Event-based automation systems are a revolutionary paradigm in the healthcare information technology activities that respond to the critical issue of operational resiliency in the growing and increasingly complex digital health ecosystems. The SupportPlus model proves how intelligent automation may radically rethink the validation process, moving away from a manual process of reactivity towards validation to a system-driven mechanism of assurance. Using microservices architecture, serverless computing, and container orchestration on the Azure Kubernetes service, the framework integrates validation, monitoring, and compliance functions in a metadata-based ecosystem. Application to mission-critical healthcare services also demonstrates significant operational benefits such as radical decreases in manual validation cost, faster incident response settings, and system availability that is approaching five-nines reliability. The architecture uses event-driven orchestration to instigate automated validation gates built into continuous delivery pipelines, modifying compliance with periodic manual audits into continuous compliance processes. The self-healing functionality is defined as the capability of the system to automatically recover most of the validation failures, and this aspect is fundamentally changing the dynamic between the operations of IT and the system reliability. In addition to the short-term efficiency, the framework harmonizes the validation practices throughout organizational teams, eradicates tribal knowledge dependence whatsoever, and develops ongoing compliance evidence that can be examined during a regulatory audit. The measurable effect proves that intelligent healthcare IT automation is not only cost optimization but also meets the main demands of patient safety, care continuity, and trust of people in digital health infrastructure.

Healthcare organizations struggle with integrating heterogeneous legacy systems in real-time environments. This 18-month prospective study examined Apache Camel and Red Hat Fuse implementations across three U.S. healthcare delivery systems (patient populations: 1.2M, 850K, and 2.1M respectively). We collected performance metrics from 23 production integration flows, conducted semi-structured interviews with 18 IT practitioners and clinical users, and documented 7 significant implementation failures with detailed root cause analyses. Contrary to vendor claims, we found that median data integration latency was reduced from 2,847 ms (legacy point-to-point baseline) to 189 ms (Camel/Fuse implementation), representing a 93% improvement (p<0.001). However, we also discovered critical deployment challenges: initial CPU overhead was 340% higher than expected due to inadequate JVM tuning, requiring 4 months of performance optimization. Clinical workflow time savings averaged 34% per transaction, translating to approximately 2.7 full-time employee equivalents recovered per institution annually. This paper presents raw experimental data, comprehensive failure case analyses with remediation timelines, and practitioner perspectives rarely discussed in vendor materials. Our implementation framework emphasizes modular architecture, phased rollout strategies, and organizational change management that practitioners identified as essential for success. The work contributes quantifiable evidence for healthcare IT leaders evaluating enterprise integration platforms in complex operational environments, with particular attention to the gap between vendor expectations and real-world deployment realities.

This research proposal investigates the implementation of Health Information Exchange (HIE) in Malaysia, focusing on understanding the technological, policy, and financial challenges and opportunities associated with its adoption and effectiveness. Through a comprehensive survey approach targeting healthcare practitioners, policymakers, IT professionals, and patients, the study aims to elucidate the current state of HIE, assess interoperability with international standards, and identify pathways for enhancement. Key objectives include analyzing technological barriers, evaluating policy and regulatory impacts, and exploring sustainable financial models for HIE. Findings indicate a positive trajectory towards HIE implementation, underscored by a broad recognition of its potential to transform healthcare delivery. However, challenges such as system integration, policy clarity, infrastructural readiness, and privacy concerns remain. Recommendations for future improvement emphasize strengthening infrastructure, clarifying policies, enhancing security measures, providing continuous training, fostering innovation, and increasing patient engagement. Furthermore, this study highlights the alignment of HIE implementation with the Sustainable Development Goals (SDGs), particularly SDG 3, to ensure universal health coverage and enhance the healthcare workforce's capacity for process innovation. Incorporating international best practices and a validated framework will further strengthen Malaysia’s healthcare system in the digital age.

