A Scalable Pseudonymization Tool for Rapid Deployment in Large Biomedical Research Networks: Development and Evaluation Study

Background

The SARS-CoV-2 pandemic has demonstrated once again that rapid collaborative research is essential for the future of biomedicine. Large research networks are needed to collect, share, and reuse data and biosamples to generate collaborative evidence. However, setting up such networks is often complex and time-consuming, as common tools and policies are needed to ensure interoperability and the required flows of data and samples, especially for handling personal data and the associated data protection issues. In biomedical research, pseudonymization detaches directly identifying details from biomedical data and biosamples and connects them using secure identifiers, the so-called pseudonyms. This protects privacy by design but allows the necessary linkage and reidentification.

Objective

Although pseudonymization is used in almost every biomedical study, there are currently no pseudonymization tools that can be rapidly deployed across many institutions. Moreover, using centralized services is often not possible, for example, when data are reused and consent for this type of data processing is lacking. We present the ORCHESTRA Pseudonymization Tool (OPT), developed under the umbrella of the ORCHESTRA consortium, which faced exactly these challenges when it came to rapidly establishing a large-scale research network in the context of the rapid pandemic response in Europe.

Methods

To overcome challenges caused by the heterogeneity of IT infrastructures across institutions, the OPT was developed based on programmable runtime environments available at practically every institution: office suites. The software is highly configurable and provides many features, from subject and biosample registration to record linkage and the printing of machine-readable codes for labeling biosample tubes. Special care has been taken to ensure that the algorithms implemented are efficient so that the OPT can be used to pseudonymize large data sets, which we demonstrate through a comprehensive evaluation.

Results

The OPT is available for Microsoft Office and LibreOffice, so it can be deployed on Windows, Linux, and MacOS. It provides multiuser support and is configurable to meet the needs of different types of research projects. Within the ORCHESTRA research network, the OPT has been successfully deployed at 13 institutions in 11 countries in Europe and beyond. As of June 2023, the software manages data about more than 30,000 subjects and 15,000 biosamples. Over 10,000 labels have been printed. The results of our experimental evaluation show that the OPT offers practical response times for all major functionalities, pseudonymizing 100,000 subjects in 10 seconds using Microsoft Excel and in 54 seconds using LibreOffice.

Conclusions

Innovative solutions are needed to make the process of establishing large research networks more efficient. The OPT, which leverages the runtime environment of common office suites, can be used to rapidly deploy pseudonymization and biosample management capabilities across research networks. The tool is highly configurable and available as open-source software.

Balancing Protection and Quality in Big Data Analytics Pipelines

Existing data engine implementations do not properly manage the conflict between the need of protecting and sharing data, which is hampering the spread of big data applications and limiting their impact. These two requirements have often been studied and defined independently, leading to a conceptual and technological misalignment. This article presents the architecture and technical implementation of a data engine addressing this conflict by integrating a new governance solution based on access control within a big data analytics pipeline. Our data engine enriches traditional components for data governance with an access control system that enforces access to data in a big data environment based on data transformations. Data are then used along the pipeline only after sanitization, protecting sensitive attributes before their usage, in an effort to facilitate the balance between protection and quality. The solution was tested in a real-world smart city scenario using the data of the Oslo city transportation system. Specifically, we compared the different predictive models trained with the data views obtained by applying the secure transformations required by different user roles to the same data set. The results show that the predictive models, built on data manipulated according to access control policies, are still effective.

Patients' experiences and perspectives regarding the use of digital technology to support exercise-based cardiac rehabilitation: a qualitative interview study

Introduction

Despite the well-known benefits of exercise-based cardiac rehabilitation for the secondary prevention of cardiovascular disease, participation in cardiac rehabilitation programmes and adherence to secondary prevention recommendations remain limited. Digital technologies have the potential to address low participation and adherence but attempts at implementing digital health interventions in real-life clinical practice frequently encounter various barriers. Studies about patients' experiences and perspectives regarding the use of digital technology can assist developers, researchers and clinicians in addressing or pre-empting patient-related barriers. This study was therefore conducted to investigate the experiences and perspectives of cardiac rehabilitation patients in Austria with regard to using digital technology for physical activity and exercise.

Methods

Twenty-five current and former cardiac rehabilitation patients (18 men and 7 women, age range 39 to 83) with various cardiac conditions were recruited from a clinical site in Salzburg, Austria. Semi-structured qualitative interviews were audio-recorded and transcribed verbatim. The analysis followed a descriptive phenomenological approach, applying the framework analysis method.

Results

The sample was diverse, including interviewees who readily used digital devices to support their physical activity, exercise and health monitoring, and interviewees who did not. Simplicity, convenience and accessibility were highlighted as important facilitators for the use of digital technology, while annoyance with digital devices, concerns about becoming dependent on them, or simply a preference to not use digital technology were commonly stated reasons for non-use. Interviewees' views on data protection, data sharing and artificial intelligence revealed wide variations in individuals' prior knowledge and experience about these topics, and a need for greater accessibility and transparency of data protection regulation and data sharing arrangements.

Discussion

These findings support the importance that is attributed to user-centred design methodologies in the conceptualisation and design of digital health interventions, and the imperative to develop solutions that are simple, accessible and that can be personalised according to the preferences and capabilities of the individual patient. Regarding data protection, data sharing and artificial intelligence, the findings indicate opportunity for information and education, as well as the need to offer patients transparency and accountability in order to build trust in digital technology and digital health interventions.

Joint controllers in large research consortia: a funnel model to distinguish controllers in the sense of the GDPR from other partners in the consortium

Large European research consortia in the health sciences face challenges regarding the governance of personal data collected, generated and/or shared during their collective research. A controller in the sense of the GDPR is the entity which decides about purposes and means of the data processing. Case law of the Court of Justice of the European Union (CJEU) and Guidelines of the European Data Protection Board (EDPB) indicate that all partners in the consortium would be joint controllers. This paper summarises the case law, the Guidelines and literature on joint controllership, gives a brief account of a webinar organised on the issue by Lygature and the MLC Foundation. Participants at the webinar agreed in large majority that it would be extreme if all partners in the consortium would become joint controllers. There was less agreement how to disentangle partners who are controllers of a study from those who are not. In order to disentangle responsibilities, we propose a funnel model with consecutive steps acting as sieves in the funnel. It differentiates between two types of partners: all partners who are involved in shaping the project as a whole versus those specific partners who are more closely involved in a sub-study following from the DoA or the use of the data Platform. If the role of the partner would be comparable to that of an outside advisor, that partner would not be a data controller even though the partner is part of the consortium. We propose further nuances for the disentanglement which takes place in various steps. Uncertainty about formal controllership under the GDPR can stifle collaboration in consortia due to concerns over (shared) responsibility and liability. Data subjects' ability to exercise their right can also be affected by this. The funnel model proposes a way out of this conundrum.

