Ambulance Dispatch Software Free Download

Objective: Ambulance-based secondary telephone triage systems have been established in ambulance services to divert low-acuity cases away from emergency ambulance dispatch. However, some low-acuity cases still receive an emergency ambulance dispatch following secondary triage. To date, no evidence exists identifying whether these cases required an emergency ambulance. The aim of this study was to investigate whether cases were appropriately referred for emergency ambulance dispatch following secondary telephone triage.

Methods: A retrospective cohort analysis was conducted of cases referred for emergency ambulance dispatch in Melbourne, Australia following secondary telephone triage between September 2009 and June 2012. Appropriateness was measured by assessing the frequency of advanced life support (ALS) treatment by paramedics, and paramedic transport to hospital.

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Results: There were 23,696 cases included in this study. Overall, 54% of cases received paramedic treatment, which was similar to the state-wide rate for emergency ambulance cases (55.5%). All secondary telephone triage cases referred for emergency ambulance dispatch had transportation rates higher than all metropolitan emergency ambulance cases (82.2% versus 71.1%). Two-thirds of the cases that were transported were also treated by paramedics (66.5%), and 17.7% of cases were not transported to hospital by ambulance following paramedic assessment.

Conclusions: Overall, the cases returned for emergency ambulance dispatch following secondary telephone triage were appropriate. Nevertheless, the paramedic treatment rates in particular indicate a considerable rate of overtriage requiring further investigation to optimize the efficacy of secondary telephone triage.

Methods: This was a retrospective whole-of-population study of emergency ambulance dispatch in Perth, Western Australia, 1 January 2014 to 30 June 2015. Dispatch priority was categorised as either Priority 1 (high priority), or Priority 2 or 3. Patient condition was categorised as time-critical for patient(s) transported as Priority 1 to hospital or who died (and resuscitation was attempted by paramedics); else, patient condition was categorised as less time-critical. The 2 statistic was used to compare chief complaints by false omission rate (percentage of Priority 2 or 3 dispatches that were time-critical) and positive predictive value (percentage of Priority 1 dispatches that were time-critical). We also reported sensitivity and specificity.

Results: There were 211 473 cases of dispatch. Of 99 988 cases with Priority 2 or 3 dispatch, 467 (0.5%) were time-critical. Convulsions/seizures and breathing problems were highlighted as having more false negatives (time-critical despite Priority 2 or 3 dispatch) than expected from the overall false omission rate. Of 111 485 cases with Priority 1 dispatch, 6520 (5.8%) were time-critical. Our analysis highlighted chest pain, heart problems/automatic implanted cardiac defibrillator, unknown problem/collapse, and headache as having fewer true positives (time-critical and Priority 1 dispatch) than expected from the overall positive predictive value.

Conclusion: Scope for reducing under-triage and over-triage of ambulance dispatch varies between chief complaints of the Medical Priority Dispatch System. The highlighted chief complaints should be considered for future research into improving ambulance dispatch system performance.

In this paper, we solve the ambulance dispatch problem with a reinforcement learning oriented strategy. The ambulance dispatch problem is defined as deciding which ambulance to pick up which patient. Traditional studies on ambulance dispatch mainly focus on predefined protocols and are verified on simple simulation data, which are not flexible enough when facing the dynamically changing real-world cases. In this paper, we propose an efficient ambulance dispatch method based on the reinforcement learning framework, i.e., Multi-Agent Q-Network with Experience Replay(MAQR). Specifically, we firstly reformulate the ambulance dispatch problem with a multi-agent reinforcement learning framework, and then design the state, action, and reward function correspondingly for the framework. Thirdly, we design a simulator that controls ambulance status, generates patient requests and interacts with ambulances. Finally, we design extensive experiments to demonstrate the superiority of the proposed method.

Public Safety Dispatchers are the first line of communication with the general public, whether answering 911 emergency calls or handling non-emergency requests for service for both the Police and Fire Departments. Public Safety Dispatchers (911 Operators) attend to incoming calls on twelve 911 phone lines, five non-emergency lines, and one silent witness line. Dispatchers enter all calls for service into the Computer-Aided Dispatch (CAD) system, classify and prioritize the calls, and dispatch public safety personnel where they are needed.

