Abstract
BackgroundTranslational researchers need robust IT solutions to access a range of data types, varying from public data sets to pseudonymised patient information with restricted access, provided on a case by case basis. The reason for this complication is that managing access policies to sensitive human data must consider issues of data confidentiality, identifiability, extent of consent, and data usage agreements. All these ethical, social and legal aspects must be incorporated into a differential management of restricted access to sensitive data.MethodsIn this paper we present a pilot system that uses several common open source software components in a novel combination to coordinate access to heterogeneous biomedical data repositories containing open data (open access) as well as sensitive data (restricted access) in the domain of biobanking and biosample research. Our approach is based on a digital identity federation and software to manage resource access entitlements.ResultsOpen source software components were assembled and configured in such a way that they allow for different ways of restricted access according to the protection needs of the data. We have tested the resulting pilot infrastructure and assessed its performance, feasibility and reproducibility.ConclusionsCommon open source software components are sufficient to allow for the creation of a secure system for differential access to sensitive data. The implementation of this system is exemplary for researchers facing similar requirements for restricted access data. Here we report experience and lessons learnt of our pilot implementation, which may be useful for similar use cases. Furthermore, we discuss possible extensions for more complex scenarios.
Highlights
Translational researchers need robust IT solutions to access a range of data types, varying from public data sets to pseudonymised patient information with restricted access, provided on a case by case basis
Ability to perform data analyses other than those for which clinical trials were originally conducted is another opportunity that clinical data sharing offers [10], which is relevant in the translational research field, enabling approaches like comparative genomics [11, 12]
In this paper we report on a pilot implementation that aims at integrating research resources and clinical resources, including data bound to a varying range of access policies, from fully open to data requiring access approval
Summary
Translational researchers need robust IT solutions to access a range of data types, varying from public data sets to pseudonymised patient information with restricted access, provided on a case by case basis. An obvious example is the wish and right of patients to keep their health information private, which can be motivated by various reasons, including the kind of relationship that an individual wants to maintain with his relatives and social relations [17, 18], the social stigma associated to certain diseases [19, 20], and access to private healthcare [21] Another reason to resist data sharing lies in the commercial or academic interests of researchers, including the willingness to be the first to submit unpublished research, and the wish to produce evidence useful to file patent applications [22, 23]. Anonymization and reidentification-prevention techniques, which are used to grant data access while ensuring patient privacy, imply that data essential for a research goal might be concealed from the researchers [24, 25]
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