In 2020, ** countries in Europe reported an average answering time needed to get in contact with emergency services that was less than ** seconds, with Ireland having the fastest average response time at *** seconds.

This data set represents the average response time for emergency vehicles. Average response times have been calculated for each agency (Police, Fire and EMS), quarter of the year and call priority. This data set contains response time only for Fulton County agencies serving the unincorporated part of the county.

In 2021, the average time an ambulance required to get a sick or wounded person to a hospital was **** minutes, more than four minutes longer than in 2012. The time needed to arrive at the scene after the first emergency call also increased throughout the surveyed period, reaching an average of *** minutes in the same year.

DATASET DESCRIPTION: This Dataset includes the average response time by Call Priority across days of the week and hours of the day. Response Times reflect the same information contained in the APD 911 Calls for Service 2019-2024 dataset. AUSTIN POLICE DEPARTMENT DATA DISCLAIMER 1. The data provided is for informational use only and may differ from official Austin Police Department data. The Austin Police Department’s databases are continuously updated, and changes can be made due to a variety of investigative factors including but not limited to offense reclassification and dates. Reports run at different times may produce different results. Care should be taken when comparing against other reports as different data collection methods and different systems of record may have been used. 4.The Austin Police Department does not assume any liability for any decision made or action taken or not taken by the recipient in reliance upon any information or data provided. City of Austin Open Data Terms of Use -https://data.austintexas.gov/stories/s/ranj-cccq

Participant: Hanna Hoover Affiliation: Independent Participant Insights: “I chose to plot the average response times by neighborhood for 911 Calls for Service (Last 30 Days). In creating this visualization, I excluded officer-initiated calls and responses where the value for the dispatch time was missing. I also made the assumption that the units in the average response time field in the 911 Calls for Service (Last 30 Days) data set was in minutes. Perhaps not too surprisingly, police officers respond quicker to 911 calls the closer they are to the city center.”

This statistic illustrates the average time period between an emergency call and the arrival of a healthcare emergency vehicle in Italy in 2015, broken down by region. According to data, Liguria was the region which registered the shortest average time between the call and the arrival of the emergency vehicle (** minutes), followed by Lombardy (** minutes), Lazio and Tuscany (** minutes).

Latest Data: January 2022 to July 2022 .Average response times of emergency services to fires under a city mandate by area. Historic Data: 2021;2020;2019;2018;2017;2016;2016 - 2015;2015 - 2014. read more

EMS average response time

This table provides summary data representing annual averages for Advanced Life Support (ASL) response time. The data shows the average performance across the entire calendar year for response time less than or equal to 7 minutes.Data is based on calls received by the Phoenix 911 system and given an Advanced Life Support (ALS) response code, indicating the nature of the call. Alarm Processing Time is calculated from the time Phoenix 911 answers the call to the time Phoenix 911 notifies a Fire department Unit. This is also known as Dispatch Time to Notification Time. Turnout Time is calculated from the time a Fire Department Unit is notified of the call to the time the unit rolls out of the station or begins proceeding to the incident. This is also known as Acknowledgment Time to Roll Time. Travel Time is calculated from the time a Fire department Unit starts proceeding to an incident to the time it arrives at the incident. This is also known as Roll Time to Arrival Time.The performance measure dashboard is available at 1.01 ALS Response Time.Additional Information Source: ImageTrend softwareContact:  Mariam CoskunContact E-Mail:  Mariam_Coskun@tempe.govData Source Type:  TabularPreparation Method:  Queried from ImageTrend using the Report Writer feature.Publish Frequency:  AnnualPublish Method:  ManualData Dictionary

