Search Results (189)

Pharmacy-based point-of-care (POC) services have evolved from pilot initiatives to an essential component of decentralized healthcare delivery. These services—ranging from rapid infectious-disease screening to chronic-disease monitoring—improve access, reduce diagnostic delays and empower pharmacists as front-line healthcare providers. The present paper is an updated, in-depth review of the evolution of pharmacy POC services worldwide, combined with the analysis of the regulatory and educational frameworks supporting implementation, technological drivers such as biosensors, mobile health and artificial intelligence and in-depth socioeconomic considerations. Benefits for patients, pharmacies and healthcare systems are contrasted with challenges including variable reimbursement, uneven regulatory oversight and workforce preparedness. Full article

Parenteral nutrition (PN) is essential for patients who are unable to tolerate oral or enteral feeding, providing them with necessary nutrients intravenously, including dextrose, amino acids, electrolytes, vitamins, trace elements, and lipid emulsions. Clinical pharmacists (CPs) play a critical role in PN management by ensuring proper formulation, monitoring therapy, preventing complications, and optimizing patient outcomes. In Saudi Arabia, limited literature exists on CPs’ involvement in total parenteral nutrition (TPN) administration, health information management (HIM) systems, and pharmacist staffing ratios. This paper examines the evolving role of CPs in PN management, addressing key challenges such as the optimal patient-to-CP ratio, the impact of HIM systems on PN prescribing, and the advantages and limitations of centralized versus decentralized PN prescription models. It highlights the need for standardized staffing levels, structured pharmacist training, and improved HIM integration to enhance workflow efficiency and prescribing accuracy. Additionally, the study examines how the adoption of advanced HIM systems can streamline documentation, reduce prescribing errors, and enhance interdisciplinary collaboration. This paper provides a framework for optimizing PN delivery, enhancing healthcare quality, and strengthening CPs’ contributions to nutrition support by addressing these factors. Implementing these recommendations will improve patient outcomes and establish a more efficient PN management system in Saudi Arabia, reinforcing the vital role of CPs in multidisciplinary care. Full article

In recent years, the convergence of advanced technologies has enabled real-time data access and sharing across diverse devices and networks, significantly amplifying cybersecurity risks. For organizations with digital infrastructures, network security is crucial for mitigating potential cyber-attacks. They establish security policies to protect systems and data, but employees may intentionally or unintentionally bypass these policies, rendering the network vulnerable to internal and external threats. Detecting these policy violations is challenging, requiring frequent manual system checks for compliance. This paper addresses key challenges in safeguarding digital assets against evolving threats, including rogue access points, man-in-the-middle attacks, denial-of-service (DoS) incidents, unpatched vulnerabilities, and AI-driven automated exploits. We propose a Blockchain-Enhanced Network Scanning and Monitoring (BENSAM) Framework, a multi-layered system that integrates advanced network scanning with a structured database for asset management, policy-driven vulnerability detection, and remediation planning. Key enhancements include device profiling, user activity monitoring, network forensics, intrusion detection capabilities, and multi-format report generation. By incorporating blockchain technology, and leveraging immutable ledgers and smart contracts, the framework ensures tamper-proof audit trails, decentralized verification of policy compliance, and automated real-time responses to violations such as alerts; actual device isolation is performed by external controllers like SDN or NAC systems. The research provides a detailed literature review on blockchain applications in domains like IoT, healthcare, and vehicular networks. A working prototype of the proposed BENSAM framework was developed that demonstrates end-to-end network scanning, device profiling, traffic monitoring, policy enforcement, and blockchain-based immutable logging. This implementation is publicly released and is available on GitHub. It analyzes common network vulnerabilities (e.g., open ports, remote access, and disabled firewalls), attacks (including spoofing, flooding, and DDoS), and outlines policy enforcement methods. Moreover, the framework anticipates emerging challenges from AI-driven attacks such as adversarial evasion, data poisoning, and transformer-based threats, positioning the system for the future integration of adaptive mechanisms to counter these advanced intrusions. This blockchain-enhanced approach streamlines security analysis, extends the framework for AI threat detection with improved accuracy, and reduces administrative overhead by integrating multiple security tools into a cohesive, trustworthy, reliable solution. Full article