The healthcare sector faces a critical shortage of healthcare workers, creating significant challenges in healthcare delivery. The use of Information Technology (IT) solutions in healthcare presents potential remedies to reduce the negative consequences of this problem. The purpose of this study was to identify IT solutions implemented to mitigate the effects of medical shortages and improve administrative processes and care access. The study used a systematic approach integrating desk research, national expert consultations and comparative analysis to examine IT solutions in healthcare systems. Five European countries were selected for the in-depth analysis: Poland, the Netherlands, Spain, Finland, and Croatia. The impact on administrative processes, care access, and the functioning of healthcare systems was assessed. The study identified a variety of regulatory frameworks, common implementation strategies and the institutions responsible for these activities. All compared countries used telemedicine, e-prescriptions and various types of health applications. It was found that the most frequently used IT solutions were electronic health record (EHR) and e-prescription systems. However, IT training, its organization, financing and mandatory nature differed in individual countries. In addition, common barriers were identified across all countries, such as financial constraints and interoperability issues. Integrating IT solutions offers opportunities to address health workforce shortages and enhance healthcare efficiency. Tailored strategies and collaborative efforts are essential to address financial constraints and interoperability issues. Implementing best practices identified in this study can improve administrative processes and care access. Future research should prioritize longitudinal impact assessments and explore new technologies to optimize healthcare IT solutions.

This research presents a comprehensive CWPP-enabled framework tailored for securing healthcare cloud workloads, specifically focusing on cloud-based health data warehouses (HDWs) that integrate heterogeneous clinical data sources for analytics and decision support. Drawing on contemporary literature, industry research, and real-world implementations, this paper analyzes the healthcare cloud threat landscape, evaluates CWPP architectural components, and proposes an end-to-end framework integrating runtime monitoring, micro segmentation, continuous compliance, and DevSecOps-aligned scanning. Demonstrations and system diagrams illustrate how CWPPs intervene in attack chains, reduce breach impact, and defend PHI-processing workloads. A comparative analysis of leading CWPP solutions (Prisma Cloud, Microsoft Defender for Cloud, Trend Micro Deep Security) is included to highlight operational relevance for healthcare IT environments. Findings show that CWPPs significantly enhance resilience, reduce misconfigurations, and strengthen compliance readiness in healthcare HDW ecosystems. The proposed framework can guide healthcare organizations toward establishing workload-centric, adaptive, and regulatory-aligned security architecture suitable for modern cloud operations.

The growing complexity of healthcare information technology (IT) infrastructures, driven by the proliferation of electronic health records, connected medical devices, telemedicine platforms, and cloud-based clinical services, has created an urgent need for advanced analytical methods capable of modeling, optimizing, and safeguarding large-scale interconnected systems. Graph theory, with its ability to mathematically represent relationships among discrete components, provides a powerful foundation for understanding the structural and functional behavior of healthcare IT networks. This study examines how graph-theoretic principles and network analysis techniques, integrated with artificial intelligence (AI), can be systematically applied to the intelligent design and evaluation of healthcare IT infrastructures where scalability, reliability, data security, and performance efficiency are critical. The research models core healthcare infrastructure components—including clinical servers, hospital information systems, medical IoT devices, data centers, and communication links—as graph structures that capture both connectivity and operational dependencies. Network analysis metrics such as centrality measures, clustering coefficients, cut-sets, and shortest-path algorithms are applied to identify critical nodes, communication bottlenecks, and points of vulnerability that may compromise service continuity or patient safety. In parallel, spectral and flow-based graph analyses are employed to assess load distribution, latency patterns, and potential failure propagation across healthcare networks. Building upon these graph-derived insights, AI and machine learning techniques are incorporated to enable predictive maintenance, intelligent load balancing, and adaptive infrastructure planning. Dynamic graph modeling allows the system to capture temporal variations in healthcare data traffic, detect anomalies in real time, and anticipate structural weaknesses before they escalate into operational failures. The framework also supports comparative evaluation of alternative healthcare network architectures—including centralized, distributed, hybrid, and software-defined models—revealing critical trade-offs in responsiveness, fault tolerance, data availability, and scalability. The findings demonstrate that the integration of graph theory, network analysis, and AI significantly enhances the intelligence, resilience, and efficiency of healthcare IT infrastructure design. By providing a rigorous, quantifiable basis for architectural decision-making, this approach aligns technological capabilities with clinical performance objectives and regulatory requirements. The study concludes that graph-theoretic and AI-driven methodologies are essential components of next-generation healthcare IT engineering, enabling secure, adaptive, and future-ready digital health ecosystems.