Dicing with data: the risks, benefits, tensions and tech of health data in the iToBoS project

This paper will discuss the European funded iToBoS project, tasked by the European Commission to develop an AI diagnostic platform for the early detection of skin melanoma. The paper will outline the project, provide an overview of the data being processed, describe the impact assessment processes, and explain the AI privacy risk mitigation methods being deployed. Following this, the paper will offer a brief discussion of some of the more complex aspects: (1) the relatively low population clinical trial study cohort, which poses risks associated with data distinguishability and the masking ability of the applied anonymisation tools, (2) the project's ability to obtain informed consent from the study cohort given the complexity of the technologies, (3) the project's commitment to an open research data strategy and the additional privacy risk mitigations required to protect the multi-modal study data, and (4) the ability of the project to adequately explain the outputs of the algorithmic components to a broad range of stakeholders. The paper will discuss how the complexities have caused tension which are reflective of wider tensions in the health domain. A project level solution includes collaboration with a melanoma patient network, as an avenue for fair and representative qualification of risks and benefits with the patient stakeholder group. However, it is unclear how scalable this process is given the relentless pursuit of innovation within the health domain, accentuated by the continued proliferation of artificial intelligence, open data strategies, and the integration of multi-modal data sets inclusive of genomics.

Assessing and Improving Data Integrity in Web-Based Surveys: Comparison of Fraud Detection Systems in a COVID-19 Study

BACKGROUND

Web-based surveys increase access to study participation and improve opportunities to reach diverse populations. However, web-based surveys are vulnerable to data quality threats, including fraudulent entries from automated bots and duplicative submissions. Widely used proprietary tools to identify fraud offer little transparency about the methods used, effectiveness, or representativeness of resulting data sets. Robust, reproducible, and context-specific methods of accurately detecting fraudulent responses are needed to ensure integrity and maximize the value of web-based survey research.

OBJECTIVE

This study aims to describe a multilayered fraud detection system implemented in a large web-based survey about COVID-19 attitudes, beliefs, and behaviors; examine the agreement between this fraud detection system and a proprietary fraud detection system; and compare the resulting study samples from each of the 2 fraud detection methods.

METHODS

The PhillyCEAL Common Survey is a cross-sectional web-based survey that remotely enrolled residents ages 13 years and older to assess how the COVID-19 pandemic impacted individuals, neighborhoods, and communities in Philadelphia, Pennsylvania. Two fraud detection methods are described and compared: (1) a multilayer fraud detection strategy developed by the research team that combined automated validation of response data and real-time verification of study entries by study personnel and (2) the proprietary fraud detection system used by the Qualtrics (Qualtrics) survey platform. Descriptive statistics were computed for the full sample and for responses classified as valid by 2 different fraud detection methods, and classification tables were created to assess agreement between the methods. The impact of fraud detection methods on the distribution of vaccine confidence by racial or ethnic group was assessed.

RESULTS

Of 7950 completed surveys, our multilayer fraud detection system identified 3228 (40.60%) cases as valid, while the Qualtrics fraud detection system identified 4389 (55.21%) cases as valid. The 2 methods showed only "fair" or "minimal" agreement in their classifications (κ=0.25; 95% CI 0.23-0.27). The choice of fraud detection method impacted the distribution of vaccine confidence by racial or ethnic group.

CONCLUSIONS

The selection of a fraud detection method can affect the study's sample composition. The findings of this study, while not conclusive, suggest that a multilayered approach to fraud detection that includes conservative use of automated fraud detection and integration of human review of entries tailored to the study's specific context and its participants may be warranted for future survey research.

Purpose definition as a crucial step for determining the legal basis under the GDPR: implications for scientific research

The General Data Protection Regulation (GDPR) of the European Union, which became applicable in 2018, contains a new accountability principle. Under this principle, controllers (ie parties determining the purposes and the means of the processing of personal data) are responsible for ensuring and demonstrating the overall compliance with the GDPR. However, interpretive uncertainties of the GDPR mean that controllers must exercise considerable judgement in designing and implementing an appropriate compliance strategy, making GDPR compliance both complex and resource-intensive. In this article, we provide conceptual clarity around GDPR compliance with respect to one core aspect of the law: the determination and relevance of the purpose of personal data processing. We derive from the GDPR's text concrete requirements for purpose specification, which we subsequently apply to the area of secondary use of personal data for scientific research. We offer guidance for correctly specifying purposes of data processing under different research scenarios. To illustrate the practical necessity of purpose specification for GDPR compliance, we then show how our proposed approach can enable controllers to meet their compliance obligations, using the example of the overarching GDPR principle of lawfulness to highlight the relevance of purpose specification for the identification of a suitable legal basis.

Enhancing Safety on Construction Sites: A UWB-Based Proximity Warning System Ensuring GDPR Compliance to Prevent Collision Hazards

Construction is known as one of the most dangerous industries in terms of worker safety. Collisions due the excessive proximity of workers to moving construction vehicles are one of the leading causes of fatal and non-fatal accidents on construction sites internationally. Proximity warning systems (PWS) have been proposed in the literature as a solution to detect the risk for collision and to alert workers and equipment operators in time to prevent collisions. Although the role of sensing technologies for situational awareness has been recognised in previous studies, several factors still need to be considered. This paper describes the design of a prototype sensor-based PWS, aimed mainly at small and medium-sized construction companies, to collect real-time data directly from construction sites and to warn workers of a potential risk of collision accidents. It considers, in an integrated manner, factors such as cost of deployment, the actual nature of a construction site as an operating environment and data protection. A low-cost, ultra-wideband (UWB)-based proximity detection system has been developed that can operate with or without fixed anchors. In addition, the PWS is compliant with the General Data Protection Regulation (GDPR) of the European Union. A privacy-by-design approach has been adopted and privacy mechanisms have been used for data protection. Future work could evaluate the PWS in real operational conditions and incorporate additional factors for its further development, such as studies on the timely interpretation of data.

A proposal for an international Code of Conduct for data sharing in genomics

As genomic research becomes commonplace across the world, there is an increased need to coordinate practices among researchers, especially with regard to data sharing. One such way is an international code of conduct. In September 2020, an expert panel consisting of representatives from various fields convened to discuss a draft proposal formed via a synthesis of existing professional codes and other recommendations. This article presents an overview and analysis of the main issues related to international genomic research that were discussed by the expert panel, and the results of the discussion and follow up responses by the experts. As a result, the article presents as an annex a proposal for an international code of conduct for data sharing in genomics that is meant to establish best practices.

Does data protection law in South Africa apply to pseudonymised data?