Blockchain has been widely proposed to strengthen data management security through decentralization, immutability, and auditable transactions, capabilities increasingly recognized as enablers of sustainable digital ecosystems and resilient institutions; however, existing studies remain dispersed across domains and rarely consolidate governance, interoperability, and evaluation criteria. This paper conducts a systematic literature review of 70 peer-reviewed studies published between 2018 and 2024, using IEEE Xplore, Scopus, Springer, ScienceDirect, and ACM Digital Library as primary sources and following Kitchenham’s guidelines and the PRISMA 2020 flow, to examine how blockchain has been applied to secure data in healthcare, IoT, smart cities, supply chains, and cloud environments. The analysis identifies four methodological streams—empirical implementations, cryptographic/security protocols, blockchain–machine learning integrations, and conceptual frameworks—and shows that most contributions are technology-driven, with limited attention to standard metrics, regulatory compliance, and cross-platform integration. In addition, the review reveals that very few works articulate governance models that align technical solutions with organizational policies, which creates a gap for institutions seeking trustworthy, auditable, and privacy-preserving deployments. The review contributes a structured mapping of effectiveness criteria (confidentiality, auditability, availability, and compliance) and highlights the need for governance models and interoperable architectures to move from prototypes to production systems. Future work should prioritize large-scale validations, policy-aligned blockchain solutions, and comparative evaluations across sectors. Full article

Plasma separation is an essential step in blood-based diagnostics. While traditional centrifugation is effective, it is costly and usually restricted to centralized laboratories because it requires relatively expensive equipment, a supply of consumables, and trained personnel. In an effort to alleviate these shortcomings, microfluidic and point-of-care devices offering rapid and low-cost plasma separation from small sample volumes, such as finger-stick samples, are quickly emerging as an alternative. Such microscale plasma separation systems enable reduced costs, rapid test results, self-testing, and broader accessibility, particularly in resource-limited or remote settings and facilitate the integration of separation, fluid handling, and downstream analysis into portable, automated lab-on-a-chip platforms. This review highlights advances in microfluidic systems and lab-on-a-chip devices for plasma separation categorized in design strategies, separation principles and characteristics, application purposes, and future directions for the decentralization of healthcare and personalized medicine. Full article

The integration of artificial intelligence into remote patient monitoring (RPM) offers significant benefits for proactive and continuous healthcare, but also raises critical concerns regarding privacy, integrity, and robustness. Federated Learning (FL) provides a decentralized approach to model training that preserves data locality, yet most existing solutions address only isolated security aspects and lack contextual adaptability for clinical use. This paper presents MedGuard-FL, a context-aware FL framework tailored to e-healthcare environments. Spanning device, edge, and cloud layers, it integrates encryption, adaptive differential privacy, anomaly detection, and Byzantine-resilient aggregation. At its core, a policy engine dynamically adjusts privacy and robustness parameters based on the patient’s status and the system’s risk. Evaluations on real-world clinical datasets show MedGuard-FL maintains high model accuracy while neutralizing various adversarial attacks (e.g., label-flip, poisoning, backdoor, membership inference), all with manageable latency. Compared to static defenses, it offers improved trade-offs between privacy, utility, and responsiveness. Additional edge-level privacy analyses confirm its resilience, with attack effectiveness near random. By embedding clinical risk awareness into adaptive defense mechanisms, MedGuard-FL lays a foundation for secure, real-time federated intelligence in RPM. Full article

