Search Results (2,582)

Background/Objectives: Discharging patients after hospitalization for an acute exacerbation of chronic obstructive pulmonary disease (COPD) is a critical transition in care associated with a high risk of early readmission. This survey aimed to describe physician-reported discharge practices following COPD exacerbations, identify perceived gaps and organizational barriers, explore attitudes toward structured COPD discharge summaries, and use a SWOT analysis as an interpretative framework. Methods: In this cross-sectional observational survey, 100 physicians involved in COPD care were recruited from the official mailing list of the Respiratory Society of Serbia, which represents approximately 71% of the Society’s members. The survey assessed discharge procedures, multidisciplinary practices, patient education, comorbidity management, perceived causes of readmission, and barriers to structured discharge summaries. Data were analyzed descriptively and complemented with a structured SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis. Results: Most respondents worked in tertiary care settings and were involved in managing patients hospitalized for COPD exacerbations. Although 24% of physicians routinely used structured discharge summaries, 45% reported never using them. The most frequently perceived contributors to 30-day readmissions were active smoking (90%), poor treatment adherence (81%), comorbidities (77%), and incorrect inhaler technique (72%). Major barriers to implementing structured discharge summaries included the lack of standardized templates, time constraints, poor coordination across healthcare levels, and technical limitations. Willingness to implement structured discharge tools was high (mean score 8.86/10). SWOT analysis identified strong professional support for discharge standardization alongside organizational and system-level barriers to implementation. Conclusions: This exploratory survey identified important gaps between recommended and routine COPD discharge practices and highlighted organizational barriers to implementation. The findings may inform future evaluation and development of structured discharge tools in this healthcare setting. Full article

Background/Objectives: Medication-related harm is a leading global patient safety challenge, yet patients’ lived experiences of medication safety remain underexplored in Central and Eastern European healthcare systems, where structural constraints significantly shape everyday medication use. Methods: This study provides an in-depth qualitative analysis of Hungarian patients’ online narratives, building on a prior netnographic mixed-methods study. Using grounded theory-informed principles and a patient-centred medication safety framework, we inductively analysed 5174 publicly accessible Hungarian-language comments posted on health forums and social media platforms between August 2020 and August 2023. The COM-B model was applied as a secondary lens to map findings onto modifiable behavioural determinants. Results: Access to services and communication emerged as the dominant medication safety concerns. Patients reported long waiting times, limited rural emergency services, and brief consultations leading to delayed or inadequate treatment. Communication gaps included insufficient information on medication duration, side effects, and follow-up, as well as conflicting advice from multiple sources, all of which eroded trust and prompted treatment discontinuation or reliance on informal online communities. Community pharmacists were largely absent from patients’ mental models of care, representing a significant missed opportunity given their accessibility. Less frequently mentioned were medication shortages, healthcare professional workload, and systemic safety culture. Conclusions: Clear, respectful communication and timely access to care are central to medication safety from the patient perspective. Netnography combined with a grounded theory-informed methodology offers a valuable approach for capturing authentic patient perspectives in structurally burdened healthcare systems, with findings relevant beyond the Hungarian context. Full article

Background/Objectives: Dementia and cognitive decline place increasing demands on healthcare systems, rehabilitation services, long-term care, and community-based prevention. Structured exercise training is a promising strategy for adults at risk of cognitive decline, but it remains unclear how intervention studies integrate cerebrovascular and cognitive outcomes in ways that can inform clinical translation, rehabilitation planning, and exercise prescription. Methods: This scoping review followed PRISMA-ScR guidance. PubMed/MEDLINE and Scopus were searched for peer-reviewed English-language studies published from 2010 to 2026, supplemented by reference list checking and citation chasing. Eligible studies were human intervention studies involving structured exercise training and at least one cerebrovascular, vascular, brain-related, or cognitive outcome. Studies were mapped by exercise modality, population risk profile, grouped outcome domain, and outcome-integration category. Results: Fifty-four studies were included. A central finding was the vascular cognitive integration gap: only 7 studies assessed both cerebrovascular and cognitive outcomes within the same intervention design, whereas 38 studies reported cognitive outcomes only and 9 reported cerebrovascular or vascular outcomes only. Aerobic training formed the most developed evidence cluster for direct cerebrovascular outcomes, whereas other modalities were more often represented in cognition-focused studies but less frequently included direct cerebrovascular measures. Conclusions: Current evidence is limited by a major vascular cognitive integration gap. Because most exercise intervention studies separate cerebrovascular and cognitive outcomes, the field cannot yet determine whether exercise-induced cerebrovascular adaptations correspond to cognitive improvements in the same participants. Future trials should combine cerebrovascular assessment, domain-specific cognitive testing, dose reporting, adherence monitoring, safety reporting, feasibility evaluation, and mechanistic biomarkers to support more precise exercise prescription for dementia risk mitigation and cognitive health promotion. Full article

