Tim K. Mackey, Alec J. Calac, Tiana McMann, Ken Miyachi · 10 authors
Background: Historic and ongoing problematic practices regarding the collection, storage, and use of Indigenous health data have led to the need to ensure principles of Indigenous Data Sovereignty (IDS) are followed in research practices and technology development. Objective: This project, a partnership between UC San Diego and the Native BioData Consortium (NativeBio), sought to explore the practical application of blockchain technology and its potential to facilitate Indigenous-led research collaboration. Methods: This project first undertook purposeful relationship building with NativeBio to form a Community Advisory Board (CAB) for identifying community and technology needs for a blockchain research collaboration platform with an initial focus on genomic data. Over a 2-year project period, a series of public meetings and presentations at Indigenous-led conferences introduced the concept of exploring compatibility between blockchain and IDS principles, followed by iterative prototyping and co-design of a blockchain platform with NativeBio, using Ethereum as the underlying protocol. Results: Direct engagement with NativeBio and the CAB informed the initial design and development of a "b-IDS" proof-of-concept (POC) blockchain platform. The POC consists of three main components: (1) the web front-end layer, (2) the Ethereum network that executes the smart contract and blockchain storage aspects of the framework, and (3) the back-end database that stores off-chain interactions and data for future use with external genomic data repositories. After refinement of the POC, a community-based participatory research (CBPR) use case aligned with IDS principles was identified as a practical workflow and incorporated into the design of the POC for implementation. Conclusions: The findings from this project demonstrated the potential use of operationalizing IDS through blockchain technology with proactive and sustained engagement with Indigenous partners. Blockchain technology may have certain advantages over other data governance approaches and systems, facilitating timely oversight, shared decision-making and consent structures, and direct involvement of Indigenous communities in technology design, respecting the core principles of IDS and CBPR. Future development of the blockchain-IDS POC will need to incorporate other research practices and ethics frameworks to expand its use to other public health and biomedical research use cases.
Mohammad Shahid, Paritosh Ramanan, Mohammad Fili, Guiping Hu · 5 authors
Analysis of clinical data is a cornerstone of biomedical research with applications in areas such as genomic testing and response characterization of therapeutic drugs. Maintaining strict privacy controls is essential because such data typically contains personally identifiable health information of patients. At the same time, regulatory compliance often requires study managers to demonstrate the integrity and authenticity of participant data used in analyses. Balancing these competing requirements of privacy preservation and verifiable accountability remains a critical challenge. In this paper, we present CoSMeTIC, a zero-knowledge computational framework that proposes computational Sparse Merkle Trees (SMTs) as a means to generate verifiable inclusion and exclusion proofs for individual participants' data in clinical studies. We formally analyze the zero-knowledge properties of CoSMeTIC and evaluate its computational efficiency through extensive experiments. We demonstrate the framework on Huntington's disease and HIV-1 case studies, using simulated CAG-repeat cohorts derived from published summary statistics and published de-identified clinical lab measurements of virus samples. Using two-sample Kolmogorov-Smirnov and likelihood-ratio hypothesis tests, along with logistic-regression-based genomic analyses on the de-identified datasets, we show that CoSMeTIC achieves strong privacy guarantees while maintaining statistical fidelity. Our results suggest that CoSMeTIC provides a scalable and practical alternative for achieving regulatory compliance with rigorous privacy protection in large-scale clinical research.
Over 30 pilot programs worldwide are now sequencing newborn genomes, collectively screening tens of thousands of infants. Yet no governance framework exists to protect the resulting data from breaches, commercial exploitation, or institutional misuse. I argue that whole genome sequencing at birth should be recognized as a universal right, what I term genomic sovereignty . Under this model, each newborn’s genome is sequenced shortly after birth and delivered to the parents on an encrypted physical device, a genomic birth certificate, with no institutional copy retained. The sequencing facility purges its records upon delivery, and zero-knowledge proof protocols ensure that no traceable metadata links the genome to the child’s identity. Parents serve as temporary custodians until the child reaches adulthood and assumes full control over the data, including the right to re-encrypt, seek clinical interpretation, participate in research, or decline engagement entirely. This approach eliminates the centralized databases that have proven vulnerable to breaches and commercial exploitation, as demonstrated by recent high-profile data breaches and corporate bankruptcies in the commercial genomics sector. The genome’s lifelong value as a health resource, one that appreciates as medical knowledge advances, means that data acquired at birth will become increasingly informative over decades. Health systems already administering newborn screening are the natural stewards of this initiative, ensuring equitable access regardless of geography or income. As sequencing costs approach $100 per genome and converge with what public health systems already spend on traditional newborn screening, the economic and ethical case for universal implementation becomes compelling.
