Search Results (261)

Background/Objectives: Survivors of sexual violence and domestic violence/intimate partner violence (IPV) often require support beyond the immediate emergency encounter; however, post-acute care remains inconsistently defined, unevenly organised or conceptualised, and fragmented across service systems. This scoping review mapped international post-acute follow-up, care, assistance, and support pathways, with particular attention to organisational models, continuity mechanisms, loss to follow-up after first access, and implications for the Italian context. Methods: We conducted an international health-services scoping review of post-acute follow-up, care, assistance, and support interventions for survivors of sexual violence and domestic violence/IPV. Searches were performed in PubMed/MEDLINE, Scopus, Web of Science Core Collection, Embase, APA PsycINFO via EBSCOhost, and CINAHL via EBSCOhost. Eligible studies were published from 2013 onward and had to describe an identifiable post-acute component beyond the initial emergency, forensic, or first-contact phase. The review followed a Population–Concept–Context framework and was reported in accordance with PRISMA-ScR. Results: Forty-four studies were included in the core synthesis, comprising 16 studies on sexual violence/sexual assault, 27 on domestic violence/IPV, and one mixed domestic, family, and sexual violence outreach model. The sexual violence literature clustered around early trauma-focused interventions, sexual assault care centre pathways, medical follow-up, follow-up attendance, and digital continuity tools. The IPV literature was broader and included psychotherapy, advocacy and case-management models, housing-first and trauma-informed stabilisation approaches, nurse-led and clinic-based services, outreach and safety-contact programmes, digital interventions, and programmes for system-involved survivors. Across both fields, the pathways most consistently described as supporting continuity combined structured re-contact, coordinated support, and multi-component responses over time. Conclusions: The mapped literature supports conceptualising post-acute responses to sexual violence and domestic violence/IPV as continuity pathways that extend beyond first contact and link healthcare, psychological, advocacy, and social supports. Systems may be better positioned to support continuity when they provide structured follow-up, warm handoffs, coordinated navigation, and context-sensitive recovery models. These findings point to provisional, evidence-informed organisational questions for strengthening post-acute pathways, including in Italy, particularly around structured re-contact, warm handoffs, survivor navigation, and integration between healthcare, anti-violence, psychological, and territorial social-support services. Full article

Persons with Disabilities (PWDs) face persistent barriers to healthcare access, welfare services, and timely medical assistance, particularly where hospital information is fragmented across institutions. In Thailand, these challenges are exacerbated by heterogeneous Hospital Information Systems (HISs) across provincial, district, and sub-district hospitals. This study presents the design, implementation, and evaluation of an integrated mobile application and a hybrid Hospital Information Exchange (HIE) system to enhance healthcare accessibility and service coordination for PWDs. The platform integrates a user-centered mobile application (iOS and Android) with a hybrid data exchange architecture (MedEx Hybrid) combining an application programming interface (API) and Message Queuing Telemetry Transport (MQTT). This enables real-time and on-demand data exchange while accommodating hospitals with limited infrastructure. Key functionalities include disability registration, emergency medical service (1669) integration, appointment management, rights notification, service location mapping, teleconsultation, and peer communication. Deployment across 159 hospitals nationwide demonstrates system scalability and interoperability. The system supports secure access to electronic medical records and enables emergency responders to retrieve patient information during SOS events, improving continuity of care. Findings confirm the feasibility of the proposed system and its potential to support inclusive digital health and national healthcare interoperability. Full article

Background: Over the past two decades, teleophthalmology has become an effective approach for glaucoma screening and follow-up, with its adoption markedly accelerated by the COVID-19 pandemic. Objectives: The aim of the present study was to explore and analyze the attitudes of ophthalmologists in Bulgaria toward the application of teleglaucoma, digital communication, and artificial intelligence in clinical practice. Methods: A cross-sectional survey study was conducted among 113 ophthalmologists between September 2024 and March 2025, representing 10.5% of all licensed ophthalmologists in Bulgaria (n = 1074). Results: Age, professional experience, and specialization influenced the level of involvement in managing glaucoma patients. The level of awareness regarding the term ‘teleophthalmology’ was higher among respondents with a specialization in ophthalmology and those holding a doctoral degree (p = 0.001). Among the ophthalmologists surveyed, 35.4% (n = 40) provided teleophthalmology services, while an additional 19.5% (n = 22) reported no prior provision of such services but planned to do so in the future. The most preferred method for conducting teleophthalmology consultations was telephone communication (n = 27; 67.5%), followed by communication via Skype, Viber, or Messenger (n = 23; 57.5%). Physicians with longer professional experience more frequently conducted remote consultations with patients they already knew (p = 0.006). A substantial proportion of respondents (85.0%, n = 96) expressed willingness to participate in training related to contemporary trends and the provision of remote medical services. More than half of respondents expressed positive attitudes toward the use of artificial intelligence in ophthalmology, although practical implementation remained limited. Conclusions: The present study outlined the current landscape of attitudes among ophthalmologists in Bulgaria toward teleglaucoma, digital communication, and the use of artificial intelligence in clinical practice. The findings indicated a moderately positive yet cautious stance—remote services were perceived primarily as complementary tools, particularly for the follow-up of previously known patients and for real-time collaboration between specialists. Full article

