Preventing Emergency Vehicle Crashes: Status and Challenges of Human Factors Issues

Ambulance Traffic Crashes in Japan: Characteristics of Casualties and Efforts to Improve Ambulance Safety

BACKGROUND

An ambulance traffic crash not only leads to injuries among emergency medical service (EMS) professionals but also injures patients or their companions during transportation. We aimed to describe the incidence of ambulance crashes, seating location, seatbelt use for casualties (ie, both fatal and nonfatal injuries), ambulance safety efforts, and to identify factors affecting the number of ambulance crashes in Japan.

METHODS

We conducted a nationwide survey of all fire departments in Japan. The survey queried each fire department about the number of ambulance crashes between January 1, 2017, and December 31, 2019, the number of casualties, their locations, and seatbelt usage. Additionally, the survey collected information on fire department characteristics, including the number of ambulance dispatches, and their safety efforts including emergency vehicle operation training and seatbelt policies. We used regression methods including a zero-inflated negative binomial model to identify factors associated with the number of crashes.

RESULTS

Among the 726 fire departments in Japan, 553 (76.2%) responded to the survey, reporting a total of 11,901,210 ambulance dispatches with 1,659 ambulance crashes (13.9 for every 100,000 ambulance dispatches) that resulted in a total of 130 casualties during the 3-year study period (1.1 in every 100,000 dispatches). Among the rear cabin occupants, seatbelt use was limited for both EMS professionals (n = 3/29, 10.3%) and patients/companions (n = 3/26, 11.5%). Only 46.7% of the fire departments had an internal policy regarding seatbelt use. About three-fourths of fire departments (76.3%) conducted emergency vehicle operation training internally. The output of the regression model revealed that fire departments that conduct internal emergency vehicle operation training had fewer ambulance crashes compared to those that do not (odds of being an excessive zero -2.20, 95% CI: -3.6 to -0.8).

CONCLUSION

Two-thirds of fire departments experienced at least one crash during the study period. The majority of rear cabin occupants who were injured in ambulance crashes were not wearing a seatbelt. Although efforts to ascertain seatbelt compliance were limited, Japanese fire departments have attempted a variety of methods to reduce ambulance crashes including internal emergency vehicle operation training, which was associated with fewer ambulance crashes.

Effect of urgency level on prehospital emergency transport times: a natural experiment

Accurate estimation of ambulance transport time from the scene of incident to arrival at the emergency department (ED) is important for effective resource management and emergency care system planning. Further, differences in transport times between different urgency levels highlight the benefits of ambulance transports with highest urgency level in a setting where ambulances are allowed to not follow standard traffic rules. The objective of the study is to compare ambulance urgency level on the differences in estimates of ambulance transport times generated by Google Maps and the observed transport times in a prehospital setting where emergency vehicles have their own traffic laws. The study was designed as a natural experiment and register study. Ambulance transports dispatched with different levels of urgency (Level A and B) were included in the Central Denmark Region (a mixed urban and rural area) from March 10 to June 11, 2021. Ambulance transports for highest urgency level were compared to lowest urgency level with Google Maps estimated transport times as reference. We analyzed 1981 highest urgency level and 8.958 lowest urgency level ambulance transports. Google Maps significantly overestimated the duration of transports operating at highest level of urgency (Level A) by 1.9 min/10 km (95% CI 1.8; 2.0) in average and 4.8 min/10 km (95% CI 3.9; 5.6) for the first driven 10 km. Contrary, Google Maps significantly underestimated the duration of transports operating at lowest level of urgency (Level B) by -1.8 min/10 km (95% CI -2.1; -1.5) in average and -4.4 min/10 km (95% CI -5.4; -3.5) for the first driven 10 km. Google Maps systematically overestimates transport times of ambulance transports driven with Level A, the highest level of urgency in a setting where ambulances are allowed to not follow standard traffic rules. The results highlight the benefit of using urgency Level A and provide valuable information for emergency care management.

