Search Results (138)

Introduction: Efficient communication and collaboration among local health departments (LHDs), healthcare organizations, governmental entities, and other community stakeholders are critical for public health preparedness and response. This study evaluates (1) the impact of informatics on LHDs’ frequency and collaboration in creating consistent COVID-19 messaging; (2) the influence of informatics on targeted messaging for vulnerable populations; and (3) LHD characteristics linked to their consistent and/or targeted messaging engagement. Methods: This study analyzed the 2020 National Association of County and City Health Officials (NACCHO) Forces of Change (FOC) survey, the COVID-19 Edition. Of the 2390 LHDs invited to complete the core questionnaire, 905 were asked to fill out the module questionnaire as well. The response rate for the core was 24% with 587 responses, while the module received 237 responses, achieving a 26% response rate. Descriptive analyses and six logistic regression models were utilized. Results: Over 80% (183) of LHDs collaborated regularly with public health partners, and 95% (222) used information management applications for COVID-19. Most interacted with local and state agencies, but only half with federal ones. LHDs that exchanged data with local non-health agencies, engaged with local non-health agencies, and communicated weekly to daily with the public about long-term/assisted care had higher odds of creating consistent messages for the public, and about the use and reuse of masks had lower odds of collaborating with public health partners to develop consistent messages for the public. Conclusion: Our findings underscore the centrality of informatics infrastructure and collaboration in ensuring equitable public health messaging. Strengthening public health agencies and investing in targeted training are crucial for effective communication across the communities served by these agencies. Full article

Background: Antimicrobial resistance (AMR) poses a critical global health challenge, particularly in resource-limited settings. A hub-and-spoke model, decentralising expertise and distributing resources to peripheral facilities, has been proposed as a strategy to enhance the antimicrobial stewardship (AMS) capacity in low- and middle-income countries. Aim: This study sought to understand healthcare professionals’ experiences of a hub-and-spoke AMS model in the Volta Region of Ghana and its influence on clinical practice, leadership, and collaborative endeavours to address AMR. Methods: A qualitative descriptive design was adopted. In-depth interviews were conducted with 11 healthcare professionals who participated in the AMS program. Thematic analysis was used to identify key themes related to the knowledge and skills that were gained, clinical and leadership practice changes, capacity building, and challenges. Results: Participants reported an increased awareness of AMR, particularly regarding the scale and clinical implications of antimicrobial misuse. The clinical practice improvements included more judicious prescribing and enhanced adherence to infection prevention and control measures. Many respondents highlighted stronger leadership skills and a commitment to capacity building through AMS committees, multidisciplinary collaboration, and cross-organisational knowledge exchange. Despite resource constraints and logistical hurdles, participants expressed optimism, citing data-driven approaches such as point prevalence surveys to track progress and inform policy. Engagement with hospital management and public outreach were viewed as essential to sustaining AMS efforts and curbing over-the-counter antibiotic misuse. Conclusions: The hub-and-spoke model caused observable improvements in AMS knowledge, clinical practice, and leadership capacity among healthcare professionals in Ghana. While challenges remain, particularly in securing sustainable resources and shifting community behaviours, these findings underscore the potential of network-based programs to catalyse systemic changes in tackling AMR. Future research should explore long-term outcomes and strategies for embedding AMS practices more deeply within healthcare systems and communities. Full article

Fog computing technology grants computing and storage resources to nearby IoT devices, enabling a fast response and ensuring data locality. Thus, fog-enabled IoT environments provide real-time and convenient services to users in healthcare, agriculture, and road traffic monitoring. However, messages are exchanged on public channels, which can be targeted to various security attacks. Hence, secure authentication protocols are critical for reliable fog-enabled IoT services. In 2024, Harbi et al. proposed a remote user authentication protocol for fog-enabled IoT environments. They claimed that their protocol can resist various security attacks and ensure session key secrecy. Unfortunately, we have identified several vulnerabilities in their protocol, including to insider, denial of service (DoS), and stolen verifier attacks. We also prove that their protocol does not ensure user untraceability and that it has an authentication problem. To address the security problems of their protocol, we propose a security-enhanced blockchain-based secure authentication protocol for fog-enabled IoT environments. We demonstrate the security robustness of the proposed protocol via informal and formal analyses, including Burrows–Abadi–Needham (BAN) logic, the Real-or-Random (RoR) model, and Automated Verification of Internet Security Protocols and Applications (AVISPA) simulation. Moreover, we compare the proposed protocol with related protocols to demonstrate the excellence of the proposed protocol in terms of efficiency and security. Finally, we conduct simulations using NS-3 to verify its real-world applicability. Full article

