Search Results (1,270)

Background: Current evidence on diabetes care interventions is either fragmented or focused on specific interventions targeting healthcare professionals or patients. This study aims to categorize diabetes care interventions involving physicians or nurses and their impact on health outcomes for individuals with diabetes. Additionally, it aimed to determine the proportion of favorable findings across healthcare intervention categories. Methods: This scoping review was conducted in primary care settings and guided by a health system framework. We searched articles from inception to June 2022 in databases including CENTRAL, MEDLINE, Embase, PsycINFO, and CINAHL. The classification of healthcare interventions was guided by the Cochrane Effective Practice and Organization of Care taxonomy and health system framework. Results: Results were reported following the PRISMA Extension for Scoping Reviews. From the initial pool of 13,406 articles, 116 met the eligibility criteria and reported interventions conducted across 119 countries or regions, of which 94 were high-level economies. Five healthcare intervention categories were identified: transforming the workforce; service content; re-designing the service delivery system; information and communication technology; and multifaceted. Patient health outcomes were classified into 3 overarching categories with 11 subcategories: clinical outcomes, behavioral outcomes, and psychosocial outcomes. The proportion of statistically significant favorable findings differed across intervention categories and outcome domains. Multifaceted interventions showed relatively higher proportions of favorable findings for clinical outcomes, whereas re-designing the service delivery system interventions showed relatively higher proportions for behavioral outcomes. ICT interventions frequently reported favorable findings related to blood glucose indicators. Conclusions: This study provides a comprehensive overview of healthcare interventions and health outcomes for diabetes in primary care settings. It emphasizes the importance of tailored interventions, delivery methods, and technology integration for effective diabetes management, advocating for a comprehensive approach from individual to societal levels. Full article

Health systems are fundamental to ensuring the right to health and maintaining the stability of welfare states. In this broad framework, this narrative review takes a look at Italy and China—two countries with very different historical backgrounds and institutional paths—as they deal with challenges like demographic aging, increasing chronic illnesses, and ongoing disparities in access to care. This paper aims to examine the most relevant peer-reviewed studies, as well as current legal and policy documents, in order to outline a comparative overview of the approaches to financing, coverage, and service provision in Italy and China, two very different countries. The Italian National Health Service, financed through general taxation and designed to be universal, aims to ensure equitable access to healthcare while having to contend with significant regional differences and financial difficulties. On the other hand, China, which operates with a mixed model, has rapidly expanded its insurance coverage using a variety of schemes and embracing technological advances, which has enabled large-scale access to healthcare, although considerable differences remain between urban and rural areas and provinces. When compared, each system shows particular strengths: Italy’s local care networks and focus on continuous services offer a model for inclusive welfare, while China’s use of digital tools demonstrates how innovation might help overcome obstacles in settings with limited resources and maintain continuity of care. There is a noticeable overlap in how both countries handle chronic diseases and their shared concern for equity in health policy. Considering these points, mixed approaches that combine universal coverage, financial viability, and flexible use of technology could provide useful ideas for crafting more fair, effective, and resilient health policies in various settings. Rather than proposing direct policy transfer, the comparison identifies context-dependent strategies that may inform future healthcare reforms. Full article

