PubMed · 12832571
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Michael W Vannier, Ronald M Summers. 2003. Sharing images.. https://doi.org/10.1148/radiol.2281021654
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The sharing of data generated by clinical genetic and genomic testing without explicit consent is important for timely diagnosis and treatment. While many jurisdictions permit the sharing of identifiable data for direct clinical care, institutional policies vary in how clearly they specify key elements, including when sharing is permitted, what data are covered, and what safeguards apply. Greater clarity around these elements may support responsible data sharing while balancing timely care with transparency and appropriate protections. We conducted a mixed-methods content analysis of data-sharing and privacy policies from 33 clinical genomic institutions across 17 countries and regions. Using a predefined analytical framework, we assessed how policies document key governance elements relevant to sharing without explicit consent. Two independent reviewers extracted information about clinical contexts, data types, justifications, and protections. Although 70% of institutions described circumstances permitting data sharing without explicit consent, most policies did not clearly define the scope or governance of such sharing. Policies also rarely distinguished clinical from research or secondary use and inconsistently specified privacy and security safeguards. While sharing was commonly justified for clinical care (78.3%) or testing services (43.5%), data recipient roles and onward-sharing expectations were often left undefined. This uneven documentation could make it difficult for clinical teams and institutional decision-makers to identify and justify decisions about what is permitted and under what conditions. A guidance framework specifying core governance elements and corresponding protections could help institutions communicate their governance choices more clearly and support comparable baseline practices for responsible data sharing.
MOTIVATION: Biomedical models improve when trained on data pooled across institutions, but sensitive patient records (e.g. genomics, clinical data, and medical images) are difficult to share due to privacy constraints. Moreover, data collected at different sites often have shifted distributions because of covariate differences (including batch effects), so privacy-preserving sharing alone cannot simply resolve cross-site mismatch. Methods that protect individuals while explicitly aligning distributions are needed to enable reliable multi-institutional analyses. RESULTS: We present NoisyFlow, a three-stage differentially private framework for cross-institutional harmonization under distribution shift. In stage I, each site learns a differentially private flow-based generator of its local labeled distribution. In stage II, it learns a neural optimal transport map to a shared reference distribution. In stage III, a central server composes the released models to generate reference-aligned pseudo-data for downstream analysis without accessing raw records. Across four biomedical settings spanning single-cell genomics, histopathology, neurogenomics, and wearable sensing, NoisyFlow reduces distribution shift while preserving downstream utility under formal differential privacy guarantees. AVAILABILITY AND IMPLEMENTATION: The implementation of NoisyFlow is available at https://github.com/gersteinlab/NoisyFlow.
The advance of technology proceeds through an evolutionary process, with many different new departures in competition with each other and with prevailing practice, and with ex-post selection determining the winners and losers. In modern times what gives power to the process is the strong base of scientific and technological understanding and technique that guides the efforts of those seeking to advance the technology. Most of that base is part of a commons open to all who have expertise in a field. The proprietary aspects of technology traditionally have comprised a small topping on the commons. But recently parts of the commons have become privatized. While the justification for the policies and actions that have spurred privatization of the commons is that this will spur technological progress, the argument here is that the result can be just the opposite.