Search Results (1,063)

Endemic and (re)emerging infectious diseases pose a major threat to public health and military readiness in Central and South America. The high biodiversity and the differing transmission pathways require the use of highly sensitive methods to track the emergence and spread of these diseases. High-throughput sequencing technologies have become a critical resource for the field of infectious disease monitoring, specifically for tracking pathogen evolution, antimicrobial resistance, and transmission. This review provides an overview of the implementation of genomics-informed infectious disease surveillance conducted by the U.S. Naval Medical Research Unit SOUTH, focusing on operational, infrastructural, and logistical challenges in resource-limited settings and outlining strategies for building sustainable regional capacity. Selected case studies focused on prominent biothreats including dengue, Oropouche, malaria, and antimicrobial resistance demonstrate the significance and applicability of targeted and agnostic sequencing approaches to elucidate transmission pathways, pathogen discovery, and guide public health responses and field-deployable applications. Full article

In response to the challenge of balancing accuracy and generalization in medical image classification using convolutional neural networks (CNNs), this paper proposes ResKAN18, a hybrid structure that embeds the learnable spline function of the Kolmogorov–Arnold network (KAN) into the ResNet18 classification head for intelligent diagnosis of COVID-19 in chest X-ray images. ResKAN18 includes three variants: ResKAN18—Large (four layers of KAN, hidden-layer dimensions [256, 128, 64]), ResKAN18—Standard (four layers of KAN, hidden-layer dimensions [128, 64, 32]), and ResKAN18—Simple (three layers of KAN, hidden-layer dimensions [64, 32]), which can achieve a flexible balance between accuracy and efficiency with different depths of KAN. A systematic comparison has been conducted between four classic CNN baselines including ResNet18, VGG16, DenseNet121, ShuffleNetV2, and three ResKAN18 variants on a benchmark dataset containing 3880 chest X-rays (COVID-19, normal, viral pneumonia). The results have shown that ResKAN18—Large can achieve an accuracy of 98.80% on the independent test set, which is 1.21% higher than ResNet18 and 0.69% higher than DenseNet121—its parameter count is 13.97M, inference delay is 8.25 ms/image, and training–validation accuracy difference is only 1.50%. The accuracy and performance stability of the dataset under random partitioning conditions are superior to the other two variants and all classic CNN baselines. All ResKAN18 variants have achieved zero missed diagnoses for COVID-19, while ResNet18 has shown missed diagnoses (0.9944). Taking into account the trade-off between accuracy, generalization, and inference efficiency, ResKAN1—Large is recommended as the default configuration, while for edge deployment scenarios with severely limited resources, ResKAN18—Simple can provide a cost-effective alternative with an extremely low latency of 2.50 ms/image and only 3.1% parameter increment compared to ResNet18. Full article