The use of pseudonymised datasets is increasingly commonplace as research institutions seek to balance data utility with data security. Yet, a crucial question arises: How does South Africa's Protection of Personal Information Act (POPIA) govern these datasets, especially given their ambiguous state between de-identification and possible re-identification? A thorough examination of POPIA suggests that the determination of whether a pseudonymised dataset is personal information-and thus whether processing the dataset falls within POPIA's purview-must be informed by the specific context of the responsible party in possession of the pseudonymised dataset. When a research institution retains both the pseudonymised dataset and its linking dataset, the pseudonymised dataset remains identifiable and is thus personal information that falls within POPIA's purview. However, when only the pseudonymised dataset-without the linking dataset-is transferred to another entity, it is non-personal information in the hands of such a recipient, thus freeing the recipient from POPIA compliance. Such a delineation offers research institutions greater flexibility in sharing and using pseudonymised datasets. Importantly, because the original provider of the pseudonymised dataset (who has the means to re-identify the dataset) remains governed by POPIA, the privacy rights of data subjects are not undermined.

Common governance model: a way to avoid data segregation between existing trusted research environment

Background

Trusted Research Environments provide a legitimate basis for data access along with a set of technologies to support implementation of the "five-safes" framework for privacy protection. Lack of standard approaches in achieving compliance with the "five-safes" framework results in a diversity of approaches across different TREs. Data access and analysis across multiple TREs has a range of benefits including improved precision of analysis due to larger sample sizes and broader availability of out-of-sample records, particularly in the study of rare conditions. Knowledge of governance approaches used across UK-TREs is limited.

Objective

To document key governance features in major UK-TRE contributing to UK wide analysis and to identify elements that would directly facilitate multi TRE collaborations and federated analysis in future.

Method

We summarised three main characteristics across 15 major UK-based TREs: 1) data access environment; 2) data access requests and disclosure control procedures; and 3) governance models. We undertook case studies of collaborative analyses conducted in more than one TRE. We identified an array of TREs operating on an equivalent level of governance. We further identify commonly governed TREs with architectural considerations for achieving an equivalent level of information security management system standards to facilitate multi TRE functionality and federated analytics.

Results

All 15 UK-TREs allow pooling and analysis of aggregated research outputs only when they have passed human-operated disclosure control checks. Data access requests procedures are unique to each TRE. We also observed a variability in disclosure control procedures across various TREs with no or minimal researcher guidance on best practices for file out request procedures. In 2023, six TREs (40.0%) held ISO 20071 accreditation, while 9 TREs (56.2%) participated in four-nation analyses.

Conclusion

Secure analysis of individual-level data from multiple TREs is possible through existing technical solutions but requires development of a well-established governance framework meeting all stakeholder requirements and addressing public and patient concerns. Formation of a standard model could act as the catalyst for evolution of current TREs governance models to a multi TRE ecosystem within the UK and beyond.

Reconciling the ideals of open science with data privacy in the context of health research in Nigeria: A legal analysis

Background

The Nigerian Revised National Policy on Science, Technology and Innovation 2022 formally proposed the adoption of open science principles among researchers and all other stakeholders in Nigeria. With regard to health research, open science would allow the dissemination and sharing of scientific data and other research outputs among health researchers, thereby accelerating the scientific process and the development of innovative solutions for health challenges. However, in this context, the existing privacy and data protection rules can profoundly affect the adoption and sustainability of open science in Nigeria. This is because researchers rely heavily on accessing and sharing personal information and the biological samples of human subjects. Therefore, this study identifies and examines possible legal issues and challenges arising from the existing privacy and data protection rules as researchers adopt an open science approach to health research in Nigeria.

Methods

This study adopts a qualitative approach, providing a legal analysis of existing national, regional and international instruments on privacy, data protection and health research in the context of open science in Nigeria. The study also draws on relevant research and non-research articles on open science, health research, privacy and data protection published in English. Searches for the articles were conducted on various sites through the Google search engine, using terms such as 'sharing of data', 'data privacy', 'principles of data protection', 'open science', etc.

Results/Conclusion

The study finds that the right to privacy and data protection could affect the promotion, adoption and sustainability of open science among health researchers in Nigeria, as well as their ability to engage in open collaborative research with their peers in Africa and other jurisdictions. To prevent infringements of the privacy and data protection rules by researchers and thereby ensure the adoption of open science approaches in health research, the study further identifies various legal options for researchers, including using personal data or material transfer arrangements, which, among other things, require recipients or users of human data or biological samples to commit not to re-identify human subjects.

Cybersecurity: a critical priority for digital mental health

There has been a surge in the supply and demand of digital mental health support services in recent times. There have also been high profile cyberattacks specifically targeting mental health and behavioral services, along with a shift toward targeting vulnerable people directly. Cyberattacks involving personal health data, especially sensitive mental health data, could have devastating consequences to vulnerable people, those close to them, and many other stakeholders. This article calls for the immediate examination of the current state of cybersecurity in the digital mental healthcare industry to collectively identify risks and to protect user and provider vulnerabilities. This article points to the need to build a global cybersecurity culture within digital mental health while also working closely with other industries. The article concludes by making some preliminary recommendations to help support the creation of standards that will enhance the collective preparedness for future responses to cybersecurity threats and attacks.

Enhancing Data Protection in Dynamic Consent Management Systems: Formalizing Privacy and Security Definitions with Differential Privacy, Decentralization, and Zero-Knowledge Proofs

Dynamic consent management allows a data subject to dynamically govern her consent to access her data. Clearly, security and privacy guarantees are vital for the adoption of dynamic consent management systems. In particular, specific data protection guarantees can be required to comply with rules and laws (e.g., the General Data Protection Regulation (GDPR)). Since the primary instantiation of the dynamic consent management systems in the existing literature is towards developing sustainable e-healthcare services, in this paper, we study data protection issues in dynamic consent management systems, identifying crucial security and privacy properties and discussing severe limitations of systems described in the state of the art. We have presented the precise definitions of security and privacy properties that are essential to confirm the robustness of the dynamic consent management systems against diverse adversaries. Finally, under those precise formal definitions of security and privacy, we have proposed the implications of state-of-the-art tools and technologies such as differential privacy, blockchain technologies, zero-knowledge proofs, and cryptographic procedures that can be used to build dynamic consent management systems that are secure and private by design.

Artificial intelligence ethics in precision oncology: balancing advancements in technology with patient privacy and autonomy

Precision oncology is a rapidly evolving field that uses advanced technologies to deliver personalized cancer care based on a patient's unique genetic and clinical profile. The use of artificial intelligence (AI) in precision oncology has shown great potential to improve diagnosis, treatment planning, and treatment outcomes. However, the integration of AI in precision oncology also raises important ethical considerations related to patient privacy, autonomy, and protection from bias. In this opinion paper, an overview is provided of previous studies that have explored the use of AI in precision oncology and the ethical considerations associated with this technology. The conclusions of these studies are compared, and the importance of approaching the use of AI in precision oncology with caution is emphasized. It is stressed that patient privacy, autonomy, and protection from bias should be made central to the development and use of AI in precision oncology. Clear guidelines and regulations must be established to ensure that AI is used ethically and for the benefit of patients. The use of AI in precision oncology has the potential to revolutionize cancer care, but it should be ensured that it striked a balance between advancements in technology and ethical considerations. In conclusion, the use of AI in precision oncology is a promising development that has the potential to improve cancer outcomes. However, ethical considerations related to patient privacy, autonomy, and protection from bias must be central to the development and use of AI in precision oncology.