Chronic diseases remain among the most critical national health challenges in the United States, contributing substantially to morbidity, mortality, and healthcare costs. Digital health record systems including electronic health records, patient portals, and integrated care platforms are central to improving care coordination, patient engagement, and long-term disease management. However, many implementation efforts fail due to inadequate project management, limited stakeholder participation, and insufficient patient-centered design. This paper synthesizes existing research on project management and patient-centered digital health implementation to propose a Strategic Project-Management Framework for enhancing chronic disease outcomes. Drawing from peer-reviewed literature, policy documents, and healthcare IT reports across major databases (PubMed, Scopus, Web of Science, and Google Scholar), the review identifies critical success factors, barriers, and emerging trends shaping digital health system adoption. The analysis demonstrates that integrating structured project management methodologies with patient-centered principles is essential for creating usable, sustainable, and clinically effective digital health systems. We present a Strategic Project-Management Framework that integrates the core phases of initiation, planning, execution, monitoring, and closure with co-design, feedback, and participatory engagement mechanisms. This integration promotes stronger system adoption, improved care coordination, and better chronic disease outcomes. The framework establishes a replicable, adaptable model for healthcare organizations and provides a foundation for future empirical validation and policy development.

Digital transformation of Health Information Systems (HIS) in developing countries introduces significant vulnerabilities in data security, like data tampering, lack of transparency and lack of trust among stakeholders due to the centralised nature of these HISs, which in turn hinders effective healthcare delivery. Blockchain technology, with its decentralised, immutable, and transparent nature, offers a promising solution to fortify HIS against these challenges; however, its adoption feasibility in contexts like Tanzania remains underexplored. This study aimed to assess the security vulnerabilities within the existing HIS at Temeke Referral Hospital, Dar es Salaam, Tanzania, evaluate the feasibility of integrating a permissioned Hyperledger Fabric Blockchain Network (HFBN) in the existing HIS, develop HFBN prototype and evaluate performances of its two consensus protocols (Raft and PBFT (Practical Byzantine Fault Tolerance)) in terms of Transaction Throughput (TT) and Transaction Latency (TL). A quantitative research approach was employed to collect and analyse data, with quantitative data on security challenges and feasibility being gathered via structured questionnaires with 28 healthcare professionals, IT staff, administrators and managers and analysed via Descriptive Statistics and Inferential Statistics methods, while performance results (TT and TL) were gathered through testing experiments on a developed HFBN prototype. Secure Hash Algorithm 256 (SHA 256) was used to ensure immutability of transactions, while Elliptic Curve Digital Signature Algorithm (ECDSA) was used to ensure authenticity of transactions in the developed HFBN. The findings revealed Raft consensus protocol has better performance with a higher TT of 693 transactions per second (TPS) and a lower TL of 1.8 seconds compared to PBFT, which achieved a TT of 150 TPS and a TL of 2.1 seconds, concluding that Raft is better suited than Raft in practical deployment of the developed HFBN. The findings on security vulnerabilities revealed that the existing HIS exhibited moderate security, with significant concerns regarding staff training and inconsistent audits. Also, stakeholders strongly believed in blockchain's potential to enhance security. Major barriers for adopting the blockchain technology included a lack of technical expertise, financial constraints and infrastructural readiness. Blockchain technology presents a viable solution for strengthening HIS security at Temeke Hospital. However, successful implementation is contingent upon addressing key resource constraints through phased deployment, targeted training programs, and strategic investment. This study provides a scalable framework for similar healthcare institutions in Tanzania and other resource-constrained environments.