The deployment of artificial intelligence in healthcare is increasingly constrained by privacy, equity, and regulatory compliance challenges, especially in multilingual and cross-border contexts.Traditional centralized machine learning approaches are limited by restrictions on patient data sharing, raising both ethical and legal concerns.Federated learning offers a promising solution by enabling distributed training across institutions without transferring raw data, yet ensuring trust and privacy in federated systems remains a critical barrier.This study proposes a novel framework that combines transformer architectures with encrypted federated datasets anchored by blockchain zero-knowledge proofs (ZKPs) to achieve privacy-preserving, equitable, and multilingual healthcare diagnostics.Transformer-based models, known for their strength in natural language processing and multimodal learning, are adapted to operate on encrypted federated datasets spanning diverse linguistic and demographic contexts.Blockchain provides a decentralized trust layer, while zero-knowledge proofs ensure verifiable model updates without exposing sensitive patient information.This combination allows healthcare providers to collaboratively train diagnostic models that maintain strong predictive performance while adhering to strict privacy guarantees.The framework also advances health equity by enabling multilingual diagnostics that address disparities in underrepresented populations.By integrating explainability mechanisms, stakeholders gain insights into model reasoning across diverse cultural and linguistic datasets.Case applications in federated medical imaging, multilingual clinical notes, and genomic diagnostics highlight the framework's capacity to balance accuracy, privacy, and fairness.Overall, the integration of transformers, federated learning, and blockchain ZKPs represents a pathway toward trustworthy and equitable AI-driven healthcare, enabling collaborative innovation while safeguarding patient rights.
Marielle S. Gross, Ananya Dewan, Mario Macis, Eve Budd · 9 authors
Introduction Organoids are living, patient-derived tumor models that are revolutionizing precision medicine and drug development, however current privacy practices strip identifiers, thereby undermining ethics, efficiency, and effectiveness for patients and research enterprises alike. Decentralized biobanking “de-bi” applies non-fungible tokens (NFTs) to empower privacy-preserving specimen tracking and data sharing for networks of scientists, donors, and physicians. We design, develop, and demonstrate a functional de-bi platform for a real-world organoid biobank. Methods Ethnography of the organoid biobanking ecosystem was performed in 2022–2023, with site visits, interviews, focus groups, and structured observations of stakeholder interactions. An initial ERC-721 prototype was developed and tested, informing the design of a comprehensive NFT model. Web and mobile app prototypes were developed with a suite of ERC-1155 protocols representing ecosystem constituents as NFTs. We demonstrated the platform with publicly available Human Cancer Models Initiatives organoids to establish proof-of-concept for decentralized biobanking as the foundation of a democratized biomedical metaverse, or “biomediverse.” Results Scientists revealed key challenges for organoid research and development under policy, scientific, and economic constraints of the life science landscape. We advanced decentralized biobanking as a blockchain overlay network solution with potential to overcome barriers, enhance utility and unlock value by uniting collaborators in a privacy-preserving biomediverse. Dedicated smart contracts created “soulbound” NFTs as de-identified digital twins of patients, physicians, and scientists in a networked organoid ecosystem. We modeled biospecimen collection, processing, and distribution, including generation and expansion of organoids, via an auditable on-chain mechanism. Key features included the ability to bootstrap the digital twin NFT model onto an established organoid biobank, visibility of patient-linked biospecimens and related research activities for all ecosystem participants, as well as tooling for multisided data exchange. Implementing de-bi with ERC-1155 showed potential to minimize gas costs of on-chain activity vs ERC-721, though complementary layer-2 solutions will be essential for economic viability. Conclusion Decentralized biobanking has the potential to enhance efficiency, increase translational impact and drive research discovery through implementation of NFT digital twins for organoid research networks. Importantly, this approach also bolsters ethical practices by fostering inclusion, ensuring transparency, and enhancing accountability across the research ecosystem. Next steps include live pilot testing, market design research to align stakeholder incentives, and technical solutions to support a sustainable, scalable and mutually rewarding biomediverse.
