Search Results (192)

Background: Remote patient monitoring (RPM) for chronic heart failure (CHF) management has demonstrated clinical and economic benefits. However, data on its organisational impact remain limited, particularly in the French healthcare context following the integration of RPM into standard care pathways. The objective of this study was to describe the organisational impact of RPM for CHF management from the perspective of healthcare professionals across diverse practice settings in France. Methods: A cross-sectional survey was conducted among 28 French cardiology departments and community practices (14 CHG, 9 CHU, 4 community practices, 1 private clinic) actively using RPM for CHF management. The questionnaire assessed organisational changes across five domains: care process modifications, human resources allocation, professional training and task delegation, coordination with community-based physicians, and perceived impact on patient quality of life and working conditions. Results: Nearly all structures (96.4%) had established a dedicated RPM team (mean size: 4.8 ± 2.3 professionals). A reduction in time between cardiac decompensation and medical care was reported by 96.4% of respondents. Three-quarters (75.0%) had implemented specific consultations for emergency alerts, and 57.1% had established direct admission protocols bypassing emergency departments. Task delegation was widespread (89.3%), primarily involving alert monitoring (100%), patient inclusion (92.0%), and patient contact (84.0%). However, only 53.6% of professionals had received specific training. The perceived impact on patient quality of life was unanimously positive (64.3% very positive, 35.7% positive). Working conditions were perceived as stable by 64.3% and improved by 32.1%. The main advantages cited were reduction in hospitalisations (89.3%) and improved patient communication (85.7%). The primary area for improvement was interprofessional coordination (53.6%). Conclusions: RPM implementation has driven substantial organisational restructuring across French cardiology departments and community practices. While dedicated teams and task delegation are now standard, challenges remain in professional training, coordination with community physicians, and dedicated time allocation. These findings provide an updated organisational assessment that complements prior clinical and economic evaluations. Full article

Municipal social services remain among the least examined public functions in the local-government efficiency literature, owing to the scarcity of structured administrative data and the difficulty of measuring their outputs. Addressing that gap, this study provides the first Data Envelopment Analysis (DEA)-based efficiency assessment of municipal primary social-care departments in the Valencian Community (Spain), drawing on a purpose-built dataset that combines 2023 administrative survey, budgetary, and socio-demographic information. Its methodological contribution is an integrated evaluation architecture articulating well-established components—input-oriented DEA under variable and constant returns to scale, the Banker–Morey treatment of non-discretionary contextual variables across an exhaustive lattice of specifications, and a super-efficiency ranking—into a single design that disentangles managerial, scale, and environmental sources of inefficiency. Applied to 18 municipalities, the analysis detects substantial technical inefficiency but shows that much of it reflects scale constraints and structurally demanding socio-demographic environments rather than managerial underperformance; several municipalities with high social vulnerability and below-average income attain full efficiency once context is incorporated, while a small set of units emerges as consistent benchmarks. The architecture yields objective evidence for inter-municipal benchmarking, offers a replicable template for data-poor public services, and cautions against context-blind efficiency metrics in allocating social-policy resources. Full article

Truck–drone collaborative delivery (TDCD) refers to a coordinated logistics paradigm in which drones are deployed from delivery trucks to serve nearby customers, enabling parallelized last-mile operations. Much of the existing TDCD literature relies on synthetic datasets and manufacturer-declared drone specifications, which may overestimate performance in real-world operations. This study develops an empirically informed, route-based Mixed-Integer Linear Programming (MILP) framework that integrates empirically derived drone performance models with constrained fleet allocation decisions. Using delivery routes from the Amazon Last Mile Routing Dataset (2021), we consider three electric trucks departing from a common depot, each equipped with drones drawn from a shared fleet of 10 units. Drone flight time and energy consumption are modeled using regression functions calibrated with real flight test data from a DJI Matrice 100 platform, capturing observed variations due to payload and operational conditions. The optimization jointly determines truck stop selection, customer assignments, and drone allocation while minimizing a weighted combination of route makespan, total energy consumption, and fleet size under operational and energy constraints. The results indicate that coordinated truck–drone delivery can achieve substantial reductions in both delivery completion time and energy consumption relative to conventional truck-only delivery. These findings demonstrate the effectiveness of coordinated truck–drone operations under realistic constraints and highlight the importance of data-driven modeling and fleet-level resource allocation in improving last-mile delivery performance. Full article