The increasing complexity of healthcare data ecosystems demands educational technologies capable of supporting data-intensive learning through advanced analytics, immersive interfaces, and learning analytics. This paper presents a cloud-based reference architecture and a prospective evaluation protocol for integrating Business Intelligence (BI), Extended Reality (XR), and learning analytics in health data science education. The proposed architecture is informed by a systematic literature review conducted according to the PRISMA 2020 guidelines, which screened 613 records retrieved from four databases and retained 56 studies for qualitative synthesis. The review indicates that, although BI and XR technologies have independently been associated with educational benefits, empirical evidence supporting integrated educational architectures combining BI, XR, and learning analytics remains limited, particularly in health data science education. Based on these findings, the paper specifies a layered reference architecture comprising a cloud analytics engine, an immersive visualization engine, an interoperability layer, and a learning analytics pipeline designed to support adaptive and AI-assisted educational services during subsequent implementation phases. The reference architecture is partially instantiated within the curricular unit Health Data Analysis and Visualization of the Digital Health programme at the Polytechnic University of Porto, where the BI and XR components are currently deployed and used within the course, while the interoperability middleware, learning analytics infrastructure, and AI-assisted services remain under development or are specified as architectural capabilities. To support future empirical validation, the paper also defines a comprehensive prospective evaluation protocol comprising predefined outcomes, established instruments with published psychometric properties, together with an expert-developed health data literacy assessment undergoing content validation, research hypotheses, power analysis, a statistical analysis plan, and ethical and data-governance provisions. The manuscript makes four principal research contributions: (i) a cloud-based reference architecture for BI–XR integration, (ii) a computational learning analytics pipeline specification, (iii) an interoperable system design for health data science education, and (iv) a prospective evaluation protocol to guide the future validation of the proposed reference architecture. Full article

Healthcare delivery fragmentation can be understood as a structural coordination problem within a socio-technical System of Systems, where autonomous but interdependent providers, insurers, suppliers, information systems, support services, and care recipients interact through uneven incentives, information flows, and dependencies. This paper develops a graph-theoretic structural assessment method for examining how an external governance influence may alter the modeled coordination conditions of such a system before empirical evaluation. A synthetic network is constructed to compare a status quo healthcare SoS with a proposed governance-augmented configuration in which an external governing entity introduces governance interfaces among constituent systems while preserving their operational autonomy. The two configurations are assessed using eigenvector centrality, PageRank, Katz centrality, clustering coefficient, HITS hubs and authorities, anchored structural-priority scenarios, and Monte Carlo sensitivity analysis. Results show that the governance-augmented configuration redistributes modeled structural priority rather than uniformly improving or suppressing any category. Providers and Care Recipients gain prominence and inbound recognition, Insurance and Information Communication Technology retain routing roles despite lower inbound prominence, and reduced clustering indicates lower modeled local closure. Scenario scoring and Monte Carlo analysis show that these interpretations depend on policy-weighting assumptions: Providers and Care Recipients have high probabilities of higher structural-priority scores, Insurance remains near neutral, and Information Communication Technology is slightly lower on average. These findings show that external governance can be modeled as an interface-setting mechanism that reshapes coordination structures while preserving constituent autonomy, without claiming that governance alone would resolve healthcare fragmentation or improve outcomes. Full article

Background: Cholangiocarcinoma (CCA) is a highly heterogeneous malignancy with limited therapeutic options and poor prognosis. The increasing complexity of molecular stratification and treatment selection has stimulated interest in computational and biological modeling approaches for precision oncology. Objective. This narrative review aims to provide a comprehensive overview of digital twins (DTs), DT-enabling computational models, and biological twins (BTs) in CCA, discussing their applications, limitations, and potential integration within hybrid precision medicine frameworks. Methods: A narrative literature review was conducted. To inform the twin-focused sections, a structured PubMed search was performed using predefined keywords related to CCA and twin-related technologies, including organoids, xenografts, organ-on-chip systems. Particular attention was devoted to recent studies addressing computational modeling, patient-derived experimental systems, and translational applications. Results: DT development in CCA is supported by an ecosystem of DT-enabling technologies, including radiomics, artificial intelligence, multi-omics integration, and simulation-based models. However, fully realized medical DTs remain unavailable. BTs, including patient-derived organoids, xenografts, and microfluidic platforms, enable functional validation of therapeutic hypotheses but face challenges related to scalability, standardization, and clinical feasibility. Emerging hybrid DT-BT frameworks seek to combine computational prediction with biological validation through iterative feedback loops, potentially improving patient stratification and treatment personalization. Conclusions: DTs and BTs represent complementary components of an evolving precision oncology ecosystem in CCA. Although technical, biological, regulatory, and implementation challenges remain, the convergence of computational models, longitudinal molecular monitoring, and patient-derived systems may facilitate clinically actionable hybrid twin frameworks. Successful translation will require both technological innovation and healthcare-system improvements to precision medicine access. Full article

