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Background: Sharing health data within and across jurisdictions is important for research and improving healthcare quality; however, researchers, governments and funders must balance the benefits of data sharing with data privacy. Though frameworks exist to guide data sharing it can be difficult to translate these into practice. Therefore, our aim was to create a practical example of data sharing for researchers in pediatric rheumatology.
Methods: We utilized expert consultation with leaders in child health, genomics, rheumatology, bioethics, privacy and records, bioinformatics, and legal counsel to better understand barriers and enablers for sharing of health data. We used these barriers to frame the learnings of UCAN CAN-DU (Understanding Childhood Arthritis Network Canada-Netherlands Personalized Medicine Network in Childhood Arthritis and Rheumatic Diseases) which is a collaboration that collects and shares phenotypic, genomic, health economic and patient reported data across centers in Canada and the Netherlands in order to provide a real-life, practical example of data sharing across borders in pediatric rheumatology.
Results: Barriers to data sharing include lack of standardized consent, ethics review processes for multi-site projects, developing data governance frameworks aligned with institutional and regulatory requirements, differing data standards and a lack of interoperability, and managing data access. UCAN CAN-DU provides lessons for navigating these barriers through standardized consent forms, centralized ethics review and reciprocity agreements; building a network to support data interoperability and harmonization of procedures; documentation to support data sharing, including legal agreements, utilization of a secure healthcare data storage compute facility, and a data access advisory committee with clear policies for secondary data use.
Conclusion: We have shared how UCAN CAN-DU navigated barriers to data sharing, providing an example of data sharing for researchers in pediatric rheumatology. This work highlights the importance of research networks in establishing interoperability including minimal data sets, standard operating procedures, and institutional legal/contracts partnerships that ultimately support data sharing. The barriers and enablers presented are broadly applicable across countries and provide direction on areas for future research and initiatives to foster data sharing.
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http://dx.doi.org/10.1186/s12969-025-01139-7 | DOI Listing |
JAMA Cardiol
September 2025
Department of Medicine, Cardiovascular Medicine, Stanford University, Stanford, California.
Importance: Consumer wearable technologies have wide applications, including some that have US Food and Drug Administration clearance for health-related notifications. While wearable technologies may have premarket testing, validation, and safety evaluation as part of a regulatory authorization process, information on their postmarket use remains limited. The Stanford Center for Digital Health organized 2 pan-stakeholder think tank meetings to develop an organizing concept for empirical research on the postmarket evaluation of consumer-facing wearables.
View Article and Find Full Text PDFCereb Cortex
August 2025
Research Imaging Institute, University of Texas Health Science Center at San Antonio, 8403 Floyd Curl Drive, San Antonio, TX 78229, United States.
Statistical Parametric Mapping (SPM) adheres to rigorous methodological standards, including: spatial normalization, inter-subject averaging, voxel-wise contrasts, and coordinate reporting. This rigor ensures that a thematically diverse literature is amenable to meta-analysis. BrainMap is a community database (www.
View Article and Find Full Text PDFPharmacoeconomics
September 2025
Department of Pharmacy, Uppsala University, Box 580, 751 23, Uppsala, Sweden.
Background: Immune checkpoint inhibitors (ICIs) are clinically beneficial but associated with high costs that represent a growing challenge for healthcare budgets and may affect affordability, especially in resource-limited settings. Moreover, the healthcare sector is a significant source of greenhouse gas emissions, and medication-related waste-such as that from vial-based therapies-has been identified as a contributing factor. Alternative dosing strategies could reduce the environmental and financial impact of ICI therapy while maintaining clinical safety and efficacy.
View Article and Find Full Text PDFAm J Cardiovasc Drugs
September 2025
Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
Drugs Aging
September 2025
Dalla Lana School of Public Health, University of Toronto, V1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
Background And Objectives: Older adults living with dementia are a heterogeneous group, which can make studying optimal medication management challenging. Unsupervised machine learning is a group of computing methods that rely on unlabeled data-that is, where the algorithm itself is discovering patterns without the need for researchers to label the data with a known outcome. These methods may help us to better understand complex prescribing patterns in this population.
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