This article examines the transformative potential of blockchain technology and its integration with artificial intelligence (AI) in clinical trials, focusing on their combined ability to enhance integrity, operational efficiency, and transparency in the data governance. Through an in-depth analysis of recent advancements, the article highlights how blockchain and AI address critical challenges, including patient data privacy, regulatory compliance, and security. The article also identifies key barriers to adoption in the mentioned integration, such as scalability limitations, association with existing healthcare systems, and high implementation costs. By presenting a comprehensive overview of the current research and proposing strategic directions, this work emphasizes how the synergy between blockchain and AI can revolutionize clinical trials through process automation, improved stakeholder trust, and robust transparency.
Open access
Artificial Intelligence in Healthcare and Education
Caspar Barnes, Mateo Aboy, Timo Minssen, Jemima Winifred Allen · 7 authors
Participation in research is supposed to be voluntary and informed. Yet it is difficult to ensure people are adequately informed about the potential uses of their biological materials when they donate samples for future research. We propose a novel consent framework which we call "demonstrated consent" that leverages blockchain technology and generative AI to address this problem. In a demonstrated consent model, each donated sample is associated with a unique non-fungible token (NFT) on a blockchain, which records in its metadata information about the planned and past uses of the sample in research, and is updated with each use of the sample. This information is accessible to a large language model (LLM) customized to present this information in an understandable and interactive manner. Thus, our model uses blockchain and generative AI technologies to track, make available, and explain information regarding planned and past uses of donated samples.
Open access
Ethics in Clinical Research
Artificial Intelligence in Healthcare and Education
Jingcheng Zhang, Yingxuan Ren, Man Ho Au, Ka-Ho Chow · 9 authors
Abstract With the rapid developments in sequencing technologies, individuals now have unprecedented access to their genomic data. However, existing data management systems or protocols are inadequate for protecting privacy, limiting individuals’ control over their genomic information, hindering data sharing, and posing a challenge for biomedical research. To fill the gap, an owner-governed system that fulfills owner authority, lifecycle data encryption, and verifiability at the same time is prompted. In this paper, we realized Governome, an owner-governed data management system designed to empower individuals with absolute control over their genomic data during data sharing. Governome uses a blockchain to manage all transactions and permissions, enabling data owners with dynamic permission management and to be fully informed about every data usage. It uses homomorphic encryption and zero-knowledge proofs to enable genomic data storage and computation in an encrypted and verifiable form for its whole lifecycle. Governome supports genomic analysis tasks, including individual variant query, cohort study, GWAS analysis, and forensics. Query of a variant’s genotype distribution among 2,504 1kGP individuals in Governome can be efficiently completed in under 18 hours on an ordinary server. Governome is an open-source project available at https://github.com/HKU-BAL/Governome .
William Sánchez, Larue Linder, Robert C. Miller, Amelia Hood · 5 authors
Introduction: Scientists use donated biospecimens to create organoids, which are miniature copies of patient tumors that are revolutionizing precision medicine and drug discovery. However, biobanking platforms remove donor identifiers to protect privacy, precluding patients from benefiting from their contributions or sharing information that may be relevant to research outcomes. Decentralized biobanking (de-bi) leverages blockchain technology to empower patient engagement in biospecimen research. We describe the creation of the first de-bi prototype for an organoid biobanking use case. Methods: We designed and developed a proof-of-concept non-fungible tokens (NFTs) framework for an organoid research network of patients, physicians, and scientists within a synthetic dataset modeled on a real-world breast cancer organoid ecosystem. Our implementation deployed multiple smart contracts on Ethereum test networks, minting NFTs representing each stakeholder, biospecimen, and organoid. The system architecture was designed to be composable with established biobanking programs. Results: Our de-bi prototype demonstrated how NFTs representing patients, physicians, scientists, and organoids may be united in a privacy-preserving platform that builds upon relationships and transactions of existing biobank research networks. The mobile application simulated key features, enabling patients to track their biospecimens, view organoid images and research updates from scientists, and allow physicians to participate in peer-to-peer communications with basic scientists and patients alike, all while ensuring compliance with de-identification requirements. Discussion: We demonstrate proof-of-concept for a web3 platform engaging patients, physicians, and scientists in a dynamic research community, unlocking value for a model organoid ecosystem. This initial prototype is a critical first step for advancing paradigm-shifting de-bi technology that provides unprecedented transparency and suggests new standards for equity and inclusion in biobanking. Further research must address feasibility and acceptability considering the ethical, legal, economic, and technical complexities of organoid research and clinical translation.
