Search Results (953)

Hospitals are the most energy-intensive buildings in the tertiary sector because they have continuous and high demand for heating and cooling (to meet strict thermal comfort conditions), hot water, kitchen facilities, electricity, etc. Investigation of the energy performance of hospital buildings is crucial for defining energy savings and developing benchmarks and design guidelines for nearly Zero-Energy Hospitals (nZenHs). This study investigates the energy efficiency of hospital buildings in Greece and the necessary retrofit strategies to transform them to nearly Zero-Energy Buildings (nZEBs). Six building typologies were recognized, based on the building’s floor plan, and energy upgrade scenarios were investigated for each typology. The first scenarios aimed at improving the building’s energy efficiency, and the last one exploited the use of renewable energy source (RES) systems to minimize energy consumption. More specifically, a rooftop photovoltaic system was examined. The results showed differences in hospitals’ energy performance according to typology and climatic zone. They strongly confirm that hospitals can be transformed into buildings with nearly zero-energy consumption, irrespective of their design. The significant energy savings achieved by transforming hospitals into NZEBs highlight the crucial role in enhancing energy efficiency in tertiary sector buildings. Full article

The Church of San Juan del Hospital in Valencia (Spain) is a Gothic church whose main architectural feature—the western façade—remained unresolved, posing structural and compositional challenges. The intervention addressed this issue while preserving the historical integrity of the building and its heritage context. A systematic methodology was applied, following principles of reversibility, sustainability, and compatibility with medieval ribbed-vault construction. The project resolved five key aspects: completion of the nave’s façade, coverage of the former atrium remains, access from the north courtyard, compositional coherence of the west courtyard front, and integration of the church and museum entrances. Contemporary materials and techniques, including aluminum, recycled wood, and handmade ceramic brick, were selected to harmonize with historic stonework, ensure durability, and minimize environmental impact. Design strategies guided visual perception, emphasizing the lower façade and resolving dispersive compositional elements, while creating functional spaces for ventilation, climate control, and circulation. This intervention demonstrates how a methodical, heritage-sensitive approach can solve complex architectural problems, combining innovation with historical authenticity, and enhancing both the functionality and aesthetic experience of the Church of San Juan del Hospital. Full article

Survey campaigns in non-residential buildings show that occupants are often dissatisfied with the indoor environmental quality (IEQ), including the indoor air quality (IAQ) conditions. Occupant-centric controls (OCCs) have the potential to improve occupants’ satisfaction with IAQ and thermal comfort. Currently, applications of OCC systems with IAQ perceptions are limited due to a lack of a suitable modelling approach to predict occupants’ subjective IAQ assessment. In addition, a comprehensive overview of possible confounding variables for subjective IAQ in non-residential buildings is missing. This paper presents a systematic review of 46 papers on subjective IAQ assessments during field investigations in non-residential buildings. The following characteristics of the studies are examined: (1) the study context, (2) study and survey type, (3) dataset and sample size, (4) subjective IAQ assessment scales, (5) analysis and modelling techniques, and (6) associated variables. The review identified 46 different assessment scales and 20 different analysis techniques, respectively, indicating a lack of uniformity across the studies. The vast majority of studies were conducted in classrooms or offices. Other non-residential buildings, such as hospitals and sports halls, were underrepresented. Moreover, most of the studies failed to elaborate on the choice of a statistical technique and to report on the required sample size, compromising the validity of the statistical results. Furthermore, the review highlighted the limited scope of the subjective IAQ assessment analysis, with half of the reviewed studies investigating no more than four different variables. Lastly, only three of the reviewed papers focused on determining an accurate predictive model for subjective IAQ assessment. Full article

