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.

Novel and Proven Models of Public, Private, and Public-Private Partnerships in Healthcare: An Update.

Initiatives to share assets in the life science sector through dedicated partnerships had and still have a multitude of different aspects in the past few decades. The range goes from industry partners, small and big companies, in bilateral agreements with academic institutions up to large privately and publicly funded consortia. In general, the term public-private partnership (PPP) is used when at least one public (non-profit, academic, and/or government) part and one or more private for-profit partners are involved. A Public-Private Partnership is often driven by a public body, i.e. a ministry or a public agency. Their synergism has been described 10 years ago (Dearing, Science 315(19):344-347, 2007; Casty and Wieman, Ther Innov Regul Sci 47(3):375-383, 2013; Stevens et al., Biotechnol Law Rep 34(4):153-165, 2015). So why view this synergism again today? It will be shown that the situation in life science has changed: novel partners acting digital, data expertise being involved on many levels and novel partnering models arising. Success and challenges will be described in this chapter.

[Legal aspects in digital cardiology]. / Rechtliche Aspekte in der digitalen Kardiologie.

Digital assistants have become an indispensable tool in modern cardiology. The associated technological progress offers a significant potential to increase the efficiency of medical processes, enable more precise diagnoses in a shorter time, and thus improve patient care. However, the integration of digital assistants into clinical cardiology also raises new challenges and questions, particularly regarding the handling of legal issues. This review article aims to raise awareness of individual legal issues resulting from the use of digital technologies in cardiology. The focus is on how to deal with various legal challenges that cardiologists face, including issues related to treatment freedom, professional confidentiality and data protection. The integration of digital assistants in cardiology leads to a noticeable improvement in efficiency and quality of patient care, but at the same time, it involves a variety of legal challenges that need to be carefully addressed.

Acceptance of Social Media Recruitment for Clinical Studies Among Patients With Hepatitis B: Mixed Methods Study.

BACKGROUND: Social media platforms are increasingly used to recruit patients for clinical studies. Yet, patients' attitudes regarding social media recruitment are underexplored. OBJECTIVE: This mixed methods study aims to assess predictors of the acceptance of social media recruitment among patients with hepatitis B, a patient population that is considered particularly vulnerable in this context. METHODS: Using a mixed methods approach, the hypotheses for our survey were developed based on a qualitative interview study with 6 patients with hepatitis B and 30 multidisciplinary experts. Thematic analysis was applied to qualitative interview analysis. For the cross-sectional survey, we additionally recruited 195 patients with hepatitis B from 3 clinical centers in Germany. Adult patients capable of judgment with a hepatitis B diagnosis who understood German and visited 1 of the 3 study centers during the data collection period were eligible to participate. Data analysis was conducted using SPSS (version 28; IBM Corp), including descriptive statistics and regression analysis. RESULTS: On the basis of the qualitative interview analysis, we hypothesized that 6 factors were associated with acceptance of social media recruitment: using social media in the context of hepatitis B (hypothesis 1), digital literacy (hypothesis 2), interest in clinical studies (hypothesis 3), trust in nonmedical (hypothesis 4a) and medical (hypothesis 4b) information sources, perceiving the hepatitis B diagnosis as a secret (hypothesis 5a), attitudes toward data privacy in the social media context (hypothesis 5b), and perceived stigma (hypothesis 6). Regression analysis revealed that the higher the social media use for hepatitis B (hypothesis 1), the higher the interest in clinical studies (hypothesis 3), the more trust in nonmedical information sources (hypothesis 4a), and the less secrecy around a hepatitis B diagnosis (hypothesis 5a), the higher the acceptance of social media as a recruitment tool for clinical hepatitis B studies. CONCLUSIONS: This mixed methods study provides the first quantitative insights into social media acceptance for clinical study recruitment among patients with hepatitis B. The study was limited to patients with hepatitis B in Germany but sets out to be a reference point for future studies assessing the attitudes toward and acceptance of social media recruitment for clinical studies. Such empirical inquiries can facilitate the work of researchers designing clinical studies as well as ethics review boards in balancing the risks and benefits of social media recruitment in a context-specific manner.

Ethical Considerations and Fundamental Principles of Large Language Models in Medical Education: Viewpoint.

