Blockchain in healthcare today最新文献

We present a graphics processing unit (GPU)-accelerated Proof-of-Work (PoW) blockchain design tailored for secure healthcare data management. Our Compute Unified Device Architecture (CUDA)-optimized PoW achieves throughput improvements of approximately 5× to 100× and reduces block-formation latency compared to Central Processing Unit (CPU) mining, making blockchain practical for high-volume health records. We benchmark against standard platforms-Bitcoin, known for its robust security but slow block times; Ethereum (legacy PoW), widely adopted yet less efficient; and Hyperledger Fabric, a permissioned enterprise framework-to quantify performance gains. Empirical tests show GPU-Advanced Encryption Standard in Counter Mode (AES-CTR) processes large health-record payloads in under one second, while our PoW mining throughput improves by approximately 5×, to 100× relative to unaccelerated baselines. We also evaluate end-to-end encryption latency and discuss privacy trade-offs, including that lightweight Advanced Encryption Standard (AES) yields minimal delay, whereas fully homomorphic methods, although privacy-preserving, remain impractical for real-time permissionless blockchains and are not included in our design. We explicitly address regulatory compliance: personal health data are stored off-chain (e.g., Interplanetary File System [IPFS]), preserving the "right to erasure" via deletion of off-chain records, and we implement strict access controls to meet Health Insurance Portability and Accountability Act (HIPAA) security rules. The design includes validator selection rules that limit Sybil attacks by requiring costly work (or stake) and supports post-quantum cryptographic agility (e.g., Falcon signatures). We define our research question ("Can CUDA-accelerated PoW enable a high-performance yet compliant health data blockchain?") and hypothesize that GPU parallelism will yield substantial increases in speed. Results confirm our hypothesis: throughput and latency are significantly improved while preserving data privacy and compliance. This work makes a comprehensive contribution by detailing implementation methods, performance benchmarking, and analysis of security and legal requirements in a unified blockchain framework for healthcare.

This study underscores blockchain technology's potential to tackle critical healthcare challenges, including data security, interoperability, and collaboration, delivering a scalable and efficient framework that enhances patient trust, operational efficiency, and compliance with global data protection standards. The advent of blockchain technology has radically altered centralized data management to adopt decentralized distributed systems with their inherent features-such as transparency, immutability, and security-that offer a promising answer for the challenges faced by modern healthcare systems. The authors introduce a smart healthcare solution for a secured digital identity of a patient by maintaining its Confidentiality, Integrity, and Availability (CIA-triad). It customizes an open-source Hyperledger Fabric-based framework for developing and utilizing the healthcare ecosystem as per the requirements of maintaining the digital identity of a patient. In addition, it uses fabrics' key components, such as privacy-preserving channels, endorsing peers, anchor peers, orderer nodes, and a secure consensus for an efficient collaboration among stakeholders that ensures data integrity and confidentiality. The decentralized storage feature allows secure Secure Hash Algorithm 256-bit (256-bit) during digital signature generation and verification algorithms across the network, and its one-way cryptographic function feature adds an advantage to maintain digital identity encryption both on-chain and off-chain during sharing and storing. This acts as a resistance to different cyberattacks as a record of the Common Vulnerability Scoring System scorecard. The efficiency of the proposed operational model tested in a closed experimental network gets a more balanced output than that of a test network, which may be chosen for an adoption.

The authors explored the primary organizational and environmental factors that influence the adoption of blockchain-integrated artificial intelligence systems aimed at enhancing financial transparency within health insurance institutions. Building on established models of technology acceptance and organizational change, a conceptual framework was developed to examine the interaction of technological readiness, management support, regulatory compliance, and workforce capability. Data collected from 272 professionals working in various health insurance entities were analyzed using structural equation modeling to assess direct and indirect pathways. The findings underscore that internal drivers-particularly employee training, executive leadership commitment, and digital infrastructure-are far more significant in shaping adoption outcomes than external forces like regulatory mandates or market competition. Moreover, financial transparency emerges as a critical outcome and a mediating factor that reinforces trust in technology adoption. This article presents practical insights for policymakers and healthcare administrators to promote ethical, efficient, and transparent digital transformation in the insurance sector.

Disruptive digital health technologies are reshaping how patients interact with health professionals, how data are shared among providers, and how treatment plans and health outcomes are determined. While the COVID-19 pandemic has accelerated the adoption of digital technologies, challenges remain in realizing the potential of digital transformation programs in healthcare. Specifically, health data need to remain secure, usable, and shareable across multiple stakeholder groups in a world where silos between organizations and information systems persist. The implementation of innovative and disruptive digital technologies such as blockchain can offer a solution to these challenges. This article explores how blockchain technology can be used to accelerate digital health transformation programs. It provides an overview of the technology applications (i.e. data management, Internet of Medical Things [IoMT], supply chain management, and health insurance) and key players based on a literature review and secondary data. It also identifies challenges and success factors in implementing blockchain in healthcare. At the organizational level, we discuss the careful planning and specialized expertise required to overcome the technical, regulatory, and adoption-related hurdles associated with implementing blockchain technology. At the system level, the authors discuss the regulatory constraints, standardization and interoperability issues, and stakeholder engagement challenges linked to implementing blockchain technology.