Background: Over the years, it has become increasingly clear that neurological conditions, such as multiple sclerosis (MS), commonly exhibit other health problems. Therefore, this is the first study aimed at investigating the prevalence of and factors associated with three binary outcomes: depression, anxiety, and an overactive bladder (OAB) among MS patients in the Qassim region, Saudi Arabia. Methods: This cross-sectional study was conducted in the neurological department of King Fahad Specialist Hospital in the Qassim region, Saudi Arabia, from January to December 2024. Data on age, sex, marital status, occupation, body mass index (BMI), MS duration, comorbidities, anxiety, depression, and OAB symptoms (frequency, nocturia, urgency, and urge incontinence) were obtained. Results: Of the 262 MS patients in this study, 184 (70.2%) were females, and 78 (29.8%) were males. The median values [IQR] of age and MS duration were 34 [26–40] and 5 [2–9] years, respectively. The prevalence of depression, anxiety, and OAB were 53.4%, 43.9%, and 50%, respectively. Nocturia was the most frequent urinary symptom, and urge incontinence was significantly higher among females. Multiple logistic regression analyses were conducted to assess factors associated with three binary outcomes: depression, anxiety, and OAB. For depression, being single and anxiety were associated with increased risk. Regarding anxiety, being a student was related to decreased risk, while being female and having depression were associated with increased risk. For OAB, only anxiety was associated with increased risk. Conclusions: Approximately one in two MS patients experience either depression or OAB, while anxiety was reported by fewer than half of the patients. This high prevalence of the three outcomes has critical implications for healthcare policy and resource allocation. Thus, screening, early diagnosis, and intervention, as well as integrated care, should be prioritized by healthcare institutions and practitioners to address these conditions and improve MS patients’ quality of life. Full article

Background/Objectives: Complex chronic patients represent a heterogeneous and high-risk population, for whom accurate prognostic tools are essential to guide clinical decision-making, optimize resource allocation, and support tailored interventions. The PROFUND index was developed for mortality prediction in polypathological patients, but its performance has not yet been evaluated in an ambulatory integrated care model. Methods: A retrospective observational study was conducted using two cohorts. Cohort H included complex chronic patients admitted to the Internal Medicine Department between March 2023 and February 2024. Cohort A comprised complex chronic patients followed by a multidisciplinary chronic care program between November 2016 and December 2023. PROFUND scores were derived from electronic health records. Discrimination for 12-month mortality was assessed using Kaplan–Meier curves, log-rank tests, and receiver operating characteristic curve analysis. Calibration was evaluated by comparing observed mortality with expected mortality based on the original PROFUND index and improved through intercept and slope recalibration. Results: A total of 660 patients were included in cohort H and 540 in cohort A. One-year mortality was 38.0% and 30.2%, respectively. Discriminatory performance was good in hospitalized patients (AUC 0.760; 95% CI 0.724–0.797) and moderate to good in ambulatory patients (AUC 0.705; 95% CI 0.656–0.754). Calibration analyses demonstrated systematic overestimation of mortality, particularly in the ambulatory cohort and intermediate–high risk strata, while recalibration improved agreement between predicted and observed risks. Conclusions: The PROFUND index provides useful risk stratification for 12-month mortality in CCP across care settings but overestimates absolute risk, particularly in ambulatory case management populations. Local recalibration may improve prognostic accuracy, support individualized care planning, and advance care planning discussions and allocation of multidisciplinary follow-up intensity. Full article

Background/Objectives: Portal vein thrombosis (PVT) is a clinically significant condition in which early risk stratification remains challenging, particularly in emergency settings where rapid decision-making is required. This study aimed to evaluate the prognostic value of the Systemic Immune-Inflammation Index (SII), Systemic Inflammation Response Index (SIRI), and the Model for End-Stage Liver Disease (MELD) score in predicting the need for intensive care in patients with PVT. Methods: A retrospective analysis was conducted on adult patients (>18 years) diagnosed with PVT in the emergency department between January 2018 and December 2024. A total of 29 patients meeting the inclusion and exclusion criteria were included. Demographic characteristics, laboratory parameters, Intensive Care Unit (ICU) admission status, and 90-day mortality were analyzed. The sensitivity and specificity of MELD, SII, and SIRI for predicting ICU admission were calculated. Non-normally distributed variables were expressed as median (interquartile range, IQR) and compared using the Mann–Whitney U test. Results: The mean age of patients was 60.5 ± 16.2 years, and 18/29 (62.1%) were male. ICU admission was required in 9/29 (31.0%) of cases. MELD score (median 18.7 [11.0–21.9] vs. 7.9 [6.7–13.5], p = 0.003), bilirubin (median 2.4 [1.0–4.2] vs. 0.7 [0.4–1.1], p = 0.016), and SIRI (median 6.4 [2.3–21.3] vs. 1.4 [0.6–9.3], p = 0.038) were significantly higher in ICU-admitted patients. MELD score showed 66.7% sensitivity and 95% specificity, while SIRI had 88.9% sensitivity and 55% specificity for ICU prediction. Conclusions: MELD score, bilirubin, and SIRI are significantly associated with ICU admission in PVT patients. Their integration into emergency department protocols may assist in early risk stratification and resource allocation. Full article