The energy performance of a building reflects its typical energy use and is influenced by factors such as the building envelope (insulation and windows), system efficiency (particularly for heating, cooling, and domestic hot water), and the integration of renewable energy sources. Improving energy performance helps save energy, boost energy independence and security, lower energy costs, and reduce the need for grid investments. Standardizing energy performance assessments enables benchmarking and comparison of building efficiency, encouraging informed decision-making. In this context, the paper presents a knowledge discovery-driven intelligent decision-making system, designed, developed, and tested to identify the best strategies for prioritizing buildings in the envelope process. The system combines data mining techniques with statistical analysis to precisely rank and thoroughly evaluate low-energy-performance buildings and to develop scenario-based strategies for enveloping the buildings to achieve high energy efficiency (associated with nearly zero-energy buildings) under real-world conditions. Testing of the proposed intelligent decision-making system was conducted using a real building database of approximately 3900 records, uploaded from the Romanian central administration website. Under the highest-performance scenario of the envelope-priority strategy, which includes nearly zero-energy building standards, energy savings exceeded 50% across all categories: 51.70% for healthcare, 53.40% for residential, 60.11% for administrative and office buildings, and 69.92% for educational institutions. Overall, the average savings across all building types were 59.81% (644.86 GWh/year). Full article

Agentic artificial intelligence (AI) offers new opportunities for intelligent clinical decision support, but deployment in emergency and crisis settings remains challenging because time-critical recommendations must often be generated under incomplete patient information and system constraints. Conventional clinical decision support systems rely on rule-based workflows that degrade when structured data are absent, while standalone language models lack coordination mechanisms to enforce mandatory safety checks. We present TriAgent, a multi-agent framework that unifies adaptive orchestration, iterative retrieval, embedded safety verification, and end-to-end auditability within a single crisis clinical decision support workflow. An Orchestrator Agent dynamically selects specialist modules for clinical assessment, retrieval, treatment planning, safety verification, and system coordination, with routing determined by model reasoning rather than fixed execution paths. A retrieval sub-agent performs iterative query refinement and relevance grading over 49,000 MIMIC-IV discharge notes, while medication-conflict screening and allergy-risk assessment are invoked in parallel only when clinically indicated. A Critique Agent reviews the full reasoning trace before recommendation finalization. In a retrospective evaluation on 1000 real emergency presentations under synthesized incomplete-information inputs, TriAgent achieved 85.0% critical-case recall and 65.7% overall triage accuracy, versus at most 14.7% and 43.4% for matched single-model and retrieval-only baselines, with safety checks executed on every continuation pathway and adaptive routing invoking only the modules each case required. These results support multi-agent orchestration as a promising design pattern for transparent and auditable AI in healthcare. These gains are internal system properties; clinical-safety benefit remains to be established through prospective, clinician-involved validation. Full article

