A standardised graphic method for describing data privacy frameworks in primary care research using a flexible zone model

Kuchinke, Wolfgang; Ohmann, Christian; Verheij, Robert; Veen, Evert-Ben van; Arvanitis, Theodoros N.; Taweel, Adel; Delaney, Brendan

doi:10.1016/j.ijmedinf.2014.08.009

Cited by 36 publications

(54 citation statements)

References 23 publications

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“…A graphical method was used to map required information flows within a limited number of privacy zones [22]. We nominated privacy zones as Care Zone, Non-care Zone and Research Zone [23] but additionally incorporated a use-case driven Trustworthy Research Environment [24] for data analytics. The Trustworthy Research Environment (commonly abbreviated to TRE) is a fullyimplemented system supporting secure, regulated (authenticated researcher) access to datasets and tools.…”

Section: Consistent Matching Of Information Governance Requirements Tmentioning

confidence: 99%

GDPR Compliance Challenges for Interoperable Health Information Exchanges (HIEs) and Trustworthy Research Environments (TREs)

Conley¹,

Pocs²

2018

ejbi

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and processors once it comes into force in the spring of 2018. Fundamentally, GDPR aims to provide a set of standardized data protection laws across EU countries. This is intended to make it easier for EU citizens to understand how their data is being used and to raise any complaints. For implementers, it has potential to reduce fragmentation and administrative burdens where business activities flow through local, regional, national and international data exchanges. A full treatment of the data protection principles that drive compliance to the GDPR is beyond the scope of this paper, but an abundance of introductory resources is available on the Internet.

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Section: Consistent Matching Of Information Governance Requirements Tmentioning

confidence: 99%

GDPR Compliance Challenges for Interoperable Health Information Exchanges (HIEs) and Trustworthy Research Environments (TREs)

Conley¹,

Pocs²

2018

ejbi

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“…Moreover, a risk assessment has to be considered concerning the risk of re-identification from individual data. This risk depends on the nature and availability of contextual information, and also on IT capabilities [46]. Unlike the United States Health Insurance Portability and Accountability Act (HIPAA), which exempts data from regulation if 18 specific personal identifiers are removed, GDPR considers data as anonymous only when it cannot be identified by any means "reasonably likely to be used (...) either by the controller or by any other person" (Rec.…”

Section: Legal Frameworkmentioning

confidence: 99%

Health Data for Public Health: Towards New Ways of Combining Data Sources to Support Research Efforts in Europe

Burgun¹,

Bernal‐Delgado²,

Kuchinke³

et al. 2017

Yearb Med Inform

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“…The TRANSFoRm project has developed a research data architecture to implement the learning health system in general practice, using outcomes data to develop new evidence that can be delivered back as computable rules, to improve clinical decision making . TRANSFoRm has specified a strong separation between zones for clinical use and research use, and the provision of pseudoidentification and linkage services managed within a trusted “middle” zone …”

Section: Promoting and Enabling The Trustworthy Reuse Of Health Data mentioning

confidence: 99%

The European Institute for Innovation through Health Data

Kalra

Stroetmann

Sundgren

et al. 2016

Learning Health Systems

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The European Institute for Innovation through Health Data (i~HD, http://www.i-hd.eu) has been formed as one of the key sustainable entities arising from the Electronic Health Records for Clinical Research (IMI‐JU‐115189) and SemanticHealthNet (FP7‐288408) projects, in collaboration with several other European projects and initiatives supported by the European Commission. i~HD is a European not‐for‐profit body, registered in Belgium through Royal Assent. i~HD has been established to tackle areas of challenge in the successful scaling up of innovations that critically rely on high‐quality and interoperable health data. It will specifically address obstacles and opportunities to using health data by collating, developing, and promoting best practices in information governance and in semantic interoperability. It will help to sustain and propagate the results of health information and communication technology (ICT) research that enables better use of health data, assessing and optimizing their novel value wherever possible. i~HD has been formed after wide consultation and engagement of many stakeholders to develop methods, solutions, and services that can help to maximize the value obtained by all stakeholders from health data. It will support innovations in health maintenance, health care delivery, and knowledge discovery while ensuring compliance with all legal prerequisites, especially regarding the insurance of patient's privacy protection. It is bringing multiple stakeholder groups together so as to ensure that future solutions serve their collective needs and can be readily adopted affordably and at scale.

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A standardised graphic method for describing data privacy frameworks in primary care research using a flexible zone model

Cited by 36 publications

References 23 publications

GDPR Compliance Challenges for Interoperable Health Information Exchanges (HIEs) and Trustworthy Research Environments (TREs)

GDPR Compliance Challenges for Interoperable Health Information Exchanges (HIEs) and Trustworthy Research Environments (TREs)

Health Data for Public Health: Towards New Ways of Combining Data Sources to Support Research Efforts in Europe

The European Institute for Innovation through Health Data

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