Background: Hand trauma accounts for up to one-third of trauma-related emergency department (ED) visits and ranges from minor lacerations to complex multi-structural injuries. As healthcare systems, workflows, and patient behavior evolve, contemporary epidemiological data are crucial to guide triage, optimize resource allocation, and adapt patient care pathways. Methods: We performed a retrospective observational study of all hand-related ED consultations at a Swiss university hospital in 2013, 2016, 2019, and 2022. In total, 8644 cases were analyzed for demographics, diagnosis, anatomical localization, injury complexity (≥2 functional structures), seasonal distribution, and animal-related injuries. Temporal trends and demographic or clinical shifts were assessed using descriptive and inferential statistics. Results: Among 8644 cases, most patients were male (63.8%) with a median age of 38 years (IQR 26–55). Lacerations (32.2%) and blunt trauma (29.1%) were the most frequent diagnoses, primarily involving digits II–V. The proportion of complex injuries declined significantly from 40.0% in 2013 to 32.5% in 2022 (p < 0.001), while cat-bite injuries almost doubled from 1.3% in 2013 to 2.3% in 2022. Case volumes peaked in spring and early summer. Conclusions: Over the analyzed decade, hand trauma cases in the ED have shifted toward a rising proportion of minor, low-acuity conditions, possibly reflecting reduced primary care access and evolving referral patterns. These trends highlight the need for adaptive triage models, strengthened outpatient care, and structural responses to primary care shortages to ensure efficient resource use and maintain high-quality hand trauma management. Full article

The urgency of global sustainability challenges increased policy attention to Education for Sustainable Development (ESD), particularly in relation to Sustainable Development Goal 4.7, which calls for the systematic integration of sustainability competences across education systems. This article examines how organisational and structural approaches to continuous professional development (CPD) can support the institutionalisation of ESD beyond individual teacher training. The article adopts a case-based analytical approach drawing on programme documentation and evaluation data from two initiatives coordinated by the teacher training department of the District Government of Arnsberg in North Rhine-Westphalia, Germany: the Erasmus+ consortium EFFORT-A, which links international mobility with school development processes, and the regional programme WIRkstatt Zukunft, which implements the Whole School Approach through modular training and school-based consultancy. The analysis indicates that multi-level governance, structured networking, leadership engagement, and formal contracting mechanisms are associated with the integration of ESD within school cultures, curricula, and organisational routines. Challenges are identified regarding resource allocation, policy coherence, and the long-term sustainability of project-based formats. The article concludes that sustained ESD implementation requires CPD systems that combine international perspectives with regionally anchored support structures and align individual professional learning with institutional development strategies, offering recommendations for policymakers and educational leaders. Full article

As nuclear energy expands, nuclear emergency response systems increasingly exhibit strong human–machine–environment (H–M–E) coupling, long-duration operations, and multi-department coordination, in which minor disturbances can be amplified by feedback loops into cascading failures and loss of situational control. To address the inability of conventional static and linear methods to represent dynamic risk evolution and chaotic uncertainty, this study proposes an integrated “risk network–chaotic evolution–synchronization control” framework. Based on 12-year-old on-site comprehensive drill reports from a Chinese nuclear power base, we construct a directed H–M–E risk network in a semi-quantitative, qualitative–quantitative manner and identify critical nodes using a composite betweenness–PageRank risk metric. We further abstract the system into a three-dimensional nonlinear coupled dynamical model; phase portraits, Lyapunov exponents, and bifurcation analysis confirm threshold effects, period-doubling routes, and chaotic attractors, revealing nonlinear amplification under strong coupling. Finally, an adaptive chaotic synchronization controller driven by network coupling strength is designed. Simulations show all strategies suppress chaos and achieve synchronization, while the machine-dominated strategy offers the best speed–energy trade-off for emergency resource allocation. Full article