We introduce the Onco-Hem Connectome (OHC), a patient similarity network (PSN) designed to organize real-world hemato-oncology inpatients by exploratory phenotypes with potential clinical utility. Background: Polypharmacy and drug–drug interactions (DDIs) are pervasive in hemato-oncology and vary with comorbidity and treatment intensity. Methods: We retrospectively analyzed a 2023 single-center cohort of 298 patients (1158 hospital episodes). Standardized feature vectors combined demographics, comorbidity (Charlson, Elixhauser), comorbidity polypharmacy score (CPS), aggregate DDI severity score (ADSS), diagnoses, and drug exposures. Cosine similarity defined edges (threshold ≥ 0.6) to build an undirected PSN; communities were detected with modularity-based clustering and profiled by drugs, diagnosis codes, and canonical chemotherapy regimens. Results: The OHC comprised 295 nodes and 4179 edges (density 0.096, modularity Q = 0.433), yielding five communities. Communities differed in comorbidity burden (Kruskal–Wallis ${\epsilon }^2$: Charlson 0.428, Elixhauser 0.650, age 0.125, all FDR-adjusted p < 0.001) but not in utilization (LOS, episodes) after FDR (${\epsilon }^2$ ≈ 0.006–0.010). Drug enrichment (e.g., enoxaparin $\Delta$ = +0.13 in Community 2; vinblastine $\Delta$ = +0.09 in Community 3) and principal diagnoses (e.g., C90.0 23%, C91.1 15%, C83.3 15% in Community 1) supported distinct clinical phenotypes. Robustness analyses showed block-equalized features preserved communities (ARI 0.946; NMI 0.941). Community drug signatures and regimen signals aligned with diagnosis patterns, reflecting the integration of resource-use variables in the feature design. Conclusions: The Onco-Hem Connectome yields interpretable, phenotype-level insights that can inform supportive care bundles, DDI-aware prescribing, and stewardship, and it provides a foundation for phenotype-specific risk models (e.g., prolonged stay, infection, high-DDI episodes) in hemato-oncology. Full article

We develop a unified likelihood-based framework for limited outcomes built on the two-piece normal family. The framework includes a censored specification that accommodates boundary inflation, a doubly truncated specification on $(0,1)$ for rates and proportions, and a survival formulation with a log-two-piece normal baseline and Gamma frailty to account for unobserved heterogeneity. We derive closed-form building blocks (pdf, cdf, survival, hazard, and cumulative hazard), full log-likelihoods with score functions and observed information, and stable reparameterizations that enable routine optimization. Monte Carlo experiments show a small bias and declining RMSE with increasing sample size; censoring primarily inflates the variability of regression coefficients; the scale parameter remains comparatively stable, and the shape parameter is most sensitive under heavy censoring. Applications to HIV-1 RNA with a detection limit, household food expenditure on $(0,1)$, labor-supply hours with a corner solution, and childhood cancer times to hospitalization demonstrate improved fit over Gaussian, skew-normal, and beta benchmarks according to AIC/BIC/CAIC and goodness-of-fit diagnostics, with model-implied censoring closely matching the observed fraction. The proposed formulations are tractable, flexible, and readily implementable with standard software. Full article

Hospitals are among the most energy-intensive buildings, yet their heating systems often operate below optimal efficiency due to outdated controls and limited sensing. Existing facilities often provide only a few accessible measurement points, many of which are locked within proprietary master controllers and not integrated into the Building Automation System (BAS). To address these limitations, this study proposes a data-driven feature selection approach that supports the development of gray-box emulators for complex, real-world central heating plants. A year of operational and weather data from a large hospital was used to train multiple machine learning models to predict the heating demand and return water temperature of a hospital heating plant system. The model’s performance was evaluated under reduced-sensor conditions by intentionally removing unpredictable values such as the VFD speed and flow rate. XGBoost achieved the highest accuracy with full sensor data and maintained a strong performance when critical sensors were omitted. An explainability analysis using Shapley Additive Explanations (SHAP) is applied to interpret the models, revealing that outdoor temperature and time of day (as an occupancy proxy) are the dominant predictors of boiler load. The results demonstrate that, even under sparse instrumentation, an AI-driven digital twin of the heating plant can reliably capture system dynamics. Full article