Chapter 7 unites the different dimensions explored in each of the earlier chapters into a cohesive whole to understand the artificial intelligence (AI)-powered global public health ecosystem as humanity’s common home: its decentralized organic design (financing and integral sustainable development), framework (data architecture and political economics), inhabitants (culture and demographics), and foundation (ethics and human security balancing national security). It summarizes the key findings for these domains from the earlier chapters while highlighting emblematic AI case uses. It considers financing advances, including in universal health coverage, public–private partnerships, digital global health diplomacy, finance tracking, and value-based health. This chapter moves on to integral sustainable development advances, including in AI for the sustainable development goals, precision agriculture, climate change, affordable clean energy, equity, and generative AI (including ChatGPT). It then considers data architecture advances, including in the United Kingdom’s hybrid data architecture, India’s federated data architecture, swarm learning, gossip learning, blockchain, edge computing, application programming interfaces, augmented public health intelligence, quantum computing, zero-trust security, blockchain, and data solidarity. This chapter then considers political economic advances particularly from the perspective of Political Liberalism–bridging democracies and autocracies, including in data governance models (spanning Europe’s general data protection regulation and Japan’s agile governance), managed strategic competition, and World Health Organization coordination. Finally, this chapter considers AI ethics for the health ecosystem. Particular emphasis is given to how population aging, multicultural diversity, and human security requires more inclusive discussion of diverse perspectives, as through Personalist Social Contract ethics to generate and sustain substantive convergence on the unifying values of human dignity, rights, and sovereignty that then give rise to effective and equitable collective action. This chapter concludes by applying the abovesaid dimensions to concrete AI use cases for the global public health ecosystem that illustrates this integral approach, including ethics by design or embedded AI ethics (within existing ecosystem operations and structures), democratizing AI or personalizing AI (as with end-to-end AI platforms and edge computing expanding and interlinking free and affordable AI services for larger audiences), and ecosystem interoperability (uniting political economic interoperability, data interoperability, and moral interoperability to leverage global resources and insights for local communities leading their own projects).
Open access
Artificial Intelligence in Healthcare and Education
Wendy Charles, Mark B. van der Waal, J. Flach, Arno Bisschop · 7 authors
BACKGROUND: Blockchain has been proposed as a critical technology to facilitate more patient-centric research and health information sharing. For instance, it can be applied to coordinate and document dynamic informed consent, a procedure that allows individuals to continuously review and renew their consent to the collection, use, or sharing of their private health information. Such has been suggested to facilitate ethical, compliant longitudinal research, and patient engagement. However, blockchain-based dynamic consent is a relatively new concept, and it is not yet clear how well the suggested implementations will work in practice. Efforts to critically evaluate implementations in health research contexts are limited. OBJECTIVE: The objective of this protocol is to guide the identification and critical appraisal of implementations of blockchain-based dynamic consent in health research contexts, thereby facilitating the development of best practices for future research, innovation, and implementation. METHODS: The protocol describes methods for an integrative review to allow evaluation of a broad range of quantitative and qualitative research designs. The PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) framework guided the review's structure and nature of reporting findings. We developed search strategies and syntax with the help of an academic librarian. Multiple databases were selected to identify pertinent academic literature (CINAHL, Embase, Ovid MEDLINE, PubMed, Scopus, and Web of Science) and gray literature (Electronic Theses Online Service, ProQuest Dissertations and Theses, Open Access Theses and Dissertations, and Google Scholar) for a comprehensive picture of the field's progress. Eligibility criteria were defined based on PROSPERO (International Prospective Register of Systematic Reviews) requirements and a criteria framework for technology readiness. A total of 2 reviewers will independently review and extract data, while a third reviewer will adjudicate discrepancies. Quality appraisal of articles and discussed implementations will proceed based on the validated Mixed Method Appraisal Tool, and themes will be identified through thematic data synthesis. RESULTS: Literature searches were conducted, and after duplicates were removed, 492 articles were eligible for screening. Title and abstract screening allowed the removal of 312 articles, leaving 180 eligible articles for full-text review against inclusion criteria and confirming a sufficient body of literature for project feasibility. Results will synthesize the quality of evidence on blockchain-based dynamic consent for patient-centric research and health information sharing, covering effectiveness, efficiency, satisfaction, regulatory compliance, and methods of managing identity. CONCLUSIONS: The review will provide a comprehensive picture of the progress of emerging blockchain-based dynamic consent technologies and the rigor with which implementations are approached. Resulting insights are expected to inform best practices for future research, innovation, and implementation to benefit patient-centric research and health information sharing. TRIAL REGISTRATION: PROSPERO CRD42023396983; http://tinyurl.com/cn8a5x7t. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50339.
