Using Big Data to optimize dynamic ambulance availability maps: bridging the gap in emergency services

Prehospital care including recognition of stroke symptoms by the public and professionals combined with an efficient and effective emergency medical service (EMS) is essential to increase access to effective acute stroke care. We undertook a survey to document the status of stroke prehospital care globally. A survey was distributed via email to the World Stroke Organization (WSO) members. Information was sought on the current status of stroke prehospital delay globally, including (1) ambulance availability and whether payment for use is required, (2) ambulance response times and the proportion of patients arriving at hospital by ambulance, (3) the proportion of patients arriving within 3 h and more than 24 h after symptom, (4) whether stroke care training of paramedics, call handlers, and primary care staff, (5) availability of specialist centers, and (6) the proportion of patients taken to specialist centers. Respondents were also asked to identify the top three changes in prehospital care that would benefit their population. Data were analyzed descriptively at both country and continent level. Responses were received from 116 individuals in 43 countries, with a response rate of 4.7%. Most respondents (90%) reported access to ambulances, but 40% of respondents reported payment was required by the patient. Where an ambulance service was available (105 respondents) 37% of respondents reported that less than 50% of patients used an ambulance and 12% less than 20% of patients used an ambulance. Large variations in ambulance response times were reported both within and between countries. Most of the participating high-income countries (HIC) offered a service used by patients, but this was rarely the case for the low- and middle-income countries (LMIC). Time to admission was often much longer in LMIC, and there was less access to stroke training for EMS and primary care staff. Significant deficiencies in stroke prehospital care exist globally especially in LMIC. In all countries, there are opportunities to improve the quality of the service in ways that would likely result in improved outcomes after acute stroke.

BackgroundPre-hospital and emergency services in Indonesia are still developing. Despite recent improvements in the Indonesian healthcare system, issues with the provision of pre-hospital and emergency services persist. The demand for pre-hospital and emergency services has not been the subject of previous research and, therefore, has not been fully understood. Our research explored the utilization of emergency medical services by patients attending hospital emergency departments in Jakarta, Indonesia.MethodsThe study used a cross-sectional survey design involving five general hospitals (four government-funded and one private). Each patient’s demographic profile, medical conditions, time to treatment, and mode of transport to reach the hospital were analysed using descriptive statistics.ResultsA total of 1964 (62%) patients were surveyed. The median age of patients was 44 years with an interquartile range (IQR) of 26 to 58 years. Life-threatening conditions such as trauma and cardiovascular disease were found in 8.6 and 6.6% of patients, respectively. The majority of patients with trauma travelled to the hospital using a motorcycle or car (59.8%). An ambulance was used by only 9.3% of all patients and 38% of patients reported that they were not aware of the availability of ambulances. Ambulance response time was longer as compared to other modes of transportation (median: 24 minutes and IQR: 12 to 54 minutes). The longest time to treatment was experienced by patients with neurological disease, with a median time of 120 minutes (IQR: 78 to 270 minutes). Patients who used ambulances incurred higher costs as compared to those patients who did not use ambulances.ConclusionThe low utilization of emergency ambulances in Jakarta could be contributed to patients’ lack of awareness of medical symptoms and the existence of ambulance services, and patients’ disinclination to use ambulances due to high costs and long response times. The emergency ambulance services can be improved by increasing population awareness on symptoms that warrant the use of ambulances and reducing the cost burden related to ambulance use.

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.

