Decentralizing Health Care: History and Opportunities of Web3

This paper explores the relationship between the development of the internet and health care, highlighting their parallel growth and mutual influence. It delves into the transition from the early, static days of Web 1.0, akin to siloed physician expertise in health care, to the more interactive and patient-centric era of Web 2.0, which was accompanied by advancements in medical technologies and patient engagement. This paper then focuses on the emerging era of Web3-the decentralized web-which promises a transformative shift in health care, particularly in how patient data are managed, accessed, and used. This shift toward Web3 involves using blockchain technology for decentralized data storage to enhance patient data access, control, privacy, and value. This paper also examines current applications and pilot projects demonstrating Web3's practical use in health care and discusses key questions and considerations for its successful implementation.

Shaping the Future of Health Data: A Scenario-Based Approach

BACKGROUND

The European health care industry faces massive changes which impose new challenges on its stakeholders.

OBJECTIVE

In this paper, we present the results of a market-based analysis for upcoming changes in the European health care industry and what this specifically means for issues corresponding to data.

METHOD

Scenarios are a common tool to explain and analyze future changes in business environments. This method was used in a series of workshops together with an interdisciplinary group of experts.

RESULTS

Ten individual scenarios represent potential futures with distinctive subsets of data landscapes. Their assessment shows that the expected future of health data is still rather unclear, while desired changes are quite distinct.

CONCLUSION

The Health Data Scenarios offer a comprehensive framework for analyzing future data-driven developments in the health care industry.

Passive Data Use for Ethical Digital Public Health Surveillance in a Postpandemic World

There is a fundamental need to establish the most ethical and effective way of tracking disease in the postpandemic era. The ubiquity of mobile phones is generating large amounts of passive data (collected without active user participation) that can be used as a tool for tracking disease. Although discussions of pragmatism or economic issues tend to guide public health decisions, ethical issues are the foremost public concern. Thus, officials must look to history and current moral frameworks to avoid past mistakes and ethical pitfalls. Past pandemics demonstrate that the aftermath is the most effective time to make health policy decisions. However, an ethical discussion of passive data use for digital public health surveillance has yet to be attempted, and little has been done to determine the best method to do so. Therefore, we aim to highlight four potential areas of ethical opportunity and challenge: (1) informed consent, (2) privacy, (3) equity, and (4) ownership.

Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed

Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science. These initiatives offer greater transparency in science, with the opportunity for external research groups to reproduce, replicate, and extend research findings. Further, larger datasets offer the opportunity to identify homogeneous patterns within subgroups of individuals, where these patterns may be obscured by the heterogeneity of the neurobiological measure in smaller samples. However, data sharing and data pooling initiatives are not without their challenges, especially with new laws that may at first glance appear quite restrictive for open science initiatives. Interestingly, what is key to some of these new laws (i.e, the European Union's general data protection regulation) is that they provide greater control of data to those who "give" their data for research purposes. Thus, the most important element in data sharing is allowing the participants to make informed decisions about how they want their data to be used, and, within the law of the specific country, to follow the participants' wishes. This framework encompasses obtaining thorough informed consent and allowing the participant to determine the extent that they want their data shared, many of the ethical and legal obstacles are reduced to just monsters under the bed. In this manuscript we discuss the many options and obstacles for data sharing, from fully open, to federated learning, to fully closed. Importantly, we highlight the intersection of data sharing, privacy, and data ownership and highlight specific examples that we believe are informative to the neuroimaging community.

Edge Computing to Secure IoT Data Ownership and Trade with the Ethereum Blockchain

With an increasing penetration of ubiquitous connectivity, the amount of data describing the actions of end-users has been increasing dramatically, both within the domain of the Internet of Things (IoT) and other smart devices. This has led to more awareness of users in terms of protecting personal data. Within the IoT, there is a growing number of peer-to-peer (P2P) transactions, increasing the exposure to security vulnerabilities, and the risk of cyberattacks. Blockchain technology has been explored as middleware in P2P transactions, but existing solutions have mainly focused on providing a safe environment for data trade without considering potential changes in interaction topologies. we present EdgeBoT, a proof-of-concept smart contracts based platform for the IoT built on top of the ethereum blockchain. With the Blockchain of Things (BoT) at the edge of the network, EdgeBoT enables a wider variety of interaction topologies between nodes in the network and external services while guaranteeing ownership of data and end users’ privacy. in EdgeBoT, edge devices trade their data directly with third parties and without the need of intermediaries. This opens the door to new interaction modalities, in which data producers at the edge grant access to batches of their data to different third parties. Leveraging the immutability properties of blockchains, together with the distributed nature of smart contracts, data owners can audit and are aware of all transactions that have occurred with their data. we report initial results demonstrating the potential of EdgeBoT within the IoT. we show that integrating our solutions on top of existing IoT systems has a relatively small footprint in terms of computational resource usage, but a significant impact on the protection of data ownership and management of data trade.

