Search Results (67)

Background/Objectives: Snow can disrupt emergency medical services (EMSs); however, previous studies have mainly measured snowfall or prefecture-level exposure. These measures may not capture snow remaining on the ground or conditions within ambulance operating areas. We examined whether the daily snow depth assigned at the fire department level was associated with EMS time intervals and 1-month survival after out-of-hospital cardiac arrest (OHCA). Methods: This retrospective cohort study included 7395 adults with OHCA from the Akita Prefecture Utstein-style emergency transport registry between 2019 and 2023. Daily snow depth from the nearest Automated Meteorological Data Acquisition System (AMeDAS) station was assigned to each case by the fire department. Snow exposure was analyzed as >0 cm versus 0 cm, as five depth categories, and as a continuous variable using natural splines. Multivariable models were adjusted for age, sex, cardiac origin, initial rhythm, fire department area, witnessed status, bystander cardiopulmonary resuscitation, and year. Results: Call-to-scene time and total EMS time were longer with snow cover than without snow cover (median, 9 vs. 8 min and 34 vs. 31 min, respectively; both p < 0.001). Snow cover was associated with lower 1-month survival after adjustment (odds ratio [OR], 0.73; 95% confidence interval [CI], 0.54–0.98), but this association was attenuated after additional adjustment for call-to-scene time (OR, 0.77; 95% CI, 0.57–1.03). Category-based and spline analyses showed no clear dose–response relationship. Conclusions: Daily snow depth is consistently associated with longer EMS response and transport times. However, its association with 1-month survival remains unclear. This survival association may reflect broader winter conditions rather than snow cover itself. Full article

Emergency medical response is complex. The need to make time-based decisions that can impact people’s health requires careful examination. Network analysis, among other methods, can support that time-based decision making. This study explores network analysis through a multi-modal transportation network model to represent both fixed-wing air and ground Emergency Medical Services (EMS) resources. Methods: The study utilized open and EMS industry data to build a geospatial multi-modal network to model potential patient transfer across Alberta (Canada). Results: Within the study’s service area, ground transportation alone is more effective within 101 Km, at which threshold the addition of aerial transport begins to be more time effective, saving 9.7 min over ground transportation only. Between this distance and 417 Km, results show a mixed-use area where a combination of ground only and aerial travel is recommended based on the event pickup location, aircraft availability, and ambulance station location relative to high-speed roads. Beyond 417 Km, aerial transportation is consistently more efficient. There is a high correlation (R² = 0.82) between trip length and time difference between using ground only mode and combined air and ground. Lastly, the data showed air travel is 6.6 times more expensive than ground travel, with no modeled transfers identifying air as more time-effective than ground travel. Conclusions: Fixed-wing aircraft travel can have a positive impact on patient transfers; however, fluctuations in flight routes and times may require response agencies to implement time buffers to account for these variabilities. No cost savings were seen using fixed-wing aircraft, and the benefit of their use would be realized with efficient patient transfer times, as well as leaving ground ambulances in localized areas. Full article

Background: Stroke is a major global health issue. It is among the leading causes of disability and mortality worldwide. Early stroke detection and treatment are significant in enhancing long-term outcomes. Awareness of neuroimaging is also essential because neuroimaging must be completed urgently within a limited time to diagnose and treat stroke patients correctly. This study aims to investigate awareness of stroke symptoms, risk factors, and utilization of neuroradiology services among the public in Saudi Arabia. Methods: This is an online survey study that was conducted in Saudi Arabia using social media platforms between January to February 2026. The questionnaire tool for this study was adapted from previous research and examined stroke awareness, symptoms, and risk factors. In addition, it examined neuroradiology awareness and utilization. The multivariable logistic regression analysis was used to identify predictors of better awareness of stroke. Results: A total of 415 participants were involved in this study. Around 46.7% of them were aged 25–34 years. Females formed the majority of the study sample, comprising 76.4%. Bachelor’s degree holders formed 61.4% of the study sample. Around 42.9% of the study sample were unemployed. Married participants contributed 64.3%. Almost half of the study sample (47.2%) reported that their monthly income is less than 5000 SAR. In this study, the participants demonstrated a moderate level of knowledge of stroke. The majority of the participants (70.8%) reported that they are aware of radiology centers near them that they can refer to in case of stroke emergency or follow-up, and 79.0% reported that they think that radiological imaging is important for diagnosing and treating stroke. The majority of the participants (72.3%) reported that they have heard of interventional radiology procedures for stroke. Participants aged 35–44 years and 55–64 years were less likely to have better knowledge of stroke compared to others (aOR: 0.21 (CI: 0.06–0.83); p-value: 0.026) and aOR: 0.17 (CI: 0.04–0.81); p-value: 0.026, respectively). Furthermore, participants who reported that their income level is 5000–9999 SAR and 10,000–14,999 SAR were less likely to have better knowledge of stroke compared to others (aOR: 0.32 (CI: 0.13–0.80); p-value: 0.014 and aOR: 0.21 (CI: 0.09–0.53); p-value: <0.001), respectively). On the other hand, participants who are unemployed were more likely to have better knowledge of stroke compared to others (aOR: 3.63 (CI: 1.09–12.05); p-value: 0.035). Conclusions: The current investigation demonstrated a moderate level of knowledge about strokes among the public in Saudi Arabia. Targeted interventions are mandated to improve the level of awareness about strokes, with a focus on knowledge of the correct emergency response, specifically calling an ambulance. Full article

