Blockchain in healthcare today最新文献

{"title":"Optimization of Health Service Utilization Among Elderly People With Chronic Diseases in Rural Ethnic Minorities in Northwest Yunnan Using Graph Neural Networks.","authors":"Jing Zhang, Haitao Fan","doi":"10.30953/bhty.v8.436","DOIUrl":"10.30953/bhty.v8.436","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":72422,"journal":{"name":"Blockchain in healthcare today","volume":"8 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12860443/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146108033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Blockchain-Based Framework With Zero-Knowledge Proof Incorporated for Safeguarded Sharing of Genomic Data Through Health Record Systems.","authors":"Nandini Krishappa, Girisha Gowdra Shivappa, Sharon Zachariah, Thanushree, Kavyashree I Pattan, Arpita Paria, Savitha Hiremath, Revathi Vaithiyanathan","doi":"10.30953/bhty.v8.419","DOIUrl":"10.30953/bhty.v8.419","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":72422,"journal":{"name":"Blockchain in healthcare today","volume":"8 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12860433/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146108677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Converge2Xcelerate (ConV2X) Driving Platforms and Decentralized Technology in Healthcare and Life Sciences: Keynote Address: Existential Times.","authors":"Tory Cenaj","doi":"10.30953/bhty.v8.458","DOIUrl":"https://doi.org/10.30953/bhty.v8.458","url":null,"abstract":"","PeriodicalId":72422,"journal":{"name":"Blockchain in healthcare today","volume":"8 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12860427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146108772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trust By Design: Enabling Responsible Precision Health Through Blockchain-Powered Digital Twins And Trusted AI.","authors":"Ingrid Vasiliu-Feltes, Christina Yan Zhang, Stephen Dennis, Elliot Siegel, Daniel Uribe","doi":"10.30953/bhty.v8.453","DOIUrl":"10.30953/bhty.v8.453","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":72422,"journal":{"name":"Blockchain in healthcare today","volume":"8 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12865783/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146121147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trust By Design: Enabling Responsible Precision Health Through Blockchain-Powered Digital Twins And Trusted AI.","authors":"Ingrid Vasiliu-Feltes, Christina Yan Zhang, Stephen Dennis, Elliot Siegel, Daniel Uribe","doi":"10.30953/bhty.v10.453","DOIUrl":"10.30953/bhty.v10.453","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":72422,"journal":{"name":"Blockchain in healthcare today","volume":"10 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146108656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain Technology in Digital Health and Medical Technologies.","authors":"Muhammet Damar, Ömer Aydın, Fatih Safa Erenay","doi":"10.30953/bhty.v8.409","DOIUrl":"10.30953/bhty.v8.409","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":72422,"journal":{"name":"Blockchain in healthcare today","volume":"8 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12860446/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146108678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping the AI Landscape in Life Sciences: A Framework for Evaluation and Adoption.","authors":"Jennifer Hinkel, Fraser Peck, Cory Kidd","doi":"10.30953/bhty.v8.398","DOIUrl":"10.30953/bhty.v8.398","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":72422,"journal":{"name":"Blockchain in healthcare today","volume":"8 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12860428/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146108817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Proof-of-Work for Secure Health Data Blockchain Using Compute Unified Device Architecture.","authors":"Seid Mehammed","doi":"10.30953/bhty.v8.421","DOIUrl":"10.30953/bhty.v8.421","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":72422,"journal":{"name":"Blockchain in healthcare today","volume":"8 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12860445/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146108393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyperledger Fabric-Powered Digital Identity Scheme: Transforming CIA-Triad Security in IoMT Integrated Healthcare Eco-System.","authors":"Sanjay Jena, Ram Chandra Barik, Saroj Padhan","doi":"10.30953/bhty.v8.411","DOIUrl":"10.30953/bhty.v8.411","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":72422,"journal":{"name":"Blockchain in healthcare today","volume":"8 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12860441/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146108802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling Drivers of Blockchain-Based AI Adoption to Improve Financial Transparency in Health Insurance Organizations.","authors":"Sepideh Mohammadi Tong Andri, Sahar Mohammadi Tong Andri","doi":"10.30953/bhty.v8.427","DOIUrl":"10.30953/bhty.v8.427","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":72422,"journal":{"name":"Blockchain in healthcare today","volume":"7 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12860440/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146108720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}