Anchored in the strategic framework of Vision 2030, the research departs from anecdotal or survey-based approaches by exclusively leveraging publicly available, auditable data from national ministries, international university rankings, and scholarly publication databases. An original Integrated Green Transformation Framework (IGTF) is operationalized through fixed-effects regression modeling, longitudinal policy document analysis, and cross-sectional benchmarking of sustainability performance indicators across twelve Saudi universities. The findings demonstrate a statistically significant and temporally coherent association between national green policy milestones, such as the Saudi Green Initiative and the National Renewable Energy Program 2018, and measurable improvements in university-level sustainability strategies, operational efficiency, and research output. The average share of renewable energy utilization across sampled institutions increased from 2.1 percent in 2016 to 18.7 percent in 2023, representing substantial progress yet remaining below the Vision 2030 national target of 50%, while per-student water consumption declined by 34 percent over the same period. Scholarly publications in green economy domains rose by 638 percent, with a strong positive correlation (r = 0.76, p < 0.001) between research intensity and curriculum integration of sustainability content. Despite these advances, persistent disparities exist in resource allocation and implementation depth, particularly between historically endowed universities and newer regional institutions, highlighting a “sustainability divide” that requires targeted policy intervention. Full article

In the early stages of the COVID-19 pandemic, uncertainty around the extent of SARS-CoV-2 spread hampered policymakers’ understanding of the epidemic’s extent. Mathematical models, which proved vital for aiding decision-making, relied primarily on reported cases that were unreliable due to significant underdetection and underreporting. While serological data was used to improve understanding of the epidemiology, it can be costly and difficult to implement without bias. To counter these issues, we integrated serological data from 7229 remnant serum samples collected in 15 Maryland emergency departments (EDs) in Maryland between August and December 2020 into a Bayesian modeling approach to derive an estimate of the incidence of infection and the case fatality rate during the pandemic’s initial wave. We estimated that 5.2% (95% CI, 3.7–7.2%) of the population of Maryland had been infected by late fall 2020. The inferred reporting rate that was estimated started low (<10% in March 2020) and increased to 32% (95% HDI = 26–41%) by the fall, while the estimated infection fatality rate was likely initially higher but fell to 0.51% (95% HDI = 0.43–0.68%) after 1 September 2020. These results demonstrate how existing ED infrastructure can be leveraged to generate less biased, more accurate estimates of the true prevalence of a disease, improving the ability to make decisions and allocate resources under uncertainty. Full article

Background/Objectives: Hospitals prioritize effective resource allocation and patient satisfaction as key performance indicators. Improving the performance of the ultrasonography department remains a major challenge for hospital management due to the inherently unplanned and stochastic nature of its operations. Arrival patterns vary throughout the day, and examination durations differ depending on patients’ clinical pathways and examination types. This study focuses on the ultrasonography department of a private healthcare facility located in one of the most densely populated regions of Istanbul. The primary objective of this study was to improve departmental performance in terms of average waiting time, total time spent in the system, and resource utilization. Methods: To address the variability in patient arrivals and service times across different ultrasonography procedures, a simulation-based optimization approach was employed. Current system performance was evaluated, and multiple alternative operational scenarios were developed and simulated. In addition, the potential impact of Internet of Things applications on the performance of the ultrasonography department was investigated by incorporating alternative system configurations into the simulation model. Results: The simulation results enabled a comparative evaluation of alternative scenarios based on key performance indicators. The findings demonstrate that optimized system configurations can significantly reduce patient waiting times and total system time while improving resource utilization. The inclusion of Internet of Things applications further contributed to performance improvements in the selected scenarios. Conclusions: The proposed simulation-based approach provides a systematic decision-support framework for evaluating alternative operational scenarios in ultrasonography departments. By optimizing resource allocation and leveraging Internet of Things applications, hospital managers can improve operational efficiency and patient satisfaction. The results highlight the value of data-driven decision-making in managing complex and stochastic healthcare systems. Full article