Personal health records (PHRs) are digital repositories that allow individuals to access, manage, and share their health information. By enabling patients to track their health over time and communicate effectively with healthcare providers, personal health records support more personalized care and improve the quality of healthcare. Their integration with emerging technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and blockchain, enhances the utility and security of health data management, facilitating continuous health monitoring, automated decision support, and secure, decentralized data exchange. Despite their potential, PHR systems face significant challenges, including privacy concerns, security issues, and digital accessibility problems. This paper discusses the fundamental concepts, requirements, system architectures, and data sources that underpin modern PHR implementations, highlighting how they enable continuous health monitoring through the integration of wearable sensors; mobile health applications; and IoT-enabled medical devices that collect, process, and transmit data to support proactive care and personalized treatments. The benefits and limitations of PHR systems are also discussed in detail, with a focus on interoperability, adoption drivers, and the role of advanced technologies in supporting the development of secure and scalable health information systems for a smarter world. Full article

Decision making in modern supply chain management relies heavily on data-driven decision support. Companies show a growing interest in building insights not only on data from within the company’s own boundaries, but also from collaborators and other actors in the market. While the topic of data and information sharing has been the focus of previous works, there has been a lack of studies focusing on practical implementations in the supply chain domain. Our aim is to conduct a technical feasibility study of data sharing in supply chain management. We analyze the requirements for cross-company data sharing in supply chains, and discuss existing technologies that enable such collaboration. We apply a distributed analytics framework that has already been implemented in the healthcare domain to a simulated use case of key performance indicator (KPI) exchange between supply chain actors. We find that the application is able to compute and exchange KPIs from the simulated companies’ datasets without requiring centralization of the databases. Furthermore, we find that the framework supports integration of data quality assessment and privacy preservation mechanisms. The application thus yields promising results with regard to technical feasibility. Factors that may facilitate scalability are discussed as directions for future research. Full article

Sarcomas, a rare and complex group of cancers, require multidisciplinary care across multiple healthcare settings, often leading to delays, redundant testing, and fragmented data. This fragmented care landscape obstructs the implementation of Value-Based Healthcare (VBHC), where care efficiency is tied to measurable patient outcomes.ShapeHub, an interoperable digital platform, aims to streamline sarcoma care by centralizing patient data across providers, akin to a logistics system tracking an item through each stage of delivery. ShapeHub integrates diagnostics, treatment records, and specialist consultations into a unified dataset accessible to all care providers, enabling timely decision-making and reducing diagnostic delays. In a case study within the Swiss Sarcoma Network, ShapeHub has shown substantial impact, improving diagnostic pathways, reducing unplanned surgeries, and optimizing radiotherapy protocols. Through AI-driven natural language processing, Fast Healthcare Interoperability Resources, and Health Information Exchanges, HIEs, the platform transforms unstructured records into real-time, actionable insights, enhancing multidisciplinary collaboration and clinical outcomes. By identifying redundancies, ShapeHub also contributes to cost efficiency, benchmarking treatment costs across institutions and optimizing care pathways. This data-driven approach creates a foundation for precision medicine applications, including digital twin technology, to predict treatment responses and personalize care plans. ShapeHub offers a scalable model for managing rare cancers and complex diseases, harmonizing care pathways, improving precision oncology, and transforming VBHC into a reality. This article outlines the potential of ShapeHub to overcome fragmented data barriers and improve patient-centered care. Full article