Objective: To analyze the response time and transport time taken by the emergency medical services (EMS), considering their urban or rural location, to attend traffic accident casualties that occurred in the different geographical areas of Extremadura (Spain) from 2012 to 2015. Methods: This was a cross-sectional study of the data recorded by the Emergency Response Coordination Center 112 (ERCC-112) from traffic accidents attended by EMS. Response time was defined as the time elapsed from the request-for-care receipt until arrival of the EMS at the accident scene, and transport time as that from leaving the scene until arrival to the referral hospital. Rural EMS were those based in locations where there is no hospital, and urban EMS those located in towns or cities with a hospital. Results: During the 4-year period studied, 5,572 traffic accidents requested assistance through the ERCC-112. From the 2,875 accidents (51.9%) in which EMS were mobilized, 55.4% occurred in urban roads and the remaining in interurban ones. A total of 113 people (mean age 48.4 ± 19.0 years, range 15–84 years) died at the accident scene or before arrival to the hospital, 88.5% of them in interurban accidents. The average response time of urban and rural EMS was 10.7 ± 7.3 and 18.0 ± 12.6 min (p < 0.001), respectively, and the average transport time was 13.2 ± 11.7 and 45.2 ± 25.0 min (p = 0.009). Response time was longer than the 30-min optimum only in the most peripheral areas of Extremadura, while transport time exceeded the optimum of 90 min in the eastern regions of two health areas (Cáceres and Don Benito-Villanueva). 19.1% of the victims attended by rural EMS were classified as having a serious prognosis or as having died, as compared with 11.2% (p = 0.048) of those attended by urban EMS. Conclusions: The geographical location of EMS in Extremadura (Spain) guarantees adequate response times in traffic accidents, both in rural and urban areas. However, recommended transport times were occasionally exceeded in the most peripheral areas, due to hospital location.

Average Turnout times for Fire services

This Indicator measures the median response times of calls for service that were routed to patrol. The measurement is broken down between Priority 1 and Priority 2 calls as well as by police area. Priority 1 Calls are defined as those that include potential danger for serious injury to persons, prevention of violent crimes, serious public hazards, felonies in progress with possible suspect on scene. Priority 2 Calls are defined as urgent but not an emergency situation, hazardous / sensitive matters, in-progress misdemeanors and crimes where quick response may facilitate apprehension of suspect(s). There are 5 police areas in Oakland each of which consist of a defined set of police beats and therefore cover a specific geographic part of Oakland. For more information and maps of areas, see here: http://www2.oaklandnet.com/government/o/OPD/o/BFO/index.htm

Since April 2014, Local Health Boards have been responsible for providing emergency ambulance services (999 calls) for their local residents; the Welsh Ambulance Services NHS Trust (WAST) is commissioned to deliver emergency ambulance services on their behalf. As announced in a statement by the Deputy Minister for Health, a new clinical response model was implemented in Wales from 1 October 2015. The new model has three new categories of calls – Red, Amber and Green: • Red - Immediately life-threatening (someone is in imminent danger of death, such as a cardiac arrest). There is a target for 65 per cent of these calls to have a response within 8 minutes. • Amber- Serious but not immediately life-threatening (patients who will often need treatment to be delivered on the scene, and may then need to be taken to hospital). There will be no time-based target for amber calls; instead a range of clinical outcome indicators will be introduced to measure the quality, safety and timeliness of care being delivered alongside patient experience information, which will be published every quarter. • Green - Non urgent (can often be managed by other health services) and clinical telephone assessment. There is no official time based target for these calls. Running calls (operational crews who arrive at the scene of an unrecorded incident without prior receipt of an emergency call) are counted as red calls, as are calls answered by either a Health Care Professional on Scene with a Defibrillator (MEDIC), or a Public Access Defibrillator (PAD). Health Care Professionals¹ (HCP) Urgent & Planned Calls are identified as green; where an HCP call poses an immediate threat to life, these calls will be prioritised according the final Medical Priority Dispatch System priority. ¹ Doctor, General Practitioner, Emergency Care Practitioner, Nurse, District Nurse, Midwife, Paramedic, Dentist, Approved Social Worker. As a result of these changes, nearly all of the data from the trial is not comparable to that for before October 2015. Some of the differences include: • Call categories A & C have been removed and replaced by colour coding. • Call handlers are allowed up to an additional two minutes to accurately identify both the severity and nature of a patient’s condition (for those calls that are not immediately life threatening), and the clinical resource they require before dispatching an ambulance. • A small proportion of calls that were classed as red 2 calls have been moved to the red category and a proportion of calls have been re-categorised from red 1. This means that comparisons cannot be made between performance against the old red1/2 categories and the current red category. • The changes will result in a reduction in the number of calls received with a time target. • An 8 minute response time target is only applied to red calls and therefore comparisons of the 8 minute target performance cannot be made for before and after 1 October 2015. The total number of calls received prior to 1 October 2015 can still be compared with total calls under the current model. This is done by adding in the GP urgent calls - which were classed as urgent not emergency - prior to December 2011. Therefore only overall call volumes can be compared over time, whilst all other measures during the trial period can only be compared within the trial model. CHANGES DURING THE TRIAL: From 11 November, calls which are originally coded as amber or green and during the initial call taking process the patient deteriorates, these are then re-coded through Professional Question & Answering or by the use of a manual dispatch code/override to red. For these calls, the clock start is re-registered as the time the call is re-coded to red. Calls which are originally coded as red and during the initial call taking process the patient’s condition improves these are then re-coded through Professional Question & Answering or by the use of a manual dispatch code/override to amber or green depending on condition. For these calls clock start is re-registered as the time the call is re-coded to amber or green. The overall impact of this meant that – from 11 to 30 November - an additional 37 calls arrived at the scene within the 8 minute target time, increasing the percentage arriving within the target time by 2.2 percentage points.

