Search Results (33)

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

The adverse effects of short-term exposure to pollutants are the focus of many epidemiological studies. Little is known about the modification effects of season and temperature on the association between pollutants and the acute onset of circulatory diseases. The aim of this study was to investigate the effect of PM_2.5 on emergency ambulance dispatches (EADs) due to circulatory system diseases in different seasons and temperature levels, and to locate the vulnerable population. We collected data on daily emergency ambulances, meteorological data, and air pollution concentration in Shenzhen from 2013 to 2020. A distributed lag nonlinear model was conducted to assess the effect of PM_2.5 on circulatory system disease emergency ambulance dispatches modified by season. In addition, generalized additive models were used to detect the interactive effect of PM_2.5 and temperature on emergency ambulance dispatches due to circulatory disease. A 10 μg/m³ increase in PM_2.5 concentration was associated with a 2.43% (1.47–3.40%) increase in the risk of circulatory system disease emergency ambulance dispatches over lags of 0–5 days during the cold season, compared to 0.75% (−0.25–1.76%) during the warm season. This trend was consistent across temperature levels, with a significant 2.42% (1.47–3.10%) increase on low-temperature days, while no significant effect was observed on high-temperature days. For young people, the effect of PM_2.5 on circulatory system disease emergency ambulance dispatches was higher in the cold season and low temperature days. The cold season and low temperature significantly enhanced the adverse effect of PM_2.5 on the acute onset of circulatory system diseases, especially in young people. It is critical to focus on the synergistic effects of temperature and pollutants on the health of different vulnerable populations in different regions and climates. Full article

With the rising incidence of traffic accidents and growing environmental concerns, the demand for advanced systems to ensure traffic and environmental safety has become increasingly urgent. This paper introduces an automated highway safety management framework that integrates computer vision and natural language processing for real-time monitoring, analysis, and reporting of traffic incidents. The system not only identifies accidents but also aids in coordinating emergency responses, such as dispatching ambulances, fire services, and police, while simultaneously managing traffic flow. The approach begins with the creation of a diverse highway accident dataset, combining public datasets with drone and CCTV footage. YOLOv11s is retrained on this dataset to enable real-time detection of critical traffic elements and anomalies, such as collisions and fires. A vision–language model (VLM), Moondream2, is employed to generate detailed scene descriptions, which are further refined by a large language model (LLM), GPT 4-Turbo, to produce concise incident reports and actionable suggestions. These reports are automatically sent to relevant authorities, ensuring prompt and effective response. The system’s effectiveness is validated through the analysis of diverse accident videos and zero-shot simulation testing within the Webots environment. The results highlight the potential of combining drone and CCTV imagery with AI-driven methodologies to improve traffic management and enhance public safety. Future work will include refining detection models, expanding dataset diversity, and deploying the framework in real-world scenarios using live drone and CCTV feeds. This study lays the groundwork for scalable and reliable solutions to address critical traffic safety challenges. Full article

Background: The timely management of rapidly evolving epidemiological scenarios caused by disease outbreaks is crucial to prevent devastating consequences. However, delayed laboratory diagnostics can hamper swift health policy and epidemic response, especially in remote regions such as the western Brazilian Amazon. The aim of the article is to analyze the impact of the COVID-19 pandemic on the volume and characteristics of emergency medical services (EMS) in Manaus, focusing on how the pandemic affected sensitive indicators such as response time and the use of advanced life support ambulances. Additionally, the study seeks to understand how changes in prehospital EMS patterns, triggered by the pandemic, could be utilized as health surveillance tools, enabling a more rapid response in epidemic scenarios. Methods: This retrospective, descriptive study included data from the SAMU (Serviço de Atendimento Móvel de Urgência) medical records between January and June 2020. Results: A total of 45,581 calls resulted in mobile units being dispatched during this period. These patients were predominantly male (28,227, 61.9%), with a median age of 47 years (IQR 30–67). The median response time significantly increased during the pandemic, reaching a median of 45.9 min (IQR 30.6–67.7) (p < 0.001). EMS calls were reduced for trauma patients and increased for other medical emergencies, especially respiratory conditions, concomitantly to an escalation in the number of deaths caused by SARS and COVID-19 (p < 0.001). The employment of advanced life support ambulances was higher during the pandemic phase (p = 0.0007). Conclusion: The COVID-19 pandemic resulted in a temporary disorder in the volume and reason for EMS calls in Manaus. Consequently, sensitive indicators like the response time and the employment of advanced life support ambulances were negatively affected. Sudden prehospital EMS pattern changes could play an important role in health surveillance systems, allowing for earlier establishment of countermeasures in epidemics. The impact of the COVID-19 pandemic on prehospital EMS and its role in health surveillance should be further explored. Full article

