Reconsidering Anonymization-Related Concepts and the Term "Identification" Against the Backdrop of the European Legal Framework

[Nationally standardized broad consent in practice: initial experiences, current developments, and critical assessment]

BACKGROUND

The digitalization in the healthcare sector promises a secondary use of patient data in the sense of a learning healthcare system. For this, the Medical Informatics Initiative's (MII) Consent Working Group has created an ethical and legal basis with standardized consent documents. This paper describes the systematically monitored introduction of these documents at the MII sites.

METHODS

The monitoring of the introduction included regular online surveys, an in-depth analysis of the introduction processes at selected sites, and an assessment of the documents in use. In addition, inquiries and feedback from a large number of stakeholders were evaluated.

RESULTS

The online surveys showed that 27 of the 32 sites have gradually introduced the consent documents productively, with a current total of 173,289 consents. The analysis of the implementation procedures revealed heterogeneous organizational conditions at the sites. The requirements of various stakeholders were met by developing and providing supplementary versions of the consent documents and additional information materials.

DISCUSSION

The introduction of the MII consent documents at the university hospitals creates a uniform legal basis for the secondary use of patient data. However, the comprehensive implementation within the sites remains challenging. Therefore, minimum requirements for patient information and supplementary recommendations for best practice must be developed. The further development of the national legal framework for research will not render the participation and transparency mechanisms developed here obsolete.

Datenschutzgerechte Wege zur Nutzung von Real World Data

Hintergrund

Die Nachnutzung vorhandener realweltlicher Daten wird als vielversprechende, die Durchführung klassischer Studien ergänzende Methode der medizinischen Forschung angesehen. Real World Data werden in sehr unterschiedlichen Situationen erhoben und unterliegen damit auch datenschutzrechtlich heterogenen Rahmenbedingungen.

Ziel der Arbeit

Ziel der Arbeit ist die Unterstützung der datenschutzgerechten Nutzung von Real World Data.

Material und Methoden

Neben dem allgemeinen Datenschutzrecht auf europäischer, nationaler und bundeslandspezifischer Ebene werden auch für Gesundheitsdaten spezifische Rechtsgebiete wie die ärztliche Schweigepflicht oder das Sozialrecht beleuchtet. Schutzmethoden wie die Pseudonymisierung und Anonymisierung werden untersucht und eingeordnet.

Ergebnisse

Die Verarbeitung von Real World Data führt im Regelfall zur Anwendung des Datenschutzrechts. Die Klärung der datenschutzrechtlichen Verantwortlichkeit kann bei komplexen Verbundvorhaben anspruchsvoll sein. Die Art der möglichen Rechtsgrundlage für die Verarbeitung hängt von spezifischen Rahmenbedingungen sowie der Art der Verarbeitung ab. Zudem sind die Daten während der Verarbeitung durch technische und organisatorische Maßnahmen zu schützen.

Schlussfolgerung

Die datenschutzrechtlichen Rahmenbedingungen für die Verarbeitung von Real World Data sind komplex. Eine Vereinfachung und Harmonisierung wurde mit der europäischen Datenschutz-Grundverordnung nicht einmal innerhalb Deutschlands erreicht. Bestimmte Wege zur Nutzung dieser Daten, z. B. auf Basis eines „broad consent“ oder mit Hilfe einer abgestimmten Bewertung gemäß einer Forschungsklausel, sind mit viel Aufwand verbunden und stehen damit im Regelfall nur größeren Projekten oder Infrastrukturen zur Verfügung.

You Can't Have AI Both Ways: Balancing Health Data Privacy and Access Fairly

Artificial intelligence (AI) in healthcare promises to make healthcare safer, more accurate, and more cost-effective. Public and private actors have been investing significant amounts of resources into the field. However, to benefit from data-intensive medicine, particularly from AI technologies, one must first and foremost have access to data. It has been previously argued that the conventionally used “consent or anonymize approach” undermines data-intensive medicine, and worse, may ultimately harm patients. Yet, this is still a dominant approach in European countries and framed as an either-or choice. In this paper, we contrast the different data governance approaches in the EU and their advantages and disadvantages in the context of healthcare AI. We detail the ethical trade-offs inherent to data-intensive medicine, particularly the balancing of data privacy and data access, and the subsequent prioritization between AI and other effective health interventions. If countries wish to allocate resources to AI, they also need to make corresponding efforts to improve (secure) data access. We conclude that it is unethical to invest significant amounts of public funds into AI development whilst at the same time limiting data access through strict privacy measures, as this constitutes a waste of public resources. The “AI revolution” in healthcare can only realise its full potential if a fair, inclusive engagement process spells out the values underlying (trans) national data governance policies and their impact on AI development, and priorities are set accordingly.