Open Data in the Era of the GDPR: Lessons from the Human Cell Atlas

The Human Cell Atlas (HCA) is striving to build an open community that is inclusive of all researchers adhering to its principles and as open as possible with respect to data access and use. However, open data sharing can pose certain challenges. For instance, being a global initiative, the HCA must contend with a patchwork of local and regional privacy rules. A notable example is the implementation of the European Union General Data Protection Regulation (GDPR), which caused some concern in the biomedical and genomic data-sharing community. We examine how the HCA's large, international group of researchers is investing tremendous efforts into ensuring appropriate sharing of data. We describe the HCA's objectives and governance, how it defines open data sharing, and ethico-legal challenges encountered early in its development; in particular, we describe the challenges prompted by the GDPR. Finally, we broaden the discussion to address tools and strategies that can be used to address ethical data governance.

Healthcare and Cybersecurity: Taking a Zero Trust Approach

This article looks at Healthcare and the issues that exist with current cybersecurity measures. As hacks including ransomware attacks become more commonplace it is important to provide safeguards to protect the data of the patients and the healthcare organization. Examples of breaches are looked at with insights on how they happened and what could have been done to prevent them. A newer approach known as Zero-Trust which addresses 7 key areas to protect is reviewed and shown how its applicability to healthcare can make a difference in protecting individuals and organizations.

Genetic data are not always personal-disaggregating the identifiability and sensitivity of genetic data

In both the EU and USA, genetic data are recognized as a special category of data that requires heightened privacy protection. Identifiability and sensitivity are central pillars of the regulatory framework in both jurisdictions: the privacy concerns stem from the assumption that genetic data are capable of identifying the individual and reveals sensitive information about them. But not all genetic data are identifiable and sensitive, nor are genetic data necessarily different from other types of big data in terms of these issues. This article argues that a more nuanced approach is needed to assess the threat to privacy interests posed by uses of genetic data. The privacy interests involved should be distinguished in terms of proposed use, the amount of data in question, and its uniqueness and informational content. When these factors are disaggregated, it is clear that both regulatory schemes could better achieve their goals by focusing more on the ways genetic data can be used rather than on their status as a special category of data.

Enhancing Data Protection via Auditable Informational Separation of Powers Between Workflow Engine Based Agents: Conceptualization, Implementation, and First Cross-Institutional Experiences

German best practice standards for secondary use of patient data require pseudonymization and informational separation of powers assuring that identifying data (IDAT), pseudonyms (PSN), and medical data (MDAT) are never simultaneously knowable by any party involved in data provisioning and use. We describe a solution meeting these requirements based on the dynamic interaction of three software agents: the clinical domain agent (CDA), which processes IDAT and MDAT, the trusted third party agent (TTA), which processes IDAT and PSN, and the research domain agent (RDA), which processes PSN and MDAT and delivers pseudonymized datasets. CDA and RDA implement a distributed workflow by employing an off-the-shelf workflow engine. TTA wraps the gPAS framework for pseudonym generation and persistence. All agent interactions are implemented via secured REST-APIs. Rollout to three university hospitals was seamless. The workflow engine allowed meeting various overarching requirements, including auditability of data transfer and pseudonymization, with minimal additional implementation effort. Using a distributed agent architecture based on workflow engine technology thus proved to be an efficient way to meet technical and organizational requirements for provisioning patient data for research purposes in a data protection compliant way.

The Promise of Artificial Intelligence in Digestive Healthcare and the Bioethics Challenges It Presents

With modern society well entrenched in the digital area, the use of Artificial Intelligence (AI) to extract useful information from big data has become more commonplace in our daily lives than we perhaps realize. Medical specialties that rely heavily on imaging techniques have become a strong focus for the incorporation of AI tools to aid disease diagnosis and monitoring, yet AI-based tools that can be employed in the clinic are only now beginning to become a reality. However, the potential introduction of these applications raises a number of ethical issues that must be addressed before they can be implemented, among the most important of which are issues related to privacy, data protection, data bias, explainability and responsibility. In this short review, we aim to highlight some of the most important bioethical issues that will have to be addressed if AI solutions are to be successfully incorporated into healthcare protocols, and ideally, before they are put in place. In particular, we contemplate the use of these aids in the field of gastroenterology, focusing particularly on capsule endoscopy and highlighting efforts aimed at resolving the issues associated with their use when available.

Biobanking for Neurodegenerative Diseases: Challenge for Translational Research and Data Privacy

Biobanking has emerged as a strategic challenge to promote knowledge on neurological diseases, by the application of translational research. Due to the inaccessibility of the central nervous system, the advent of biobanks, as structure collecting biospecimens and associated data, are essential to turn experimental results into clinical practice. Findings from basic research, omics sciences, and in silico studies, definitely require validation in clinically well-defined cohorts of patients, even more valuable when longitudinal, or including preclinical and asymptomatic individuals. Finally, collecting biological samples requires a great effort to guarantee respect for transparency and protection of sensitive data of patients and donors. Since the European General Data Protection Regulation 2016/679 has been approved, concerns about the use of data in biomedical research have emerged. In this narrative review, we focus on the essential role of biobanking for translational research on neurodegenerative diseases. Moreover, we address considerations for biological samples and data collection, the importance of standardization in the preanalytical phase, data protection (ethical and legal) and the role of donors in improving research in this field.

Predicting Teens' Privacy Management and Attitude Toward Data Protection on Social Media

This study contributes to ongoing debates on the protection of teens' privacy on social media. While ample research is focused on teens' privacy management, less is known about their attitude toward data protection. Drawing from cross-sectional survey data, this study examines whether predictors of privacy management, namely privacy literacy, privacy concern, and perceptions of data control, also predict teens' attitude toward data protection. Structural equation modeling indicates that all three factors positively predict this facet of privacy. No significant differences were found in the strength of predictors for boys versus girls for privacy management, although the findings do suggest some gender differences for attitude toward data protection that call for further research.

The application of data altruism in clinical research through empirical and legal analysis lenses

Background

The legal framework for clinical research in the EU is complex and the lack of harmonization of the relevant legal and ethical rules remains one of the main challenges for stakeholders in the field. The recently adopted Data Governance Act (DGA) and the proposal for a European Health Data Space (EHDS) promise to solve the existing challenges with respect to access to and (re)use of personal data for research, but also risk to further complexify the field. The DGA introduced a novel mechanism - data altruism. Data altruism is understood as the voluntary sharing of personal and non-personal data, based on the consent of data subjects or the permission of natural and legal persons, without seeking a reward and for objectives of general interest. This study aimed to gain insights into the opinion of clinical research stakeholders on data altruism, and to critically discuss key issues pertaining to the application of data altruism from a legal point of view.