In December 2021, one of the authors of the present paper (AR) took part in the peer review of the paper “Safety and immunogenicity of an inactivated virus particle vaccine for SARS-CoV-2, BIV1-CovIran: findings from double-blind, randomized, placebo-controlled, phase I and II clinical trials among healthy adults” for the BMJ Open1, 2. The manuscript described clinical phases I and II of the COVID-19 vaccine BIV1-CovIran by Shifa Pharmed Industrial Group. The article was accepted for publication in March 2021 after three review rounds, with a total of six reviewers involved. On May 2022, AR received an email from Yeganeh Torbati, a Washington Post reporter who was investigating the development of BIV1-CovIran. Torbati asked AR for a general opinion about the data presented in the above article. AR replied that no serious anomalies were highlighted, although he specified that the peer review process was too superficial to guarantee complete integrity. Subsequently, through an article published in the Washington Post in August 2022, Torbati disclosed serious misconduct dynamics3. In support of her claims, an official correction was published in the BMJ Open in November 2022, in which the authors were forced to admit various conflicts of interest and the occurrence of vaccine-related adverse effects1. The relevant fact is that not even six peer reviewers and one editor have discovered such a hidden scenario. This is not intended to blame the journal or the reviewers but only to denounce that the world of scientific publication is currently subject to easy ethical violations. Although financial relationships can markedly bias biomedical research, marginal importance is given to this aspect4, 5. In this regard, this letter proposes a set of practices to counteract some major integrity problems.What can authors do?A1. Authors should facilitate research reproducibility to boost peer-review speed and accuracy. This includes i) sharing codes, calculations, and data (raw and elaborated), and ii) providing a step-by-step description of the ideas that led to the realization of the project, the implementation of the methods, and the procedures to assess the tests' assumptions.A2. Authors should adopt frameworks for enhancing quality in preclinical data since this can significantly increase transparency and trust in results and allow errors to be prevented rather than detected too late6.A3. Regarding clinical trials, authors should publicly share audit/monitoring documents, information about the contract research organization that monitored the study, and data submitted to regulatory agencies (at least on the clinical testing front, which does not seem to threaten intellectual property or industry secrets).A4. Authors should release a preprint version so as to allow the scientific community to review the results independently and rapidly.What can academic journals do?J1. Journal editors should evaluate the paper’s health sensitivity and decide whether it is a high-sensitivity topic. All research involving novel drugs, vaccines, and therapeutic strategies should be considered at high sensitivity.J2. For high-sensitivity topics, journals should compulsorily require A1-A4. Any draft version that has passed peer review should be released at the very moment of approval, explicitly indicating that it is an unedited peer-reviewed version. Reviewers' reports and authors' responses should always be published, ensuring easy citability (e.g., DOI). Reviewers' names and affiliations should also be published unless they express reasonable fears for their safety. This should help reduce the problem of coercive citations7. Finally, journals should allow authors to publicly share editorial rejection decisions, including reviewers' reports.J3. For high-sensitivity topics, journal editors should stratify the peer review to ensure the validity of the key elements. Alongside a general assessment, each methodological aspect (e.g., design, population, data collection, statistical analysis, and results) should be carefully evaluated by one or more independent specific experts, especially when dealing with high-complexity data or multidisciplinary approaches. Regarding clinical trials, editors should involve expert figures to evaluate pharmacological and public health aspects (e.g., adverse reaction reports). Journals should also include a specific mandatory section in which reviewers declare the limitations of their review (e.g., “I'm not an expert on Bayesian methods”), so that editors and readers have a clear understanding of what the reviewers assessed. Finally, double-blind review should be required to reduce authorship bias8.J4. For high-sensitivity topics, journal editors should create a dedicated section made up of two or more journalists experienced in detecting ethical violations. Such supervision should extend to the authors but also the reviewers, who could voluntarily influence the publication process. The inquiry must only concern researchers' professional relationships and activities, without affecting the private sphere, in order to safeguard their privacy. The academic journal should propose to the reporters to sign a non-disclosure agreement regarding the data found and guarantee the quality of the investigation.J5. For high-sensitivity topics, journals should compulsorily require that the data are