This article presents a case study from a Social and Health Care School in Denmark, where a rooftop garden was designed to promote student health and support nature-based teaching across subject areas. A novel aspect of the project is the formal integration of the garden into teaching, implying that its long-term impact may extend beyond the students to the end-users they will later encounter in nursing homes and hospitals nationwide. This study applies the Evidence-Based Health Design in Landscape Architecture (EBHDL) process model, encompassing evidence collection, programming, and concept design, with the University of Copenhagen acting in a consultancy role. A co-design process with students and teachers was included as a novel source of case-specific evidence. Methodologically, this is a participatory practice-based case study focusing on the full design and construction processes, combining continuous documentation with reflective analysis of ‘process insights,’ generating lessons learned from the application of the EBHDL process model. This study identifies two categories of lessons learned. First, general insights emerged concerning governance, stakeholder roles, and the critical importance of site selection, procurement, and continuity of design responsibility. Second, specific insights were gained regarding the application of the EBHDL model, including its alignment with Danish and international standardised construction phases. These insights are particularly relevant for project managers in nature-based initiatives. The results also show how the EBHDL model aligns with Danish and international standardised construction phases, offering a bridge between health design methods and established building practice. The case focuses on the EBHDL process rather than verified outcomes and demonstrates how evidence-based and participatory approaches can help structure complex design processes, facilitate stakeholder engagement, and support decision-making in institutional projects. Full article

Background/Objectives: Healthcare workers at the frontline of managing pandemics are at increased risk for adverse physical and mental health outcomes, which has been shown to result in burnout. The relationship between personal resilience and burnout among clinical and non-clinical healthcare staff working in an acute care setting was assessed at the start of the COVID-19 pandemic. Methods: A prospective cross-sectional survey design with electronic questionnaires was used to measure resilience (Connor-Davidson Resilience Scale,) and burnout (Maslach Burnout Inventory—Human Services Survey). Linear regression analyses were conducted to examine the relationship between resilience and emotional exhaustion, depersonalization, and personal accomplishment. Results: A significant inverse relationship between resilience and both emotional exhaustion and depersonalization, and a positive relationship between resilience and personal accomplishment were identified. Higher resilience scores were significantly associated with lower emotional exhaustion and depersonalization and higher personal accomplishment under pandemic conditions. Conclusions: Strategies to boost resilience organization-wide amongst healthcare staff providing patient care are critical for providing skills to reduce the onset of burnout and support employee mental health. From a pandemic preparedness lens, organizational-level emergency management should consider the importance of resilience-building among staff to proactively prevent burnout and its subsequent effects on patient-care and general hospital functioning. Full article

Background: Patient-centered care (PCC) is a cornerstone of quality, yet its translation into managerial decision-making remains underexplored. Middle nurse managers (MNMs) play a pivotal role in enabling patient-centeredness, but their perspectives on PCC decisions are rarely investigated. Aim: This study explored MNMs’ perceptions of what constitutes a patient-centered decision in hospital settings and identified the essential dimensions underpinning such decisions. Methods: A qualitative descriptive design was adopted using semi-structured interviews. Thirty-eight MNMs from three hospitals in central Italy were included. Data were analyzed using Elo and Kyngäs’ content analysis approach. Results: Two overarching themes emerged as central to patient-centered managerial decision-making (PCMDM): “Meaning and definition of PCMDM,” and “Influencing dimensions of PCMDM”. MNMs described PCMDM as an evolving and adaptable process shaped by patient needs and organizational constraints and unfolding across distinct phases. Key influencing dimensions included the manager’s role, organizational environment, human resource management and knowledge of the patient. Conclusions: PCMDM is a continuous, ethical, and reflective process mediated by MNMs, who reconcile institutional priorities, team dynamics, and patient needs to create conditions for high-quality PCC. Implications for Practice: Strengthening PCMDM requires coordinated action aimed at equipping nurse managers with advanced leadership capabilities, building organizational structures that sustain patient-centered decisions, and empowering patients to actively co-shape the care process. Full article