False Alarm Effects in Early Warnings for Emergency Vehicles: Exploring Drivers' Move-Over Behavior

OBJECTIVE

This study investigated drivers' move-over behavior when receiving an Emergency Vehicle Approaching (EVA) warning. Furthermore, the possible effects of false alarms, driver experience, and modality on move-over behavior were explored.

BACKGROUND

EVA warnings are one solution to encourage drivers to move over for emergency vehicles in a safe and timely manner. EVA warnings are distributed based on the predicted path of the emergency vehicle causing a risk of false alarms. Previous EVA studies have suggested a difference between inexperienced and experienced drivers' move-over behavior.

METHOD

A driving simulator study was conducted with 110 participants, whereof 54 inexperienced and 56 experienced drivers. They were approached by an emergency vehicle three times. A control group received no EVA warnings, whereas the experimental groups received either true or false warnings, auditory or visual, 15 seconds before the emergency vehicle overtook them.

RESULTS

Drivers who received EVA warnings moved over more quickly for the emergency vehicle compared to the control group. Drivers moved over more quickly for each emergency vehicle interaction. False alarms impaired move-over behavior. No difference in driver behavior based on driver experience or modality was observed.

CONCLUSION

EVA warnings positively affect drivers' move-over behavior. However, false alarms can decrease drivers' future willingness to comply with the warning.

APPLICATION

The findings regarding measurements of delay can be used to optimize the design of future EVA systems. Moreover, this research should be used to further understand the effect of false alarms in in-car warnings.

Children and Restraints Study in Emergency Ambulance Transport: An Observational Study and Analysis of Current Pediatric Ambulance Transport Practices

OBJECTIVES

The aims of this study were to identify the pediatric transport methods used by Emergency Medical Services (EMS) personnel in our area and to highlight the need for federal standards to unify prehospital transport of children.

METHODS

Children and Restraints Study in Emergency Ambulance Transport is a retrospective observational study of EMS arrivals to an academic pediatric emergency department for 1 year. Review of existing security footage from the ambulance entrance focused on the appropriateness of the selected restraints and the correctness of their application. A total of 3034 encounters were adequate for review and were matched to an emergency department encounter. Weight and age were identified from the chart. Patient weight was used in conjunction with video review to assess for the appropriateness of restraint selection.

RESULTS

A total of 53.5% (1622) of patients were transported using a weight appropriate device or restraint system. In 77.1% of all cases (2339), the devices or restraint systems were applied incorrectly. The best results were observed for commercial pediatric restraint devices (54.5% secured appropriately) and for convertible car seats (55.5%). Ambulance cot was used alone in 69.35% of all transports despite it being the appropriate choice in just 18.2% of transports.

CONCLUSIONS

Our findings confirmed that most pediatric patients transported by EMS are not appropriately secured and are at increased injury in a crash and potentially during normal vehicle operation. Opportunity exists for regulators, industry, and leaders in EMS and pediatrics to develop fiscally and operationally prudent techniques and devices to improve the safety of children in ambulances.

Medical fitness to drive, emergency service vehicles and crash risk

BACKGROUND

Emergency service vehicle (ESV) drivers are an important part of the health, fire and police services. ESV driving is associated with increased crash risk, but little guidance exists in the literature on relevant medical conditions among drivers and their potential for adding to higher crash risks.

AIMS

We undertook a narrative review to examine the role of medical and other conditions in crash risk of ESV drivers.

METHOD

A literature search was conducted using the ScienceDirect and Transport Research International Documentation (TRID) databases. There was no time frame for the search, and results were restricted to review and research articles.

RESULTS

Of 570 papers identified, 13 remained after screening and full-text review. A range of factors have been shown to have an impact on increased crash risk, including the nature of the task, physical features of the equipment, training, experience, environmental conditions and secondary tasks. There was scant information on medical conditions other than alcohol use disorders.

CONCLUSIONS

Given issues of speed, vehicle and environment, it would seem prudent to mandate levels of medical fitness to drive similar to and sometimes exceeding (i.e. colour blindness for traffic signals and alerts, hearing impairment as first responders) those for group 2 drivers with extra stipulations relating to specific service needs such as enhanced visual (such as colour blindness and contrast sensitivity) and auditory function. Further research is needed on the prevalence and emergence of relevant medical conditions among ESV drivers, with due consideration of their application to the driving tasks in each service.

