Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2025-01-03T17:17:39.380Z Has data issue: false hasContentIssue false

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
Get access

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ambur, MY and others, ‘Big Data Analytics and Machine Intelligence Capability Development at NASA Langley Research Center: Strategy, Roadmap, and Progress’ (2015), https://ntrs.nasa.gov/citations/20170000676.Google Scholar
Angst, C and Agarwal, R, ‘Adoption of Electronic Health Records in the Presence of Privacy Concerns: The Elaboration Likelihood Model and Individual Persuasion’ (2009) 33(2) Management Information Systems Quarterly 339–70.CrossRefGoogle Scholar
Article 29 Data Protection Working Party, ‘Guidelines on Automatic Individual Decision-Making and Profiling for the Purposes of Regulation 2016/679’ 17/EN, WP251rev.01 (2018), https://ec.europa.eu/newsroom/article29/items/612053.Google Scholar
Article 29 Data Protection Working Party, ‘Guidelines on Transparency Under Regulation 2016/679’ 17/EN, WP260 rev.01 (2018), https://ec.europa.eu/newsroom/article29/items/612053.Google Scholar
Article 29 Data Protection Working Party, ‘Opinion 03/2013 on Purpose Limitation’ 00569/13/EN, WP203 (2013), https://ec.europa.eu/newsroom/article29/items/612053.Google Scholar
Banik, A and Bandyopadhyay, SK, ‘Big Data-A Review on Analysing 3Vs’ (2016) 3(1) Journal of Scientific and Engineering Research 21–24.Google Scholar
Boucher P for the European Parliament, ‘Artificial Intelligence: How Does It Work, Why Does It Matter; and What Can We Do about It?’ (2020).Google Scholar
Buchner, B and Tinnefeld, M-T, Art 89. ‘Datenschutz-Grundverordnung’ in Kühling, J and Buchner, B (eds), Bundesdatenschutzgesetz (C.H.Beck 2018) 1–31.Google Scholar
Chandrika, M and others, ‘Impact of Big Data and Emerging Research Trends’ (2015) International Journal of Innovative Technology and Research. International Conference on Computational Systems for Health & Sustainability (CSFHS) 17–18 April 2015 14–17.Google Scholar
Chico, V, ‘The Impact of the General Data Protection Regulation on Health Research’ (2018) 128(1) British Medical Bulletin 109–18.Google Scholar
Commission Nationale Informatique & Libertés, ‘Comment Permettre À L’homme de Garder la Main ?’ (2017), www.cnil.fr/sites/default/files/atoms/files/cnil_rapport_garder_la_main_web.pdf.Google Scholar
Commission Recommendation on a European Electronic Health Record Exchange Format (C(2019)800) of 6 February 2019, https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32019H0243&from=EN.Google Scholar
Council Directive 93/13 EEC of 5 April 1993 on Unfair Terms in Consumer Contracts, https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:31993L0013&from=EN.Google Scholar
Council of Europe, Convention 108 +, Convention for the protection of individuals with regard to the processing of personal data (2018), www.europarl.europa.eu/meetdocs/2014_2019/plmrep/COMMITTEES/LIBE/DV/2018/09-10/Convention_108_EN.pdf.Google Scholar
Council of Europe, ‘Explanatory Report Modernised Convention’ (2018), https://rm.coe.int/cets-223-explanatory-report-to-the-protocol-amending-the-convention-fo/16808ac91a.Google Scholar
Council of Europe, ‘Guidelines on the Protection of Individuals with Regard to the Processing of Personal Data in a World of Big Data’ (2017), https://rm.coe.int/16806ebe7a.Google Scholar
Council of Europe, ‘Report on Artificial Intelligence’, T-PD(2018)09Rev (2019), https://rm.coe.int/artificial-intelligence-and-data-protection-challenges-and-possible-re/168091f8a6.Google Scholar
Dahlem, D and others, ‘Predictability Bounds of Electronic Health Records’ (2015) 5(1) Scientific Reports 1–9.Google Scholar
Datatilsynet (The Norwegian Data Protection Authority), ‘Artificial Intelligence and Privacy’ (2018), www.datatilsynet.no/globalassets/global/english/ai-and-privacy.pdf.Google Scholar
Diebold, FX, ‘On the Origin(s) and Development of the Term “Big Data”’ (2012), 12(037) PIER Working Paper.Dorland, WA Newman, Dorland’s Illustrated Medical Dictionary (28th ed., WB Saunders 1994).Google Scholar