According to a study conducted by Wilde, a one-minute increase in ambulance response time resulted in an 8-17% increase, on average, in mortality for patients in cardiac arrest or suffering a stroke.1 In another study by Peyravi, ambulances with a two-minute shorter response time had a mortality rate of 1.5%, 1.1-percentage points lower than the average ambulance response time.2

EMS organizations know that the key to reduce response time is to deploy ambulances near where the next emergencies are likely to happen. However, predicting where the next emergencies will occur is the main challenge. Some EMS organizations can generate a demand analytics report based on call volume associated with specific shifts, areas and time of day. They will simply look over the report and make a gut decision to predict when and where the next emergencies will occur. Other EMS organizations who are serious about reducing response time even employ statisticians to analyze historical demand, but even that is not accurate enough. That is why AI and machine learning are deployed to assist.

Four clustering algorithms were used. These algorithms takes into consideration the population density of a specific region using residential address, merging smaller clusters with neighboring clusters based on certain rules, building type (hospitals, schools, etc.). The goal is to know what types of activities takes place at a certain region and where people are at a given time-interval. Finally, dispatch data containing location coordinates were used to determine if dispatch were needed based on building type, time, and other factors.

Although the logistic regression model outperformed the baseline in total (97.9% vs. 94.1), it was worse at predicting when dispatches actually happened (positive samples).3 The results were not as expected, and further investigation is needed.

This study conducted by Lin5 used 10-year ambulance demand data recorded by Singapore Civil Defense Force (SCDF). Unlike the previous method of using clusters, this study used a massive dataset based on regional characteristics, historical ambulance records, and incident records. Their goal is to predict next-day demand for ALL regions, so staffing and other preparations could be easier.

Other datasets contain details of each ambulance call, which include time of incident, its classification, patient status, patient age, ambulance origin and destination hospital, gender, exact patient location, etc.

This study was conducted using the knowledge, expertise and dispatch policy for the Dutch EMS region of Babant Zuid-Oost (BZO) in the Netherlands.6 The main goal is to help EMS deploy resources more efficiently so they can meet the national target of having response time within 15 minutes for highly urgent ambulance requests 95% of the time.

The results were significant. On-time response performance for highly urgent requests increased by 0.77 of a percentage point. The study claims that this performance gain is equivalent of adding more than seven weekly ambulance shifts.6

For evaluation and simulation, real-world data is leveraged, which included (1) EMS request records, (2) ambulance stations, (3) hospitals, and (4) road networks. Below is a visual of the spatial (left) and temporal (right) distribution of EMS request in Tianjin.

The system uses IBM predictive analytics and machine learning to analyze 200,000 ambulance trips to estimate the total time of an ambulance transport, including the time needed to get a patient in and out of an ambulance. Datasets analyzed include patient demographics, medical conditions and the required medical services needed.

Even with all the demand prediction models mentioned earlier, unanticipated transport requests can still disrupt your entire dispatch schedule. Not only that, rerouting trips to adjust to sudden changes expends manpower, is error-prone, and can increase the mix of dead leg trips. Traumasoft, who provides an all-in-one EMS management system, developed real-time routes for NEMT scheduling that addresses this issue.

The system uses machine learning and artificial intelligence to enable the programmatic and dynamic routing and re-routing of trips, helping dispatchers create the best routes in seconds. Datasets from 100,000+ ambulance trips were used to establish baselines and artificial intelligence algorithms.

Although machine learning models that improve ambulance response time are promising, their results have yet to be perfected. Before implementing these models to real life-and-death situations, more development and data gathering are required. Further, the combined learnings of each of these approaches will pave the way for data scientists to design and create better models in the future.

5. Lin AX, Ho AFW, Cheong KH, Li Z, Cai W, Chee ML, Ng YY, Xiao X, Ong MEH. Leveraging machine learning techniques and engineering of multi-nature features for national daily regional ambulance demand prediction. Singapore: International Journal of Environmental Research and Public Health; 2020. 006ab0faaa