Informed consent is fundamental to Australian newborn bloodspot screening (NBS), but emerging genomic screening technologies pose new challenges to clinical care and research. Optimizing consent processes is necessary to support ethical practice and maintain public trust as NBS evolves. This study aimed to identify gaps and opportunities to improve NBS consent processes in Queensland, Australia, while also exploring preliminary insights into the evolving complexity of consent in the context of genomic NBS (gNBS) and NBS-related research. A qualitative study design was used, with two facilitated interest-holder workshops (a total of 12 h) involving 86 participants (healthcare professionals, policy-makers, researchers, genomics experts, and consumer representatives). Workshop 1 (n = 25) was held virtually, and Workshop 2 (n = 61) was held in person. Thematic analysis was used to identify practical recommendations and ethical considerations. Participants identified three priority domains for improving consent in Queensland’s NBS program: (1) revision of the Guthrie Card and consent statement, (2) development of consistent, antenatal information resources across healthcare providers, and (3) standardized consent delivery training for healthcare staff. Discussions also highlighted tensions around information requirements for informed consent, revealing growing complexities regarding layered consent models. While recommendations on research consent were not fully developed at the workshop, insights highlighted the growing complexity and divergence of views on layered consent models. Findings suggest that improving NBS consent requires both operational reform and reassessment of ethical standards, alongside broader interest-holder engagement and feasible, scalable models also suited to genomic technologies. A nationally consistent framework is needed. Full article

Continuous monitoring of Activities of daily living (ADLs) requires sensing systems that are privacy-preserving, low-power, and robust to environmental variation. Ambient sensing technologies provide an alternative to RGB video and wearable devices, but individual sensing modalities exhibit characteristic limitations. Sparse mmWave radar provides strong motion sensitivity but limited posture detail, low-resolution thermal sensing preserves posture-related spatial information, and smart plug telemetry captures only appliance-mediated behavioural interaction. To address these limitations, this paper proposes a layered multimodal ambient-sensing framework comprising a sparse-track 24-GHz FMCW radar, a $32\times 24$ low-resolution thermal sensor, and a Moko smart plug. It experimentally evaluates a radar–thermal HAR branch together with a separate smart plug appliance-recognition branch. The framework proposes three streams to enable continuous non-wearable monitoring while maintaining redundancy and reduced privacy exposure for intelligent-building and ambient assisted living environments. Radar and thermal streams are jointly evaluated on binary motion and four-class posture and activity recognition tasks collected across multiple environmental configurations using recording-grouped cross-validation, while the appliance stream is evaluated using per-plug telemetry from residential-grade appliances. The radar–thermal streams use a single-subject, fixed-placement dataset of binary-motion windows and four-class posture and motion windows collected across six furniture configurations. The separate intrusive load monitoring stream utilises smart plugs to classify appliances. Regarding binary motion recognition, radar $(F_{1,\mathrm{Transformer}}=0.882\pm 0.034)$ and thermal $(F_{1,\mathrm{XGBoost}}=0.870\pm 0.069)$ pipelines achieved similar macro F1 performance. On the four-class posture and activity recognition task, thermal features $(F_{1,\mathrm{thermal}}=0.775\pm 0.053)$ substantially outperformed radar $(F_{1,\mathrm{radar}}=0.609\pm 0.110)$. Weighted late fusion produced only modest descriptive gains. Separately, smart plug telemetry demonstrated strong appliance recognition performance using lightweight tree-based models suitable for constrained edge deployment. The results support a scoped redundancy argument. Sparse track-level radar carries gross motion, while low-resolution thermal sensing carries posture. The smart plug appliance monitoring extends the framework toward appliance-mediated instrumental activity of daily living (IADL) monitoring, with lightweight tree-based models achieving strong recognition performance under constrained edge deployment conditions. The findings support a layered multimodal sensing architecture for privacy-preserving ADL monitoring, where radar contributes motion-sensitive coverage, thermal sensing contributes posture-aware spatial context, and smart plug telemetry contributes appliance-level behavioural evidence within intelligent healthcare and ambient assisted living environments. Full article

