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Ambulance demand: random events or predicable patterns?
  1. Kate Cantwell1,2,
  2. Paul Dietze3,
  3. Amee E Morgans2,4,
  4. Karen Smith1,2,5
  1. 1Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
  2. 2Ambulance Victoria, Doncaster, Victoria, Australia
  3. 3Burnet Institute, Melbourne, Victoria, Australia
  4. 4Department of Community Emergency Health and Paramedic Practice, Monash University, Melbourne, Victoria, Australia
  5. 5Emergency Medicine Department, University of Western Australia, Western Australia, Australia
  1. Correspondence to Kate Cantwell, Department of Epidemiology and Preventive Medicine, Monash University, The Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004; Australia; Katharine.cantwell{at}


Background Occupational, social and recreational routines follow temporal patterns, as does the onset of certain acute medical diseases and injuries. It is not known if the temporal nature of injury and disease transfers into patterns that can be observed in ambulance demand. This review examines eligible study findings that reported temporal (time of day, day of week and seasonal) patterns in ambulance demand.

Methods Electronic searches of Medline and Cumulative Index of Nursing and Allied Health Literature were conducted for papers published between 1980 and 2011. In addition, hand searching was conducted for unpublished government and ambulance service documents and reports for the same period.

Results 38 studies examined temporal patterns in ambulance demand. Six studies reported trends in overall workload and 32 studies reported trends in a subset of ambulance demand, either as a specific case type or demographic group. Temporal patterns in overall demand were consistent between jurisdictions for time of day but varied for day of week and season. When analysed by case type, all jurisdictions reported similar time of day patterns, most jurisdictions had similar day of week patterns except for out-of-hospital cardiac arrest and similar seasonal patterns, except for trauma. Temporal patterns in case types were influenced by age and gender.

Conclusions Temporal patterns are present in ambulance demand and importantly these populations are distinct from those found in hospital datasets suggesting that variation in ambulance demand should not be inferred from hospital data alone. Case types seem to have similar temporal patterns across jurisdictions; thus, research where demand is broken down into case types would be generalisable to many ambulance services. This type of research can lead to improvements in ambulance service deliverables.

  • Emergency Ambulance Systems, Systems
  • Prehospital Care
  • Paramedics
  • Pre-Hospital
  • Research, Epidemiology

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