IntroductionPre-hospital ambulance service is the most important part of healthcare service. Client satisfaction with the service indicates the degree of adaptation to the appropriate quality and quantity of services. Patients’ dissatisfaction with the service can affect their expectations of the overall services that they will receive later in the definitive care facility. However, it is not a well-addressed area in developing countries, including Ethiopia.ObjectiveThis study aimed to identify the ambulance service satisfaction level and associated factors among service users in Addis Ababa, Ethiopia.MethodsA cross-sectional study was conducted in five governmental hospitals in Addis Ababa city. A face-to-face exit interview technique was employed on a total of 410 consecutively selected participants using a pretested tool developed from similar sources. The cleaned data was entered into the Epi-Data Manager 4.6 version and then exported to SPSS version 26 for analysis. The dependent variable was dichotomized into satisfied and unsatisfied to compute bivariate logistic regression. In the multivariate logistic regression model, predictors with a p-value < 0.05 at the 95% CI were considered to have a significant association.ResultA total of 410 respondents were included in the study. The mean of participants’ responses regarding ambulance personnel, call operator, treatment on the scene, and ambulance subscale was 3.64, 3.48, 3.40, and 3.43, respectively. The study found that only 21.5% of participants were satisfied by the ambulance service they received. There was a statistically significant association between ambulance service satisfaction and age (AOR = 3.52, 95% CI: 1.01–12.36), monthly income (AOR = 3.13, 95% CI: 1.41–6.94), ambulance response time (AOR = 10.33, 95% CI: 2.09–51.06), type of ambulance used (AOR = 4.55, 95% CI: 2.19–9.43), and previous ambulance usage (AOR = 2.33, 95% CI: 1.34–4.05).ConclusionThe study found a low level of satisfaction among ambulance users. The findings suggest that ambulance personnel performance is a key determinant of user satisfaction, while treatment at the scene and in the ambulances, and call operator areas require improvement. Age, monthly income, ambulance response time, type of ambulance, and previous ambulance use also influenced satisfaction. Improving the quality of services, reducing response time, and ensuring call operators are trained are vital steps to enhance satisfaction.

Background: Chest pain is a leading complaint in emergency settings. Timely emergency medical services (EMS) responses can reduce delays to treatment and improve clinical outcomes for acute myocardial infarction patients and other medical emergencies. We investigated national-level EMS response, scene, and transport times for acute chest pain patients in the United States. Methods: A retrospective analysis was performed using 2015–2016 data from the National EMS Information System (NEMSIS). Eligible patients were identified as having a provider impression of chest pain or discomfort and not due to trauma or resulting in cardiac arrest during EMS care. Descriptive analyses of prehospital time intervals and patient-, response-, and system-level covariates were performed. Multivariable logistic regression was used to measure associations between meeting response and scene time benchmarks (8-min and 15-min, respectively) and covariates. Results: Our study identified 1,672,893 eligible EMS encounters of chest pain. Patients had a mean age of 63.1 years (SD = 14.8). The population was evenly distributed by sex (51% male; 49% female). Most encounters occurred in home or residence (58%) and had lights and sirens response to scene (84%). Most encounters were in urban areas (78%). The median (interquartile range, IQR) response time was 7 (5–10) minutes. The median (IQR) scene time was 16 (12–20) minutes. The median (IQR) transport time was 13 (8–20) minutes. Generally, median response and transport times were longer in rural and frontier areas compared to urban and suburban areas. Only 65% and 49% met the 8-min response and 15-min scene time benchmarks. Responding with lights and sirens was associated with greater compliance with EMS response time benchmark. EMS care of older age groups and females was less likely to meet the scene time benchmark. Conclusions: Substantial proportions of EMS encounters for chest pain did not meet response and scene time benchmarks. Regional and urban-rural differences were observed in adherence with the response time benchmark. Our findings also suggest age and gender disparities in on-scene delays by EMS. Our study contributes important evidence on timely EMS responses for cardiac chest pain and provides suggestions for EMS system benchmarking and quality improvement.

Objectives. To identify regulatory programs for ambulance response times used by California counties, to inventory their foci and application, and to identify differences around the state. By studying the variety of programs used in one state, this study establishes a framework for evaluation of state and local regulatory programs elsewhere. Methods. This study surveyed all California local EMS agencies (LEMSAs), California's equivalent of regional EMS organizations. The survey achieved a 100% response rate and all data involve population parameters obviating the need for inferential statistics. Results. Fifty-seven percent of California counties regulate response times. Large-population counties and those that operate their own LEMSAs are more likely to use response time regulations than are small counties and those that participate in multicounty EMS agencies. Most of the counties with response time standards measure from dispatch to arrival at the scene and most use a fractile measurement to determine compliance. Many of the ambulance enforcement programs in California have enforcement mechanisms that are unlikely to promote compliance. Conclusions. Response time regulations are intended to improve the effectiveness of prehospital care. Few counties use standards that comply with recommended state standards. A large number of counties are unable to determine how well ambulance services actually comply with the standards and are not in a position to enforce them. The use of these regulatory programs is questionable based on their application.