Methods

Semi-structured interviews with (1) data protection officers (DPOs) and legal experts working with commercial and academic sponsors of clinical trials, (2) investigators, and (3) members of research ethics committees. Data underwent framework analysis. The legal discussion was comprised of legal doctrinal research with focus on the DGA, EHDS proposal, and the interplay with the EU General Data Protection Regulation (GDPR).

Results

Fourteen experts took part in the interviews, more than half of which were DPOs/legal experts. Interviewees were based in seven EU Member states and the United Kingdom. The majority of participants were critical towards the data altruism mechanism and pointed out challenges and risks associated with its application.

Conclusion

Although data altruism holds the potential to facilitate data sharing, its application in clinical research at the moment is still riddled with uncertainties. The interplay of the DGA rules with the provisions of the GDPR and the EHDS proposal are insufficiently clear and further efforts from the legislator are required to build a working, patient-centered, and research fostering data altruism system.

Challenges related to data protection in clinical research before and during the COVID-19 pandemic: An exploratory study

Background

The COVID-19 pandemic brought global disruption to health, society and economy, including to the conduct of clinical research. In the European Union (EU), the legal and ethical framework for research is complex and divergent. Many challenges exist in relation to the interplay of the various applicable rules, particularly with respect to compliance with the General Data Protection Regulation (GDPR). This study aimed to gain insights into the experience of key clinical research stakeholders [investigators, ethics committees (ECs), and data protection officers (DPOs)/legal experts working with clinical research sponsors] across the EU and the UK on the main challenges related to data protection in clinical research before and during the pandemic.

Materials and methods

The study consisted of an online survey and follow-up semi-structured interviews. Data collection occurred between April and December 2021. Survey data was analyzed descriptively, and the interviews underwent a framework analysis.

Results and conclusion

In total, 191 respondents filled in the survey, of whom fourteen participated in the follow-up interviews. Out of the targeted 28 countries (EU and UK), 25 were represented in the survey. The majority of stakeholders were based in Western Europe. This study empirically elucidated numerous key legal and ethical issues related to GDPR compliance in the context of (cross-border) clinical research. It showed that the lack of legal harmonization remains the biggest challenge in the field, and that it is present not only at the level of the interplay of key EU legislative acts and national implementation of the GDPR, but also when it comes to interpretation at local, regional and institutional levels. Moreover, the role of ECs in data protection was further explored and possible ways forward for its normative delineation were discussed. According to the participants, the pandemic did not bring additional legal challenges. Although practical challenges (for instance, mainly related to the provision of information to patients) were high due to the globally enacted crisis measures, the key problematic issues on (cross-border) health research, interpretations of the legal texts and compliance strategies remained largely the same.

Publishing Identifiable Patient Photographs in Scientific Journals: Scoping Review of Policies and Practices

Background

Publishing identifiable patient data in scientific journals may jeopardize patient privacy and confidentiality if best ethical practices are not followed. Current journal practices show considerable diversity in the publication of identifiable patient photographs, and different stakeholders may have different opinions of and practices in publishing patient photographs.

Objective

This scoping review aimed to identify existing evidence and map knowledge gaps in medical research on the policies and practices of publishing identifiable photographs in scientific articles.

Methods

We performed a comprehensive search of the Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, CINAHL with Full Text, Database of Abstracts of Reviews of Effects, Ovid MEDLINE, and Scopus. The Open Science Framework, PROSPERO, BASE, Google Scholar, OpenGrey, ClinicalTrials.gov, the Campbell Collaboration Library, and Science.gov were also searched.

Results

After screening the initial 15,949 titles and abstracts, 98 (0.61%) publications were assessed for eligibility at the full-text level, and 30 (0.19%) publications were included in this review. The studies were published between 1994 and 2020; most had a cross-sectional design and were published in journals covering different medical disciplines. We identified 3 main topics. The first included ethical aspects of the use of facial photographs in publications. In different clinical settings, the consent process was not conducted properly, and health professionals did not recognize the importance of obtaining written patient consent for taking and using patient medical photographs. They often considered verbal consent sufficient or even used the photographs without consent. The second topic included studies that investigated the practices and use of medical photography in publishing. Both patients and doctors asked for confidential storage and maintenance of medical photographs. Patients preferred to be photographed by their physicians using an institutional camera and preferred nonidentifiable medical photographs not only for publication but also in general. Conventional methods of deidentification of facial photographs concealing the eye area were recognized as unsuccessful in protecting patient privacy. The third topic emerged from studies investigating medical photography in journal articles. These studies showed great diversity in publishing practices regarding consent for publication of medical photographs. Journal policies regarding the consent process and consent forms were insufficient, and existing ethical professional guidelines were not fully implemented in actual practices. Patients’ photographs from open-access medical journals were found on public web-based platforms.

Conclusions

This scoping review showed a diversity of practices in publishing identifiable patient photographs and an unsatisfactory level of knowledge of this issue among different stakeholders despite existing standards. Emerging issues include the availability of patients’ photographs from open-access journals or preprints in the digital environment. There is a need to improve standards and processes to obtain proper consent to fully protect the privacy of patients in published articles.

An Enhanced Steganography Network for Concealing and Protecting Secret Image Data

The development of Internet technology has provided great convenience for data transmission and sharing, but it also brings serious security problems that are related to data protection. As is detailed in this paper, an enhanced steganography network was designed to protect secret image data that contains private or confidential information; this network consists of a concealing network and a revealing network in order to achieve image embedding and recovery separately. To reduce the system's computation complexity, we constructed the network's framework using a down-up structure in order to compress the intermediate feature maps. In order to mitigate the input's information loss caused by a sequence of convolution blocks, the long skip concatenation method was designed to pass the raw information to the top layer, thus synthesizing high-quality hidden images with fine texture details. In addition, we propose a novel strategy called non-activated feature fusion (NAFF), which is designed to provide stronger supervision for synthetizing higher-quality hidden images and recovered images. In order to further boost the hidden image's visual quality and enhance its imperceptibility, an attention mechanism-based enhanced module was designed to reconstruct and enhance the salient target, thus covering up and obscuring the embedded secret content. Furthermore, a hybrid loss function that is composed of pixel domain loss and structure domain loss was designed to boost the hidden image's structural quality and visual security. Our experimental results demonstrate that, due to the elaborate design of the network structure and loss function, our proposed method achieves high levels of imperceptibility and security.