Background: Cataract diagnosis and management pose a significant global health challenge, contributing to 17 million cases of blindness and over 83 million cases of vision impairment worldwide in 2020. This issue is particularly acute in regions lacking adequate ophthalmological services, where a shortage of eye care clinicians and specialized equipment like slit-lamp cameras leads to late diagnoses. To address this accessibility gap, we developed a computer-assisted cataract grading system using smartphone-acquired external eye photographs. This approach utilizes image processing and deep learning on a standard, hardware-free smartphone, offering a low-cost and portable alternative to traditional equipment. Methods: The study introduces a new advanced algorithm to stratify cataract severity into three distinct stages: normal, pre-mature, and mature. The methodology was developed using a combined dataset of 799 images sourced from the Cataract v01 Computer Vision Project and the Indian Institute of Technology, Delhi. A key step is isolating the iris and lens using a region of interest (ROI) extraction procedure powered by the open-source MediaPipe framework. Key to the algorithm’s efficacy is the use of transfer learning, adapting four customized ResNet architectures (ResNet-18, ResNet-34, ResNet-50, and ResNet-101) to address medical image analysis intricacies. These models were fine-tuned with specific modifications, including dropout layers and the Adam optimizer, for analyzing the digital periocular images. Results: Evaluation of the models shows varied performance across the various architectures when classifying cataract stages. While the simpler ResNet-18 model exhibited the lowest performance, the deeper models showed significant improvement. The ResNet-50 architecture achieved the highest accuracy of 94%. This model also demonstrated excellent precision (94%), recall (95%), and an F1-score of 95% in multi-class classification, outperforming the other tested models. Its depth enables precise cataract classification, positioning it as a robust and reliable tool for potential medical diagnostic deployment. Conclusions: Deep learning-based analysis of smartphone-acquired external eye images demonstrated feasibility for cataract detection in this study. This method could be a scalable and easy-to-use addition to screening, especially in places where resources are limited. Further work is needed to expand the dataset and to validate the algorithm against established clinical grading systems before broader clinical implementation. Full article

Perinatal depression (PND), encompassing both major and minor depressive episodes during pregnancy and up to one year postpartum, is a prevalent and debilitating condition with profound consequences for maternal, infant, and family well-being. Clinical screening of PND remains challenging due to obstacles in early detection, symptom overlap with normal perinatal experiences, lack of standardized screening protocols, and considerable interpatient variability. Furthermore, the complexity of treating PND arises from multiple interconnected factors, including medication safety considerations during pregnancy and lactation, information barriers resulting from the separation of mental health services from obstetric and pediatric care systems, and unique sociocultural obstacles. The absence of systematically integrated care pathways often leads to severe and potentially irreversible outcomes for both mothers and infants. Hence, this review summarizes recent advances in PND screening and treatment, emphasizing the critical transition toward integrated or collaborative care models. Notably, future efforts should focus on overcoming implementation barriers, digital health solutions, task-sharing frameworks, and personalized treatment strategies to ensure equitable access to these innovations for affected populations. Full article