Data mining approach to explore emergency vehicle crash patterns: A comparative study of crash severity in emergency and non-emergency response modes

Emergency vehicle crashes, involving police vehicles, ambulances, and fire trucks, pose a serious traffic safety concern causing severe injury and deaths to first responders and other road users. However, limited research is available focusing on the contributing factors and their interactions related to these crashes. This research aims to address this gap by 1) identifying patterns of emergency vehicle crashes based on severity levels in both emergency and non-emergency modes and 2) comparing the associations by response modes for the related fatal, nonfatal injury, and no-injury crashes. Two national crash databases, Fatality Analysis Reporting System (FARS) and Crash Report Sampling System (CRSS), were utilized for police-reported emergency vehicle crashes from January 2016 to February 2020. Association rule mining (ARM) was employed to reveal the association between factors that strongly contributed to these crashes. The generated rules were validated using the lift increase criterion (LIC). The results showed the complex nature of risk factors influencing the severity of emergency vehicle crashes. The fatal consequences of speeding with no seatbelt usage were evident for emergency mode, whereas none of these risky driving attributes was observed for non-emergency mode. In addition, the analysis identified the risk of fatal emergency vehicle crashes involving pedestrians in dark-lighted conditions in both response modes. Regarding nonfatal injury severity, angle collisions were more likely to occur at urban intersections during emergencies, while rear-end crashes were more frequent on segments with a posted speed limit of 40-45 mph during non-emergency incidents. The outcomes also revealed that the no-injury crashes involving fire trucks exhibited different patterns depending on the response mode. The findings of this study can guide in making effective strategies to improve safe driving behavior of first responders. The identified associations provide insights into the factors that can be controlled to ensure safe operation of emergency vehicles on the road.

The Epidemiology of Prehospital Ambulance Crashes: A National Experience Across Saudi Red Crescent Authority

Introduction Road traffic injuries (RTIs) have a significant impact on the healthcare system as well as the global economy. RTIs involving ambulance crashes not only cause delays in patient transfers but also endanger ambulance occupants and other road users. Due to the rising numbers of RTIs in the Kingdom of Saudi Arabia (KSA), the country's primary provider of prehospital services the Saudi Red Crescent Authority (SRCA) has established a new Trauma Epidemiology Center (TEC) following the KSA 2030 vision. Objective This current study was conducted to determine the causes and management-related aspects of RTIs involving ambulance crashes in KSA. We aim to highlight the current status and guide further efforts to fill gaps in knowledge and on-ground changes to prevent ambulance crashes, enhance patient care, and reduce morbidity and mortality. Method This is a descriptive, retrospective cross-sectional observational study of ambulance crashes in Saudi Arabia between January 2020 and July 2022. The data was extracted from the SRCA electronic database platform. Results One hundred and sixty accidents were recorded between 2020 and 2022 with the highest proportion of accidents recorded in Riyadh 44.29%; followed by the Eastern Province and Makkah, 15% and 12%, respectively. Ninety percent of ambulance crashes were due to direct collisions and caused by other parties in 58% of cases. It is worth mentioning that ambulance drivers fastened their seat belts in 99% of crashes. Conclusion This study highlights the epidemiology and distribution of prehospital ambulance crashes in the KSA. Most crashes occurred in urban areas with a reasonable contribution of the public in such crashes. Understanding contributing factors related to the vector, driver behaviors, and the surrounding environment is crucial to guide national preventive measures and help decision-makers to implement proper corrective actions.

