Search Results (1,251)

The widespread fragmentation of patient information across heterogeneous systems and the lack of standardized integration mechanisms hinder efficient and comprehensive medical diagnostics. To address these limitations, this work presents an architecture framework designed to support physicians in the diagnostic process by integrating clinical and socio-health information (patient medical histories), structured documents extracted from Health Information System (HIS), and data automatically extracted from diagnostic images using Artificial Intelligence (AI) techniques. The proposed architecture is made by several modules, in particular a Decision Support System (DSS) that enables risk assessment related to specific patient’s clinical conditions. In addition, the clinical information retrieved is aggregated, standardized, and transmitted to external systems for follow up. Standardization and data interoperability are ensured through the adoption of the international HL7 Fast Healthcare Interoperability Resources (FHIR) standard, which facilitates seamless connection with HIS. An Android application has been developed to communicate with different HISs in order to: (i) retrieve information, (ii) aggregate clinical data, (iii) calculate patient risk scores using AI algorithms, (iv) display results to healthcare professionals, and (v) generate and share relevant clinical information with external systems in a standardized format. To demonstrate architecture’s applicability, a case study on breast cancer diagnosis is presented. In this context, an AI-based Risk Assessment module was developed using the Breast Ultrasound Images Dataset (BUSI), which includes benign, malignant, and normal cases. Machine Learning algorithms were applied to perform the classification task. Model performance was evaluated using a 4-fold cross-validation strategy to ensure robustness and generalizability. The best results were achieved using the Multilayer Perceptron method, with a competitive F1-score of 0.97. Full article

Background: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that causes severe motor, respiratory and communication impairment and imposes a high psychosocial burden on patients and families. Recent evidence shows that integrated neuropalliative care—early collaboration between neurology and palliative services with community support—improves quality of life and reduces avoidable hospitalisations. Yet there are few descriptions of how such integration is operationalised. Objective: This study examines a Community-Integrated Neuropalliative Pathway (CINP) implemented in the province of Modena (Emilia-Romagna, Italy), analysing how neurology, palliative care and emergency services collaborate to provide continuous, person-centred care for people with ALS. Methods: A single, holistic case study was conducted following Yin’s analytical approach. Data sources included ten semi-structured interviews with neurologists, palliative physicians, nurses, home-care professionals and emergency clinicians; ethnographic observations in the ALS outpatient clinic; relevant organisational documents (the regional Clinical Pathway on ALS); and aggregated quantitative data from the palliative care registry (January 2023–December 2024). Thematic analysis with investigator triangulation was used to explore care integration, advance care planning and emergency coordination. Quantitative data were summarised descriptively. Results: Three interrelated themes were identified: (1) Progressive and flexible integration between neurology and palliative care. Neurologists remained longitudinal reference points while palliative teams were activated in response to evolving needs and became more relevant with the progression of the disease. Regular multidisciplinary meetings and shared discharge planning facilitated coordination. (2) The shared culture of advance care planning. Professionals framed advance care planning (ACP) as a relational, iterative process anchored in therapeutic relationships. Shared care plans, once completed, triggered an electronic Emergency Warning (“warning 118”) procedure that notified the emergency service of patient preferences. (3) The integration of palliative and emergency services. The warning system enabled emergency clinicians to respect care plans and avoid aggressive interventions during crises. Quantitative data on 47 ALS patients followed by territorial palliative services showed that 16 had an active Emergency Warning flag; among these, most died at home or in a hospice rather than in hospital. Conclusions: The Modena CINP exemplifies how a public health system can operationalise early neuropalliative integration and connect hospital, community and emergency services. The qualitative findings illustrate the cultural and organisational shifts required for continuous care, while the quantitative data show that the system is correctly used and that patients with the Emergency Warning activation died mostly at home or in a hospice. Lessons from this analytical case study can inform the development of similar pathways in other regions, although further research is needed to assess outcomes in larger populations and such models need to be adapted to local contexts. Full article

Healthcare collaborative processes still encounter major challenges, particularly regarding the interoperability of heterogeneous information systems, the traceability of medical interventions, and the secure sharing of patient data under strict privacy regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). This paper presents a patient-centric, blockchain-based framework designed to overcome these limitations. The proposed solution integrates smart contracts and non-fungible tokens (NFTs) within the Ethereum blockchain to ensure data integrity, traceability, and privacy preservation. Furthermore, a compliance-by-design mechanism is embedded into the smart contracts to enable self-supervision of collaborative workflows without third-party intervention. A Proof-of-Authority (PoA) consensus protocol is also adopted to optimize validation efficiency and significantly reduce computational and energy costs. Full article

