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

Peter Hill1,2, Jakob Lederman3,4, Daniel Jonsson5

  • 1Region Stockholm Health and Medical Care Administration, The Department for Specialized Care, Stockholm, Sweden. peter.hill@ki.se.

BMC Medical Informatics and Decision Making
|March 25, 2025
PubMed
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This summary is machine-generated.

Machine learning models effectively identified key factors influencing Emergency Medical Services (EMS) response times, including weather and call priority. This research supports adaptive resource allocation for improved emergency care efficiency and patient outcomes.

Area of Science:

  • Emergency Medical Services Research
  • Machine Learning Applications in Healthcare
  • Public Health Informatics

Background:

  • Optimizing Emergency Medical Services (EMS) response times is crucial for patient outcomes in critical situations.
  • This study investigates the determinants of EMS response times using advanced machine learning (ML) techniques.
  • The goal is to enhance resource allocation and operational efficiency within EMS systems.

Purpose of the Study:

  • To identify and analyze the key factors influencing EMS response times.
  • To leverage machine learning for predicting and understanding response time variability.
  • To inform strategies for improving EMS operational efficiency and patient care.

Main Methods:

  • Retrospective analysis of over one million EMS missions in Stockholm (2017-2022).
Keywords:
Emergency care optimizationEmergency medical servicesMachine learningPredictive analyticsResource allocationResponse times

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  • Application of Gradient Boosting machine learning models to assess variable impacts.
  • Feature engineering and statistical validation to determine predictor relationships with response times.

    • Main Results:

    • Identified weather conditions, call priority, and resource availability as primary drivers of response time variability.
    • Gradient Boosting models accurately quantified the impact of these factors on EMS response times.
    • Demonstrated the effectiveness of ML in predicting response times across various scenarios.

    Conclusions:

    • Machine learning insights can significantly enhance EMS resource allocation strategies.
    • Integrating real-time data enables adaptive deployment models, reducing response times and improving equity.
    • This research provides a framework for predictive analytics in EMS to improve efficiency and patient outcomes.