The proliferation of electronic health records necessitates secure and privacy-preserving data sharing frameworks to combat escalating cybersecurity threats in healthcare. Current systems face critical limitations including centralized data repositories vulnerable to breaches, static consent mechanisms, and inadequate audit capabilities. This paper introduces an integrated blockchain and federated learning framework that enables privacy-preserving collaborative AI across healthcare institutions without centralized data pooling. The proposed approach combines federated distillation for heterogeneous model collaboration with dynamic differential privacy that adapts noise injection to data sensitivity levels. A novel threshold key-sharing protocol ensures decentralized access control, while a dual-layer Quorum blockchain establishes immutable audit trails for all data sharing transactions. Experimental evaluation on clinical datasets (Mortality Prediction and Clinical Deterioration from eICU-CRD) demonstrates that our framework maintains diagnostic accuracy within 3.6% of centralized approaches while reducing communication overhead by 71% and providing formal privacy guarantees. For Clinical Deterioration prediction, the framework achieves 96.9% absolute accuracy on the Clinical Deterioration task with FD-DP at $ϵ$ = 1.0, representing only 0.14% degradation from centralized performance. The solution supports HIPAA-aligned technical safeguards, mitigates inference and membership attacks, and enables secure cross-institutional data sharing with real-time auditability. This work establishes a new paradigm for privacy-preserving healthcare AI that balances data utility, regulatory requirements, and protection against emerging threats in distributed clinical environments. Full article

Background/Objectives: Point-of-care testing (POCT) is increasingly adopted in primary healthcare to facilitate rapid screening and monitoring of chronic conditions. Ensuring that its analytical quality is comparable to central laboratory testing is crucial for safe and effective implementation. This study aims to rigorously evaluate the analytical performance of the Allegro POCT system against established central laboratory reference methods to determine its suitability for decentralized healthcare settings. Methods: We assessed the correlation, concordance, and bias of glycated hemoglobin (HbA1c), glucose (GLUC), total cholesterol (CHOL), high-density lipoprotein cholesterol (HDL), triglycerides (TRIG), creatinine (CREA), and C-reactive protein (CRP). Using a cohort of 100 residual patient samples, measurements from the Allegro POCT system were compared against reference methods on the Atellica CH 930 Analyzer and TOSOH G8 system. The statistical analysis was performed using Deming regression, Bland–Altman plots, and Pearson correlation. Results: HbA1c and GLUC demonstrated strong linearity and correlation (Pearson’s r = 0.9863 and r = 0.9994, respectively). A slight positive bias was noted for HbA1c at higher concentrations. In the lipid panel, CHOL showed a significant positive bias (mean bias +14.2 mg/dL), while TRIG exhibited a substantial negative bias (mean bias −37.0 mg/dL) and wide limits of agreement. HDL and CREA showed good linearity but only moderate agreement. CRP demonstrated excellent concordance with the reference method (Pearson’s r = 0.9955) and minimal bias. Conclusions: The Allegro system exhibits acceptable analytical performance for GLUC and CRP, rendering it suitable for decentralized use. HbA1c and CREA performance is adequate, though caution is advised due to observed biases. However, the significant biases for CHOL and TRIG underscore the indispensable role of central laboratory oversight in any POCT program. Rigorous initial validation and continuous quality monitoring under a robust governance framework are essential to ensure the reliability and clinical utility of POCT. Full article

