Examining Gaps in Institutional Policies for Clinical Genomic Data Sharing: A Cross-Jurisdictional Study
Ju, Z.; Xue, Y.; Rud, A.; Savatt, J. M.; Lerner-Ellis, J.; Rehm, H. L.; Joly, Y.; Uberoi, D.
Show abstract
BackgroundThe sharing of data generated through the course of clinical genetic and genomic testing without explicit patient consent is increasingly important for timely diagnosis and treatment. While many jurisdictions permit the sharing of identifiable data for direct patient care, institutional policies vary in how clearly they specify key elements. When do policies permit sharing of data without explicit consent? What data types may be shared, with whom, and under what safeguards? Greater clarity around these elements may support responsible data sharing while balancing timely care with transparency and appropriate protections. MethodsWe conducted a qualitative content analysis of data-sharing and privacy policies from 33 clinical genomic institutions across 17 jurisdictions. 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, documenting areas of inconsistency across institutions. ResultsAlthough 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%), recipient roles, access conditions, and onward-sharing expectations were often left undefined. ConclusionThis uneven documentation could make it difficult for clinical teams, laboratories, and institutional decision-makers to identify and justify key decisions about what is permitted and under what conditions. A guidance framework specifying core policy elements and corresponding protections could help institutions communicate their governance choices more clearly while supporting more comparable baseline practices for responsible data sharing across settings.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Low adherence to existing model reporting guidelines by commonly used clinical prediction models 88%
- If you build it, will they use it? Use of a Digital Assistant for Self-Reporting of COVID-19 Rapid Antigen Test Results during Large Nationwide Community Testing Initiative 88%
- COVID-19 outcomes, risk factors and associations by race: a comprehensive analysis using electronic health records data in Michigan Medicine 88%
Similar papers in this journal
- Large Language Models Facilitate the Generation of Electronic Health Record Phenotyping Algorithms 90%
- Observer: Creation of a Novel Multimodal Dataset for Outpatient Care Research 90%
- Clinical Implementation Of Preemptive Pharmacogenomics Testing For Personalized Medicine At An Academic Medical Center 90%
Similar papers in this journal
- User Testing of a Diagnostic Decision Support System with Machine-assisted Chart Review to Facilitate Clinical Genomic Diagnosis 91%
- Cracking the Code: A Scoping Review to Unite Disciplines in Tackling Legal Issues in Health Artificial Intelligence 90%
- Influence of social determinants of health and county vaccination rates on machine learning models to predict COVID-19 case growth in Tennessee 86%
Similar papers in this journal
- Reduced turnaround times through multi-sectoral collaboration during the first surge of SARS-CoV-2 in Louisiana, March-April 2020 90%
- Feasibility and lessons learned on remote trial implementation from TestBoston, a fully remote, longitudinal, large-scale COVID-19 surveillance study 89%
- Clinical code sets and the problem of redundancy in code set repositories 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.