Blockchain in healthcare today最新文献

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.

Objective: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Genomic data sharing remains a core problem in precision medicine because genomic data are highly sensitive and unchangeable. In this article, we propose a blockchain-based framework that utilizes zero-knowledge proofs (ZKPs), smart contracts, and off-chain storage to facilitate secure, privacy-preserving data sharing within health record systems. We implemented and evaluated a proof-of-concept prototype in Python on a simulated genomic dataset. The prototype uses a hybrid storage system where metadata is retained on a blockchain and encrypted data are placed in an emulated InterPlanetary File System (IPFS). Rule-based access is controlled using smart contracts, while privacy and security are achieved using ZKPs with interactive Schnorr protocol and elliptic curve cryptography (ECC). Empirical analysis using real-time testing over 100 iterations reported an average zero-knowledge proof with blockchain (ZKPB) query latency of 5.83 ms with a 90.00% accuracy, smart contract latency of under 0.01 ms with 90.00% accuracy, blockchain query time of 0.01 ms with 90.00% accuracy, and ECC latency of 8.72 ms with 90.00% accuracy. These empirical findings validate the effectiveness and privacy guarantees of the framework, which can be utilized in healthcare research, clinical genomics, and personalized medicine workflows.

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.

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.

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.

Background: The adoption of Artificial Intelligence (AI) is accelerating in the life sciences sector, offering opportunities to enhance biopharmaceutical research, development, and commercialization. However, the sector lacks structured tools to prioritize AI initiatives across varied business domains.

Objective: This study presents a framework to categorize and evaluate potential AI applications in the life sciences industry, organizing use cases along two critical dimensions: Phase of Product Lifecycle and Operational Domain.

Methods: A structured mixed-methods approach was employed, including a modified Delphi consensus process with industry experts, a qualitative case study review, and iterative framework refinement between August 2023 and August 2024.

Results: The resulting matrix framework enables life sciences professionals to assess AI opportunities across research, clinical development, commercialization, and post-marketing activities. Key findings highlight the pervasive nature of AI impact, the emphasis on data-driven strategies, and the regulatory and ethical challenges facing biopharma firms.

Conclusions: This framework provides a practical model for strategic AI adoption decisions within the life sciences sector and lays the groundwork for future research, policy development, and enterprise transformation efforts.