Millions of healthcare workers experience percutaneous exposure to bloodborne communicable infectious disease pathogens annually, with the risk of contracting occupationally acquired infections. In this study, we aimed to assess the status of occupational safety and outbreak preparedness in Congolese nurses and laboratory technicians in Kongo central and the Katanga area, amidst multiple ongoing public health emergencies in the Democratic Republic of the Congo (DRC). This was a multicenter analytical cross-sectional study conducted in five referral hospitals located in Kongo central province and the Katanga area between 2019 and 2020 amidst Ebola, Yellow fever, Cholera and Chikungunya outbreaks. Participants were adult A0 grade nurses, A1 nurses, A2 nurses and medical laboratory technicians (N = 493). They answered a structured, self-administered questionnaire related to hospital hygiene and standard precautions for occupational infection prevention. The majority of the respondents were females (53.6%), and 30.1% of them have never participated in a training session on hospital infection prevention during their career. The proportions of those who have been immunized against hepatitis B virus (HBV) was markedly low, at 16.5%. Of the respondents, 75.3% have been using safety-engineered medical devices (SEDs), whereas 93.5% consistently disinfected medical devices after use. Moreover, 78% of the respondents used gloves during medical procedures and 92.2% wore masks consistently. A large majority of the respondents, 82.9%, have been recapping the needles after use. Regarding participation in outbreak response, 24.5% and 12.2% of the respondents were Chikungunya and Cholera epidemic responders, respectively; 1.8% have served in Ebola outbreak sites. The proportion of the respondents who sustained at least one percutaneous injury by needlestick or sharp device, blood/body fluid splash or both in the previous 12-month period was high, 89.3% (41.8% for injury, 59.2% for BBF event), and most of them (73%) reported over 11 events. Compared to laboratory technicians, nurses had higher odds for sustaining percutaneous injury and BBF events [OR = 1.38 (0.16); p < 0.01], whereas respondents with longer working experience were less likely to sustain those events [OR = 0.47 (0.11); p < 0.001]. Findings from this study suggest that Congolese nurses and laboratory technicians experience a high frequency of injury and BBF events at work, and remain at high risk for occupationally acquired infection. There is a need for periodic capacity-building training for the healthcare workforce to improve infection prevention in health settings, the provision of sufficient and appropriate PPE and SEDs, post-exposure follow-up and keeping records of occupational injuries in hospitals in Congolese healthcare settings. Full article

Location estimation is significant in this era of the Internet of Things (IoT). Satellite and cellular signals are often blocked indoors, prompting researchers to explore alternative wireless technologies for indoor positioning. Among these, Wi-Fi Received Signal Strength (RSS) with fingerprinting is dominant in large, multi-floor buildings due to its existing infrastructure, acceptable accuracy, low cost, easy deployment, and scalability. This study aims to systematically search and review the literature on the use of real Wi-Fi RSS fingerprints for indoor localization or positioning in large, multi-floor buildings, in accordance with PRISMA guidelines, to identify current trends, performance, and gaps. Our findings highlight three main public datasets in this fields (covering areas over 10,000 sq.m). Recent trends indicate the widespread adoption of Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNNs) and Stacked Autoencoders (SAEs). While buildings (in the same vicinity) and their respective floors are accurately identified, the maximum average error remains around 7 m. A notable gap is the lack of public datasets with detailed room or zone information. This review intends to serve as a guide for future researchers looking to improve indoor location estimation in large, multi-floor structures such as universities, hospitals, and malls. Full article

Background/Objectives: It is critical to monitor real-world COVID-19 vaccine effectiveness (VE) in older adults, as they have been identified as a priority group for vaccination. This is the first study that aims to estimate national absolute vaccine effectiveness (aVE) against severe COVID-19 outcomes among Canadian older adults aged ≥50 years. Methods: The screening method (SM) was implemented using standard and spline-based logistic regression models to estimate aVE and 95% confidence intervals (CIs) by outcome, age group, vaccination status, time since last dose, vaccine schedules, and variant of concern (VOC) period. Results: From 1 August 2021 to 30 November 2023, there were 103,822 severe COVID-19 cases, of which 72.9% were hospitalized, 8.2% were admitted to ICU, and 18.9% had died. A total of 23.1% of these cases were unvaccinated against COVID-19, 21.9% completed a primary series only, and 55.0% received at least one additional/booster dose. National aVE against severe COVID-19 outcomes remained moderate to high during Delta and original Omicron VOC predominance periods. Monthly age-specific aVE of at least two additional/booster doses remained stable during recombinant XBB.1.5/EG.5 VOC predominance, ranging from 61.0% (95% CI: 51.9–68.4%) to 69.8% (95% CI: 67.5–72.0%) against hospitalization, and 71.0% (95% CI: 62.8–77.4%) to 77.2% (95% CI: 74.2–79.9%) against ICU admission/death. Adjusted aVE was higher for last booster doses received within the past six months and with heterologous mRNA vaccine schedules. Conclusions: The SM is a useful method to estimate aVE in near real-time, enabling the assessment of temporal changes in aVE, guiding vaccine policy, and building vaccine confidence among populations at higher risk of severe outcomes. Full article