Edwards, L and Veale, M, ‘Slave to the Algorithm? Why a “Right to an Explanation” Is Probably Not the Remedy You Are Looking for’ (2017) 16(1) Duke Law and Technology Review 18–84.Google Scholar
El Arass, M and Souissi, N, ‘Data Lifecycle: From Big Data to SmartData’ IEEE 5th International Congress on Information Science and Technology, 208, 80–7. doi: 10.1109/CIST.2018.8596547.CrossRefGoogle Scholar
European Commission – European Group on Ethics in Science and New Technologies, ‘Statement on Artificial Intelligence, Robotics and “Autonomous” Systems’ (2018), https://op.europa.eu/en/publication-detail/-/publication/dfebe62e-4ce9-11e8-be1d-01aa75ed71a1.Google Scholar
European Data Protection Board, ‘Guidelines 05/2020 on Consent under Regulation 2016/679’ (2020), https://edpb.europa.eu/sites/default/files/files/file1/edpb_guidelines_202005_consent_en.pdf.Google Scholar
European Union Agency for Fundamental Rights and Council of Europe, Handbook on European Data Protection Law (Publications Office of the European Union, 2018) 24–27, https://fra.europa.eu/sites/default/files/fra_uploads/fra-coe-edps-2018-handbook-data-protection_en.pdf.Google Scholar
Explanatory Memorandum of the Modernised Convention (2018), https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52018PC0449&from=HUGoogle Scholar
Forgó, N and others, ‘The Principle of Purpose Limitation and Big Data’ in Corrales, M and others (eds), New Technology, Big Data and the Law (Springer Nature Singapore 2017) 17–42.Google Scholar
Goodfellow, I and others, Deep Learning (MIT Press 2016).Google Scholar
Hardesty, L, ‘Crowdsourcing Big-Data Analysis’, MIT News, 30 October 2017, https://news.mit.edu/2017/crowdsourcing-big-data-analysis-1030, accessed 9 November 2019.Google Scholar
Hedge, V and Usmani, S, ‘Parallel and Distributed Deep Learning’ (2016), https://web.stanford.edu/~rezab/classes/cme323/S16/projects_reports/hedge_usmani.pdf.Google Scholar
Herbst, T, ‘Recht auf Löschung’ in Kühling, J and Buchner, B (eds), ‘Datenschutz-Grundverordnung‘, Bundesdatenschutzgesetz (General Data Protection Regulation, Federal Data Protection Act) (C.H. Beck 2018) 459.Google Scholar
Independent High-Level Expert Group on Artificial Intelligence set up by the European Commission, ‘Ethics Guidelines for Trustworthy AI’ (2019), www.aepd.es/sites/default/files/2019-12/ai-ethics-guidelines.pdf.Google Scholar
InteropEHRate, ‘InteropEHRate in a Nutshell’ InteropEHRate (2019), www.interopehrate.eu/interopehrate-in-a-nutshell/, accessed 10 November 2019.Google Scholar
Jensen, PB and others, ‘Mining Electronic Health Records: Towards Better Research Applications and Clinical Care’ (2012) 13(6) Nature Reviews Genetics 395–405.Google Scholar
Kent, J, ‘Big Data Analytics, Precision Medicine Top Priorities in 2020’ Health IT Analytics, 25 November 2019, https://healthitanalytics.com/news/big-data-analytics-precision-medicine-top-priorities-in-2020?eid=CXTEL000000519439&elqCampaignId=12525&utm_source=nl&utm_medium=email&utm_campaign=newsletter&elqTrackId=4e968b96b27c420ab1fff6c2e4a654f5&elq=c801b7efe6444ef394d45338b78dc755&elqaid=13156&elqat=1&elqCampaignId=12525, accessed 30 November 2019.Google Scholar
Khennou, F and others, ‘Improving the Use of Big Data Analytics within Electronic Health Records: A Case Study based OpenEHR’ (2018) 127(1) Procedia Computer Science 60–68.Google Scholar
Männer, R and Lange, R, ‘Rechnen mit Neuronalen Netzen: Wie funktionieren Neuronale Netze und wie werden sie trainiert?’ (Computing with neural networks: How neural networks work and how are they trained?) (1994) 50(5) Physikalische Blätter 445–49.Google Scholar
Martini, M, ‘Art 21 Widerspruchsrecht’ in Paal, BP and Pauly, DA (eds), Datenschutz-Grundverordnung (General Data Protection Regulation) (Beck’sche Kompakt-Kommentare 2017) 60.Google Scholar
Mathworks, ‘Deep Learning Examples: Training a Model from Scratch’, www.mathworks.com/solutions/deep-learning/examples/training-a-model-from-scratch.html, accessed 10 October 2019.Google Scholar
