By Gauri Chawla
Introduction
The emergency medical services (EMS) system is a vital part of the national healthcare system, responding to time-sensitive, life threatening emergencies and transporting patients to healthcare facilities for further care. One of the key aspects of the EMS systems is their ability to respond quickly to increase survival rates and decrease the long term effects of the emergency.
Responding rapidly to 911 calls is extremely important as mere seconds or minutes can make a large difference for patient outcome. According to John Herlitz et al., starting CPR within one minute results in a 33% survival rate, whereas taking longer than one minute results in only a 14% survival rate.1
Abboud et al. notes that when EMS correctly and quickly identifies a stroke, the patient is more likely to receive treatment from a physician faster than other patients who didn’t use EMS.2 It is important that stroke patients get care quickly because it makes it easier for the patient to recover faster and get closer to their baseline.3
Therefore, rapid EMS response is important as it helps better a patients’ chance at survival and fast tracks the process of getting the patient care in a hospital setting.
According to National Fire Protection Association (NFPA) standards, first responders, including EMS, must respond to at least 90% of calls without delays to meet the national requirement.4 However, there are many factors that are making it more difficult for EMS to respond as efficiently as they should.
For the past few years, EMS calls have been increasing around the nation. A chart from the 2021 Annual EMS Data Report displays an increase in calls each month and year from 2019 to 2021.5 At the same time, there are staffing shortages, with 39% of EMS positions and 55% of paramedic positions left vacant.6 A combination of these two factors places a strain on the EMS system.
Although there are studies researching the frequency of delay, research addressing each type of delay and potential mechanisms to help decrease the delay is limited. It is important to study the frequency of different types of delay as it will indicate which areas EMS should focus on improving first so that patients can be treated efficiently.
As such, this study seeks to investigate the percentage of delays each year and the frequency of each type of delay. In this way, it is possible to see if the US EMS system is meeting the NFPA’s response time standards and propose potential ways to lessen the occurrence and impact of these delays.
Methods
Data from the National Emergency Medical Services Information System (NEMSIS), a database that collects EMS data from 54 states and territories to assess national performance and quality of care, was used in this study. NEMSIS gets data directly from over 14,000 EMS agencies and from state data submissions.
After downloading the data from 2017 to 2022, the number of distinct calls and the number of distinct calls with delays were calculated. This data was used to determine the percentage of calls that are experiencing some time of delay. This data was then further sub-categorized to determine the amount and frequency of dispatch delays, response delays, scene delays, transport delays and turn around delays.
The data was converted into a graph to see whether the frequency within each type of delay varied from year to year. A graph of the average frequency of each type of delay was made with error bars to see whether the difference between the frequency of each type of delay was statistically significant.
Results
Discussion
The aim of this study was to find the overall percent of calls with delays and the frequency of each type of delay. While there is much research investigating the frequency of delay and the implications of EMS delay, research comparing the frequency of each type of delay is still limited. This research is important as it identifies where the main problem lies, helping EMS stations determine where to focus their resources to best address delays and improve response times.
Each year from 2017 to 2021, the frequency of overall delays increased, but in 2022, there was a slight decrease in the percent of delayed calls. Every year after 2017, national EMS response times did not meet the NFPA standards according to this set of data, as more than 10% of calls experienced a delay (Figure 1).
There was not much variation in the frequency within each type of delay from one year to the next (Figure 3). However, there was a difference in the average frequency of each type of delay from 2017 to 2022. The most common delay was a turn-around delay, followed by scene delays.
The least common type of delay was a dispatch delay. There was not a statistically significant difference between the frequencies of turn-around and scene delays or between the frequencies of response and scene delays (Figure 2). Therefore, EMS stations should focus primarily on increasing the efficiency of turn around delays and scene delays to minimize the number of delays.
The category of dispatch-related delays includes reasons such as an uncooperative caller, a high call volume, a language barrier, or technical failure. Each year, high call volume was the largest reason for a dispatch-related delay. Some methods in which the frequency of dispatch-related delays can be decreased is by increasing the number of staff, doing regular maintenance of the technology, and creating a more efficient translation service.
