Dynamic ambulance reallocation for the reduction of ambulance response times using system status management

Assessing Efficiency in a Static-Based 9-1-1 Ambulance Service: An Analysis of Operational Performance Metrics

OBJECTIVES

This study sought to evaluate performance indicators to assist a static-based 9-1-1 agency in defining its response efficiency.

METHODS

Initial assessment of three metrics-unit hour utilization (UHU), fractile response intervals, and level 0 frequency (occurrence when no ambulances are available to respond)-suggested the agency's response over its four coverage zones was inefficient, so an operational change was implemented: an ambulance was relocated from one service area to another to improve the overall response productivity. A 2-year retrospective analysis was performed to determine the impact ambulance relocation had on the three targeted measurements.

RESULTS

The operational change resulted in a statistically significant change in unit hour utilization, a non-significant increase in fractile response intervals, and a statistically significant reduction in level 0 frequency from pre- to post-operational change times.

CONCLUSIONS

These findings suggest a way to evaluate the efficiency of static-based ambulance deployment and potentially identify strategies for redeployment.

Optimizing remote and rural prehospital resources using air transport of thrombectomy candidates

BACKGROUND

In Finland, the yearly number of mechanical thrombectomies for acute stroke is increasing and more patients are transported over 100 km to the comprehensive stroke centre (CSC) for definitive care. This leaves the rural townships without immediate emergency medical services (EMS) for hours. In this study we compare the EMS' estimated return times to own station after the handover of a thrombectomy candidate between two transport methods: (1) using ground transportation with an ambulance to the CSC or (2) using a hydrid strategy starting the transportation with an ambulance and continuing by air with a helicopter emergency medical services unit (HEMS).

METHODS

We reviewed retrospectively all thrombectomy candidates' transportations from the hospital district of South Ostrobothnia to definitive care at the nearest CSC, Tampere University Hospital from June 2020 to October 2022. The dispatch protocol stated that a thrombectomy candidate's transport begins immediately with an ambulance and if the local HEMS unit is available the patient is handed over to them at a rendezvous. If not, the patient is transported to the CSC by ground. Transport times and locations of the patient handovers were reviewed from the CSC's EMS database and the driving time back to ambulance station was estimated using Google maps. The HEMS unit's pilot's log was reviewed to assess their mission engagement time.

RESULTS

The median distance from the CSC to the ambulances' stations was 188 km (IQR 149-204 km) and from the rendezvous with the HEMS unit 70 km (IQR 51-91 km, p < 0.001). The estimated median driving time back to station after the patient handover at the CSC was 145 min (IQR 117-153 min) compared to the patient handover to the HEMS unit 53 min (IQR 38-68 min, p < 0.001). The HEMS unit was occupied in thrombectomy candidate's transport mission for a median of 136 min (IQR 127-148 min).

CONCLUSION

A hybrid strategy to transport thrombectomy candidates with an ambulance and a helicopter reallocates the EMS resources markedly faster back to their own district.

Dynamic ambulance relocation: a scoping review

OBJECTIVES

Dynamic ambulance relocation means that the operators at a dispatch centre place an ambulance in a temporary location, with the goal of optimising coverage and response times in future medical emergencies. This study aimed to scope the current research on dynamic ambulance relocation.

DESIGN

A scoping review was conducted using a structured search in PubMed, Scopus and Web of Science. In total, 21 papers were included.

RESULTS

Most papers described research with experimental designs involving the use of mathematical models to calculate the optimal use and temporary relocations of ambulances. The models relied on several variables, including distances, locations of hospitals, demographic-geological data, estimation of new emergencies, emergency medical services (EMSs) working hours and other data. Some studies used historic ambulance dispatching data to develop models. Only one study reported a prospective, real-time evaluation of the models and the development of technical systems. No study reported on either positive or negative patient outcomes or real-life chain effects from the dynamic relocation of ambulances.

