In today’s digital era, secure and efficient management of health information is a critical challenge. Centralized health data systems often expose sensitive information to security risks, enabling unauthorized access, modifications, and data sharing without patients’ consent. These challenges require a patient-centric, standardized approach to managing health data. This article presents a decentralized health information exchange framework that leverages blockchain technology to address these issues. The framework combines the Interplanetary File System for scalable data storage with Ethereum (ETH) smart contracts to enforce secure and transparent access control. By integrating these technologies, the proposed solution presented here enhances data security, transparency, and interoperability while reducing costs and reliance on intermediaries. Experiments conducted on the ETH blockchain demonstrate the framework’s efficiency, with smart contracts evaluated for transaction costs and accuracy. In addition to examining scalability and security, the authors discuss the framework’s limitations and its potential for broader application. To foster further research and collaboration, the source code for the smart contracts is openly available on GitHub.

Metaverse is heralded by some as the next iteration of the internet. It offers three-dimensional, immersive virtual spaces, where users, represented as avatars, can synchronously work, play, interact, and transact. Initially developed within the realm of massive multiplayer online role-playing games, the metaverse now extends into sectors such as music, entertainment, retail, real estate, and, more recently, healthcare. Users engage in the metaverse using augmented reality or virtual reality (AR/VR) headsets that interoperate with other sensory devices to integrate biofeedback and multimodal data streams.1 In this article, the authors offer a conceptual synthesis and anticipatory policy analysis grounded in a narrative review of current trends reported in public-facing news reports as well as interdisciplinary sources from bioethics, law, digital-health policy, and science-and-technology studies. There are seven key ethical, legal, and social issue areas for consideration in the delivery of metaverse-enabled healthcare. We additionally issue a call to action to explore the metaverse through a bioethics lens. The authors begin by providing a primer on the metaverse, including the various forces shaping its development. Next, the authors describe what distinguishes the metaverse from other digital-health technologies and illustrate emerging use cases for the metaverse. We then outline what we consider the most pressing ethical issues raised as the metaverse matures in parallel with (or in advance of) policy guidance and regulation. Finally, we conclude with a research agenda that treats the metaverse as a serious topic of normative and empirical inquiry and argue for sustained engagement from diverse user communities to support its ethical design and development.

The demand for health services among elderly patients with chronic diseases in rural ethnic minority areas of northwest Yunnan is increasing. Yet, service utilization remains imbalanced. Existing studies mainly focus on disease combinations, overlooking temporal and spatial variations in medical behavior. This study applies graph neural networks to construct a heterogeneous graph integrating patients, medical institutions, and geographic units, modeling dynamic service paths to identify high-frequency and potentially lost-contact patients. Using a heterogeneous graph attention network for feature embedding and a graph attention network classifier, the model captures behavioral similarity and service path patterns. Geographic and social variables such as ethnicity, terrain, and road accessibility further enhance sensitivity to regional disparities.Based on node centrality and path distribution, targeted service optimization strategies-such as mobile medical points and cross-regional collaboration nodes-are proposed for resource allocation. Experimental results reveal marked spatial and structural disparities: Diqing Prefecture shows an accessibility index of 68 min versus 29 min in Dali; multimorbidity (3+) groups have a 68.6% matching rate but a 1.138 utilization rate, indicating resource imbalance; and mountain unit G18's coverage index is only 0.31. The proposed model achieves a Macro-F1 of 0.83, outperforming XGBoost (0.76), effectively identifying high-risk groups, locating service bottlenecks, and supporting precise health resource optimization.

The rapid evolution of digital health technologies has created an urgent need for secure, transparent, and interoperable data management systems. The core problem addressed in this study is the fragmentation of healthcare data and the lack of trust among stakeholders in existing digital health infrastructures. The main goal is to examine how blockchain technology can drive digital health transformation through decentralized data governance and integration with other emerging technologies. To achieve this, the research employs a mixed bibliometric and systematic review methodology, analyzing peer-reviewed publications indexed in the Web of Science and comparing topic hierarchies with outputs from Google Scholar between 2017 and 2024. Using keyword co-occurrence and thematic mapping, six major domains were identified: genomics and precision medicine, telemedicine and mobile health, immersive technologies such as augmented and virtual reality, the Internet of Things and Health 5.0 systems, artificial intelligence and big data integration, and global and regional health management. The findings indicate that blockchain enhances healthcare by improving data security, ensuring traceability, facilitating interoperability across platforms, and enabling real-time data sharing in clinical and research environments. It also supports regulatory compliance and patient-centered data ownership. In conclusion, blockchain serves as a foundational technology for future digital health ecosystems, promoting transparency and decentralization across global health networks. This study contributes to the literature by offering a comprehensive framework for integrating blockchain with digital health innovations, providing valuable guidance for researchers, policymakers, and healthcare technologists.