This study investigates the role of modern small-scale cities in addressing public health challenges through the lens of spatial justice, using the city of Tartu, Estonia, as a case study. Tartu has been recognized for its progressive public health initiatives, including the Tartu Health Care College, Mental Health Centre, Smoke-Free Tartu campaign, Health Trail network, Healthy School Program, and an expanding smart bike-sharing system. By employing Geographic Information Systems (GIS), we map and analyze the spatial distribution and accessibility of health-promoting infrastructure, such as healthcare facilities, green and blue spaces, health trails, and mobility services, across the urban landscape. A population-weighted accessibility assessment indicates that, although Tartu’s central districts (e.g., Kesklinn (HRI: 0.972)) are well-served, peripheral and densely populated districts such as Annelinn (HRI: 0.351) and Ropka (HRI: 0.377) exhibit notable deficits in health-related infrastructure. However, access to green infrastructure and mobility services is more evenly distributed citywide, reflecting a relatively equitable provision of non-clinical health assets. These findings highlight both the strengths and spatial gaps in Tartu’s health-oriented urban design, emphasizing the need for targeted investment in underserved areas. The study contributes to emerging studies on health-justice planning in small-scale urban contexts and demonstrates how spatial analytics can be guided to advance distributional justice in the provision of public health infrastructure. Ultimately, this research indicates the essential role of spatial analysis in guiding inclusive and data-informed health planning in urban environments. Full article

Background: This study reports on a community engagement knowledge-translation world café hosted in British Columbia, built on the research project “Enhancing cancer navigation for newly diagnosed, treated and post-treatment of people living with breast cancer in interior region”. The aim was to co-create a knowledge translation initiative with community stakeholders to enhance interdisciplinary approaches to psychosocial oncology. Methods: This study drew on implementation science and the consolidated framework for implementation research, which emphasize the importance of creating partnerships between researchers and engaging people for whom the research is meant to be of use—knowledge users and service users. Guided world café and purposeful sampling were used to engage a diverse range of stakeholders. Eighty stakeholders participated in this study from April 2023 to April 2024. Thematic analysis was conducted through familiarization, coding, theme development, review, definition, and reporting. Results: Eleven key themes emerged, including compassionate connection, time as a healing gift, empowering health literacy, informed compassion, holistic support ecosystem, empowering patient navigators, shared decision-making, empowering partnerships, digital–physical synergy, person-centered transformation, and accountability and collaboration. Conclusions: The key findings highlighted the need for continuous professional development for primary care providers, integrating patient-reported outcomes in electronic health records, leveraging digital health tools, and establishing community-engaged psychosocial oncology hubs to enhance care in rural communities. Recommendation: Recommendations include ongoing professional learning, embedding patient voices and lived experiences into care planning through digital tools, and empowering rural and diverse communities through inclusive and accessible cancer models of care. Full article

Among animal diseases, those characterized with transboundary potential enhance their interconnection to the One Health principle. Zoonoses with a higher capacity to spread compared to other diseases with a lower level of transmissibility multiply their potential impact on human populations. The routes and speed of transmission and virulence may also increase the impact on animal health in the zootechnic sector and in wild animals. This risk, especially in endangered species, has the potential to alter biodiversity, negatively affecting the environment. The characteristics of these pathogens represent a global health danger that requires knowledge and the capacity for prevention and control, considering the possibility of natural outbreak occurrence together with the deliberate use of such pathogens as biological weapons for terrorist attacks. Animal pathogens, particularly those with zoonotic potential, have long been considered for use in bioterrorism. International conventions prohibit the use of microbiological and toxin weapons. Furthermore, recent European legislation has also addressed the potential misuse of animal pathogens in bioterrorism. In this context, the Parliamentary Assembly of the Mediterranean (PAM) and its Center for Global Studies are committed to preventing global health threats by promoting transboundary cooperation, especially through a One Health approach that links human, animal, and environmental health. In the face of future emergencies, PAM is also committed to promoting greater information sharing for harmonized legislative frameworks and equitable access to resources, to strengthen the resilience of global health systems, especially in developing countries. In both the past and recent history, various outbreaks have been attributed to proven or alleged bioterrorist attacks targeting human or animal populations. This study discusses the general characteristics of several relevant transboundary diseases. Paying high attention to One Health is of utmost importance. However, for a full understanding, it is necessary to consider all related aspects and implications. Full article