Vehicle crash simulations for safety: Introduction of connected and automated vehicles on the roadways

Traffic accidents are one main cause of human fatalities in modern society. With the fast development of connected and autonomous vehicles (CAVs), there comes both challenges and opportunities in improving traffic safety on the roads. While on-road tests are limited due to their high cost and hardware requirements, simulation has been widely used to study traffic safety. To make the simulation as realistic as possible, real-world crash data such as crash reports could be leveraged in the creation of the simulation. In addition, to enable such simulations to capture the complexity of traffic, especially when both CAVs and human-driven vehicles co-exist on the road, careful consideration needs to be given to the depiction of human behaviors and control algorithms of CAVs and their interactions. In this paper, the authors reviewed literature that is closely related to crash analysis based on crash reports and to simulation of mixed traffic when CAVs and human-driven vehicles co-exist, for studying traffic safety. Three main aspects are examined based on our literature review: data source, simulation methods, and human factors. It was found that there is an abundance of research in the respective areas, namely, crash report analysis, crash simulation studies (including vehicle simulation, traffic simulation, and driving simulation), and human factors. However, there is a lack of integration between them. Future research is recommended to integrate and leverage different state-of-the-art transportation-related technologies to contribute to road safety by developing an all-in-one-step crash analysis system.

Process improvement using telemedicine consultation to prevent unnecessary interfacility transfers for low-severity blunt head trauma

OBJECTIVE

Mild traumatic brain injuries (MTBI) associated with intracranial haemorrhage are commonly transferred to tertiary care centres. Recent studies have shown that transfers for low-severity traumatic brain injuries may be unnecessary. Trauma systems can be overwhelmed by low acuity patients justifying standardisation of MTBI transfers. We sought to evaluate the impact of telemedicine services on mitigating unnecessary transfers for those presenting with low-severity blunt head trauma after sustaining a ground level fall (GLF).

METHOD

A process improvement plan was developed by a task force of transfer centre (TC) administrators, emergency department physicians (EDP), trauma surgeons and neurosurgeons (NS) to facilitate the requesting EDP and the NS on-call to converse directly to mitigate unnecessary transfers. Consecutive retrospective chart review was performed on neurosurgical transfer requests between 1 January 2021 and 31 January 2022. A comparison of transfers preintervention and postintervention (1 January 2021 to 12 September 2021)/(13 September 2021 to 31 January 2022) was performed.

RESULTS

The TC received 1091 neurological-based transfer requests during the study period (preintervention group: 406 neurosurgical requests; postintervention group: 353 neurosurgical requests). After consultation with the NS on-call, the number of MTBI patients remaining at their respective ED's with no neurological degradation more than doubled from 15 in the preintervention group to 37 in the postintervention group.

CONCLUSION

TC-mediated telemedicine conversations between the NS and the referring EDP can prevent unnecessary transfers for stable MTBI patients sustaining a GLF if needed. Outlying EDPs should be educated on this process to increase efficacy.

Quantification of human behavior levels by extending Rasmussen's SRK model and the effects of time pressure and training on the levels switching

Human factor-related accidents account for an increasing portion of the total accidents through the advancing level of system automation. Human reliability becomes the key issue in human-machine systems especially for safety-relevant tasks and operations. Rasmussen's SRK (skill-rule-knowledge) model is well known in the field of human factors. Likewise, it is well known that skill-based behaviors have the highest human reliability, while knowledge-based behaviors are associated with the lowest reliability scores. Although numerous studies exist on human error probability (HEP), correspondingly typically attributed directly or indirectly to these three levels of behavior, a coherent, consistent representation, especially using data sources, has not been available. In this contribution, the quantification of human behavior levels with Rasmussen's SRK model is given based on three databases for the first time. Effects of time pressure and training on human reliability switching are also analyzed based on related publications. To determine the HEP of these three levels, three databases, technique for human error rate prediction (THERP), Savannah river site human reliability analysis (SRS-HRA) and nuclear action reliability assessment (NARA), from human reliability analysis (HRA) methods are used. The procedure contains identifying the tasks including the operator involved and the assumptions the analysts made and classifying the tasks into suitable cognitive behavior mode (CBM). In this case, the relationship between SRK levels and HEP is mapped. The effects of the two in automation context very relevant performance shaping factors (PSFs), time pressure and training/knowledge degradation, on human behavior levels switching are analyzed and the explanations of the SRK switching are presented. In this case, a more general structure is established to illustrate the dynamic behavior of levels switching with six directions under different conditions. From the results we conclude that skill, rule, and knowledge behavior levels are continuous in terms of HEP and therefore allow a new inside into this key aspect of human factor quantification. Based on this analysis the consequences of daily automation in the context of autonomous transport systems in combination with human qualification and reliability degrading are from this specific and in the current automation discussion very intensively discussed. The presented discussion linking SRK levels and HEP gives a new perspective on the foreseeable consequences of further automation in application areas with increasing automation of everyday tasks (like using a highly automated vehicle).