Wearable technology and the Internet of Things have increased access to personal data, enabling applications that deliver individualized treatment and therapy within clinical pathways. To optimize coaching and interventions within such pathways, it is essential to identify all relevant factors in the available data. Feature selection can be a useful tool for achieving this, but with small, high-dimensional datasets, common in healthcare, it can be challenging. The goal of this study is to develop a method for identifying the most relevant features in small, high-dimensional datasets and to introduce a new ordering index that measures the quality of the orderings produced by feature selection methods. This novel index is sensitive to the quality of feature ordering and to the prediction model’s performance metrics when combined with a feature selection method. The index reaches its maximum when the number-of-features-versus-accuracy graph has an ideal concave-downward shape, reflecting increasing accuracy with each informative feature added and decreasing accuracy with each confounding feature added. Using this index, we define six feature orderings derived from the results of four standard feature selection methods. Using the performance metrics and our new ordering index, we show that the resulting orderings can identify more relevant features and improve the overall performance of the classification models, and that the ordering index is a useful contribution to feature selection techniques. Full article

Patients with congenital heart disease (CHD) represent a special healthcare challenge due to their high complexity, which accompanies them throughout all life stages. Consequently, this population faces increased morbidity and mortality rates, often linked to hemodynamic shifts in pulmonary flow or cardiac output. These risks are further compounded by potential arrhythmias and heart failure decompensation, which may lead to the progressive progression toward advanced stages of the disease. Unfortunately, standard outpatient follow-up is often not capable of responding to the continuous monitoring needs presented by these patients and their families. This selective review frames information on the wearable devices that have emerged as a key solution for continuous and remote monitoring. Beyond clinical tracking, research is increasingly focusing on their role in assessing physical activity—a critical determinant of health outcomes in the CHD population. This review examines the existing literature on wearable technology in both pediatric and adult patients while also addressing the current limitations that hinder their integration into routine clinical practice. Full article

Background/Objectives: Health literacy and patient education play a key role in supporting informed decision-making, treatment adherence, and disease self-management. While educational interventions have traditionally focused on improving knowledge and understanding, emotions are increasingly recognized as factors associated with learning processes and healthcare experiences. This scoping review mapped the role of emotions in health literacy and patient education by identifying the emotions investigated, assessment methods, technologies employed, educational outcomes, and opportunities for emerging technologies. Methods: A scoping review was conducted following PRISMA-ScR and Joanna Briggs Institute guidance. Searches were finalized in Scopus and PubMed on 6 June 2026, with no publication-date restrictions; only English-language journal articles and reviews were eligible. Two reviewers screened records, extracted data, and classified studies, resolving disagreements by consensus and consulting methodological or clinical co-authors when needed. Results: A total of 135 studies were included. Anxiety and emotional distress were the most frequently investigated emotional categories, whereas emotional support, reassurance, and emotional engagement received less attention. Interviews, surveys, questionnaires, and standardized scales were the predominant assessment methods. Emotional well-being and understanding were the most frequently reported educational outcomes, followed by adherence, communication, and emotional support. Educational programs and educational materials were the most commonly employed approaches, while digital health solutions appeared less frequently. Limited use of artificial intelligence and objective emotion-recognition methods was identified. Conclusions: Emotional factors were associated with several patient-education outcomes, but the heterogeneous and predominantly self-reported evidence does not establish causality. Emerging technologies warrant further evaluation before their effectiveness, feasibility, and safety in patient education can be established. Full article