Background: Attention-Deficit/Hyperactivity Disorder (ADHD) affects an estimated 6% of adults in the United States and contributes to a significant economic burden. Telehealth has emerged as a vital tool in the management of ADHD, offering improved access to care, especially for individuals in underserved communities. Despite its growing role, there remain critical gaps in understanding how social determinants of health (SDOH) are associated with disparities in telehealth utilization for ADHD treatment. Objectives and Methods: This study analyzed data from the National Center for Health Statistics (NCHS) Rapid Surveys System (RSS) Round 2: ADHD (October–November 2023), a nationally fielded survey of U.S. adults. Respondents were classified into three groups: never diagnosed, previously diagnosed, and currently diagnosed with ADHD. The study aimed to (1) compare the distribution of SDOH across ADHD status groups and the general adult population to identify factors associated with ADHD diagnosis; (2) assess the homogeneity of SDOH distributions across ADHD groups; (3) evaluate telehealth utilization among adults currently diagnosed with ADHD; and (4) examine the relationship between SDOH and telehealth use for ADHD treatment. Multivariable logistic regression (MVLR) served as a benchmark model, while machine learning (ML) models—including regularized linear regression, support vector machine (SVM), random forest (RF), LightGBM, multilayer perceptron (MLP), and Few-Shot Learning (FSL)—were trained to identify key predictors. Results: A total of 7009 survey responses were analyzed: 124 had a past diagnosis, 444 were currently diagnosed, and the remainder had never been diagnosed with ADHD, corresponding to a current ADHD prevalence of 6.3%. Adults with current ADHD were more likely to be male, single, younger, white, non-homeowners, and frequent users of online health resources. They also reported lower education, income, and financial security. About 70% used telehealth for counseling and prescriptions; insurance covered telehealth visits for 82.32% of users, yet 38.76% reported no coverage of ADHD-related diagnostic or treatment costs. Nineteen SDOH elements across four domains—demographic, socioeconomic, neighborhood/built environment, and healthcare access—were identified as predictors. ML models outperformed MVLR, with SVM and FSL achieving the highest F1 (both 0.63), and FSL the highest recall (0.69). Age, race, marital status, difficulty paying bills, home ownership, education, and household size were the most consistently important variables. Limitations: This study is limited by a cross-sectional design, reliance on self-reported ADHD diagnoses, and a lack of genetic or family-history measures. Additionally, the omission of complex sampling weights limits the national representativeness of these findings. Finally, the small effective sample size poses risks of model overfitting, and the generalizability of the models could not be externally validated due to the unavailability of comparable independent datasets. Conclusions: Despite widespread internet access, disparities in telehealth use for ADHD persist. Among 19 SDOH predictors, age (aOR = 0.56), difficulty paying medical bills (aOR = 2.52), and race (aOR = 1.37) were significantly associated with telehealth use, and all ML models outperformed the MVLR benchmark, though bootstrap CIs overlapped. Future research should incorporate inclusive data collection and stratified modeling to better represent disadvantaged populations and inform equitable access strategies. Full article

Background: Emergency aeromedical services, including helicopter emergency medical service (HEMS) and medical emergency evacuation (MEDEVAC), are critical to time-sensitive care. Dispatch decisions are complex and consequential, determining whether, which, and under what conditions to deploy aeromedical resources. This study reviews modelling approaches for emergency aeromedical dispatch. Methods: Following PRISMA, studies between 2003 and 2026 (June) were screened, yielding 42 studies. Models were classified as predictive and learning-based, sequential decision, and prescriptive optimisation-based, with solution techniques, operational applications, and policy contexts analysed. Results: Markov decision process and approximate dynamic programming models dominate the sequential decision literature, particularly in military MEDEVAC. Prescriptive models support resource allocation, base location, coverage planning, and dispatch optimisation, while predictive and AI/ML-based approaches remain limited but emerging. Key challenges include computational complexity, data uncertainty, policy fragmentation, and ethical concerns. Sequential models reflect dispatch’s dynamic, stochastic nature, where current deployments constrain future resource availability. Priority-aware policies outperform closest-unit rules, but limited real-world validation hinders adoption. Conclusions: This review maps methods and provides evidence-based guidance for researchers and dispatch organisations selecting decision support models. Future opportunities include AI-assisted dispatch, hybrid predictive–prescriptive modelling, real-time adaptive algorithms, sustainability-oriented optimisation, improved helicopter landing zone identification, and standardised ethical and regulatory frameworks. Full article