Background: Sharing substance use disorder (SUD) data is essential for advancing equitable healthcare and improving outcomes for marginalized populations. However, concerns about privacy, stigma, and adherence to data privacy regulations often hinder effective data sharing. This study explores patient preferences and considerations related to sharing SUD-related medical records, with a focus on the sociocultural and systemic factors that shape their willingness to share. Methods: A total of 357 adult patients from four community-based clinics in Arizona participated in a cross-sectional electronic survey. The survey assessed sociodemographic factors, experiences of stigma (self-directed, anticipated, and provider-based), trust in healthcare providers, satisfaction with care, and willingness to share SUD data across various scenarios. Data were analyzed using descriptive statistics, Pearson correlations, and one-way ANOVA to uncover key associations. Results: Patients identified SUD history, diagnoses, and treatment information as particularly sensitive. Stigma was significantly correlated with increased sensitivity and reduced willingness to share data, especially with providers outside their primary facility (p < 0.001). In contrast, trust in providers and higher satisfaction with care were linked to greater willingness to share data with all providers (p < 0.01). Patients were more inclined to share SUD data during emergencies or for direct treatment purposes than for administrative or research applications (p < 0.001). Discussion: These findings underscore the ethical imperative to address stigma and foster trust to promote equitable SUD data sharing. Policies must empower patients with control over sensitive health information while ensuring cultural competence and fairness in care delivery. Ensuring that patients feel confident in how their data are used may encourage greater participation in health information exchange, ultimately supporting more effective and individualized SUD care. Full article

Very few studies, all employing surveys, have investigated the perceptions of community pharmacists regarding antimicrobial stewardship (AMS). A qualitative inquiry exploring factors affecting community pharmacists’ participation in AMS may assist in the implementation of AMS in the primary care setting. This study aimed to explore the perceived barriers and enablers of community pharmacists’ participation in AMS. One-on-one semi-structured telephone interviews were conducted with a sample of community pharmacists from across Australia. Interviews were transcribed verbatim and analysed using the Framework Analysis method. Twenty community pharmacists (70% female), representing urban, regional, and remote areas of Australia participated in the study. Pharmacists identified a discord between clinical needs of patients and practice policies as the primary source of excessive prescribing and dispensing of antibiotics. The fragmented nature of the primary healthcare system in Australia was seen as limiting information exchange between community pharmacists and general practitioners about antibiotic use, that was encouraging inappropriate and, at times, unsupervised use of antibiotics. The existing community pharmacy funding model in Australia, where individual pharmacists do not benefit from any financial incentives associated with clinical interventions, was also discouraging their participation in AMS. Pharmacists suggested restricting default antibiotic repeat supplies, reducing legal validity of antibiotic prescriptions to less than the current 12 months, and adopting a treatment duration-based approach to antibiotic prescribing instead of the ‘quantity-based’ approach, where the quantity prescribed is linked to the available pack size of the antibiotic. Structural changes in the way antibiotics are prescribed, dispensed, and funded in the Australian primary care setting are urgently needed to discourage their misuse by the public. Modifications to the current funding model for pharmacist-led cognitive services are needed to motivate pharmacists to participate in AMS initiatives. Full article

Secure and privacy-preserving health status representation learning has become a critical challenge in clinical prediction systems. While deep learning models require substantial high-quality data for training, electronic health records are often restricted by strict privacy regulations and institutional policies, particularly during emerging health crises. Traditional approaches to data integration across medical institutions face significant privacy and security challenges, as healthcare providers cannot directly share patient data. This work presents MultiProg, a secure federated learning framework for clinical representation learning. Our approach enables multiple medical institutions to collaborate without exchanging raw patient data, maintaining data locality while improving model performance. The framework employs a multi-channel architecture where institutions share only the low-level feature extraction layers, protecting sensitive patient information. We introduce a feature calibration mechanism that ensures robust performance even with heterogeneous feature sets across different institutions. Through extensive experiments, we demonstrate that the framework successfully enables secure knowledge sharing across institutions without compromising sensitive patient data, achieving enhanced predictive capabilities compared to isolated institutional models. Compared to state-of-the-art methods, our approach achieves the best performance across multiple datasets with statistically significant improvements. Full article