Background/Objectives: Altered level of consciousness (ALC) is a common emergency department (ED) presentation with high mortality. We evaluated etiologies and early ED-course prognostic markers for mortality. Methods: We retrospectively identified adult ED visits with ALC (September 2023–August 2025) and classified etiologies using the ALC-10 framework. Patients transferred directly to other hospitals were excluded because post-transfer outcomes were unavailable; sensitivity analyses were performed. Overall mortality was ED death or in-hospital death, and ED mortality was death during the ED stay. Nested logistic models were prespecified: overall-mortality Model A included age, initial Glasgow Coma Scale (GCS), etiologic category, and ICU admission, and Model B added vasopressor use and mechanical ventilation within 1 h; ED-mortality Model A included age and initial GCS, and Model B added vasopressor use and mechanical ventilation. Results: ALC accounted for 2.85% (2194/76,957) of adult ED visits; 1932 patients were analyzed after excluding 262 transfer-outs. Systemic infection (25.8%) and metabolic causes (23.7%) were most frequent. Observed overall mortality was 23.6% (455/1932), including ED mortality of 6.4% (124/1932); model-based sensitivity analysis estimated adjusted overall mortality to be 23.2% (95% uncertainty interval, 22.9–23.7) among all ALC visits. In adjusted models, older age, lower initial GCS, and vasopressor use were associated with higher odds of both outcomes, while ICU admission and mechanical ventilation were associated with overall mortality. Model B showed improved discrimination (AUC 0.795 overall; 0.869 ED). Conclusions: These findings highlight the prognostic significance of age, initial neurologic status, and etiology. This study may assist in risk stratification and early resource allocation. Full article

Introduction: Large language models (LLMs) have proven effective in many different fields, including the allocation of scarce resources. Triage within emergency departments (ED) is a core process that ensures the sickest patients are seen in a timely manner. Relatively little research has examined the use of existing LLMs in the triage process. Methods: 12 widely available LLMs were provided with real-world patient triage vital sign data from an academic trauma center in a major metropolitan area. The LLMs were asked to assign a triage score to each patient based on this information alone. The deviation between each LLM triage score and the real-world triage score for each patient was calculated, and the absolute value of the deviation was calculated and then averaged across the entire dataset per LLM. The average absolute value of deviation (AAVD) could then be used to compare LLMs against each other. All LLMs were blinded to the real-world triage score and received no additional training or instruction. Results: The models with the highest concordance with real-world triage scores were Claude Sonnet 4.5 (AAVD: 0.37; 62.37% concordance), ChatGPT-5 Instant (AAVD: 0.39; 62.89% concordance), and Claude Opus 4.1 (AAVD: 0.40; 62.37% concordance). The least accurate models were Gemini 2.5 Flash (AAVD: 0.42; 43.81% concordance), ChatGPT-4o Mini (AAVD: 0.49; 45.36% concordance), and ChatGPT-o3 (AAVD: 0.48; 48.45% concordance). Conclusions: This study analyzes the ability of LLMs to triage emergency department patients based primarily on vital sign data. Certain LLMs demonstrated moderate concordance with real-world triage scores. LLMs may be able to synthesize objective vital sign data and provide a triage recommendation. Further study could involve clinical validation against patient outcomes. Full article

Background/Objectives: Efficient allocation of hospital resources is crucial in managing lower extremity trauma. Selected patients with stable injuries may not require inpatient hospitalization while awaiting surgical fixation. This study describes the feasibility and safety of a structured Home-based Preoperative Management (HPM) pathway for such patients. Methods: We conducted a retrospective, single-center observational study of 187 adult patients with isolated lower extremity fractures managed with HPM between 2019 and 2022. All patients were discharged home from the Emergency Department with standardized instructions, immobilization, anticoagulation, and planned follow-up. No comparator group was included. Results: Of 187 patients (mean age 49.7 y), 23 patients (12.3%) returned to the Emergency Department during the preoperative waiting period. The mean time from Emergency Department presentation to surgery was 8.5 days. Overall, 164 patients (87.7%) completed the preoperative waiting period at home without requiring an additional Emergency Department visit. Within one year after surgery, 51 patients (27.3%) presented to the Emergency Department; 29 of these visits (56.9%) were considered surgery-related. Patients who returned to the Emergency Department before surgery had a higher likelihood of postoperative Emergency Department visits within one year compared with those who did not (69.6% versus 21.3%, p < 0.001). Time to surgery was not associated with postoperative Emergency Department visits (p = 0.763). Conclusions: In this retrospective cohort, Home-Based Preoperative Management was feasible and appeared safe for carefully selected patients with lower extremity trauma. Most patients were able to await surgery at home without unplanned Emergency Department visits. Given the absence of a comparator group, no conclusions regarding comparative effectiveness or superiority over inpatient management can be drawn. Full article