The increasing volume of digital health data generated through Electronic Health Records (EHRs), emergency care systems, and real-time monitoring technologies has intensified the need for secure cross-institutional healthcare analytics. However, privacy concerns, regulatory restrictions, institutional mistrust, and risks associated with centralized data aggregation continue to limit large-scale healthcare data sharing. This paper presents OPAL4Health, a governance-oriented and privacy-preserving distributed healthcare analytics framework grounded in the MIT Open Algorithms (OPAL) paradigm. The framework integrates federated analytics, blockchain-based auditability, explainable artificial intelligence (XAI), and institutional governance mechanisms within a unified computation-to-data healthcare ecosystem. Unlike conventional federated healthcare systems that primarily focus on decentralized computation alone, OPAL4Health emphasizes governance, transparency, auditability, and policy-aligned distributed analytics while preserving institutional data sovereignty. The privacy protections supported by OPAL4Health are primarily architecture-based and governance-oriented, relying on local institutional data retention, controlled query execution, and blockchain-auditable analytical workflows rather than formally provable cryptographic privacy guarantees. The framework was evaluated through a real-world urgent care pilot across seven hospitals in Riyadh, Saudi Arabia, using 184 anonymized patient cases collected between May 2015 and September 2016. Analytical findings identified a median onset-to-arrival delay of 285 min (95% Confidence Interval (CI): 270–302), low ambulance utilization (18.5%), and hospital bypass behavior in 42% of cases. Peak Emergency Department (ED) congestion periods were also identified. Scenario-based modeling projected potential long-term healthcare savings of approximately $602 million over 15 years through improved Emergency Medical Services (EMS) allocation and reduced disability-adjusted life years (DALYs). The findings demonstrate the feasibility of governance-oriented, privacy-preserving distributed healthcare analytics within OPAL4Health while generating actionable operational and policy-relevant insights without centralizing sensitive patient-level records. The proposed framework provides a transferable model for secure, transparent, and accountable digital health collaboration across healthcare ecosystems. Full article

China’s low-altitude economy (LAE) is moving from policy experimentation to coordinated industrial deployment, yet existing assessments often treat the LAE as a homogeneous sector or equate aircraft capability with deployment readiness. This study develops a scenario-gated sustainability readiness framework for six representative LAE and urban air mobility (UAM) scenarios in China: emergency medical logistics and disaster response, infrastructure inspection and public-service monitoring, urban instant logistics, airport shuttle and intermodal passenger transfer, urban air taxi, and low-altitude tourism. The proposed framework consists of a scenario layer, an eight-dimensional readiness layer, and a decision layer integrating 0–4 ordinal scoring, evidence-confidence tagging, non-compensatory gate conditions, and readiness classification. The eight dimensions cover mission and demand fit; airspace and traffic controllability; infrastructure and site readiness; digital communication, navigation, surveillance, and data security; vehicle, energy, and environmental performance; weather and route-environment robustness; workforce and organizational readiness; and social acceptance and legal legitimacy. The illustrative application indicates that infrastructure inspection is the only routine scaling candidate; emergency medical logistics and urban instant logistics are suitable for bounded routine operation; airport shuttle and tourism should remain controlled pilot candidates; and open-network urban air taxi is still at the pre-pilot stage. The study contributes a scenario-based deployment logic for sustainable aviation and UAM governance. Full article

Knee Osteoarthritis (KOA) is a leading contributor to global physical disability, where delayed diagnosis often results in irreversible joint damage and socio-economic cost. Early diagnosis remains challenging due to subtle radiographic biomarkers and limited access to specialized expertise, particularly in distributed healthcare settings. Within the evolving landscape of the Future Internet, characterized by Internet of Medical Things (IoMT), edge–cloud computing, and intelligent digital health infrastructures, there is an increasing demand for scalable, low-latency, and explainable AI-driven diagnostic solutions. In this work, we propose a Dual-Stream Wavelet Fusion Network (DS-WFN) alongside a distributed edge-cloud architectural roadmap tailored for deployment in distributed and edge-enabled healthcare ecosystems. The framework integrates a spatial morphological stream with a spectral wavelet stream, augmented by an Adaptive Wavelet Selection Mechanism (AWSM). The AWSM dynamically selects optimal frequency bases (Haar, Symlet, Daubechies) to preserve fine-grained diagnostic features typically lost in conventional CNN architectures. An Adaptive Spatial Alignment (ASA) module further ensures efficient fusion of heterogeneous representations, enabling robust feature integration across computational nodes. Experimental results across a five-fold patient-isolated cross-validation protocol demonstrate that the DS-WFN achieves a mean classification accuracy of 76.3% (95% CI: 71.6–80.8%) and a macro-averaged F1-score of 0.747 (95% CI: 0.697–0.795), consistently outperforming single-stream baselines while preventing patient-level data leakage. Furthermore, Grad-CAM visualizations provide interpretable outputs aligned with clinical diagnostic criteria, supporting trustworthy AI integration into digital healthcare workflows. Furthermore, we disclose a methodological framework for edge-based implementation, highlighting how localized inference ensures data sovereignty and real-time clinical support. By combining multiscale signal processing with deep learning under a Future Internet paradigm, this work contributes a scalable, explainable, and edge-ready diagnostic framework for early KOA detection, enabling intelligent, connected, and resource-efficient healthcare services. Full article