suitably standardized to allow decentralized analysis through automated tools, software, or artificial intelligence algorithms6, 9. Specific guidelines should be provided to help authors with the A1 point. Means for decentralized analysis should be provided to reviewers. Should a unique international standardization be chosen by regulatory agencies, journals would have to adhere to it.J6. For high-sensitivity topics, journals should pay peer reviewers and editors. Indeed, paying peer reviewers – a sustainable practice, as shown by the editorial policies of various journals – would foster excellence thanks to an economic reward proportional to the reviewer's skill (competition mechanism). One of the main obstacles to publication, namely the difficulty in finding available reviewers, would be quickly overcome. Scientists could play this role on a permanent and ongoing basis thanks to the benefits of true job performance. Paid work would increase the actual responsibility of peer reviewers and editors.What can abstracting service groups do?I1. Tiered indexing should be introduced by abstracting services. The top rank should only be granted to academic journals that meet J1-J6. Indeed, since indexing in recognized databases is a source of prestige (so much so that, in most cases, journals reserve a special section of their websites to this scope), doing so would drive health journals to adjust to the new standards. Moreover, this would help the public to identify the most authoritative and reliable journals. Similar initiatives are already underway10.What can regulatory agencies, funders, and institutions do?R1. Funders and regulatory agencies should require necessary authors' compliance with points A1-A3.R2. Regulatory agencies should agree on a unique international data standardization (see point J5) so as to strengthen and accelerate scrutiny by the whole scientific community.R3. Institutions and employers should actively encourage and support scientific refereeing. Moreover, funders should be willing to finance an extra amount to properly perform points J3, J4, and J6.In conclusion, we do ask the scientific community to take a clear position and make itself heard with a stentorian voice to protect public health from ethical misconduct. This renewal would lead not only to direct benefits to the research but also to the public image of the whole scientific world, thanks to a novel, more transparent, efficient, and effective procedure of academic publication. We are aware that these guidelines are tailored to the medical field and that some of our requests could be not applicable or not stringent enough. Therefore, if needed, specific recommendations should be added or lifted based on the research field.
Veerasathpurush Allareddy, Sankeerth Rampa, Shankar Rengasamy Venugopalan, Mohammed H. Elnagar · 7 authors
There is a paucity of largescale collaborative initiatives in orthodontics and craniofacial health. Such nationally representative projects would yield findings that are generalizable. The lack of large-scale collaborative initiatives in the field of orthodontics creates a deficiency in study outcomes that can be applied to the population at large. The objective of this study is to provide a narrative review of potential applications of blockchain technology and federated machine learning to improve collaborative care. We conducted a narrative review of articles published from 2018 to 2023 to provide a high level overview of blockchain technology, federated machine learning, remote monitoring, and genomics and how they can be leveraged together to establish a patient centered model of care. To strengthen the empirical framework for clinical decision making in healthcare, we suggest use of blockchain technology and integrating it with federated machine learning. There are several challenges to adoption of these technologies in the current healthcare ecosystem. Nevertheless, this may be an ideal time to explore how best we can integrate these technologies to deliver high quality personalized care. This article provides an overview of blockchain technology and federated machine learning and how they can be leveraged to initiate collaborative projects that will have the patient at the center of care.
Open access
Artificial Intelligence in Healthcare and Education
BACKGROUND AND OBJECTIVE: One of the main tasks in a biobank consists in storing biological samples in a high-quality condition in order to future research. At moment, there exist many applications to manage a biobank. However, in general, these are web-based applications. In these web-based applications different tasks can be done. Among them, it is possible to remark the following: informed consent, confidentiality, non-profit, respect for quality and safety standards, including traceability of samples. In this paper, we describe a blockchain smart contract to ensure the traceability of the processes done in a biobank meaning a step forward to guarantee this traceability. METHODS: Use of blockchain technology to improve security, integrity and traceability of the processes carried out in a biobank. In particular IBM Hyperledger Fabric. RESULTS: As a result, a set of smart contracts have been developed describing the biobank processes. CONCLUSIONS: Improvement of the security, integrity, and traceability of samples in biobanks.