This viewpoint article first explores the ethical challenges associated with the future application of large language models (LLMs) in the context of medical education. These challenges include not only ethical concerns related to the development of LLMs, such as artificial intelligence (AI) hallucinations, information bias, privacy and data risks, and deficiencies in terms of transparency and interpretability but also issues concerning the application of LLMs, including deficiencies in emotional intelligence, educational inequities, problems with academic integrity, and questions of responsibility and copyright ownership. This paper then analyzes existing AI-related legal and ethical frameworks and highlights their limitations with regard to the application of LLMs in the context of medical education. To ensure that LLMs are integrated in a responsible and safe manner, the authors recommend the development of a unified ethical framework that is specifically tailored for LLMs in this field. This framework should be based on 8 fundamental principles: quality control and supervision mechanisms; privacy and data protection; transparency and interpretability; fairness and equal treatment; academic integrity and moral norms; accountability and traceability; protection and respect for intellectual property; and the promotion of educational research and innovation. The authors further discuss specific measures that can be taken to implement these principles, thereby laying a solid foundation for the development of a comprehensive and actionable ethical framework. Such a unified ethical framework based on these 8 fundamental principles can provide clear guidance and support for the application of LLMs in the context of medical education. This approach can help establish a balance between technological advancement and ethical safeguards, thereby ensuring that medical education can progress without compromising the principles of fairness, justice, or patient safety and establishing a more equitable, safer, and more efficient environment for medical education.

The potential of federated learning for public health purposes: a qualitative analysis of GDPR compliance, Europe, 2021.

BackgroundThe wide application of machine learning (ML) holds great potential to improve public health by supporting data analysis informing policy and practice. Its application, however, is often hampered by data fragmentation across organisations and strict regulation by the General Data Protection Regulation (GDPR). Federated learning (FL), as a decentralised approach to ML, has received considerable interest as a means to overcome the fragmentation of data, but it is yet unclear to which extent this approach complies with the GDPR.AimOur aim was to understand the potential data protection implications of the use of federated learning for public health purposes.MethodsBuilding upon semi-structured interviews (n = 14) and a panel discussion (n = 5) with key opinion leaders in Europe, including both FL and GDPR experts, we explored how GDPR principles would apply to the implementation of FL within public health.ResultsWhereas this study found that FL offers substantial benefits such as data minimisation, storage limitation and effective mitigation of many of the privacy risks of sharing personal data, it also identified various challenges. These challenges mostly relate to the increased difficulty of checking data at the source and the limited understanding of potential adverse outcomes of the technology.ConclusionSince FL is still in its early phase and under rapid development, it is expected that knowledge on its impracticalities will increase rapidly, potentially addressing remaining challenges. In the meantime, this study reflects on the potential of FL to align with data protection objectives and offers guidance on GDPR compliance.

[Short paths to diagnosis with artificial intelligence: systematic literature review on diagnostic decision support systems]. / Kurze Wege zur Diagnose mit künstlicher Intelligenz ­ systematische Literaturrecherche zu "diagnostic decision support systems".

BACKGROUND: Rare diseases are often recognized late. Their diagnosis is particularly challenging due to the diversity, complexity and heterogeneity of clinical symptoms. Computer-aided diagnostic aids, often referred to as diagnostic decision support systems (DDSS), are promising tools for shortening the time to diagnosis. Despite initial positive evaluations, DDSS are not yet widely used, partly due to a lack of integration with existing clinical or practice information systems. OBJECTIVE: This article provides an insight into currently existing diagnostic support systems that function without access to electronic patient records and only require information that is easily obtainable. MATERIALS AND METHODS: A systematic literature search identified eight articles on DDSS that can assist in the diagnosis of rare diseases with no need for access to electronic patient records or other information systems in practices and hospitals. The main advantages and disadvantages of the identified rare disease diagnostic support systems were extracted and summarized. RESULTS: Symptom checkers and DDSS based on portrait photos and pain drawings already exist. The degree of maturity of these applications varies. CONCLUSION: DDSS currently still face a number of challenges, such as concerns about data protection and accuracy, and acceptance and awareness continue to be rather low. On the other hand, there is great potential for faster diagnosis, especially for rare diseases, which are easily overlooked due to their large number and the low awareness of them. The use of DDSS should therefore be carefully considered by doctors on a case-by-case basis.