Household Personal Emergency Response System (PERS) and Medical Alert System Market Outlook 2032

The global household personal emergency response system (PERS) and medical alert system market size was USD XX Billion in 2023 and is projected to reach USD XX Billion by 2032, expanding at a CAGR of XX% during 2024–2032. The market growth is attributed to the increasing aging population across the globe.



Growing aging population is expected to boost the market during the assessment years. As people age, they are likely to experience health emergencies, necessitating the need for reliable and efficient emergency response systems. The high demand for these systems among the elderly has fueled the market.

Impact of Artificial Intelligence (AI) in Household Personal Emergency Response System (PERS) and Medical Alert System Market

Artificial Intelligence (AI) has significantly transformed the household personal emergency response system (PERS) and medical alert system market. The integration of AI into these systems has enhanced their efficiency and reliability, thereby improving the quality of care provided to the elderly and individuals with health conditions.



AI algorithms are being used to analyze data from wearable devices, enabling the systems to detect falls or health emergencies accurately and promptly. This has reduced the response time and minimized the risk of false alarms. Furthermore, AI-powered voice recognition technology has made it possible for users to activate the system and call for help using voice commands, thereby making the systems accessible and user-friendly.



The use of AI has facilitated remote patient monitoring, allowing healthcare providers to track the health status of the users in real-time and intervene promptly when necessary. This has resulted in improved patient outcomes and reduced healthcare costs. Therefore, the integration of AI into the market has revolutionized the way care is provided t

An emergency responder management (ERM) system dispatches responders, such as ambulances, when it receives requests for medical aid. ERM systems can also proactively reposition responders between predesignated waiting locations to cover any gaps that arise due to the prior dispatch of responders or significant changes in the distribution of anticipated requests. Optimal repositioning is computationally challenging due to the exponential number of ways to allocate responders between locations and the uncertainty in future requests. The state-of-the-art approach in proactive repositioning is a hierarchical approach based on spatial decomposition and online Monte Carlo tree search, which may require minutes of computation for each decision in a domain where seconds can save lives. We address the issue of long decision times by introducing a novel reinforcement learning (RL) approach, based on the same hierarchical decomposition, but replacing online search with learning. To address the computational challenges posed by large, variable-dimensional, and discrete state and action spaces, we propose: (1) actor-critic based agents that incorporate transformers to handle variable-dimensional states and actions, (2) projections to fixed-dimensional observations to handle complex states, and (3) combinatorial techniques to map continuous actions to discrete allocations. We evaluate our approach using real-world data from two U.S. cities, Nashville, TN and Seattle, WA. Our experiments show that compared to the state of the art, our approach reduces computation time per decision by three orders of magnitude, while also slightly reducing average ambulance response time by 5 seconds

Evanston Fire Department average response time to non-emergency and emergency situations from 2012 to 2015.