In recent years, healthcare is evolving to meet the needs of a growing and ageing population. To support better and more reliable care, a comprehensive and up-to-date Personal Health Record (PHR) is essential. Ideally, the PHR should contain all health-related information about an individual and be available for sharing with healthcare institutions. However, due to interoperability issues of the medical and fitness devices, most of the times, the PHR only contains the same information as the patient Electronic Health Record (EHR). This results in lack of health-related information (e.g., physical activity, working patterns) essential to address medical conditions, support prescriptions, and treatment follow-up. This paper introduces the B-Health IoT Box, a fog IoT computing framework for eHealth interoperability and data collection that enables seamless, secure integration of health and contextual data into interoperable health records. The system was deployed in real-world settings involving over 4500 users, successfully collecting and transmitting more than 1.5 million datasets. The validation shown that data was collected, harmonized, and properly stored in different eHealth platforms, enriching data from personal EHR with mobile and wearable sensors data. The solution supports real-time and near real-time data collection, fast prototyping, and secure cloud integration, offering a modular, standards-compliant gateway for digital health ecosystems. The health and health-related data is available in FHIR format enabling interoperable eHealth ecosystems, and better equality of access to health and care services. Full article

Background/Objectives: Projected augmented reality (PAR) enables real-time projection of digital surgical information directly onto the operative field. This offers a hands-free, headset-free platform that is universally visible to all members of the surgical team. Compared to head-mounted display systems, which are limited by restricted fields of view, ergonomic challenges, and user exclusivity, PAR provides a more intuitive and collaborative surgical interface. When paired with artificial intelligence (AI), PAR has the potential to automate aspects of surgical planning and deliver high-precision guidance in both high-resource and global health settings. Our team is working on the development and validation of a PAR platform to dynamically project surgical and anatomic markings directly onto the patients intraoperatively. Methods: We developed a PAR system using a structured light scanner and depth camera to generate digital 3D surface reconstructions of a patient’s anatomy. Surgical markings were then made digitally, and a projector was used to precisely project these points directly onto the patient’s skin. We also developed a trained machine learning model that detects cleft lip landmarks and automatically designs surgical markings, with the plan to integrate this into our PAR system. Results: The PAR system accurately projected surgeon and AI-generated surgical markings onto anatomical models with sub-millimeter precision. Projections remained aligned during movement and were clearly visible to the entire surgical team without requiring wearable hardware. Conclusions: PAR integrated with AI provides accurate, real-time, and shared intraoperative guidance. This platform improves surgical precision and has broad potential for remote mentorship and global surgical training. Full article

Background: Neurological disorders are the leading cause of disability, affecting over three billion people worldwide. Amyotrophic lateral sclerosis (ALS) is among the most feared and uniformly fatal neurodegenerative diseases, with no therapy capable of restoring lost function. Methods: We report the first application of therapeutic fever to ALS using Computerized Brain-Guided Intelligent Thermofebrile Therapy (CBIT²). This fully noninvasive treatment, delivered through an FDA-approved computerized platform, digitally reengineers the 1927 Nobel Prize-recognized malarial fever therapy into a modern treatment guided by the Brain–Eyelid Thermoregulatory Tunnel. CBIT² induces therapeutic fever through synchronized hypothalamic feedback, activating heat shock proteins, which are known to restore proteostasis and neuronal function. Case presentation: A 56-year-old woman was diagnosed with progressive ALS at the Mayo Clinic, with electromyography (EMG) demonstrating fibrillation and fasciculation indicative of denervation corroborated by neurological and MRI findings; the patient was informed that she had an expected survival of three to five years. A neurologist from Northwestern University confirmed the diagnosis and thus maintained the patient on FDA-approved ALS drugs (riluzole and edaravone). Her condition rapidly worsened despite pharmacological treatment, and she underwent CBIT², resulting in (i) electrophysiological reversal with complete disappearance of denervation; (ii) biomarker correction, including reductions in neurofilament and homocysteine, IL-10 normalization (previously linked to mortality), and robust HSP70 induction; (iii) restoration of gait, swallowing, respiration, speech, and cognition; (iv) reconstitution of tongue structure; and (v) return to complex motor tasks, including golf, pickleball, and swimming. Discussion: This case provides the first documented evidence that ALS can be reversed through digitally reengineered fever therapy aligned with thermoregulation, which induces heat shock response and upregulates heat shock proteins, resulting in the patient no longer meeting diagnostic criteria for ALS and discontinuation of ALS-specific medications. Beyond ALS, shared protein-misfolding pathology suggests that CBIT² may extend to Alzheimer’s, Parkinson’s, and related disorders. By modernizing this Nobel Prize-recognized therapeutic principle with computerized precision, CBIT² establishes a framework for large-scale clinical trials. A century after fever therapy restored lost brain function and so decisively reversed dementia paralytica such that it earned the 1927 Nobel Prize in Medicine, CBIT² now safely harnesses the therapeutic power of fever through noninvasive, intelligent, brain-guided thermal modulation. Amid a global brain health crisis, fever-based therapies may offer a path to preserve thought, memory, movement, and independence for the more than one-third of humanity currently affected by neurological disorders. Full article