This study proposes DtMin, a novel protocol for implementing data minimization principles in medical information sharing between healthcare providers (HCPs) and electronic health record providers (EHRPs). DtMin utilizes a multi-type encryption approach, combining attribute-based encryption (ABE) and hybrid encryption techniques. The protocol classifies patient data attributes into six categories based on sensitivity, consent status, and sharing requests. It then applies differential encryption methods to ensure only the intersection of patient-consented and EHRP-requested attributes is shared in decipherable form. DtMin’s security is formally analyzed and proven under the ICR-DB and ICR-IS security games. Performance analysis demonstrates efficiency across various data volumes and patient numbers. This study explores the integration of DtMin with advanced cryptographic techniques such as lattice-based ABE and lightweight ABE variants, which can potentially enhance its performance and security in complex healthcare environments. Furthermore, it proposes strategies for integrating DtMin with existing healthcare information systems and adapting it to future big data environments processing over 100,000 records. These enhancements and integration strategies position DtMin as a scalable and practical solution for implementing data minimization in diverse healthcare settings, from small clinics to large-scale health information exchanges. Full article

Policymakers face a dilemma in determining the optimal approach to regulating highly concentrated markets, especially in the context of digital government initiatives in healthcare. The current study investigates whether vendor market concentration facilitates or impedes efficiency in health information sharing. Utilizing data from the American Hospital Association’s annual surveys and information technology (IT) surveys from 2014 to 2016, we find that the market concentration of vendors, both at the state and county levels, indeed promotes information sharing among stakeholders. Specifically, when more hospitals in a given state or county use the same IT vendors for information sharing, providers exchange a more detailed level of information with a broader range of stakeholders compared to when using different vendors. Furthermore, we found that such facilitation varies among ownership types. Our findings highlight the complexities faced by policymakers in digital government, as restricting vendor market concentration too hastily could hinder the efficiency of information sharing and collaboration among healthcare providers. Additionally, such concentration may, in fact, enhance information sharing, especially for not-for-profit and for-profit hospitals. Overall, understanding the relationship between market concentration and information sharing can help healthcare administrators allocate resources more strategically, leading to better planning and less resource wastage, while aligning operational strategies with broader sustainability goals. Full article

Objectives: Heart failure is associated with frequent hospital admissions and high mortality. Digital medical technologies could help to improve information exchange between healthcare providers and patients to prevent recurrent cardiac decompensation. Methods: Eligible patients aged between 18 and 85 (mean age 65 ± 12; 35.4% female) with symptomatic heart failure were included in this cross-sectoral telemedicine network (sekTOR-HF) study (n = 79) with a 12-month intervention period. Depending on the severity of heart failure at the time of inclusion, patients in the intervention group were labeled either as inpatients (NYHA III–IV) or outpatients (NYHA I–II). All patients not included served as the control group. Nearest Neighbor Propensity Score Matching was performed to obtain a control group of the same size. Patients in the intervention group received an electronic patient record with all relevant health data in an eHealth portal and the option to use learning modules. A coordinating network office supported all patients in the intervention group. Monitoring included patient self-measurement of blood pressure, weight, heart rate, and oxygen saturation and a digital electrocardiogram. The primary endpoint was all-cause rehospitalization in both groups. Results: The cumulative incidence for all-cause rehospitalization was lower in the intervention group compared to the control group (sHR 1.86; 95% CI: 1.12–3.09). There was no difference in all-cause mortality (HR 1.5; 95% CI: 0.53–4.21). Conclusions: Intervention management in this cross-sectoral telemedicine network led to a lower cumulative incidence of all-cause rehospitalization even in the early phase of intervention. Full article