CONCLUSIONS

Current knowledge on dynamic relocation of ambulances is dominated by mathematical and technical support data that have calculated optimal locations of ambulance services based on response times and not patient outcomes. Conversely, knowledge of how patient outcomes and the working environment are affected by dynamic ambulance dispatching is lacking. This review has highlighted several gaps in the scientific coverage of the topic. The primary concern is the lack of studies reporting on patient outcomes, and the limited knowledge regarding several key factors, including the optimal use of ambulances in rural areas, turnaround times, domino effects and aspects of working environment for EMS personnel. Therefore, addressing these knowledge gaps is important in future studies.

Use of discrete event simulation and genetic algorithms to estimate the necessary resources to respond in a timely manner in the Medical Emergency System in Bogotá

Introduction

Bogotá has a Medical Emergency System of public and private ambulances that respond to health incidents. However, its sufficiency in quantity, type and location of the resources demanded is not known.

Objective

Based on the data from the Medical Emergency System of Bogotá, Colombia, we first sought to characterize the prehospital re- sponse in cardiac arrest and determine with the model which is the least number of resources necessary to respond within eight minutes, taking into account their location, number, and type.

Methods

A database of incidents reported in administrative records of the district health authority of Bogotá (2014 to 2017) was obtained. Based on this information, a hybrid model based on discrete event simulation and genetic algorithms was designed to establish the amount, type and geographic location of resources according to the frequencies and typology of the events.

Results

From the database, Bogotá presented 938 671 ambulances dispatches in the period. 47.4% high priority, 18.9% medium and 33.74% low. 92% of these corresponded to 15 of 43 medical emergency codes. The response times recorded were longer than expected, especially in out-of-hospital cardiac arrest (median 19 minutes). In the proposed model, the best scenario required at least 281 ambulances, medicalized and basic in a 3:1 ratio, respectively, to respond in adequate time.

Conclusions

Results suggest the need for an increase in the resources that respond to these incidents to bring these response times to the needs of our population.

Does Every Minute Really Count? Road Time as an Indicator for the Economic Value of Emergency Medical Services

OBJECTIVES

This article builds on the literature regarding the association between emergency medical service (EMS) response times and patient outcomes (death and severe injury). Three issues are addressed in this article with respect to the empirical estimation of this relationship: the endogeneity of response time (systematically quicker response for higher degrees of urgency), the nonlinearity of this relationship, and the variation between such estimations for different patient outcomes.

METHODS

Binomial and multinomial logistic regression models are used to estimate the impact of response time on the probabilities of death and severe injury using data from French Fire and Rescue Services. These models are developed with response time as an explanatory variable and then with road time (dispatch to arrival) hypothesized as representing the exogenous variation within response time. Both models are also applied to data subsets based on response time intervals.

RESULTS

The results show that road time yields a higher estimate for the impact of response time on patient outcomes than (total) response time. The impact of road time on patient outcomes is also shown to be nonlinear. These results are of both statistical significance (model coefficients are significant at the 95% confidence level) and economical significance (when taking into account the number of annual interventions performed).

CONCLUSIONS

When using heterogeneous data on EMS interventions where endogeneity is a clear issue, road time is a more reliable indicator to estimate the impact of EMS response time on patient outcomes than (total) response time.

Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

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.

Impact of crowding in local ambulance demand on call-to-ambulance scene arrival in out-of-hospital cardiac arrest

BACKGROUND

Rapid emergency medical service (EMS) response is an important prognostic factor in out-of-hospital cardiac arrest (OHCA). This study aims to evaluate the association between local hourly EMS demand and ambulance response in OHCA.

METHODS

OHCA occurring in 24 districts of Seoul from 2013 to 2018 was analyzed. Hourly ambulance demand per ambulance in each local district of patient location at the hour of cardiac arrest was calculated as the crowding index. The crowding index was categorized according to quartiles (1Q: ≤0.43, 2Q: 0.44-0.67, 3Q: 0.68-0.99, 4Q: ≥1.0 calls/h\r/ambulance). The primary outcome was ambulance dispatched within 1 km of the OHCA scene. Multivariable logistic regression analysis was performed to test the association between the local hourly ambulance demand and outcomes.