Medication adherence is a critical determinant of treatment effectiveness; however, its measurement and integration into routine healthcare practice remain challenging in many healthcare systems. This study aimed to compare medication adherence measurement methods and examine the legal and regulatory frameworks governing adherence-related tools in North Macedonia, Romania, Serbia, and Bulgaria. A structured comparative, two-phase document review was conducted between January and April 2026. The first phase identified and compared medication adherence measurement methods reported in research and recognized within national healthcare systems. The second phase analyzed legislation, regulatory documents, and policy frameworks relevant to adherence-related tools, including digital technologies. Searches were performed in PubMed, Scopus, and Web of Science, together with official governmental, regulatory, and institutional websites. The final evidence base comprised 84 documents, including scientific publications, official legal and policy documents, and regulatory and institutional sources. Across North Macedonia, Romania, and Bulgaria, medication adherence monitoring was not systematically integrated into routine clinical practice, despite differences in digital health infrastructure and regulatory maturity. Traditional indirect methods, particularly prescription refill data and clinician assessment, were more consistently reported, whereas validated direct and digital adherence measurement approaches remained unevenly implemented. Regulatory frameworks differed substantially in scope, specificity, and formal recognition of adherence-related tools, reflecting varying levels of alignment with European digital health policies and contributing to fragmented implementation across healthcare systems. Although existing digital health infrastructure provides a foundation for systematic medication adherence monitoring, implementation remains fragmented because of limited interoperability, inconsistent regulatory support, and the absence of standardized national approaches. Strengthening regulatory guidance, harmonizing adherence measurement standards, integrating adherence indicators into routine healthcare information systems, and formally supporting pharmacist-led adherence services could support more consistent medication adherence monitoring and improve medication management across the healthcare systems of the four analyzed countries. Full article

Durable medical devices (DMD) are essential healthcare assets, yet their management in public hospitals is constrained by fragmentation, limited traceability, reactive maintenance, and weak lifecycle integration. This study proposes a framework for digitally enabled, sustainable, and circular DMD management. A mixed-methods design integrated a literature review, expert consultation using the Best–Worst Method, weighted technology nominations, and case-based process mapping in Brazilian hospitals. Eleven experts assessed the criteria guiding Industry 4.0 technology selection for DMD management and the technologies best responding to these priorities; nine consistent judgments were aggregated. Patient-Centered Care, Operational Efficiency, and Resource Efficiency and Cost Reduction emerged as the leading influences on technology selection. Big Data and Analytics, Artificial Intelligence, the Internet of Things, Cloud Computing, Cyber-Physical Systems, Smart Sensors, and Machine Learning formed the priority portfolio, accounting for 84% of the weighted score. The cases contextualized these priorities by revealing discontinuous information flows, limited asset visibility, corrective maintenance, fragmented governance, and weak end-of-life practices. By connecting decision priorities and technological capabilities with observed gaps, the TO-BE framework organizes sustainable procurement, traceable use, predictive maintenance, redeployment, refurbishment, and responsible disposal through material and information flows, providing a pathway for digital and circular transformation in resource-constrained healthcare systems. Full article