The global trend of population aging and increasing life expectancy leads to the need to develop and implement national strategies for promoting a healthy lifestyle and preventing chronic noncommunicable diseases at the population level. The article analyzes the best practices of world experience in popularizing healthy lifestyle among different age groups, concepts of maintaining health and increasing life expectancy, active longevity, as well as effective measures to prevent CVD. The analysis of publications posted in the electronic databases PubMed, CochraneLibrary, WoS, Scopus, eLibrary, CyberLeninka, 15 years deep, as well as documents of the United Nations on Sustainable Development Goals and the World Health Organization “Global Action Plan for the Prevention of Noncommunicable Diseases for 2013– 2020”, revised and extended in many ways until 2030.The rapid development of information technology in healthcare, the growth of data volumes and the complexity of diagnostic and treatment methods have led to the fact that artificial intelligence has begun to be used more and more often in this industry. The global experience of a number of Countries confirms that the prevention of chronic noncommunicable diseases and the formation of a healthy lifestyle are multi-level tasks that require a combination of political will, a developed healthcare system and public participation.

Online healthcare consultation services (OHCS) hold immense potential to transform the healthcare industry. This study seeks to investigate the impact of doctors' participation in OHCS on offline outpatient and inpatient costs, addressing a critical yet underexplored area in the healthcare IT literature. Utilizing an exhaustive dataset from a high-level comprehensive hospital in China over a period of 42 months, we find that doctors' OHCS participation increases outpatient costs by 63.2% (¥268.510) and inpatient costs by 22.2% (¥2,365.092). These results remain robust after addressing endogeneity concerns through instrumental variable (IV) approaches and propensity score matching (PSM). Notably, mechanism tests reveal that OHCS participation enhances doctors' service volume expansion, cost-upgrading within services and patient mix changes, leading to induced demand and higher offline medical costs. Moreover, these effects are amplified for doctors with higher professional status. However, the COVID-19 pandemic and higher department crowding levels tend to mitigate these cost increases. This study makes several significant theoretical contributions. First, it expands the healthcare IT literature by uncovering the "dark side" of OHCS participation, showing that it raises medical costs by altering supply-demand dynamics, contrary to the cost-saving potential often associated with traditional healthcare IT. Second, it advances research on OHCS by identifying the mechanisms—service volume expansion, cost-upgrading within services and patient mix changes—through which OHCS participation influences offline costs. Third, it explores the heterogeneous effects of physician, environmental, and departmental characteristics on medical costs. Practically, our findings suggest that hospitals and policymakers should promote widespread OHCS participation to balance supply-demand dynamics, advance treatment standardization and differentiated payment models, establish a multi-dimensional physician performance evaluation system, and leverage AI technology to improve doctor-patient matching and resource scheduling—enabling the realization of online healthcare's potential without inadvertently driving up medical costs. This study highlights the nuanced implications of OHCS on offline healthcare costs, offering insights for improving its implementation in healthcare systems.

q-rung linear diophantine fuzzy Schweizer and Sklar power aggregation operators: risk evaluation of third-party vendor selection based on cybersecurity in healthcare IT

Digital Health Challenges and Opportunities for Health-Care IT Professionals

To use more precise measures of which hospitals are electronically connected to determine whether health information exchange (HIE) is associated with lower emergency department (ED)-related utilization. We combined 2018 Medicare fee-for-service claims to identify beneficiaries with 2 ED encounters within 30days, and Definitive Healthcare and AHA IT Supplement data to identify hospital participation in HIE networks (HIOs and EHR vendor networks). We determined whether the 2 encounters for the same beneficiary occurred at: the same organization, different organizations connected by HIE, or different organizations not connected by HIE. Outcomes were: (1) whether any repeat imaging occurred during the second ED visit; (2) for beneficiaries with a treat-and-release ED visit followed by a second ED visit, whether they were admitted to the hospital after the second visit; (3) for beneficiaries discharged from the hospital followed by an ED visit, whether they were admitted to the hospital. In adjusted mixed effects models, for the first two outcomes, beneficiaries returning to the same organization had significantly lower utilization compared to those going to different organizations; for the third outcome, those returning had higher utilization. Comparing only those going to different organizations, HIE was not associated with lower levels of repeat imaging or likelihood of admission following hospital discharge. HIE was associated with lower likelihood of hospital admission following a treat-and-release ED visit (1.83 percentage points [-3.44 to 0.21]). Differing utilization for beneficiaries returning to the same organization could reflect better access to information or other factors such as aligned incentives. HIE is not consistently associated with utilization outcomes reflecting more coordinated care in the ED setting.