Legal and ethical framework for global health information and biospecimen exchange - an international perspective

Background

The progress of electronic health technologies and biobanks holds enormous promise for efficient research. Evidence shows that studies based on sharing and secondary use of data/samples have the potential to significantly advance medical knowledge. However, sharing of such resources for international collaboration is hampered by the lack of clarity about ethical and legal requirements for transfer of data and samples across international borders.

Main text

Here, the International Clinical Trial Center Network (ICN) reports the legal and ethical requirements governing data and sample exchange (DSE) across four continents. The most recurring requirement is ethical approval, whereas only in specific conditions approval of national health authorities is required. Informed consent is not required in all sharing situations. However, waiver of informed consent is only allowed in certain countries/regions and under certain circumstances. The current legal and ethical landscape appears to be very complex and under constant evolution. Regulations differ between countries/regions and are often incomplete, leading to uncertainty.

Conclusion

With this work, ICN illuminates the unmet need for a single international collaborative framework to facilitate DSE. Harmonising requirements for global DSE will reduce inefficiency and waste in research. There are many challenges to realising this ambitious vision, including inconsistent terminology and definitions, and heterogeneous and dynamic legal constraints. Here, we identify areas of agreement and significant difference as a necessary first step towards facilitating international collaboration. We propose the establishment of a working group to continue the comparison across jurisdictions, create a standardised glossary and define a set of basic principles and fundamental requirements for DSE.

Use and Understanding of Anonymization and De-Identification in the Biomedical Literature: Scoping Review

Background

The secondary use of health data is central to biomedical research in the era of data science and precision medicine. National and international initiatives, such as the Global Open Findable, Accessible, Interoperable, and Reusable (GO FAIR) initiative, are supporting this approach in different ways (eg, making the sharing of research data mandatory or improving the legal and ethical frameworks). Preserving patients’ privacy is crucial in this context. De-identification and anonymization are the two most common terms used to refer to the technical approaches that protect privacy and facilitate the secondary use of health data. However, it is difficult to find a consensus on the definitions of the concepts or on the reliability of the techniques used to apply them. A comprehensive review is needed to better understand the domain, its capabilities, its challenges, and the ratio of risk between the data subjects’ privacy on one side, and the benefit of scientific advances on the other.

Objective

This work aims at better understanding how the research community comprehends and defines the concepts of de-identification and anonymization. A rich overview should also provide insights into the use and reliability of the methods. Six aspects will be studied: (1) terminology and definitions, (2) backgrounds and places of work of the researchers, (3) reasons for anonymizing or de-identifying health data, (4) limitations of the techniques, (5) legal and ethical aspects, and (6) recommendations of the researchers.

Methods

Based on a scoping review protocol designed a priori, MEDLINE was searched for publications discussing de-identification or anonymization and published between 2007 and 2017. The search was restricted to MEDLINE to focus on the life sciences community. The screening process was performed by two reviewers independently.

Results

After searching 7972 records that matched at least one search term, 135 publications were screened and 60 full-text articles were included. (1) Terminology: Definitions of the terms de-identification and anonymization were provided in less than half of the articles (29/60, 48%). When both terms were used (41/60, 68%), their meanings divided the authors into two equal groups (19/60, 32%, each) with opposed views. The remaining articles (3/60, 5%) were equivocal. (2) Backgrounds and locations: Research groups were based predominantly in North America (31/60, 52%) and in the European Union (22/60, 37%). The authors came from 19 different domains; computer science (91/248, 36.7%), biomedical informatics (47/248, 19.0%), and medicine (38/248, 15.3%) were the most prevalent ones. (3) Purpose: The main reason declared for applying these techniques is to facilitate biomedical research. (4) Limitations: Progress is made on specific techniques but, overall, limitations remain numerous. (5) Legal and ethical aspects: Differences exist between nations in the definitions, approaches, and legal practices. (6) Recommendations: The combination of organizational, legal, ethical, and technical approaches is necessary to protect health data.

Conclusions

Interest is growing for privacy-enhancing techniques in the life sciences community. This interest crosses scientific boundaries, involving primarily computer science, biomedical informatics, and medicine. The variability observed in the use of the terms de-identification and anonymization emphasizes the need for clearer definitions as well as for better education and dissemination of information on the subject. The same observation applies to the methods. Several legislations, such as the American Health Insurance Portability and Accountability Act (HIPAA) and the European General Data Protection Regulation (GDPR), regulate the domain. Using the definitions they provide could help address the variable use of these two concepts in the research community.

A Multiagent System for Dynamic Data Aggregation in Medical Research

The collection of medical data for research purposes is a challenging and long-lasting process. In an effort to accelerate and facilitate this process we propose a new framework for dynamic aggregation of medical data from distributed sources. We use agent-based coordination between medical and research institutions. Our system employs principles of peer-to-peer network organization and coordination models to search over already constructed distributed databases and to identify the potential contributors when a new database has to be built. Our framework takes into account both the requirements of a research study and current data availability. This leads to better definition of database characteristics such as schema, content, and privacy parameters. We show that this approach enables a more efficient way to collect data for medical research.