In Healthcare 4.0, we are witnessing a fundamental shift from provider-centric systems to patient-centric models, where individuals, empowered by technologies such as blockchain, the Internet of Medical Things, and artificial intelligence (AI), assume the role of the Self-Sovereign Patient, exercising control over their health data and care journey. These technologies enable new forms of data ownership, interoperability, and personalized care, building on the structured reliability of legacy systems. However, significant challenges remain. Tensions between blockchain immutability and regulatory rights such as data erasure, the unresolved question of digital inheritance, and ethical concerns surrounding consent, monetization, and health equity must all be addressed. In addition, institutional barriers such as clinical integration, data governance, and uneven access to digital infrastructure pose risks of deepening existing disparities. AI agents, when responsibly deployed, offer promising pathways to augment care delivery and alleviate workforce burdens. Realizing this vision requires coordinated action across clinical, technical, legal, and ethical domains to design trustworthy, privacy-preserving systems that enhance transparency and accountability.

Objective: The authors assessed how research in primary healthcare was affected by the COVID-19 pandemic and identified the potential of blockchain technology to address pandemic-related challenges.

Methods: This quantitative bibliometric research study used machine learning techniques. A comprehensive analysis of all primary healthcare (PHC) research was conducted using bibliometric data from the WOs. We examined co-authorship, co-occurrences, citation and co-citation, thematic mapping, factorial, document, and Latent Dirichlet Allocationtopic analyses. Our main dataset was 1,885 articles produced by 9,185 researchers from 3,132 institutions in 113 countries.

Results: The most cited studies in the PHC field during the pandemic related to telemedicine and remote consultation, along with clinical conditions such as mental health, diabetes, vaccinations, risks during pregnancy, and healthcare of the elderly. In addition, the impact of COVID-19 on educational outcomes, changes to the organization of care, experiences and challenges to PHC physicians and other health professionals, and the diversity of COVID-19 symptoms were prominent.

Conclusions: The PHC researchers adapted quickly to the pandemic and conducted multidisciplinary research that helped to mitigate the impact on individuals, health systems, and society. Within this context, blockchain technology can be used to facilitate the security of health data, resource management (e.g., monitoring of the vaccine supply chain), and global collaboration toward pandemic control. By providing transparency, security, and efficiency in these areas, blockchain technology might lead to more effective pandemic preparedness and management in the future.

Background: Blockchain healthcare insurance offers substantial potential for improving data transparency, efficiency, and security. However, many blockchain healthcare projects struggle to scale up and achieve commercial success. Despite technical feasibility studies, limited research exists on the commercial success of these projects. This study explores the technical, regulatory, and strategic factors that contribute to market success in blockchain healthcare insurance initiatives.

Methods: Using exploratory mixed methods, we combined quantitative market data analysis from CoinGecko Application Programming Interface (API) with manual metadata curation from publicly available data. We conducted descriptive statistics, Pearson correlation, and multiple regression analysis to evaluate relationships between key variables and market success.

Results: The Health Insurance Portability and Accountability Act of 1996 (HIPAA) compliance emerged as a significant predictor of higher market cap (β = +12.6, p = 0.006), while insurance partnerships negatively impacted success due to early-stage complexity (β = -15.3, p = 0.009). The model explains 95.7% of market cap variance (adjusted R² = 0.957).

Findings: These findings demonstrate how crucial technical preparedness and regulatory alignment are to the successful commercialization of blockchain-based health insurance. The importance of organizational scale is highlighted by a moderate association (r = 0.83) between team size and market success. The importance of strategic alliances and regulatory compliance is further shown by the theme analysis of white papers. Investors should concentrate on projects with well-defined regulatory policies, while entrepreneurs should give HIPAA compliance top priority early on. It is recommended that policymakers create more precise regulatory frameworks for blockchain in the medical field. For more thorough insights, future studies should increase the sample size.

In the context of Code Generation AI (Code Gen AI), "rich" and "quality" data refer to datasets that are not only syntactically and structurally sound but also context-aware, domain-specific, and semantically aligned with the target application. Unlike large-scale, general-purpose code corpora scraped from open repositories, rich datasets are curated to reflect regulatory requirements, architectural patterns, and problem-solving conventions within a given field. This distinction is critically important when deploying Code Gen AI in the healthcare sector, where software must meet rigorous standards for safety, auditability, and compliance. Blindly scaling models with low-quality or irrelevant data may lead to brittle, error-prone systems-posing risks not only to patients and providers but also to the integrity of digital healthcare infrastructure. This issue has not been fully addressed in the Code Gen AI research to date. This article evaluates the critical trade-offs between "rich data" and "data quantity" strategies in Code Gen AI and autonomous code agents, focusing on high-integrity sectors such as healthcare. While Code Gen AI can enhance productivity by up to 55% in controlled environments, models trained on unfiltered, large-scale datasets often increase code duplication, churn, and error rates. The central challenge is balancing performance gains with reliability, maintainability, and ethical accountability. In healthcare, codebases must embody accuracy, traceability, and data privacy-attributes often diluted in large but uncurated training sets. Using Self-Evolving Software as a case study, this article contrasts the outcomes of both approaches and introduces a weighted data selection matrix tailored to Code Gen AI systems. The findings demonstrate that rich, curated, domain-specific datasets consistently produce more robust, compliant, and sustainable code, especially in sectors where quality and governance are non-negotiable.