The Internet of Medical Things (IoMT) integrates sensors, medical devices, and Internet of Things (IoT) technologies to provide data-driven healthcare systems. The systems facilitate medical monitoring and decision-making; however, there are significant concerns about data leakage and patient consent. Additionally, a shortage of large, high-quality IoMT datasets to study the surrounding issues is problematic. Federated learning (FL) is a decentralized machine learning approach that potentially offers substantial amounts of capacity, so that compound Smart Healthcare Systems (SHSs) can further personalize and contextualize the secrecy of data and strong system structures. Additionally, to protect against advanced and shifting computational intelligence-based cyber threats, especially in operational health environments, the use of Intruder Detection Systems (IDSs) is quite essential. However, traditional approaches to implementing IDSs are usually computationally costly and inappropriate for the narrow contours of deploying medical IoT devices. To address these challenges, the proposed study introduces PriFed-IDS, a novel, privacy-preserving FL-based IDS framework based on FL and reinforcement learning. The proposed model leverages reinforcement learning to uncover latent patterns in medical data, enabling accurate anomaly detection. A dynamic federation and aggregation strategy is implemented to optimize model performance while minimizing communication overhead by adaptively engaging clients in the training process. Experimental evaluations and theoretical analysis demonstrate that PriFed-IDS significantly outperforms existing benchmark IDS models in terms of detection accuracy and efficiency, underscoring its practical applicability for securing real-world IoMT networks. Full article

Verifiable Credentials (VCs) are a core component of decentralized identity systems, enabling individuals to prove claims without centralized intermediaries. However, managing VC revocation across segregated blockchain networks remains a key interoperability challenge. In this paper, we present a bidirectional blockchain bridge that enables the cross-chain verification of VCs between two Ethereum-compatible private blockchain networks: Geth and Besu. The system allows credentials issued and revoked on one chain to be validated from another without duplicating infrastructure or compromising security. Our architecture combines on-chain smart contracts with an off-chain relay, ensuring auditable, low-latency credential checks across chains. Our proposal is validated through an open-source working prototype. It is particularly relevant for domains where independent organizations must validate shared credentials across segregated blockchain infrastructures, including education, healthcare, and governmental identity services. Full article

Healthcare access in Latin America is highly unequal, with rural and peri-urban populations disproportionately excluded from essential and specialized services. To address the persistent gaps often obscured by conventional urban–rural classifications, this study developed a machine learning framework integrating the Functional Urban Area (FUA) model with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Shannon entropy optimization to refine urbanization gradients and quantify inequities across 11 countries. High-resolution population density data from the Meta High Resolution Settlement Layer (HRSL, 2020) and CIESIN’s Gridded Population of the World (GPWv4, rev. 11), combined with healthcare facility locations from Healthsites.io, were processed in R to generate population-facility networks. Entropy optimization dynamically determined country-specific DBSCAN distance thresholds, ensuring representative clustering of functional urban and rural areas. Facilities were categorized by care level, and per-capita densities were compared across clusters. Results showed that entropy-optimized DBSCAN improved spatial precision over traditional approaches and revealed systemic urban bias: Peru, Chile, and Venezuela had the lowest hospital densities, while Ecuador, Bolivia, and Paraguay displayed the strongest rural deficits in primary care. Specialized services were overwhelmingly concentrated in urban clusters. This reproducible framework establishes a quantitative baseline for healthcare inequities, providing data-driven insights to inform the design of decentralized strategies to improve equitable access to care across Latin America. Full article

The Internet of Things (IoT) has become deeply embedded in modern society, enabling applications across smart homes, healthcare, industrial automation, and environmental monitoring. However, as billions of interconnected devices continuously collect and exchange sensitive data, privacy and security concerns have escalated. This survey systematically reviews the state-of-the-art privacy-preserving techniques in IoT systems, emphasizing approaches that protect user data during collection, transmission, and storage. Peer-reviewed studies from 2016 to 2025 and technical reports were analyzed to examine applied mechanisms, datasets, and analytical models. Our analysis shows that blockchain and federated learning are the most prevalent decentralized privacy-preserving methods, while homomorphic encryption and differential privacy have recently gained traction for lightweight and edge-based IoT implementations. Despite these advancements, challenges persist, including computational overhead, limited scalability, and real-time performance constraints in resource-constrained devices. Furthermore, gaps remain in cross-domain interoperability, energy-efficient cryptographic designs, and privacy solutions for Unmanned Aerial Vehicle (UAV) and vehicular IoT systems. This survey offers a comprehensive overview of current research trends, identifies critical limitations, and outlines promising future directions to guide the design of secure and privacy-aware IoT architectures. Full article