Application of a Blockchain Platform to Manage and Secure Personal Genomic Data: A Case Study of LifeCODE.ai in China

Background

The rapid development of genetic and genomic technologies, such as next-generation sequencing and genome editing, has made disease treatment much more precise and effective. The technologies’ value can only be realized by the aggregation and analysis of people’s genomic and health data. However, the collection and sharing of genomic data has many obstacles, including low data quality, information islands, tampering distortions, missing records, leaking of private data, and gray data transactions.

Objective

This study aimed to prove that emerging blockchain technology provides a solution for the protection and management of sensitive personal genomic data because of its decentralization, traceability, encryption algorithms, and antitampering features.

Methods

This paper describes the case of a blockchain-based genomic big data platform, LifeCODE.ai, to illustrate the means by which blockchain enables the storage and management of genomic data from the perspectives of data ownership, data sharing, and data security.

Results

Blockchain opens up new avenues for dealing with data ownership, data sharing, and data security issues in genomic big data platforms and realizes the psychological empowerment of individuals in the platform.

Conclusions

The blockchain platform provides new possibilities for the management and security of genetic data and can help realize the psychological empowerment of individuals in the process, and consequently, the effects of data self-governance, incentive-sharing, and security improvement can be achieved. However, there are still some problems in the blockchain that have not been solved, and which require continuous in-depth research and innovation in the future.

Aspects of Data Ethics in a Changing World: Where Are We Now?

Ready data availability, cheap storage capacity, and powerful tools for extracting information from data have the potential to significantly enhance the human condition. However, as with all advanced technologies, this comes with the potential for misuse. Ethical oversight and constraints are needed to ensure that an appropriate balance is reached. Ethical issues involving data may be more challenging than the ethical challenges of some other advanced technologies partly because data and data science are ubiquitous, having the potential to impact all aspects of life, and partly because of their intrinsic complexity. We explore the nature of data, personal data, data ownership, consent and purpose of use, trustworthiness of data as well as of algorithms and of those using the data, and matters of privacy and confidentiality. A checklist is given of topics that need to be considered.

Who Owns the Data? Open Data for Healthcare

Research on large shared medical datasets and data-driven research are gaining fast momentum and provide major opportunities for improving health systems as well as individual care. Such open data can shed light on the causes of disease and effects of treatment, including adverse reactions side-effects of treatments, while also facilitating analyses tailored to an individual's characteristics, known as personalized or "stratified medicine." Developments, such as crowdsourcing, participatory surveillance, and individuals pledging to become "data donors" and the "quantified self" movement (where citizens share data through mobile device-connected technologies), have great potential to contribute to our knowledge of disease, improving diagnostics, and delivery of -healthcare and treatment. There is not only a great potential but also major concerns over privacy, confidentiality, and control of data about individuals once it is shared. Issues, such as user trust, data privacy, transparency over the control of data ownership, and the implications of data analytics for personal privacy with potentially intrusive inferences, are becoming increasingly scrutinized at national and international levels. This can be seen in the recent backlash over the proposed implementation of care.data, which enables individuals' NHS data to be linked, retained, and shared for other uses, such as research and, more controversially, with businesses for commercial exploitation. By way of contrast, through increasing popularity of social media, GPS-enabled mobile apps and tracking/wearable devices, the IT industry and MedTech giants are pursuing new projects without clear public and policy discussion about ownership and responsibility for user-generated data. In the absence of transparent regulation, this paper addresses the opportunities of Big Data in healthcare together with issues of responsibility and accountability. It also aims to pave the way for public policy to support a balanced agenda that safeguards personal information while enabling the use of data to improve public health.