Search Results (2,255)

Artificial intelligence (AI) is now embedded across many aspects of healthcare, yet most implementations remain fragmented, task-specific, and layered onto legacy workflows. This paper does not review AI applications in healthcare per se; instead, it examines what an AI-first healthcare system would look like, one in which AI functions as a foundational organizing principle of care delivery rather than an adjunct technology. We synthesize evidence across ambulatory, inpatient, diagnostic, post-acute, and population health settings to assess where AI capabilities are sufficiently mature to support system-level integration and where critical gaps remain. Across domains, the literature demonstrates strong performance for narrowly defined tasks such as imaging interpretation, documentation support, predictive surveillance, and remote monitoring. However, evidence for longitudinal orchestration, cross-setting integration, and sustained impact on outcomes, costs, and equity remains limited. Key barriers include data fragmentation, workflow misalignment, algorithmic bias, insufficient governance, and lack of prospective, multi-site evaluations. We argue that advancing toward AI-first healthcare requires shifting evaluation from accuracy-centric metrics to system-level outcomes, emphasizing human-enabled AI, interoperability, continuous learning, and equity-aware design. Using hypertension management and patient journey exemplars, we illustrate how AI-first systems can enable proactive risk stratification, coordinated intervention, and continuous support across the care continuum. We further outline architectural and governance requirements, including cloud-enabled infrastructure, interoperability, operational machine learning practices, and accountability frameworks—necessary to operationalize AI-first care safely and at scale, subject to prospective validation, regulatory oversight, and post-deployment surveillance. This review contributes a system-level framework for understanding AI-first healthcare, identifies priority research and implementation gaps, and offers practical considerations for clinicians, health systems, researchers, and policymakers. By reframing AI as infrastructure rather than isolated tools, the AI-first approach provides a pathway toward more proactive, coordinated, and equitable healthcare delivery while preserving the central role of human judgment and trust. Full article

This study investigates how digital transformation in healthcare shapes sustainable customer value by analyzing the role of digital quality and its influence on satisfaction and loyalty within an AI-based diabetes prognostic service. Drawing on system, information, and service quality as core dimensions of digital quality, the study examines their direct effects on satisfaction and their contribution to loyalty formation relative to traditional service factors. Using survey data collected from over 1000 users of a digital healthcare platform equipped with an AI-driven diabetes prognostic algorithm, 800 valid responses were analyzed through PLS-SEM in SmartPLS 4.0. The results show that both traditional service attributes and digital quality significantly enhance customer satisfaction, which in turn promotes loyalty. However, digital quality does not strengthen the satisfaction–loyalty linkage, indicating that its value lies in establishing baseline trust and usability rather than amplifying loyalty outcomes. Environmental uncertainty—captured as technological and market uncertainty—also positively affects loyalty. This study contributes to digital healthcare research by providing empirical evidence from an AI-based long-term prognostic service and clarifying that digital quality operates as a foundational hygiene factor essential for sustainable customer value, rather than as a competitive differentiator. Full article

In response to the growing demand for environmentally friendly and sustainable yet functional technical textiles, this research developed a spun-bonded nonwoven from the biodegradable thermoplastic starch-based biopolymer BIOPLAST^®, incorporating fruit extracts as natural sources of polyphenolic compounds and surface-active additives. Extracts from Vaccinium myrtillus L. and Sambucus nigra L. were applied onto a nonwoven’s surface via aerographic spraying using a water/ethanol system. The resulting materials were characterized in terms of morphology, physicochemical and mechanical behavior, surface characteristics, and stability under accelerated ageing and hydrolytic conditions. Treatment with the extracts increased the tensile strength by roughly 38% and elongation at break by about 50%, and it changed the surface from hydrophobic (contact angle of 115°) to hydrophilic, with contact angles of 83° for the blueberry-modified nonwoven and 55° for the elderberry-modified nonwoven. The modified nonwovens also showed sustained release of polyphenolic compounds over 72 h, which is beneficial for biomedical, healthcare, and cosmetic applications, where short-term use, controlled release of active compounds, and bioactivity are more important than long-term durability. Overall, the results indicate that BIOPLAST^®-based spun-bonded nonwovens can serve as fully bio-based carriers for fruit extracts in MedTech-related technical textiles, offering a straightforward way to introduce additional functionality into biodegradable nonwovens. Full article