A Retrospective, Single-Agency Analysis of Ambulance Crashes during a 3-Year Period: Association with EMS Driver Characteristics and a Telematics-Measured Safe Driving Score

BACKGROUND

Driver demographics and aggressive driving behavior are established risk factors for traffic accidents, yet their role in ambulance crashes remains poorly studied. We reviewed all ambulance crashes that occurred in our emergency medical services (EMS) agency during a 3-year period, and examined incidence rates (IR) by driver characteristics and telematics-measured driver behavior.

METHODS

This retrospective study was conducted in a U.S. EMS agency that operates 75 Type III ambulances and requires personnel to document all ambulance collisions, regardless of severity. Crashes reported between September 2017 and August 2020 were reviewed, and established criteria were used to classify injury and vehicle damage severity. Serious crashes were defined as events with any injury and/or functional or disabling damage. A vehicle telematics system installed fleet-wide in 2017 continuously captures driver-specific data, including miles driven and indicators related to speeding, harsh cornering and braking, and seatbelt use. A composite score characterizes compliance with safe driving behaviors (1 = low compliance to 5 = high compliance). Crash IR per 100,000 miles, IR ratios (IRR), and Poisson regression were used in analysis. Driver sex, age, agency tenure, miles driven, and safe driving score were examined.

RESULTS

Clinicians reported 214 crashes and the IR of any crash and serious crash were 2.1 and 0.63 per 100,000 miles, respectively. Injuries occurred in 8% of crashes and were all of low acuity. About one third of crashes produced functional (21%) or disabling (8%) vehicle damage, and the ambulance required towing in 10%. In a multivariate model, female sex (IRR = 1.50, 95%CI = 1.13-1.97), age 18-24 (IRR = 1.67, 95%CI = 1.06-2.66), and being in the lowest quartile of safe driving score (IRR = 1.51, 95%CI = 1.14-2.02) were EMS driver factors independently associated with an increased risk of any collision.

CONCLUSION

Most ambulance crashes are minor events, but the proportion that result in injury and/or functional or disabling vehicle damage may be as high as one-third. Poor driver compliance with objectively measured safe driving behaviors may increase risk for collisions independent of driver sex and age. The EMS industry would benefit from additional studies that examine the full spectrum of ambulance crashes and expand understanding of EMS driver-related risk factors.

Driving Ambulances Safely: Findings of Ten Years of Japanese Ambulance Crash Data

OBJECTIVE

Rules and regulations for ambulance operations differ across countries and regions, however, little is known about ambulance crashes outside of the United States. Japan is unique in several aspects, for example, routine use of lights and sirens during response and transport regardless of the urgency of the case and low speed limits for ambulances. The aim of this study was to describe the incidence and characteristics of ambulance crashes in Japan.

METHODS

We retrospectively analyzed data from the Institute for Traffic Accident Research and Data Analysis (ITARDA) that include all traffic crashes resulting in injury or death in Japan. The study included all ambulance crashes from 2009 to 2018. We compared crashes that occurred during emergency operations with lights and sirens (i.e., when responding to a call or transporting a patient) to those that occurred during non-emergency operations without lights or sirens. We also used data on total number of ambulance dispatches from the Japanese Fire and Disaster Management Agency to calculate ambulance crash risk.

RESULTS

During the 10-year period, we identified a total of 486 ambulance crashes out of 59,208,761 ambulance dispatches (0.82 in every 100,000 dispatches or one crash for every 121,829 dispatches) that included two fatal crashes. Among all ambulance crashes, 142 (29.2%) occurred during emergency operations. The incidence of ambulance crashes decreased significantly over the 10-year period. Ambulance crashes at an intersection occurred more frequently during emergency operations than during non-emergency operations (72.5% vs. 58.1%; 14.4% difference, 95% CI 5.0-22.9).

