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. 2020 Apr 21:8:1900410.
doi: 10.1109/JTEHM.2020.2987008. eCollection 2020.

A Data-Driven Simulator for the Strategic Positioning of Aerial Ambulance Drones Reaching Out-of-Hospital Cardiac Arrests: A Genetic Algorithmic Approach

Conor Mackle  1 , Raymond Bond  1 , Hannah Torney  1   2 , Ronan Mcbride  3 , James Mclaughlin  4 , Dewar Finlay  4 , Pardis Biglarbeigi  4 , Rob Brisk  5 , Adam Harvey  2 , David Mceneaney  3
Affiliations

A Data-Driven Simulator for the Strategic Positioning of Aerial Ambulance Drones Reaching Out-of-Hospital Cardiac Arrests: A Genetic Algorithmic Approach

Conor Mackle et al. IEEE J Transl Eng Health Med. .

Abstract

Objective: The Internet of Things provide solutions for many societal challenges including the use of unmanned aerial vehicles to assist in emergency situations that are out of immediate reach for traditional emergency services. Out of hospital cardiac arrest (OHCA) can result in death with less than 50% of victims receiving the necessary emergency care on time. The aim of this study is to link real world heterogenous datasets to build a system to determine the difference in emergency response times when having aerial ambulance drones available compared to response times when depending solely on traditional ambulance services and lay rescuers who would use nearby publicly accessible defibrillators to treat OHCA victims.

Method: The system uses the geolocations of public accessible defibrillators and ambulance services along with the times when people are likely to have a cardiac arrest to calculate response times. For comparison, a Genetic Algorithm has been developed to determine the strategic number and positions of drone bases to optimize OHCA emergency response times.

Conclusion: Implementation of a nationwide aerial drone network may see significant improvements in overall emergency response times for OHCA incidents. However, the expense of implementation must be considered.

Keywords: AED; OHCA; UAV; ambulance drone.

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Figures

FIGURE 1.
FIGURE 1.
Probability density function for publicly accessed AEDs for OHCA incidents by hour.
FIGURE 2.
FIGURE 2.
Probability density function for publicly accessed AEDs for OHCA incidents by day.
FIGURE 3.
FIGURE 3.
Map plots for Northern Ireland.
FIGURE 4.
FIGURE 4.
Generating points flow chart.
FIGURE 5.
FIGURE 5.
Fitness test simulations.
FIGURE 6.
FIGURE 6.
Final drone base locations in Northern Ireland.
FIGURE 7.
FIGURE 7.
The effect of adding more drone bases on emergency response times for OHCA incidents.
FIGURE 8.
FIGURE 8.
OHCA response time groups after drone network implemented.
FIGURE 9.
FIGURE 9.
Comparison of average response times before and after ambulance drone network for health and social care trusts.
FIGURE 10.
FIGURE 10.
Emergency service count for OHCA incidents before and after drone network is implemented.
FIGURE 11.
FIGURE 11.
Boxplots for health and social care trusts response times before and after simulated ambulance drone network is introduced (differences in the before and after response times were assessed using a Wilcoxon test).
See this image and copyright information in PMC

References

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