All incident segments for each of the first responding agencies (PD, FD and EMS) that contribute to the end-to-end response times. This data set provides call volumes broken down by incident type for each Week Start time period as well as the timestamps and average response times (in seconds) for each segment of the call. For the Incident Type Definitions please refer to this link.

The Emergency Health Services data set includes various service-related metrics and indicators related to the Emergency Health Services (EHS). This data includes measures for the number of 911 calls that EHS respond to, the average EHS response time (in minutes), and the average offload time (in minutes).

Equipment for Ambulances Market Outlook

The global market size for equipment used in ambulances is projected to grow significantly, with a compound annual growth rate (CAGR) of 6.5% from 2024 to 2032. In 2023, the market size was valued at approximately USD 1.5 billion, and by 2032, it is expected to reach around USD 2.6 billion. A key growth factor driving this market is the increasing incidence of road accidents and chronic diseases requiring emergency medical attention.

One of the primary growth factors for the equipment for ambulances market is the rising number of road accidents worldwide. As urbanization and the number of vehicles on the road increase, so does the frequency of accidents, which subsequently boosts the demand for advanced medical equipment in ambulances. Additionally, the aging population globally is more susceptible to chronic conditions such as heart disease, diabetes, and respiratory disorders, necessitating efficient emergency medical services equipped with state-of-the-art devices.

Another major growth driver is technological advancements in medical equipment. Innovations such as portable defibrillators, advanced ventilators, and compact infusion pumps enhance the capabilities of ambulance services, providing better patient outcomes. These advancements not only ensure timely intervention in emergencies but also improve the overall efficiency of medical services. The integration of IoT and AI in medical devices is further pushing the market forward by enabling real-time monitoring and data analysis, which are crucial for critical care during transportation.

Government initiatives and policies aimed at improving healthcare infrastructure also play a significant role in market growth. Many countries are investing heavily in upgrading their emergency medical services, which includes procuring advanced ambulance equipment. Subsidies and grants for healthcare providers to purchase these high-tech devices are becoming more common, especially in developing regions. Furthermore, stringent regulations regarding the quality and functionality of ambulance equipment ensure a steady demand for certified and reliable products.

Emergency Medical Services Products play a pivotal role in enhancing the efficiency and effectiveness of ambulance services. These products encompass a wide range of medical devices and equipment that are essential for providing immediate care to patients during transportation. From advanced monitoring systems to life-saving defibrillators, these products ensure that medical teams are well-equipped to handle emergencies. The demand for high-quality and reliable Emergency Medical Services Products is increasing as healthcare providers strive to improve patient outcomes and reduce response times. With continuous advancements in medical technology, these products are becoming more sophisticated, offering enhanced capabilities and functionalities that are crucial for critical care situations.

Regionally, North America dominates the equipment for ambulances market, driven by well-established healthcare infrastructure and high healthcare expenditure. However, Asia Pacific is expected to witness the highest growth rate during the forecast period. The increasing healthcare awareness, coupled with rising investments in healthcare infrastructure and the growing incidence of chronic diseases, are key factors propelling the market in this region. Europe also holds a significant market share, supported by strong healthcare systems and stringent regulatory frameworks.

Product Type Analysis

The equipment for ambulances market is segmented by product type, including defibrillators, ventilators, stretchers, suction pumps, infusion pumps, and others. Defibrillators, which are crucial for restoring normal heart rhythms in patients experiencing cardiac arrest, constitute a significant portion of the market. The demand for defibrillators is driven by their life-saving potential and advancements in portable, user-friendly models. The growing prevalence of cardiovascular diseases globally further propels this segment.

Ventilators are another critical component of ambulance equipment, especially for patients with respiratory issues. The COVID-19 pandemic underscored the importance of ventilators in emergency care, leading to increased d

The dataset contains incident response data from B&T’s seven bridges and two tunnels in New York City. Each record represents a monthly summary for a facility with the number of incidents responded to and the average response time for all incidents. Response time is defined as the time elapsed between the reporting of an incident and the arrival of the emergency response vehicle.