Microplastics (MPs) are pervasive in freshwater and may threaten aquatic ecosystem health. We surveyed surface waters of Shijiu Lake and its inflowing tributaries during the dry (January–March) and rainy (May–July) seasons of 2024. MP abundance ranged within 17.54–30.93 items/L, with higher values in the rainy than in the dry season (28.18 ± 6.03 vs. 24.53 ± 5.68 items/L; one-way ANOVA, p < 0.05). Abundance correlated positively with turbidity (r = 0.44; R² = 0.20; p < 0.05), whereas associations with total nitrogen, total phosphorus, and suspended solids were not significant (p > 0.05). Small particles (38–75 μm) dominated and were slightly more prevalent in the dry season, while the fraction of larger particles (>150 μm) was relatively higher in the rainy season. Granules predominated across sites, but their share decreased in the rainy season, accompanied by a notable increase in fibers. The Pollution Load Index (PLI) indicated slight but spatially uneven pollution (PLI = 1.00–1.43), generally higher during the rainy season and consistently elevated at the lake center; the Nongkan River exhibited the lowest levels. Ecologically, the patterns indicate rainfall-driven inputs and hydrodynamic controls (runoff, resuspension, residence time), identifying the lake center and tributary interfaces as priority zones for monitoring and mitigation. These results provide lake-scale evidence to refine seasonal monitoring and inform source-reduction strategies in similar inland waters. Full article

Floating offshore wind turbines (FOWTs) face complex aero-hydro-servo-elastic interactions that challenge conventional modeling, monitoring, and control. This review critically examines how artificial intelligence (AI) is being applied across four domains—design and surrogate modeling, structural health monitoring, control and operations, and digital twins—with explicit attention to uncertainty and reliability. Using PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), a Scopus search identified 412 records; after filtering for articles, conference papers, and open access, 115 studies were analyzed. We organize the literature into a taxonomy covering classical supervised learning, deep neural surrogates, physics-informed and hybrid models, reinforcement learning, digital twins with online learning, and uncertainty-aware approaches. Neural surrogates accelerate coupled simulations; probabilistic encoders improve structural health monitoring; model predictive control and trust-region reinforcement learning enhance adaptive control; and digital twins integrate reduced-order physics with data-driven calibration for lifecycle management. The corpus reveals progress but also recurring limitations: simulation-heavy validation, inconsistent metrics, and insufficient field-scale evidence. We conclude with a bias-aware synthesis and propose priorities for future work, including shared benchmarks, safe RL with stability guarantees, twin-in-the-loop testing, and uncertainty-to-decision standards that connect model outputs to certification and operational risk. Full article

Patient stratification strategies based on digital databases and advanced information technology can predict inpatient rehabilitation outcomes and support safe hospital discharge for patients who underwent joint replacement for hip and knee osteoarthritis. The degree of continuity between surgery and rehabilitation, the perioperative process integration, and the setting where rehabilitation is provided are crucial factors to improve care effectiveness, access, minimize readmissions, and cost increase. The primary aim of this scoping review of the literature is to identify perioperative variables that are predictive of inpatient rehabilitation stay after hip and knee arthroplasty for osteoarthritis. These factors are divided by time of assessment through the perioperative pathway and surgical procedure site. The secondary aim is to explore how different data sources and facilities are linked into a patient-centered perioperative pathway. An electronic search of the literature was performed on PubMed, Embase, and Scopus. No time restrictions were applied. All primary research studies investigating predictive factors of inpatient rehabilitation stay after hip and knee osteoarthritis were included. In total, 25 studies were included in the review. Age, caregiver presence, presence of comorbidities, sex, Body Mass Index, Risk Assessment and Prediction Tool composite score, pre-operative Clinician-Reported Outcome Measures, pre-operative Patient-Reported Outcome Measures, and post-operative Barthel Index of autonomy in the Activities of Daily Living were predictive of some degree of inpatient rehabilitation stay in more than one study. The studies were fairly distributed between retrospective and prospective, with multicentric databases more spread among the latter. Data collection occurred in acute hospitals more than in specialized rehabilitation facilities. Using comprehensive models supported by electronic health records and powerful information technologies, analyzing specific inpatient rehabilitation LOS as distinguished from surgical ward rehabilitation, using institutional registries, and including specific rehabilitation factors in these registries, and promoting vocabulary and federated data sharing can strongly enhance the predictivity of models investigating rehabilitation outcomes and support appropriate discharge from inpatient rehabilitation units. Full article