Feature selection represents a critical step in developing accurate and interpretable models for early breast cancer detection. Despite extensive research in the field of mammographic image analysis, no consensus has yet been reached on the optimal feature subsets that distinguish normal from malignant tissues. To address this gap, the present study aims to identify the most discriminative and significant features through a comprehensive multi-criterion selection framework. The aim is to integrate, as new frameworks, different combinations of t-test, ANOVA, Mutual Information (MI), and Equal Grouping Methods (EGM) to rank 19 linear and nonlinear features extracted from mammographic images. The objective is to maximize feature relevance while minimizing redundancy and enhancing diagnostic and healthcare systems. Linear features were assessed alongside nonlinear descriptors. A framework combining t-test, ANOVA, and EGM, guided by MI relevance, was employed to balance feature contributions across categories. The experimental results demonstrated that hybrid feature selection significantly enhanced diagnostic accuracy using optimal linear and nonlinear attributes. The optimization results suggested using a hybrid of six linear and eight nonlinear features. Linear features were highly accurate for detecting cancer. Haralick entropy obtained the highest average accuracy and performance, 94.14% and 93.45%; followed by kurtosis, 93.49% and 92.59%; perimeter irregularity, 93.43% and 92.65%; skewness, 93.01% and 92.25%; and volume/area, 92.82% and 91.92%. Despite the reliable discriminative power of linear descriptors, their overall effectiveness in representing intricate tissue characteristics was limited. The comparison of statistical characteristics shows a distinct performance benefit of nonlinear descriptors over linear ones for detecting breast cancer. Nonlinear descriptors, however, showcased higher accuracy and performance, with an average accuracy of 97.81% in contrast to 94.43% for linear approaches. Local phase congruency achieved the top average accuracy and performance, 97.81% and 96.61%, respectively; succeeded by wavelet entropy, 97.62% and 96.42%; Laplacian spectrum features, 97.52% and 96.32%; nonlinear diffusion, 97.10% and 95.90%; and clustering coefficient, 96.70% and 95.50%; then Shannon, Tsallis, and Rényi entropies. The results indicate that statistically validated nonlinear characteristics significantly outperform linear ones across accuracy and performance measures. Their combination significantly improves the strength and discriminative power of computer-assisted breast cancer diagnostic systems, affirming their suitability for integration into sophisticated machine learning and deep learning models. The results also show that the new multi-criterion framework’s early detection performance surpassed that of the statistical and deep learning models explored, with an average of 98.6% accuracy, 98% sensitivity, 98.9% precision, and 98.4% F1 score of early detection of breast cancer. The incorporation of statistically validated nonlinear descriptors, particularly local phase congruency and wavelet entropy, improves the discriminative ability, robustness, and clinical understanding of breast cancer computer-assisted diagnostic systems. Overall, the proposed framework confirms that integrating hybrid features substantially enhances robustness and plays a pivotal role in computer-assisted breast cancer detection. These selected features may be fed to more advanced algorithms in the future, potentially yielding improved performance. Full article

Background: Late-stage presentation of oral cancer remains a major challenge in low- and middle-income countries and contributes substantially to poor clinical outcomes. Data describing oral cancer presentation patterns in Indonesia remain limited. This study aimed to characterize the clinicopathological profile, stage distribution, treatment patterns, and exposure-related characteristics of oral cancer patients treated at a national referral center in Indonesia. Methods: A retrospective study was conducted using medical records of 404 patients with histopathologically confirmed oral malignancies treated between 2021 and 2025. Descriptive analyses were performed to summarize demographic, clinicopathological, staging, treatment-related, and exposure-related characteristics. Results: The mean age at diagnosis was 49.17 ± 14.11 years, with a relatively balanced sex distribution. The tongue was the most common primary tumor site (76.0%), and oral squamous cell carcinoma (OSCC) represented the predominant histopathological diagnosis (81.9%). Late-stage presentation (stage III–IV) was observed in 64.1% of all cases and increased to 70.7% among patients with available staging information, while 29.2% of patients had incomplete or undefined staging data. Surgical treatment, either alone or combined with adjuvant therapies, was the most frequently employed treatment modality. Notably, 21.5% of patients had no documented definitive oncologic treatment during the recorded treatment period. Smoking was reported by 35.4% of patients, alcohol consumption by 4.0%, and a family history of cancer by 24.8%. Conclusions: Advanced-stage oral cancer was highly prevalent in this referral-based cohort. The substantial burden of late-stage disease, together with incomplete staging information and the proportion of patients without documented definitive treatment, highlights challenges related to staging completeness, treatment documentation, and cancer care monitoring. These findings support efforts to strengthen early detection, referral coordination, and cancer care monitoring within the Indonesian healthcare system. Full article