Background: Increasing evidence shows that the COVID-19 pandemic has disproportionately impacted certain populations, particularly those facing structural marginalization, such as immigrants and refugees. Additionally, research highlights that structurally marginalized populations living with chronic conditions, such as cancer and/or mental health and addiction (MH&A) disorders, are more vulnerable to the adverse effects of COVID-19. These individuals face higher susceptibility to infection and worse health outcomes, including increased rates of hospitalization, severe illness, and death. To better understand the challenges faced by people living at the intersection of social and clinical disadvantages, we organized a series of Think Tank sessions to engage stakeholders in exploring barriers and identifying community-based solutions for immigrants and refugees living with cancer and/or MH&A disorders during the current and future pandemics. Objectives: Our main objectives were to gauge how earlier findings resonated with stakeholders, to identify any gaps in the work, and to co-develop actionable solutions to safeguard health and well-being during COVID-19 and future crises. Methods: Two virtual Think Tank sessions were held in September 2023 as integrative knowledge exchange forums. The Cancer Think Tank was attended by 40 participants, while the MH&A disorders Think Tank included 41 participants. Each group comprised immigrants and refugees living with or affected by cancer (in the Cancer Think Tank) or MH&A disorders (in the MH&A disorders Think Tank), alongside service providers, policymakers, and researchers from Ontario. This paper presents the key discussions and outcomes of these sessions. Results: Participants identified and prioritized actionable strategies during the Think Tank sessions. In the Cancer Think Tank, participants emphasized the importance of leveraging foreign-trained healthcare providers to address workforce shortages, creating clinical health ambassadors to bridge gaps in care, and connecting immigrants with healthcare providers immediately upon their arrival in Canada. In the MH&A disorders Think Tank, participants highlighted the need to remove silos by fostering intersectoral collaboration, empowering communities and building capacity to support mental health, and moving away from one-size-fits-all approaches to develop tailored interventions that better address diverse needs. Conclusions: The Think Tank sessions enhanced our understanding of how the COVID-19 pandemic has impacted immigrants and refugees living with cancer and/or MH&A disorders. The insights gained informed a series of actionable recommendations to address the unique needs of these populations during the current pandemic and in future public health crises. Full article

We sought to incorporate a community-based solution with a family health history (FHH) clinical support program (MeTree) integrated into well-patient appointments with the novel partnership of a public health state-level health information exchange (HIE). The Arkansas—Making History pilot project tested informatics compatibility among these systems and the patients’ electronic medical record (EPIC) in a rural clinic in the north central region of the state, having the state HIE as a means for patients to store and share their FHHs across multiple healthcare providers with updates in real time. We monitored for unexpected issues during the pilot and asked for the perspectives of patients and healthcare providers throughout the project to have a clear understanding of how to implement this project on a larger scale. The greatest barrier to project implementation was the inability of the state HIE to host or share the FHH data. We compensated for the lack of systems compatibility and documented valuable information about patient acceptability and usability of the MeTree platform, as well as gleaning important clinical outcome data from those who completed MeTree FHH accounts in an underserved area. Rural patients need additional technological support in the larger scaling of this project, both in available linkages to community clinics with patient-controlled options for how their data is stored and shared and in Internet connectivity and software options available for ease of use. Full article

The rapid advancement and widespread adoption of artificial intelligence (AI) across diverse industries, including healthcare, finance, manufacturing, and retail, underscore the transformative potential of AI technologies. This necessitates the development of viable AI model marketplaces that facilitate the development, trading, and sharing of AI models across the pervasive industrial domains to harness and streamline their daily activities. These marketplaces act as centralized hubs, enabling stakeholders such as developers, data owners, brokers, and buyers to collaborate and exchange resources seamlessly. However, existing AI marketplaces often fail to address the demands of modern and next-generation application domains. Limitations in pricing models, standardization, and transparency hinder their efficiency, leading to a lack of scalability and user adoption. This paper aims to target researchers, industry professionals, and policymakers involved in AI development and deployment, providing actionable insights for designing robust, secure, and transparent AI marketplaces. By examining the evolving landscape of AI marketplaces, this paper identifies critical gaps in current practices, such as inadequate pricing schemes, insufficient standardization, and fragmented policy enforcement mechanisms. It further explores the AI model life-cycle, highlighting pricing, trading, tracking, security, and compliance challenges. This detailed analysis is intended for an audience with a foundational understanding of AI systems, marketplaces, and their operational ecosystems. The findings aim to inform stakeholders about the pressing need for innovation and customization in AI marketplaces while emphasizing the importance of balancing efficiency, security, and trust. This paper serves as a blueprint for the development of next-generation AI marketplaces that meet the demands of both current and future application domains, ensuring sustainable growth and widespread adoption. Full article