The growing economic pressures on healthcare systems have heightened the need for effective and sustainable cost management strategies. This study presents a PRISMA-based systematic review of 210 peer-reviewed articles published between 1974 and 2024, retrieved from the Scopus and Web of Science databases. Following a structured selection and screening process, the articles were analysed to identify dominant cost control tools, contextual applications, and methodological trends across diverse health systems. The findings highlight a strong prevalence of Activity-Based Costing (ABC), Diagnosis-Related Groups (DRG), and benchmarking practices, predominantly in public hospital settings. However, significant thematic gaps remain, particularly concerning low-income countries, interdisciplinary integration, and the evaluation of digital technologies for financial optimisation. This review provides a comprehensive thematic synthesis of international research, consolidating knowledge in healthcare cost management and offering evidence-based recommendations to guide future empirical research, policy design, and strategic planning in health finance. Full article

This paper addresses the critical need for efficient energy management in healthcare facilities, where fluctuating energy demands pose challenges to both operational reliability and sustainability objectives. Traditional energy management approaches often fall short in healthcare settings, resulting in inefficiencies and increased operational costs. To address this gap, the paper explores AI-driven methods for demand forecasting and load balancing and proposes an integrated framework combining Long Short-Term Memory (LSTM) networks, a genetic algorithm (GA), and SHAP (Shapley Additive Explanations), specifically tailored for healthcare energy management. While LSTM has been widely applied in time-series forecasting, its use for healthcare energy demand prediction remains relatively underexplored. In this study, LSTM is shown to significantly outperform conventional forecasting models, including ARIMA and Prophet, in capturing complex and non-linear demand patterns. Experimental results demonstrate that the LSTM model achieved a Mean Absolute Error (MAE) of 21.69, a Root Mean Square Error (RMSE) of 29.96, and an R² of approximately 0.98, compared to Prophet (MAE: 59.78, RMSE: 81.22, R² ≈ 0.86) and ARIMA (MAE: 87.73, RMSE: 125.22, R² ≈ 0.66), confirming its superior predictive performance. The genetic algorithm is employed both to support forecasting optimisation and to enhance load balancing strategies, enabling adaptive energy allocation under dynamic operating conditions. Furthermore, SHAP analysis is used to provide interpretable, within-model insights into feature contributions, improving transparency and trust in AI-driven energy decision-making. Overall, the proposed LSTM–GA–SHAP framework improves forecasting accuracy, supports efficient energy utilisation, and contributes to sustainability in healthcare environments. Future work will explore real-time deployment and further integration with reinforcement learning to enable continuous optimisation. Full article

Hard-to-heal wounds remain a significant challenge for healthcare professionals, particularly in aging populations. Although most chronic wounds are associated with diabetes or chronic venous insufficiency, rare etiologies should also be considered. One such cause is envenomation by Phoneutria spp. (native to South America, rare in Europe). Their venom contains potent neurotoxins. While systemic manifestations are more commonly reported, localized necrotic skin lesions may also occur. This case report presents a rare chronic wound following a suspected Phoneutria spider bite and highlights the importance of an individualized, multimodal treatment approach. A 61-year-old male patient with a progressive thigh wound following a spider bite sustained during work. Despite initial self-treatment and pharmacotherapy the wound deteriorated. The patient was admitted to the authors’ facility, where surgical treatment included necrosectomy and a sandwich graft using an acellular dermal matrix combined with a split-thickness skin graft. Adjunctive therapies included negative pressure wound therapy and hyperbaric oxygen therapy. After discharge, outpatient wound care was continued. Treatment was monitored with photographic documentation and serial microperfusion measurements. Complete wound closure was achieved after 4 months of specialized therapy. Management of chronic wounds requires a multidisciplinary and individualized approach with surgical intervention, advanced wound care and specialized outpatient follow-up. Full article