CONCLUSIONS

Ambulance crashes occurred infrequently in Japan with crash rates much lower than previously reported crash rates in the United States. Ambulance crashes during emergency operations occurred more frequently at intersections compared to non-emergency operations. Further investigation of the low Japanese ambulance crash rates could provide opportunities to improve ambulance safety in other countries.

Modeling road traffic fatalities in Iran's six most populous provinces, 2015-2016

BACKGROUND

Prevention of road traffic injuries (RTIs) as a critical public health issue requires coordinated efforts. We aimed to model influential factors related to traffic safety.

METHODS

In this cross-sectional study, the information from 384,614 observations recorded in Integrated Road Traffic Injury Registry System (IRTIRS) in a one-year period (March 2015-March 2016) was analyzed. All registered crashes from Tehran, Isfan, Fras, Razavi Khorasan, Khuzestan, and East Azerbaijan provinces, the six most populated provinces in Iran, were included in this study. The variables significantly associated with road traffic fatality in the uni-variate analysis were included in the multiple logistic regression.

RESULTS

According to the multiple logistic regression, thirty-two out of seventy-one different variables were identified to be significantly associated with road traffic fatality. The results showed that the crash scene significantly related factors were passenger presence(OR = 4.95, 95%CI = (4.54-5.40)), pedestrians presence(OR = 2.60, 95%CI = (1.75-3.86)), night-time crashes (OR = 1.64, 95%CI = (1.52-1.76)), rainy weather (OR = 1.32, 95%CI = (1.06-1.64)), no intersection control (OR = 1.40, 95%CI = (1.29-1.51)), double solid line(OR = 2.21, 95%CI = (1.31-3.74)), asphalt roads(OR = 1.95, 95%CI = (1.39-2.73)), nonresidential areas(OR = 2.15, 95%CI = (1.93-2.40)), vulnerable-user presence(OR = 1.70, 95%CI = (1.50-1.92)), human factor (OR = 1.13, 95%CI = (1.03-1.23)), multiple first causes (OR = 2.81, 95%CI = (2.04-3.87)), fatigue as prior cause(OR = 1.48, 95%CI = (1.27-1.72)), irregulation as direct cause(OR = 1.35, 95%CI = (1.20-1.51)), head-on collision(OR = 3.35, 95%CI = (2.85-3.93)), tourist destination(OR = 1.95, 95%CI = (1.69-2.24)), suburban areas(OR = 3.26, 95%CI = (2.65-4.01)), expressway(OR = 1.84, 95%CI = (1.59-2.13)), unpaved shoulders(OR = 1.84, 95%CI = (1.63-2.07)), unseparated roads (OR = 1.40, 95%CI = (1.26-1.56)), multiple road defects(OR = 2.00, 95%CI = (1.67-2.39)). In addition, the vehicle-connected factors were heavy vehicle (OR = 1.40, 95%CI = (1.26-1.56)), dark color (OR = 1.26, 95%CI = (1.17-1.35)), old vehicle(OR = 1.46, 95%CI = (1.27-1.67)), not personal-regional plaques(OR = 2.73, 95%CI = (2.42-3.08)), illegal maneuver(OR = 3.84, 95%CI = (2.72-5.43)). And, driver related factors were non-academic education (OR = 1.58, 95%CI = (1.33-1.88)), low income(OR = 2.48, 95%CI = (1.95-3.15)), old age (OR = 1.67, 95%CI = (1.44-1.94)), unlicensed driving(OR = 3.93, 95%CI = (2.51-6.15)), not-wearing seat belt (OR = 1.55, 95%CI = (1.44-1.67)), unconsciousness (OR = 1.67, 95%CI = (1.44-1.94)), driver misconduct(OR = 2.51, 95%CI = (2.29-2.76)).

