GlaxoSmithKline opens the door on clinical data sharing

2012 ◽  
Vol 11 (12) ◽  
pp. 891-892 ◽  
Author(s):  
Charlotte Harrison
Keyword(s):  
2016 ◽  
Vol 7 ◽  
Author(s):  
Frank Emmert-Streib ◽  
Matthias Dehmer ◽  
Olli Yli-Harja
Keyword(s):  

Author(s):  
Curtis L Cole ◽  
Soumitra Sengupta ◽  
Sarah Rossetti (née Collins) ◽  
David K Vawdrey ◽  
Michael Halaas ◽  
...  

Abstract Digital medical records have enabled us to employ clinical data in many new and innovative ways. However, these advances have brought with them a complex set of demands for healthcare institutions regarding data sharing with topics such as data ownership, the loss of privacy, and the protection of the intellectual property. The lack of clear guidance from government entities often creates conflicting messages about data policy, leaving institutions to develop guidelines themselves. Through discussions with multiple stakeholders at various institutions, we have generated a set of guidelines with 10 key principles to guide the responsible and appropriate use and sharing of clinical data for the purposes of care and discovery. Industry, universities, and healthcare institutions can build upon these guidelines toward creating a responsible, ethical, and practical response to data sharing.


Author(s):  
Brett K. Beaulieu-Jones ◽  
Zhiwei Steven Wu ◽  
Chris Williams ◽  
Ran Lee ◽  
Sanjeev P. Bhavnani ◽  
...  

2021 ◽  
pp. 174077452110385
Author(s):  
Enrique Vazquez ◽  
Henri Gouraud ◽  
Florian Naudet ◽  
Cary P Gross ◽  
Harlan M Krumholz ◽  
...  

Background/Aims: Over the past decade, numerous data sharing platforms have been launched, providing access to de-identified individual patient-level data and supporting documentation. We evaluated the characteristics of prominent clinical data sharing platforms, including types of studies listed as available for request, data requests received, and rates of dissemination of research findings from data requests. Methods: We reviewed publicly available information listed on the websites of six prominent clinical data sharing platforms: Biological Specimen and Data Repository Information Coordinating Center, ClinicalStudyDataRequest.com , Project Data Sphere, Supporting Open Access to Researchers–Bristol Myers Squibb, Vivli, and the Yale Open Data Access Project. We recorded key platform characteristics, including listed studies and available supporting documentation, information on the number and status of data requests, and rates of dissemination of research findings from data requests (i.e. publications in a peer-reviewed journals, preprints, conference abstracts, or results reported on the platform’s website). Results: The number of clinical studies listed as available for request varied among five data sharing platforms: Biological Specimen and Data Repository Information Coordinating Center (n = 219), ClinicalStudyDataRequest.com (n = 2,897), Project Data Sphere (n = 154), Vivli (n = 5426), and the Yale Open Data Access Project (n = 395); Supporting Open Access to Researchers did not provide a list of Bristol Myers Squibb studies available for request. Individual patient-level data were nearly always reported as being available for request, as opposed to only Clinical Study Reports (Biological Specimen and Data Repository Information Coordinating Center = 211/219 (96.3%); ClinicalStudyDataRequest.com  = 2884/2897 (99.6%); Project Data Sphere = 154/154 (100.0%); and the Yale Open Data Access Project = 355/395 (89.9%)); Vivli did not provide downloadable study metadata. Of 1201 data requests listed on ClinicalStudyDataRequest.com , Supporting Open Access to Researchers–Bristol Myers Squibb, Vivli, and the Yale Open Data Access Project platforms, 586 requests (48.8%) were approved (i.e. data access granted). The majority were for secondary analyses and/or developing/validating methods ( ClinicalStudyDataRequest.com  = 262/313 (83.7%); Supporting Open Access to Researchers–Bristol Myers Squibb = 22/30 (73.3%); Vivli = 63/84 (75.0%); the Yale Open Data Access Project = 111/159 (69.8%)); four were for re-analyses or corroborations of previous research findings ( ClinicalStudyDataRequest.com  = 3/313 (1.0%) and the Yale Open Data Access Project = 1/159 (0.6%)). Ninety-five (16.1%) approved data requests had results disseminated via peer-reviewed publications ( ClinicalStudyDataRequest.com  = 61/313 (19.5%); Supporting Open Access to Researchers–Bristol Myers Squibb = 3/30 (10.0%); Vivli = 4/84 (4.8%); the Yale Open Data Access Project = 27/159 (17.0%)). Forty-two (6.8%) additional requests reported results through preprints, conference abstracts, or on the platform’s website ( ClinicalStudyDataRequest.com  = 12/313 (3.8%); Supporting Open Access to Researchers–Bristol Myers Squibb = 3/30 (10.0%); Vivli = 2/84 (2.4%); Yale Open Data Access Project = 25/159 (15.7%)). Conclusion: Across six prominent clinical data sharing platforms, information on studies and request metrics varied in availability and format. Most data requests focused on secondary analyses and approximately one-quarter of all approved requests publicly disseminated their results. To further promote the use of shared clinical data, platforms should increase transparency, consistently clarify the availability of the listed studies and supporting documentation, and ensure that research findings from data requests are disseminated.


2020 ◽  
Author(s):  
◽  

Data sharing, particularly of potentially sensitive information, is not always straightforward. This Data Sharing Toolkit has been developed to collate practical information and resources related to the topic. The Data Management Basics section includes explanations covering different aspects of working with data, from a list of free version control tools, through explanation of metadata, to real-life examples of data problems encountered by data managers handling clinical data. The Data Sharing Steps provide an overview of the core elements of the process of depositing your data into a repository. The Repository Finder has been developed in order to aid users in choosing a suitable repository to submit to. The toolkit also included an extensive collection of resources linked to working with and sharing data.


2017 ◽  
Vol 2017 ◽  
pp. 1-24 ◽  
Author(s):  
R. Gazzarata ◽  
B. Giannini ◽  
M. Giacomini

The eSource Data Interchange Group, part of the Clinical Data Interchange Standards Consortium, proposed five scenarios to guide stakeholders in the development of solutions for the capture of eSource data. The fifth scenario was subdivided into four tiers to adapt the functionality of electronic health records to support clinical research. In order to develop a system belonging to the “Interoperable” Tier, the authors decided to adopt the service-oriented architecture paradigm to support technical interoperability, Health Level Seven Version 3 messages combined with LOINC (Logical Observation Identifiers Names and Codes) vocabulary to ensure semantic interoperability, and Healthcare Services Specification Project standards to provide process interoperability. The developed architecture enhances the integration between patient-care practice and medical research, allowing clinical data sharing between two hospital information systems and four clinical data management systems/clinical registries. The core is formed by a set of standardized cloud services connected through standardized interfaces, involving client applications. The system was approved by a medical staff, since it reduces the workload for the management of clinical trials. Although this architecture can realize the “Interoperable” Tier, the current solution actually covers the “Connected” Tier, due to local hospital policy restrictions.


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