Background: Prehospital detection and triage of stroke patients mostly rely on the use of large vessel occlusion prediction scales to decrease onsite time. These quick but simplified scores, though useful, prevent prehospital providers from detecting posterior strokes and isolated symptoms such as limb ataxia or hemianopia. Case report: In the present case, an ambulance was dispatched to a 46-year-old man known for ophthalmic migraines and high blood pressure, who presented isolated visual symptoms different from those associated with his usual migraine attacks. Although the assessment advocated by the prehospital guideline was negative for stroke, the paramedic who assessed the patient was one of the few trained in the National Institutes of Health Stroke Scale assessment. Based on this assessment, the paramedic activated the fast-track stroke alarm and an ischemic stroke in the right temporal lobe was finally confirmed by magnetic resonance imaging. Discussion and conclusions: Current prehospital practice enables paramedics to detect anterior strokes but often limits the detection of posterior events or more subtle symptoms. Failure to identify such strokes delay or even forestall the initiation of thrombolytic therapy, thereby worsening patient outcomes. We therefore advocate a two-step prehospital approach: first, to avoid unnecessary delays, the prehospital stroke assessment should be carried out using a fast large vessel occlusion prediction scale; then, if this assessment is negative but potential stroke symptoms are present, a full National Institutes of Health Stroke Scale assessment could be performed to detect neurological deficits overlooked by the fast stroke scale. Full article

Personal Emergency Response Systems (PERSs) are fall-detection devices supporting users in any situation. No previous studies have investigated the differences in events and the use of PERS between users financially supported by public authorities (public users) and those who privately afford the PERS cost (private users). More than two years of data collected by the Telemergency Operation Centre (TOC) were downloaded. All users who sent at least one real alert to request support were included. No differences were found for falls (37, 16.7% vs. 95, 13.4%) and medical problems (46, 20.7% vs. 122, 17.2%). The dispatch of an ambulance was necessary for all medical problems, while for falls, this was only in half of cases. Public users significantly asked more for service demand, while private users asked for support calls. The TOC staff directly managed most of the service demands (398, 97.3%) and support calls. PERS could be a valid instrument for promoting independent living and helping manage chronic conditions in older adults. The results suggest that PERSs might improve in-home care services, facilitating the connection to in-home services. Full article

Background: Ambulance emergency calls (AECs) are seen as a more suitable metric for syndromic surveillance due to their heightened sensitivity in reflecting the health impacts of air pollutants. Limited evidence has emphasized the combined effect of hourly air pollutants on AECs. This study aims to investigate the combined effects of multipollutants (i.e., PM_2.5, PM₁₀, Ozone, NO₂, and SO₂) on all-cause and cause-specific AECs by using the quantile g-computation method. Methods: We used ambulance emergency dispatch data, air pollutant data, and meteorological data from between 1 January 2013 and 31 December 2019 in Shenzhen, China, to estimate the associations of hourly multipollutants with AECs. We followed a two-stage analytic protocol, including the distributed lag nonlinear model, to examine the predominant lag for each air pollutant, as well as the quantile g-computation model to determine the associations of air pollutant mixtures with all-cause and cause-specific AECs. Results: A total of 3,022,164 patients were identified during the study period in Shenzhen. We found that each interquartile range increment in the concentrations of PM_2.5, PM₁₀, Ozone, NO₂, and SO₂ in 0–8 h, 0–8 h, 0–48 h, 0–28 h, and 0–24 h was associated with the highest risk of AECs. Each interquartile range increase in the mixture of air pollutants was significantly associated with a 1.67% (95% CI, 0.12–3.12%) increase in the risk of all-cause AECs, a 1.81% (95% CI, 0.25–3.39%) increase in the risk of vascular AECs, a 1.77% (95% CI, 0.44–3.11%) increase in reproductive AECs, and a 2.12% (95% CI, 0.56–3.71%) increase in AECs due to injuries. Conclusions: We found combined effects of pollutant mixtures associated with an increased risk of AECs across various causes. These findings highlight the importance of targeted policies and interventions to reduce air pollution, particularly for PM, Ozone, and NO₂ emissions. Full article