Background and Purpose: There are no contemporary national-level data on Emergency Medical Services (EMS) response times for suspected stroke in the United States (US). Because effective stroke treatment is time-dependent, we characterized response times for suspected stroke, and examined whether they met guideline recommendations. Methods: Using the National EMS Information System dataset, we included 911 calls for patients ≥ 18 years with an EMS provider impression of stroke. We examined variation in the total EMS response time by dispatch notification of stroke, age, sex, race, region, time of day, day of the week, as well as the proportion of EMS responses that met guideline recommended response times. Total EMS response time included call center dispatch time (receipt of call by dispatch to EMS being notified), EMS dispatch time (dispatch informing EMS to EMS starts moving), time to scene (EMS starts moving to EMS arrival on scene), time on scene (EMS arrival on scene to EMS leaving scene), and transport time (EMS leaving scene to reaching treatment facility). Results: We identified 184,179 events with primary impressions of stroke (mean age 70.4 ± 16.4 years, 55% male). Median total EMS response time was 36 (IQR 28.7–48.0) minutes. Longer response times were observed for patients aged 65–74 years, of white race, females, and from non-urban areas. Dispatch identification of stroke versus “other” was associated with marginally faster response times (36.0 versus 36.7 minutes, p < 0.01). When compared to recommended guidelines, 78% of EMS responses met dispatch delay of <1 minute, 72% met time to scene of <8 minutes, and 46% met on-scene time of <15 minutes. Conclusions: In the United States, time from receipt of 9-1-1 calls to treatment center arrival takes a median of 36 minutes for stroke patients, an improvement upon previously published times. The fact that 22%–46% of EMS responses did not meet stroke guidelines highlights an opportunity for improvement. Future studies should examine EMS diagnostic accuracy nationally or regionally using outcomes based approaches, as accurate recognition of prehospital strokes is vital in order to improve response times, adhere to guidelines, and ultimately provide timely and effective stroke treatment.

Statistical modeling of emergency medical services’ response and rescue times to road traffic crashes in the Kingdom of Saudi Arabia

196 Comparing Response Times, Intensity of Care and Outcomes between Private versus Municipal Emergency Medical Services Systems

ObjectiveEmergency medical dispatch is intended to ensure that emergency medical services (EMS) allocate appropriate resources for the treatment of patients with status epilepticus (SE). However, it is unclear whether dispatch algorithms accurately identify those patients having a seizure-related medical emergency and how dispatch algorithms influence what prehospital resources are allocated for the encounter.MethodsWe performed a cross-sectional analysis of prehospital encounters for SE using data from the 2019 ESO Data Collaborative. We included patients who were ≥18 years of age, had an EMS diagnostic impression of SE, and did not have a cardiac arrest. We examined the dispatch-determined complaint designated by the emergency medical dispatch (EMD) code, dispatch-determined level of acuity (A, B, C, D), ambulance response, and training level of the responding prehospital professional.ResultsOf the 18,515 patient encounters for SE with an EMD code, 8,279 (44.9%) were women, and the mean age was 40.0 years (SD 19.7). There were 13,829 (75%) encounters that received a dispatch code for seizures/convulsions and 4,686 (25%) with a dispatch code for a non-seizure-related condition. Among encounters for SE identified by dispatch as seizures/convulsions, 6,412 (46.4%) were designated high acuity, 6,626 (63.6%) were designated low acuity, and the majority received emergent ambulance responses (98.1% among those designated high acuity and 81.8% among those designated low acuity) and an Advanced Life Support-trained responder (93.7% among those designated high acuity and 92.7% among those designated low acuity). Median response times were similar for all acuity levels (9.1, 8.8, 9.1, and 8.3 minutes for A-D, respectively).ConclusionApproximately one-fourth of SE cases were categorized as a non-seizure related condition at dispatch, and fewer than half received the highest acuity determinant code. Despite this, dispatch-assigned acuity did not have a strong relationship with the ambulance response or training level of the EMS responder or response time, suggesting that use of dispatch algorithms might be further optimized and highlighting a potential area to improve quality of EMS care.