Background/Objectives: Oman Vision 2040, the national blueprint for socio-economic transformation, aims to elevate the Sultanate to developed nation status, with the “Health” priority committed to building a “Leading Healthcare System with International Standards” via a Health in All Policies (HiAP) approach. This paper critically reviews Oman’s strategic health directions and implementation frameworks under Vision 2040, assessing their alignment with global Sustainable Development Goals (SDGs) and serving as a case model for health system transformation. Methods: This study employs a critical narrative synthesis based on a comprehensive literature search that included academic, official government reports, and international organization sources. The analysis is guided by the World Health Organization’s (WHO) Health Systems Framework, providing a structured interpretation of progress across its six building blocks. Results: Key interventions implemented include integrated governance (e.g., Committee for Managing and Regulating Healthcare), diversified health financing (e.g., public private partnership (PPPs), Health Endowment Foundation), and strategic digital transformation (e.g., Al-Shifa system, AI diagnostics). Performance metrics show progress, with a rise in the Legatum Prosperity Index ranking and an increase in the Community Satisfaction Rate. However, critical challenges persist, including resistance to change during governance restructuring, cybersecurity risks from digital adoption, and system fragmentation that complicates a unified Non-Communicable Disease (NCD) response. Conclusions: Oman’s integrated approach, emphasizing decentralization, quality improvement, and investment in preventive health and human capital, positions it for sustained progress. The transformation offers generalizable insights. Successfully realizing Vision 2040 demands rigorous, evidence-informed policymaking to effectively address equity implications and optimize resource allocation. Full article

The rapid rise of virtual hospitals has created an urgent need for secure, interoperable, and patient-centered (PC) access to medical data across distributed healthcare environments. However, most existing hospital information systems and electronic medical records (EMRs) were not designed to support decentralized service delivery or cross-institutional collaboration. While many prior solutions advocate replacing legacy systems with new architectures, such approaches often face significant cost, integration, and adoption challenges. This paper introduces a novel blockchain-based Virtual Care Access Control (VCAC) model that extends—rather than replaces—legacy systems and EMRs to support secure data sharing across virtual hospital ecosystems. Leveraging the core features of distributed ledger technology (DLT)—including immutability, decentralized auditability, and consensus-driven access—the VCAC framework embeds a six-tier PC information classification scheme into a blockchain-based layer. This model enables fine-grained, role-based access to clinical data, supporting PC treatment in comorbidity-aware contexts, emergency access, and policy-driven governance while maintaining institutional autonomy. We demonstrate how VCAC mitigates key confidentiality, integrity, and availability risks common to legacy systems. The model is evaluated through a breast cancer outpatient use case, illustrating its practical potential to transform fragmented infrastructures into secure, interoperable, and PC virtual care platforms—without disrupting existing healthcare operations. Full article

The integration of artificial intelligence (AI) and blockchain technology opens new avenues for decentralized, transparent, and secure data-driven systems. However, ensuring privacy and verifiability in collaborative AI environments remains a key challenge, especially when model updates or decisions must be recorded immutably on-chain. In this paper, we propose a novel privacy-preserving framework that leverages an ElGamal-based aggregate signature scheme with aggregate public keys to enable secure, verifiable, and unlinkable multi-party contributions in blockchain-based AI ecosystems. This approach allows multiple AI agents or data providers to jointly sign model updates or decisions, producing a single compact signature that can be publicly verified without revealing the identities or individual public keys of contributors. The design is particularly well-suited to resource-constrained or privacy-sensitive applications such as federated learning in healthcare or finance. We analyze the security of the scheme under standard assumptions and evaluate its efficiency in different terms. The study and experimental results demonstrate the potential of our framework to enhance trust and privacy in AI collaborations over decentralized networks. Full article