Implementation of a pan-European ecosystem and an interoperable platform for Smart and Healthy Ageing in Europe: An Innovation Action research protocol

As life expectancy continues to increase in most EU Member States, smart technologies can help enable older people to continue living at home, despite the challenges accompanying the ageing process. The Innovation Action (IA) SHAPES 'Smart and Healthy Ageing through People Engaging in Supportive Systems' funded by the EU under the Horizon 2020 Research and Innovation Programme (grant agreement number 857159) attends to these topics to support active and healthy ageing and the wellbeing of older adults. This protocol article outlines the SHAPES project's objectives and aims, methods, structure, and expected outcomes. SHAPES seeks to build, pilot, and deploy a large-scale, EU-standardised interoperable, and scalable open platform. The platform will facilitate the integration of a broad range of technological, organisational, clinical, educational, and social solutions. SHAPES emphasises that the home is much more than a house-space; it entails a sense of belonging, a place and a purpose in the community. SHAPES creates an ecosystem - a network of relevant users and stakeholders - who will work together to scale-up smart solutions. Furthermore, SHAPES will create a marketplace seeking to connect demand and supply across the home, health and care services. Finally, SHAPES will produce a set of recommendations to support key stakeholders seeking to integrate smart technologies in their care systems to mediate care delivery. Throughout, SHAPES adopts a multidisciplinary research approach to establish an empirical basis to guide the development of the platform. This includes long-term ethnographic research and a large-scale pan-European campaign to pilot the platform and its digital solutions within the context of seven distinct pilot themes. The project will thereby address the challenges of ageing societies in Europe and facilitate the integration of community-based health and social care. SHAPES will thus be a key driver for the transformation of healthcare and social care services across Europe.

Proportionate response to the COVID-19 threat? Use of apps and other technologies for monitoring employees under the European Union's data protection framework

This article explores the potential uses by employers of contact-tracing apps and other monitoring technologies to mitigate the spread of COVID-19, and the potential concerns that these raise in the context of the European Union's General Data Protection Regulation. Given the imbalance of power in the employment relationship, the authors call for national laws to strengthen employees' ability to refuse the use of such apps and technologies after the end of the COVID-19 pandemic. When such tools are no longer needed to keep employees safe, additional regulations and guidance will be necessary to prevent future problems, such as function creep and other misuse by employers.

Why are you offline? The issue of digital consent and discrimination of Roma communities during pandemic in Slovakia

The article is focused on the issue of discrimination against Roma communities in the use of educational online platforms on account of failure to provide digital consent. The digital education model used during the COVID-19 pandemic in the Slovak Republic somehow forgot about Roma communities, thus preventing Roma students from accessing education through online platforms. This contribution is aimed to foster the discussion of discrimination against marginalized Roma communities in accessing education through online platforms and to point out the inadequacy of national legislation on the provision of digital consent.

A Consent Tool for Secondary Use of Biomedical Data

To pursue scientific goals with patient data usually requires informed consent from the data subjects. Such a consent constitutes a contract between the research institute and the patient. Several issues must be included in the consent to be valid, for example, how the data is processed and stored as well as specifics of the research questions for which the data is going to be used. Here, we describe the development and the implementation of a user-friendly IT solution that supports the process-oriented obtainment of consents. Current solutions often focus only on the benefits for the researcher. Our solution intends to add value to all participants and to reduce paperwork to a minimum. The consent Tool was evaluated by a usability test using the UEQ Method (User Experience Questionnaire) and received positive feedback - both efficiency and originality were rated above the average UEQ-Benchmark. Nevertheless, the lack of compatibility with the technical infrastructure of the hospital was a significant shortcoming. Hence, although there is a general interest in digitized solutions in the healthcare sector, there are still many hurdles to implement them and roll them out.

Artificial intelligence in laboratory medicine: fundamental ethical issues and normative key-points

The contribution of laboratory medicine in delivering value-based care depends on active cooperation and trust between pathologist and clinician. The effectiveness of medicine more in general depends in turn on active cooperation and trust between clinician and patient. From the second half of the 20th century, the art of medicine is challenged by the spread of artificial intelligence (AI) technologies, recently showing comparable performances to flesh-and-bone doctors in some diagnostic specialties. Being the principle source of data in medicine, the laboratory is a natural ground where AI technologies can disclose the best of their potential. In order to maximize the expected outcomes and minimize risks, it is crucial to define ethical requirements for data collection and interpretation by-design, clarify whether they are enhanced or challenged by specific uses of AI technologies, and preserve these data under rigorous but feasible norms. From 2018 onwards, the European Commission (EC) is making efforts to lay the foundations of sustainable AI development among European countries and partners, both from a cultural and a normative perspective. Alongside with the work of the EC, the United Kingdom provided worthy-considering complementary advice in order to put science and technology at the service of patients and doctors. In this paper we discuss the main ethical challenges associated with the use of AI technologies in pathology and laboratory medicine, and summarize the most pertaining key-points from the guidelines and frameworks before-mentioned.

Confidentiality breaches in hospital: the experiences of young people and parents

BACKGROUND

Healthcare professionals, including nurses, have a vital role in protecting patient confidentiality. However, evidence shows that breaches of confidentiality are common in hospitals for various reasons, including the ward design and issues related to healthcare staff's professionalism. The situation can be complicated further in paediatric care because of the age range of patients and their associated needs and rights about information sharing, confidentiality and consent.

AIM

To explore the views and experiences of young people admitted to hospital, and their parents and related caregivers, about the effects of confidentiality breaches.

METHOD

A descriptive questionnaire-based survey was conducted in 2018 in two regional hospitals in Denmark over two weeks. A total of 214 surveys were completed by parents and related caregivers (n=173) and by young people (n=41).

FINDINGS

Many parents and young people reported that they had overheard healthcare professionals discussing care, including information about named patients, test results, personal disclosures and various comments or opinions. In many cases these breaches of confidentiality affected patients' overall experience of hospital and led some to withhold important information.

CONCLUSION

This study demonstrates the challenges of preserving confidentiality in children's wards. Situations in which confidentiality breaches were reported appear to have been affected by the physical environment, such as ward design, as well as staff behaviour and attitudes. Therefore, nurses and other healthcare professionals need to enhance their understanding of issues related to confidentiality and pay attention to how and where information about patients is shared.

Solutions to Big Data Privacy and Security Challenges Associated With COVID-19 Surveillance Systems

The growing dependency on digital technologies is becoming a way of life, and at the same time, the collection of data using them for surveillance operations has raised concerns. Notably, some countries use digital surveillance technologies for tracking and monitoring individuals and populations to prevent the transmission of the new coronavirus. The technology has the capacity to contribute towards tackling the pandemic effectively, but the success also comes at the expense of privacy rights. The crucial point to make is regardless of who uses and which mechanism, in one way another will infringe personal privacy. Therefore, when considering the use of technologies to combat the pandemic, the focus should also be on the impact of facial recognition cameras, police surveillance drones, and other digital surveillance devices on the privacy rights of those under surveillance. The GDPR was established to ensure that information could be shared without causing any infringement on personal data and businesses; therefore, in generating Big Data, it is important to ensure that the information is securely collected, processed, transmitted, stored, and accessed in accordance with established rules. This paper focuses on Big Data challenges associated with surveillance methods used within the COVID-19 parameters. The aim of this research is to propose practical solutions to Big Data challenges associated with COVID-19 pandemic surveillance approaches. To that end, the researcher will identify the surveillance measures being used by countries in different regions, the sensitivity of generated data, and the issues associated with the collection of large volumes of data and finally propose feasible solutions to protect the privacy rights of the people, during the post-COVID-19 era.