Traffic variability along heavily congested signalised urban corridors undermines roadway safety, reduces energy efficiency, weakens operational reliability, and can hinder emergency response. Although many simulation-based studies have examined the impacts of Autonomous Vehicles (AVs), relatively few have combined high-resolution congestion observations with link-level microscopic calibration in a real urban network, particularly when evaluating implications for emergency mobility. This study develops and calibrates a microscopic Aimsun traffic simulation model for the Atakum district of Samsun, Türkiye, using a 10 min Google Traffic Index (GTI) observation stream converted into a four-level ordinal congestion scale. The calibration process began with an origin–destination (OD) matrix derived from 2020 traffic counts and was refined through link-level GTI synchronization, iterative OD scaling on mismatched corridors, and signal retiming at key intersections. GTI was validated as an ordinal congestion proxy through both categorical agreement and volumetric consistency, achieving 83% class agreement and GEH values below 5 for more than 90% of links. Five AV penetration scenarios (0%, 25%, 50%, 75%, and 100%) were simulated under peak-hour conditions. Network performance was evaluated using delay, stop time, mean speed, throughput, missed turns, and total journey time, while emergency mobility was assessed along a representative ambulance corridor on Atatürk Boulevard using seconds per kilometre. The results indicate that increasing AV penetration improves flow stability more clearly than nominal capacity. Mean speed increased from 36.2 to 39.2 km/h, delay and stop time declined steadily, and throughput remained nearly constant at 22.2–22.5 thousand vehicles/h. Along the ambulance corridor, travel time improved by 11.5%, from 112.4 to 99.4 s/km, between the baseline and full automation scenarios. These findings provide scenario-based evidence that, within a calibrated signalised urban network, increasing AV penetration can enhance operational stability and emergency response efficiency. More broadly, the study demonstrates the practical value of integrating GTI-based congestion observations with microscopic simulation for AV impact assessment in real urban networks. Full article

Emergency facility location and layout are critical to the efficiency of emergency rescue and resource allocation. However, practical emergency scenarios are plagued by two key challenges: the risk of facility failure due to various uncertain factors and the presence of complex polygonal barriers (including convex and concave polygons) that hinder transportation. Existing studies often overlook concave polygonal barriers or fail to prioritize time satisfaction, a core demand in emergency response. To address these gaps, this paper proposes a reliable emergency facility location optimization model with the objective of maximizing time satisfaction, considering constraints such as capacity, cost, and demand. The model integrates three key methods: a convex hull algorithm to convert concave barriers into convex ones for simplified calculation, a path optimization algorithm to find the shortest bypass routes around barriers, and an Artificial Ecosystem Optimization (AEO) algorithm to solve the nonlinear programming model. Through numerical experiments (single-facility, multi-facility, and medium-scale scenarios) and a practical case study in the Meknès region of Morocco for ambulance deployment, the feasibility and effectiveness of the model and algorithms are verified. The results show that the model achieves high time satisfaction (all above 0.8, with most exceeding 0.9) and efficiently optimizes facility locations and resource allocation. Sensitivity analysis indicates that increased failure risk parameters ($\alpha$ and $\theta$) lead to a gradual decrease in average time satisfaction. This research provides a systematic mathematical model and practical method for emergency facility location decision-making, effectively addressing the challenges of complex barriers and facility failure. Full article