In New Orleans, one district has begun using a two way AI-based translation service that helps make calls 70% faster. This replaces the old translation mechanism, which connects to a third party to translate for both the caller and dispatcher.
The connecting process itself can take up to a minute, which can be vital in an emergency situation.7 Therefore, EMS agencies across the nation should implement new AI-based translation technology as one of the measures to decrease dispatch-related delays.
Some of the reasons for response delays were crowds, diversions, route obstructions and weather. The most frequent reason for a response related delay was distance; each year from 2017 to 2022, delays caused by distance made up an average of 43.1% of response delays.
To help mitigate this problem, more stations could be built to increase coverage and mobile stations could be released. In doing so, each station would have a smaller area to cover, and responses would likely be more efficient.
Additionally, EMS specific lanes could be created in places with higher traffic, such as freeways or large complexes. Kwan and Lee of Ohio State University and Minnesota State University suggest the implementation of a GIS-based intelligent emergency response system (GIERS).
The GIERS system will help EMTs find patients in larger, more complex buildings by providing them with a navigation system inside the building, pinpointing the exact location of the patient.8 This system has helped decrease delays in various instances and should therefore be implemented in all EMS stations.
Examples of scene delays include hazardous material, a language barrier, distance, patient access, traffic, triage or multiple patients and mechanical issues. The most frequent reason for a scene delay was a staff delay. Staff training periods can be increased to ensure that all staff are fully prepared for different scenarios, and the number of staff in each unit can be increased to help decrease the delays.
Moreover, maintenance checks can be performed more often to decrease the likelihood of mechanical issues on call. Similar to with dispatch-related delays, an AI-based translation service7 can be implemented in these cases to make response and treatment more efficient.
Transport delays include EMS being unable to locate the scene, diversions, distance, a route obstruction, staff delay, traffic, vehicle crash or failure, etc. To decrease any EMS training related transport delays, all EMTs should receive an increased amount of training driving the routes in the area.
The most frequent reason for delay is a turn-around delay, which includes clean-up, decontamination, documentation, emergency department (ED) overcrowding, equipment replenishment, and vehicle crash or failure.
To make this process more efficient, hospitals could increase staffing in the ED and designate a team to assist with turn-around. During training, EMTs should receive increased training or practice in cleaning and preparing the unit for the next call so that it is faster.
Additionally, a system such as the one being developed by Grekousis and Liu of the Sun-Yat Sen University in China could be implemented. They developed a system that predicts the location and time of the next EMS call with 68% accuracy.9 By implementing this, EMTs could prepare for the next call in a faster manner and could change their initial plan in preparation for the next 911 call.
While these issues are being addressed, it is difficult to implement these changes across the country, as many areas have limited funding to expand the EMS service. Additionally, these changes require lots of time to be implemented.
Another solution is to employ bystanders to provide life-saving care until the ambulance arrives. In a study done by Leung et al. in Canada, they investigated the impact of sending drones with AEDs ahead of the ambulance so that bystanders could assist the patient. They found that the median response interval was significantly less when a drone was used, which could potentially increase survival rates.10
It is also important to educate bystanders so that they can assist a patient in case of an EMS delay. With increased community-level first aid education, bystanders are more likely to assist the patient if an emergency occurs.
According to research done by Hoeyweghen et al. in Belgium, in the event of a cardiac arrest, bystanders accurately performing CPR increases survival rates from 4% to 16%.11 This is significant as bystanders’ assistance can be life-altering when there is an EMS delay.
Limitations and Future Studies
One of the primary limitations in this study was that the geographical location of each data point could not be seen, so an analysis of where certain delays are more frequent could not be performed.
Additionally, analysis regarding which types of delays tend to be coupled was not possible given the information provided in the dataset. Future studies seek to analyze the frequency of delay by county or district to assess EMS effectiveness and the level of patient care in specific parts of the nation.
Gauri Chawla is a volunteer at the Stanford (CA) ValleyCare’s Emergency Department and a certified nursing assistant at the Vineyards Healthcare center. She is also the founder and president of Every Beat Countz, a non-profit organization aiming to spread free CPR training.
References
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