Background: Health assessment in custodial settings represents a core medico-legal function, particularly when clinical changes may influence detention compatibility and judicial decisions. Objective documentation of disease progression during incarceration is essential to ensure respect for the principle of healthcare equivalence and the protection of fundamental rights. Methods: A retrospective descriptive medico-legal analysis was conducted on 37 male inmates referred for forensic evaluation. Standardized assessments included anamnesis, physical and psychiatric examination, evaluation of medical records, and comparative evaluation of pre-detention and detention-period health status. Results: Pre-existing conditions accounted for 73% of pathologies, while 27% developed during imprisonment. Clinical deterioration occurred in 35.1% of inmates (13 cases). Psychiatric disorders were frequent (27%, 10 inmates), predominantly depressive. Conclusions: The findings indicate that imprisonment may constitute a setting that contributes to the progression of medical and psychiatric conditions, highlighting structural and organizational challenges within penitentiary healthcare. Strengthening standardized medico-legal assessment protocols, improving multidisciplinary collaboration, and ensuring continuity of care between prison and community health services are crucial steps toward protecting inmates’ health. This study provides an exploratory assessment of health conditions in a cohort of inmates within the Italian correctional system, emphasizing the central role of healthcare in custodial settings. Since many Italian correctional facilities are equipped with well-developed healthcare services and appropriate diagnostic and therapeutic resources, numerous acute and chronic medical conditions can be effectively managed within the prison setting, ensuring continuity of care while limiting the need for transfer to external healthcare facilities. Full article

Introduction: Infective endocarditis (IE) remains a serious life-threatening condition associated with substantial morbidity and mortality. Within the Republic of Ireland, there is limited national data regarding the clinical and economic impact of IE. This study aimed to describe IE in Ireland. Methods: We conducted a retrospective cohort study using national Hospital Inpatient Enquiry (HIPE) data accessed through the National Quality Assurance and Improvement System (NQAIS). All admissions with a principal diagnosis of acute or subacute infective endocarditis (ICD10: I33.0, I33.8) from 1 January 2018–30 July 2025 were included. Duplicate episodes, incomplete records, nonemergency admissions, and cases without documented transoesophageal echocardiography (TOE) were excluded. Demographic, clinical, procedural, and outcome variables were explored. Costs were assigned using Healthcare Pricing Office guidance for length of stay (LOS). Results: A total of 441 patients met inclusion criteria. The mean age was 64.1 years (SD 17.0), and 75.5% were male. Surgical intervention occurred in 18.4% of cases. Surgically managed patients were younger than medically managed patients (mean age 58.8 years vs. 65.7 years, p < 0.001) and experienced a longer LOS (mean of 51 days vs. 36 days, p < 0.001). The overall in-hospital mortality rate was 9.5% (n = 42). On multivariable analysis, higher comorbidity burden (measured by Charlson comorbidity index (CCI)) was independently associated with in-hospital mortality (OR 1.09, 95% CI 1.06–1.13, p < 0.001). The total expenditure was €20.6 million. Discussion: IE in Ireland affects predominantly older, comorbid patients and is associated with significant healthcare resource utilization. In-hospital mortality rates appear favourable compared with published international series. Full article

Background: Malnutrition is prevalent among older adults (OAs), ≥65 years, receiving home care services. Despite recommendations for early detection and multifactorial interventions, a gap remains between routine practice and evidence. We therefore developed a complex intervention for early detection of malnutrition risk in a Danish home care setting, comprising an implementation strategy with staff learning activities and a screening procedure with a start-up conversation and follow-up screenings. This article outlines the feasibility test and process evaluation of the intervention. Methods: Guided by the Medical Research Council (MRC/NIHR) framework for complex interventions and MRC process evaluation guidance, the evaluation was conducted in two parts. Part 1 focused on case-level feasibility, while Part 2 examined implementation feasibility within routine home care practice. Qualitative data were collected through participant observations, informal interviews, semi-structured individual interviews, focus group interviews, and a participatory workshop. Quantitative data were extracted from the electronic patient record (NEXUS, Systematic A/S, Aarhus, Denmark). Qualitative data were analysed using qualitative content analysis, and quantitative data using descriptive statistics. Results: All eligible OAs (12/12) received a start-up conversation in Part 1 compared with 5/16 (31%) in Part 2, while follow-up screenings declined from 61/71 (86%) to 37/50 (74%). Overall, staff perceived the learning activities as useful and were able to deliver the screening procedure. However, delivery and implementation were influenced by time constraints, leadership support, workplace culture, and organisational infrastructures. OAs showed willingness to participate, yet their prerequisites for engagement were more limited than anticipated. Conclusion: The intervention could be delivered in a real-world home care setting, but delivery, implementation, and activation of proposed mechanisms were constrained by resource limitations, competing priorities, workplace culture, and organisational infrastructures. To improve uptake, intervention adaptations and a stepwise implementation approach are recommended. Full article