[Chat messenger use in the care of patients with Parkinson's disease]. / Chat-Messenger-Nutzung in der Parkinson-Versorgung.

BACKGROUND: The demand for chat messaging apps for communication between physicians, therapists and patients is increasing. The expectations for this form of communication and uncertainties regarding introduction and use are heterogeneous. OBJECTIVE: The implementation of chat messengers in the care of patients with Parkinson's disease should be facilitated by recommendations regarding introduction and usage. METHODS: Semi-structured interviews with neurologists and physiotherapists were conducted to capture the expectations and needs regarding the use of chat messengers. From the data analysis, recommendations were derived. RESULTS: The expectations for technical functionality exceeded the chat messenger functions. This concerns, e.g., the connection of the chat messenger to the electronic patient file. There is a great deal of uncertainty, particularly when it comes to the applicable General Data Protection Regulations (GDPR). The recommendations relating to the use of chat messengers, data protection aspects, the design of such tools and methodological considerations can help to implement the tool as an additional communication channel. CONCLUSION: Practical recommendations regarding functionality, the use of chat messengers in everyday life and in relation to data protection are derived from the results. By improving knowledge, physicians and therapists can contribute to the successful establishment of chat messengers as an additional communication tool.

[Open educational resources for otorhinolaryngology : A pilot study on needs assessment and implementation]. / Open Educational Resources für die HNO-Heilkunde : Ein Pilotprojekt zur Bedarfsanalyse und Implementierung.

BACKGROUND: Open educational resources (OER) are educational materials licensed openly by authors, permitting usage, redistribution, and in some instances, modification. OER platforms thereby serve as a medium for distributing and advancing teaching materials and innovative educational methodologies. OBJECTIVE: This study aims to determine the present state of OER in otorhinolaryngology and to examine the prerequisites for seamlessly integrating OER into the curricular teaching of medical schools, specifically through the design of two OER blended learning modules. METHODS: OER content in the field of otorhinolaryngology was analyzed on OER platforms, ensuring its relevance to the German medical curriculum. Data protection concerns were addressed with legal counsel. The blended learning modules were developed in collaboration with medical students and subsequently published as OER. RESULTS AND CONCLUSION: This project yielded the first OER from a German ENT department, tailored to the German medical curriculum. One significant barrier to OER use in medicine, more than in other fields, is data protection. This challenge can be navigated by obtaining consent to publish patient data as OER. OER hold the promise to play a pivotal role in fostering cooperation and collaboration among educators, aiding educators in lesson preparation, and simultaneously enhancing didactic quality.

[Artificial intelligence and secure use of health data in the KI-FDZ project: anonymization, synthetization, and secure processing of real-world data]. / Künstliche Intelligenz und sichere Gesundheitsdatennutzung im Projekt KI-FDZ: Anonymisierung, Synthetisierung und sichere Verarbeitung für Real-World-Daten.

The increasing digitization of the healthcare system is leading to a growing volume of health data. Leveraging this data beyond its initial collection purpose for secondary use can provide valuable insights into diagnostics, treatment processes, and the quality of care. The Health Data Lab (HDL) will provide infrastructure for this purpose. Both the protection of patient privacy and optimal analytical capabilities are of central importance in this context, and artificial intelligence (AI) provides two opportunities. First, it enables the analysis of large volumes of data with flexible models, which means that hidden correlations and patterns can be discovered. Second, synthetic - that is, artificial - data generated by AI can protect privacy.This paper describes the KI-FDZ project, which aims to investigate innovative technologies that can support the secure provision of health data for secondary research purposes. A multi-layered approach is investigated in which data-level measures can be combined in different ways with processing in secure environments. To this end, anonymization and synthetization methods, among others, are evaluated based on two concrete application examples. Moreover, it is examined how the creation of machine learning pipelines and the execution of AI algorithms can be supported in secure processing environments. Preliminary results indicate that this approach can achieve a high level of protection while maintaining data validity. The approach investigated in the project can be an important building block in the secure secondary use of health data.