Background: The increasing prevalence of comorbidities worldwide has spurred the need for time-effective pre-hospital emergency medical services (EMS). Some pre-hospital emergency calls requesting EMS result in patient non-conveyance. Decisions for non-conveyance are sometimes driven by the patient or the clinician, which may jeopardize the patients’ healthcare outcomes. This study aimed to explore the distribution and determinants of patient non-conveyance to hospitals in a Middle Eastern national Ambulance Service that promotes the transportation of all emergency call patients and does not adopt clinician-based non-conveyance decision. Methods: Using R Language, descriptive, bivariate, and binary logistic regression analyses were conducted for 334,392 multi-national patient non-conveyance emergency calls from June 2018 to July 2022, from a total of 1,030,228 calls to which a response unit was dispatched. Results: After data pre-processing, 237,862 cases of patient non-conveyance to hospital were retained, with a monthly average of 41.96% (n = 8799) of the emergency service demands and a standard deviation of 5.49% (n = 2040.63). They predominantly involved South Asians (29.36%, n = 69,849); 64.50% (n = 153,427) were of the age category from 14 to 44 years; 61.22% (n = 145,610) were male; 74.59% (n = 177,424) from the urban setting; and 71.28% (n = 169,552) had received on-scene treatment. Binary logistic regression with full variables and backward methods identified the final models of the determinants of patient non-conveyance decisions with an Akaike information criterion prediction estimator, respectively, of (250,200) and (250,169), indicating no significant difference between both models (Chi-square test; p-value = 0.63). Conclusions: Despite exercising a cautious protocol by encouraging patient transportation to hospital, patient non-conveyance seems to be a problem in the healthcare system that strains the pre-hospital medical response teams’ resources. Policies and regulations should be adopted to encourage individuals to access other primary care centers when required rather than draining emergency services for non-emergency situations. Full article

The concept of a catchment area is often used to establish equitable access to essential services such as ambulance emergency medical services. In a time-sensitive environment, taking the wrong decision when there is a need for a short travel time can have serious consequences. In ambulance management, a mistaken dispatch which may result in the late arrival of an ambulance can lead to a life-and-death situation. In addition, finding the optimal route to reach the destination within a minimum amount of time is a significant problem. A spatial routing analysis based on travel times within the emergency services catchment area can quickly find the best routes to emergency points and may overcome this problem. In this study, a smart spatial routing and accessibility analysis system is proposed for EMS using catchment areas of the Voronoi spatial model and time-based Dijkstra’s routing algorithm (TDRA) to support the route analysis of emergencies and to facilitate the dispatch of appropriate units that are able to respond within a reasonable time frame. Our simulation shows that the system can successfully predict and determine the nearest candidate ambulance unit within the catchment area and candidate ambulance services in the adjacent catchment area that has a minimum travel time to the demand point taking TDRA construction into account. Full article

The reorganization of an emergency medical system means that we look for new locations of ambulance stations with the aim of improving the accessibility of the service. We applied two tools that are well known in the operations research community, namely mathematical programming, and computer simulation. Using the hierarchical pq-median model, we proposed optimal locations of the stations throughout the country and within large towns. Several solutions have been calculated that differ in the number of stations that are supposed to be relocated to new positions. The locations proposed by the mathematical programming model were evaluated via computer simulation. The approach was demonstrated under the conditions of the Slovak Republic using real historical data on ambulance dispatches. We have concluded that (i) the distribution of the stations proposed by the hierarchical pq-median model overcomes the current distribution; the performance of the system has significantly improved even if only 10% of the stations are relocated to new municipalities; (ii) the variant that relocates 40% of the stations is a reasonable compromise between the benefits and induced costs; (iii) optimizing station locations in big towns can significantly improve the local as well as the nationwide performance indicators; the response times in two regional capitals has reduced by more than 4 min. Full article

Approximately 7% of all dispatched ambulances in Denmark are for patients for whom breathing difficulties are the main cause for using ambulance services. Objective measurements are routinely carried out in the ambulances, but little is known of the patients’ subjective experience of dyspnea. The purpose of this study was to investigate how patients with acute dyspnea, transported to hospital by ambulance, experience their situation, along with their experience of the use of a dyspnea scale. The study was carried out in the North Denmark Region. Transcribed patient interviews and field notes were analyzed and interpreted with inspiration from Paul Ricoeur. For interviews, we included 12 patients with dyspnea who were transported to the hospital by ambulance: six women and six men all aged 60 years or above. Observations were made over six ambulance transports related to dyspnea. Three themes emerged: “anxiety”, “reassurance in the ambulance” and “acceptance of the dyspnea measurements in the ambulance”. Several patients expressed anxiety due to their dyspnea, which was substantiated by observations in the ambulance. The patients expressed different perspectives on what improved the situation (treatment, reassurance by ambulance professionals). The patients and the ambulance personnel were, in general, in favor of the dyspnea scale. Full article