The ambulance service in Victoria, Australia implemented a revised clinical response model (CRM) in 2016 which was designed to increase the diversion of low-acuity Triple Zero (000) calls to secondary telephone triage and reduce emergency ambulance dispatches. The present study evaluates the influence of the revised CRM on emergency ambulance response times and ED presentations. A retrospective study of emergency calls for ambulance between 1 January 2015 and 31 December 2018. Ambulance data were linked with ED presentations occurring up to 48 h after contact. Interrupted time series analyses were used to evaluate the impact of the revised CRM. A total of 2 365 529 calls were included. The proportion allocated a Code 1 (time-critical, lights/sirens) dispatch decreased from 56.6 to 41.0% after implementation of the revised CRM. The proportion of calls not receiving an emergency ambulance increased from 10.4 to 19.6%. Interrupted time series analyses demonstrated an improvement in Code 1 cases attended within 15 min (Key Performance Indicator). However, for patients with out-of-hospital cardiac arrest or requiring lights and sirens transport to hospital, there was no improvement in response time performance. By the end of the study period, there was also no difference in the proportion of callers presenting to ED when compared with the estimated proportion assuming the revised CRM had not been implemented. The revised CRM was associated with improved Code 1 response time performance. However, there was no improvement in response times for high acuity patients, and no change in the proportion of callers presenting to ED.

Prehospital care is a critical component of efforts to lower trauma mortality. In less-developed countries, scarce resources dictate that any improvements in prehospital care must be low in cost. In one Latin American city, recent efforts to improve prehospital care have included an increase in the number of sites of ambulance dispatch from two to four and introduction of the Prehospital Trauma Life Support (PHTLS) course. The effect of increased dispatch sites was evaluated by comparing response times before and after completion of the change. The effect of PHTLS was evaluated by comparing prehospital treatment for the 3 months before initiation of the course (n = 361 trauma patients) and the 6 months after (n = 505). Response time decreased from a mean of 15.5 +/- 5.1 minutes, when there were two sites of dispatch, to 9.5 +/- 2.7 minutes, when there were four sites. Prehospital trauma care improved after initiation of the PHTLS course. For all trauma patients, use of cervical immobilization increased from 39 to 67%. For patients in respiratory distress, there were increases in the use of oropharyngeal airways (16-39%), in the use of suction (10-38%), and in the administration of oxygen (64-87%). For hypotensive patients, there was an increase in use of large-bore intravenous lines from 26 to 58%. The improved prehospital treatment did not increase the mean scene time (5.7 +/- 4.4 minutes before vs. 5.9 +/- 6.8 minutes after). The percent of patients transported who died in route decreased from 8.2% before the course to 4.7% after. These improvements required a minimal increase (16%) in the ambulance service budget. Increase in sites of dispatch and increased training in the form of the PHTLS course improved the process of pre-hospital care in this Latin American city and resulted in a decrease in prehospital deaths. These improvements were low cost and should be considered for use in other less developed countries.