BACKGROUND: A clinical trial management system (CTMS) is a suite of specialized productivity tools that manage clinical trial processes from study planning to closeout. Using CTMSs has shown remarkable benefits in delivering efficient, auditable, and visualizable clinical trials. However, the current CTMS market is fragmented, and most CTMSs fail to meet expectations because of their inability to support key functions, such as inconsistencies in data captured across multiple sites. Blockchain technology, an emerging distributed ledger technology, is considered to potentially provide a holistic solution to current CTMS challenges by using its unique features, such as transparency, traceability, immutability, and security. OBJECTIVE: This study aimed to re-engineer the traditional CTMS by leveraging the unique properties of blockchain technology to create a secure, auditable, efficient, and generalizable CTMS. METHODS: A comprehensive, blockchain-based CTMS that spans all stages of clinical trials, including a sharable trial master file system; a fast recruitment and simplified enrollment system; a timely, secure, and consistent electronic data capture system; a reproducible data analytics system; and an efficient, traceable payment and reimbursement system, was designed and implemented using the Quorum blockchain. Compared with traditional blockchain technologies, such as Ethereum, Quorum blockchain offers higher transaction throughput and lowers transaction latency. Case studies on each application of the CTMS were conducted to assess the feasibility, scalability, stability, and efficiency of the proposed blockchain-based CTMS. RESULTS: A total of 21.6 million electronic data capture transactions were generated and successfully processed through blockchain, with an average of 335.4 transactions per second. Of the 6000 patients, 1145 were matched in 1.39 seconds using 10 recruitment criteria with an automated matching mechanism implemented by the smart contract. Key features, such as immutability, traceability, and stability, were also tested and empirically proven through case studies. CONCLUSIONS: This study proposed a comprehensive blockchain-based CTMS that covers all stages of the clinical trial process. Compared with our previous research, the proposed system showed an overall better performance. Our system design, implementation, and case studies demonstrated the potential of blockchain technology as a potential solution to CTMS challenges and its ability to perform more health care tasks.
Patrick Silva, Deborah Vollmer Dahlke, Matthew Lee Smith, Wendy Charles · 7 authors
Current best practices in tumor registries provide a glimpse into a limited time frame over the natural history of disease, usually a narrow window around diagnosis and biopsy. This creates challenges meeting public health and healthcare reimbursement policies that increasingly require robust documentation of long-term clinical trajectories, quality of life, and health economics outcomes. These challenges are amplified for underrepresented minority (URM) and other disadvantaged populations, who tend to view the institution of clinical research with skepticism. Participation gaps leave such populations underrepresented in clinical research and, importantly, in policy decisions about treatment choices and reimbursement, thus further augmenting health, social, and economic disparities. Cloud computing, mobile computing, digital ledgers, tokenization, and artificial intelligence technologies are powerful tools that promise to enhance longitudinal patient engagement across the natural history of disease. These tools also promise to enhance engagement by giving participants agency over their data and addressing a major impediment to research participation. This will only occur if these tools are available for use with all patients. Distributed ledger technologies (specifically blockchain) converge these tools and offer a significant element of trust that can be used to engage URM populations more substantively in clinical research. This is a crucial step toward linking composite cohorts for training and optimization of the artificial intelligence tools for enhancing public health in the future. The parameters of an idealized clinical genomic registry are presented.
Fidelia Cascini, Flavia Beccia, Francesco Andrea Causio, Andrea Gentili · 7 authors
The recent progress of genomics research is providing unprecedented insight into human genetic variance, susceptibility to disease and risk stratification. Current trends predict that a massive amount of genomic data will be produced in the upcoming years which, when coupled with the fast-paced development of the field, will create new social, ethical, and legal challenges. In the complex legislative environment of the European Union, genomic data sharing policies will have to weigh the benefits of scientific discovery against the ethical risks posed by the act of sharing sensitive data. In this complex, interconnected environment, blockchain provides a unique and novel solution to accountability, traceability, and transparency issues regarding genomic data sharing. Implementing a distributed ledger technology-based database could empower both patients and citizens to responsibly use genomic data pertaining to them because it allows for a higher degree of control over the recipients of their data and their uses. The blockchain technology will engage both data owners and policymakers to address the multiple issues of genomic data sharing and allow us to redefine the way we look at genomics.