Welcome to Decentralized Science (DeSci) — a new framework reimagining how research is funded, shared, and rewarded. The Coalition for Decentralized Healthcare and Life Sciences (CDHLS) brought together three pioneers at the intersection of science, technology, and decentralized technology for a candid conversation about where DeSci stands today, the challenges it faces, and what needs to change for mainstream adoption. Meet The Panel Jelani Clarke, Executive Lead at DeSci World, and Sales & Marketing Head at AminoChain Filippo Frangioni, Product Manager at Novo Nordisk, and Head of Partnerships at Nordic Blockchain Jim Nasr, CEO, Acoer Themes tackled include: The perception problem: Why does DeSci still sound like sci-fi to most scientists? Funding friction: Can decentralized models compete with traditional grants and venture capital? Data ownership: Who really owns research data, and how can blockchain change that? Regulatory roadblocks: What happens when innovation moves faster than policy? Real world implementation: Beyond the hype, where is DeSci actually working today? From pharmaceutical giants to grassroots research collectives, these leaders shared unfiltered insights on what's holding DeSci back—and what could accelerate its adoption. Intrigued? This conversation is the first in a new podcast series launching in 2026 that will dive deeper into the decentralized science revolution. We'll explore: Case studies of successful DeSci projects Interviews with researchers and policy makers Delve into tokenomics, DAOs, and data sovereignty The intersection of AI, biotech, and decentralized systems The future of science is being built by innovators, technologists, and researchers willing to challenge the status quo. Ready to learn more?Watch the full panel discussion or download it at your leisure. Learn about CDHLS here.

As we head into 2026, artificial intelligence (AI), blockchain, and other emerging technologies are moving from experiments into core healthcare systems. That shift promises tangible benefits: fewer people left untreated, faster discovery of lifesaving treatments, and simpler, lower‑cost ways to move money and data across borders. It also brings real risks-speculative hype, erosion of institutional trust, and rushed rollouts that fail patients-so adoption must be disciplined and values-driven. This annual predictions article, informed by ConV2X Symposium speakers, highlights practical advances likely to matter at the bedside and beyond: programmable stablecoins that lower cross‑border payment friction; AI that surfaces pediatric risks earlier; verifiable digital credentials that ease clinician mobility; post‑quantum cryptography to safeguard sensitive records; domain‑specific AI designed for regulatory compliance; consumer apps that put usable health tools in people's pockets; and the rise of Decentralized Science (DeSci) to restore transparency and funding momentum to stalled research. Realizing these possibilities will require deliberate choices, commitment, and coordinated stewardship across innovators, clinicians, and policymakers. With that effort, these tools can help build a more verifiable, equitable, and resilient global healthcare system-technology shaped to serve people, not the other way around; aspirations for healing, dignity, and universal well-being. While uncertainties persist, the path forward is clear: responsible innovation today will shape a healthier, more inclusive tomorrow.

Despite blockchain technology's demonstrated potential to enhance security, transparency, and efficiency in healthcare systems, adoption rates remain significantly lower than predicted, creating a persistent gap between perceived benefits and adoption feasibility. This study addresses the critical question of what explains this adoption paradox by developing and testing a comprehensive theoretical framework that integrates the Technology Acceptance Model (TAM) with the Technology-Organization-Environment (TOE) framework. A systematic literature review is conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to synthesize existing research on blockchain adoption in health care. This study develops four key propositions examining how technology characteristics, organizational factors, external environmental pressures, perceived risks, and system quality collectively influence healthcare organizations' blockchain adoption intentions. The analysis reveals that blockchain adoption in health care is influenced by a complex interplay of facilitating and inhibiting factors. Technology characteristics such as perceived usefulness (PU) and ease of use, combined with organizational innovation readiness and technology compatibility, positively influence adoption intention. External factors enhance perceived technology benefits and consequently affect adoption decisions. However, perceived risks moderate the relationship between PU and adoption intention. Blockchain technology represents a transformative solution for persistent healthcare challenges, but successful adoption requires a holistic approach that simultaneously addresses technology, organizational, and environmental factors. The adoption gap can be bridged through strategic planning that aligns institutional readiness with user incentives, comprehensive risk management, and supportive regulatory frameworks. Future research should focus on establishing ethical governance models to support broad blockchain adoption in health care.

This study develops a big data-driven predictive platform for hospital staff attrition, integrating machine learning (ML) with psychological constructs. Negotiable Fate (NF), a culturally rooted belief system, is examined as a predictor of turnover via psychological capital (PC) and organizational citizenship. Structured HR data from 400+ employees at a tertiary public hospital, covering 20+ features, were analyzed. Due to attrition imbalance (~5%), SMOTE was applied to balance the dataset. Four ML classifiers-logistic regression, decision tree, random forest, and XGBoost-were evaluated using accuracy, precision, recall, and F1-score. Statistical analyses assessed mediation, moderation, and construct validity using survey variables: NF, PC, perceived organizational support, job performance (JP), and organizational citizenship behavior. Random Forest and XGBoost achieved superior recall for attrition cases. Feature importance consistently highlighted working hours, income, job type, and satisfaction as key predictors. NF significantly predicted JP (β = 0.30, p < 0.001) and organizational citizenship (β = 0.36, p < 0.001) through PC (β = 0.33, p < 0.001). Perceived organizational support moderated the NF → PC pathway (β = 0.16, p < 0.001), confirming mediated moderation. Integrating ML with psychological theory enhances both the prediction and understanding of hospital staff attrition. The platform enables culturally sensitive, data-driven HR interventions, helping administrators identify high-risk employees and implement targeted strategies to reduce attrition, stabilize the workforce, and improve patient care.

The Executive Session, Trust by Design: Enabling Responsible Precision Health through Blockchain-Powered Digital Twins and Trusted AI, explores how the convergence of blockchain, artificial intelligence, genomics, 6G wireless technology, and other advanced technologies can be leveraged to power precision health digital twins. The dialogue focused on governance, interoperability, cybersecurity, and the impact of blockchain and trusted AI-powered digital twins on advancing precision healthcare and personalized medicine. Use cases—for genomics, radiology, theranostics, and end-of-life care—illustrated both opportunities and barriers. Throughout the discussion, speakers emphasized the centrality of trust, patient sovereignty, and resilient infrastructures for the next generation of healthcare.