The discovery and development of new drugs is a lengthy, complex, and costly process, often requiring 10–20 years to progress from initial concept to market approval, with clinical trials representing the most resource-intensive stage. In recent years, Artificial Intelligence (AI) has emerged as a transformative technology capable of reshaping the entire pharmaceutical research and development (R&D) pipeline. The purpose of this narrative review is to examine the role of AI in drug discovery and development, highlighting its contributions, challenges, and future implications for pharmaceutical sciences and global public health. A comprehensive review of the scientific literature was conducted, focusing on published studies, reviews, and reports addressing the application of AI across the stages of drug discovery, preclinical development, clinical trials, and post-marketing surveillance. Key themes were identified, including AI-driven target identification, molecular screening, de novo drug design, predictive toxicity modelling, and clinical monitoring. The reviewed evidence indicates that AI has significantly accelerated drug discovery and development by reducing timeframes, costs, and failure rates. AI-based approaches have enhanced the efficiency of target identification, optimized lead compound selection, improved safety predictions, and supported adaptive clinical trial designs. Collectively, these advances position AI as a catalyst for innovation, particularly in promoting accessible, efficient, and sustainable healthcare solutions. However, substantial challenges remain, including reliance on high-quality and representative biomedical data, limited algorithmic transparency, high implementation costs, regulatory uncertainty, and ethical and legal concerns related to data privacy, bias, and equitable access. In conclusion, AI represents a paradigm shift in pharmaceutical research and drug development, offering unprecedented opportunities to improve efficiency and innovation. Addressing its technical, ethical, and regulatory limitations will be essential to fully realize its potential as a sustainable and globally impactful tool for therapeutic innovation. Full article

Even though environmental health and climate change are rapidly intensifying the severity of determinants of disease and inequity, training for health professionals in these areas remains fragmented across Europe. To address this gap, the European Medical Association (EMA), in collaboration with the European Network on Climate and Health Education (ENCHE), the International Network on Public Health and Environment Tracking (INPHET) and University College London, convened a one-day hybrid roundtable in London on 17 September 2025, focused on “Preparing Health Professionals for Environmental Health and Climate Change: A Challenge for Europe”. The programme combined keynote presentations on global and European policy, health economics and curriculum design with three disease-focused roundtables (respiratory, cardiovascular and neurological conditions), each examining the following topics: (A) climate and environment as preventable causes of disease; (B) healthcare as a source of environmental harm; and (C) capacity building through education and training. Contributors highlighted how environmental epidemiology, community-based prevention programmes and sustainable clinical practice can be integrated into teaching, illustrating models from respiratory, cardiovascular, surgical and neurological care. EU-level speakers outlined the policy framework (European Green Deal, Zero Pollution Action Plan and forthcoming global health programme) and tools through which professional and scientific societies can both inform and benefit from European action on environment and health. Discussions converged on persistent obstacles, including patchy national commitments to decarbonising healthcare, isolated innovations that are not scaled and curricula that do not yet embed sustainability in examinable clinical competencies. The conference concluded with proposals to develop an operational education package on environmental and climate health; map and harmonise core competencies across undergraduate, postgraduate and Continuing -professional-development pathways; and establish a permanent EMA-led working group to co-produce a broader position paper with professional and scientific societies. This conference report summarises the main messages and is intended as a bridge between practice-based experience and a formal EMA position on environmental-health training in Europe. Full article

Translation of evidence-based resuscitation practices into clinical settings remains slow and inconsistent, a gap that significantly impacts survival and neurological outcomes. Implementation science offers a structured approach to accelerate adoption by identifying context-specific barriers—such as dispatcher workload, team choreography, and resource constraints—and tailoring strategies to overcome them. This paper applies the Knowledge-to-Action (KTA) framework to resuscitation, emphasizing stakeholder engagement, iterative monitoring, and sustainability. We provide detailed guidance across key resuscitation settings, including dispatch-assisted cardiopulmonary resuscitation (DA-CPR), in-hospital code teams, and emergency medical services (EMS). The manuscript introduces a comprehensive outcomes framework encompassing implementation, service/system, and patient-level metrics, and illustrates practical application through case examples such as DA-CPR and real-time feedback devices. To enhance scientific utility, we also present a decision-oriented table for pilot testing, offering healthcare institutions a roadmap for sustainable integration of evidence-based resuscitation protocols. Full article