Ambulance offload delay (AOD) occurs when incoming ambulance patients cannot be transferred promptly from paramedics to emergency department (ED) staff, usually due to ED and hospital congestion. This study empirically examines how AOD affects ambulance system efficiency in Nova Scotia, Canada. Using 12 months of call data from an integrated provincial EMS system and the electronic patient care reporting system, the analysis quantifies AOD impacts on the number of ambulances at EDs, turnaround time, total call time, response time, and ambulance availability across all regions. Findings show that AOD in the Central Region negatively affects all performance measures locally and in adjacent regions, prolonging turnaround and total call times, lengthening response times, and reducing ambulance availability where resources are shared. These results highlight the scale of AOD’s system-wide impact and provide a generalizable methodological framework that other EMS operators can adapt to assess and manage AOD in their specific operational contexts, recognizing that region-specific factors significantly influence outcomes. Full article

Background: Rapid helicopter air ambulance (HAA) response is a cornerstone of emergency medical logistics, yet the “time-to-care” metric remains highly sensitive to uncertainties in base posture, readiness, and operational disruptions. This study evaluates how these factors jointly influence response-time reliability and identifies strategies for improving service performance. Methods: A Monte Carlo simulation was developed to model the end-to-end HAA mission chain, including dispatch, wheels-up delay, en-route flight, and patient handoff, while accounting for uncertainty from weather, airspace congestion, and flight dynamics. Scenario experiments incorporated training improvements and alternative response protocols (Ground vs. Airborne Standby). Results: Simulation results indicate that operational factors reduced mean and tail response times, with Airborne Standby reducing the probability of exceeding a 45 min threshold by over 90% in urban night scenarios. Performance gains were most prominent in rural service areas and night operations, where disruption risks were highest. Conclusions: The findings offer evidence-based guidance for EMS logistics planners by clarifying how standby policies and readiness enhancements mitigate logistical risks. Full article

Emergency Medical Services (EMS) require timely and equitable ambulance allocation supported by accurate demand estimation. In our prior work, we developed a statistical forecasting module based on Overall Smoothed Average Demand (OSAD) and Average Maximum (AMX) to estimate proportional EMS demand across spatial zones. Although this approach was interpretable and computationally efficient, it was limited in modeling nonlinear spatiotemporal dependencies and adapting to dynamic demand variations. This paper presents a unified deep learning-based EMS planning framework that integrates spatiotemporal demand forecasting with adaptive ambulance allocation. Specifically, the statistical OSAD/AMX estimators are replaced by graph-based spatiotemporal forecasting models capable of capturing spatial interactions and temporal dynamics. The predicted demand is then incorporated into a reinforcement learning-based allocator that dynamically optimizes ambulance placement under fairness, coverage, and operational constraints. Experiments conducted on real-world EMS datasets demonstrate that the proposed end-to-end framework not only improves demand forecasting accuracy but also translates these improvements into tangible operational benefits, including enhanced equity in resource distribution and reduced response distance. Compared with traditional statistical and heuristic-based baselines, the proposed approach provides a more adaptive and decision-aware solution for EMS planning. Full article

Background/Objectives: Intensive care units (ICU) immobility and weakness impair recovery, yet practice for Physical Therapist (PT)-led therapeutic exercise and mobility varies in definitions, dosing, safety, and documentation, which limits comparability and complicates quality assessment. This study aims to integrate adult ICU evidence and present PT-led operational definitions, dose progression principles, safety parameters, outcome measurement, and a documentation minimum dataset. Methods: A scoping review following PRISMA-ScR is used. Eligibility used Population, Concept, and Context: adults in ICU; PT-led therapeutic exercise or mobility; and ICU-initiated or directed care. Primary studies and prespecified quality-improvement reports were included. Data were extracted with a standardized form and summarized descriptively without meta-analysis. Results: Sixty studies were included. Based on the extracted data, this review synthesizes current evidence to propose standardized PT-led operational definitions and a graded progression from in-bed exercise to ambulation. While the individual components are derived from the literature, the conceptual framework for safety parameters and the stop rules were integrated and elaborated to guide clinical decision-making. Adverse events were uncommon and manageable. Outcome measurement centered on validated mobility and function measures at prespecified time points. A concise electronic record minimum dataset specifies provider attribution, timing and duration, activity level with assistance or device, planned and delivered dose with progression, in-session responses, and adverse events, supporting unit-level quality review and comparisons across ICU. Conclusions: A PT-led, graded program that applies shared thresholds, uses validated outcome measures, and employs standardized electronic documentation is feasible and supports safe delivery, clinically meaningful change, and quality improvement across adult ICU. Full article

