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FirstAED emergency dispatch, global positioning of community first responders with distinct roles - a solution to reduce the response times and ensuring an AED to early defibrillation in the rural area Langeland

Author

Listed:
  • Finn Lund Henriksen
  • Per Schorling
  • Bruno Hansen
  • Henrik Schakow
  • Mogens Lytken Larsen

Abstract

FirstAED is a supplement to the existing emergency response systems. The aim is to shorten the community first responder response times at emergency calls to below five minutes in a bridge connected island area. FirstAED defines a way to dispatch the nearby three first responders and organise their roles in a team structure to reduce response times, ensure citizens' safety and offer equal possibility of early defibrillation. First aid is provided by community first responders who use their smartphone. FirstAED global positioning system (GPS)-tracks the nine nearby first responders and enables the emergency dispatcher to send an organised team of three first responders with distinct roles to the scene automatically. During the first 24 months the FirstAED system was used 718 times. Three first responders arrived in ~89% of the cases, and they arrived before the ambulance in ~94% of the cases. FirstAED entailed a significant reduction in median first responder response time to four minutes nine seconds and in median time to AED on site to five minutes and 47 seconds.

Suggested Citation

  • Finn Lund Henriksen & Per Schorling & Bruno Hansen & Henrik Schakow & Mogens Lytken Larsen, 2016. "FirstAED emergency dispatch, global positioning of community first responders with distinct roles - a solution to reduce the response times and ensuring an AED to early defibrillation in the rural are," International Journal of Networking and Virtual Organisations, Inderscience Enterprises Ltd, vol. 16(1), pages 86-102.
    • Handle: RePEc:ids:ijnvor:v:16:y:2016:i:1:p:86-102
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    Cited by:

    1. Adrian Xi Lin & Andrew Fu Wah Ho & Kang Hao Cheong & Zengxiang Li & Wentong Cai & Marcel Lucas Chee & Yih Yng Ng & Xiaokui Xiao & Marcus Eng Hock Ong, 2020. "Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction," IJERPH, MDPI, vol. 17(11), pages 1-15, June.
    2. Niki Matinrad & Melanie Reuter-Oppermann, 2022. "A review on initiatives for the management of daily medical emergencies prior to the arrival of emergency medical services," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(1), pages 251-302, March.

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