The rapid expansion of mindfulness research has generated both enthusiasm and controversy regarding its actual clinical value. While meditation is often regarded as the central mechanism of mindfulness-based interventions, other components such as psychoeducation and informal practice may play an equally significant role in improving mental health outcomes. This critical review examines the relative contributions of these elements to the therapeutic impact of mindfulness and clarifies the extent to which its effects are comparable to established treatments, particularly Cognitive Behavioral Therapy (CBT). Evidence from meta-analyses and high-quality trials indicates that mindfulness programs achieve moderate efficacy in reducing symptoms of anxiety, depression, and stress, but effect sizes are frequently inflated by methodological limitations. Importantly, cognitive and emotional regulation skills, especially acceptance and non-judgment, appear to sustain long-term benefits more consistently than meditation alone. These findings highlight the need for rigorous longitudinal studies and component-focused designs to identify the mechanisms that drive clinical change. By distinguishing between evidence-based applications and overstated claims, this review contributes to a more balanced understanding of mindfulness and its appropriate integration into healthcare. Full article

Background: Healthcare is a large contributor to greenhouse gas (GHG) emissions, contributing to climate change and health impairments. However, the magnitude of health and climate benefits of local and regional GHG mitigation strategies has not been well quantified. Few studies have demonstrated the use of public tools for this purpose in healthcare facilities. Methods: We evaluated several renewable energy and energy efficiency scenarios focused on one academic medical center in New York State. We used the Environmental Protection Agency’s (EPA) publicly available AVoided Emissions and geneRation Tool to estimate avoided GHG and health-harmful air pollutant emissions. The economic value of the resulting avoided health and climate damages was quantified using EPA’s CO-Benefits Risk Assessment screening tool. Results: Transitioning one healthcare institution to 100% solar energy and improving energy efficiency by 25% could yield approximately $807,000 to $1.5 million in annual health savings, with an additional $2.3 million benefits in avoided climate damages. There is an approximate $108.5–$196.6 million in annual climate and health benefits when extrapolating these energy solutions to hospitals across the same state. Conclusions: There are significant health savings from healthcare GHG mitigation strategies. This application of publicly available and accessible tools demonstrates ways to integrate climate and health benefits into local decision-making around climate change mitigation and sustainability efforts. Full article

Integrating antibacterial fabrics into wearable technology represents a transformative advancement in healthcare, fashion, and personal hygiene. Antibacterial fabrics, designed to inhibit microbial growth, are gaining prominence due to their potential to reduce infections, enhance durability, and maintain cleanliness in wearable devices. These fabrics offer effective antimicrobial properties while retaining comfort and functionality by incorporating nanotechnology and advanced materials, such as silver nanoparticles, zinc oxide, titanium dioxide, and graphene. The production techniques for antibacterial textiles range from chemical and physical surface modifications to biological treatments, each tailored to achieve long-lasting antibacterial performance while preserving fabric comfort and breathability. Advanced methods such as nanoparticle embedding, sol–gel coating, electrospinning, and green synthesis approaches have shown significant promise in enhancing antibacterial efficacy and material compatibility. Wearable technology, including fitness trackers, smart clothing, and medical monitoring devices, relies on prolonged skin contact, making the prevention of bacterial colonization essential for user safety and product longevity. Antibacterial fabrics address these concerns by reducing odor, preventing skin irritation, and minimizing the risk of infection, especially in medical applications such as wound dressings and patient monitoring systems. Despite their potential, integrating antibacterial fabrics into wearable technology presents several challenges. This review provides a comprehensive overview of the key antibacterial agents, the production strategies used to fabricate antibacterial textiles, and their emerging applications in wearable technologies. It also highlights the need for interdisciplinary research to overcome current limitations and promote the development of sustainable, safe, and functional antibacterial fabrics for next-generation wearable. Full article