Sepsis remains a critical medical emergency caused by a dysregulated immune response to infection, with timely detection and intervention being essential for improving survival rates. Traditional methods often rely on clinician intuition and structured scoring systems, which may be time-intensive and prone to variability. To address these limitations, Machine Learning (ML) offers a powerful alternative, bringing precision and efficiency to sepsis detection. This study investigates both white-box and complex black-box ML models applied to patient data collected across the continuum of care, including monitoring at the urgent care, en route in ambulances, and diagnostics conducted within hospital emergency department settings themselves. White-box models, such as logistic regression and decision trees, are valued for their interpretability, allowing healthcare providers to understand and trust the reasoning behind predictions. Meanwhile, black-box models like deep neural networks and support vector machines deliver superior accuracy but pose challenges in clinical transparency. This trade-off between explainability and performance is explored in detail, supported by experimental results aimed at identifying the most effective computational strategies for early sepsis recognition across diverse healthcare environments. Full article

Introduction: Paramedics are increasingly being recognized as essential contributors to sports medicine, where their role extends beyond emergency response to prevention, planning, and collaboration with other medical professionals. Yet their scope of practice and effectiveness across sporting levels and regions remain insufficiently synthesized. Methods: This scoping review mapped international evidence on paramedics in sports medicine. Literature published in English between 2013 and 2023 was systematically searched in PubMed, Scopus, and ScienceDirect, and eligible studies were analyzed thematically. Thirty studies were included, spanning professional and amateur sports in North America, Europe, Asia, Oceania, and Africa. Results: The findings demonstrate that paramedics provide critical value across six domains. First, rapid emergency response, supported by innovations such as motorcycle-based ambulances, significantly reduced access times and improved survival rates. Second, preparedness and ongoing training, including physical fitness and interprofessional education, were shown to enhance effectiveness in demanding sporting environments. Third, collaboration with athletic trainers and other professionals improved on-field care and reduced unnecessary hospital transfers. Fourth, paramedics contributed to injury prevention programmes that lowered injury incidence and healthcare costs. Fifth, their involvement at mass gatherings ensured safety, streamlined triage, and reduced pressure on hospitals. Finally, evidence indicates that paramedic-led initiatives are cost-effective, generating both clinical and economic benefits. Conclusions: Paramedics play a multifaceted role in athlete care, emergency response, and injury prevention. Strengthening their integration through targeted training, protocol standardization, and equitable resource allocation can improve both athlete safety and system efficiency. Future research should focus on grassroots contexts and the use of paramedic-generated data to inform prevention and policy. Full article

Background/Objectives: In Japan, task shifting reduces the working hours of pediatricians, who face excessive workloads. The status of task shifting under the Ministry of Health, Labor, and Welfare’s reforms remains unclear. This study aimed to evaluate the current status and barriers of task shifting in pediatric care in Japan. Methods: A questionnaire survey was conducted among pediatricians working in hospitals in Japan. The results were compared with those from 2020. Results: Questionnaires were sent to 835 hospitals, and valid responses were received from 815 pediatricians in 316 hospitals (response rate: 37.8%). The largest group (31.0%) was 40–49 years, and 34.4% of the participants were women. Among the items surveyed, most pediatricians indicated “shifted” in “Patient transfer (transporting between hospitals using an ambulance)” and “Intravenous injection of antibiotics.” Most physicians believed task shifting improved care quality; 10.3% felt it worsened. The most common estimate for daily working hour reduction due to task shifting was “1 to <2 h” (44.9%). Precisely 15.8% of pediatricians believed that task shifting had “not progressed at all,” with rural areas and non-university hospitals showing lower task-shifting implementation. National university hospitals had a higher likelihood of task shifting than public hospitals. No significant associations were observed for the total hospital bed count or the number of full-time pediatricians. Conclusions: Task shifting in pediatric care remains underdeveloped. While many pediatricians support the concept and report modest reductions in working hours, actual implementation remains limited. Future efforts must address systemic, institutional, and regulatory challenges to facilitate meaningful task redistribution and improve healthcare delivery. Full article

