Understanding EMS response times: a machine learning-based analysis.

Journal: BMC medical informatics and decision making
PMID: 40128718

Abstract

BACKGROUND: Emergency Medical Services (EMS) response times are critical for optimizing patient outcomes, particularly in time-sensitive emergencies. This study explores the multifaceted determinants of EMS response times, leveraging machine learning (ML) techniques to identify key factors such as urgency levels, environmental conditions, and geographic variables. The findings aim to inform strategies for enhancing resource allocation and operational efficiency in EMS systems.

Authors

  • Peter Hill
    Region Stockholm Health and Medical Care Administration, The Department for Specialized Care, Stockholm, Sweden. peter.hill@ki.se.
  • Jakob Lederman
    Region Stockholm Health and Medical Care Administration, The Department for Specialized Care, Stockholm, Sweden.
  • Daniel Jönsson
    Department of Periodontology, Malmö University, Malmö and Swedish Dental Service of Skane, Lund, Sweden.
  • Peter Bolin
    Region Stockholm Health and Medical Care Administration, The Department for Specialized Care, Stockholm, Sweden.
  • Veronica Vicente
    The Ambulance Medical Service in Stockholm (AISAB), Stockholm, Sweden.

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