JMIR Publications

ABSTRACT

Secondary use is now ordinary in data science health research. Electronic health records collected for care become prediction tools and generative-AI inputs; imaging archives become foundation-model corpora; genomic datasets become resources for polygenic risk scores; and legacy biospecimens become renewable cell lines. Governance has responded by emphasizing verifiable instruments: provenance logs, repository approvals, broad-consent forms, data-use agreements, model cards, records of processing, and locality-preserving architectures. These instruments are necessary, but they are not sufficient. We define ethical continuity as the persistence of normatively relevant relationships between the original conditions of data generation or material collection and subsequent downstream uses, such that current uses remain justifiable in light of the expectations, permissions, meanings, and relational obligations present at entrustment. We define the Continuity Trap as a review-stage governance error in which a salient signal of continuity in one domain is treated as sufficient evidence of ethical continuity overall, causing inquiry into other continuity domains to close prematurely. The trap is not ordinary noncompliance, ethics creep, or a demand for universal re-review. It is a cross-domain inference error. We operationalize ethical continuity across provenance, semantics, authorization, and relational standing. We then apply the framework to consent and non-consent settings, including public health surveillance, immunization registries, syndromic surveillance, wastewater surveillance, polygenic risk scores, induced pluripotent stem cells, federated learning, and health-related large language models. The policy implication is trigger-based continuity review: investigators and reviewers should identify the weakest continuity domain at the present data-stage and impose a domain-matched safeguard.