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8 - The Principle of Transparency in Medical Research

Applying Big Data Analytics to Electronic Health Records

from Part III - Knowledge, Risk and Control

Published online by Cambridge University Press:  08 September 2022

By
Nikolaus Forgó  and
Marie-Catherine Wagner
Edited by
Marcelo Corrales Compagnucci ,
Michael Lowery Wilson ,
Mark Fenwick ,
Nikolaus Forgó  and
Till Bärnighausen
Marcelo Corrales Compagnucci
Affiliation:
University of Copenhagen
Michael Lowery Wilson
Affiliation:
University of Turku, Finland
Mark Fenwick
Affiliation:
Kyushu University, Japan
Nikolaus Forgó
Affiliation:
Universität Wien, Austria
Till Bärnighausen
Affiliation:
Universität Heidelberg
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Summary

In recent years, the amount of data provided by Electronic Health Records (EHRs) worldwide has expanded. The more data is collected, the more can be learned from it and the better decisions can be made. Projects such as InteropEHRate try to provide models on how health data can be made interoperable and available to medical research organisations. The processing of personal data might intervene with the fundamental right to data protection and/or privacy. In the EU, the GDPR treats specific forms of data processing differently if they could be protected by fundamental rights and freedoms. GDPR provides some privileges for scientific research and allows additional derogations by member states. When health data are analysed on the basis of machine learning, special attention needs to be paid to transparency, a fundamental feature in EU law that is both needed and challenged when machines, replacing or supporting humans, take decisions. This chapter analyses the principle of transparency and its compatibility with Big Data analytics in medical research. Apart from an evaluation of the current European legal framework, including Convention 108+, we will also examine global initiatives, such as the ‘Recommendation on the Protection and Use of Health-Related Data’.

Keywords

GDPRtransparencymedical researchelectronic health recordsInteropEHRateBig Data analyticsConvention 108+UN recommendation
Type
Chapter
Information
AI in eHealth
Human Autonomy, Data Governance and Privacy in Healthcare
 , pp. 209 - 250
Publisher: Cambridge University Press
Print publication year: 2022

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