Comparing definitions of data and information in data protection law and machine learning: A useful way forward to meaningfully regulate algorithms?

The notion of information is central to data protection law, and to algorithms/machine learning. This centrality gives the impressions that algorithms are just yet another data processing operation to be regulated. A more careful analysis reveals a number of issues. The notion of personal data is notoriously under-defined, and attempts at clarification from an information theory perspective are also equivocal. The paper therefore attempts a clarification of the meaning of data and information in the context of information theory, which it uses in order to clarify the notion of personal data. In doing so, it shows that data protection law is grounded in the logic of knowledge communication, which stands in stark contrast with machine learning, which is predicated upon the logic of knowledge production, and hence, upon different definitions of data and information. This is what ultimately explains the failure of data protection to adequately regulate machine learning algorithms.

Data protection, information governance and the potential erosion of ethnographic methods in health care?

With the most recent developments to the European General Data Protection Regulations (GDPR) introduced in May 2018, the resulting legislation meant a new set of considerations for study approvers and health-care researchers. Compared with previous legislation in the UK (The Data Protection Act, 1998), it introduced more extensive and directive principles, requiring anybody 'processing' personal data to specifically define how this data will be obtained, stored, used and destroyed. Importantly, it also emphasised the principle of accountability, which meant that data controllers and processors could no longer just state that they planned to adhere to lawful data protection principles, they also had to demonstrate compliance. New questions and concerns around accountability now appear to have increased levels of scrutiny in all areas of information governance (IG), especially with regards to processing confidential patient information. This article explores our experiences of gaining required ethical and regulatory approvals for an ethnographic study in a UK health-care setting, the implications that the common law duty of confidentiality had for this research, and the ways in which IG challenges were overcome. The purpose of this article was to equip researchers embarking on similar projects to be able to navigate the potentially problematic and complex journey to approval.

Establishment of a national DNA database in the Republic of Serbia: Legal aspects and implications for the future

Forensic DNA databases have been established in the vast majority of European countries and represent an essential personal identification instrument in the criminal justice system. The implementation of suitable legislation and accompanying legal practice is an imperative to ensure sensible use of the DNA repository, without interfering with the rights, freedoms and privacy of individuals and their families. Thus, the complex matter of DNA databank effectiveness and intrusiveness is a delicate balancing act, which has resulted in diverse database governing regimes among different countries. In 2018, the National Assembly of the Republic of Serbia passed the first Law on the National DNA Register, which was created by the Ministry of Interior. This short and imprecise regulation was announced without prior consultation with professional stakeholders or the public, resulting in controversy. In this article, we examine essential questions pertinent to launching a forensic DNA database through the lens of the new Law on the National DNA Register in the Republic of Serbia and in comparison to the other European approaches and guidelines to regulating forensic DNA repositories. We further discuss the resulting legal, social and ethical implications and concerns and propose future actions intended to improve the legal framework. We believe that stimulating such discussions throughout the scientific and professional community will result in the advancement of DNA profiling and databasing in a future European Union member state and, more generally, the manner in which biological data are managed in various countries, particularly those that are developing.

Maximizing the positive and minimizing the negative: Social media data to study youth mental health with informed consent

Social media usage impacts upon the mental health and wellbeing of young people, yet there is not enough evidence to determine who is affected, how and to what extent. While it has widened and strengthened communication networks for many, the dangers posed to at-risk youth are serious. Social media data offers unique insights into the minute details of a user's online life. Timely consented access to data could offer many opportunities to transform understanding of its effects on mental wellbeing in different contexts. However, limited data access by researchers is preventing such advances from being made. Our multidisciplinary authorship includes a lived experience adviser, academic and practicing psychiatrists, and academic psychology, as well as computational, statistical, and qualitative researchers. In this Perspective article, we propose a framework to support secure and confidential access to social media platform data for research to make progress toward better public mental health.

Ethics and governance for internet-based conservation science research

Internet-based research is increasingly important for conservation science and has wide-ranging applications and contexts, including culturomics, illegal wildlife trade, and citizen science. However, online research methods pose a range of ethical and legal challenges. Online data may be protected by copyright, database rights, or contract law. Privacy rights may also restrict the use and access of data, as well as ethical requirements from institutions. Online data have real-world meaning, and the ethical treatment of individuals and communities must not be marginalized when conducting internet-based research. As ethics frameworks originally developed for biomedical applications are inadequate for these methods, we propose that research activities involving the analysis of preexisting online data be treated analogous to offline social science methods, in particular, nondeceptive covert observation. By treating internet users and their data with respect and due consideration, conservationists can uphold the public trust needed to effectively address real-world issues.

International federation of genomic medicine databases using GA4GH standards

We promote a shared vision and guide for how and when to federate genomic and health-related data sharing, enabling connections and insights across independent, secure databases. The GA4GH encourages a federated approach wherein data providers have the mandate and resources to share, but where data cannot move for legal or technical reasons. We recommend a federated approach to connect national genomics initiatives into a global network and precision medicine resource.

Signalling Standards for Progress: Bridging the Divide Between a Valid Consent to Use Patient Data Under Data Protection Law and the Common Law Duty of Confidentiality

In this article, we analyse the legal components of disclosing confidential patient information under the UK's common law duty of confidentiality (CLDoC) and processing personal (health) data under the UK's General Data Protection Regulation (GDPR) and Data Protection Act 2018. We describe the ostensible divide between the CLDoC and data protection law when it comes to the requirements of a valid signal of consent by a patient to use and disclose patient information, obtained by a health professional in the context of direct care, for health care and health research purposes. Ultimately, our analysis suggests that we are saddled, at least in the medium term, with two regimes operating with different standards of a valid consent-while putatively protecting similar interests. There is, however, opportunity for progress. It is possible to improve professional guidance on the interaction between the regimes and to achieve significant normative alignment without aligning the signalling standard for consent; this would promote consistent protection of reasonable expectations of patients across both regimes. Further coherence would require aligning not only the standard, but also the role played by consent under each regime. Here we argue that, in relation to direct care, any such shift should be away from consent as the normal justification. In relation to health research, on the contrary, it should be toward consent as the normal justification for use and disclosure of patient information under both the CLDoC and data protection law.

A scalable software solution for anonymizing high-dimensional biomedical data

BACKGROUND

Data anonymization is an important building block for ensuring privacy and fosters the reuse of data. However, transforming the data in a way that preserves the privacy of subjects while maintaining a high degree of data quality is challenging and particularly difficult when processing complex datasets that contain a high number of attributes. In this article we present how we extended the open source software ARX to improve its support for high-dimensional, biomedical datasets.

FINDINGS

For improving ARX's capability to find optimal transformations when processing high-dimensional data, we implement 2 novel search algorithms. The first is a greedy top-down approach and is oriented on a formally implemented bottom-up search. The second is based on a genetic algorithm. We evaluated the algorithms with different datasets, transformation methods, and privacy models. The novel algorithms mostly outperformed the previously implemented bottom-up search. In addition, we extended the GUI to provide a high degree of usability and performance when working with high-dimensional datasets.

