Ambulance dispatch prioritisation for traffic crashes using machine learning: A natural language approach

Ceklic, Ellen; Ball, Stephen; Finn, Judith; Brown, Elizabeth; Brink, Deon; Bailey, Paul; Whiteside, Austin; Brits, Rudolph; Tohira, Hideo

doi:10.1016/j.ijmedinf.2022.104886

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…Most variables used in the models presented were static, gathered from GIS systems or similar techniques. New techniques, such as AI, may offer additional tools for decision-making and dispatching of ambulances in the future 21. However, dispatchers also use other variables, such as medical priority, type of medical care required, clinical judgement and their own professional experience, when making decisions on ambulance dispatching 22.…”

Section: Discussionmentioning

confidence: 99%

“…New techniques, such as AI, may offer additional tools for decision-making and dispatching of ambulances in the future. 21 However, dispatchers also use other variables, such as medical priority, type of medical care required, clinical judgement and their own professional experience, when making decisions on ambulance dispatching. 22 Therefore, an important consideration is to identify how fixed variables can be incorporated into decision-making systems, while also recognising the limitations of these tools.…”

Section: Discussionmentioning

confidence: 99%

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Dynamic ambulance relocation: a scoping review

Becker,

Kurland,

Höglund

et al. 2023

BMJ Open

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

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Dynamic ambulance relocation: a scoping review

Becker,

Kurland,

Höglund

et al. 2023

BMJ Open

View full text Add to dashboard Cite

show abstract

“…Another study scrutinized the application of AI algorithms in ambulance dispatch protocols for motor vehicle accidents. Through predictive analytics, this study anticipated optimal deployment times and locations for ambulances to expedite response times ( 31 ). We envision a future state where similar AI strategies can be deployed to position ambulances based on factors such as time of day, regional demographics, and other conflicting emergency calls to ensure fast response.…”

Section: Future State: Artificial Intelligence To Enhance Prehospital...mentioning

confidence: 99%

Artificial intelligence to enhance prehospital stroke diagnosis and triage: a perspective

Wolcott,

English

2024

Front. Neurol.

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As health systems organize to deliver the highest quality stroke care to their patients, there is increasing emphasis being placed on prehospital stroke recognition, accurate diagnosis, and efficient triage to improve outcomes after stroke. Emergency medical services (EMS) personnel currently rely heavily on dispatch accuracy, stroke screening tools, bypass protocols and prehospital notification to care for patients with suspected stroke, but novel tools including mobile stroke units and telemedicine-enabled ambulances are already changing the landscape of prehospital stroke care. Herein, the authors provide our perspective on the current state of prehospital stroke diagnosis and triage including several of these emerging trends. Then, we provide commentary to highlight potential artificial intelligence (AI) applications to improve stroke detection, improve accurate and timely dispatch, enhance EMS training and performance, and develop novel stroke diagnostic tools for prehospital use.

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Increased number of dispatches in emergency medical services correlates to response time extension

Ishikawa,

Tomita,

Shimazui

et al. 2024

Acute Medicine & Surgery

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AimThis study investigated the correlation between the number of emergency medical service (EMS) dispatches and response time extension. In addition, we conducted a simulation to assess the potential for reducing response times by relocating the ambulance based on the number of dispatches.MethodsThis retrospective observational study analyzed data on patients treated with EMS between May 1 and June 25, 2021, in an urban area (Chiba City, Japan). Spearman's rank correlation tests were used to analyze the correlations among the number of dispatches, response time extension, and ambulance distance. We created a heat map to visualize the number of dispatches and distribution of emergency case occurrences, and simulated the relocation of the EMS team with the lowest number of dispatches to the closest EMS team with the highest number of dispatches.ResultsIn total, 7915 emergency cases were included. The median response time across all dispatches was 9 min, whereas that for the response time extension cases was 12 min. There was a significant positive correlation between the increased number of dispatches, response time extension (r = 0.94, p < 0.0001), and ambulance distance (r = 0.95, p < 0.0001). The relocation simulation significantly shortened the average response time from 13 min and 30 s to 12 min and 11 s (9.9% decrease, p < 0.0001).ConclusionAn increased number of dispatches significantly increased the response time extension cases and ambulance distance. Our simulation suggests that EMS relocation can potentially shorten the response time. While increased dispatches influence the response time extension, optimal EMS allocation may improve response times.

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Ambulance dispatch prioritisation for traffic crashes using machine learning: A natural language approach

Cited by 6 publications

References 19 publications

Dynamic ambulance relocation: a scoping review

Dynamic ambulance relocation: a scoping review

Artificial intelligence to enhance prehospital stroke diagnosis and triage: a perspective

Increased number of dispatches in emergency medical services correlates to response time extension

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