RESULTS

A total of 26,479 patients were analyzed. The rate of ambulance dispatched within 1 km decreased according to the crowding quartile (1Q: 31.3%, 2Q: 30.0%, 3Q: 28.8%, and 4Q: 26.6%). Compared to 1Q, adjusted odds ratios (95% CIs) of dispatch distance within 1 km in 2Q, 3Q, and 4Q were 0.92 (0.86-0.99), 0.86 (0.80-0.94), and 0.77 (0.71-0.84), respectively.

CONCLUSION

Crowding in local ambulance demand was associated with less ambulance dispatched within 1 km and delayed response to the scene in OHCA. Strategies to mitigate and adjust to ambulance demand crowding may be considered for better EMS response performance.

Factors Influencing the Emergency Medical Service Response Time for Cardiovascular Disease in Guangzhou, China

While emergency medical service (EMS) response time (ERT) is a major factor associated with the survival of patients with cardiovascular disease (CVD), relatively few studies have explored the factors associated with ERT. This study aimed to assess the current status of ERT and to identify the factors affecting ERT in patients with CVD in China. Between January 1, 2011 and December 31, 2015, EMS responses to CVD incidents in Guangzhou, China, were examined. The primary outcome was ERT, defined as the time from receipt of an emergency call to the arrival of paramedics on the scene. Factors associated with ERT were evaluated by multivariable logistic regression. A total of 44 383 CVD incidents were analysed. The median ERT was 12.58 min (interquartile range=9.98-15.67). Among the risk factors, distance (OR=13.73, 95% CI=11.76-16.04), level of hospital (OR=1.57, 95% CI=1.40-1.75), and site of the incident (OR=1.53, 95% CI=1.38-1.69) were the top three significant factors affecting the ERT. Our results suggest that greater attention should be given to factors affecting the ERT. It is essential to make continuous efforts to promote the development of effective interventions to reduce the response time.

Association Between Emergency Medical Service Response Time and Motor Vehicle Crash Mortality in the United States

Importance

Motor vehicle crashes (MVCs) are a leading public health concern. Emergency medical service (EMS) response time is a modifiable, system-level factor with the potential to influence trauma patient survival. The relationship between EMS response time and MVC mortality is unknown.

Objectives

To measure the association between EMS response times and MVC mortality at the population level across US counties.

Design, Setting, and Study Population

This population-based study included MVC-related deaths in 2268 US counties, representing an estimated population of 239 464 121 people, from January 1, 2013, through December 31, 2015. Data were analyzed from October 1, 2017, through April 30, 2018.

Exposure

The median EMS response time to MVCs within each county (county response time), derived from data collected by the National Emergency Medical Service Information System.

Main Outcomes and Measures

The county rate of MVC-related death, calculated using crash fatality data recorded in the Fatality Analysis Reporting System of the National Highway Traffic Safety Administration.

Results

During the study period, 2 214 480 ambulance responses to MVCs were identified (median, 229 responses per county [interquartile range (IQR), 73-697 responses per county]) in 2268 US counties. The median county response time was 9 minutes (IQR, 7-11) minutes. Longer response times were significantly associated with higher rates of MVC mortality (≥12 vs <7 minutes; mortality rate ratio, 1.46; 95% CI, 1.32-1.61) after adjusting for measures of rurality, on-scene and transport times, access to trauma resources, and traffic safety laws. This finding was consistent in both rural/wilderness and urban/suburban settings, where a significant proportion of MVC fatalities (population attributable fraction: rural/wilderness, 9.9%; urban/suburban, 14.1%) were associated with prolonged response times (defined by the median value, ≥10 minutes and ≥7 minutes, respectively).

Conclusions and Relevance

Among 2268 US counties, longer EMS response times were associated with higher rates of MVC mortality. A significant proportion of MVC-related deaths were associated with prolonged response times in both rural/wilderness and urban/suburban settings. These findings suggest that trauma system-level efforts to address regional disparities in MVC mortality should evaluate EMS response times as a potential contributor.

