Secondary use of biospecimens and longitudinal rheumatology data often fails at the governance layer rather than the assay layer: participants withdraw, consent scopes evolve, protocols are amended, and downstream datasets can become detached from the exact permissions under which they were assembled. I hypothesize that revocation-aware consent provenance tags attached to each specimen, derived dataset, and model training batch will reduce noncompliant secondary data use in autoimmune and rheumatology biobanks without materially degrading recruitment yield or usable cohort size.

Why this is plausible

• Many compliance failures arise from weak linkage between participant permissions and downstream analytical assets rather than overt misconduct.
• Fine-grained provenance tagging is already technically feasible in modern data-lake and sample-tracking architectures.
• If revocation propagates automatically to derived assets, research teams may catch governance conflicts earlier, before publication or external sharing.

Testable design

• Prospective comparison across biobank programs or a stepped-wedge rollout within one multi-site rheumatology network.
• Intervention arm uses machine-readable consent objects with versioning, scope labels, revocation timestamps, and automatic propagation to specimen IDs, analysis tables, and model-training manifests.
• Control arm uses conventional document-based consent tracking with manual downstream reconciliation.
• Primary endpoint: rate of noncompliant secondary-use incidents per 1,000 consented participants, defined as use outside the current consent scope or after effective withdrawal.
• Key secondary endpoints: time required to process a withdrawal, number of downstream assets requiring quarantine after revocation, recruitment completion rate, and percentage change in usable cohort size at 12 months.

Falsifiable predictions

1. Programs using revocation-aware provenance tags will reduce noncompliant secondary-use incidents by at least 30%.
2. Median withdrawal-processing time will fall by at least 50%.
3. Recruitment completion rate will not decrease by more than 3 percentage points compared with standard workflows.

Assumptions

• Consent scopes can be represented in machine-readable form without losing legally relevant nuance.
• Sample and dataset identifiers remain consistently linked across local and federated infrastructure.
• Sites are willing to treat provenance enforcement as an operational control rather than a documentation formality.

Limitations

• Better traceability may initially increase detected violations even if underlying behavior is unchanged.
• Dynamic consent preferences may differ across jurisdictions, especially where revocation rights for de-identified derivatives are limited or ambiguously defined.
• Smaller sites may lack the informatics maturity needed to implement propagation across all derived assets.

References

1. Mikkelsen RB, Gjerris M, Waldemar G, Sandøe P. Broad consent for biobanks is best provided it is also deep. BMC Med Ethics. 2019;20(1):71. DOI: 10.1186/s12910-019-0404-3
2. Budin-Ljøsne I, Teare HJA, Kaye J, et al. Dynamic Consent: a potential solution to some of the challenges of modern biomedical research. BMC Med Ethics. 2017;18(1):4. DOI: 10.1186/s12910-016-0162-9
3. ICH Harmonised Guideline E6(R3) Good Clinical Practice. International Council for Harmonisation. Final adopted guideline, 2025.

DNAI • Ethical DeSci Governance