Data protection-compliant ways to use real world data

被引:0
|
作者
Drepper, Johannes [1 ]
机构
[1] TMF Technol & Methodenplattform Vernetzte Med For, Berlin, Germany
关键词
Data protection law; Informed consent; Technical and organisational measures; Pseudonymization; Anonymization;
D O I
10.1007/s11553-022-00991-9
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Background The secondary use of existing real world data is seen as a promising method of medical research that complements the conduct of closely controlled studies. However, these real world data are collected in very different situations and are therefore subject to heterogeneous framework conditions in terms of data protection. Objectives Supporting the privacy-compliant use of real world data. Materials and methods In addition to general data protection laws at the European, national, and state levels, areas of law specific to health data, such as medical confidentiality or social law, are also examined. Protection methods such as pseudonymization and anonymization are examined and classified. Results The processing of real world data usually leads to the application of data protection law. Clarifying responsibility under data protection law can be challenging in complex collaborative projects. The type of possible legal basis for processing depends on specific framework conditions as well as the type of processing. In addition, the data must be protected during processing by technical and organizational measures. Conclusions The data protection legal framework for the processing of real world data is complex. Simplification and harmonization have not even been achieved within Germany with the European General Data Protection Regulation. Certain ways of using this data, e.g., on the basis of broad consent or with the help of an agreed assessment in accordance with a research clause, involve a great deal of effort and expense and are thus generally only available to larger projects or infrastructures.
引用
收藏
页数:8
相关论文
共 50 条
  • [21] Real world data in rheumatology
    Hyrich, Kimme L.
    SEMINARS IN ARTHRITIS AND RHEUMATISM, 2019, 49 : S22 - S24
  • [22] REAL-WORLD DATA
    STROCK, JM
    POLICY REVIEW, 1993, 63 : 96 - 96
  • [23] Sifting data in the real world
    Block, MM
    NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT, 2006, 556 (01): : 308 - 324
  • [24] Oseltamivir: the real world data
    Freemantle, Nick
    Shallcross, L. J.
    Kyte, D.
    Rader, T.
    Calvert, M. J.
    BMJ-BRITISH MEDICAL JOURNAL, 2014, 348
  • [25] Data Quality in the Real World
    Haebich, W.
    1998, (11):
  • [26] Blockchain based general data protection regulation compliant data breach detection system
    Ansar, Kainat
    Ahmed, Mansoor
    Malik, Saif Ur Rehman
    Helfert, Markus
    Kim, Jungsuk
    PEERJ COMPUTER SCIENCE, 2024, 10
  • [27] Data Science Methods for Real-World Evidence Generation in Real-World Data
    Liu, Fang
    ANNUAL REVIEW OF BIOMEDICAL DATA SCIENCE, 2024, 7 : 201 - 224
  • [28] Federated machine learning in data-protection-compliant research
    Brauneck, Alissa
    Schmalhorst, Louisa
    Majdabadi, Mohammad Mahdi Kazemi
    Bakhtiari, Mohammad
    Voelker, Uwe
    Saak, Christina Caroline
    Baumbach, Jan
    Baumbach, Linda
    Buchholtz, Gabriele
    NATURE MACHINE INTELLIGENCE, 2023, 5 (01) : 2 - 4
  • [29] Federated machine learning in data-protection-compliant research
    Alissa Brauneck
    Louisa Schmalhorst
    Mohammad Mahdi Kazemi Majdabadi
    Mohammad Bakhtiari
    Uwe Völker
    Christina Caroline Saak
    Jan Baumbach
    Linda Baumbach
    Gabriele Buchholtz
    Nature Machine Intelligence, 2023, 5 : 2 - 4
  • [30] DATA PROTECTION IN AN INCREASINGLY GLOBALIZED WORLD
    Palmieri, Nicholas F., III
    INDIANA LAW JOURNAL, 2019, 94 (01) : 297 - 329