During the 2022–2023 cholera outbreak in Lebanon, cases were reported through the District Health Information System 2 (DHIS2). We developed automated procedures in R computing language to improve completeness of routinely notified variables, apply case definition criteria, improve geographic accuracy and documentation of laboratory results. We developed R scripts for data cleaning, standardization, and reclassification, plotted epidemic curves and produced maps to display cholera incidence rates and rapid diagnostic test (RDT) coverage by district. We shared the R scripts on GitHub platform for open adaptation and use. Prior to cleaning, missingness reached 99.7% for inpatient status and 17–35% for other key variables. After cleaning, all fields were complete. Initially, 92.8% of cases were notified through DHIS2 as suspected and 7.2% as confirmed. Following reclassification, 40% were classified as suspected, 5.8% as confirmed, and 48.6% with unspecified classification. Laboratory data revealed that 5.8% of cases were culture positive, 2.2% RDT positive, and 65.1% had no documented testing. Among facility-entered cases (n = 5953), 11.4% were reported from a different governorate than the patient’s residence. At the time of the outbreak, the daily maps were generated based on place of residence. Integrating R-based analytics with DHIS2 enhanced data completeness, improved case classification, and enabled more better spatial and laboratory analysis. This combined approach provided a clearer epidemiological picture of the cholera outbreak, supporting data-driven public health decision-making and highlighting the value of integrating analytical tools with routine surveillance systems. Full article

Bathing water quality (BWQ) monitoring and prediction are essential to safeguard public health by informing bathers about the risk of exposure to faecal indicator bacteria (FIBs). Traditional monitoring approaches, such as manual sampling and laboratory analysis, while effective, are often constrained by delayed reporting, limited spatial and temporal coverage, and high operational costs. The integration of artificial intelligence (AI), particularly machine learning (ML), with automated data sources such as environmental sensors and satellite imagery has offered novel predictive and real-time monitoring opportunities in BWQ assessment. This systematic literature review synthesises current research on the application of AI in BWQ assessment, focusing on predictive modelling techniques and remote sensing approaches. Following the PRISMA methodology, 63 relevant studies are reviewed. The review identifies dominant modelling techniques such as Artificial Neural Networks (ANN), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Hybrid and Ensemble Boosting algorithms. The integration of AI with remote sensing platforms such as Google Earth Engine (GEE) has improved the spatial and temporal solution of BWQ monitoring systems. The performance of modelling approaches varied depending on data availability, model flexibility, and integration with alternative data sources like remote sensing. Notable research gaps include short-term faecal pollution prediction and incomplete datasets on key environmental variables, data scarcity, and model interpretability of complex AI models. Emerging trends point towards the potential of near-real-time modelling, Internet of Things (IoT) integration, standardised data protocols, global data sharing, the development of explainable AI models, and integrating remote sensing and cloud-based systems. Future research should prioritise these areas while promoting the integration of AI-driven BWQ systems into public health monitoring and environmental management through multidisciplinary collaboration. Full article

Background: Recovery is an emerging approach. In Chile, attempts are being made to introduce the Recovery Model with specific guidelines for the care of people diagnosed with Severe Mental Disorders. The participation of nurses in this process is peripheral to the biomedical model. Objectives: To explore the participation of nurses in the recovery process of people hospitalised in Psychiatric Intensive Care between 2023 and 2024. Methods: Qualitative research, collective-case multisite study design in four hospitals. With the approval of four ethics committees, 18 nurses who signed informed consent were interviewed. Rapid qualitative analysis was performed. Results: Nursing care is mainly related to the caregiving, educational, and management roles. Recovery is associated with clinical improvement, and different components are identified, such as family and social support, the ability to resume control of one’s life, the existence of a future life plan, and the ability to manage one’s own illness. Conclusions: The results are consistent with elements described in contemporary approaches to recovery, incorporating autonomy, confidence in the person’s abilities, and shared decision-making. However, they are still far from modern approaches to personal and non-clinical recovery. Nursing needs to redirect its efforts toward recovery with a paradigm shift toward a model in which the person affected by a mental health condition is the protagonist of their own health process. Full article