Optimizing ambulance deployment is a critical task in emergency medical services (EMS), as it directly affects patient outcomes and system efficiency. This study proposes a cyber-secure, machine learning-based framework for predicting region-specific ambulance allocation and response times across South Korea. The model integrates heterogeneous datasets—including demographic profiles, transportation indices, medical infrastructure, and dispatch records from 229 EMS centers—and incorporates real-time IoT streams such as traffic flow and geolocation data to enhance temporal responsiveness. Supervised regression algorithms—Random Forest, XGBoost, and LightGBM—were trained on 2061 center-month observations. Among these, Random Forest achieved the best balance of accuracy and interpretability (MSE = 0.05, RMSE = 0.224). Feature importance analysis revealed that monthly patient transfers, dispatch variability, and high-acuity case frequencies were the most influential predictors, underscoring the temporal and contextual complexity of EMS demand. To support policy decisions, a Lasso-based simulation tool was developed, enabling dynamic scenario testing for optimal ambulance counts and dispatch time estimates. The model also incorporates the coefficient of variation (CV) of workload intensity as a performance metric to guide long-term capacity planning and equity assessment. All components operate within a cyber-secure architecture that ensures end-to-end encryption of sensitive EMS and IoT data, maintaining compliance with privacy regulations such as GDPR and HIPAA. By integrating predictive analytics, real-time data, and operational simulation within a secure framework, this study offers a scalable and resilient solution for data-driven EMS resource planning. Full article

Firefighters experience high noise levels from various sources, such as sirens, alarms, pumps, and emergency vehicles. Unlike industrial workers who experience continuous noise exposure, firefighters are subject to intermittent high-intensity noise, increasing their risk of noise-induced hearing loss (NIHL). Despite global concerns regarding firefighters’ auditory health, research on Korean firefighters remains limited. This study aimed to assess personal noise exposure among Korean firefighters across three primary job roles—fire suppression, rescue, and emergency medical services (EMS)—and to predict worst-case noise exposure scenarios. This study included 115 firefighters from three fire stations (one urban, two suburban). We measured personal noise exposure using dosimeters attached near the ear following the Korean Ministry of Employment and Labor (MOEL) and International Organization for Standardization (ISO) criteria. Measurements included threshold levels of 80 dBA, exchange rates of 5 dB (MOEL) and 3 dB (ISO), and a peak noise criterion of 140 dBC. We categorized firefighters’ activities into routine tasks (shift handovers, equipment checks, training) and emergency responses (fire suppression, rescues, EMS calls). We performed statistical analyses to compare noise levels across job roles, vehicle types, and specific tasks. The worst-case exposure scenarios were estimated using 10th percentile recorded noise levels. The average 8 h time-weighted noise exposure levels varied significantly by job role. Rescue personnel exhibited the highest mean noise exposure (MOEL: 71.4 dBA, ISO: 81.2 dBA; p < 0.05), whereas fire suppression (MOEL: 66.5 dBA, ISO: 74.2 dBA) and EMS personnel (MOEL: 68.6 dBA, ISO: 73.0 dBA) showed no significant difference. Peak noise levels exceeding 140 dBC were most frequently observed in rescue operations (33.3%), followed by fire suppression (30.2%) and EMS (27.2%). Among vehicles, noise exposure was the highest for rescue truck occupants. Additionally, EMS personnel inside ambulances had significantly higher noise levels than drivers (p < 0.05). Certain tasks, including shift handovers, equipment checks, and firefighter training, recorded noise levels exceeding 100 dBA. Worst-case scenario predictions indicated that some work conditions could lead to 8 h average exposures surpassing MOEL (91.4 dBA) and ISO (98.7 dBA) limits. In this study, Korean firefighters exhibited relatively low average noise levels. However, when analyzing specific tasks, exposure was sufficiently high enough to cause hearing loss. Despite NIHL risks, firefighters rarely used hearing protection, particularly during routine tasks. This emphasizes the urgent need for hearing conservation programs, including mandatory hearing protection during high-noise activities, noise exposure education, and the adoption of communication-friendly protective devices. Future research should explore long-term auditory health outcomes and assess the effectiveness of noise control measures. Full article

This retrospective multicentre study investigated haemorrhagic myelopathy as a rare complication of steroid-responsive meningitis-arteritis (SRMA) in nine dogs. The affected dogs exhibited varied neurological deficits, including cervical hyperesthesia, generalised stiffness, ambulatory tetraparesis, and, in the most severe cases, paraplegia without nociception. MRI findings primarily localised haemorrhagic lesions to the thoracolumbar (T3-L3) region, with intradural–extramedullary haemorrhages being the most common type. Most cases responded favourably to immunosuppressive therapy with prednisolone, either alone or in combination with cytarabine. Surgical intervention, performed in a case of compressive extradural haemorrhage, led to a successful recovery of ambulation. Two cases presented or developed paraplegia without nociception, despite immunosuppression. These findings emphasise the importance of advanced imaging and timely therapeutic interventions in addressing atypical and severe manifestations of SRMA. Full article