Background: Artificial intelligence-driven digital therapeutics (AI-DTx) are rapidly emerging as a transformative paradigm in healthcare, integrating machine learning, deep learning, and generative AI into digital interventions across diverse clinical domains. Despite rapid growth, the evidence landscape remains fragmented, with heterogeneous methodologies, diverse application contexts, and limited cross-domain synthesis. Aim: This narrative view aims to provide an evidence-informed narrative synthesis of the available secondary literature on AI-driven digital therapeutics, primarily focusing on systematic reviews and meta-analyses, to identify emerging patterns, cross-cutting trends, and future directions across clinical and technological domains. Methods: A narrative synthesis of secondary evidence was conducted, focusing on 23 systematic reviews, meta-analyses, and relevant review articles addressing AI-driven digital therapeutics. The identified literature was analyzed to explore recurring themes across clinical domains, technological approaches, and implementation challenges. Findings were further contextualized through selected recent randomized controlled trials and translational studies to provide insights into emerging clinical applications and real-world perspectives. Results: Across the available literature, AI-driven digital therapeutics demonstrate a broad and rapidly evolving expansion across mental health, chronic disease management, rehabilitation, and behavioral health. The field is characterized by a progressive shift from static, rule-based interventions toward more adaptive systems supported by machine learning, deep learning, and generative AI. A key emerging theme is the role of AI as an enabling layer for personalization, adaptation, and dynamic intervention delivery rather than as a standalone therapeutic modality. Mental health represents the most extensively studied domain, particularly through conversational agents and cognitive behavioral therapy-informed interventions, while other clinical areas are progressively expanding their translational potential. Persistent challenges include methodological heterogeneity, limited long-term validation, and incomplete integration into routine clinical workflows. Discussion: The current evidence suggests a transition toward hybrid human–AI models of care, in which digital systems may support and augment clinical practice through adaptive and data-driven approaches. However, the field remains characterized by fragmented evidence, evolving evaluation approaches, and challenges related to standardization, validation, and real-world implementation. Conclusions: AI-driven digital therapeutics are evolving toward increasingly adaptive and clinically oriented healthcare solutions. Future progress will depend on improving methodological consistency, strengthening long-term evaluation, and supporting responsible integration into clinical pathways to ensure safe, scalable, and meaningful impact. Full article

The rapid proliferation of Internet of Things (IoT) technologies has transformed the modern home into a complex cyber–physical ecosystem encompassing hundreds of millions of connected devices globally. Smart homes support automation, energy management, and healthcare monitoring, but they also introduce a broad and evolving range of security and privacy challenges. This review examines 233 sources published between 2018 and May 2025, selected through a PRISMA-informed process covering five major academic databases and relevant standards and technical reports. It discusses communication protocols, including Matter, develops a Threat-Layer-Defense synthesis matrix covering ten attack categories; examines the practical limitations of AI-based anomaly detection and blockchain-based trust management; and derives recommendations for manufacturers, platform providers, users, and regulators. Privacy challenges, regulatory frameworks, and user behavior are considered alongside technical threats. The findings suggest that scalable smart home security requires coordinated progress in protocol standardization, enforceable device update lifecycles, gateway-level anomaly detection, and privacy-preserving local analytics rather than reliance on a single technical solution. Full article

Haptic technology restores the sense of touch to robotic systems and has become increasingly important for safe and intuitive human–robot interaction in healthcare. Despite substantial advances over the past two decades, widespread clinical adoption remains limited, highlighting a persistent gap between laboratory research and real-world medical deployment. This review synthesizes research from robotics, human–computer interaction, neuroscience, and clinical medicine based on a systematic literature search conducted in IEEE Xplore, PubMed, and Scopus (2000–2025). The review adopts an information-centric perspective, focusing on the clinically relevant information conveyed through haptic feedback rather than force reproduction alone. The review examines tactile, kinesthetic, and hybrid feedback modalities; summarizes key principles of haptic rendering, stability, and control; and evaluates applications in surgical robotics, teleoperation, rehabilitation, prosthetics, and medical training. Evidence indicates that haptic feedback can improve performance, reduce excessive forces, and enhance situational awareness, although benefits remain task-dependent. Clinical translation continues to be constrained by sensing limitations, miniaturization challenges, stability requirements, human factors, and regulatory considerations. Current research is increasingly directed toward sensorless force estimation, artificial intelligence-assisted haptic rendering, wearable and soft haptic interfaces, and neurohaptic technologies, reflecting a shift toward task-oriented and information-centric feedback. Future progress will depend less on maximizing physical realism and more on delivering clinically meaningful information through stable, interpretable, and user-centered haptic systems. This review provides a roadmap for advancing clinically deployable haptic technologies in healthcare. Full article