Background: Culturally and Racially Marginalised (CaRM) communities in Australia encounter subtle and covert forms of prejudice, commonly referred to as “new racism”. Within healthcare settings, these experiences can shape trust, engagement, and patterns of help-seeking. Mental health nurses are often the first point of contact in care delivery, and their ability to recognise, respond to, and mitigate the impacts of new racism is critical for fostering therapeutic relationships and supporting equitable access. Understanding how CaRM communities perceive the conditions that influence their mental health service use is fundamental for informing more equitable and culturally responsive care. Objective: This study explored the viewpoints of CaRM community members regarding the factors they consider important for addressing new racism in healthcare systems and supporting engagement with mental health services. Design: Q methodology was used to identify statistically derived viewpoints that reflect shared viewpoints about the conditions perceived as critical for addressing the impacts of new racism on mental health service use. Setting: Participants were recruited from culturally and linguistically diverse communities across Australia through community settings, social media, and professional networks. Participants: Thirty-five individuals from CaRM backgrounds completed the Q-sort. Methods: This Q methodology consisted of five steps: (1) set up of the Q-sorting instrument, (2) selection of participants, (3) data collection, (4) factor analysis, and (5) factor interpretation. Results: Three distinct viewpoints were identified: (1) raising awareness of mental health issues within CaRM communities (community-focused), (2) providing visible anti-racism and culturally safe services (service-focused), and (3) recognising and formally addressing new racism within healthcare systems (policy-focused). Conclusions: This study offers the first empirically derived, community-informed set of viewpoints on addressing new racism in Australian mental healthcare. While exploratory, the findings highlight multi-level considerations that are potentially relevant to mental health nursing practice, and may be useful to inform future research, policy development, and service redesign aimed at strengthening cultural responsiveness and equity in mental health systems. Full article

Healthcare-associated infections (HAIs) in long-term care facilities (LTCFs) are a public health problem worldwide. The aim of this narrative review is to summarize and to provide a comprehensive overview of the current surveillance methods used in LTCFs. We conducted a search of the PubMed database starting from 2000 onward. We found 324 articles that were systematically evaluated by the titles, abstracts, and full texts, and we selected 42 records. These articles were dispersed across North America, Europe, Australia, and Asia. We identified articles describing the results from active or passive surveillance and from a point prevalence study (PPS). The most frequently used method was PPSs in 30 studies, followed by 12 articles reporting active surveillance results and 3 articles describing active surveillance findings. The case definitions applied in 16 articles were those provided by the Centers for Disease Control and Prevention, followed by the McGeer and European Centre for Disease Prevention and Control definitions in 15 and 13, respectively. Twenty-six studies described the method used to calculate the HAI indicator (incidence, incidence density, or prevalence). The articles on PPSs reported prevalence per 100 patients (22 articles), whereas those on active and passive surveillance reported the incidence density per 1000 patient-days. PPSs effectively identified HAIs in LTCFs through standardized data collection and essential interpretive information. The variability in surveillance methods, case definitions, HAI types, and indicator calculation approaches highlights the need for standardized surveillance protocols and methodologies in LTCFs worldwide. Furthermore, these findings emphasize the importance of implementing appropriate surveillance systems and targeted public health measures to effectively address the specific needs and vulnerabilities of the LTCF population in our country. Full article

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

Background/Objectives: Digital technologies and data-driven approaches are rapidly transforming healthcare practice and enabling more personalized and preventive care. As personal health data becomes increasingly embedded in healthcare systems, understanding how future healthcare professionals interpret these developments is essential for shaping responsive health education. This study aims to identify the factors that students in health-related programs consider important regarding the increasing use of personal health data in healthcare. Methods: An exploratory qualitative focus group study was conducted between March 2024 and July 2025 across five higher education institutions in Australia, Canada, and the Netherlands. Seven focus groups were conducted with forty students from health-related programs, including nursing, public health, occupational therapy, and social work. Participants discussed the use of personal health data in healthcare and reflected on short fictional future scenarios designed to stimulate discussion about possible developments in data-driven healthcare. Data were analyzed using reflexive thematic analysis using ATLAS.ti. Results: Three overarching domains were identified: (1) personalization and prevention, (2) data quality and ethical considerations, and (3) organizational implications and conditions. Students described personal health data as a powerful tool for personalization, prevention, and informed decision-making. At the same time, they raised concerns about data reliability, overreliance on automated systems, patient anxiety, potential dehumanization of care, privacy risks, and emerging inequalities related to access to and representation within data systems. Overall, students appeared neither purely techno-optimistic nor technophobic, but articulated nuanced ethical, cultural, and professional tensions surrounding data-driven care. Conclusions: Preparing future healthcare professionals for data-driven healthcare requires integrating critical data literacy, ethical reflection, interdisciplinary collaboration and opportunities to critically engage with the societal and professional implications of data-driven technologies into health professional education, while ensuring that organizational conditions support the responsible use of personal health data. 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

