Using machine learning to assess the extent of busy ambulances and its impact on ambulance response times: A retrospective observational study.

Journal: PloS one
PMID: 38181019

Abstract

BACKGROUND: Ambulance response times are considered important. Busy ambulances are common, but little is known about their effect on response times.

Authors

  • Lars Eide Næss
    Department of Research and Development, The Norwegian Air Ambulance Foundation, Oslo, Norway.
  • Andreas Jørstad Krüger
    Department of Research and Development, The Norwegian Air Ambulance Foundation, Oslo, Norway.
  • Oddvar Uleberg
    Department of Emergency Medicine and Pre-Hospital Services, St. Olav's University Hospital, Trondheim, Norway.
  • Helge Haugland
    Department of Research and Development, The Norwegian Air Ambulance Foundation, Oslo, Norway.
  • Jostein Dale
    Department of Emergency Medicine and Pre-Hospital Services, St. Olav's University Hospital, Trondheim, Norway.
  • Jon-Ola Wattø
    Department of Emergency Medicine and Pre-Hospital Services, St. Olav's University Hospital, Trondheim, Norway.
  • Sara Marie Nilsen
    Center for Health Care Improvement, St. Olav's University Hospital, Trondheim, Norway.
  • Andreas Asheim
    Center for Health Care Improvement, St. Olav's University Hospital, Trondheim, Norway.

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