Emergency medical services (EMS) around the world face the challenging task of allocating resources to efficiently respond to medical emergencies within a geographical area. While several studies have been done to improve various aspects of EMS, such as ambulance dispatch planning and station placement optimization, few works have focused on the assessment of existing rich real-world emergency response data to systematically identify areas of improvement. In this paper, we propose DAPI (data-driven analysis of potential response inefficiencies), a general tool for analyzing inefficiencies in emergency response datasets. DAPI efficiently identifies potential response bottlenecks based on spatial distributions of ambulance responses and statistically assesses them with respect to inferred activity levels of relevant dispatch stations to aid causality analysis. DAPI is applied on a dataset containing all medical emergency responses in mainland Portugal, in which we find statistical evidence that inefficiencies are correlated with high levels of activity of stations closer to an emergency location. We present these findings, along with the associated patterns and geographical clusters, serving as a valuable decision support tool to aid EMS in improving their operations. Full article

Extreme weather events present significant global threats to health. The National Ambulance Syndromic Surveillance System collects data on 18 syndromes through chief presenting complaint (CPC) codes. We aimed to determine the utility of ambulance data to monitor extreme temperature events for action. Daily total calls were observed between 01/01/2018–30/04/2019. Median daily ’Heat/Cold’ CPC calls during “known extreme temperature” (identified a priori), “extreme temperature”; (within 5th or 95th temperature percentiles for central England) and meteorological alert periods were compared to all other days using Wilcoxon signed-rank test. During the study period, 12,585,084 calls were recorded. In 2018, median daily “Heat/Cold” calls were higher during periods of known extreme temperature: heatwave (16/day, 736 total) and extreme cold weather events (28/day, 339 total) compared to all other days in 2018 (6/day, 1672 total). Median daily “Heat/Cold” calls during extreme temperature periods (16/day) were significantly higher than non-extreme temperature periods (5/day, p < 0.001). Ambulance data can be used to identify adverse impacts during periods of extreme temperature. Ambulance data are a low resource, rapid and flexible option providing real-time data on a range of indicators. We recommend ambulance data are used for the surveillance of presentations to healthcare related to extreme temperature events. Full article

In this paper, an analytic review of the recent methodologies tackling the problem of dynamic allocation of ambulances was carried out. Considering that state-of-the-art is moving to deal with more extensive and dynamic problems to address in a better way real-life instances, this research looks to identify the evolution and recent applications of this kind of problem once the basic models are explored. This extensive review allowed us to identify the most recent developments in this problem and the most critical gaps to be addressed. In this sense, it is essential to point out that the dynamic location of emergency medical services (EMS) is nowadays a relevant topic considering its impact on the healthcare system outcomes. Issues related to forecasting, simulation, heterogeneous fleets, robustness, and solution speed for real-life problems, stand out in the identified gaps. Applications of machine learning the deployment challenges during epidemic outbreaks such as SARS and COVID-19 were also explored. At the same time, a proposed notation tries to tackle the fact that the word problem in this kind of work refers to a model on many occasions. The proposed notation eases the comparison between the different model proposals found in the literature. Full article

The aim of this study was to analyse medical management in geriatric patients in the Hospital Emergency Departments in the Biała Podlaska County and Chełm County (Poland) between 2016 and 2018 in a group of patients ≥65 years of age. We analysed medical records of 829 patients transported to Hospital Emergency Departments by Medical Emergency Teams. The research was conducted in the period from June 2019 to March 2020. We analysed emergency medical procedure forms and medical records of patients transported to the hospitals. Cardiovascular diseases were diagnosed in 40% of patients. Mortality cases accounted for 3.1% of the 1200 interventions analysed. Ambulance dispatch resulted in the patient being transported to the Hospital Emergency Departments in more than ²/₃ of cases. The concordance between the diagnoses made by the Medical Emergency Teams and those made at the Hospital Emergency Departments was confirmed for 78% patients admitted to the department (n = 647), whereas the concordance of classification at the group level was estimated at 71.7% (n = 594). Further in-patient treatment was initiated in some of the patients admitted to the department (n = 385). The mean time of hospital stay was 10.1 days. In conclusion, differences between the initial diagnosis made by the heads of the Medical Emergency Teams and the diagnosis made by the doctor on duty in the Hospital Emergency Departments depended on the chapter of diseases in the ICD-10 classification, but they were acceptable. The majority of the patients were transported to Hospital Emergency Departments. The most common groups of diseases that require Hospital Emergency Departments admission include cardiovascular diseases, injuries due to external causes, and respiratory diseases. A moderate percentage of patients were qualified for further specialist treatment in hospital departments. Full article