The emergence and dissemination of antimicrobial resistance (AMR) are driven by complex, interconnected mechanisms involving microbial communities, environmental factors, and human activities, with climate change playing a pivotal and accelerating role. Rising temperatures, altered precipitation patterns, and other environmental disruptions caused by climate change create favorable conditions for bacterial growth and enhance the horizontal gene transfer (HGT) of antibiotic resistance genes (ARGs). Thermal stress and environmental pressures induce genetic mutations that promote resistance, while ecosystem disturbances facilitate the stabilization and spread of resistant pathogens. Moreover, climate change exacerbates public and animal health risks by expanding the range of infectious disease vectors and driving population displacement due to extreme weather events, further amplifying the transmission and evolution of resistant microbes. Livestock agriculture represents a critical nexus where excessive antibiotic use, environmental stressors, and climate-related challenges converge, fueling AMR escalation with profound public health and economic consequences. Environmental reservoirs, including soil and water sources, accumulate ARGs from agricultural runoff, wastewater, and pollution, enabling resistance spread. This review aims to demonstrate how the Mediterranean’s strategic position makes it an ideal living laboratory for the development of integrated “One Health” frameworks that address the mechanistic links between climate change and AMR. By highlighting these interconnections, the review underscores the need for a unified approach that incorporates sustainable agricultural practices, climate mitigation and adaptation within healthcare systems, and enhanced surveillance of zoonotic and resistant pathogens—ultimately offering a roadmap for tackling this multifaceted global health crisis. Full article

Background/Objectives: Digital transformation is increasingly essential for healthcare organizations to improve operational efficiency and service quality. However, in developing countries such as Indonesia, many lower-middle-class hospitals lag due to limited financial, human, and infrastructural resources. This study examines how environmental dynamism—comprising regulatory changes, market pressures, and technological shifts—affects the digital capabilities of these hospitals. Methods: A quantitative, cross-sectional survey was conducted in Class C and D hospitals across Indonesia. Respondents included hospital directors, deputy directors, and IT heads. Data were collected through structured questionnaires measuring environmental dynamism and digital capability using a six-point Likert scale. Reliability testing yielded Cronbach’s alpha values above 0.96 for both constructs. Correlation analysis was performed to examine the relationship between environmental dynamism and digital capability. Results: Findings reveal a weak positive correlation (r = 0.1816) between environmental dynamism and digital capability. Although external factors such as policy regulations and technological competition encourage digital adoption, hospitals with limited internal resources struggle to translate these pressures into sustainable transformation. Key challenges include low ICT budgets, inconsistent staff training, and insufficient infrastructure. Conclusions: The results suggest that environmental change alone cannot drive digital readiness without internal capacity development. To foster resilient digital healthcare ecosystems, policy interventions should integrate regulatory frameworks with practical support programs that strengthen resources, leadership, and human capital in lower-middle-class hospitals. Full article

The COVID-19 pandemic highlighted critical issues in health services and public policy, particularly in long-term care facilities across Canada. Failures in these facilities revolving around chronic underfunding, staffing shortages, inadequate infection control, and inconsistent regulatory oversight, underscore the need to rethink health service interventions, especially considering varying implementation contexts among provinces. The Ontario Long-Term Care COVID-19 Commission Final Report pointed to long-standing systemic issues as the primary causes of the sector’s failures. To explore this issue, a narrative review was conducted with findings indicating that the long-term care crisis in Canada cannot be solved by more privatization, regulation or efficiency measures, as these have contributed to the problem’s root causes. Ontario’s long-term care crisis stems from systemic misalignments in policy, structure and stakeholder dynamics, requiring a shift toward systems thinking and resident-centered care to build an equitable and sustainable long-term care sector. Ultimately, governments must lead a policy redesign that reflects shared responsibility, stakeholder interdependence, and public involvement, offering a model for broader healthcare reform. Full article