A mathematical model for efficient emergency transportation in a disaster situation

This work focuses on a real-life patient transportation problem derived from emergency medical services (EMS), whereby providing ambulatory service for emergency requests during disaster situations. Transportation of patients in congested traffic compounds already time sensitive treatment. An urgent situation is defined as individuals with major or minor injuries requiring EMS assistance simultaneously. Patients are either (1) slightly injured and treated on site or (2) are seriously injured and require transfer to points of care (PoCs). This paper will discuss enhancing the response-time of EMS providers by improving the ambulance routing problem (ARP). A genetic based algorithm is proposed to efficiently guide the ARP while simultaneously solving two scenarios.

Simulation-based decision support framework for dynamic ambulance redeployment in Singapore

OBJECTIVE

Dynamic ambulance redeployment policies tend to introduce much more flexibilities in improving ambulance resource allocation by capitalizing on the definite geospatial-temporal variations in ambulance demand patterns over the time-of-the-day and day-of-the-week effects. A novel modelling framework based on the Approximate Dynamic Programming (ADP) approach leveraging on a Discrete Events Simulation (DES) model for dynamic ambulance redeployment in Singapore is proposed in this paper.

METHODS

The study was based on the Singapore's national Emergency Medical Services (EMS) system. Based on a dataset comprising 216,973 valid incidents over a continuous two-years study period from 1 January 2011-31 December 2012, a DES model for the EMS system was developed. An ADP model based on linear value function approximations was then evaluated using the DES model via the temporal difference (TD) learning family of algorithms. The objective of the ADP model is to derive approximate optimal dynamic redeployment policies based on the primary outcome of ambulance coverage.

RESULTS

Considering an 8min response time threshold, an estimated 5% reduction in the proportion of calls that cannot be reached within the threshold (equivalent to approximately 8000 dispatches) was observed from the computational experiments. The study also revealed that the redeployment policies which are restricted within the same operational division could potentially result in a more promising response time performance. Furthermore, the best policy involved the combination of redeploying ambulances whenever they are released from service and that of relocating ambulances that are idle in bases.

CONCLUSION

This study demonstrated the successful application of an approximate modelling framework based on ADP that leverages upon a detailed DES model of the Singapore's EMS system to generate approximate optimal dynamic redeployment plans. Various policies and scenarios relevant to the Singapore EMS system were evaluated.

Characterizing the influence of transportation infrastructure on Emergency Medical Services (EMS) in urban area-A case study of Seoul, South Korea

In highly urbanized area where traffic condition fluctuates constantly, transportation infrastructure is one of the major contributing factors to Emergency Medical Service (EMS) availability and patient outcome. In this paper, we assess the impact of traffic fluctuation to the EMS first response availability in urban area, by evaluating the k-minute coverage under 21 traffic scenarios. The set of traffic scenarios represents the time-of-day and day-of-week effects, and is generated by combining road link speed information from multiple historical speed databases. In addition to the k-minute area coverage calculation, the k-minute population coverage is also evaluated for every 100m by 100m grid that partitions the case study area of Seoul, South Korea. In the baseline case of traveling at the speed limit, both the area and population coverage reached nearly 100% when compared to the five-minute travel time national target. Employing the proposed LoST (Loss of Serviceability due to Traffic) index, which measures coverage reduction in percentage compared to the baseline case, we find that the citywide average LoST for area and population coverage are similar at 34.2% and 33.8%. However, district-wise analysis reveals that such reduction varies significantly by district, and the magnitude of area and population coverage reduction is not always proportional. We conclude that the effect of traffic variation is significant to successful urban EMS first response performance, and regional variation is evident among local districts. Complexity in the urban environment requires a more adaptive approach in public health resource management and EMS performance target determination.

Timely bystander CPR improves outcomes despite longer EMS times

OBJECTIVES

This study aimed to determine the impact of bystander CPR on clinical outcomes in patients with increasing response time from collapse to EMS response.