Background: Primary healthcare systems continue to face patient safety challenges, particularly misdiagnosis and medication errors, which contribute to preventable harm and reduced quality of care. Electronic Medical Records (EMRs) have the potential to improve clinical documentation, support decision-making, and reduce risks; however, these benefits depend on effective utilization in routine clinical practice. This study examined factors influencing EMR utilization in primary healthcare settings. Methods: A sequential explanatory mixed-methods design was conducted across 42 community health centers in one Indonesian city. Quantitative data from general practitioners were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships among clinical workflow fit, digital health competency, governance, system capabilities, interprofessional collaboration, perceived patient engagement, and EMR utilization. Qualitative interviews were subsequently conducted to provide a contextual explanation of the quantitative findings. Results: Clinical workflow fit and digital health competency emerged as the strongest factors associated with EMR utilization. Their effects operated through interprofessional collaboration and perceived patient engagement, indicating the importance of integrating EMRs into everyday clinical workflows. Governance structures and system capabilities primarily functioned as enabling conditions rather than direct determinants of utilization. Qualitative findings further highlighted the importance of practical workflow integration, communication processes, and user competency in supporting meaningful system use. Conclusions: EMR utilization may contribute to improved care coordination, patient engagement, and service efficiency in primary healthcare settings. Strengthening workflow alignment and digital competency may help support safer and more reliable care delivery, particularly in resource-constrained environments where risks of misdiagnosis and medication errors remain significant. Full article

Background/Objectives: Individuals with limitations in their daily activities use assistive technology (AT), which helps them restore body structures and functions. During the pandemic, to prevent the spread of infection, health policies have disrupted the traditional delivery mode of AT service, and the lack of preparedness for contingency measures has further caused AT service disruptions, making the continuity of AT services a major challenge. This study explores the critical success factors (CSFs) for preventing AT service interruptions in the post-pandemic era and supporting decision-makers in responding rapidly to similar infectious disease pandemics in the future, while ensuring delivery of high-quality AT services. Methods: A systematic literature review was conducted, and then, the multicriteria decision-making (MCDM) model, combined with a decision-making trial and evaluation laboratory (DEMATEL) and an analytic network process (ANP), was applied to stratify complex problems in a structured manner, thereby constructing a multicriteria decision analysis structure for identifying the CSFs for avoiding AT service interruptions in the post-pandemic era. Results: The study results revealed that the three most influential direct factors are improving the providers’ telemedicine capabilities, enhancing access to digital AT service support, and establishing a digital AT ecosystem. Indirect factors include addressing resource shortages. Conclusions: To avoid repeating past mistakes during future pandemics involving similar infectious diseases, strengthening the telemedicine capabilities of medical staff and ensuring a complete AT service delivery system are the most essential priorities. Full article

Medical waste management has been receiving increasing attention in recent years. The National Health Service (NHS) of the United Kingdom has started planning its waste strategy to comply with its Net Zero Goals. Waste management does not only involve waste disposal; the process includes segregation, collection, storage, and the transportation of waste from one point to another. Unusual characteristics of waste from the healthcare industry are that waste can be infectious and needs special storage conditions and specific transportation criteria to maintain the waste’s quality. However, entities working with the waste lack knowledge about the waste they receive and need assistance to verify the quality of the waste as well. Limited knowledge can lead to injuries, contamination, or the spread of pathogens. The global monitoring guidelines of medical waste are studied to understand the monitoring requirements and the stakeholders who are working with the waste. Application and research contributions to the digitisation of medical waste monitoring are scrutinised to look for the monitoring gaps. This paper proposes a digital system designed to connect all waste stakeholders within a blockchain environment, supported by automated data collection. A framework for stakeholder communication with data is designed. The data gathered from transporters is analysed before sending the status to the blockchain. Furthermore, the paper outlines a dashboard showcasing the digitisation of waste management, backed by a case study used for validation. A hypothetical case study in managing waste using existing manual waste monitoring in the United Kingdom is compared with monitoring using the system. By employing a proving method of all activities approach with blockchain technology, this method has achieved a 25.17% improvement in medical waste management time-taken efficiency and a 27.85% improvement while virtually eliminating the risk of fraudulent documentation. Full article