Minsky, M and Papert, S, Perceptrons: An Introduction to Computational Geometry (MIT Press 1969).Google Scholar
Miotto, R and others, ‘Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records’ (2016) 6(26094) Scientific Reports 1–10.Google Scholar
Mitrou, L, ‘AI and GDPR Study’ (2019).Google Scholar
Nguyen, G and others, ‘Machine Learning and Deep Learning Frameworks and Libraries for Large-Scale Data Mining: A Survey’ (2019) 52 Artificial Intelligence Review 77–124.Google Scholar
Nielsen, MA, Neural Networks and Deep Learning (Determination Press 2015).Google Scholar
Rajaraman, A, ‘More Data Usually Beats Better Algorithms’ Datawocky, 24 March 2008, https://anand.typepad.com/datawocky/2008/03/more-data-usual.html, accessed 8 November 2019.Google Scholar
Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the Protection of Natural Persons with Regard to the Processing of Personal Data and on the Free Movement of Such Data, and Repealing Directive 95/46/EC (General Data Protection Regulation), https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32016R0679&from=EN.Google Scholar
Richesson, R and others, ‘Using Electronic Health Record Data in Pragmatic Clinical Trials’ in Rethinking Clinical Trials: A Living Textbook of Pragmatic Clinical Trials (NIH Health Care Systems Research Collaboratory 2021), https://rethinkingclinicaltrials.org/chapters/design/using-electronic-health-record-data-pragmatic-clinical-trials-top/using-electronic-health-record-data-in-pragmatic-clinical-trials-introduction/, accessed 10 March 2021.Google Scholar
Rosenblatt, F, ‘The Perceptron: A Probabilistic Model For Information Storage and Organization in the Brain’ (1958) 65(6) Psychological Review 386–408.Google Scholar
Ross, MK and others, ‘“Big Data” and the Electronic Health Record’ (2014) 9(1) Yearbook of Medical Informatics 97–104.Google Scholar
Roth, C, ‘Recht für F&E; Open Innovation’ in Felten, E and others (eds), Digitale Transformation im Wirtschafts- & Steuerrecht (Digital Transformation in Business and Tax Law) (Lindeverlag 2018) 11/21.Google Scholar
Rumelhart, D and others, ‘Learning Representations by Back-Propagating Errors’ (1986) 323 Nature 533–36.Google Scholar
Schlegel, DR and Ficheur, G, ‘Secondary Use of Patient Data: Review of the Literature Published in 2016’ (2017) 26(1) Yearbook of Medical Informatics 68–71.Google Scholar
Schmidt, J and others, ‘Recent Advances and Applications of Machine Learning in Solid-State Materials Science’ (2019) 5(83) npj Computational Materials, www.nature.com/articles/s41524-019-0221-0.pdf.Google Scholar
Shkabatur, J, ‘The Global Commons of Data’ (2018) 22(2) Stanford Technology Law Review 1–46.Google Scholar
Stauton, C and others, ‘The GDPR and the Research Exemption: Considerations on the Necessary Safeguards for Research Biobanks’ (2019) 27(1) European Journal of Human Genetics 1159–67.Google Scholar
TechAmerica Foundation, ‘Demystifying Big Data’ (2012), https://bigdatawg.nist.gov/_uploadfiles/M0068_v1_3903747095.pdf.Google Scholar
UN Recommendation on the Protection and Use of Health-Related Data (2019), https://undocs.org/A/74/277.Google Scholar
Von Schomberg, R, Towards Responsible Research and Innovation in the Information and Communication Technologies and Security Technologies Fields (Publications Office of the European Union 2011).Google Scholar
Wachter, S and others, ‘Counterfactual Explanations Without Opening The Black Box: Automated Decisions and the GDPR’ (2018) 31(2) Harvard Journal of Law and Technology 841–87.Google Scholar
Watson, H, ‘Tutorial: Big Data Analytics: Concepts, Technologies, and Applications’ (2014) 34(1) Communications of the Association for Information Systems 65.Google Scholar
Zarsky, T, ‘Incompatible: The GDPR in the Age of Big Data’ (2017) 47(4) Seton Hall Law Review 995–1020.Google Scholar
Zozus, MN and others, ‘Factors Affecting Accuracy of Data Abstracted from Medical Records’ (2015) 10(10) Plos One e0138649.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×