Conceptual analysis of DIGS model in low- and middle-income countries identifies four structural gaps

BackgroundIntegrating biobanking and genomic research into health systems in low- and middle-income countries (LMICs) provides considerable opportunity to advance precision medicine and promote global health equity. However, persistent structural disconnect exists between the physical infrastructure for biospecimen collection and digital frameworks required to generate, manage, and share associated data. This biospecimen-data divide excludes LMIC populations from the benefits of genomic research and increases community vulnerability to extractive data policies and dependency on high-income-country (HIC) partners.ObjectiveThis conceptual analysis aims to: (1) deconstruct the biospecimen-data divide by describing its core elements and interrelations; (2) introduce the DIGS (Digital Infrastructure, Interoperability, Governance, and Skills) Model as a framework for reconceptualizing biobanking as integrated digital enterprise; (3) examine how decisions regarding digital infrastructure may reproduce or reduce health inequities in LMIC settings.MethodsA critical scoping review was conducted using the Arksey and O’Malley framework and reported in line with PRISMA-ScR. Five databases—PubMed, Scopus, Web of Science, Embase, and Global Index Medicus—were systematically searched, yielding 2,390 records. After deduplication (n = 880), title and abstract screening (n = 1510), and full-text review (n = 296), 154 studies were included in the final synthesis. These included peer-reviewed articles, policy documents, and grey literature on biobanking, digital health infrastructure, and data governance in LMIC contexts.ResultsFour interdependent structural gaps underpin the biospecimen-data divide: (1) infrastructure gap, reflecting constraints on data generation and flow; (2) interoperability gap, arising from incompatible systems that create data silos; (3) human-capital gap, marked by limited availability of personnel with combined laboratory and data stewardship expertise; (4) governance gap, defined by weak ethical and regulatory structures. Together, these gaps reveal deeper asymmetries between HICs and LMICs. The DIGS Model reframes biobanking as a cyclical, equitable process requiring coordinated investment across all four dimensions.DiscussionThe DIGS Model challenges the prevailing infrastructure-first paradigm, which prioritizes physical assets over digital capabilities. It advances partnership models that center LMIC leadership in data governance, redefining sustainability as the cultivation of local capacity to generate, interpret, and control data. The framework offers researchers, funders, and policymakers a shared diagnostic tool for bridging the biospecimen-data divide without reinforcing existing dependencies.