Deaf people experience significant barriers to education, healthcare, employment, and information access, resulting in inequities across a myriad of contexts. To better understand these disparities, our all-deaf research team conducted semi-structured interviews with deaf and hearing (parents, caregivers, and educators) adults across Ecuador, exploring how structural, institutional, and social factors influence daily life and well-being. Participants (n = 36) described systemic exclusion from education and employment, limited access to interpreters and assistive technologies, and constrained autonomy due to insufficient family support and institutional resources. These barriers compound health risks by restricting access to care, information, and social participation. Participants’ narratives highlighted how political and economic instability, institutional neglect, and discrimination create structural vulnerabilities that extend beyond individual-level factors. Findings underscore the importance of public health interventions that address structural and communicative inequities, including inclusive education, accessible health services, and community-based support, to improve health equity and quality of life for deaf populations in Ecuador. Full article

Background: Pregnancies tend to progress without any serious complications. Nonetheless, for a subset of women, obstetric complications may develop, ranging in severity. The most critical of these life-threatening events is referred to as Maternal Near Miss (MNM). To identify the epidemiological and obstetric characteristics, as well as clinical outcomes of MNM cases reported over the year 2021 in the Brazilian state of Paraná. This quantitative, population-based study analyzed 888 notifications that occurred in 2021, obtained from the MNM Notification System. Descriptive statistics and a one-sample Chi-square goodness-of-fit test were applied to the data. Among the women reported, 92.6% were pregnant, the mean age was 29 years, 67.1% identified as white, and 45.2% had preexisting health conditions. Regarding obstetric characteristics, 41.6% were classified as high-risk pregnancies, but nearly one-third (32.3%) of MNM cases occurred in women initially classified as usual risk. The clinical worsening event occurred most frequently during the third trimester (71.9%), and emergency cesarean section was indicated in 60.1% of cases with complete information for this variable. Cesarean delivery predominated over vaginal delivery, with an emergency-to-elective cesarean ratio of approximately 4.7:1. Most women (75%) were discharged after recovery. MNM events are not restricted to women initially classified as high-risk, underscoring the need to strengthen early detection strategies and ensure appropriate management at all levels of care. Improved training of healthcare professionals responsible for reporting and the standardization of MNM monitoring systems in Brazil are also essential. Full article

Background/Objectives: Disease-related malnutrition affects millions of patients worldwide. Nutrition support therapy (NST), namely oral nutritional supplements (ONSs), serve as a cornerstone therapeutic intervention. However, treatment effectiveness depends not only on an appropriate prescription but also on patient acceptance and adherence. This study evaluates the provision pathway of ONSs within a co-payment healthcare system, focusing on patient acceptance patterns, barriers to adherence, and the critical yet underexplored role of pharmacist–patient interactions in determining treatment outcomes. Methods: A cross-sectional observational study was conducted across 100 Croatian community pharmacies during September–October 2025. Pharmacists prospectively documented 973 patient encounters involving ONS prescriptions requiring co-payment using real-time patient record forms. Data captured patient demographics, diagnoses, prescription patterns, prior knowledge of co-payment requirements, acceptance responses, and pharmacist-assessed reasons for refusal. Results: While 65% of all patients knew about co-payment requirements in advance, 51% of first-time users arrived uninformed, leading to dramatically different acceptance patterns (93% immediate acceptance when informed versus 33% when uninformed, p < 0.05). Overall, 8–12% of patients refused or reduced prescribed ONSs. Among refusals, 59% cited the financial burden, but, critically, 23% appeared not to understand why an ONS was prescribed or what benefits to expect, revealing significant communication gaps in the care pathway. Overall, fifteen percent of patients required an explanation from the pharmacist before accepting their prescription, demonstrating pharmacists’ decisive role as gatekeepers of nutritional therapy. Conclusions: These findings suggest that the pharmacy dispensing encounter is an important decision point in the ONS care pathway, where insufficient preparation and coordination may be associated with suboptimal treatment outcomes among vulnerable patient populations. Improved prescriber–patient communication about co-payment and clinical rationale, pharmacist education in disease-specific nutrition and ONS counseling, and structured communication protocols between prescribers and pharmacists represent areas that may warrant further attention and evaluation. Full article