Safeguarding Data and Building Trust in Technology-Enabled Rehabilitation Counseling: A Review of Governance Models for Fair and Accessible Care

Abstract

Technology-enabled rehabilitation counselling, encompassing telerehabilitation, digital case management, wearable-based self-tracking, and artificial intelligence (AI) decision support, has expanded access to vocational and psychosocial rehabilitation while simultaneously increasing the potential for privacy harm, algorithmic bias, and the erosion of client trust. This structured, PRISMA-informed review synthesises 50 sources to examine how data governance models can safeguard sensitive client data and cultivate trust without sacrificing fairness or accessibility. The review integrates foundational instruments, the WHO Global Strategy on Digital Health 2020–2025 and the WHO guidance on the ethics and governance of AI for health, the UNICEF Responsible Data for Children (RD4C) and Data Governance Fit for Children frameworks, the FAIR data principles, and the regulatory baselines of the GDPR and HIPAA, with an emerging body of privacy-by-design, federated learning, and culturally responsive rehabilitation scholarship. A comparative analysis of the data and data-analysis structures used by WHO and UNICEF, alongside OECD and statutory regimes, is presented across six governance dimensions. Findings indicate that principle-led, lifecycle-oriented models (exemplified by RD4C) outperform compliance-only regimes in terms of equity, cultural responsiveness, and stewardship, while statutory regimes lead in enforceable privacy and accountability. The review proposes an integrated four-pillar governance model: data safeguarding, trust building, equity and access, and governance stewardship, operationalised through composite trust, fairness, privacy, and maturity metrics. Unlike earlier frameworks that address privacy compliance or ethical principles in isolation, the four pillars (data safeguarding, trust building, equity and access, and governance stewardship) are advanced here as one integrated model that links measurable safeguards to trust, fairness, and equity outcomes across the entire data lifecycle. It concludes that fair and accessible technology-enabled rehabilitation counselling depends on embedding measurable safeguards and participatory stewardship across the entire data lifecycle rather than at the point of collection alone.