METHODS

A population-based observational study was conducted in patients with witnessed out-of-hospital cardiac arrest (OHCA) of presumed cardiac etiology from 2012 to 2014. The time interval from collapse to CPR by EMS providers was categorized into quartile groups: fastest group (<4min), fast group (4 to <8min), late group (8 to <15min), and latest group (15 to <30min). The primary outcome was hospital discharge and the secondary outcome was survival with good neurological outcome. Multivariable logistic regression analysis was performed to evaluate the interaction between bystander CPR and the time interval from collapse to CPR by EMS providers.

RESULTS

A total of 15,354 OHCAs were analyzed. Bystander CPR was performed in 8591 (56.0%). Survival to hospital discharge occurred in 1632 (10.6%) and favorable neurological outcome in 996 (6.5%). In an interaction model of bystander CPR, compared to the fastest group, adjusted odds ratios (AORs) (95% CIs) for survival to discharge were 0.89 (0.66-1.20) in the fast group, 0.76 (0.57-1.02) in the late group, and 0.52 (0.37-0.73) in the latest group. For favorable neurological outcome, AORs were 1.12 (0.77-1.62) in the fast group, 0.90 (0.62-1.30) in the late group, 0.59 (0.38-0.91) in the latest group.

CONCLUSION

The survival from OHCA decreases as the ambulance response time increases. The increase in mortality and worsening neurologic outcomes appear to be mitigated in those patients who receive bystander CPR.

Prehospital Emergency Medical Services Departure Interval: Does Patient Age Matter?

Introduction The concept of response time with minimal interval is intimately related to the practice of emergency medicine. The factors influencing this time interval are poorly understood. Problem In a process of improvement of response time, the impact of the patient's age on ambulance departure intervals was investigated.

METHOD

This was a 3-year observational study. Departure intervals of ambulances, according to age of patients, were analyzed and a multivariate analysis, according to time of day and suspected medical problem, was performed.

RESULTS

A total of 44,113 missions were included, 2,417 (5.5%) in the pediatric group. Mean departure delay for the adult group was 152.9 seconds, whereas it was 149.3 seconds for the pediatric group (P =.018).

CONCLUSION

A statistically significant departure interval difference between missions for children and adults was found. The difference, however, probably was not significant from a clinical point of view (four seconds). Schnegg B , Pasquier M , Carron PN , Yersin B , Dami F . Prehospital Emergency Medical Services departure interval: does patient age matter? Prehosp Disaster Med. 2016;31(6):608-613.

Factors affecting the ambulance response times of trauma incidents in Singapore

OBJECTIVES

Time to definitive care is important for trauma outcomes, thus many emergency medical services (EMS) systems in the world adopt response times of ambulances as a key performance indicator. The objective of this study is to examine the underlying risk factors that can affect ambulance response times (ART) for trauma incidents, so as to derive interventional measures that can improve the ART.

MATERIAL AND METHODS

This was a retrospective study based on two years of trauma data obtained from the national EMS operations centre of Singapore. Trauma patients served by the national EMS provider over the period from 1 January 2011 till 31 December 2012 were included. ART was categorized into "Short" (<4min), "Intermediate" (4-8min) and "Long" (>8min) response times. A modelling framework which leveraged on both multinomial logistic (MNL) regression models and Bayesian networks was proposed for the identification of main and interaction effects.

RESULTS

Amongst the process-related risk factors, weather, traffic and place of incident were found to be significant. The traffic conditions on the roads were found to have the largest effect-the odds ratio (OR) of "Long" ART in heavy traffic condition was 12.98 (95% CI: 10.66-15.79) times higher than that under light traffic conditions. In addition, the ORs of "Long ART" under "Heavy Rain" condition were significantly higher (OR 1.58, 95% CI: 1.26-1.97) than calls responded under "Fine" weather. After accounting for confounders, the ORs of "Long" ART for trauma incidents at "Home" or "Commercial" locations were also significantly higher than that for "Road" incidents.

CONCLUSION

Traffic, weather and the place of incident were found to be significant in affecting the ART. The evaluation of factors affecting the ART enables the development of effective interventions for reducing the ART.