CONCLUSION

This study reveals that driving behavior, infrastructure design, and geometric road factors must be considered to avoid fatal crashes. Our results found that the above-mentioned factors had higher odds of a deadly outcome than their counterparts. Generally, addressing risk factors and considering the odds ratios would be beneficial for policy makers and road safety stakeholders to provide support for compulsory interventions to reduce the severity of RTIs.

Driving Speeds in Urgent and Non-Urgent Ambulance Missions during Normal and Reduced Winter Speed Limit Periods—A Descriptive Study

Objective: Most traffic research on emergency medical services (EMS) focuses on investigating the time saved with emergency response driving. Evidence regarding driving speed during non-urgent ambulance missions is lacking. In contrast, this descriptive study compared registered driving speeds to the road speed limit in urgent A-missions and non-urgent D-missions. Specifically, the study examined driving speeds during normal speed limits, periods of reduced winter speed limits, and speeding during non-urgent D-missions. Methods: Urgent A-missions and non-urgent D-missions were included. Registered ambulance locations and speed data from Pirkanmaa Hospital District, Finland between 1 January 2018 and 31 December 2018 were used. Ambulance locations were linked to OpenStreetMap digital road network data. The registered driving speed distribution was reported as quartiles by the effective road speed limit. Furthermore, the results during the normal speed limit and reduced winter speed limit periods were reported separately. Driving speeds in non-urgent missions were compared with current Finnish traffic violation legislation. Results: As expected, the urgent A-missions exceeded the speed limits during both the normal speed limit and reduced winter speed limit periods. On the smallest streets with speed limits of 30 km/h, the driving speeds in urgent missions were lower than the speed limit. The driving speeds in non-urgent D-missions were broadly similar throughout the whole year on high-speed roads, and mostly on lower speed limit roads. However, within the 30 km/h speed limits, the mean speed in non-urgent missions appeared to increase during the winter. One-fifth of the registered non-urgent D-missions were speeding. Conclusions: Speeding is common in urgent A-missions and non-urgent D-missions throughout the year. Stricter guidelines for EMS are needed to increase driving safety.

Driving the ambulance: an essential component of emergency medical services: an integrative review

Background

The transport of patients from one location to another is a fundamental part of emergency medical services. However, little interest has been shown in the actual driving of the ambulance. Therefore, this review aimed to investigate how the driving of the ambulance affects the patient and the medical care provided in an emergency medical situation.

Methods

A systematic integrative review using both quantitative and qualitative designs based on 17 scientific papers published between 2011 and 2020 was conducted.

Results

Ambulance driving, both the actual speed, driving pattern, navigation, and communication between the driver and the patient, influenced both the patient’s medical condition and the possibility of providing adequate care during the transport. The driving itself had an impact on prehospital time spent on the road, safety, comfort, and medical issues. The driver’s health and ability to manage stress caused by traffic, time pressure, sirens, and disturbing moments also significantly influenced ambulance transport safety.

Conclusions

The driving of the ambulance had a potential effect on patient health, wellbeing, and safety. Therefore, driving should be considered an essential part of the medical care offered within emergency medical services, requiring specific skills and competence in both medicine, stress management, and risk approaches in addition to the technical skills of driving a vehicle. Further studies on the driving, environmental, and safety aspects of being transported in an ambulance are needed from a patient’s perspective.

Online Newspaper Reports on Ambulance Accidents in Austria, Germany, and Switzerland: Retrospective Cross-sectional Review

Background

Ambulance accidents are an unfortunate indirect result of ambulance emergency calls, which create hazardous environments for personnel, patients, and bystanders. However, in European German-speaking countries, factors contributing to ambulance accidents have not been optimally researched and analyzed.

Objective

The objective of this study was to extract, analyze, and compare data from online newspaper articles on ambulance accidents for Austria, Germany, and Switzerland. We hope to highlight future strategies to offset the deficit in research data and official registers for prevention of ambulance and emergency vehicle accidents.

Methods

Ambulance accident data were collected from Austrian, German, and Swiss free web-based daily newspapers, as listed in Wikipedia, for the period between January 2014 and January 2019. All included newspapers were searched for articles reporting ambulance accidents using German terms representing “ambulance” and “ambulance accident.” Characteristics of the accidents were compiled and analyzed. Only ground ambulance accidents were covered.