Background A recent national review of English ambulance services, Taking Healthcare to the Patient: Transforming NHS Ambulance Services, 1 published by the Department of Health, recommended that pre-hospital care research topics should be prioritised to ensure that service provision and development are evidence based wherever possible and that limited available funds are targeted to the most pressing needs. Study objectives To identify gaps in research evidence related to delivery of pre-hospital care; and to rank topics in order of priority for research. Methods Research priorities were initially identified by delegates at the UK Ambulance Service Association's annual conference, AMBEX 2006. An examination of research reviews in pre-hospital care identified other research evidence gaps. Relevant websites, databases and review bibliographies were also searched. Management, service delivery and treatment recommendations in UK policy/guidance documents published since 2000 were matched to research evidence. A list of evidence gaps was circulated in a Delphi-style three-round consultation to experts in pre-hospital care, including clinicians, managers and researchers. Round 1 confirmed/identified research gaps; Round 2 focussed on ranking topics; and Round 3 reviewed the scores and provided an option to rescore. Scores were analysed using SPSS. Results Ninety-six research issues were identified for circulation and prioritisation from 52 reviews and expert consultation and these were matched against 30 policy and guidance documents. Forty people participated in the Delphi exercise. The subject receiving highest priority for research was the development of new performance measures other than emergency ambulance response times. Other highly ranked priorities included treatment of stroke, cardiac conditions, children and people who self-harm; alternatives to Accident and Emergency (A&amp;E) treatment; patient information sharing across care providers; access issues; decision support systems; and demand management systems for pre-hospital care. These priorities reflect three key issues: measuring activity to benefit patients; development of safe non-A&amp;E care; and providing appropriate evidence-based clinical care in the pre-hospital environment. Implications There are many evidence gaps related to current pre-hospital policy and practice including management, clinical and service delivery issues. This Delphi consultation combines expertise of clinicians, managers and researchers to generate consensus on future research priorities in pre-hospital care. The need to develop meaningful performance measures plus alternative methods of patient management illustrates the synergistic relationship between service delivery and performance measurement. It suggests an opportunity to identify alternatives to response times as indicators of quality of pre-hospital care. The final results from this study will be useful to commissioners when developing their strategic approach to decision making about which research should be funded to facilitate continued development of quality patient care in the pre-hospital setting.

Creating an ideal system of care to address the care forpatients with ST-elevation myocardial infarction (STEMI) iscomplex from both the system’s and patient/family’s perspec-tives. In general, this care is unlike most other hospital care.It typically involves very fast and complex decision makingand, often, sudden transportation to another facility forpercutaneous coronary intervention (PCI). All of this occurswith a potentially critically ill patient and at a time when thefamily is often not immediately available. In this report, weaddress key perspectives from the patient and public point ofview of the current system of care for STEMI patients andhighlight the barriers and gaps that must be addressed by anideal system of care (Table 1).

Introduction: EMS time factors such as total prehospital, activation, response, scene and transport intervals have been used as a measure of EMS system quality with the assumption that shorter EMS time factors save lives. The objective was to assess in adults and children accessing ground EMS (population), whether operational time factors (intervention and control) were associated with survival at hospital discharge (outcome). Methods: Medline, EMBASE, and CINAHL were searched up to January 2015 for articles reporting original data that associated EMS operational time factors and survival. Conference abstracts and non-English language articles were excluded. Two investigators independently assessed the candidate titles, abstracts, and full text with discrepant reviews resolved by consensus. Risk of bias was assessed using GRADE. Results: A total of 10,151 abstracts were screened for potential inclusion, 199 articles were reviewed in full-text, and 73 met inclusion criteria. Amongst included studies, 49 investigated response time, while 24 investigated other time factors. All articles were observational studies. Amongst the 14 (28.6%) studies where response time was the primary analysis, statistically significant associations between shorter response time and increased survival were found in 5 of 7 cardiac arrest, 1 of 5 general EMS population, and 0 of 2 trauma studies. Other time factors were reported in the primary analysis in 10 (41.7%) studies. One study reported shorter combined scene and transport intervals associated with increased survival in acute heart failure patients. Two studies in trauma patients had somewhat conflicting results with one study reporting shorter prehospital interval associated with increased survival whereas the other reported increased survival associated with longer scene and transport intervals. Study design, analysis, and methodological quality were of considerable variability, and thus, meta-analyses were not possible. Conclusion: There is a substantial body of literature describing the association between EMS time factors and survival, but evidence informing these relationships are heterogeneous and complex. Important details such as patient population, EMS system characteristics, and analytical approach must be taken into consideration to appropriately translate these findings to practice. These results will be important for EMS leaders wishing to create evidence-based time policies.