Background: Fire emergency management in healthcare facilities represents a complex challenge, particularly in historic buildings subject to architectural preservation constraints, where progressive horizontal evacuation is objectively difficult. This study analyzes the effectiveness of an evacuation sheet employed by Hospital Policlinico San Martino to improve the speed of evacuating non-self-sufficient patients in these buildings. Methods: This study involved evacuation simulations in wards previously selected based on structural characteristics. Healthcare personnel (male and female, aged between 30 and 55 years) conducted both horizontal and vertical patient evacuation drills, comparing the performance of the S-CAPEPOD^® Evacuation Sheet (Standard Model) with the conventional method (hospital bed plus and rescue sheet). This study focused on the night shift to evaluate the most critical scenario in terms of human resources. Results: The use of the evacuation sheet proved more efficient than the conventional method throughout the entire evacuation route, especially during the first 15 min of the emergency (the most critical period). Indeed, with an equal number of available personnel, the evacuation sheet enabled an average improvement of 50% in the number of patients evacuated. Conclusions: The data support the effectiveness of the device, confirming the theoretical premise that the introduction of the evacuation sheet—also due to its ease of use—can be an improvement measure for the evacuation performance of non-self-sufficient patients, despite limitations related to structural variability and the simulated nature of the trials. Full article

Stroke is one of the leading causes of death and long-term disability worldwide, and effective prevention depends on fast, reliable, and privacy-preserving risk assessment. This study proposes a federated IoT-enabled framework that combines feature-optimized machine learning (ML) with real-time patient monitoring to predict and detect brain stroke risk. The system operates in two stages: (i) a stroke prediction module that builds an ML model for risk assessment and (ii) an IoT-based framework that continuously monitors patients and triggers timely alerts. The ML pipeline starts from a clinical–physiological dataset containing 17 initial attributes and applies a feature optimization strategy based on feature importance, selection, and reduction to identify the most informative predictors of stroke. To support multi-center deployment while protecting patient confidentiality, the ML pipeline is embedded within a standard Federated Averaging (FedAvg) architecture, where multiple home or hospital IoT gateways collaboratively train a shared global model without exchanging raw patient data. In each communication round, clients perform local training and the server aggregates client model parameters to update the global model. The resulting federated global model matches the performance of the centralized baseline, achieving 99.44% test accuracy while preserving data locality. Integrated with IoT devices, the system can detect pre-stroke syndromes in real time and automatically notify family members or emergency medical services, making it suitable for both home and hospital environments and offering a practical path toward early intervention and improved stroke outcomes. Full article

Emergency medicine clinicians face disproportionately high levels of burnout and mental health strain compared to other specialties. This study examined whether perceived resilience predicted reduced mental health strain following the COVID-19 Omicron surge and whether meaningful work mediated this relationship. Participants were 197 emergency medicine professionals at a large hospital system who completed monthly surveys during the pandemic. Perceived resilience and meaningful work were measured pre-Omicron surge, and mental health strain was measured post-surge. Results showed that higher perceived resilience significantly predicted lower mental health strain and that meaningful work explained 40% of this relationship. The findings emphasize that resilience matters and that its benefits are at least partly a function of meaningful work. Broader implications extend to organizations seeking to strengthen workforce well-being. Interventions should integrate resilience-building with practices that enhance purpose and meaning at work. Full article