Background: This study examines how medical consumption is discussed in online communities among individuals who are blind or visually impaired using the Social Ecological Model (SEM) to capture multilevel healthcare experiences within digital discourse. Methods: A total of 428 posts and comments were collected from Reddit’s r/Blind community. Term frequency–inverse document frequency keyword extraction and a theory-driven LLM-based classification approach were applied to categorize texts into five SEM levels: intrapersonal, interpersonal, institutional, community, and public policy. Results: The findings show that intrapersonal (44.4%) and public policy (29.8%) levels were the most prominent, indicating a strong emphasis on personal coping experiences alongside structural constraints in healthcare access. Institutional-level discourse accounted for 15.8%, whereas interpersonal (6.2%) and community (3.8%) discourse were relatively limited. Keywords and qualitative analyses revealed themes related to emotional adaptation, social support, service accessibility, mobility constraints, and welfare policy barriers. The Jaccard similarity analysis indicated stronger associations between institutional and policy levels, whereas community-level discourse remained relatively distinct. Conclusions: These findings highlight the importance of understanding healthcare experiences, both individually and structurally, in online environments. This study also demonstrated the potential of integrating LLM-based classification with theory-driven frameworks to enable an interpretable and scalable analysis of complex health-related discourse. Full article

As modern society grows increasingly complex, access to essential services such as healthcare, legal aid, tailored education, and psychological support remains heavily gated by socio-economic, neurological, and systemic barriers. This paper explores the transformative potential of Large Language Models (LLMs) and Generative Artificial Intelligence not merely as industrial productivity enhancers, but as vital “social scaffolds” capable of fostering a more inclusive society. Crucially, we propose a paradigm shift in the concept of Ethical Computing—moving from a passive defensive framework of non-maleficence (“do no harm”) to an active mandate of beneficence, where AI systems are explicitly developed to serve marginalized and un(der)served populations. Through this expanded ethical lens, we systematically analyze the democratizing impact of AI across four primary axes of inclusivity: socio-economic (providing zero-cost medical triage and legal translation for undocumented populations), neurospicy (acting as a non-judgmental communicative bridge for individuals with Autism Spectrum Disorder), pedagogical (delivering hyper-personalized executive function support for Special Educational Needs), and psychological (serving as an accessible, first-level triage system for mental health crises). By framing LLMs as a modern social safety net, we outline a clear trajectory for future research, advocating for an “ethical-by-design” development paradigm that explicitly prioritizes equity, accessibility, and the active dismantling of historical barriers for the digitally and socially disenfranchised. Full article

Background: Artificial intelligence (AI) is increasingly embedded in routine primary care, yet how the levels of integration might affect its acceptability is unknown, especially in relationship-based service models where patients expect visible human stewardship. Prior experimental studies often treat AI adoption as a binary condition, leaving the “intensity gradient” of automation and the role of model specialization under-explored. We examine whether increasing AI integration in the clinical encounter erodes patients’ intention to seek care from family doctors in China, and whether labeling the AI as a medical-specific model buffers such erosion. Methods: We conducted a nationwide online survey experiment in China (N = 2790). Participants were randomly assigned to vignettes that varied by (i) the level of AI integration (low, medium, high) and (ii) the AI type (general-purpose vs. medical-specific large language model), with a human-only care scenario as a reference. Care-seeking intention from family doctors was assessed immediately after exposure. We estimated treatment effects using OLS regression with heteroskedasticity-robust standard errors, and examined the buffering hypothesis through an interaction term between AI integration intensity and AI type. Results: Care-seeking intention declined steadily as AI integration increased (p < 0.001), with the sharpest drop under high-intensity AI integration where clinical decisions were delegated to the AI system. Across all intensity levels, framing the system as a medical-specific AI consistently resulted in higher care-seeking intention than a general-purpose model. However, the interaction between AI intensity and the AI type was not statistically significant (p = 0.508). Conclusions: Patient acceptance of AI in primary care depends not only on whether AI is involved, but on how deeply AI is positioned in the encounter. Medical-specific AI labeling may enhance acceptance across all AI integration levels. The findings underscore the need to preserve human clinical agency in AI-embedded primary care. The results contribute to research on healthcare systems, digital health, and AI–patient interaction. Full article

The majority of people with mental disorders in low-, middle-, and high-income countries do not receive any intervention for their symptoms, despite enormous advances in developing evidence-based psychosocial treatments and medications. The perspective and viewpoint article discusses and illustrates digital and technology-based interventions and activities in everyday life that have been shown to reduce symptoms of mental disorders. The article begins with background on the treatment gap and a discussion of why treatments do not reach people in need. Digital and technology-based interventions and everyday activities are presented to complement current treatments with the goal of scaling interventions to serve more people in need and to circumvent many of the usual barriers that preclude people from seeking or receiving traditional mental health services. Interventions in each of the categories are illustrated. The challenge is to integrate such interventions in mental health practices, to better promote these at the population level, and to monitor their impact. Full article