Context: Previous studies and reviews in adult patients have shown that hope is an important coping mechanism in patients with serious health problems. Although there are individual studies in pediatric patients, there is a lack of a systematic review of how hope manifests and how it can be supported in children with life-threatening illnesses and their families. Objectives: This systematic review aims to identify the key factors that are associated with the experience of hope in pediatric palliative care. It examines how hope is fostered or diminished in children with life-limiting illnesses, their parents, and other family members. Methods: A systematic search was conducted across major medical databases for studies published between 1990 and 2024. Thematic analysis was used to identify factors associated with hope. The quality of the included studies was critically appraised. Results: Out of 1373 identified records, 105 studies met the inclusion criteria. Most studies were qualitative in design and met a high proportion of the applicable criteria in their respective design-specific appraisal tools. Factors associated with increased hope included open and empathetic communication, trusting relationships with healthcare providers, strong social and family support, spiritual and religious faith, effective symptom management, involvement in care decisions, and access to practical resources. Conversely, reduced hope was associated with poor communication, social isolation, uncontrolled symptoms, uncertainty, caregiver distress, financial strain, and negative healthcare experiences. These factors were consistent across cultural and diagnostic contexts, with some variation in emphasis. Conclusions: Hope in pediatric palliative care is associated with relational, psychological, spiritual, and systemic factors. Many of these are modifiable. A family-centered and multidisciplinary approach is essential to sustaining hope in pediatric palliative contexts. Full article

Dementia is a leading cause of disability and death worldwide, yet diagnosis in primary care remains substantially lower than expected, delaying access to treatment, support and future care planning. The Facing Dementia Together Practice Change Program was developed as a co-designed, multicomponent intervention to improve dementia diagnosis in Australian general practice. A mixed-methods process evaluation used the RE-AIM framework with the addition of Appropriateness. The intervention included Primary Health Network (PHN) engagement, GP education, clinical resources, audit and benchmarking reports, specialist support, and a dementia risk-alert tool. Data were collected through surveys, stakeholder interviews, website analytics and program documentation. Clinicians who participated reported increased confidence and changes in dementia-related clinical behaviours, while education, resources and benchmarking were perceived as valuable. However, overall reach was limited by competing clinical priorities, workforce pressures, lack of financial incentives, and PHN implementation challenges. The dementia risk-alert tool was feasible but achieved limited uptake because of software compatibility, usability concerns and incomplete electronic medical record data. Although the program was acceptable to participating clinicians, limited reach constrained its potential impact. These findings highlight organizational, workforce, digital infrastructure and policy factors that influenced implementation of multicomponent interventions in routine primary care. Full article

