communities. Its principal contribution is not a single algorithm or technology, but a governed ecosystem linking longitudinal community data, AI-assisted risk stratification, telemedicine, secure interoperability and a progressively validated digital-twin architecture. The platform remains a conceptual artefact pending prospective clinical, ethical and implementation validation.