Healthcare organizations today face unprecedented challenges in protecting sensitive patient information from increasingly sophisticated cyber threats. The digital transformation of healthcare has brought remarkable benefits in terms of efficiency and patient care, but it has also created new vulnerabilities that malicious actors are eager to exploit. This study examines the current landscape of cybersecurity threats facing health information systems and explores comprehensive strategies for safeguarding patient and clinical data. Through analysis of recent security incidents and evaluation of protective measures, this research identifies critical vulnerabilities in healthcare IT infrastructure and proposes practical solutions for strengthening data security. The findings reveal that successful protection of health information requires a multi-layered approach combining technical controls, staff training, policy development, and continuous monitoring. Healthcare organizations must recognize that cybersecurity is not merely an IT issue but a fundamental component of patient safety and quality care. The study emphasizes that protecting patient data requires ongoing commitment from leadership, adequate resource allocation, and a culture of security awareness throughout the organization. As cyber threats continue to evolve, healthcare providers must remain vigilant and adaptive in their security strategies to maintain patient trust and comply with regulatory requirements.

Children with medical complexity (CMC) require ongoing care, presenting unique challenges for family caregivers. These challenges often result in raised stress levels, affecting the mental health of caregivers and the well-being of the children. Health information technology (Health IT) offers opportunities to support caregivers in managing stress and promoting better mental health outcomes. Effective implementation of technologies requires a clear understanding of the mental health challenges and needs involved. This study aims to understand the mental health challenges faced by parents of CMC to design user-centered technologies that support both children and their caregivers. By addressing these challenges through supportive technologies, CMC and their parents could be empowered to more effectively manage their responsibilities and improve their overall quality of life.

ABSTRACTBlockchain technology has emerged as a pivotal solution for securing sensitive data across various domains, including artificial intelligence (AI), supply chain management, cloud computing, and healthcare. Its core attributes, confidentiality, decentralization, security, and privacy, offer significant advantages to the healthcare sector. The integration of Internet of Things (IoT) devices within healthcare systems enhances interoperability, enabling seamless communication between healthcare software and IT infrastructure. However, traditional healthcare systems face persistent security challenges, including phishing, masquerading, and identity theft. To address these issues, we propose a secure blockchain‐based decentralized application for generating, maintaining, and validating medical certificates. This application facilitates secure interactions among healthcare entities, including hospitals, patients, and IoT devices, while ensuring confidentiality, authentication, and access control through smart contracts. The proposed blockchain architecture enhances data integrity and secure transmission using the zero‐knowledge proof (ZKP) mechanism. Additionally, we incorporate the interplanetary file system (IPFS) for off‐chain data storage to optimize storage costs and enhance security through Ethereum smart contracts. Performance evaluations demonstrate the effectiveness of our approach in mitigating existing security vulnerabilities, thereby offering a robust and scalable solution for secure healthcare data management.

The adoption of digital technologies in healthcare is growing rapidly, and with it, the associated cybersecurity risks are also increasing. In particular, web applications, which can be used to manage and share sensitive health and personal information, require strong security measures to prevent data breaches and ensure compliance with regulatory standards. This paper investigates the applicability of the Open Web Application Security Project (OWASP) guidelines in the healthcare domain. Through a literature review, we identified the most common security requirements considered and used in Digital Health (DH) technologies and assessed their alignment with OWASP Application Security Verification Standard (ASVS). Furthermore, a questionnaire, involving Italian healthcare facilities and Information Technology (IT) companies operating in the healthcare sector, highlighted a significant gap between the availability of security standards and guidelines, and their actual knowledge and use in practice. Based on these findings, we propose a context-aware tool that guides developers and testers in applying OWASP standards throughout the software development lifecycle. The proposed tool aims to provide tailored security recommendations, structured checklists, and test planning based on application context, offering a practical bridge between frameworks and real-world adoption in clinical environments.