Results

In Germany, a total of 597 ambulance accidents were recorded, corresponding to 0.719 (95% CI 0.663-0.779) per 100,000 inhabitants; 453 of these accidents left 1170 people injured, corresponding to 1.409 (95% CI 1.330-1.492) per 100,000 inhabitants, and 28 of these accidents caused 31 fatalities, corresponding to 0.037 (95% CI 0.025-0.053) per 100,000 inhabitants. In Austria, a total of 62 ambulance accidents were recorded, corresponding to 0.698 (95% CI 0.535-0.894) per 100,000 inhabitants; 47 of these accidents left 115 people injured, corresponding to 1.294 (95% CI 1.068-1.553) per 100,000 inhabitants, and 6 of these accidents caused 7 fatalities, corresponding to 0.079 (95% CI 0.032-0.162) per 100,000 inhabitants. In Switzerland, a total of 25 ambulance accidents were recorded, corresponding to 0.293 (95% CI 0.189-0.432) per 100,000 inhabitants; 11 of these accidents left 18 people injured, corresponding to 0.211(95% CI 0.113-0.308) per 100,000 inhabitants. There were no fatalities. In each of the three countries, the majority of the accidents involved another car (77%-81%). In Germany and Switzerland, most accidents occurred at an intersection. In Germany, Austria, and Switzerland, 38.7%, 26%, and 4%, respectively, of ambulance accidents occurred at intersections for which the ambulance had a red light (P<.001). In all three countries, most of the casualties were staff and not uncommonly a third party. Most accidents took place on weekdays and during the daytime. Ambulance accidents were evenly distributed across the four seasons. The direction of travel was reported in 28%-37% of the accidents and the patient was in the ambulance approximately 50% of the time in all countries. The cause of the ambulance accidents was reported to be the ambulance itself in 125 (48.1% of accidents where the cause was reported), 22 (42%), and 8 (40%) accidents in Germany, Austria, and Switzerland, respectively (P=.02), and another vehicle in 118 (45.4%), 29 (56%), and 9 (45%) accidents, respectively (P<.001). A total of 292 accidents occurred while blue lights and sirens were used, which caused 3 deaths and 577 injuries.

Conclusions

This study draws attention to much needed auxiliary sources of data that may allow for creation of a contemporary registry of all ambulance accidents in Austria, Germany, and Switzerland. To improve risk management and set European standards, it should be mandatory to collect standardized goal-directed and representative information using various sources (including the wide range presented by the press and social media), which should then be made available for audit, analysis, and research.

Discovering Spatio-Temporal Clusters of Road Collisions Using the Method of Fast Bayesian Model-Based Cluster Detection

Public availability of geo-coded or geo-referenced road collisions (crashes) makes it possible to perform geovisualisation and spatio-temporal analysis of road collisions across a city. This study aims to detect spatio-temporal clusters of road collisions across Greater London between 2010 and 2014. We implemented a fast Bayesian model-based cluster detection method with no covariates and after adjusting for potential covariates respectively. As empirical evidence on the association of street connectivity measures and the occurrence of road collisions had been found, we selected street connectivity measures as the potential covariates in our cluster detection. Results of the most significant cluster and the second most significant cluster during five consecutive years are located around the central areas. Moreover, after adjusting the covariates, the most significant cluster moves from the central areas of London to its peripheral areas, while the second most significant cluster remains unchanged. Additionally, one potential covariate used in this study, length-based road density, exhibits a positive association with the number of road collisions; meanwhile count-based intersection density displays a negative association. Although the covariates (i.e., road density and intersection density) exhibit potential impact on the clusters of road collisions, they are unlikely to contribute to the majority of clusters. Furthermore, the method of fast Bayesian model-based cluster detection is developed to discover spatio-temporal clusters of serious injury collisions. Most of the areas at risk of serious injury collisions overlay those at risk of road collisions. Although not being identified as areas at risk of road collisions, some districts, e.g., City of London, are regarded as areas at risk of serious injury collisions.