Background: Hospital-wide centralized bed allocation, known in China as the “one-bed-for-the-whole-hospital” model, aims to improve inpatient access by pooling beds across specialties. Existing studies have mainly examined operational outcomes, process risks, or staff competence. Less is known about how bed redistribution interacts with clinical responsibility, professional roles, functional support, and shared governance. Objectives: This study explored healthcare professionals’ perspectives on the barriers, facilitators, and strategies for sustaining the hospital-wide “One Bed” model. It also examined how bed integration and care integration aligned or diverged across the dimensions of the Rainbow Model of Integrated Care. Methods: A single-center descriptive qualitative study was conducted in a Grade A tertiary hospital in Shanghai, China, where the model had operated across 11 pilot wards for approximately 48 months. Between December 2024 and March 2025, 25 healthcare professionals, including 14 clinical nurses, eight head nurses, and three physicians, completed face-to-face semi-structured interviews. Data were analyzed using inductive qualitative content analysis. After themes and subthemes were developed from participants’ accounts, the Rainbow Model of Integrated Care was used as an interpretive framework to map the findings across clinical, professional, organizational, system, functional, and normative integration. Results: Five themes were generated. Participants perceived centralized bed allocation as shortening waiting time and improving bed use, but also as intensifying ward workload and making single efficiency indicators insufficient. Patients could move to available wards before medical response, responsibility, and physician visibility were fully aligned. Cross-specialty case mixes exceeded what nurses could manage through temporary learning alone. Information, logistics, space, equipment, and supplies did not always move with patients, leaving nurses to maintain workflow through manual and often invisible coordination. Sustained bed sharing also depended on clearer boundaries for patient selection, specialty fit, severity, nursing workload, leadership authority, resources, and incentives. Together, these themes showed that bed resources were integrated faster than care processes, professional capability, functional support, and shared governance. Conclusions: Hospital-wide centralized bed allocation should be understood as an uneven process of integration rather than only as a bed-management strategy. These findings primarily reflect the perceptions and experiences of nurses and nursing managers, supplemented by limited physician input. The distinctive finding of this study is that beds may be pooled, and patients may move rapidly, while medical response, nursing competence, functional support, workload recognition, and governance arrangements do not always move at the same pace. Safe and sustainable implementation therefore requires bounded flexibility: bed allocation should be guided not only by bed vacancy but also by clinical suitability, specialty fit, nursing workload, timely medical response, functional systems that move with patients, and shared accountability. Full article

Background: Increasing demand for emergency medical services (EMS) challenges prehospital resource availability, particularly for time-critical emergencies requiring advanced life support (ALS). Although tiered BLS and ALS response models are established in many non-physician-led EMS systems, evidence regarding qualification-based dispatch stratification within physician-led EMS systems remains limited. This study evaluated the clinical and operational characteristics of an expanded BLS dispatch segment in Berlin EMS. Methods: A retrospective observational study analysed 2,132,246 EMS missions in Berlin, Germany, between 2020 and 2024. Missions were retrospectively classified according to dispatch codes included in the expanded BLS segment, designed to allocate incidents with lower expected requirements for ALS-level interventions to appropriately qualified EMS personnel. Analyses included dispatch characteristics, clinical findings from electronic patient care records, observed safety-related indicators, and ALS response intervals. Results: Overall, 28.7% of EMS missions were classified within the expanded BLS segment. Traumatic and psychiatric presentations represented the most frequent diagnostic groups. Based on predefined clinical indicators, no immediately life-threatening condition was documented in more than 95% of missions. Cardiopulmonary resuscitation occurred in 0.03% of cases, and emergency physician involvement was documented in approximately 3% of cases. Conclusions: A substantial proportion of EMS missions represented a population with predominantly lower expected prehospital treatment complexity within a qualification-based dispatch framework. Structured emergency call interrogation combined with dispatch classification and linked clinical data enabled retrospective evaluation of this approach. Further validation against independent clinical reference standards and linkage with downstream outcomes are required to determine broader applicability. Full article

Early identification of COVID-19 patients requiring intensive care is critical for improving treatment prioritization, supporting clinical decision-making, and managing limited hospital resources. While structured electronic health record (EHR) data provide important physiological information, chest computed tomography (CT) imaging contains additional indicators related to disease severity. This study presents a multimodal clinical decision support framework for a multimodal risk prediction framework for severe COVID-19 outcomes using structured emergency clinical features and patient-level CT imaging from the COVID Data for Shared Learning (CDSL) dataset. After multimodal cohort construction, 784 patients with both structured clinical records and CT imaging were included in the analysis. Three predictive settings were evaluated: EHR-only prediction using Gradient Boosting, CT-only prediction using ResNet50-based feature extraction with Logistic Regression, and multimodal prediction using weighted late fusion. The experimental results indicate that the CT-based model surpassed the clinical baseline, yielding an F1-score of 0.42 and an ROC-AUC of 0.772, whereas the EHR-only model achieved scores of 0.30 and 0.715, respectively. Overall, the multimodal fusion framework achieved the strongest results among the approaches tested, reaching an F1-score of 0.47 and an ROC-AUC of 0.782. Taken together, these findings indicate that, although CT imaging alone carries meaningful predictive power for evaluating ICU risk, combining it with clinical data leads to predictions that are more reliable and robust. The proposed framework offers a practical and interpretable foundation for multimodal clinical decision support and demonstrates the potential of combining structured clinical data with medical imaging for intelligent critical care applications. Full article