Despite significant diagnostic and therapeutic advances in valvular heart disease (VHD) care, all populations do not yet benefit equally. Established evidence reveals persistent disparities across the VHD care continuum, particularly affecting underserved populations such as women, racial and ethnic minorities, low-income individuals, and rural residents. In recent years, advancements in healthcare information technology (IT) infrastructures and the rapid emergence of artificial intelligence (AI) models have equipped us with smarter, more capable electronic health record (EHR) systems and digital tools to help tackle underdiagnosis and undertreatment challenges. However, without a specific equity lens embedded in both research initiatives and real-world implementations, these technological advancements risk leaving disparities unaddressed. In this review, we call for translating current awareness about disparities in VHD care into measurable actions. First, we review documented disparities across three main domains: diagnosis/detection, surveillance/referral, and treatment. Then, we discuss promising IT-based solutions aimed at mitigating these disparities, focusing on three key areas: AI-augmented diagnostic tools, EHR-based clinical decision support systems, and telehealth.

Abstract In today's healthcare environments, especially for ones that are motivated by digital transformation and remote patient engagement, scalability of infrastructure is key to providing predictable access to services like Electronic Health Records (EHR), telemedicine platforms, and analytics dashboards for health. Classic auto-scaling of cloud environments like Azure Kubernetes Service (AKS) relies heavily on reactive thresholds mostly CPU or memory utilization to scale in terms of provisioning/deprovisioning containers. These approaches typically struggle with unpredictable traffic of high variance characteristic in healthcare (e.g., telehealth spikes in flu season or pandemics). This paper outlines an intelligent auto-scaling architecture with AI-driven predictive models specifically Long Short-Term Memory (LSTM) networks and Facebook’s Prophet to predict system demand. These models process past usage data from a simulated telehealth application and automatically invoke Kubernetes-based Event-Driven Autoscaler (KEDA) for adaptive management of pods. We have a real-case study scenario that emulates traffic surges in a digital health application and tests the system against conventional Horizontal Pod Autoscaler (HPA) approaches. Our results demonstrate improved response latency of 35% and cloud compute cost savings of 22% while sustaining 100% uptime. Our architecture met this twin need for resilience and cost-effectiveness in high-priority healthcare infrastructure and positions it as a building block for intelligent, scalable healthcare cloud solutions. Keywords Azure Kubernetes Service (AKS), Auto-scaling in cloud computing, Predictive scaling models, Healthcare cloud infrastructure, LSTM workload prediction, Prophet time-series forecasting, KEDA (Kubernetes Event-Driven Autoscaler), Telehealth optimization, AI in healthcare operations, Cloud-native architecture, HIPAA-compliant cloud solutions, MLOps in cloud scaling, Latency reduction in healthcare IT, Cost-efficient cloud resource management, Real-time digital health platforms

This paper examines the complex relationship between political instability and the acceleration of brain drain in Pakistan during the tenure of Prime Minister Imran Khan from 2018 to 2022. Employing a qualitative -methods approach, this research analyzes longitudinal emigration data, sector-specific workforce trends, and qualitative insights from professionals who emigrated or considered emigrating during this period. The study finds that political turbulence during these years, characterized by frequent policy changes, civil-military tensions, and no-confidence motions, exacerbated pre-existing economic vulnerabilities and significantly contributed to the decision of highly skilled professionals to seek opportunities abroad. The emigration of critical workforce segments including healthcare professionals, engineers, IT experts, and academics intensified, depriving Pakistan of essential human capital needed for sustainable development. This research contributes to the broader understanding of how political factors interact with economic determinants in influencing migration decisions and offers policy recommendations for mitigating brain drain through institutional stability, economic reforms, and diasporas engagement strategies.