CONCLUSION

With our additions we have significantly enhanced ARX's ability to handle high-dimensional data in terms of processing performance as well as usability and thus can further facilitate data sharing.

Exploring the prevalence and profile of epilepsy across Europe using a standard retrospective chart review: Challenges and opportunities

OBJECTIVE

This study aimed to determine the prevalence of epilepsy in four European countries (Austria, Denmark, Ireland, and Romania) employing a standard methodology. The study was conducted under the auspices of ESBACE (European Study on the Burden and Care of Epilepsy).

METHODS

All hospitals and general practitioners serving a region of at least 50 000 persons in each country were asked to identify patients living in the region who had a diagnosis of epilepsy or experienced a single unprovoked seizure. Medical records were accessed, where available, to complete a standardized case report form. Data were sought on seizure frequency, seizure type, investigations, etiology, comorbidities, and use of antiseizure medication. Cases were validated in each country, and the degree of certainty was graded as definite, probable, or suspect cases.

RESULTS

From a total population of 237 757 in the four countries, 1988 (.8%) patients were identified as potential cases of epilepsy. Due to legal and ethical issues in the individual countries, medical records were available for only 1208 patients, and among these, 113 had insufficient clinical information. The remaining 1095 cases were classified as either definite (n = 706, 64.5%), probable (n = 191, 17.4%), suspect (n = 153, 14.0%), or not epilepsy (n = 45, 4.1%).

SIGNIFICANCE

Although a precise prevalence estimate could not be generated from these data, the study found a high validity of epilepsy classification among evaluated cases (95.9%). More generally, this study highlights the significant challenges facing epidemiological research methodologies that are reliant on patient consent and retrospective chart review, largely due to the introduction of data protection legislation during the study period. Documentation of the epilepsy diagnosis was, in some cases, relatively low, indicating a need for improved guidelines for assessment, follow-up, and documentation. This study highlights the need to address the concerns and requirements of recruitment sites to engage in epidemiological research.

COVID-19 digital contact tracing applications and techniques: A review post initial deployments

The coronavirus disease 2019 (COVID-19) is a severe global pandemic that has claimed millions of lives and continues to overwhelm public health systems in many countries. The spread of COVID-19 pandemic has negatively impacted the human mobility patterns such as daily transportation-related behavior of the public. There is a requirement to understand the disease spread patterns and its routes among neighboring individuals for the timely implementation of corrective measures at the required placement. To increase the effectiveness of contact tracing, countries across the globe are leveraging advancements in mobile technology and Internet of Things (IoT) to aid traditional manual contact tracing to track individuals who have come in close contact with identified COVID-19 patients. Even as the first administration of vaccines begins in 2021, the COVID-19 management strategy will continue to be multi-pronged for the foreseeable future with digital contact tracing being a vital component of the response along with the use of preventive measures such as social distancing and the use of face masks. After some months of deployment of digital contact tracing technology, deeper insights into the merits of various approaches and the usability, privacy, and ethical trade-offs involved are emerging. In this paper, we provide a comprehensive analysis of digital contact tracing solutions in terms of their methodologies and technologies in the light of the new data emerging about international experiences of deployments of digital contact tracing technology. We also provide a discussion on open challenges such as scalability, privacy, adaptability and highlight promising directions for future work.

Data Protection Using Polymorphic Pseudonymisation in a Large-Scale Parkinson's Disease Study

This paper describes an advanced form of pseudonymisation in a large cohort study on Parkinson's disease, called Personalized Parkinson Project (PPP). The study collects various forms of biomedical data of study participants, including data from wearable devices with multiple sensors. The participants are all from the Netherlands, but the data will be usable by research groups worldwide on the basis of a suitable data use agreement. The data are pseudonymised, as required by Europe's General Data Protection Regulation (GDPR). The form of pseudonymisation that is used in this Parkinson project is based on cryptographic techniques and is 'polymorphic': it gives each participating research group its own 'local' pseudonyms. Still, the system is globally consistent, in the sense that if one research group adds data to PPP under its own local pseudonyms, the data become available for other groups under their pseudonyms. The paper gives an overview how this works, without going into the cryptographic details.

Ethical implications of AI in robotic surgical training: A Delphi consensus statement

CONTEXT

As the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them.

OBJECTIVES

To provide ethical guidance on developing narrow AI applications for surgical training curricula. We define standardised approaches to developing AI driven applications in surgical training that address current recognised ethical implications of utilising AI on surgical data. We aim to describe an ethical approach based on the current evidence, understanding of AI and available technologies, by seeking consensus from an expert committee.

EVIDENCE ACQUISITION

The project was carried out in 3 phases: (1) A steering group was formed to review the literature and summarize current evidence. (2) A larger expert panel convened and discussed the ethical implications of AI application based on the current evidence. A survey was created, with input from panel members. (3) Thirdly, panel-based consensus findings were determined using an online Delphi process to formulate guidance. 30 experts in AI implementation and/or training including clinicians, academics and industry contributed. The Delphi process underwent 3 rounds. Additions to the second and third-round surveys were formulated based on the answers and comments from previous rounds. Consensus opinion was defined as ≥ 80% agreement.

EVIDENCE SYNTHESIS

There was 100% response from all 3 rounds. The resulting formulated guidance showed good internal consistency, with a Cronbach alpha of >0.8. There was 100% consensus that there is currently a lack of guidance on the utilisation of AI in the setting of robotic surgical training. Consensus was reached in multiple areas, including: 1. Data protection and privacy; 2. Reproducibility and transparency; 3. Predictive analytics; 4. Inherent biases; 5. Areas of training most likely to benefit from AI.

CONCLUSIONS

Using the Delphi methodology, we achieved international consensus among experts to develop and reach content validation for guidance on ethical implications of AI in surgical training. Providing an ethical foundation for launching narrow AI applications in surgical training. This guidance will require further validation.

PATIENT SUMMARY

As the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them.In this paper we provide guidance on ethical implications of AI in surgical training.

Ensuring Purpose Limitation in Large-Scale Infrastructures with Provenance-Enabled Access Control

The amount of data generated in today’s world has a fair share of personal information about individuals that helps data owners and data processors in providing them with personalized services. Different legal and regulatory obligations apply to all data owners collecting personal information, specifying they use it only for the agreed-upon purposes and in a transparent way to preserve privacy. However, it is difficult to achieve this in large-scale and distributed infrastructures as data is continuously changing its form, such as through aggregation with other sources or the generation of new transformed resources, resulting often in the loss or misinterpretation of the collection purpose. In order to preserve the authorized collection purposes, we propose data is added as a part of immutable and append-only resource metadata (provenance), to be retrieved by an access control mechanism when required for data-usage verification. This not only ensures purpose limitation in large-scale infrastructures but also provides transparency for individuals and auditing authorities to track how personal information is used.