Background: The Objective Structured Clinical Examination (OSCE) has been the backbone of assessment in medical education, and the number of stations required to achieve sufficient reliability in high-stakes examinations is an important consideration for resource-limited programs. Methods: The correlation between the number of stations and psychometric performance was simulated using post hoc resampling of the 17-station Final MBBS Medicine and Therapeutics OSCE across three campuses of the University of the West Indies. Blueprint coverage was maintained in balanced subsets of six, eight, ten, and twelve stations. For subsets and the full exam, we compared failure rates, internal consistency, generalizability, and correlation of scores. Results: Failure rates increased as the number of stations decreased; however, none of the differences between subset and full-exam performance were significant. Station count was positively related to internal consistency using alpha (α ≈ 0.32–0.61 at six stations; α ≈ 0.64–0.70 at 17 stations) and G-coefficients (≈0.18–0.42 at six and ≈0.36–0.70 at 17 stations). However, all reliability coefficients were <0.80. Strong internal consistency was maintained for all subsets, even at six stations (r ≥ 0.79), and approached 1.0 for the 17-station subset. There was a subset size effect on mean values for Campuses 1 and 2 but not for Campus 3. Conclusions: Shorter subset exams maintained strong internal consistency. Reliability at ten–twelve stations approached that of the 17-station total. Intercampus variability highlighted the need for examiner training, station quality checks, and improved standard setting. Full article

This preliminary study aims to compare homeschooling in Greece and the UK in terms of experiences, arithmetical attainments, parental motivations, implementation practices, and support structures. For this purpose, we employed a multiple case study design, enrolling a total of five Greek and fifteen British homeschooling families. Results suggested that the Greek parents primarily chose homeschooling in response to their child’s severe and complex medical needs and reported limited access to support systems. British parents provided more varied motivations, higher engagement with educational resources, and stronger integration into informal support networks. The sample of UK children demonstrated higher arithmetical attainment than the Greek sample. Cultural, structural, and regulatory factors appear to shape homeschooling experiences differently across the Greek and UK educational systems. Differences also emerged in terms of daily routines, access to support, and parental confidence. Results are discussed in relation to educational provision and policy development. Full article

Background: Population growth and rising healthcare demand increasingly strain public healthcare in emerging economies. Further, poor supply visibility, frequent stock-outs, and medicine expiry weaken healthcare supply chain performance (SCP) and patient outcomes. In response, the Internet of Things (IoT) offers promising ways to improve the monitoring, distribution, and management of medical supplies. However, empirical evidence on how technological, organisational, and environmental factors influence IoT adoption and its effect on public healthcare SCP remains limited. This study examined factors influencing IoT adoption and its effect on supply chain performance in public healthcare facilities. Methods: Data were collected from 102 respondents drawn from 90 public healthcare facilities. Results: Factors associated with technological factors have the most significant influence on the adoption of IoT in public healthcare SCs. The lack of significance of organisational and environmental factors may be attributed to the early stage of IoT adoption in public healthcare SCs. Conclusions: This study contributes to the theoretical understanding of IoT adoption by highlighting the dominant role of technological factors over organisational and environmental considerations in resource-constrained public healthcare settings. From a practical perspective, the findings encourage policymakers and healthcare managers to prioritise investments in relevant ICT infrastructure to accelerate IoT adoption. Full article