Baldwin C. Mak, Bryan T. Addeman, Jia Chen, Kim Papp · 9 authors
Objective: Despite the implementation of quality assurance procedures, current clinical trial management processes are time-consuming, costly, and often susceptible to error. This can result in limited trust, transparency, and process inefficiencies, without true patient empowerment. The objective of this study was to determine whether blockchain technology could enforce trust, transparency, and patient empowerment in the clinical trial data management process, while reducing trial cost. Design: In this proof of concept pilot, we deployed a Hyperledger Fabric-based blockchain system in an active clinical trial setting to assess the impact of blockchain technology on mean monitoring visit time and cost, non-compliances, and user experience. Using a parallel study design, we compared differences between blockchain technology and standard methodology. Results: A total of 12 trial participants, seven study coordinators and three clinical research associates across five sites participated in the pilot. Blockchain technology significantly reduces total mean monitoring visit time and cost versus standard trial management (475 to 7 min; P = 0.001; €722 to €10; P = 0.001 per participant/visit, respectively), while enhancing patient trust, transparency, and empowerment in 91, 82 and 63% of the patients, respectively. No difference in non-compliances as a marker of trial quality was detected. Conclusion: Blockchain technology holds promise to improve patient-centricity and to reduce trial cost compared to conventional clinical trial management. The ability of this technology to improve trial quality warrants further investigation.
Marielle S. Gross, Amelia Hood, Robert C Miller Jr
Henrietta Lacks' deidentified tissue became HeLa cells (the paradigmatic learning health platform). In this article, we discuss separating research on Ms Lacks' tissue from obligations to promote respect, beneficence, and justice for her as a patient. This case illuminates ethical challenges for the secondary use of biospecimens, which persist in contemporary learning health systems. Deidentification and broad consent seek to maximize the benefits of learning from care by minimizing burdens on patients, but these strategies are insufficient for privacy, transparency, and engagement. The resulting supply chain for human cellular and tissue-based products may therefore recapitulate the harms experienced by the Lacks family. We introduce the potential for blockchain technology to build unprecedented transparency, engagement, and accountability into learning health system architecture without requiring deidentification. The ability of nonfungible tokens to maintain the provenance of inherently unique digital assets may optimize utility, value, and respect for patients who contribute tissue and other clinical data for research. We consider the potential benefits and survey major technical, ethical, socioeconomic, and legal challenges for the successful implementation of the proposed solutions. The potential for nonfungible tokens to promote efficiency, effectiveness, and justice in learning health systems demands further exploration.
Open access
Ethics in Clinical Research
Biomedical Ethics and Regulation
Neuroethics, Human Enhancement, Biomedical Innovations
Abstract Introduction/Objective My role model when I was a medical technologist intern was a chief pathologist who taught me to speak up when something is unsafe and to serve willingly, do what is right, be fair and excellent in work. His character impacted my whole life and career. Every moment matters; life is precious and something to protect. The patient and their care teams depend upon accurate, safe and high-quality clinical laboratory tests result to achieve positive patient outcomes. Methods/Case Report Clinical Practice Results (if a Case Study enter NA) We can do better. Everybody can be surveyors with greater understanding of pathophysiologic processes in disease, extensive experience working in laboratories and in-depth knowledge about complaince, quality, mistake-proofing care, patient-focused and laboratory management. Diagnostic testing would be the method to screen for disease, confirm disease, and monitor disease in hopes of secondary prevention - to identify latent disease to “catch it early.” The screening tests could be anything from newborn screening for inborn metabolism errors, adult screening tests (like mammograms, pap smears, and colonoscopies), to high-risk population screenings to detect HIV, RPR, and gonorrhea. The disease must have a high prevalence to justify the expense of therapy and should be detectable before symptoms arise. The test must not have many false positives but extremely high sensitivity. The results of these tests could lead to the three different methods of prevention. Primary prevention would reduce the risk of developing cardiovascular disease or stroke. Secondary prevention would detect the disease early to prevent progression of the disease. Tertiary prevention would reduce disabilty and promotion of rehabilitation from the disease like strokes or rehab programs. Conclusion In conclusion, this information provides clinicians with a laboratory test menu guidelines to improve clinical practice. We can all learn well to focus on what really matters. Together, we can make healthcare better, more patient-centered, less costly, and safer.
Approaches developed based on the blockchain concept can provides a framework for the realization of open science. The traditional centralized way of data collection and curation is a labor-intensive work that is often not updated. The fundamental contribution of developing blockchain format of microbial databases includes: 1. Scavenging the sparse data from different strain database; 2. Tracing a specific thread of access for the purpose of evaluation or even the forensic; 3. Mapping the microbial species diversity; 4. Enrichment of the taxonomic database with the biotechnological applications of the strains and 5. Data sharing with the transparent way of precedent recognition. The plausible applications of constructing microbial databases using blockchain technology is proposed in this paper. Nevertheless, the current challenges and constraints in the development of microbial databases using the blockchain module are discussed in this paper.