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Klin Monbl Augenheilkd 2024; 241(06): 758-767
DOI: 10.1055/a-2165-9815
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Implementation and Execution of Big Data-based Studies in Ophthalmology within the Framework of the GDPR

Article in several languages: English | deutsch

Authors

  • Benedikt Siebelmann

    1   Hamburg, Osborne Clarke Rechtsanwälte Steuerberater Partnerschaft mit beschränkter Berufshaftung, Hamburg, Deutschland

  • Guido Grass

    2   Ethik-Kommission, Medizinische Fakultät der Universität zu Köln, Deutschland

  • Mario Matthaei 

    3   Zentrum für Augenheilkunde, Universitätsklinikum Köln, Deutschland

  • Claus Cursiefen

    3   Zentrum für Augenheilkunde, Universitätsklinikum Köln, Deutschland

  • Till Gerhardt

    1   Hamburg, Osborne Clarke Rechtsanwälte Steuerberater Partnerschaft mit beschränkter Berufshaftung, Hamburg, Deutschland

  • Juliane Koeberlein-Neu

    4   Bergisches Kompetenzzentrum für Gesundheitsökonomik und Versorgungsforschung, Bergische Universität Wuppertal, Fakultät für Wirtschaftswissenschaft – Schumpeter School of Business and Economics, Wuppertal, Deutschland

  • Sebastian Siebelmann 

    3   Zentrum für Augenheilkunde, Universitätsklinikum Köln, Deutschland
    4   Bergisches Kompetenzzentrum für Gesundheitsökonomik und Versorgungsforschung, Bergische Universität Wuppertal, Fakultät für Wirtschaftswissenschaft – Schumpeter School of Business and Economics, Wuppertal, Deutschland
    5   Augenärzte Solingen, Augenärzte Kölner Höfe, Solingen, Deutschland
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Abstract

The processing of the retrospective data pool for ophthalmology holds huge potential, especially for the research sector. “Big Data” enables medical science to draw conclusions for the future from historical data. Based on the evaluation of such data, algorithms could be trained, for instance, that are capable of making decisions with the help of artificial intelligence. As a result, the medical decision-making process on certain issues could be accelerated, enriched in qualitative and quantitative terms, or even completely be taken over. Ophthalmology is a rapidly evolving field. Due to the multitude of partly automated medical imaging technologies and the predestined accessibility of the eye for such technologies, ophthalmology, similarly to radiology or dermatology, is well suited for artificial intelligence-assisted image data analysis and the frequently associated initiation of diagnosis and therapy. Meanwhile, numerous studies exist based on AI-assisted image data analysis of ophthalmological image data. To the extent that the algorithms filter out results from the data pools by means of calculation rules and are even capable of making independent decisions on the basis of decision trees, the enormous benefit and, simultaneously, the profit for scientific research is quite obvious. Accordingly, it would be desirable to have unrestricted and comprehensive possibility of corresponding data processing of these health data for ophthalmological research. In spite of the potential for ophthalmology, for which there is only fragmentary evidence, the question of practical feasibility arises. In particular, the legal requirements and limits of European and national data protection law must be taken into account, prior to any unreflected processing of personal (health) data. Only by doing so can we circumvent existing obstacles and pitfalls, which can lead to severe fines. Most important are to date the requirements of two legal texts: The General Data Protection Regulation (GDPR) and the Federal Data Protection Act (BDSG). This article provides an overview of the relevant legal requirements applicable in the field of ophthalmology and highlights the major pitfalls and implementation requirements.

Key words

Big Data - retrospective data pools - artificial intelligence - GDPR - German Data Protection Act


Publication History

Received: 29 January 2023

Accepted: 31 August 2023

Accepted Manuscript online:
04 September 2023

Article published online:
28 June 2024

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